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Contents
CONTRIBUTORS
TO
VOLUME 53 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ix
The Bacterial Response to the Chalcogen Metalloids Se and Te
Davide Zannoni, Francesca Borsetti, Joe J. Harrison and
Raymond J. Turner
1.
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8.
Abbreviations . . . . . . . . . . . . . . . . . . . . . . . .
Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . .
Biological Uses of Se and Te . . . . . . . . . . . . .
Resistance Towards Se and Te Oxyanions . . . .
Microbial Processing of Metalloid Chalcogens .
Chalcogens and Bacterial Physiology. . . . . . . .
Other Chalcogens and Metalloids . . . . . . . . . .
Concluding Remarks . . . . . . . . . . . . . . . . . . .
Acknowledgments . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . .
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51
Gaining Insight into Microbial Physiology in the Large Intestine: A
Special Role for Stable Isotopes
Albert A. de Graaf and Koen Venema
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3.
4.
5.
6.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
The Gut Microbial Ecosystem . . . . . . . . . . . .
Stable Isotopes . . . . . . . . . . . . . . . . . . . . . . .
Genomic Inventories of Intestinal Bacteria . . .
Proteomic Aspects of Intestinal Microbial Life.
Metabolomics . . . . . . . . . . . . . . . . . . . . . . . .
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75
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110
115
vi
CONTENTS
7. Metabolic Flux Analysis Applied to the Gut . . . . . . . . . . . .
8. Emerging Picture of the Role of Microorganisms Integrated
in Man . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9. New Aspects in the Study of Intestinal Bacterial Physiology .
10. Conclusions and Future Prospects . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Copper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
232
233
245
Bacterial Physiology, Regulation and Mutational Adaptation in a
Chemostat Environment
Thomas Ferenci
1. General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. The Chemostat Environment and Its Applications to
Studies of Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. The Physiological Changes in an Organism Inoculated into
a Chemostat: The Example of Glucose-Limited
Escherichia coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Variations in Responses Within and Between Species . . . . . . .
5. Steady State or Constant Change in a Chemostat Population?
6. Mutation Rates and Mutators in Chemostat Populations . . . .
7. Mutational Takeovers and Population Changes . . . . . . . . . . .
8. A Mutational Sweep in Detail: The Physiological Advantage
and Spread of mgl Mutations in Glucose-Limited E.coli . . . . .
9. Other Mutations in Chemostat Populations and Their
Physiological Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10. Emerging Diversity in Chemostat Populations . . . . . . . . . . . .
11. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Metallosensors, The Ups and Downs of Gene Regulation
Amanda J. Bird
CONTENTS
4. Zinc . . . . . . . . . . .
5. Cadmium . . . . . . .
6. Conclusions . . . . .
Acknowledgements
References . . . . . .
vii
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AUTHOR INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
269
SUBJECT INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
303
Colour Plate Section to be found in the back of this book
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Contributors to Volume 53
AMANDA J. BIRD, Division of Hematology, Department of Internal
Medicine, University of Utah Health Sciences Center, Salt Lake City, UT
84132, USA
FRANCESCA BORSETTI, Department of Biology, Unit of General Microbiology,
Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna,
Italy
ALBERT A. de GRAAF, Wageningen Center for Food Sciences, P.O. Box 557,
6700 AN Wageningen, The Netherlands; Department of Surgery, University
of Maastricht, Maastricht, The Netherlands
THOMAS FERENCI, School of Molecular and Microbial Biosciences G08, The
University of Sydney, NSW 2006, Australia
JOE J. HARRISON, Department of Biological Sciences, University of Calgary,
Calgary, Alta., Canada
RAYMOND J. TURNER, Department of Biological Sciences, University of
Calgary, Calgary, Alta., Canada
KOEN VENEMA, Wageningen Center for Food Sciences, P.O. Box 557, 6700
AN Wageningen, The Netherlands; TNO Quality of Life, P.O. Box 360,
3700 AJ Zeist, The Netherlands
DAVIDE ZANNONI, Department of Biology, Unit of General Microbiology,
Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna,
Italy
The Bacterial Response to the Chalcogen
Metalloids Se and Te
Davide Zannoni1, Francesca Borsetti1, Joe J. Harrison2 and
Raymond J. Turner2
1
Department of Biology, Unit of General Microbiology, Faculty of Sciences, University of
Bologna, Via Irnerio 42, 40126 Bologna, Italy
2
Department of Biological Sciences, University of Calgary, Calgary, Alta., Canada
ABSTRACT
Microbial metabolism of inorganics has been the subject of interest since
the 1970s when it was recognized that bacteria are involved in the
transformation of metal compounds in the environment. This area of
research is generally referred to as bioinorganic chemistry or microbial
biogeochemistry. Here, we overview the way the chalcogen metalloids Se
and Te interact with bacteria. As a topic of considerable interest for basic
and applied research, bacterial processing of tellurium and selenium
oxyanions has been reviewed a few times over the past 15 years. Oddly,
this is the ﬁrst time these compounds have been considered together and
their similarities and differences highlighted. Another aspect touched on
for the ﬁrst time by this review is the bacterial response in cell–cell or
cell–surface aggregates (bioﬁlms) against the metalloid oxyanions.
Finally, in this review we have attempted to rationalize the considerable
amount of literature available on bacterial resistance to the toxic
metalloids tellurite and selenite.
ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53
ISBN 978-0-12-373713-7
DOI: 10.1016/S0065-2911(07)53001-8
Copyright r 2008 by Elsevier Ltd.
All rights reserved
2
DAVIDE ZANNONI ET AL.
Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Tellurium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2. Selenium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Biological uses of Se and Te . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1. Use in Medicine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2. Use in Structural Biochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3. Use in Selective Bacterial Growth Media . . . . . . . . . . . . . . . . . . . .
3.4. Isolates from the Environment with Te and Se Oxyanions . . . . . . . .
3.5. Applications of Te and Se in Biotechnology/Industry/Bioremediation.
4. Resistance toward Se and Te oxyanions . . . . . . . . . . . . . . . . . . . . . . .
4.1. Tellurium and TeR Determinants . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2. Tellurate Resistance Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3. Selenite/Selenate Resistance Genes . . . . . . . . . . . . . . . . . . . . . . .
5. Microbial processing of metalloid chalcogens . . . . . . . . . . . . . . . . . . . .
5.1. Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2. Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3. Biofilms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6. Chalcogens and bacterial physiology . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1. Selenium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2. Tellurium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3. Mechanism(s) of Chalcogen Toxicity . . . . . . . . . . . . . . . . . . . . . . .
7. Other chalcogens and metalloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1. Polonium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2. Other Metalloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ABBREVIATIONS
Ch
COX
DPA
GSH
NMR
MBC
MBEC
MIC
PDTC
QS
R
ROS
chalcogen
cytochrome c oxidase
dipicolinic acid
reduced glutathione
nuclear magnetic resonance
minimum bactericidal concentration
bioﬁlms eradication concentration
minimum inhibitory concentration
pyridine-2,6-bis-thiocarboxylic acid
quorum sensing
organic constituent
reactive oxygen species
. .2
. .3
. .4
. .5
. .6
. .7
. .7
. .8
. .9
. 10
. 11
. 13
. 14
. 21
. 21
. 22
. 22
. 29
. 30
. 38
. 38
. 42
. 45
. 49
. 49
. 50
. 50
. 51
. 52
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
SAM
SCV
Se
SEM
SEM-EDS
SRB
TA
Te
3
S-adenosylmethionine
small colony variant
Selenium
scanning electron microscopy
scanning electron microscopy energy dispersive spectroscopy
sulfate reducing bacteria
toxin-antitoxin
tellurium
1. INTRODUCTION
Considering that heavy metals have been reasonably abundant throughout
the majority of the Earth’s history, one needs to acknowledge that bacteria
have had to deal with their toxic forms since the beginning. This view,
pointed out by Silver and Phung (2005a), implies that metal resistance in
bacteria is not a recent evolutionary event. Although levels of metals in
localized environments become higher from time to time due to geological
events, human activities have provided unique metal combinations and levels from industrial and pollution events. Regardless of the explanation of
tolerance and biogeochemical interaction between heavy metals and bacteria, there is an amazingly wide occurrence of bacterial genetic elements with
deﬁned metal resistances. Thus, bacteria have found ways to eke out a life
with such metals and the chalcogens Se and Te.
Metal metabolism and resistance in bacteria has been of interest since the
1970s when it was recognized that microorganisms are involved in the
transformation of metal compounds in the environment (Jernelov and
Martin, 1975; Saxena and Howard, 1977; Summers and Silver, 1978). This
area of research is beginning to be referred to as environmental bioinorganic
chemistry or microbial biogeochemistry. Bacterial processing of selenium
and tellurium oxyanions has been explored since these early years and remains a topic of interest.
Here we overview the ways in which the chalcogen metalloids Se and Te
interact with bacteria. Tellurite toxicity and resistance in bacteria has been
reviewed a few times (Walter and Taylor, 1992; Taylor, 1999; Turner, 2001).
While the focus of the literature on selenium in bacteria has been primarily
on its incorporation into the amino acid selenocysteine (the 21st amino acid)
(Bo¨ck et al., 1991), an overview of selenium processing in bacteria has been
published (Turner et al., 1998). However, this is the ﬁrst time these compounds have been considered together and their similarities and differences
highlighted.
4
DAVIDE ZANNONI ET AL.
2. CHEMISTRY
Wilhelm Blitz of the Institute of Inorganic Chemistry at the University of
Hannover, Germany, coined the term ‘‘chalcogen’’ sometime around 1930.
Whereas other groups of elements had names, the Group 16 elements (formally Group VI A in USA labeling and VI B in European labeling) O, S, Se,
and Te lacked a good collective term. The result was the term ‘‘chalcogens’’
(‘‘ore formers’’ from chalcos old Greek for ‘‘ore’’) for these elements and
‘‘chalcogenides’’ for their compounds (Fischer, 2001). The chalcogen elements (pronounced with a hard ‘‘C’’ as in chemistry) other than oxygen can
be generally referred to in chemical structures by ‘‘Ch’’ and this abbreviation
will also be used here.
The most common compounds of the non-oxygen chalcogens are
chalcogenide glasses. The most abundant materials in the earth’s crust are
silicates (various compounds of silicon dioxide). ‘‘Chalcogenide glasses’’ are
distinguished from these as non-silicate glasses.
Se and Te generate compounds that are structurally related to their sulfur
analogues, but that exhibit different properties and reactivities and are thus
considerably more toxic. As one descends the column, the chalcogens become larger and more polarizable than sulfur. Selenium has a lower electronegativity and forms weaker bonds than sulfur (Whitham, 1995). The
chemists ﬁnd that selenium can be easily introduced into molecules as a
radical, a nucleophile, or an electrophile. Tellurium has even greater metallike properties and is a true metalloid. In part, due to its polarizability, the
C–Se bond is weaker than C–S bonds; C–Te bonds are weaker yet and tend
to decompose in aqueous environments. This difference in bond energies
may explain why telluromethionine and tellurocysteine amino acids have
not been naturally found while selenocysteine (the 21st amino acid) has been
found in all but a few organisms. Although a considerable amount of selenium chemistry has been studied, tellurium chemistry is still somewhat in
the dark ages.
Se and Te can exist in a number of redox states, namely:
Ch2 or ChðIIÞ ! Ch0 or Chð0Þ
2
! ChO2
3 or ChðIVÞ ! ChO4 or ChðVIÞ
Selenide ðSe2 Þ ! elemental ðSe0 Þ
2
! selenite ðSeO2
3 Þ ! selenate ðSeO4 Þ
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
5
Telluride ðTe2 Þ ! elemental ðTe0 Þ
2
! tellurite ðTeO2
3 Þ ! tellurate ðTeO4 Þ
Bacteria are exposed to these elements mainly as their oxidized ions in the
form of the oxyanions, as well as in organometalloid forms (RCh). However, the exact ionic form of the chalcogen to which microorganisms are
exposed is unknown. For example, in solution at physiological pH, As(III) is
primarily in the form of the undissociated acid arsenic trioxide [As(OH)3]
and not the oxyanion arsenite (Ramirez-Solis et al., 2004). At physiological
1
2
pH, Se(IV) is predominantly HSeO
3 (pKa ¼ 2.6 and pKa ¼ 7.3) and Se(VI)
2
is SeO4 . Although selenide is a key metabolic intermediate, its ionic form is
probably not Se2 but HSe. Te(IV) at pH 7.0 exists at a ratio of HTeO
3/
TeO2
of 104/1. Te(VI) would likely be TeO2
3
4 . Thus, the standard reduction potential of the Te/TeO2
3 couple (0.42 V) at basic pH would be
2
4+
raised to 0.12 V for the couple HTeO
3 /TeO3 at pH 7.0, with no Te
present due to its instability in water (Di Tomaso et al., 2002).
2.1. Tellurium
Tellurium was named from the Latin ‘‘tellus’’, meaning ‘‘earth’’, and was
discovered by F.J. Mueller von Reichenstein in 1782 from ores mined in the
gold districts of Transylvania (Bragnall, 1966; Cooper, 1971). Tellurium is
occasionally found native, but is more often found as the telluride of gold
(calaverite) or combined with other metals. It is recovered commercially
from anode muds produced during the electrolytic reﬁning of blister copper.
The U.S., Canada, Peru, and Japan are its main producers. The concentration of total Te in the earth’s crust is estimated to be 0.002 ppm ranking
Te as approximately 75th in abundance of earth’s elements (Bragnall, 1966;
Cooper, 1971). Crystalline tellurium has a silvery-white appearance and
when pure it exhibits a metallic luster. Amorphous tellurium is found by
precipitating tellurium from a solution of tellurous acid. Tellurium is a
p-type semiconductor, and shows greater conductivity in certain directions,
depending on the alignment of the atoms. Tellurium has been used in blasting caps, and is added to cast iron for chill control and to steel for toughness. It is increasingly being used in ceramics and photovoltaic cells (Lide,
2005) and is presently very popular as a coloring and property-modifying
agent in various types of glasses. It is also used as a reagent (tellurium
chloride and tellurium dioxide) in producing the black ﬁnish on silverware.
The addition of Te0 and Te diethyldithiocarbamate as primary vulcanizing
agents to rubber allows it to withstand temperature ﬂuctuations and
6
DAVIDE ZANNONI ET AL.
enhance the overall lifetime of natural and synthetic rubber. Usually in
combination with Pt, Te is also used as an accelerant/catalyst in a variety of
reactions. In metallurgy, Te is used to modify and improve the properties
and machinability of cast iron, lead, and copper alloys; its addition to
lead decreases the corrosive action of acids and improves its strength and
hardness.
In the environment, Te exists in its elemental (Te0), inorganic – (telluride
2
(Te2), tellurite (TeO2
3 ), and tellurate (TeO4 )), and organic (dimethyl
telluride (CH3TeCH3)) forms (Cooper, 1971). Of these, its oxyanion forms
are more common than its non-toxic, elemental state (Summers and Jacoby,
1977). Presently, sparse research into anthropogenic emissions of Te-based
compounds has been conducted and the implications of Te in the air have
yet to be investigated.
2.2. Selenium
Berzelius discovered selenium in 1818. Its name is derived from the Greek
word selene, meaning ‘‘moon’’. Selenium is found in a few rare minerals
such as crooksite and clausthalite. Previously, it has been obtained from ﬂue
dusts remaining from processing copper sulﬁde ores, but the anode metal
from electrolytic copper reﬁneries now provides the main source, as for
tellurium. Elemental selenium has been said to be practically non-toxic and
is considered to be an essential trace element; however, hydrogen selenide
and other selenium compounds are extremely toxic, and resemble arsenic in
their physiological reactions. Selenium exists in several allotropic forms,
although three are generally recognized. Selenium can be prepared with
either an amorphous or a crystalline structure. Amorphous selenium is
either red (in powder form) or black (in vitreous form). Crystalline monoclinic selenium is deep red; crystalline hexagonal selenium, which is the most
stable variety, is a metallic gray. Selenium exhibits both photovoltaic action
and photoconductive action; therefore, it ﬁnds use in photocells. Selenium is
also able to convert a.c. to d.c. electricity and is used in rectiﬁers. As a p-type
semiconductor, selenium has many uses in electronic and solid-state applications. Selenium is used in Xerography for copying documents. Like
tellurium, it is used by the glass industry as an additive to stainless steel
(Lide, 2005).
As opposed to tellurium, selenium is a very important, essential element
for most organisms, including humans. This requirement stems from its
incorporation into proteins as part of the 21st amino acid as selenocysteine
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
7
(Bo¨ck et al., 1991) and many selenoproteins have now been identiﬁed
(Gromer et al., 2005).
Although considered a key trace element, Se can be highly toxic depending on its concentration and speciation. In the environment, Se occurs in
a variety of oxidation states from the water-soluble oxyanions selenite
2
(SeO2
3 ) and selenate (SeO4 ). Under anoxic conditions, it is found in its
insoluble elemental form of Se(0) and mineralized selenides. Redox transformations can occur in natural systems to increase or decrease the mobility
and bioavailability of the element. Although such transformations can occur
abiotically (Myneni et al., 1997; Zhang et al., 2004), the reduction of selenate
and selenite to elemental Se clearly involves microorganisms.
3. BIOLOGICAL USES OF SE AND TE
3.1. Use in Medicine
Tellurium found applications in the treatment of microbial infections prior
to the discovery of antibiotics. Early documentation in 1926 reports its use
in the treatment of syphilis. Its oxyanion tellurite, TeO2
3 , has been used in
microbiology since the 1930s when Alexander Fleming reported its antibacterial properties (Fleming, 1932; Fleming and Young, 1940). In 1984, it
was suggested that TeO2
could be a potential antisickling agent of red
3
blood cells in the treatment of sickle cell anemia (Asakura et al., 1984). In
1988, tellurium-containing immuno-modulating drugs were proposed as
treatment agents for AIDs; however, little has been done on it since (Jacobs,
1989). This compound, AS-101, inhibits the production of IL-10, IFNgamma, IL-2R, and IL-5 (Shohat et al., 2005). A new use of tellurium compounds is in bone marrow stem cell protection during chemotherapy.
Trichloro[dioxoethylene-O,O0 ]tellurite shows promise compared with other
compounds (Guest and Uetrecht, 2001). In another recent example,
organoselenium and organotellurium compounds are being explored as
pharmaceuticals for defense against oxidative and nitrosative stress (Klotz
et al., 2003).
Selenium is intrinsically useful through its role in selenocysteine. Selenium
has undergone a revolution since the days when it was only considered to be
a toxin. Now, Se is not only recognized as an essential trace element, but has
started to be considered the champion of antioxidants (Tapiero et al., 2003)
and in cancer prevention (Fleming et al., 2001). It would be impossible in the
context of this review to highlight all the eukaryotic biology of selenium.
8
DAVIDE ZANNONI ET AL.
The reader is directed toward some other reviews for such purposes
(Neve, 1991; Whanger et al., 1996; Burk, 2002; Hatﬁeld, 2002; Klein, 2004).
However, it is worth pointing out here that selenium can modify the toxicity
of other heavy metals including mercury (Watanabe, 2002) and arsenate
(Gailer et al., 2002; Manley et al., 2006) that occur through glutathione–Se–As/Hg complexes (Gailer et al., 2000, 2002).
3.2. Use in Structural Biochemistry
The functional and structural properties of chalcogen analogues of sulfurand oxygen-containing amino acids in peptides and proteins is now possible
with new synthetic and recombinant technologies. Applications are being
increasingly explored with both natural and synthetic proteins (reviewed by
Moroder, 2005). Selenocysteine has been recognized as a tool for the production of selenoenzymes with new catalytic activities. By exploiting the
highly negative redox potential of selenols, disulﬁde replacement with diselenide is well suited to increase the robustness of cysteine frameworks in
cystine-rich peptides and proteins and can even be used in the de novo design
of non-native cysteine frameworks. The isomorphous character of seleniumand tellurium-containing amino acids can be easily exploited for the production of metalloid mutants of proteins. Such modiﬁed proteins have been
shown to be useful in protein spectroscopy. Both selenomethionine and
telluromethionine have been incorporated into proteins as heavy metal
derivatives of proteins in protein crystallography and nuclear magnetic
resonance (Boles et al., 1995; Budisa et al., 1995). Tellurium acts as a
phasing vehicle for solving X-ray diffraction patterns and in NMR as an
internal probe to examine structure/function biochemistry following the
125
Te signal. The ﬁrst use of telluromethionine as a tool for phasing X-ray
diffraction data was its incorporation in dihydrofolate reductase (Boles
et al., 1994) and was also used in solving the structures of the phage P22
tailspike protein (Steinbacher et al., 1997) and pyrrolidone carboxypeptidase
(Boles et al., 1997). To carry out these experiments, the protein is expressed
in a bacterial methionine auxotroph host, typically Escherichia coli, in
minimal media supplemented with selenomethionine or telluromethionine.
Bioincorporation of telluromethionine is difﬁcult due to its toxicity,
presumably because of isotope effects on enzyme activities. Additionally,
telluromethionine in aqueous solution is unstable and degrades to produce
the toxic TeO2
3 .
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
9
3.3. Use in Selective Bacterial Growth Media
Tellurite has been extensively explored as an additive to growth media for
the selection and identiﬁcation of various microorganisms, particularly
those resistant to tellurite, for almost 90 years. It is often employed in
selective media to isolate a wide range of pathogens including: Corynebacterium diphtheriae, Vibrio cholerae (Shimada et al., 1990), Shigella spp.
(Rahaman et al., 1986), and verocytotoxigenic E. coli O157:H7 (the ‘‘hamburger disease’’ bacterium) (Zadic et al., 1993; Kormutakova et al., 2000).
Considerable work has been focused on the pathogenic E. coli O157:H7.
This E. coli strain contains the terABCDEF TeR determinant on its chromosome as part of the O pathogenicity island (Taylor, 1999; Taylor et al.,
2002). Because of the high level of resistance, several groups have explored
the use of tellurite-enriched media for its identiﬁcation and isolation.
Tellurite is reduced in these strains resulting in a dark black colony that led
to the adage ‘‘Beware the Black E. coli’’ (see Fig. 1). Although the resistance
from this toxin-producing E. coli originates from the ter resistance determinant (see below), there is diversity in the number of gene copies present
and there are even examples without the ter genes (Taylor et al., 2002).
Tellurite is highlighted as a key selection ingredient (De Boer and
Heuvelink, 2000) and is also used in media to select Shiga toxin-producing
E. coli (STEC) O26 (Hiramatsu et al., 2002). However, a study on E. coli
O46 and O15:H7 suggests that there is no correlation between the TeR and
the ability to produce Shiga toxin (Taylor et al., 2002).
In addition to E. coli strains, tellurite has been used in selection media for
other organisms, including Mycobacterium avium complex (Afghani and
Fujiyama, 2001), which also give black colonies, and in the selective media
for methicillin-resistant Staphylococcus aureus (MSRA) (Zadic et al., 2001).
Furthermore, tellurite is also used as an additive to culture media for the
isolation of pathogeneic Vibrio spp. (Donovan and van Netten, 1995).
Ceﬁxime-tellurite media has been used for isolating organisms from minced
beef (Dogan et al., 2003), rectal swabs of cattle (Yilmaz et al., 2002), raw
vegetables (Fujisawa et al., 2002), and sprouts (Fujisawa et al., 2000).
Tellurite and tellurate have also been proposed for use in selective media
for fecal Streptococci (Saleh, 1980). It is clear that tellurite has proven to be
a useful amendment for selection media in clinical laboratory settings and
will continue to do so. However, this approach should be used with caution
since non-pathogenic strains can acquire tellurite resistance determinants,
for example the ter genes present in the pathogenic E. coli O157:H7, thereby
appearing in many clinical assays as false positives. Conversely, as the
biochemistry of Te is far from understood, it needs to be recognized that as
10
DAVIDE ZANNONI ET AL.
Figure 1 Biogeochemical transformation of tellurite and selenite by bacteria. The
coloration of black cells (tellurite) and red-orange (selenite) is due to the reduction to
Ch(0) product within the cells. (A) Pseudomonas aeruginosa grown in microtitre plate
planktonically with tellurite. (B) P. aeruginosa grown on Calgary Bioﬁlm Device pegs
with tellurite. (C) P. aeruginosa grown in microtitre plate planktonically with selenite.
(D) P. aeruginosa grown on Calgary Bioﬁlm Device pegs with selenite. (E) E. coli
grown on solid Luria Bertani broth showing the black colonies. (F) Thin section
electron micrograph of E. coli grown in the presence of tellurite. The ﬁgure shows the
precipitation of black crystals along the membrane. (G) E. coli harboring various
tellurite resistance determinants. The non-colored culture of the ars is reﬂective of the
resistance being an efﬂux system. (See plate 1 in the color plate section.)
yet unidentiﬁed physiological responses to this chalcogen may give rise to
false negatives.
3.4. Isolates from the Environment with Te and Se Oxyanions
Apart from the isolation and selection of infectious organisms described
above by the augmentation of growth media with potassium tellurite, other
bacteria have also been selected through the use of chalcogen oxyanions.
Tellurite was found to be an excellent selective agent for Agrobacterium spp.
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
11
(Mougel et al., 2001). It was also utilized to characterize a gene segment in
an unculturable rove beetle symbiont that was found to have a functional
terZABCDEF tellurite resistance operon (Piel et al., 2004). This organism is
thought to be closely related to Pseudomonas aeruginosa. Recently, a proposal to use tellurite in a bioassay for quantiﬁcation of cell viability in
environmental samples has been put forward (Lloyd-Jones et al., 2006). The
assay is based on the assumption that the tellurite reduction to the black
precipitate only occurs in metabolically competent bacteria.
Yurkov’s group has been involved in isolating bacteria from various
unique and extreme environments. His group has concentrated investigations on the microorganisms located in close proximity to the hydrothermal
vent of the Juan de Fuca Ridge in the Paciﬁc Ocean (Rathgeber et al., 2002).
Ocean hydrothermal vents emit an array of heavy metal/metalloid compounds into the aquatic environment, including TeO2
3 . Tellurite- and
selenite-reducing strains were isolated in large numbers from the bacterial
bioﬁlms and sulﬁde-rich rocks near the hydrothermal vents. The isolates
were found to be from the genus Pseudoalteromonas, were salt-, pH-, and
heat-tolerant, and gave rise to very high MICs (1500–2500 mg K2TeO3)
(Rathgeber et al., 2002). Some of these organisms were found to utilize
2
SeO2
3 or TeO3 as terminal electron acceptors. Recently, a strain performing anaerobic respiration on tellurate (TeO2
4 ) was isolated from the hydrothermal vent sulﬁde worm Paralvinella sulfincola (Csotonyi et al., 2006).
3.5. Applications of Te and Se in Biotechnology/Industry/
Bioremediation
There are unique challenges in following fates of genetically modiﬁed bacteria released into the environment. The exploitation of microorganisms for
the bioremediation of contaminated areas is of particular interest. The use of
antibiotic resistance markers for following released organisms has deleterious ramiﬁcations in the spread of multi-drug resistance. Sanchez-Romero
et al. (1998) have shown that the kilAtelAB tellurite resistance determinant
can be used to trace Pseudomonas putida following environmental release for
organic degradation. Tellurite has also been used to detect and quantify the
release of Pseudomonas pseudoalcaligenes KF707 in soils for polychlorinated
biphenyl (PCB) degradation (Zanaroli et al., 2002).
Bacteria can mediate bioremediation of Se and Te either through direct
sequestration, bioreduction, or biomethylation. In sequestration, bacteria
do not biotransform the chalcogen oxyanions into a less toxic compound;
the accumulation may occur either through uptake or interaction with
12
DAVIDE ZANNONI ET AL.
surface biomolecules acting in the form of an ion-exchange matrix. Bioreduction instead reduces the more toxic oxyanion forms to the ‘‘non-toxic’’
Ch(0) form. This process usually occurs intracellularly, leading to the
precipitation of the metallic form within the cell. Finally, biomethylation
leads to volatile methyl derivatives that disperse into the atmosphere. The
dimethyl chalcogens can undergo reactions with OHd , NO3 radicals, and
ozone. The methylated products and the reactive products can interact with
atmospheric particles leading to atmospheric residence times from hours to
days (Atkinson et al., 1990). Thus, the chalcogen can travel considerable
distances providing detoxiﬁcation of local contamination sites through
dilution by dispersal. Below, we explore some examples of Se and Te bioremediation studies. Bioremediation of selenium-contaminated environments
has been reviewed by Frankenberger and Arshad (2001).
The tellurite resistance determinants kilAtelAB, ter, tehAB, and arsABC
were investigated for use in tellurite remediation. The use of the plasmidborne tellurite resistance determinant tehAB was found to facilitate the
largest amount of uptake of tellurite from the external media (Turner et al.,
1994a). Highly resistant microbes could also potentially be used for
Te bioremediation. Strains of marine purple non-sulfur bacteria with
resistance to 5 mM tellurite were found to decrease the concentration of
tellurite in the external media 100-fold and led to accumulation of Te(0)
deposits in the cells (Yamada et al., 1997). Similar levels of activity
have been reported for strains of obligate anaerobes (Yurkov et al., 1996).
This bioreduction, leading to sequestration and chemical transformation
of chalcogen oxyanions, could have promise for aquifer contamination
sites. Tellurium oxyanions could also be remediated through biotransformation via volatilization through production of methylated derivatives.
Methylation and reduction processes are discussed further below.
Both aerobic and anaerobic reduction processes of selenium oxyanions
are considered to be useful for removing toxic forms of Se from Se-contaminated water. In certain aquatic systems, the effective bioremediation
must include the physical removal of the precipitated Se(0) to prevent
its re-oxidation to Se(IV) and Se(VI) and subsequent remobilization
(Zhang et al., 2004). An interesting approach to the bioremediation of
metals is to combine phytoremediation with microbial remediation. This
would involve metal processing through plant rhizoﬁltration and is based
on plant rhizobacteria interactions. An example is the isolation of Bacillus
mycoides and Stenotrophomonas maltophilia from the rhizosphere of
Astragalus bisulcatus, a plant that is able to hyper-accumulate selenium.
These organisms were highly efﬁcient in reducing selenite to Se(0) (Vallini
et al., 2005).
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
13
Microbial bioﬁlms show potential for various industrial processes. The
ability of metalloids to adsorb to and/or react with microbial biomass has
been exploited as a means for detecting industrial pollutants in rivers (Mages
et al., 2004). Moreover, bioﬁlms grown on membrane bioﬁlm reactors are
now being explored as a means to extract SeO2
4 from industrial waste-water
and mine tailings, either chalcogens alone or in the presence of other metals
such as chromate or arsenate (Chung et al., 2006b,c). Bioremediation of
selenium-contaminated water sources is promising. Early experiments
have explored bioremediation of chalcogens using algal–bacterial mixtures
(Gerhardt et al., 1991). Drainage water treatment using the selenate-respiring bacterium Thauera selenatis has been explored (Macy et al., 1993). In a
system at the Panoche Water District in California, USA, a medium-packed
biological reactor amended with acetate as the carbon source demonstrated
a 98% reduction in selenium oxyanions levels. The Se was bioprocessed to
Se(0) and then removed using Nalmet 8072, a Se precipitant coagulant
(Cantaﬁo et al., 1996). Another example using this organism utilized wheyamended fermentor to removal of up to 98% selenium oxyanions in the
contaminated drainage water (Bledsoe et al., 1999).
4. RESISTANCE TOWARD SE AND TE OXYANIONS
The ﬁeld of toxic metal resistance microbiology has been frequently
reviewed in the past 25 years. Notably, a number of extensive reviews have
been written by Simon Silver and others (Trevors et al., 1985; Silver, 1996,
1998; Silver and Phung, 1996; Summers, 2005). However, with the exception
of As, the metal oxyanions have not received much attention. In fact, in the
recent review of Silver and Phung (2005a), the authors dedicate barely a
paragraph to tellurium and do not discuss selenium. In general, the so-called
heavy metals (although it would be more correct to refer to them as ‘‘toxic
metals’’ as some heavy metals are not very toxic; Mo, for example) are toxic
as they form stable long-lived complexes with sulfur, thus disrupting the
thiol chemistry within the cell. The chalcogens Se and Te are no different
and demonstrate interesting thiol chemistry within bacteria as described in
the sections below.
A lack of understanding of the toxicity of the Ch oxyanions has hampered
investigations of the mechanism of several of the cloned resistance determinants. Although oxidative damage has been suggested as a mode of toxicity, generally speaking, tellurite resistance determinants generally do not
provide global protection to other oxidants and tend to be very speciﬁc to
14
DAVIDE ZANNONI ET AL.
tellurite. Recruitment of genes from the metabolic operons responsible for
managing the toxicity of normal metabolites or from the genes targeted by
the toxic agent are common themes in resistance determinants (Summers,
2005). This can be seen in the resistance mechanisms displayed for other
metals. However, this is less evident in tellurite resistance to date. Furthermore, it is interesting to note that there is no cross-resistance observed for
any of the deﬁned resistance determinants, suggesting a speciﬁc evolution of
Ter genes.
4.1. Tellurium and TeR Determinants
Genes responsible for tellurite resistance in various organisms have been
isolated and characterized by a number of groups. The Ter genes ﬁrst
appeared associated with plasmids; however, several determinants and
plasmid homologues have now been found associated with the chromosome.
Tellurium resistance mediated by plasmids was ﬁrst described by Anne
Summer and Diane Taylor in the 1970s (Summers and Jacoby, 1977; Taylor
and Summers, 1979; Taylor et al., 1988). Taylor reviewed bacterial tellurite
resistance in 1999, and the mechanisms of toxicity in E. coli were explored in
2001 by Turner.
It has been recognized for some time that metal resistance determinants
are found on conjugative plasmids (Summers and Jacoby, 1977; Izaki,
1978). Reviews focusing on plasmid-mediated tellurite resistance (Ter)
include: Walter and Taylor (1992), Taylor (1999), and Turner (2001).
Plasmid-encoded tellurite resistance determinants are generally associated
with plasmids of the H and P incompatibility groups (Hou and Taylor,
1994). Additionally, a number of chromosomal genes have been found to be
associated with tellurite resistance or to directly mediate tellurite resistance.
To date, ﬁve genetically distinct chromosomal and plasmid-borne bacterial
tellurite resistance systems have been described (Taylor, 1999; Turner et al.,
1999; Turner, 2001; Taylor et al., 2002). However, there are also several
unrelated Ter determinants emerging from various bacterial families, suggesting that these determinants provide some selective advantage in natural
environments. The nature of this advantage may be unrelated to the Ter
phenotype, as the levels of resistance demonstrated in the laboratory do not
correlate with the levels of tellurium ion species present in the ecological or
pathogenic environment. An interesting characteristic of the genes encoding
Ter is that many confer other phenotypes as well. Thus, it is highly likely
that the genes identiﬁed to be associated with tellurite resistance may act by
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
15
encoding ‘‘moonlighting’’ enzymes. Our present understanding of the Ter
determinants is summarized below.
4.1.1. ter
The majority of plasmids within the incompatibility Groups HI-2 and HII
confer protection against colicins and resistance to potassium tellurite
(Taylor and Summers, 1979; Taylor, 1999). The isolated tellurite resistance
determinants mediated by these plasmids convey a very high MIC
(1024 mg/ml) and has been primarily studied in the plasmids pMER610
and R478 (Jobling and Ritchie, 1987, 1988; Whelan et al., 1995). The phenotypes of resistance to tellurite, bacteriophage (Phi), and pore-forming
colicins (PacB) are associated with a large cluster of genes (terZABCDEF)
referred to as the ter Ter determinant (Walter and Taylor, 1992; Whelan
et al., 1995, 1997). This determinant was later identiﬁed to be on the chromosome of E. coli H157:O7, associated with the pathogenicity island
(Tarr et al., 2000; Taylor et al., 2002).
A few studies have been performed on the regulation of the ter operon.
The terABCDE operon from the plasmid pMER610 was initially considered
to be inducible (Jobling and Ritchie, 1987) but later was shown to be constitutively expressed (Hill et al., 1993). A study examining the ter determinant in pathogenicity islands of pathogens using reverse transcriptase-PCR
analysis demonstrated that the majority of ter genes showed constitutive
expression. However, a few isolates were recently found to be telluriteregulated and involved induction of the terB and terC genes (Taylor et al.,
2002). Transposon mutagenesis suggests that only the terB, -C, -D, and -E
genes are required for Ter (Kormutakova et al., 2000) and the data from
Taylor et al. (2002) suggest a common transcriptional region for strains with
high MIC. However, those with intermediate levels of resistance probably
have separate tellurite-regulated promoters before terCDE and terZ as well
as before terB. The regulatory sensor protein has not yet been identiﬁed.
A terZABCDE operon was identiﬁed in Proteus mirabilis and was also
found to be inducible as a single transcript (Toptchieva et al., 2003). This ter
operon was inducible by tellurite and to a lesser extent by oxidative stress
inducers, such as hydrogen peroxide and methyl viologen. The promoter
resembled the OxyR-consensus sequence. From this work, it appeared that
this determinant was common in the Proteus genus. Overall, the ter operon
may be differentially regulated in different organisms.
Other examples of the ter determinant include the E. coli KL53 conjugative plasmid pTE53, which contains homologous terBCDEF genes responsible for the TeR (Kormutakova et al., 2000) as well as the so-called
16
DAVIDE ZANNONI ET AL.
protective region of genes terXYW (Vavrova et al., 2006). The terF gene was
not required in this case to mediate full resistance. Burian et al. (1998) noted
that this plasmid gives twice the tellurite uptake, compared with the cured
strain. There is a considerable degree of homology between the ter genes on
IncHI2 plasmid R478, which originated in Serratia marcescens, and pTE53
from the E. coli clinical isolate.
The biochemical mechanism of resistance toward tellurite by the ter
determinant remains unknown. However, it is clear that reduced uptake or
efﬂux is not involved (Lloyd-Jones et al., 1991, 1994; Turner et al., 1995a).
Additionally, there was no increased accumulation of tellurite from the
media (Turner et al., 1994a). From the accumulation of Te(0) crystals in
E. coli expressing this Ter, it has been inferred that this determinant facilitates the reduction (Lloyd-Jones et al., 1994). No in vitro reduction using
cell extracts has been demonstrated (Lloyd-Jones et al., 1991). However, as
the Te(0) deposits are closely associated with the membrane and a required
protein, TerC, an integral membrane protein, it remains a viable hypothesis
that ter components tap into the electron pool in the membrane for functionality (Lloyd-Jones et al., 1994). Additionally, the ter operon is able to
protect against tellurite-mediated thiol oxidation (Turner et al., 1999).
The resistance to channel-forming colicins has been reviewed by Alonso
et al. (2000b). No clues arise from our present understanding of colicins and
other cholicin resistance mechanisms on how the ter determinants might
mediate Pac and Phi resistance. Bioinformatic analysis suggests a weak
homology between TerC and some transporters. TerD is homologous to the
cAMP binding protein. TerA, TerD, and TerE show some homology and
are related to a stress response protein in a variety of organisms. Overall,
bioinformatic analysis does not give any clues to biochemical activity other
than the fact that ter operon homologues are found on the chromosomes of
a wide range of bacteria. The observation of co-resistance to phage, colicins,
and tellurite remains an unresolved biochemical and physiological link.
4.1.2. tehAB
The tehAB genes were ﬁrst described as a Ter determinant that was believed
to have originated from the IncHII plasmid pHH1508a (Walter and Taylor,
1989; Walter et al., 1991). Later, these genes were localized on the E. coli
genome (Taylor et al., 1994). The genes were thought to be speciﬁc to E. coli
at that time based on hybridization and PCR approaches. However,
through genome sequencing projects, homologues have been clearly shown
to be present on many other bacterial genomes. In E. coli, cloning the tehAB
genes into a multicopy plasmid or over-expressing them behind an inducible
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
17
promoter leads to a tellurite MIC of 128 mg/ml. These genes do not appear
to mediate resistance when encoded on the chromosome and no difference in
the basal resistance is observed in a deletion mutant. No work to date has
explored the expression and regulation of these genes on the chromosome.
TehA is a polytopic integral membrane protein of 36 kDa with a putative
topology of 10 transmembrane helices. This protein shows homology to
C4-dicarboxylate transporter/malic acid transport proteins. Intriguingly, it
was observed that four of its transmembrane helices are homologous to the
small multidrug resistance (SMR) protein family (Turner et al., 1997). It was
shown that both full length TehA and a truncated construct in which helices
were removed except for the SMR homologous region could transport
quaternary ammonium compounds, which are substrates of the SMR proteins. The SMR proteins have a conserved Glu-14 that is crucial to their
activity as a proton drug antiporter and is thought to play a role in binding
both ligands (Gutman et al., 2003). In fact, TehA contains a glutamic acid in
the transmembrane region that could play a similar role.
TehB is a 23-kDa cytoplasmic protein that associates weakly with the
membrane. TehB contains three conserved motifs found in S-adenosyl-methionine (SAM)-dependent non-nucleic acid methyltransferase (Liu et al.,
2000). Mutagenesis of key residues of these motifs eliminated the resistance
mediated by the tehAB determinant. It was also shown that TehB undergoes
a conformational change upon SAM and tellurite binding (Liu et al., 2000)
and SAM can be photochemically reacted with TehB (R.J. Turner, unpublished results). Although a SAM-dependent depletion of tellurite in cultures
is observed, tellurium methylation has not been directly observed with this
determinant. In fact, expression of this determinant on a plasmid decreases
the presence of methylated tellurides in the head gas of cultures and actually
decreased the methylsulﬁde levels (van Fleet-Stalder and T.G. Chasteen,
personal communication).
A study of Liu and Taylor (1999) suggests that TehB has the ability to
mediate resistance on its own and that it is partially responsible for the
natural resistance of Streptococcus (Liu and Taylor, 1999). Additionally,
overexpression of tehB from Streptococcus pneumoniae in E. coli causes a
ﬁlamentous morphology in E. coli (Liu and Taylor, 1999). Morphological
changes upon the over-expression of Ter determinants are a common theme.
The tehAB determinant is very relevant to the physiological state of the
cell. In order to mediate full resistance, the cell must have a functioning
cysteine biosynthetic pathway, ubiquinone biosynthesis, nicotinamide
metabolism, and a thioredoxin/glutathione/glutaredoxin system (Turner
et al., 1995a). This suggests that the oxidoreductases and thiol-redox balance
are important (Turner et al., 1995a, 1999). Additionally, cysteine residues
18
DAVIDE ZANNONI ET AL.
were found to be functionally important for both TehA and TehB (DyllickBrenzinger et al., 2000). Each protein contains three cysteines and can
withstand the loss of single cysteine residues; however, TehA and TehB
mutants lacking more than one of these cysteines had a decreased tellurite
resistance level. The work also demonstrated that TehB is a dimer and at
least one Cys is involved in tellurite binding. These results, taken together,
make clear that thiol biochemistry is fundamental to the mechanism of these
genes.
Although the biochemical mechanism of TehAB-mediated tellurite
resistance is unknown, one can hypothesize a mechanism based on the observations to date. TehB is clearly a methylase but does not lead to (CH3)nTe
products. As TehAB requires glutathione to mediate resistance, it is possible
that glutathione participates in the TehB reaction leading to a GSTeCH3.
This may follow a similar mechanism that is displayed in eukaryotic multiresistance proteins that utilize glutathione conjugation of drugs and efﬂux
of the product (Deeley and Cole, 2006). TehA could then transport the
GSTeCH3 compound via a proton antiport mechanism. Preliminary metabolomic experiments examining small molecular weight compounds in the
media support such an idea (R.J. Turner, unpublished results). A direct
efﬂux mechanism, in which there is no change in molecular form has been
ruled out, giving this hypothesis further support (Turner et al., 1995a).
4.1.3. kilAtelAB/klaABtelB
IncP plasmids do not normally express the Ter phenotype. However, a
cryptic determinant was identiﬁed on the IncP plasmid RP4 or RK2 (Taylor
and Bradley, 1987). The RK2 plasmids have a complex network of coregulated genes known as the kil-kor operon. A normally cryptic Ter was
identiﬁed on some isolates as RK2TeR was mapped to the kilA locus, giving
MICs of 256 mg/ml (Walter and Taylor, 1989). The operon comprises three
genes, klaA, -B, -C, which in the Ter versions are referred to as kilAtelAB
(Walter et al., 1991; Turner et al., 1994b,c). All three of the genes are
required for resistance (Turner et al., 1994b). Furthermore, a single mutation – Ser125 to Cys125 in TelB – was identiﬁed as being responsible for the
appearance of the resistance (Turner et al., 1994c). Due to nomenclature
changes of the kil-kor region, and in order to clearly identify the Ter version,
this determinant is also referred to as klaABtelB.
The kil-kor region of the RK2 plasmid is responsible for plasmid maintenance and was named for cell killing or killing override (Goncharoff et al.,
1991). The KilA (KlaA) was observed to have a strong lethality phenotype
and was also found to inhibit assembly of lambda phage tails (Saltman et al.,
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
19
1992). The growth inhibition phenotype of the operon was examined and it
was demonstrated that all three genes provide some level of retarded growth
(Turner et al., 1994b). In this study, cells expressing KilA were found to
form non-septated ﬁlaments with distinctive evaginations or blebs on the
membranes. A working hypothesis for this phenotype is that KilA inhibits
the cell chaperone GroEL, as suggested by in vitro experiments (Rochet and
Turner, unpublished results).
Though the mutation is in TelB, all three genes are required for resistance
(Turner et al., 1994c). KlaA (28 kDa) and KlaB (42 kDa) are cytoplasmic
proteins while TelB is an integral membrane protein of 32 kDa. There is a
cysteine pair (Cys125/Cys132) in a putative loop in TelB and both cysteines
are required for resistance (Turner et al., 1994c). This suggests that thiol
chemistry is also involved for this Ter. However, as opposed to the tehAB
determinant, kilAtelAB is much less dependent on the physiological state of
the cell to mediate full resistance (Turner et al., 1995a). Additionally, this
determinant is able to protect against the tellurite-dependent glutathione
oxidation in a cell (Turner et al., 2001). Reduced uptake or efﬂux of tellurite
has been ruled out for this determinant as the resistance mechanism (Turner
et al., 1995a).
The operon appears to be unique to the IncP plasmid. KlaA is found on
the chromosome of very few organisms such as Burkholderia spp., Proteus
vulgaris, Paracoccus denitrificans, Roseobacter spp., and Acinetobacter spp.
However, there is some annotation confusion in that KlaA in these organisms is referred to as TelA and designated as a putative toxic anion resistance protein. KlaB (TelA) is found on the chromosomes of many organisms
and annotated as a hypothetical oxyanion resistance. TelB is found in only
a few organisms again showing a strong conserved domain to the TrbC
conjugal transfer protein. A telAB version is beginning to be identiﬁed on
other plasmids such as pADPTel in P. putida CR30RNS (Hirkala and
Germida, 2004). Overall, sequence analysis does not lead to any further
clues as to the biochemical mechanism of resistance.
Due to the lethality phenotype associated with this determinant, fewer
microbial and biochemical studies have been performed. At this time, the
biochemical mechanism of this determinant remains elusive.
4.1.4. tpmT
The tpm gene was cloned from the tellurite resistant Pseudomonas syringae
pathovar pisi (Cournoyer et al., 1998). This gene encodes a SAM-dependent
thiopurine methyltransferase enzyme, which led the authors to propose that
the resistance probably occurs through a volatilization of tellurite/selenite
20
DAVIDE ZANNONI ET AL.
into dimethyl telluride/selenide, a biochemical mechanism that is also
involved in the detoxiﬁcation of thiopurine drugs and their analogues.
Analysis of the genome of P. putida KT2440, a strain that has a high metal
tolerance (Canovas et al., 2003), was also found to have tpmT and an
arsRBCH, both of which likely contribute to the tellurite resistance in this
organism.
4.1.5. cysM/cysK
Chromosomally encoded genes, homologous to those involved in cysteine
biosynthesis, have been isolated and are inferred to be involved in tellurite
resistance. The cysM gene from S. aureus SH1000 was found to be functionally homologous to the O-acetyl serine (thiol)-lyase B family of cysteine
synthase proteins. A deletion in this gene gives increased sensitivity to tellurite and could mediate TeR when transformed into E. coli (Lithgow et al.,
2004). A clone of a single reading frame from pMip233, an IncHI3 plasmid,
conﬁrmed resistance against both tellurite and pore-forming colicin B. The
sequence of this clone is also homologous with O-acetyl serine sulfhydrylase
(Alonso et al., 2000a). This group designated this gene cysK and mediated
resistance to 41000 mg/ml. This enzyme is a pyridoxal 50 -phosphatedependent enzyme and catalyzes the transformation of O-acetyl-L-serine and
S2 to L-cysteine and acetate. The cysK gene was cloned and characterized in
Azospirillum brasilense, where its deletion led to an eightfold decrease in
tellurite resistance (Ramirez et al., 2006). Somehow this reductase-like
enzyme mediates resistance to pore-forming colicins and towards tellurite.
This dual phenotype is similar to that of the ter determinant above, yet
no homology exists between them. Experiments using E. coli ton and tol
mutants harboring pB22 (the cysK clone from plasmid Mip233 Inc HI3)
indicate that the product of tolC, but not that of tonB, is required for both
the PacB and Ter phenotypes (Vilchez et al., 1997).
A homologue of cysK was also identiﬁed in Geobacillus stearothermophilus
V (formerly Bacillus sterothermophilus) that is naturally resistant to tellurite
(Vasquez et al., 2001). Vasquez’s group has explored this organism’s tellurite
resistance (Vasquez et al., 1999) and has isolated different fractions from
cell lysates that demonstrate a NADH-dependent reduction of tellurite
(Moscoso et al., 1998). Additionally, the gene iscS (cysteine disulfurase) was
cloned and shown to be responsible for some of the resistance in this
organism and it could confer resistance in E. coli (Tantalean et al., 2003).
This work suggests that there may be several genes that are involved in
tellurite metabolism in this organism.
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
21
It is tempting to suggest from the above ﬁndings that tellurite resistance in
many organisms is due to genes involved in cysteine biosynthesis. However,
if this were the case, then one must question why all organisms do not
display high levels of resistance. Furthermore, a cysK mutant of E. coli
shows no change in its basic level of sensitivity to tellurite (Turner et al.,
1995b).
4.2. Tellurate Resistance Genes
Tellurate (Te(VI), TeO2
4 ) toxicity is of the same order of that of tellurite.
However, this oxyanion has been far less studied probably due to its markedly lower solubility in aqueous buffers, further indicating that our understanding of the electronic forms of tellurium metalloids remains poor. For
the most part, the tellurite resistance determinants ter, klaABtelB, teh, and
ars do not mediate resistance to tellurate (R.J. Turner, unpublished results).
Few studies have been performed exploring the effects of tellurate on
microbes. To our knowledge, no electron microscopy or other tools have
been used to investigate tellurate exposure to microbes. E. coli cultures, both
planktonic and bioﬁlm, exposed to tellurate just below their MIC turn a
gray color, compared with the black seen with tellurite. It is not clear if the
difference in color and darkness is due to different levels of Te(0) accumulation or to a different metalloid product. E. coli nitrate reductase, which
contributes to basal levels of tellurite resistance, may reduce selenate, Se(VI),
to selenite, Se(IV), and tellurite, Te(IV), to Te(0), but does not show any
tellurate, Te(VI), reduction activity (Avazeri et al., 1997). The ubiE gene of
G. stearothermophilus V encodes a methyltransferase, which upon cloning
into E. coli produced dimethyl telluride in the head gas of cultures amended
with tellurate, but not tellurite (Araya et al., 2004). Although the biochemistry of UbiE is not completely worked out, the process is likely to be SAMdependent.
4.3. Selenite/Selenate Resistance Genes
Selenite is 100- to 1000-fold less toxic than tellurite and thus speciﬁc resistance determinants have not evolved. Likewise, selenate is much less toxic
than selenite (Frankenberger and Engberg, 1998). Nonetheless, a few studies
have now been done that describe genes involved in resistance and/or bioconversion of Se oxyanions. The tpmT gene is proposed to mediate selenite
methylation in addition to tellurite methylation (Cournoyer et al., 1998).
22
DAVIDE ZANNONI ET AL.
The ubiE gene of G. stearothermophilus described above, which mediates
tellurate resistance through methylation, was found to also volatilize selenite
and selenate (Swearingen et al., 2006). The methylated compounds were
dimethyl selenide and dimethyl diselenide.
5. MICROBIAL PROCESSING OF METALLOID CHALCOGENS
5.1. Reduction
The biochemical role of reduction and its tenuous correlation to susceptibility is an important unresolved factor in the study of microbial tolerance
to Se and Te oxyanions. When cultures of microorganisms are exposed to
Se or Te oxyanions, a reaction occurs that leads to the formation of crystals
or nanoparticles of the metalloid in a reduced form (see also Fig. 2). There
are several examples that suggest that the chemistry leading to reduction
may provide a base level of resistance to an organism (Avazeri et al., 1997);
however, many of the genetic resistance determinants described above act
independently of this. For instance, while working to clone the ter operon
from pTE53, Burian et al. (1998) discovered ‘‘white-colony’’ variants with a
level of tellurite resistance comparable to ‘‘black-colony’’ variants harboring
the same plasmid. In this case, a 3.5-fold relative decrease in TeO2
3 uptake
was noted and the authors concluded that an insertion mutation had
Figure 2 There are a number of outcomes for a chalcogen oxyanion within the
cells. The primary process dictates how toxic the oxyanion will be to the microorganism and the related damage as well as any transformation of the oxyanion.
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
23
occurred in an unknown chromosomal gene likely to be responsible for
membrane transport of this anion. In another example, cultures of bacteria
harboring kilAtelAB were observed to reduce TeO2
3 at a slower rate that
growth controls lacking this determinant (Turner et al., 1994a). An attractive explanation for this was the hypothesis that kilAtelAB may encode a
2
TeO2
3 efﬂux system. However, the difference in the overall TeO3 reduction
rate of the kilAtelAB transformed culture was the result of slower metabolism in the transformant (Turner et al., 1994a) and not due to reduced
uptake (Turner et al., 1995a).
This section is focused on the bioreduction of Se and Te oxyanions by
bacteria and fungi. This biological process has a history of application to
clinical microbiology as well as to electron microscopy and may also be
important in the biogeochemical cycling of minerals. A frustrating limitation
of the data presented here is that it cannot resolve whether there is a true
correlation between metalloid reduction and the mechanism(s) of resistance.
5.1.1. Selenium
Selenium is found in four inorganic oxidation states. Comparative biological
toxicity of several selenium compounds representing the different oxidation
states of this element were originally evaluated in rats by Franke and Painter
(1938) and in humans by Vinceti et al. (2001). The soluble oxyanions
selenate and selenite were poisonous in concentrations of ppm. In contrast,
elemental selenium Se0 (0) is highly insoluble and relatively non-toxic and
occurs as a prevalent chemical species under anoxic conditions (Barceloux,
1999). Selenide, S2 (-II), is both highly reactive and highly toxic, but is
readily oxidized to Se0 through several possible, energetically favorable
inorganic and/or biochemical reactions (Turner et al., 1998). A variety of
bacteria from soil and aquatic environments have the ability to reduce
Se(VI) and Se(IV) oxyanions to insoluble Se(0). Representative genera
include Wolinella, Pseudomonas, Sulfurospirillum, Enterobacter, Thaurea,
Bacillus, and Citrobacter (Zhang and Frankenberger, 2005; Sidique et al.,
2006). Reduction of selenium oxyanions leading to bioaccumulation may
also be mediated by plants (Hurd-Karrer, 1937). There is a great deal of
interest in using resistant rhizosphere bacteria in conjunction with plant life
as low cost treatments to manage contamination in selenium-laden efﬂuents
(Di Gregorio et al., 2005; Vallini et al., 2005).
Deposition of selenium particles may occur in the extracellular milieu for
some microorganisms (Klonowska et al., 2005). For others, bioaccumulation of reduced selenium is intracellular, frequently in association with the
cell wall or membrane (Gerrard et al., 1974). Four different types of
24
DAVIDE ZANNONI ET AL.
biochemical mechanisms have been proposed that can account for the formation of nanoparticles of elemental selenium in cultures amended with
Se(VI) or Se(IV). These are: (1) the Painter-type reactions of SeO2
or
4
SeO2
with
reduced
thiols,
in
particular
with
reduced
glutathione,
(2)
the
3
enzymatic reduction of selenium oxyanions by periplasmic as well as cytosolic oxidoreductases, (3) inorganic reactions with bacterial metabolites,
and (4) the reduction–oxidation reactions of Se oxyanions involving the
siderophore pyridine-2,6-bisthiocarboxylic acid (PDTC). Some of these
pathways have been described previously in E. coli (Turner et al., 1998).
Below, we describe these four putative mechanisms focusing on the reduction to Se(0) (see also Fig. 3 for a general scheme).
Painter (1941) was the ﬁrst to observe the high reactivity of selenium
oxyanions with thiol groups, particularly in proteins in toxic cereal grains
during chemical analysis of poisonous plants growing in seleniferous soils.
He discovered that selenium forms selenotrisulﬁdes (RS-Se-SR), which may
be produced according to the following reaction:
4RSH þ H2 SeO3 ! RS-Se-SR þ RSSR þ 3H2 O
(1)
Figure 3 Biochemical pathways for the biological reduction of selenium and tellurium. The chalcogen (Ch, denoting Se or Te) oxyanions may be reduced by bacteria
to form elemental precipitates through four generalized routes via: (1) enzymatic
reduction, (2) methylation, (3) dissimilatory reduction concomitant with sulfate reduction, (4) Painter-type reactions with the thiols of proteins as well as glutathione,
and (5) a chemical reaction with the siderophore PDTC and the products of PDTC
hydrolysis. The italicized numbers refer to reactions listed and detailed in the text.
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
25
It is important to note that the ﬁrst probable step in metabolic processing
of SeO2
4 is an enzymatic (discussed below) or abiotic reduction–oxidation
reaction to form SeO2
3 . The latter is a slow but energetically favorable
reaction with glutathione (Shamberger, 1985). In this manner, some cells
2
may process SeO2
4 by the same pathways as SeO3 . As an interesting aside,
the formation of selenotrisulﬁdes by E. coli has since been conﬁrmed in vivo
using 77Se NMR to examine bacterial cultures amended with SeO2
3
(Rabenstein and Tan, 1988).
Ganther (1968) identiﬁed that an analogous, Painter-type reaction occurs
between SeO2
and the tripeptide glutathione. Recent evidence suggests
3
that when glutathione functions as an electron donor, the reduction of
SeO2
also leads to the formation of superoxide anions (O
3
2 ) (Kessi and
Hanselmann, 2004). The proposed equation for this reaction is:
6GSH þ 3H2 SeO3 ! 3GS-Se-SG þ O
2
(2)
O
2
may be removed by the combined enzymatic
In biological systems,
activity of superoxide dismutase and catalase. Generation of reactive oxygen
species (ROS) such as O
2 and H2O2 in this process may account for the
observed oxidative stress response of bacterial cells exposed to selenium
oxyanions (Bebien et al., 2002). Regardless of these later ﬁndings, Ganther
(1971) also demonstrated that the biological reduction of selenodiglutathione (GS-Se-SG) was mediated by the cellular enzyme glutathione reductase
(GR):
GS-Se-SG þ NADPH ! GSH þ GS-Se þ NADPþ
(3)
As a terminal step in this biochemical pathway, elemental selenium may
be produced by an inorganic reaction between the unstable glutathione
selenopersulﬁde (GS-Se) and a proton (H+), regenerating a single molecule
of glutathione in the process:
GS-Se þ Hþ ! GSH þ Se0
(4)
Thioredoxin is a ubiquitous protein with a redox-active dithiol/disulﬁde in
the active site. Work with thioredoxin reductase (TR) extracts from E. coli
suggest that thioredoxin (Trx) may reduce selenodiglutathione (Bjornstedt
et al., 1992). Oxidized Trx can in turn be reduced by TR in a NADPHdependent manner to regenerate reduced thioredoxin (Bjornstedt et al., 1992;
Kumar et al., 1992). This may be represented by the following two reactions:
Trx-ðSHÞ2 þ GS-Se-SG ! Trx-S2 þ GSH þ GS-Se
(5)
Trx-S2 þ NADPH þ Hþ ! Trx-ðSHÞ2 þ NADPþ
(6)
26
DAVIDE ZANNONI ET AL.
The selenopersulﬁde product from (5) may then undergo the spontaneous
dismutation reaction (4) that generates Se0.
It is important to note that the mechanism of biological reduction
of SeO2
differs from the inorganic reduction–oxidation reaction that
3
produces Se0, particularly with respect to the generation of ROS (Kessi
and Hanselmann, 2004). In summary, the likely ﬁrst step in the biochemical mechanism for generating elemental selenium in bacterial cultures
involves the reaction between Se oxyanions and reduced thiols, followed
by the subsequent action of glutathione reductase and/or thioredoxin
reductase.
Other biomolecules that contribute to the biological process of metalloid
reduction are redox active enzymes, many of which are components of bacterial electron transport chains. Various enzymatic systems, such as nitrate
2
(NO
3 ) and nitrite (NO2 ) reductases as well as sulfate (SO4 ) and sulﬁte
(SO3 ) reductases, are suspected to be involved in the overall reduction of
0
2
2
2
SeO2
4 and SeO3 to Se . For example, the reduction of SeO4 to SeO3
may be carried out by the E. coli periplasmic nitrate reductase NapA, or
through the action of the cytoplasmic nitrate reductases NarGHIJ or
NarZUWV (Avazeri et al., 1997). Selenite generated in this fashion can
undergo further reduction via reactions (1) through (6).
In another example, De Moll-Decker and Macy (1993) have suggested
that reduction of SeO2
to Se0 in T. selenatis may be catalyzed by a
3
periplasmic dissimilatory nitrite reductase. Similarly, a dissimilatory sulﬁte
reductase from Clostridium pasteurianum has shown a high selenite
reductase activity (Harrison et al., 1984). More recently, the work of Kessi
(2006) has demonstrated that there is metabolic interference between selenite
and sulﬁte as well as selenite and nitrite metabolism in logarithmic-growing
Rhodobacter capsulatus. However, R. capsulatus stationary phase cells
that can no longer reduce nitrite or sulﬁte still may metabolize selenite,
suggesting that nitrite, sulﬁte, and selenite reduction may be catalyzed by
independent pathways in this microorganism (Kessi, 2006). Overall, these
studies suggest that the catalytic speciﬁcity of oxidoreductases for SeO2
4
and SeO2
may be different or even absent from certain classes and/or
3
families of these enzymes.
It is also interesting to note that in sulfate-reducing bacteria (SRB), SO2
4
reduction is linked to the concomitant precipitation of sulfur and selenium
in SeO2
3 amended cultures. It is likely that this is not due to the direct action
of redox active enzymes; rather Hockin and Gadd (2003) postulated that
this was due to the following inorganic reaction:
þ
SeO2
! Se0 þ 2S0 þ 3H2 O
3 þ 2HS þ 4H
(7)
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
27
A ﬁnal mechanism for metalloid reduction involves siderophores – ironspeciﬁc (Fe3+) chelators produced by microorganisms under nutrientlimited conditions as part of an iron acquisition system. The chelator PDTC,
which is produced by Pseudomonas stutzeri and P. putida, has an ability to
bind a broad range of metals, including many transition metals, lanthanides
and actinides (Cortese et al., 2002). Many toxic metals form insoluble precipitates with PDTC, including toxic selenium and tellurium oxyanions
(Zawadzka et al., 2006). These workers proposed that SeO2
may be
3
reduced and bound by PDTC or its hydrolysis product, dipicolinic acid
[pyridine-2,6-bis(carboxylic acid)] (DPA). The authors represented this
reaction qualitatively, but not stoichiometrically, as the following two linked
reactions:
PDTC þ H2 O ! DPA þ Hþ þ H2 S þ e
(8)
þ
SeO2
! Se0 þ S0 þ H2 O þ DPA
3 þ PDTC þ H2 S þ H þ e
(9)
SeO2
4
SeO2
3
and
may be reduced through several mechTo summarize,
anisms within bacteria, encompassing energetically favorable reactions with
thiols, reduction–oxidation reactions mediated by enzymes, inorganic
precipitation with bioenergetically produced sulﬁde, and precipitation via
reactions with siderophores and their hydrolysis products.
5.1.2. Tellurium
Similar to selenium, tellurium has four inorganic oxidation states: the II, 0,
IV, and VI valence states. Te(II) is chemically reactive and is naturally
incorporated into organic tellurides. In fact, reduction to dimethyl telluride
is responsible for the hallmark garlic breath of acute tellurium toxicity in
animals (Hollins, 1969; Taylor, 1996) as well as in humans (Blackadder
and Manderson, 1975; Yarema and Curry, 2005). Biomethylation of Te is
further discussed below.
The tellurium oxyanions, tellurite and tellurate, have been considered in
the literature as strong oxidizers and this chemical attribute is considered to
be the explanation for their toxicity in vivo (Taylor, 1999). Gram-negative
bacteria are especially sensitive to tellurium oxyanions; hence, there is a
history of using potassium tellurite (K2TeO3) as a selective agent in microbiological growth medium for the isolation of pathogenic bacterial species
from food, clinical, and environmental samples (Zadic et al., 1993; Donovan
and van Netten, 1995). Klett (1900) was the ﬁrst to note the biochemical
transformation of TeO2
3 into a black, insoluble precipitate that, at the time,
was presumed to be metallic tellurium. This chalcogen is considered to be
28
DAVIDE ZANNONI ET AL.
relatively non-toxic in its elemental state (Te0), although there is no published data that explicitly addresses this assumption. In support of this
notion, it was observed that E. coli growth on agar plates amended with
black Te0 precipitates from spent cultures is similar to growth on agar plates
without the reduced metalloid (R.J. Turner, unpublished data). The capacity
to reduce tellurium is not restricted to resistant microorganisms, nor is it
unique to pathogens (Harrison et al., 2005c). A variety of bacterial aerobic
and anaerobic phototrophs (Moore and Kaplan, 1992), hydrothermal
vent heterotrophs (Rathgeber et al., 2002), eukaryotes, such as fungi
(Kuhn and Jerchel, 1941) and plants (Schreiner and Sullivan, 1911), and the
mitochondria in animal tissues (Barrnett and Palade, 1957) may carry out
various reactions leading to black precipitates.
In contrast to selenium, bacterial deposition of tellurium crystallites is
almost exclusively intracellular (van Iterson and Leene, 1964a,b; LloydJones et al., 1994; Klonowska et al., 2005). Transmission electron microscopy (TEM) indicates that metalloid precipitation usually occurs in close
physical proximity to the cell wall and/or lipid membranes. Reduction of
tellurium oxyanions may also occur through four documented processes: (1)
a Painter-type reaction with glutathione (Turner et al., 2001); (2) catalytic
reduction by periplasmic and cytoplasmic oxidoreductases (Avazeri et al.,
1997); (3) a reduction–oxidation reaction involving the iron siderophore
PDTC (Zawadzka et al., 2006); and (4) direct or indirect reduction by electrons siphoned from the membrane-bound respiratory chain (Trutko et al.,
2000). Tellurium and selenium chemistry are similar in many regards; this is
exempliﬁed again as the ﬁrst three mechanisms of tellurite reduction are
similar to those presented previously for selenium. However, tellurium
oxyanions may differ in their site-speciﬁc interaction(s) with components of
the bacterial respiratory chain; it has recently been shown that the redox
state of several electron transport redox components can be affected by
tellurite (Borsetti et al., 2007). In membrane fragments isolated from cells of
the facultative phototroph R. capsulatus, addition of tellurite induces an
acceleration of the QH2:cyt c oxidoreductase activity, an effect which is both
speciﬁcally inhibited by antimycin A and dependent on the presence of the
membrane-associated thiol:disulﬁde oxidoreductase DsbB. These results
not only blur the proposal by Trutko et al. (2000) that membrane-bound
oxidases are involved in tellurite reduction but also exclude the possibility
that the oxyanion has a general oxidizing effect on the membrane redox
components.
Microbiologists generally accept that the crystalline precipitates produced
by microorganisms growing in the presence of K2TeO3 are metallic tellurium. This is founded on two sets of observations: (1) chemical data,
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
29
including the observation that these precipitates rapidly dissolve in the
presence of a strong oxidizing agent, such as bromine (Morton and
Anderson, 1941); and (2) X-ray diffraction analysis of tellurium precipitates
from C. diphtheriae and Group D Streptococci (Tucker et al., 1962, 1966).
It is interesting to note that pure, metallic tellurium ore is a lustrous silverwhite, which starkly contrasts the black, reduced tellurium precipitates
recovered from bacterial cultures. To date, this discrepancy has neither been
sufﬁciently addressed nor adequately resolved in the literature. It is unclear
whether any other organic material may be associated with the tellurium
precipitates found in vivo. For instance, scanning electron microscopy
energy dispersive spectroscopy (SEM-EDS) suggests that tellurium precipitates may be in proximal association with organosulfur compounds
(Zawadzka et al., 2006).
5.2. Methylation
A common biological response to Se and Te exposure is methylation. Over
a hundred years ago, it was observed that upon Se or Te exposure, a distinct
and unpleasant garlic-like odor emanated from their biological acquisition.
We now recognize that this odor originates from methylated derivatives
of the chalcogens. Such methylated forms in microbes include: dimethyl
selenide, CH3SeCH3; dimethyl selenenyl sulﬁde, CH3SeSCH3; dimethyl
diselenide, CH3SeSeCH3; dimethyl telluride, CH3TeCH3; and dimethyl ditelluride, CH3TeTeCH3 (Chasteen and Bentley, 2003). Of these compounds,
the methylated tellurides are considered to have the more ‘‘disagreeable
character’’. An interesting anecdote is that garlic as a nutriceutical appears
to have the ability to reduce cholesterol levels, and garlic tends to accumulate Te; incidentally, tellurite has been shown to have hypocholesterolaemic
effects (Larner, 1995).
Methylation of selenium and tellurium in microorganisms has been extensively reviewed by Chasteen and Bentley (2003) and the reader is directed
there for an extensive overview. Chalcogen methylation in bacteria appears
to be reasonably common, and examples of methylated products have
been reported from the IV and VI, as well as 0, redox states of Te and Se.
Similarly, some organisms will convert organochalcogen compounds
such as selenomethionine or telluromethionine to methylated derivatives
(Chasteen and Bentley, 2003). The biochemical mechanism of the methylation has been explored in only a few organisms; nonetheless, such
work has led to the assumption that the methyl group originates from
S-adenosylmethione (SAM). Reports also suggest the possibility that methyl
30
DAVIDE ZANNONI ET AL.
cobalamin contributes to Se methylation as well (Thompson-Eagle et al.,
1989). Overall, the methylation of Se is thought to occur via a form of the
Challenger mechanism. This includes a series of reduction methylation
steps alternating the redox state of the Se from VI to IV and ﬁnally a dual
reduction of dimethylselenone through a Se(III) to Se(II) to dimethylselenide (Challenger, 1945). This mechanism was later modiﬁed to
account for dimethyl diselenide via a methyl selenide intermediate (Reamer
and Zoller, 1980).
The majority of studies have explored mixed microbial populations in
soils, waters, sediments, and efﬂuents from metal-contaminated areas as well
as in sewage sludge that has not changed much from such early reports
(Chau et al., 1976; Cooke and Bruland, 1987). Methylated metals and metalloids are commonly observed in gases released from anaerobic wastewater
treatment facilities, presumably due to the microbial activity (Michalke
et al., 2000). Chasteen and Bentley (2003) provide a list of organisms that
have been identiﬁed with the biomethylation of selenium. The surprise in the
list is that Rhodobacter sphaeroides, Rhodocyclus tenuis, and Rhodospirillum
rubrum have the ability to use Se(0) and Te(0) as a substrate (Van FleetStalder and Chasteen, 1998). This provides the possibility of biomining of
minerals of these chalcogens.
A point to consider is that methylation and reduction are likely to
be mutually exclusive activities. A study that alludes to this examines
P. fluorescens K27 where although, considerable dimethyl telluride is produced from methylation, reduction is still the fate of one third of the
amended tellurite (Basnayake et al., 2001).
5.3. Biofilms
Microbial bioﬁlms are cell–cell or solid–surface attached assemblies of bacteria that are surrounded by an extracellular matrix of polymers. Growth in
a bioﬁlm is part of the natural ecological cycle for the vast majority of
microbes (Kolter and Greenberg, 2006) and is regarded as a developmental
process likened to differentiation in multicellular organisms (Hall-Stoodley
et al., 2004; Harrison et al., 2005f). A typical bioﬁlm forms when bacteria
stick to a surface and become permanently attached, triggering a change in
physiology. The bacteria then grow and divide to form layers, clumps or
stalk, and mushroom-shaped microcolonies, all under the control of speciﬁc
bioﬁlm genes (Stoodley et al., 2002). At every stage of growth, bioﬁlm bacteria are generally more resilient to antimicrobials than their planktonic
counterparts. For example, E. coli bioﬁlms are up to 100 times more tolerant
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
31
to antibiotics and disinfectants than the corresponding logarithmic-growing
planktonic cells (Spoering and Lewis, 2001; Harrison et al., 2005b). The
exploration of bioﬁlm susceptibility to metal toxicity has only just begun
(Teitzel and Parsek, 2003; Harrison et al., 2004a).
Recently, Teitzel and Parsek (2003) demonstrated that P. aeruginosa bioﬁlms are up to 600 times more tolerant to heavy metal cations than logarithmic-growing planktonic cells. Harrison and coworkers have shown a
comparable trend in E. coli bioﬁlms exposed to the selenium and tellurium
2
oxyanions, selenite (SeO2
3 ) and tellurite (TeO3 ) (Harrison et al.,
2005b,c,d). For bacterial bioﬁlms, this tolerance is time dependent. In fact,
bioﬁlms can be eradicated under certain test conditions by a number of
2
metal ions including SeO2
and TeO2
(Harrison et al., 2004a,b,
3
3 /TeO4
2005a,b).A 24-h challenge period using potassium tellurite gives an MIC1
of 0.006 mM, an MBC2 of 0.016 mM, and an MBEC3 of 0.014 mM for
E. coli; MIC of 0.073 mM, MBC of 3.1 mM, and an MBEC of 4.4 mM for
P. aeruginosa; and MIC of 0.18 mM, MBC of 40.73 mM, and an MBEC
of 0.73 mM for S. aureus. For sodium selenite, there was no difference
between the MIC, MBC and MBEC, which were: 8.1 mM for E. coli, 28 mM
for P. aeruginosa, and 16 mM for S. aureus (Harrison et al., 2004a).
Although the physiology of bioﬁlms is considerably different from planktonic growth, the metabolism and thus the degree of protection conferred to
microbes by bioﬁlm formation varies from organism to organism. In terms
of application, understanding the molecular mechanisms that contribute to
metal tolerance is a logical ﬁrst step in utilizing bioﬁlms for bioremediation
of metal(loid) contaminated soils and wastewaters.
This section explicitly focuses on a multifactorial model of metal tolerance
that may be used to explain the reduced susceptibility of bioﬁlms to watersoluble oxyanions of selenium and tellurium. It is important to emphasize
that the altered susceptibility of bioﬁlms to these compounds occurs in the
absence of speciﬁc selenium and tellurium genetic resistance determinants;
therefore, microbial bioﬁlm formation is an innate strategy for microorganisms to survive exposure to metal toxicity. Bacterial bioﬁlms derive their
astonishing tolerance to metal toxicity from at least six contributing factors
(Harrison et al., 2005e,f), many of which also contribute to antibiotic tolerance (Lewis, 2001, 2005). This includes: (1) structure-dependent metabolic
gradients arising from restricted penetration of nutrients and oxygen
into the bioﬁlm; (2) a distinct bioﬁlm physiology controlled by a set of
1
MIC: minimum inhibitory concentration – inhibition of planktonic growth.
MBC: minimum bactericidal concentration – killing of planktonic bacteria.
3
MBEC: minimum bioﬁlm eradication concentration – killing of bioﬁlm bacteria.
2
32
DAVIDE ZANNONI ET AL.
bioﬁlm-speciﬁc genes; (3) sequestration of ions in the bioﬁlm matrix; (4) an
adaptive physiological response to metal toxicity; (5) self-generated genetic
diversity within the community that gives rise to variant cell phenotypes; and (6) a small population of specialized survivor cells termed ‘‘persisters’’. These factors and their potential contribution to the reduced susceptibility of bacterial bioﬁlms to chalcogen toxicity are outlined in the
sections below.
5.3.1. Structure and Susceptibility
The architecture of mature bioﬁlms is irregular but complex, and communities are intermingled with networks of ﬂuid-ﬁlled channels (Lawrence
et al., 1991). Bioﬁlm structure is mechanically elastic and the constituent
cells have metabolic plasticity, which together allow the bacteria to be
malleable in the face of environmental factors, such as shear, chemical, and
nutritive stresses (O’Toole and Kolter, 1998; Stoodley et al., 1999). There
are many elegant studies showing metabolic gradients within solid surfaceattached bioﬁlms, a stratiﬁcation that is correlated to the restricted penetration of oxygen and nutrients from the liquid phase to the microbial
community (Huang et al., 1998; Xu et al., 1998; Werner et al., 2004). As a
result, bacteria growing in a bioﬁlm can possess very different and distinct
physiological states, even when separated by as little as 10 mm (Xu et al.,
2000). In general, the bacteria nearest the substratum are in an anoxic zone
and are thus slow-growing, which leads to an intrinsic resistance to killing
by antibiotics relative to the fast-growers in the outer layers of the bioﬁlm
(Walters et al., 2003; Borriello et al., 2004). This structure-dependent metabolic heterogeneity may also explain, in part, the tolerance of the bacterial
bioﬁlms to metal(loid) ions. For example, since the reduction of TeO2
3 to
Te0 is suggested to be correlated with speciﬁc electron transport activity of
the respiratory chain (Trutko et al., 2000), differential expression of electron
transport chain components in aerobic, anaerobic, and microaerophillic
regions of a bioﬁlm may play a role in bioﬁlm tolerance to TeO2
3 .
This structure–function relationship has recently been examined by comparing the bioﬁlm susceptibility of a parental E. coli strain to its isogenic
twin-arginine translocase (tat) mutant (Harrison et al., 2005c). E. coli strains
lacking this membrane-associated, protein transport apparatus have a
variety of cell envelope-related defects, including abnormal cell division as
well as hypersensitivity to detergents and hydrophobic drugs (Stanley et al.,
2001). E. coli DtatABC mutants also have an impaired ability to form
bioﬁlms, in particular under nutrient-restricted conditions (Ize et al.,
2004; Harrison et al., 2005c). Bioﬁlms of E. coli DSS640 (DtatABC) that
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
33
lack the highly organized microcolony structure of the isogenic, parental
strain (E. coli TG1) still possess elevated tolerance to antimicrobials, including a 10-fold increased tolerance to TeO2
3 in comparison to the corresponding planktonic cells. However, this tolerance is still diminished
relative to the wild type E. coli bioﬁlms (Harrison et al., 2005c). This indicates that bioﬁlm structure (and the interdependent metabolic stratiﬁcation in the community) is only one amongst several contributing factors to
chalcogen tolerance.
5.3.2. Biofilm Physiology
A comparative study has shown that, with respect to the different forms of
microbial growth, bioﬁlms and planktonic cultures of the same bacterial
2
2
strain may process SeO2
3 , TeO4 , and TeO3 in different ways (Harrison
et al., 2004b). P. aeruginosa ATCC 27853 bioﬁlms and planktonic cultures
reduce selenium and tellurium oxyanions to orange and black end-products,
respectively. In all cases, P. aeruginosa is highly tolerant to killing by
these metalloid oxyanions. Similarly, planktonic cultures of S. aureus
ATCC 29213 are able to process these compounds to produce elemental
precipitates and are resilient to their toxicity. However, the corresponding
S. aureus bioﬁlm cultures do not produce colored end-products typical of
metalloid reduction and are two- and ﬁvefold more susceptible to killing by
2
TeO2
4 and TeO3 , respectively. Although the change in bioﬁlm cell physiology decreases tolerance to tellurium oxyanions, in this case it also
demonstrates that chalcogen biochemistry and chemistry may be altered in a
bioﬁlm.
An additional factor in bioﬁlm physiology is the process of cell–cell
signaling by quorum-sensing (QS). Many groups have examined QS control
of bioﬁlm formation. Although, in some cases, QS does not appear to be
involved, there are many bacterial species in which QS does inﬂuence bioﬁlm
development (Parsek and Greenberg, 2005). In relationship to defense
against chalcogen toxicity, some bacterial species, such as P. aeruginosa,
upregulate expression of cellular defense machinery, for instance genes
against ROS including superoxide dismutase (sodAB) and catalase (katA)
(Hassett et al., 1999). This is important as oxidative stress is involved in
tellurite toxicity (Rojas and Vasquez, 2005). Sublethal amounts of both
selenite and tellurite increase superoxide dismutase activity and this effect
mimics the early cellular response to oxidative stress (Bebien et al., 2002;
Borsetti et al., 2005). In this fashion, the change in physiology innate to the
bioﬁlm lifestyle may afford an additional level of protection for bacteria
against the oxidative toxicity of metalloid oxyanions.
34
DAVIDE ZANNONI ET AL.
5.3.3. Sequestration in the Biofilm Matrix
The precise composition of a bioﬁlm extracellular matrix varies with the
environment as well as the genotype(s) of the constituent microorganism(s)
(J.J. Harrison, H. Ceri, and R.J. Turner, submitted work). In general, the
bioﬁlm matrix is a highly charged, viscoelastic hydrogel that comprises
oligonucleotides (Whitchurch et al., 2002), species-speciﬁc proteins (Branda
et al., 2006), amino acids (Sutherland, 2001b), and assorted polysaccharides
(Sutherland, 2001a; Wozniak et al., 2003; Branda et al., 2005). Selenium and
tellurium oxyanions may equilibrate across the bioﬁlm matrix at a slowed
rate due to steric and/or ionic hindrance, similar to other charged molecules
(Stewart, 2003). The bioﬁlm itself has pH and reduction–oxidation gradients
(Pringault et al., 1999) that can affect anion speciation; in these microenvironments, certain constituents of the extracellular matrix may bind, as well
as react with, these oxyanions. Sequestration of chalcogens by the bioﬁlm
matrix is thus considered here as a potential contributor to metal tolerance.
The extracellular polymeric substances produced by E. coli have been well
studied. Colanic acid is the major extracellular polysaccharide for many
E. coli strains (Potrykus and Wegrzyn, 2004) and is important for the ability
of this microorganism to form bioﬁlms (Whitﬁeld and Roberts, 1999).
Colanic acid is anionic and would thus have a low afﬁnity for binding both
2
SeO2
3 and TeO3 . This is evidenced by the ability of these ions to eradicate
bioﬁlms, which necessitates that the oxyanions completely penetrate the
matrix (Harrison et al., 2005b). Furthermore, the organic chelator sodium
diethyldithiocarbamate can be used to coordinate and precipitate transition
metals and metalloid oxyanions in vitro. Its use produces visible
metal–chelator complexes in Cu2+-treated bioﬁlms; however, when used
against Se or Te oxyanion-treated E. coli bioﬁlms, no precipitate is seen
(Harrison et al., 2005b). Rather, E. coli in bioﬁlms have the propensity and
2
2
2
capacity to reduce SeO2
to their elemental
4 , SeO3 , TeO4 , and TeO3
forms, which is predominantly an intracellular phenomenon in this microorganism (Harrison et al., 2005c). Overall, this indicates that the extracellular matrix of E. coli bioﬁlms may sequester only small quantities of
selenium and tellurium.
In contrast, some microorganisms may produce chemically reactive metabolites that cause the precipitation of metalloid oxyanions in the extracellular matrix. For instance, sulfate-reducing bacteria (SRB) produce sulﬁde
(S2) through dissimilatory sulﬁde biogenesis. Under low redox conditions
and in the dark, precipitation of elemental selenium in SRB bioﬁlms may
occur via an abiotic reaction with bacterially generated S2 (Hockin and
Gadd, 2003). Boils of Shewanella oneidensis growing in anaerobic conditions
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
35
may also reduce SeO2
3 and accumulate elemental selenium in the extracellular milieu (Klonowska et al., 2005). However, the amount of SeO2
3
reduced is highly dependent on the nature of the electron donor and
terminal electron acceptor. It is important to note that S. oneidensis retains
the capacity to reduce TeO2
3 and accumulate elemental tellurium within the
cell (Klonowska et al., 2005).
The coordination and/or reaction of chalcogens with components of the
bioﬁlm matrix could sequester toxic anions away from the bacterial cells.
This may provide a level of protection commensurate with the kinetics of the
reaction equilibrium, which may restrict diffusion as well as alter biological
availability of the chalcogen oxyanions.
5.3.4. Adaptive Stress Responses
Szomolay et al. (2005) have proposed that reaction-diffusion limited penetration of bioﬁlms may result in low levels of antimicrobial exposure to
bacterial cells in deep regions of the community. Cells sheltered in this
fashion may be able to enter an adapted physiological state that is resistant
to the antimicrobial. To date, there have been no studies examining the
global changes to the bacterial transcriptome that accompany chalcogen
exposure. However, proteomic ﬁngerprinting of bioﬁlm and planktonic
E. coli exposed to TeO2
indicate that the proteome undergoes global
3
changes after only a few hours of exposure to this toxic compound. By
contrast, similarities in protein proﬁles obtained for bioﬁlms and planktonic
cultures suggest that certain features of this adaptive response may be
shared by both modes of bacterial growth (N.J. Roper, personal communication). Although the understanding of this physiological adaptation is
still superﬁcial, proteomic ﬁngerprinting suggests that TeO2
3 elicits a complex biological response from bacterial bioﬁlm populations. Accordingly, an
adaptive stress response of bioﬁlms (vs. the innate physiological difference
of bioﬁlms compared with planktonic cells) may further contribute to
chalcogen tolerance.
5.3.5. Genetic Diversity and Colony Morphology Variants
In many instances, bioﬁlm growth leads to the formation of colony morphology variants that may display altered phenotypic traits relative to the
parental, colonizing strain. Small colony variant (SCV) cells, which are frequently recovered from bioﬁlms of clinical and/or rhizosphere Pseudomonas
spp., are an example of this (Ha¨uXler, 2004; Kirisits et al., 2005; van den
Broek et al., 2005). Typically less motile, these SCV isolates are superior at
36
DAVIDE ZANNONI ET AL.
forming bioﬁlms compared with their progenitors and occur at a frequency
in the population that is increased by exposure to certain antibiotics, metal
ions, and H2O2 (Drenkard and Ausubel, 2002; Davies et al., 2007). It was
recently discovered that TeO2
3 may trigger the formation of SCV cells in
bioﬁlms of P. fluorescens (J.J. Harrison, M.L. Workentine, H. Ceri, and
R.J. Turner, unpublished data). Variant formation may be an important
contributor to chalcogen tolerance, as the switch to the SCV phenotype is
correlated with the emergence of multidrug and metal resistance (Drenkard
and Ausubel, 2002; Davies et al., 2007).
SCV cells of S. aureus are frequently auxotrophic for menadione or hemin, two compounds that are required for the biosynthesis of menaquinone
and cytochromes, respectively (McNamara and Proctor, 2000). S. aureus
strains bearing inactivating mutations in menD or hemB, which are required
for the synthesis of menadione and hemin, produce stable SCV cells (von
Eiff et al., 2006). S. aureus menD and hemB mutants with a stable SCV
2
phenotype are hypersensitive to SeO2
3 and/or TeO3 (von Eiff et al., 2006).
Similar to P. aeruginosa SCV cells, S. aureus SCV cells are hyperadherent
(Vandaux et al., 2002), providing an additional link between bioﬁlm formation and altered susceptibility to chalcogen toxicity.
Although the molecular mechanism that triggers the formation of SCV
cells is unknown, it was shown that the reversion of P. aeruginosa PA14 and
P. chlororaphis O6 SCV cells to a normal colony morphotype requires the
sensor kinase GacS (Davies et al., 2007). In many rhizosphere and laboratory strains of Pseudomonas spp., gacS is naturally prone to inactivating
mutations (Duffy and Defago, 2000; Sa´nchez-Contreras et al., 2002; van den
Broek et al., 2005). Moreover, phenotypic variation in P. fluorescens is
mediated by two site-speciﬁc recombinases, XerD and Sss, which appear to
introduce mutations into gacA and/or gacS (Martinez-Granero et al., 2005).
The stability of many types of biological systems is increased by genetic
diversity, and Boles et al. (2004) have recently reported that P. aeruginosa
may introduce genetic diversity into the bioﬁlm population in a recAdependent manner. In this manner, genetic diversity may act as ‘‘insurance’’
for microbial survival in a diverse range of environmental stresses. In the
analogous case of SCV cells, genetic diversity introduced to the bioﬁlm
community by XerD and Sss may lead to the variation in cell phenotype,
that appears to be linked to metal(loid) resistance.
5.3.6. Persister Cells
The tolerance of bacterial bioﬁlms to antimicrobials may be explained, in
part, by the presence of a large number of specialized survivor cells termed
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
37
‘‘persisters’’ within the adherent community. This subpopulation of cells is
estimated to account for 0.0001 to 0.001% of the logarithmic-growing bacteria in planktonic culture (Moyed and Bertrand, 1983), but may represent
as much as 1–10% of the cells in a bioﬁlm (Spoering and Lewis, 2001). These
slow-growing phenotypic variants are able to withstand exposure to chemically and structurally unrelated bactericidal agents (Lewis, 2005). A hallmark of the persistent phenotype is biphasic population killing kinetics by
antibiotics and disinfectants that are either time-dependent (Sufya et al.,
2003; Balaban et al., 2004; Keren et al., 2004a) or concentration-dependent
(Brooun et al., 2000; Spoering and Lewis, 2001). Both of these characteristics are common to cell death kinetics in bioﬁlms exposed to bactericidal
2
2
concentrations of metal ions, including SeO2
3 , TeO4 , and TeO3 (Harrison
et al., 2005a,b).
Bacterial persistence is best understood in E. coli and has a wellestablished genetic basis linked to the expression of chromosomal toxin–
antitoxin (TA) modules (Moyed and Bertrand, 1983; Moyed and Broderick,
1986; Scherrer and Moyed, 1988; Black et al., 1991, 1994; Korch et al.,
2003). The fraction of persisters in the E. coli population is controlled, in
part by the TA module high persistence (hipBA) operon (Keren et al.,
2004b), which encodes a toxin (HipA) and an antitoxin (HipB). The mechanism of persister formation is only partially understood, but involves the
interaction of HipA with a downstream target to arrest macromolecular
synthesis (Keren et al., 2004b; Korch and Hill, 2006). Mutants bearing
inactivating mutations in hipBA produce a smaller proportion of persisters
in stationary-phase cultures and in bioﬁlms than wild type E. coli (Keren
et al., 2004b). In fact, certain alleles of hipA increase the frequency of persisters in the bacterial population. The hipA7 allele is a gain-of-function
mutation known to mediate a 20-fold increase in relative size of the persister
cell population produced by E. coli approaching stationary phase (Korch
et al., 2003). Stationary phase cultures of the E. coli hipA7 mutant produce
up to an 80-fold increase in the relative size of the bacterial population
2
surviving exposure to TeO2
4 and TeO3 (Harrison et al., 2005b). These data
suggest that persister cells, which occur at increased frequency in bioﬁlm
bacterial populations, may mediate time-dependent tolerance to metalloid
oxyanions.
5.3.7. Fungal Biofilms
Up to this point, this review has focused on bacterial bioﬁlms. Similarly,
fungi may also form surface-adherent bioﬁlms that are innately multidrug
and metal resistant. Bioﬁlm formation by fungi is best characterized for
38
DAVIDE ZANNONI ET AL.
Candida spp. This genus of polymorphic yeast produces bioﬁlms through
a stepwise developmental process involving cellular differentiation
(Parahitiyawa et al., 2006). For instance, C. albicans produces exopolymer
entrenched, mature bioﬁlms that are composed of a basal layer of yeast cells
from which hyphae extend into the liquid medium (Chandra et al., 2001).
To date, the inﬂuence of chalcogens on fungal bioﬁlms has been examined
only in C. tropicalis. Bioﬁlms of this microorganism continue growing in
twice the concentration of SeO2
3 required to sterilize a planktonic culture
of equivalent cell density (Harrison et al., 2006). C. tropicalis bioﬁlms are
also highly resistant to TeO2
(Harrison et al., 2006). Surprisingly, low
3
concentrations of SeO2
affect
the
pattern of cellular differentiation in the
3
bioﬁlm and thus change the structure and organization of the surfaceadherent community. In particular, SeO2
3 inhibits the transition from yeast
to hyphal cell morphotypes; thus exposed bioﬁlms consist of only a ﬂat
layer of yeast cells that lack both microcolony structure as well as hyphae
(J.J. Harrison, R.J. Turner, and H. Ceri, unpublished data). Similarly,
microscopic investigation of Aspergillus parasiticus Var. globosus revealed
morphological changes to the fungi which were more marked with increased
concentration of either selenite or tellurite (Zohri et al., 1997). Since the
emergence of drug resistance coincides with multiple cell morphotypes in
bioﬁlm maturation (Chandra et al., 2001), metal(loid) ions may alter bioﬁlm
susceptibility to natural or synthetic antimicrobial agents, including the
chalcogens. These studies suggest that bioﬁlm formation may also be a
strategy for fungi to survive exposure to metalloid toxicity.
6. CHALCOGENS AND BACTERIAL PHYSIOLOGY
6.1. Selenium
The biochemistry of selenium in microbes has been discussed extensively
(Heider and Bo¨ck, 1993; Turner et al., 1998; Stolz and Oremland, 1999;
Birringer et al., 2002; Stolz et al., 2002, 2006) (see Fig. 2 for a general
scheme). As mentioned above, Se is a trace element incorporated into several proteins in bacteria, archaea, and eukaryotes as selenocysteine (Sec)
and selenomethionine. To date, hundreds of microbial selenoproteins have
been identiﬁed. They belong to 10 principal selenoprotein families, although
this number is destined to increase thanks to the development of innovative bioinformatic tools that allow the identiﬁcation of new classes of
selenoproteins (Zhang et al., 2005). Analysis of the composition of
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
39
selenoproteomes revealed that most are redox proteins, which use selenocysteine to coordinate redox-active metals (Mo, Ni, or W) or are involved in
Sec-thiol redox catalysis (Kryukov and Gladyshev, 2004; Zhang et al.,
2005).
At present, very little information exists concerning the metabolic processes responsible for assimilation of inorganic selenium (such as selenate and
selenite) into these selenoproteins. It was noted in the selenate-resistant
E. coli strains, that sulfate uptake was inhibited by selenate. This occurs to a
lesser extent in wild type strains, suggesting a connection between sulfate
and selenate transport (Springer and Huber, 1973). Due to the similarities in
the chemical properties of selenium and sulfur, it was proposed that the
two elements were assimilated through the same pathway (Shrift, 1969;
Stadtman, 1974). Selenate uptake in the model system of E. coli is considered to follow the sulfate uptake system for the most part, for incorporation into selenocysteine. However, early studies suggest that there may be
an alternative uptake pathway as inhibitors of the pathway of sulfur incorporation do not completely stop selenite assimilation (Brown and Shrift,
1982). Studies with Salmonella typhimurium have demonstrated that,
whereas selenate (SeO2
4 ) is transported by the sulfate permease (CysTWA
system), selenite (SeO2
3 ) is taken up by a different process, involving neither
the sulfate nor the sulﬁte assimilatory pathways (Brown and Shrift, 1980).
Analogous studies in E. coli have demonstrated that transport of selenate is
repressed by the presence of cysteine in the medium as well as sulfate uptake.
Selenite transport, in contrast, is not repressed in the presence of this amino
acid, suggesting that a distinct carrier for this oxyanion could exist (Brown
and Shrift, 1982). Selenite seems to be accumulated by the sulfate carrier
only when it is present at high concentrations, reﬂecting the low afﬁnity
of the sulfate permease for the oxyanion compared with sulfate anion
(Lindblow-Kull et al., 1985). Selenite uptake in C. pasteurianum was
reported to occur via both a unidirectional ATPase (probably the sulﬁte
uptake system) as well as via the DpH component of the proton motive
force (Bryant and Laishley, 1989). The existence of an alternative carrier
for selenite was also suggested for Selenomonas ruminatum, a species that
cannot metabolize sulfate and selenate (Hudman and Glenn, 1984) as well as
the phototrophic bacterium R. sphaeroides. In R. sphaeroides, a polyol ABC
transporter has been indicated as the possible carrier of selenite into the
cytoplasm (Bebien et al., 2001). It is worth noting that the uptake of the
oxyanions arsenite (As(III)) and antimonite (Sb(III)) in E. coli AW3110 is
facilitated by the glycerol facilitator GlpF, an aquaglyceroporin that
helps the movement of neutral substrates but not ions (Sanders et al.,
1997; Meng et al., 2004).
40
DAVIDE ZANNONI ET AL.
In prokaryotes, selenium also participates in dissimilatory processes in
which transformations result in the transduction of energy and/or detoxiﬁcation. Microbes that can use selenium oxyanions as terminal electron
acceptors are widespread amongst prokaryotes and this ability is often used
to distinguish between closely related species (Stolz and Oremland, 1999).
The majority of these microorganisms are able to reduce selenate to selenite
and to elemental selenium that is ﬁnally accumulated inside the cells as dense
orange-red precipitates. The ability to use selenate as an alternative acceptor
is often associated with the ability also to use arsenate (Stolz et al., 2002). By
contrast, only a few species have been isolated for their ability to use selenite
as an electron acceptor, namely the haloalkaliphilic Bacillus selenitireducens
and three strains of an Aquificales sp. (Switzer-Blum et al., 1998; Takai et al.,
2002). Reduction of selenate and selenite to elemental selenium, which is
insoluble and less toxic, may inﬂuence the mobility and hence the bioavailability of the element in the environment (Oremland et al., 1990, 1991;
Steinberg and Oremland, 1990). Although the remobilization of selenium by
oxidation is possible, this biotic process is very slow compared with dissimilatory reduction (Dowdle and Oremland, 1998). Overall, it appears that
microbial metabolism is predominately responsible for the biogeotransformation of selenium oxyanions in the environment.
Once inside the cell, selenium derived from selenate or selenite may be
incorporated into polypeptides as selenocysteine and selenomethionine.
In order for this to occur, selenium oxyanions must be reduced to selenide.
Selenite is reduced to selenide by the Painter reaction with GSH, the most
abundant reduced thiol in the cytoplasm of the cells (Painter, 1941; Fahey
et al., 1978). Although several Se-glutathione intermediates may be produced (such as GSSeO
2 and GSOSeSR), the principal adduct formed and
shown by 77SeNMR is selenodiglutathione GS-Se-SG (Milne et al., 1994).
Selenate may also react with GSH, albeit slowly (Shamberger, 1985),
although selenate reduction to selenite catalyzed by periplasmic or membrane-associated nitrate reductases may be the ﬁrst step for the further
incorporation of selenium.
In the form of selenide, selenium can be incorporated into the free-amino
acid selenocysteine by the enzyme O-acetylserine (thiol)-lyase (coded by
cysK gene) or modiﬁed by the Sel system to give the speciﬁc aminoacyltRNASec. The free amino acid is not directly ligated to tRNA but can be
esteriﬁed to tRNACys and subsequently inserted randomly into proteins in
place of cysteine (Kaiser and Young, 1975). The effect of this replacement is
deleterious for the cells as it can alter enzyme activities. The Sel system
comprises four gene products (SelA, SelB, SelC, and SelD) and is directly
involved in the biogenesis of Sec-proteins. SelC is the Sec-speciﬁc-tRNA
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
41
(tRNASec), SelA and SelD are enzymes essential for the formation of the
tRNASec from seryl-tRNA and SelB is an elongation factor that speciﬁcally
recognizes the tRNASec (Stadtman, 1996). Importantly, tRNASec is not
directly derived from ligation of selenocysteine to tRNA, but is derived from
modiﬁcation of an L-serine residue previously ligated to tRNASec by L-serine
ligase. SelD is a selenophosphate synthase, which produces an activated
form of selenium, selenophosphate. SelA is the selenocysteine synthase that
converts seryl-tRNASec to aminoacrylyl-tRNASec. Selenocysteine is incorporated into the polypeptide chains in response to the UGA codon. Though
UGA is universally recognized as a stop codon, it can cue selenocysteine
incorporation into polypeptide chains. This occurs through an RNA stemloop structure designated Sec insertion sequence (SECIS). SECIS elements
are present immediately downstream of UGA codons in bacteria (Zinoni
et al., 1990; Liu et al., 1998) and in 30 untranslated regions in archea (Rother
et al., 2001). SelB is the elongation factor that recognizes the mRNA context
of selenocysteine codons and, additionally, it can discriminate tRNASec
from other tRNA species (Heider and Bo¨ck, 1993). This recognition of
selenocysteine has come a long way from early experiments exploring the
physiology of selenium on bacterial growth, which include the work of
Huber et al. (1967) in which the effect of selenate was evaluated by its ability
to replace sulfur. It was noted that the majority of added selenate was
incorporated into proteins.
Phototrophic bacteria have been shown to grow in the presence of
0.1–10 mM selenate or selenite. When phototrophic bacteria are grown in
the presence of selenium oxyanions, small amounts of the oxyanions are
reduced and/or methylated. A study in R. sphaeroides indicated that volatilization of selenite or selenate occurs only at low levels and is an insigniﬁcant fate for the selenium oxyanions taken up by this organism
(Van Fleet-Stalder et al., 2000). Selenite was processed more efﬁciently
than selenate in this organism. Although volatilization is not signiﬁcant,
R. sphaeroides grown in the light produced more reduced volatile selenium
than cultures kept in the dark (Van Fleet-Stalder et al., 1997).
The selenium uptake system in R. sphaeroides operates at very low concentrations of selenium oxyanions. Its poor initial afﬁnity for selenite and
even lower afﬁnity for selenate seems to be compensated for by a very
effective subsequent reduction to trap any selenium that enters the cell
(Van Fleet-Stalder et al., 2000). The physiology of the organism appears
to initially put any excess Se into a form very similar to selenomethionine, or
even selenomethionine itself. Any further Se excess appears to be completely
reduced to the detoxiﬁed red elemental form Se(0), which has a very low
bioavailability (Combs et al., 1996).
42
DAVIDE ZANNONI ET AL.
It has been suggested that members of the Rhodospirillaceae family utilize
oxidized compounds, including Te and Se oxyanions, to get rid of the excess
electrons produced in anaerobic photosynthesis (Moore and Kaplan, 1992,
1994). This working hypothesis has recently garnered support by data indicating that tellurite reduction is likely to be mediated by the thiol:disulﬁde
oxidoreductase DsbB by extracting reducing equivalents from the ubiquinone-pool (Borsetti et al., 2007). Selenite reduction is observed in
R. sphaeroides f. sp. denitrificans under photosynthetic conditions after approximately 100 h lag time, the result of the induction of a molybdenumdependent enzyme (Pierru et al., 2006). This group concluded that there are
several pathways of selenite reduction in this organism, at least one involving such an enzyme.
Another example of Se oxyanion utilization in respiration is a novel,
strictly anaerobic, hyperthermophilic, facultative organotrophic archaeon
that utilizes carbon dioxide as the carbon source and can use hydrogen as an
electron donor and arsenate, thiosulfate, or selenate as electron acceptors
(Huber et al., 2000). This organism is related to Pyrobaculum aerophilum
which can utilize arsenate, selenate, and selenite for electron acceptors.
An organism of key importance in selenium microbial physiology is
T. selenatis, which contains a respiratory selenate reductase that allows
growth on selenate by utilizing it as a terminal electron acceptor (Schro¨der
et al., 1997).
6.2. Tellurium
Unlike selenite and selenate, no microorganism has been isolated for its
ability to use tellurite as a terminal electron acceptor for growth. Tellurate,
which is less toxic than tellurite, has recently been shown to sustain the
anaerobic growth of a strain, EC-Te-48, isolated from hyperthermophilic
vents (Csotonyi et al., 2006). Furthermore, it has been observed that nitrate
reductases (NRs A and Z) from E. coli present tellurite and selenate reduction activities, leading to the deposition of Te0 and Se0. A soluble nitrate
reductase is also able to reduce tellurite in anaerobically grown cells, though
this activity does not allow the growth of the microorganism under anaerobic conditions without nitrate as a terminal electron acceptor. Interestingly, E. coli is able to utilize selenate and tellurite for anaerobic respiration
when the NRA is induced in large amounts (Avazeri et al., 1997). Additionally, tellurite was found to negate the induction effect of nitrate on
NRA, while Se oxyanions decrease the nitrate reduction by 50%
(R.J. Turner and G. Giordano, unpublished results). This suggests that
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
43
NR activity on Se and Te oxyanions is an unfavorable reaction for both the
enzyme and the cell.
Periplasmic and membrane-bound nitrate reductases from Ralstonia
eutropha, P. denitrificans, Paracoccus pantotrophus, and the phototrophic
bacterium R. sphaeroides have shown the ability to reduce tellurite and
selenite in vitro as well, suggesting that tellurite- and selenate-reducing activities are a general feature of different denitrifying species. However, the
catalytic activity of the isolated Nap enzyme from R. sphaeroides is too low
to justify the high level of resistance of the microorganism, suggesting that
other mechanisms may contribute to the resistance phenotype (Sabaty et al.,
2001). Indeed, tellurite resistance without metal accumulation has been
observed in some obligately aerobic photosynthetic bacteria, showing that
tellurite resistance does not strictly depend on reduction to Te(0) (Yurkov
et al., 1996).
Trutko et al. (2000) have proposed that components of the respiratory
chain of Gram-negative bacteria are involved in the reductive process. They
postulate that the location of tellurium deposits is dependent on the plasma
membrane position of the active site of terminal membrane-bound oxidases.
However, they have also shown that rate of tellurite reduction does not
always correlate with the intensity of respiration. Indeed, in cells of P. aeruginosa PAO ML 4262, the stimulation of cytochrome c oxidase (COX)
activity by addition of ascorbate-dichlorophenolindophenol drastically lowers the Te0 deposition in cells (Trutko et al., 2000). This ﬁnding compromises the hypothesis that COX plays a direct role in reducing tellurite but is
in line with other reports indicating that COX activities in membranes from
P. pseudoalcaligenes KF707 and R. capsulatus cells grown in the presence of
tellurite, drop concurrently with a drastic decrease of the soluble c-type
heme content (Di Tomaso et al., 2002; Borsetti et al., 2003a). Additionally,
despite the polarity of the respiratory COX in the membrane, R. capsulatus
and P. pseudoalcaligenes KF707 accumulate elementary tellurium in the
cytosol only (Di Tomaso et al., 2002; Borghese et al., 2004), suggesting that
reduction of tellurite to Te0 is unlikely to be performed by respiratory
cytochrome c oxidases. By contrast, whether the modiﬁcations observed in
the plasma membrane redox chains of P. pseudoalcaligenes KF707 and
R. capsulatus are speciﬁcally required to survive in the presence of tellurite
or simply reﬂect toxic effects of the anion on the electron transport system,
remains a matter for debate. This problem has recently been challenged in
isolated plasma membrane vesicles of R. capsulatus. Borsetti et al. (2007)
showed that tellurite (0.25–2.5 mM) alters the redox equilibrium of the
Q/QH2-bc1-c2/cy segment of the redox chain. This effect is blocked by the
bc1 complex-speciﬁc inhibitor antimycin A and it is absent from membranes
44
DAVIDE ZANNONI ET AL.
of R. capsulatus MD22, a mutant lacking the thiol:disulﬁde oxidoreductase
DsbB. The latter ﬁnding is particularly important because it suggests
for the ﬁrst time a possible molecular mechanism by which tellurite can
perturb the plasma membrane redox components facing the periplasmic
space.
Little is known about the entry of tellurium oxyanions into bacterial cells.
The observation of a ‘‘white’’ tellurite resistance variant (Burian et al., 1998)
is an important factor to consider when analyzing the location of reduction
of Te(IV) to Te(0) in the cell and the existence of speciﬁc transporters. It also
leads to the question of how uptake is related to toxicity and resistance.
A ﬁrst report suggests that tellurite may be transported into E. coli cells by
the phosphate transporter (Tomas and Kay, 1986). This conclusion was
derived from two observations: ﬁrst, TeO2
3 is a strong competitive inhibitor
of the transport of phosphate in wild type strain and second, some mutants
defective in phosphate transport are collaterally resistant to high levels of
tellurite. Indeed, sensitivity to the anion is restored by a plasmid carrying the
phoB region, which is involved in phosphate transport. Likewise, tellurite
uptake in R. capsulatus cells is a DpH-dependent process strongly repressed
by the K+/H+ exchanger nigericin and by the sulfhydryl reagent NEM
(Borsetti et al., 2003b). These observations support the idea that R. capsulatus imports tellurite by a phosphate transporter belonging to the Pit
2
family, which catalyze the transport of H2PO
across the inner
4 /HPO4
+
membrane in an electro-neutral way, working as a H /solute symport system (Van Veen, 1997; Harris et al., 2001). However, these data do not
exclude the existence of additional mechanisms for the uptake of the oxyanion; indeed, recent results in aerobically grown cells of R. capsulatus suggest that tellurite may enter the cells by exploiting other carriers, such as an
as yet uncharacterized monocarboxylate transporter (R. Borghese, personal
communication) in a DpH-dependent manner as previously shown by Borsetti et al. (2003b). These tellurite transport experiments are challenging as
‘‘friendly’’ radio-isotopes of tellurite do not exist. Even so, a few studies
have utilized this approach to demonstrate levels of uptake (Lloyd-Jones et
al., 1991, 1994). With the advent of a spectroscopic method to examine free
tellurite concentration via the chelator diethyldithiocarbamate, tellurite
transport can be more easily explored (Newman et al., 1989; Turner et al.,
1992b). Using this assay, the Ter determinants, ter, kilAtelAB, and tehAB
were shown not to mediate any change in the uptake rate (Turner et al.,
1995a). However, it was observed that the arsenite/arsenate/antimonite resistance determinant arsABC, an ATP-dependent efﬂux pump, does in fact
give rise to reduced tellurite accumulation suggesting that the ars is a general
chalcogen efﬂux transporter (Turner et al., 1992a). Finally, it cannot be
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
45
ruled out that, along with other oxyanions, GlpF (discussed above regarding
Se oxyanion transport) may also facilitate movement of telluro compounds.
6.3. Mechanism(s) of Chalcogen Toxicity
While different elements have different toxicity levels toward different bacterial groups, in general the toxicity levels of the different
chalcogen oxyanions, from most toxic to least toxic, is:
2
2
2
2
TeO2
Tellurite is
3 4TeO4 AsO2 4AsO4 ; SbO2 4SeO3 SeO4 .
toxic at MIC’s on the order of 0.006–0.8 mM, whereas selenite ranges from
8.1 to 28 mM for organisms such as E. coli, S. aureus, and P. aeruginosa
(Harrison et al., 2004a). The study of the toxicity of these oxyanions in
eukaryotes is far more developed. Information within these eukaryotic
studies could provide clues to toxicity in prokaryotic systems. We brieﬂy
overview this research below.
Selenium exempliﬁes Paracelsus’ statement: ‘‘It is the dose that makes the
poison’’. Indeed, selenium is essential for animals and humans to guarantee
growth and reproductive functions (Fan, 1990). Deﬁciency in humans
results in a condition known as Keshan’s disease, a cardiomyopathy found
in some areas of China, where the selenium concentration in soils is low
(Chen et al., 1980). Another Se-responsive disease, also reported in regions
of China, Siberia, and Korea, is an osteoarthropathy called Kaschin-Beck
disease (Ge and Yang, 1993). Conversely, excess selenium in a diet leads to a
pathological status deﬁned as ‘‘selenosis’’ and acute selenium overexposure
can cause several characteristic symptoms. For more detailed information
on nutritional and toxicological aspects, see Fan (1990) and Goldhaber
(2003).
There are many proposed mechanisms by which selenium and its derivatives cause toxicity in eukaryotic cells. Metalloid Se can undergo redox
reactions with thiols (Klassen et al., 1985), which can compromise the function of structural, enzymatic, and regulatory proteins (Park et al., 2000a,b;
Gupta and Porter, 2002; Hartwig et al., 2002; Chung et al., 2006a). By
reacting with thiols, and glutathione in particular, selenium initiates a ROS
production, involving the formation of selenide (RSe). This intermediate
may enter a redox cycle and generate the superoxide anion and oxidative
stress, or may form free radicals that could inhibit further enzymes or cause
damage to cell membranes and DNA (Chaudier et al., 1992; El-Demerdash,
2001; Abul-Hassan et al., 2004; Garcia et al., 2005). Another mechanism
by which selenium and its derivatives may exert their toxicity in eukaryotic
cells is through selenium substitution for sulfur in methionine, forming
46
DAVIDE ZANNONI ET AL.
selenomethionine, which may be mis-incorporated into proteins. This
interaction could explain the teratogenic action of these compounds (Combs
and Combs, 1986) and the damage to keratin-containing proteins in adults
exposed to high levels of selenium in their diet (Fan, 1990).
It is important to note that the toxicity of Se depends on the chemical
form of the element, as this determines its bioavailability and ability to enter
the organism and/or cells. In addition, the negative action of the metalloid
can be altered by its interactions with other substances, such as sulfate,
methionine, cysteine, heavy metals (As, Cd, Cu, Pb, Hg, Ag, Zn), and
vitamins C and E (Fan, 1990). Hence, a selenium effect is a result of a
balance between antioxidant and pro-oxidant abilities in the cells.
Superoxide production may be the major mechanism of selenium toxicity
under aerobic conditions in prokaryotic cells as well, as suggested by several
studies (Turner et al., 1998; Bebien et al., 2002; Kessi and Hanselmann,
2004). Selenite is the only known compound that induces both iron and
manganese superoxide dismutases (SodB and SodA, respectively) in E. coli.
The effect of the oxyanion on the proteomic response of the microorganism
strengthens the hypothesis that selenium toxicity involves several molecular
circuits and it is not directed to a speciﬁc and single target.
6.3.1. Tellurite
Tellurium biochemistry in the context of animal and human toxicology was
last reviewed by Taylor (1996). Despite many chemical homologies between
selenium and tellurium, a nutritional role has never been identiﬁed for
tellurium; moreover, tellurium at low concentrations induces both acute and
chronic toxicity in a variety of organisms. Nevertheless, several studies have
shown that trace amounts of tellurium are present in body ﬂuids, such as
blood and urine (Goulle´ et al., 2005). Tellurocysteine and telluromethionine
can be found in bacteria (Boles et al., 1995; Budisa et al., 1995, 1997), yeast
(Yu et al., 1993), and fungi (Ramadan et al., 1989) as a result of misincorporation of tellurium in place of sulfur or selenium, thus allowing the
expression of protein analogs useful for protein structural studies. One
possibility is that tellurium may act as a metabolic antagonist of selenium,
inhibiting the catalytic activity of certain enzymes. This could be the case for
the cytosolic glutathione peroxidase (GSHpx) in hepatocytes in which
(121Te)-tellurite was shown to form adducts on the protein, with resulting
inhibition of the catalytic reduction of hydrogen peroxide by the enzyme
(Garberg et al., 1999). A contrasting situation is the one in which tellurium
and/or its oxyanion forms act on the enzyme squalene monooxygenase,
the second enzyme in the committed pathway for cholesterol biosynthesis.
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
47
Tellurium blocks cholesterol synthesis, causing a transient demyelination of
peripheral nerves (Wagner-Recio et al., 1991; Wagner et al., 1995). The same
effect has been observed with selenite and other methylselenium compounds
(Gupta and Porter, 2002). The sensitivity of squalene monooxygenase to
tellurium and selenium compounds is due to the binding of these compounds to vicinal cysteines; the methylation of tellurium in vivo may enhance
the toxicity of tellurium for this enzyme (Laden and Porter, 2001). It has
also been observed that tellurite (TeO2
3 ) ions induce the alteration of the
erythrocyte membrane and this activity is thiol-dependent as well (Deuticke
et al., 1992). Finally, most of the Te(IV) derivatives are also able to inactivate cysteine proteases, but not other families of proteases. This seems to
be related to the ability of Te(IV) compounds to react with the thiolcatalytic site of cysteine proteases. Te (VI) compounds do not exhibit any
such inhibitory activity as they are inert towards thiol moieties (Albeck
et al., 1998). Overall, the above work suggests that tellurium compounds
interact with biological systems by speciﬁc chemical interaction with
endogenous thiols.
The precise biochemical explanation for the toxicity of oxyanions of the
different chalcogens remains largely unknown in bacteria. In general, it has
been assumed that the toxicity of tellurite is a consequence of the strong
oxidizing properties. From various experiments examining tellurite resistance mechanisms, one can infer possible toxic pathways, which involve
normal bacterial physiology. For example, one recognizes that the redox
chemistry of the reaction of tellurite with nitrate reductase is unbalanced, as
nitrate reductase would undergo two electron reductions and the reduction
of TeO2
to Te0 would require four electrons. The two electron reduced
3
2
TeO3 would likely result in the formation of a radical species or radical
oxygen ions being formed. Furthermore, the reaction of tellurite with glutathione would sequester glutathione and change the glutathione/
glutaredoxin/ thioredoxin redox balance as well as produce H2O2 and
O2d , as discussed above. This latter observation corresponds with the
evidence that the induction of the cambialistic superoxide dismutase (i.e.
functional with Mn or Fe) of R. capsulatus leads to a signiﬁcant increase in
tellurite resistance. Interestingly, it has also been reported that SOD activity
is increased by the addition of a sublethal amount of K2TeO3 (Borsetti et al.,
2005). If tellurite is allowed to enter the metabolism of the cell, it can be
incorporated into enzymes as telluromethionine and tellurocysteine as well
replacing both sulfate/sulﬁte and/or phosphate in various biochemical
events. Tellurite toxicity in cells of the obligate aerobe and PCB degrader
P. pseudoalcaligenes KF707, has been linked to the production of ROS
(Tremaroli et al., 2006). This study also indicates that although Od
2
48
DAVIDE ZANNONI ET AL.
generation is clearly linked to tellurite reduction by reduced thiol (RSH)
oxidation, the time courses of the two processes are different. Experiments
with the iscS gene from G. stearothermophilus V complementing E. coli iscS
and sodA sodB mutants, support this evolving hypothesis of tellurite toxicity. The reduction of tellurite generates superoxide and other ROS
and the primary targets of the superoxide damage in E. coli may be [Fe-S]
clusters (Tantalean et al., 2003).
The interaction of tellurite with the electron transport chain via the DsbB
link to the quinone pool leads to a short circuit in the electron transfer
pathways of R. capsulatus (Borsetti et al., 2007) and also avoids overreduction of the quinone pool with a consequent stimulation of lightdependent electron transport under highly reducing conditions. Thus,
under unfavorable growth conditions, i.e. phototrophic growth under
anaerobiosis, sub-inhibitory amounts of tellurite might exert a positive effect
on the redox state of the electron transport components of the facultative
phototroph R. capsulatus.
E. coli exposed to tellurite displays a rapid loss in free thiol content
(Turner et al., 1999). In addition to this key observation, the transmembrane
DpH gradient is dissipated and intracellular ATP levels are rapidly depleted
(Lohmeier-Vogel et al., 2004). Tellurite exposure also causes a large change
in the proteome ﬁngerprint whether grown planktonically or as a bioﬁlm
(N.J. Roper, J.J. Harrison, J.M. Howell, H. Ceri and R.J. Turner, unpublished data).
6.3.2. Tellurate
Tellurate (TeO2
4 ) is about 2- to 10-fold less toxic than tellurite in most
organisms studied (Harrison et al., 2004a). However, due to its poor solubility in aqueous conditions, very little has been done with this form of
tellurium. Basnayake et al. (2001) observed that adding tellurite and tellurate to cultures of P. fluorescens was more toxic than added individually.
This work suggests a synergistic toxic effect of tellurate and tellurite
on bacterium. At this time, our understanding of Te physiology does not
provide any clues for this observation. As the observations of dark cells are
also seen in cultures exposed to tellurite, it is likely at least some similar
biochemistry is occurring or that tellurate is being reduced to tellurite.
However, at this point it is not clear what enzyme or process would carry
this reaction out, as it is clear that nitrate reductase does not have this
capacity (Avazeri et al., 1997).
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
49
6.3.3. Selenite/Selenate
Very few studies have looked at the biochemical mechanism behind the
selenite toxicity in bacteria because, at most concentrations, there is little
effect on the growth rate or accumulated biomass (Hudman and Glenn,
1984). Lohmeier-Vogel et al. (2004), using 31P-NMR on E. coli, did not see
any effect on ATP levels or on the transmembrane DpH gradient, in contrast
to the case of tellurite exposure. Examining the free thiol oxidation in E. coli
upon selenite exposure, one observes an initial loss of RSH content but this
oxidation recovers over a short time (Turner, unpublished results). This is
probably because the glutathione reductase can accept the GSSeSG as a
substrate.
Similarly, selenate is essentially non-toxic to most bacteria (Huber et al.,
1967, 2000). It is unknown if this is due to simply a lack of uptake or the
inability to reduce selenate to selenite. However, it is clear that nitrate reductases can perform this task (Avazeri et al., 1997; Sabaty et al., 2001).
Overall, the toxicity of tellurite can be considered to be a combination of
speciﬁc targeted thiol chemistry and the resulting production of oxygen
radical species that contributes to poisoning of the cell’s electrochemistry.
The difference in toxicity between the oxyanions of the different chalcogens
may lie in the rate or ability to repair thiol oxidation and/or process RS
Ch(II) species (such as GSSe vs. GSTe) and the rate of ROS production, all
of which leads to subsequent damage to respiratory and biosynthetic pathways. On a ﬁnal note, it has been observed that addition of selenite with
tellurite will increase the tellurite MIC; i.e. selenite protects against the toxic
affects of tellurite (R. Turner, unpublished results). This suggests that the
preferential, and less toxic, biochemical paths and kinetics of selenite can
dominate over that of tellurite. Thus, tellurite has a less noxious relationship
with the cell.
7. OTHER CHALCOGENS AND METALLOIDS
7.1. Polonium
Nothing has been done exploring polonium (Po) speciﬁcally due to its
extremely low natural abundance and its high radioactivity. The chemistry
of soluble forms of Po are expected to be similar to that of the other chalcogens; however, the toxicity level would be greater due to the radiation.
50
DAVIDE ZANNONI ET AL.
Although no speciﬁc investigations exist, studies by Momoshima et al.
(2001, 2002) on aquatic samples suggest that the microorganisms are able to
generate volatile Po species most likely through methylation. Additionally,
they may have the ability to reduce oxyanion forms of Po, leading to precipitates, as the authors indicate there was some accumulation of Po in some
form as well. This observation is supported by a previous study that demonstrated that phytoplankton and bacteria accumulate 210Po (Wildgust
et al., 1998). Other early work from the 1960s by various Russian groups
simply used Po as a source of radiation to explore radiation tolerance of
microbes.
7.2. Other Metalloids
By deﬁnition, the metalloids include the elements Bo, Al, Si, Ge, As, Sb, Te,
and Po. Of these, considerable research has been focused on the resistance of
arsenite, arsenate, and antimonite. The microbiology of these will not be
discussed here and the reader is referred to the following reviews: Rosen
et al. (1999), Rosen (2002c), Bhattacharjee et al. (2000), Rosen (2002a,b),
Mukhopadhyay et al. (2002), Silver and Phung (2005a,b).
However, the arsenate resistance determinant arsABC is worth bringing
up here. ArsAB is an ATP-dependent efﬂux pump and ArsC is an arsenate
reductase, reducing arsenate to arsenite. Turner et al. (1992a) observed that
this operon also provides tellurite resistance and effectively efﬂuxes tellurite,
keeping tellurite accumulation low and thus preventing the ‘‘blackening’’ of
the cells through tellurite reduction. Surprisingly, ArsC, the thiol-dependent
arsenate to arsenite reductase, was required for full resistance. Along the
same lines, arsenate reductase activity was found in the plasmid pI258 from
S. aureus. This reductase demonstrated selenate reduction and was inhibited
by tellurite (Ji et al., 1994). The overlap of the biochemistry of arsenate with
the chalcogen oxyanions remains mostly under-appreciated and under-investigated.
8. CONCLUDING REMARKS
An interesting biological puzzle of tellurite resistance arises from the
observation that levels of resistance frequently observed are higher than the
concentrations typically experienced in the environment. Levels of selenite
THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS
51
resistance are closer to environmental levels and the biochemistry of the cells
may have naturally evolved in order to coexist with this metal. Perhaps, the
striking difference between the cellular response against tellurite and the
cellular response against selenite results from the evolutionary pressure of
speciﬁc intracellular, periplasmic, and electron transfer redox components.
The recent observation that the thiol:disulﬁde oxidoreductase DsbB allows
the transfer of oxidizing equivalents from tellurite to membrane-embedded
quinols opens new perspectives for both microbial physiologists and biochemists. The possibility that membrane-bound disulﬁde proteins might act
as ‘‘electron conduits’’ between periplasmically localized metalloids and
redox complexes raises the question of whether metalloids can be considered
toxic per se or whether they might also act as ‘‘electron sinks’’ under
unfavorable reducing conditions.
Another biological puzzle concerns how metalloids get into cells and their
subsequent cytosolic fate. No speciﬁc carriers have been identiﬁed although
several non-speciﬁc mechanisms are known. Further, the reduction
mechanisms are still uncertain for both tellurite and selenite to their less
toxic elemental forms, while it is clear they are linked to generation of toxic
ROS. Thus, in the case of tellurite and probably also that of selenite, the
cellular response is more likely to be a suicide mechanism than a rescue
mechanism.
In conclusion, tellurite resistance has proved to be the greatest challenge
in the metal and metalloid resistance ﬁeld. The level of frustration was
clearly illustrated in a recent review by Silver and Phung (2005a) where the
area of tellurite resistance was given as a single paragraph indicating that the
research had not progressed much in 10 years in comparison to the research
on other metals. However, as seen here, advances have indeed been made in
both understanding the mechanism of toxicity of this metalloid and the
various biochemical processes it interacts or interferes with. We are hopeful
that this review helps to bring ‘‘tellurium microbiology’’ out of the dark age.
ACKNOWLEDGMENTS
This work was supported by MIUR (PRIN 2005) to D.Z. and NSERC to
R.J.T. R.J.T. is grateful for discussions with Andrew J. Percy and discussions on the bioﬁlm research with Howard Ceri. J.J.H. was supported by an
AHFMR studentship and a NSERC CGSD award. We also thank Bronwyn
Hasslam for the careful reading of the manuscript.
52
DAVIDE ZANNONI ET AL.
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Plate 1 Biogeochemical transformation of tellurite and selenite by bacteria. The coloration of black cells (tellurite) and redorange (selenite) is due to the reduction to Ch(0) product within the cells. (A) Pseudomonas aeruginosa grown in microtitre plate
planktonically with tellurite. (B) P. aeruginosa grown on Calgary Bioﬁlm Device pegs with tellurite. (C) P. aeruginosa grown in
microtitre plate planktonically with selenite. (D) P. aeruginosa grown on Calgary Bioﬁlm Device pegs with selenite. (E) E. coli
grown on solid Luria Bertani broth showing the black colonies. (F) Thin section electron micrograph of E. coli grown in the
presence of tellurite. The ﬁgure shows the precipitation of black crystals along the membrane. (G) E. coli harboring various
tellurite resistance determinants. The non-colored culture of the ars is reﬂective of the resistance being an efﬂux system. (For b/w
version, see page 10 in the volume)
Gaining Insight into Microbial Physiology
in the Large Intestine: A Special Role for
Stable Isotopes
Albert A. de Graaf1,2,3 and Koen Venema1,3
1
Wageningen Center for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands
2
Department of Surgery, University of Maastricht, Maastricht, The Netherlands
3
TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands
ABSTRACT
The importance of the human large intestine for nutrition, health, and
disease, is becoming increasingly realized. There are numerous
indications of a distinct role for the gut in such important issues as
immune disorders and obesity-linked diseases. Research on this longneglected organ, which is colonized by a myriad of bacteria, is a rapidly
growing ﬁeld that is currently providing fascinating new insights into the
processes going on in the colon, and their relevance for the human host.
This review aims to give an overview of studies dealing with the
physiology of the intestinal microbiota as it functions within and in
interaction with the host, with a special focus on approaches involving
stable isotopes. We have included general aspects of gut microbial life as
well as aspects specifically relating to genomic, proteomic, and
metabolomic studies. A special emphasis is further laid on reviewing
relevant methods and applications of stable isotope-aided metabolic
ﬂux analysis (MFA). We argue that linking MFA with the ‘-omics’
technologies using innovative modeling approaches is the way to go to
3
Present address: TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands.
ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53
ISBN 978-0-12-373713-7
DOI: 10.1016/S0065-2911(07)53002-X
Copyright r 2008 by Elsevier Ltd.
All rights reserved
74
ALBERT A. DE GRAAF AND KOEN VENEMA
establish a truly integrative and interdisciplinary approach. Systems
biology thus actualized will provide key insights into the metabolic
regulations involved in microbe–host mutualism and their relevance
for health and disease.
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1. Why Study Intestinal Microbial Physiology? . . . . . . . . . . . . . .
1.2. Purpose of This Review . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. The gut microbial ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Butyrate is Important . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2. Gut pH Matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3. Microbes Extend Our Genome . . . . . . . . . . . . . . . . . . . . . . .
2.4. Microbes Keep Our Immune System on Standby . . . . . . . . . .
2.5. Methods to Study Bacterial Physiology: Many Fields of
Science Come Together . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6. Stable Isotopes Offer Unique Insights . . . . . . . . . . . . . . . . . .
3. Stable isotopes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1. What are Stable Isotopes? . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2. How are Stable Isotopes Detected? . . . . . . . . . . . . . . . . . . .
3.3. What Information can be Retrieved from Stable Isotope
Experiments? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4. Important Fields of Applications of Stable Isotopes in
Biomedicine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5. Basics of Metabolic Flux Analysis . . . . . . . . . . . . . . . . . . . . .
3.6. MFA in Detecting Microbial Metabolic Stress . . . . . . . . . . . . .
4. Genomic inventories of intestinal bacteria . . . . . . . . . . . . . . . . . . . .
4.1. General Aspects: Cataloguing Intestinal Microbial
Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2. The Microbiome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3. Stable Isotope Probing: Clues to Metabolic Function from
Genomics Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. Proteomic aspects of intestinal microbial life . . . . . . . . . . . . . . . . . .
5.1. Functions of Intestinal Bacterial Enzymes . . . . . . . . . . . . . . .
5.2. Proteomic Studies of the Gut Microbiota: A Largely Unprobed
Area? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3. Can Stable Isotopes Help in Proteomics? . . . . . . . . . . . . . . .
6. Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1. Microbial Products and What They Can Mean to Us. . . . . . . .
6.2. Tracing the Fate of Prebiotics: In Vitro Models and Stable
Isotopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3. Evidence of Cross-Feeding. . . . . . . . . . . . . . . . . . . . . . . . . .
7. Metabolic flux analysis applied to the gut . . . . . . . . . . . . . . . . . . . .
7.1. Insights into Bacterial Metabolic Routes. . . . . . . . . . . . . . . . .
7.2. Get Quantitative: Mass Balances Reveal a Lot. . . . . . . . . . . .
7.3. Stable Isotope-Aided Quantification of Pathways: Functional
Genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8. Emerging picture of the role of microorganisms integrated in man . .
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MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
8.1. Energy Balance . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2. Innate Immune System . . . . . . . . . . . . . . . . . . . . .
8.3. Intestinal Microbiota: Is There a Link With Obesity? .
8.4. Role of Stable Isotopes . . . . . . . . . . . . . . . . . . . . .
9. New aspects in the study of intestinal bacterial physiology .
9.1. Microbes at War: Population Competition Models . . .
10. Conclusions and future prospects . . . . . . . . . . . . . . . . . .
10.1. Toward a Systems Biology of the Gut . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75
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147
1. INTRODUCTION
1.1. Why Study Intestinal Microbial Physiology?
What happens in our intestine? The human gastro-intestinal (GI) tract is the
primary site of food intake, food perception, and food conversion. In the
ﬁrst part, the small intestine, highly active enzymatic hydrolysis of carbohydrates, fats, and proteins takes place and the resultant digestive products
are absorbed. Thus, the bulk of our food intake is processed by the small
intestine. What then is the function of the second part of the intestine, the
large intestine (or colon)? Until recently, the large intestine was considered
just a storage place for undigested food components. However, the past
5–10 years have changed this view drastically. Nowadays, the large intestine
is called the ‘forgotten organ’. The cells of microorganisms (totaling
approximately 1014 cells) present in the colon outnumber the cells of the host
by a factor of 10 and all these bacteria contribute to nutrient processing. The
biochemical (metabolic) potential of this complex assemblage of different
microorganisms is considered equal to that of the liver. This community
of mostly anaerobic bacteria inﬂuences human gut physiology and health by
performing a number of activities including fermentation of dietary compounds which escape digestion in the small intestine, processing of mucosal
cells shed in the small intestine, and of intestinally secreted mucus. Thus,
polymers of sugars are degraded by the colonic microbes into gases such as
hydrogen, carbon dioxide, and methane as well as short-chain fatty acids
(SCFAs) (Bergman, 1990; Cummings, 1991), notably butyrate, propionate,
and acetate (Fig. 1). These SCFA are taken up by the host and contribute to
its energy and health status. In addition, the microbial community produces
a variety of other health-related compounds including vitamins (Hill, 1995)
and other growth-promoting compounds. However, toxic, mutagenic, and
carcinogenic substances (Cummings and Macfarlane, 1997) may be formed
that negatively affect the host. It is also known that the colon plays a role
76
Propionate
Carbohydrates
PropionylCoA
Diacetyl
Glycerol
CO2
Succinate
Malate
Pyr
Acetolactate
Lactate
Acetate
Acetic Aldehyde
Formate
CO2
Ethanol
AcetoAcetylCoA
CO2
AcAcetate CrotonylCoA
3-Hydroxybutyrate
n-Butanol
Butyrate
Figure 1 Schematic overview of anaerobic bacterial metabolic pathways involved in carbohydrate metabolism. The dashed
reaction represents the Wood–Ljungdahl pathway of acetate formation from CO2. Boxes show the short-chain fatty acids
(SCFAs) from C1 (formate) up to C4 (butyrate), and lactate.
ALBERT A. DE GRAAF AND KOEN VENEMA
2,3-Butanediol
CO2
AcetylCoA
Acetoin
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
77
in the modulation of the immune system (Chadwick and Anderson, 1995),
the transformation of bile acids (Aries and Hill, 1970), and the provision of
a barrier against pathogenic bacteria (Hill, 1995).
The above facts already indicate that the processes going on inside the
colon are important for us. There is indeed evidence that the bacterial metabolic processes in the colon are becoming an increasingly important issue for
man. Numerous indications of a correlation between intestinal health status
and the occurrence of various intestinal diseases, such as colon cancer,
inﬂammatory bowel disease (IBD), and irritable bowel syndrome (IBS)
(Chadwick and Anderson, 1995) have been reported. The general hypothesis
is that there is a strong correlation between proteolytic fermentation in the
(distal) colon and the occurrence of colon cancer and IBD (Macfarlane
et al., 1992a; Levine et al., 1998). Protein fermentation leads to the production of microbial metabolites that can be toxic to the host (Macfarlane
et al., 1992b; Macfarlane and Macfarlane, 1995). In particular, these may
include sulfur-containing metabolites (Hill et al., 1995; Rowland, 1995).
Carbohydrate fermentation, on the other hand, leads amongst others to
the production of SCFA, which are considered to be health promoting
(Cummings, 1991, 1995). During carbohydrate fermentation, protein is incorporated into microbial biomass (Birkett et al., 1996), preventing fermentation of protein. However, most carbohydrates are completely fermented
in the proximal colon, which leads to the depletion of these substrates in the
distal colon. Here, the microorganisms switch to fermenting protein. It
is hypothesized that it would therefore be of importance to prolong the
fermentation of carbohydrates. This can be accomplished for instance by
including more slowly fermentable carbohydrates in the diet (e.g. certain
types of resistant starch). More generally, the idea developed that changing
the diet in such a way that harmful bacteria (or their harmful activities) are
suppressed and beneﬁcial bacteria (or their beneﬁcial activities) are stimulated, may contribute to improving gut health. However, the microbial
processes occurring in the colon are hitherto largely unknown, because the
accessibility of the lumen of the human colon is severely limited in practice.
1.2. Purpose of This Review
The studies discussed above all served to demonstrate that there is a strong
yet intricate link between intestinal bacterial metabolism and gut health.
There is also little doubt that nutrition has an impact on the composition
and activity of the intestinal microbiota and thereby inﬂuences human
health and well-being. However, the mechanisms behind these processes are
78
ALBERT A. DE GRAAF AND KOEN VENEMA
still largely unknown. If we want to be able to improve human health by
optimizing nutrition, we must know what are the regulatory mechanisms
that govern how our intestinal bacteria react to various food ingredients,
and also what will be the determinant processes for the reaction of the host
(i.e. our own body) in return. While this may seem a distant goal, a key
step in this research, i.e. the characterization and analysis of the bacterial
physiological behavior, has become possible using the powerful analytical
methods available today. While genomics, proteomics, metabolomics, and
bioinformatics immediately come to mind, it is becoming increasingly clear
that the ﬁnal endpoint of the genome, proteome, and metabolome, i.e. the
ﬂuxome, is perhaps the most closely linked with physiology. Indeed, it is
argued that fully assembled metabolic pathways in living systems, rather
than genes or proteins, are the true units of function in biology and biochemistry. A corollary is that measurement of metabolic ﬂuxes (biochemical
kinetics) is thereby required to understand biochemical control and gene
function (Hellerstein, 2004). Studying the dynamics of an organism’s metabolic ﬂuxes in response to various stimuli (such as different nutritional
components) provides the unique and key insights to understand physiological regulation. As pointed out above, it is precisely this regulation which
needs to be discovered so as to ﬁnally enable a targeted modulation of
intestinal bacterial metabolism to achieve beneﬁcial effects on human health.
Therefore, the present review will put a special focus on the application
and perspectives of metabolic ﬂux analysis (MFA) and its main enabling
technology, stable isotope labeling, to study intestinal microbial physiology.
2. THE GUT MICROBIAL ECOSYSTEM
2.1. Butyrate is Important
As already eluded to above, SCFAs (primarily acetate, propionate, and
butyrate) are the major end products of bacterial fermentation in the
intestine and affect key functions of the colonic epithelium in vivo. These
compounds are probably key participants in gut maintenance (Bergman,
1990; Kruh et al., 1991; Mariadason et al., 2000) and may also be beneﬁcial
contributors to the peripheral metabolism in humans (Macfarlane and
Cummings, 1991). Therefore, SCFA have been the subject of numerous
investigations. Butyrate is considered the most important of the SCFA.
In the large intestine, butyrate is present in millimolar concentrations.
Butyrate is metabolized by epithelial cells and is responsible for 70% of their
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
79
energy needs (Roediger, 1982; Scheppach, 1994). In addition, it acts as a
signaling metabolite, affecting epithelial cell proliferation and differentiation
(Gamet et al., 1992; Gibson et al., 1992). In addition to its established role in
regulating viability, differentiation, and proliferation (Velazquez et al.,
1997), butyrate was reported to be effective in cancer suppression (McIntyre
et al., 1993; Singh et al., 1997). Furthermore, butyrate might be beneﬁcial in
preventing mucosal inﬂammation, because decreased availability of butyrate
has been associated with distinct forms of colitis (Harig et al., 1989; Chapman et al., 1994; Ahmad et al., 2000). Moreover, butyrate enemas have been
shown to be an effective treatment for mucosal inﬂammation in both
humans and animal models of colitis in some studies (Scheppach et al., 1992;
Gibson and Rosella, 1995; Butzner et al., 1996; D’Argenio et al., 1996;
Fernandez-Banares et al., 1999; Kanauchi et al., 1999; Okamoto et al., 2000;
Andoh et al., 2003; Cherbut et al., 2003). Experimental work to explain the
molecular mechanisms of this anti-inﬂammatory property of butyrate
largely focused on cultured intestinal epithelial cells, where butyrate was
shown to modulate IL-8, and macrophage inhibitory protein 2. In addition,
butyrate has been shown to inhibit activation of the transcription factor
NF-kB in cultured epithelial cells (Wu et al., 1999; Inan et al., 2000). Furthermore, butyrate has been shown to have an anti-inﬂammatory effect on
human monocytes by the potent inhibition of IL-12 and upregulation of
IL-10 production (Saemann et al., 2000). Moreover, butyrate resulted in an
increase of IgA-producing cells and mucosal IgA concentrations (Morita
et al., 2004; Roller et al., 2004), the secretion of anti-inﬂammatory cytokines
(Fusunyan et al., 1998, 1999; Saemann et al., 2002; Bocker et al., 2003), and
decreased activity of myeloperoxidase (Butzner et al., 1996; Cherbut et al.,
2003), an enzyme which aids in the defensive properties of phagocytic
cells of the human immune response. The (regulation of the) ﬂux of butyrate
(and the other SCFA for that matter) by (speciﬁc) intestinal microorganisms
in the colon and the use of the SCFA in the rest of the body, however, is still
largely unknown. It is important therefore to know which bacterial metabolic
routes exist in vivo for the synthesis of SCFA. Knowledge of the regulation
of these metabolic routes will allow the development of dietary strategies to
inﬂuence SCFA metabolism, possibly even for therapeutic purposes.
2.2. Gut pH Matters
Carbohydrate fermentation results in lowered cecal pH concomitant with
the production of SCFA (Cummings and Bingham, 1987). In sudden death
individuals, a significant trend from high to low concentrations of SCFA
80
ALBERT A. DE GRAAF AND KOEN VENEMA
has been found on passing distally from cecum to descending colon, while
pH changed from 5.670.2 in the cecum to 6.670.1 in the descending colon
(Cummings et al., 1987). Interestingly, there seems to be an inverse effect
of pH on SCFA metabolism as well; the response of human fecal microbial
communities in anaerobic continuous culture showed markedly higher ﬁnal
butyrate concentrations at pH 5.5 compared with pH 6.5, whereas acetate
and propionate were higher at pH 6.5 (Walker et al., 2005). Changes
in colon pH are also reported to alter the metabolism of protein, bile acids,
nitrate, sulfate, and other substances (Cummings and Bingham, 1987).
In contrast to SCFA, products of protein fermentation, such as ammonia,
branched chain fatty acids, and phenolic compounds, progressively increase
from the proximal (right) to the distal (left) colon, as does the pH of gut
contents (Macfarlane et al., 1992a). Epidemiological studies found a marked
correlation between pH and the incidence of colon cancer (e.g. Levy et al.,
1994), which in a subsequent study appeared to be associated with higher
animal protein and fat consumption (O’Keefe et al., 1999). However, a
direct linkage between colon cancer and alkaline colonic pH was questioned
in other experimental studies (Hove et al., 1993). This demonstrates that it is
difﬁcult to establish unequivocally the cause–effect relationships between
intestinal processes and human health due to the many interactions present.
Clearly, extensive additional systematic research is necessary to elucidate the
regulation of intestinal bacterial metabolism, the reaction of the host, and
the interaction between both.
2.3. Microbes Extend Our Genome
The colonic microbiota represents an enormous and largely unexplored
potential of metabolic pathways for synthesis and degradation of compounds
(Sekirov and Finlay, 2006). This has, for instance, been described for fatty acids
(Juste, 2005). Microbial activity may be relevant here in various ways; bacteria
may detoxify food components (Humblot et al., 2005), but also produce toxins
themselves. Due to the multitude of chemical reactions they can carry out,
intestinal bacteria may contribute significantly to drug metabolism (Shu et al.,
1991; van de Kerkhof et al., 2005). This may be exploited by having a drug
activated by intestinal bacteria that is otherwise absorbed before it can exert its
action, or by applying bacteria themselves to act directly on gut epithelial cells
(O’Hara et al., 2006). Clearly, while metabolic activation of drugs by intestinal
host cells has already been demonstrated (Gharat et al., 2001), bacteria must
also be considered able to carry out speciﬁc desired biotransformations of food
(glucosinolates, ﬂavonoids, etc.) and drugs. However, the establishment of
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
81
the highly speciﬁc biotransformations needed for drug efﬁciency may prove
difﬁcult in such a complex microbiota as that found in the colon.
2.4. Microbes Keep Our Immune System on Standby
Gut bacteria also play an important role in the development and modulation
of our immune system. A healthy mucosa shows a certain chronic, basal
inﬂammatory activity in the lamina propria. This is essential in relation to its
barrier function and is closely related to the intestinal microbiota. It is assumed that a number of important health issues, such as IBS, are associated
with an aberration of this inﬂammatory activity (Collins, 2001). In other
words, it seems that we need our intestinal bacteria to keep up a certain basal
level of activity of our intestinal immune system. The interaction between
bacteria, their metabolic products, and the colonic epithelium is of pivotal
importance for the inﬂammatory reaction. Recently, the importance of mast
cell activity for intestinal function in relation to intestinal comfort has been
described (O’Sullivan et al., 2000; Barbara et al., 2004; Siddiqui and Miner,
2004), and may well be modiﬁed by nutritional intervention (Rydzynski and
Dalen, 1994; Ju et al., 1996; Ganessunker et al., 1999; Larauche et al., 2003).
Unfortunately, a single biomarker for epithelial health is lacking. One has to
rely on markers of permeability, e.g. production of certain tight junction
proteins (Nusrat et al., 2000; Ma et al., 2004), Paneth cell defensins (Bevins,
2006), transport of paracellular and transcellular inert permeability markers
(Baumgart and Dignass, 2002), inﬂammation (Saemann et al., 2000), mucus
composition (Szentkuti et al., 1990), cell turnover and apoptosis (Andoh
et al., 2003), mast cell activation (O’Sullivan et al., 2000), and others.
Healthy individuals can be subjected to investigating these biomarkers
as many of these can be determined by non- or minimally invasive, but
sophisticated, techniques. Furthermore, in patients with established increased inﬂammatory activity, such as ulcerative colitis (UC) and pouchitis,
these techniques can also be implemented. The intestinal immune system and
the mucus layer are both important for human host defence and can be
affected by microbial metabolites (amongst others butyrate).
2.5. Methods to Study Bacterial Physiology: Many Fields of
Science Come Together
Not surprisingly, recent years have seen the development of specially
adapted experimental techniques to study the colon and its inhabitants.
These include in vitro model systems, cell culture models, animal models,
microdialysis, and breath tests (Table 1).
82
Table 1
Some characteristics
References
In vitro model
systems
-From simple anaerobic cultivation tubes to fully ﬂedged sophisticated
computer-controlled in vitro model systems
-Enable quantitative studies under deﬁned conditions using an inoculum
isolated either from feces or, in an invasive manner, from speciﬁc sites along
the GI tract
-The effect of e.g. prebiotics on the composition and activity of the microbiota
has been studied in such a model
-From CaCO-2 or HT29 cell lines to intact intestinal mucosal strips
-Enable the selective study of the properties of different types of (transformed)
colon cells in isolation
-The advantage of mucosal strips over cell cultures is that conditions probably
better approach the in vivo situation because the strips include an intact basolateral lining of cells, as e.g. reﬂected in Km values for butyrate uptake that
differ markedly from isolated colonocytes
-Especially dogs and pigs, with several methods of sampling: either (i)
dissection after sacriﬁcing the animals, (ii) use of stomas implanted in the
living animal to probe speciﬁc luminal sites in the GI tract, or (iii) multicatheterization of blood vessels so as to gain access to arterial, portal, and
hepatic venous blood all at the same time in the living, conscious animal
Minekus et al. (1999),
Jensen and Jorgensen
(1994), van Nuenen et al.
(2003), Venema et al.
(2003)
Cell culture
models
(Monogastric)
animal models
Boren et al. (2003),
Jorgensen and
Mortensen (2000),
Jorgensen and
Mortensen (2001)
Deutz et al. (1998),
Rerat (1985), Wunsche
et al. (1979)
ALBERT A. DE GRAAF AND KOEN VENEMA
Experimental
technique
Breath tests
and gas
analyses
Rooyackers et al. (2004),
Jansson et al. (2004)
Christian et al. (2002),
Jensen and Jorgensen
(1994), Slater et al.
(2006)
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
Microdialysis
-Upon chemical analysis of blood samples, the techniques described in the line
above allow for detailed quantitative studies of net splanchnic absorption and/
or intestinal production of metabolites, but such studies generally bear a
(strong) invasive character
-Can be used safely with low-grade invasiveness in humans with small
catheters placed in speciﬁc tissue beds of interest
-Allowing continuous sampling of the interstitial space over prolonged periods
of time without taking any biopsies
-Quantitative measures for metabolic activity are not easily obtained because
the exact amount of tissue involved in the dialysis is not known. Results of
intraperitoneal microdialyses were shown to strongly depend on catheter
position
-Enable non-invasive assessments of especially intestinal (carbohydrate)
metabolism
-Information is limited and quantitative aspects are not trivial since measured
values represent overall metabolism of the complete organism
83
84
ALBERT A. DE GRAAF AND KOEN VENEMA
The challenge for the coming years is to establish a truly integrative research approach that will enable the analysis and correlation in a meaningful
manner of the many diverse aspects involved in intestinal microbial metabolism and its interaction with the human host. This will require the cooperation between scientists from various different scientiﬁc disciplines such
as microbial physiology, human physiology, and gastroenterology, as well as
highly technical disciplines including genomics, proteomics, metabolomics,
analytical sciences, and bioinformatics and possibly nano-technology.
2.6. Stable Isotopes Offer Unique Insights
Perhaps one of the greatest difﬁculties in the research of intestinal metabolism and function is the requirement for adequate, minimally invasive
experimental techniques that allow for research in humans in vivo. Such
techniques will have to cope with severely limited possibilities for manipulation of experimental conditions as well as for material sampling. The
use of stable isotopes may prove to be a key factor to success here. Stable
isotope-labeled molecules follow the same metabolic routes, and function
identically in physiological processes as their natural unlabeled counterparts. The isotopic label, however, allows for their speciﬁc detection at any
desired stage after their administration, allowing indirect monitoring of the
processes in which they are involved. Over the past 20 years, stable isotopelabeling techniques have proven to be powerful tools to get quantitative
as well as qualitative information about the metabolic processes in living
organisms in general, including microorganisms, plants, animals, and
humans, and also in the colon in vivo (Moran and Jackson, 1990; Wolfe,
1992; de Graaf, 2000; Shulman and Rothman, 2001; Pouteau et al.,
2003; Kelleher, 2004; McCabe and Previs, 2004; Dolnikowski et al., 2005;
Ratcliffe and Shachar-Hill, 2006). The use of isotopically labeled compounds enables the selective study of that part of the metabolism in which
the isotopic tracer is involved, offering ample possibilities to probe microbial
as well as host metabolism, or both. Isotopic labeling in compounds can be
conveniently and specifically detected by mass spectrometry and/or nuclear
magnetic resonance (NMR)-based analytical techniques.
One indirect yet important feature of stable isotopes is that their application involves and bridges many disciplines at the same time, providing
scientists with ample opportunities to come across different ﬁelds than their
own, and stimulating cross-fertilization of ideas. Scientists from the life
sciences working with stable isotopes will acquire a sense for analytical
issues, and they will necessarily have to interact with physicists and
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
85
mathematicians to undertake their modeling work successfully. The complexity of human metabolism may make researchers in the medical ﬁeld
refrain from in-depth mechanistic analyses of metabolic regulation, yet the
impressive results obtained from stable isotope studies in microbial applications in recent years may stimulate the design of studies to tackle more
complex issues. In return, analytical scientists will broaden their views because they need to ﬁnd solutions that suit both the experimental constraints
associated with biological material, as well as the requirements of the data
modelers to reach accurate and precise parameter estimates and statistically
significant results. Mathematicians and physicists are forced to make sense
out of data that are often imprecise and show limited reproducibility, yet the
inherent ability of such investigations to detect structure and logic even in
complex multivariate data adds to the level and quality of conclusions that
can ultimately be derived from experimental work. Put differently, working
with stable isotopes conveys a natural inclination toward systems biology
thinking (Kelleher, 2004).
3. STABLE ISOTOPES
3.1. What are Stable Isotopes?
Isotopes were discovered in the 1910s after experiments conducted by
F. Soddy gave the ﬁrst demonstration that most of the elements in nature
are composed of atoms identical from the chemical point of view but slightly
different in weight. Very soon after the discovery of deuterium, for which
H.C. Urey was awarded the Nobel Prize in 1934, researchers launched
the idea of using stable isotopes in kinetic/dynamic investigations. Thus,
early studies on fat metabolism in mice with deuterium were done by
R. Schoenheimer and D. Rittenberg (Ratner et al., 1987), and studies in
nutrition by using 15N, 13C, and 18O soon followed. Tracer methods
ﬁnd applications in nearly every ﬁeld of science, be it typical life science
ﬁelds (medicine, biology, physiology, nutrition, toxicology, biotechnology),
or more technical areas (physics, chemistry, agriculture, geoscience, engineering), which have now become an integral part of everyday life. The
common issues for all these isotope labeling applications concern the
possibility of tracing the entity of interest, called tracee, which may be a
substance, or a component of a substance, like a radical, a molecule, or an
atom. An ideal tracer has the same physical, chemical, or biological properties as the tracee, but it presents some unique characteristic that enables
86
ALBERT A. DE GRAAF AND KOEN VENEMA
its detection in the system where the tracee is also present. The production
of an isotopic tracer involves the substitution of one or more naturally
occurring atoms in speciﬁc positions in the tracee molecule with an isotope
of that atom with a less common abundance. Either stable or radioactive
isotopes can be used as tracers. Natural abundancies of a number of stable
isotopes relevant for life science research are displayed in Table 2. Mass
differences of isotopes are due to different numbers of nuclear neutrons, so
that the chemical properties are not affected. Both stable and radioactive
isotopes of an element take part in the same chemical reactions of the
element. The use of a labeled tracer requires the assumption that the labeled
molecule, or atom, will not be discriminated from the unlabeled in,
for example, chemical or enzymatic reactions, and will trace the position or
movement of the unlabeled molecules. Some isotopic effects (like evaporation processes or root uptake into plants) can be observed, especially for
light elements or molecules, and should be taken into account.
Radioactive tracers have been used very intensively in the earlier years
of life sciences research (Table 2). However, their use has diminished much
in favor of stable isotopes after the health risks of radioactivity became
apparent (and radioactive waste processing costs rose significantly).
Table 2 Stable and radioactive isotopes used in life science research
Element
Isotope
Stable or radioactive
% Natural abundance
H
1
Stable
Stable
Radioactive
Stable
Stable
Radioactive
Stable
Stable
Stable
Stable
Stable
Stable
Stable
Stable
Radioactive
Stable
Stable
Radioactive
99.985
0.015
–a
98.89
1.108
–
99.63
0.37
99.76
0.037
0.204
95.00
0.76
4.22
–
0.014
100
–
C
N
O
S
P
a
H
H
3
H
12
C
13
C
14
C
14
N
15
N
16
O
17
O
18
O
32
S
33
S
34
S
35
S
36
S
31
P
32
P
2
Abundance listed for the stable isotopes only.
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
87
3.2. How are Stable Isotopes Detected?
Detection of stable isotopes is based either on their speciﬁc masses, or on
nuclear properties such as spin. Consequently, mass spectrometry (MS) and
NMR are the main detection methods for stable isotopes used today. Mass
spectroscopy is by far the more sensitive of both techniques. A great variety
of mass spectrometer instruments and applications dedicated to the detection of compounds with different characteristics exists. Newest time-of-ﬂight
(TOF) and Fourier transform (FT) mass spectrometers reach such high
mass accuracies that, from the measured mass of a molecule, its unique
composition in terms of number of carbons, nitrogens, oxygens, and protons
can be derived unequivocally, allowing for easy identiﬁcation. Especially for
MS instruments that do not have such high mass resolution, the spectrometer is often run ‘hyphenated’ with a chromatographic separation technique (either liquid chromatography (LC) or gas chromatography (GC)) to
allow for better separation of signals, namely in both the (retention) time
and the mass dimension. Illustrative examples of such two-dimensional approaches used for isotopic studies of metabolism include LC-MS of glycerol
and glucose (McIntosh et al., 2002), LC-MS of amino acids (van Eijk and
Deutz, 2004), GC-MS of amino acids (Christensen and Nielsen, 1999)
in protein hydrolysates, volatile fatty acid detection by GC-MS, and
GC-combustion-isotope ratio mass spectrometry (GC-C-IRMS) (Morrison
et al., 2004), as well as IRMS approaches for breath test analysis (Stellaard
and Elzinga, 2005). The latter authors describe also the use of infrared (IR)
spectroscopy for gas isotopic analysis.
One important difﬁculty in determination of isotopic enrichments by mass
spectrometry is the presence, especially with larger molecules, of background
isotope signals that stem from the natural abundance of stable isotopes, 13C
in most cases making the largest contribution. Powerful matrix calculation
protocols that allow for easy correction of those mass isotopomer peaks have
recently been developed (Wahl et al., 2004) (isotopomers are molecules having
an identical chemical composition but different masses due to the presence of
one or more isotopes).
NMR spectroscopy is far less sensitive than MS and can detect only nuclei
that possess a nuclear spin. Fortunately, these include such important isotopes
as 2H, 13C, 15N, and 17O. Moreover, NMR in contrast to MS offers the
advantage that it also allows one to determine the position of an isotopic label
within a molecule. This has proved such an enormous asset in studies
of biosynthetic pathways that the number of isotope-aided NMR studies in the
life science ﬁeld is seemingly endless, and growing every day. The interested
reader may ﬁnd useful information and references in de Graaf (2000).
88
ALBERT A. DE GRAAF AND KOEN VENEMA
3.3. What Information can be Retrieved from Stable Isotope
Experiments?
Basically, stable isotopes can convey two sorts of information: how fast a
speciﬁc metabolic process is running, and what the products of the processes
are. That is, the speed of incorporation of an isotopic label in a molecule gives
information on the synthesis rate of that metabolite, and the position of the
label in the molecule gives information on the biosynthetic pathway of
the molecule. In special cases, namely experimental protocols where a steady
state in time of the degree of isotopic labeling of the concerned molecules is
established, it is the steady-state degree of isotopic enrichment at different positions in the concerned molecules that gives information on the
synthesis rate (or, equivalently, the biosynthetic pathway ﬂux) of the molecule
(Fig. 2).
The principles of kinetic analysis from isotopic labeling experiments have
been described in detail (Wolfe, 1992). Recent interesting examples of ‘classical’ kinetic analysis and biosynthetic pathway elucidation can be found in
papers by Teusink et al. (2003) and Bacher et al. (1999), respectively.
endogenous
tracee
synthesis
Ra
tracer infusion
Inf
whole body
compartment
TTR
Rd
disposal
Figure 2 Principle of whole body synthesis rate determination using stable isotopes. Ra, rate of appearance of unlabeled, newly synthesized metabolite; Rd, rate of
disappearance (due to metabolism, excretion, etc.) of metabolite; and Inf, rate of
infusion of stable isotope. The tracer–tracee ratio (TTR) is determined experimentally by NMR or MS from a sample of the traced compartment. Because Inf is a
parameter that is set by the researchers and TTR can be measured, Ra and Rd can be
determined.
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
89
A great variety of dedicated experimental protocols for isotope-aided
studies have been developed to suit special needs, some of which are relevant
here. Incubation of microorganisms with [U-13C]glucose for multiple doubling times followed by biomass hydrolysis and isotope labeling NMR
analysis of the proteinaceous amino acids allows for overall analysis of the
bacterial intermediary metabolism (Szyperski, 1995). Similarly, incubation
of mammalian cells with [1,2-13C2]glucose and subsequent mass isotopomer
analysis of cellular metabolites allows the characterization of cellular intermediary metabolism (Boren et al., 2003). Primed, continuous infusion
of 13C-labeled essential amino acids combined with LC-MS detection of
amino acid labeling allowed the study of protein turnover in man (Engelen
et al., 2000). An alternative technique needs only a single tracer dose to be
injected but is mathematically more involved and requires also biopsies to
be taken (Zhang et al., 2002). Deuterium labeling of non-essential amino
acids combined with mass isotopomer distribution analysis (MIDA)
(Hellerstein and Neese, 1999) of body proteins allow the determination of
their synthesis rates (Busch et al., 2006). The indicator amino acid oxidation
method, which consists of infusion of [1-13C]phenylalanine and monitoring
its oxidation product 13CO2 in exhaled air at different supplementation rates
of another essential amino acid, may be used to determine dietary amino
acid requirements such as for L-lysine (Zello et al., 1993).
The use of multiple tracers in a single experiment may offer speciﬁc
advantages. The aim of tracer studies is to gather quantitative information
about a speciﬁc metabolic function. In case the measured isotope enrichment may be affected by other metabolic events, the necessary correction
can be performed when a second tracer, which is known to be metabolized
by all interfering metabolic events but not by the function of interest, is
added simultaneously (Stellaard, 2005). A special case of this principle is the
simultaneous administration of two tracers through both the intravenous
and oral routes of administration, which permits the understanding of
dynamic pictures of relevant processes such as ﬁrst-pass splanchnic bed
retention of nutrients in humans (Matthews et al., 1993). Smartly designed
multiple tracer techniques may also be used to resolve multiple biosynthetic
pathways leading to the same metabolite, as in the case of, for example,
arginine metabolism (Lau et al., 2000), homocysteine remethylation metabolism (Davis et al., 2004), and gluconeogenesis in humans (Ekberg et al.,
1999). Application of multiple substrates has also led to impressive results in
quantiﬁcation of complex microbial metabolic pathway networks (Petersen
et al., 2000) and theoretical frameworks have been established that allow
for optimal experimental design of stable isotope labeling experiments
(Wiechert et al., 2001).
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ALBERT A. DE GRAAF AND KOEN VENEMA
3.4. Important Fields of Applications of Stable Isotopes
in Biomedicine
As mentioned previously, stable isotopes have been applied to almost every
imaginable ﬁeld of the life sciences. In the following, a number of references
are given to literature on biomedical applications of stable isotopes that are
relevant to the study of microbial physiology, mammalian metabolism, and
their interaction.
Stable isotope studies have been highly instrumental in the elucidation of
(microbial) biosynthetic pathways (Bacher et al., 1999). Relatively recent
examples that may bear relevance to investigating gut microbial pathways
are, for example, a study on pathways of methanol conversion in anaerobic
bacteria (Paulo et al., 2003), a study of propionate metabolism by sludges
from bioreactors treating sulfate- and sulﬁde-rich wastewaters (Lens et al.,
1998) and a study demonstrating how the presence of the Bifidobacterium
pathway of acetate formation (Wolin et al., 1998) can be inferred from
isotope labeling data. Stable isotopes have found widespread application
in MFA of microbes (see following paragraphs); a recent review is Wiechert
(2001). The technique can now be applied on a large scale for screening
of metabolic ﬂux distributions in microorganisms. Illustrative applications
on Bacillus subtilis are described (Fischer and Sauer, 2005).
Many examples of stable isotope work in mammalian systems are available. The reader looking for an overview of recent work may ﬁnd reviews on
the application of isotope tracers to the study of metabolism in mouse
models (McCabe and Previs, 2004), on the estimation of ﬂuxes in mammalian metabolic physiology (Kelleher, 2004), and on the application of stable
isotopes in obesity research (Dolnikowski et al., 2005) useful. Organs that
have been very intensively studied with stable isotopes include the heart
(Des Rosiers et al., 2004), the liver (especially hepatic gluconeogenesis)
(Previs and Brunengraber, 1998), muscle, and brain (Shulman and
Rothman, 2001). Interesting areas where significant progress in recent years
has been reported include lipogenesis (Bederman et al., 2004), nitric oxide
metabolism in disease (Luiking and Deutz, 2003), intestinal and renal
metabolism of L-citrulline and L-arginine (Boelens et al., 2005), and interorgan protein metabolism (Engelen et al., 2005). Stable isotopes have also
been applied to the study of colonic metabolism (Pouteau et al., 2003); these
studies will be reviewed in the section on MFA below.
Stable isotopes are also beginning to be applied to the study of the interaction of human and gut microbial metabolism. For instance, knowing
that SCFA are important products of gut microbial metabolism, and that
they are taken up by the host, a further question is what exactly they are
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
91
used for in our bodies. SCFA are known to be oxidized to CO2 in colonic
tissue, contributing to colonocyte energy metabolism (Jorgensen and
Mortensen, 2000). The fate of butyrate carbons in metabolism of HT29
colon cells was probed with [1,2-13C2]butyrate (Boren et al., 2003) and
demonstrated their utilization in the citric acid cycle.
The study of amino acid metabolism in man is another example where
stable isotopes (in this case 15N) have been instrumental in demonstrating
man–microbe interactions. Amino acids from circulating blood may
exchange via enterocytes even with the colonic lumen, causing intermingling of endogenous and microbial amino acid metabolism. A number of
studies employing 15N-labeled urea have been performed to assess this issue.
Urea diffuses into the colon where it is hydrolyzed by bacterial urease to
ammonia before being assimilated. Thus, 15N labeling of amino acids upon
15
N urea administration is a clear indication of the activity of microbial
nitrogen metabolism (Fig. 3). The microbial origin of a significant fraction
in body protein of several amino acids that cannot be transaminated in
mammalian tissue (e.g. lysine and threonine) has thus been shown (Lien
et al., 1997; Metges, 2000). This implies that our intestinal bacteria have the
potential to supply us with essential amino acids!
While the examples mentioned mostly pertained to studies focusing
on metabolites of intermediary metabolism, recent applications with high
relevance to the study of intestinal microbial physiology also target the
synthesis of macromolecules, notably proteins and nucleic acids.
Only a few stable isotope-aided studies of proteins targeting the intestine
have appeared in the literature. These studies are focused on the synthesis of
mucins and mucoproteins (Faure et al., 2002), mainly looking at the effect
of food components (Coefﬁer et al., 2003; Faure et al., 2005).
Studies on nucleic acids are especially relevant for the study of intestinal
microbiota (see next chapter). Stable isotope probing (SIP) approaches, as
they are called, involve incubation of microorganisms with a stable isotopelabeled substrate under conditions resembling the environmental situation.
After a sufﬁciently long incubation, microbial nucleic acids are isolated and
the heavier fractions (i.e. those that show incorporation of the stable isotope) are separated and analyzed by, for example, PCR and ﬁngerprinting
techniques. Hereby, in situ isotope tracking techniques allow the unraveling
of the substrate utilization of microbes in their natural habitat by linking the
isotopic signature of biomarker molecules to their inherent phylogenetic
information (Maneﬁeld et al., 2004). These techniques are useful for a
broad and unprejudiced activity-screen in complex communities, and also to
verify whether selected groups of microbes utilize a certain substrate or not.
Recent reviews have been written (Dumont and Murrell, 2005; Egert et al.,
enterocytes
serosa
(blood)
from
arterial
92
intestinal lumen
diet
urea
microbial
compartment
NH3
urea
His, Lys, Thr
AA
AA
microbial
Protein
endogenous
Protein
NH3
AAdisp
Protein
NH3
to portal
vein
Figure 3 Schematic representation of gut-associated nitrogen metabolism, compiled from information in Metges (2000) and
references therein. Colored circles symbolize 15N isotope label originating from urea (red) or ammonia (blue), respectively, with
fading color intensity indicating isotope dilution. Urea diffuses from the blood through the enterocytes into the intestinal lumen,
where it is hydrolyzed by bacterial urease into ammonia and carbon dioxide. Ammonium is the preferred non-speciﬁc microbial
nitrogen substrate for synthesis of e.g. amino acids. Microbially synthesized amino acids may partially be released into the gut
lumen and taken up by ileal enterocytes (in the colon, bacterial cell densities are so high that microbially synthesized amino acids
probably never reach colonocytes). Therefore, bacteria may supply a significant portion of the body’s requirement for indispensable amino acids. 15N label appearance in histidine, lysine, and threonine upon 15N-urea or 15N-ammonia administration is
proof of microbial activity since these amino acids cannot be endogenously transaminated. However, after administration of e.g.
the 15N-labeled indispensable amino acid leucine, 15N label will appear in other branched-chain indispensable amino acids as well
as in dispensable amino acids (AAdisp) since the body is able to transaminate leucine, valine, and isoleucine. Due to extensive
amino acid exchange between blood, enterocytes, and intestinal microbiota, interpretation of 15N labeling experiments is often
ambiguous. Combining nitrogen-15 labeling with carbon-13 or carbon-14 labeling as done e.g. in Torrallardona et al. (2003),
therefore, may constitute a useful approach to arrive at unequivocal conclusions. (See plate 2 in the color plate section.)
ALBERT A. DE GRAAF AND KOEN VENEMA
Val, Ile, Leu
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
93
2006). Since little is known about the actual in situ functions of human
colonic microorganisms, SIP techniques appear particularly promising in
investigating these.
Ideally, to get good insight into the role of the intestinal microbiota, one
would like to be able to probe bacterial and host metabolism simultaneously
using stable isotopes, in a single approach. However, an integrative study of
gut microbial metabolism of a relevant substrate (e.g. carbohydrate) that
covers all the microbial products, and also monitors their metabolism in the
host, is still lacking today. The setup of such a study would need to include
the basic features of MFA. First, a careful mass balancing helps to ensure
that all metabolites are accounted for. Second, tracing the routes where the
microbially produced metabolites go using stable isotopes gives an insight
into the host processes that receive input from gut microbial activities.
Finally, determining the velocities of the various pathways involved
allows us to address the (relative) importance of the pathways for the host.
Likewise, the reverse routes (man to microbe) can be probed using stable
isotopes.
It is interesting to note that, whereas historically the ﬁelds of stable isotope studies in microorganisms and in mammalian cells and organisms seem
to have developed much on their own, MFA is now starting to integrate
both ﬁelds (Ramakrishna et al., 2001; Lee et al., 2003; Antoniewicz et al.,
2006). Because of the key role MFA can play in integrating different ﬁelds
and disciplines of science, the following sections will give a brief introduction to MFA with references to important literature.
3.5. Basics of Metabolic Flux Analysis
At the heart of MFA as it is being used today to characterize microbial
metabolism under steady-state conditions, stands the concept of metabolite
balancing (FBA, ﬂux balance analysis). Introduced to its full functionality
by Stephanopoulos and Vallino (1991), this simple principle has proved very
powerful in analyzing metabolic networks, and even in predicting their
behavior under various environmental and genetic conditions (Schuster
et al., 1999; Edwards and Palsson, 2000; Burgard et al., 2004). Special
powerful computational methods like minimization of metabolic adjustment
(MOMA) (Segre et al., 2002; Holzhutter, 2004), regulatory on/off minimization (ROOM) of metabolic ﬂux changes (Shlomi et al., 2005), and ﬂux
coupling analysis (FCA; Burgard et al., 2004) have been developed especially for the latter purpose. Metabolite balancing applies the principle of
material conservation for each and every metabolite pool in the metabolic
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ALBERT A. DE GRAAF AND KOEN VENEMA
network. At steady state, metabolite pools are constant, hence the total of
all metabolite ﬂuxes entering a speciﬁc pool must equal the total of ﬂuxes
leaving that same pool. This yields, for every metabolite pool, one linear
equation relating all ﬂuxes connecting with that pool. Available measurement data on ﬂuxes (such as substrate uptake rates and product excretion
rates) provide additional equations. Setting up the equations for every pool
in the network then yields a high-dimensional system of linear equations,
with the ﬂuxes as unknowns, that can be solved mathematically using matrix
procedures, provided the system is (over)determined (i.e. there are more
independent equations than there are unknowns). Here comes the problem
with FBA: it turns out that in practice, there are insufﬁcient data to fully
determine the equation system. Therefore, a solution can be obtained only
when additional assumptions (such as on the stoichiometry of the electron
transport chain, or a closed balance of cofactors such as NAD(P), etc.) are
made. This has the drawback that the calculated ﬂuxes depend more or less
strongly on the assumptions made, with the associated risk of introducing
important systematic errors. At this point, stable isotopes came in to help,
providing additional measurement data to solve the ﬂux analysis problem
without having to rely on assumptions (Wiechert and de Graaf, 1996;
de Graaf, 2000) (Fig. 4). The reason why isotopic labeling data allow this is
that now a material conservation balance for each and every single carbon
(in case of 13C labeling) for every metabolite in the network can be drawn
up, resulting in a greatly increased number of equations. Although the
number of unknown ﬂuxes also increases in the procedure, for the reason
that isotopic labeling patterns also depend on backward ﬂuxes (Wiechert
and De Graaf, 1997; Wiechert et al., 1997), the vastly increased amount
of experimental data coming with the additional labeling information
generally allows the solving of the new equations without having to rely
on assumptions.
Soon after that, it became obvious that a further increase in information
could be obtained by including isotopomer measurement data. Isotopomer
information holds knowledge as to whether a molecule is labeled in a single
position, or two or more positions at the same time, and also which
position(s) is/are labeled. Consequently, considering only natural carbon-12
and its stable isotope 13C, a molecule with a backbone of N carbons can
exist as 2N different isotopomers. Extending the mass balancing principle up
to the level of isotopomers proved to be difﬁcult at ﬁrst because the equations were no longer linear. However, an analytical solution was developed
in due time (Mollney et al., 1999; Wiechert et al., 1999), making the full
potential of MFA available. Theoretical properties of isotopomer labeling
equation systems along with their associated consequences for experimental
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
95
isotope-labeled substrate
pathway A
pathway B
metabolism
FA
13 C
12 C
FB
FB
product metabolite
= F A/ F B
Figure 4 Principle of pathway ﬂux determination at (pseudo)steady state using
stable isotopes. Via pathway A, carbons 1 and 2 of the substrate end up in carbons 1
and 2 of the product P, respectively, leading to an [1-13C]P isotopomer. In contrast,
via pathway B, these carbons end up in carbons 2 and 1, respectively, leading to the
[2-13C]P isotopomer (the fate of carbons 3 and 4 of the substrate is ignored here).
When the labeling of pool P has reached steady state, the ratio of the [1-13C]P and
[2-13C]P isotopomers present in the total pool equals the ratio FA/FB of the ﬂuxes in
the two pathways A and B. Once the total rate of synthesis of P is measured independently, the absolute ﬂuxes FA and FB are known.
design have been analyzed (Wiechert and Wurzel, 2001; Isermann and
Wiechert, 2003). The interested reader may ﬁnd a number of tutorial reviews
on MFA useful (de Graaf, 2000; Wiechert, 2001, 2002).
Recent advances concentrate on providing mathematical frameworks
that also allow metabolic ﬂuxes from full isotopomer data sets under nonsteady-state conditions to be derived (Wiechert and Noh, 2005). This may
provide a much hoped-for basis for the integration of the full potential of
isotopomer labeling with ‘classical’ kinetic tracer approaches, both with
regard to theoretical and experimental considerations.
3.6. MFA in Detecting Microbial Metabolic Stress
One of the main advantages of MFA is that it visualizes the ﬁnal effect of
genetic, proteomic, and metabolomic responses to the changing environment
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ALBERT A. DE GRAAF AND KOEN VENEMA
of an organism. In other words, it reveals how the organism adapts itself at
the metabolic level to changing requirements. This offers unique insights
into physiological regulation that cannot usually be obtained from genetic,
proteomic, or metabolomic analyses alone. To illustrate this point, the reader
is referred to work by Petersen and colleagues (Petersen et al., 2000, 2001,
2003), on the amino acid-producing bacterium Corynebacterium glutamicum.
This microorganism shows astonishingly little phenotypic change upon
deletion/inactivation of important genes for anaplerosis (pyruvate carboxylase, phosphoenolpyruvate (PEP) carboxylase) or gluconeogenesis (PEPcarboxykinase) (de Graaf et al., 2001). This has severely hampered a targeted
metabolic engineering for increased L-lysine production by genetic modiﬁcation (Sahm et al., 2000). Whereas the organism does not respond at the genetic
and proteomic level to inactivation of the aforementioned genes, metabolite
levels changed up and down only to relatively small extents, with no clear
picture discernible. Carbon-13 isotopomer-aided MFA experiments in contrast
showed very clear-cut responses of the metabolic ﬂuxes concerned (Petersen
et al., 2001). Taking into account key kinetic data of the enzymes involved,
a mathematical model of the relevant pathways was constructed of which the
unknown parameters were subsequently tuned using the available experimental data on enzyme activities (‘genome’/‘proteome’), metabolite concentrations
(‘metabolome’), and ﬂuxes (‘ﬂuxome’). Amazingly, this model could quite
accurately predict the effects of genetic manipulations on L-lysine production
(Petersen et al., 2003), making it the ﬁrst working small-scale systems biology
model to our knowledge.
As of today, research in the medical ﬁeld aiming at elucidation of disease
mechanisms largely proceeds by way of making statistical correlations of observable physiological parameters (‘phenotype’) with measured metabolite
concentrations (‘metabolome’), enzyme activities (‘proteome’), or gene transcription rates (‘genomics’/‘transcriptomics’). In our view, the advent of
‘-omics’ technologies in recent years has changed the scale of this approach,
but not the paradigm. The example with C. glutamicum demonstrates that
much can be gained from including MFA results, and from developing
appropriate modeling frameworks as opposed to purely statistical analysis
procedures.
There is nowadays a growing consensus that, before a certain disease
actually appears, there is a long preceding time period during which the
metabolism is continuously stressed, until the point where normal regulatory mechanisms can no longer compensate (van der Greef et al., 2004), and
the disease becomes manifest. Although changes in gene expression, enzyme
activities, and/or metabolite levels may be apparent during the pre-disease
period, pathway regulation often is so complex that a clear picture is very
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
97
hard to obtain from statistical analysis of these data alone. From the
example of C. glutamicum, we can expect that metabolic ﬂuxes may offer
a significantly improved view. A ﬁrst example pertains to protein turnover
measured with stable isotopes in chronic obstructive pulmonary disease
patients (Engelen et al., 2000), where no apparent net unbalance in protein
metabolism was found, but whole body protein synthesis and breakdown were significantly increased in patients (i.e. the turnover, or ﬂux, was
increased).
Summarizing, it is predicted that stable isotope-aided methods, especially
MFA, will significantly improve our understanding in the near future of
metabolic regulation in response to the dynamic environment. In the special
case of the intestinal microbiota interacting with our own metabolism, expectations are also high that stable isotopes will permit key insights into the
metabolic regulations both on the microbial, and the host side in due course.
The following sections will give an overview of important knowledge gained
in recent years in the ﬁeld of intestinal microbial metabolism on the genomic,
proteomic, and metabolomic level as well as on the level of metabolic ﬂuxes,
with a special focus on studies that employed stable isotopes.
4. GENOMIC INVENTORIES OF INTESTINAL BACTERIA
The human GI tract is colonized by a microbial community that develops
in complexity during life, resulting in a climax community of microbial
cells in adults which outnumber the host cells by an order of magnitude
(Blaut, 2003; Zoetendal et al., 2006). In addition to this temporal development, the GI tract community is characterized by a distinct spatial variation
of microbial communities that progressively develops in size and diversity distally from the stomach (Blaut, 2003; Zoetendal et al., 2006), culminating in a staggering 1012 microorganisms per gram of colonic content. The
microorganisms in our large intestine contribute significantly to nutrient
processing and are important for health and disease. While the enumeration
of bacteria by conventional culture techniques has been imprecise and
time consuming, analysis of the ecology of the intestinal microbiota has been
greatly improved by designing 16S-rRNA-targeted oligonucleotide
probes. Nowadays, many tools and techniques are available to characterize comprehensively the microbial diversity in the human gut (Wilson and
Blitchington, 1996; Zoetendal et al., 1998; Suau et al., 1999; Rigottier-Gois
et al., 2003). Use of these in molecular studies (Hugenholtz et al., 1998;
Zoetendal et al., 2004a, 2004b) have shown that the majority of the
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ALBERT A. DE GRAAF AND KOEN VENEMA
microorganisms in our gut have not yet been cultivated as pure cultures in
the laboratory, either because we do not know the nutritional requirements
or growth conditions of these microorganisms (Finegold et al., 1983;
Rigottier-Gois et al., 2003), or because they are damaged or dead
(Ben-Amor et al., 2005). Despite the fact that, over the past few years, the
use of these molecular techniques has given important insight into structure
and spatial organization of the human intestinal microbiota (Hugenholtz
et al., 1998; Zoetendal et al., 2004a, 2004b), only a limited number of tools
are to hand to investigate the activity of the microbiota at the level of
individual species. Several recent developments, discussed below, that aimed
to characterize the activity of (particular species within) a microbial community have allowed a more detailed picture of the link between its structure
and function. Frequently, these methods and tools have been developed for
other ecosystems, but they have recently found their way to the anaerobic
system of the GI tract.
Food passes relatively quickly through the stomach and the small intestine. That, in combination with a hostile environment in the upper GI-tract
(gastric acid, bile, pancreatic enzymes, immune system) precludes a dense
colonization of this region of the gut, although up to 107 cells per gram of
content may be present. Transit of undigested and indigestible food components through the colon is much slower. This allows for the development
of the diverse microbiota present in the large intestine. Here, the microbes
thrive on a variety of substrates, including some originating from the host,
such as mucin. It appears that we have established an intimate relationship
with the microbial world, a relationship of a largely symbiotic nature.
Speciﬁc host–microbial interactions develop that are now starting to be
understood and are essential for maintaining intestinal health (Hooper et al.,
2002; Freitas et al., 2003). In an elegant paper (Backhed et al., 2005), the
term ‘mutualism’ has recently been introduced as a more proper way
to account for the fact that both host and microbes proﬁt from their
coexistence. In the following sections, we will discuss the principal ﬁndings
of studies on diversity of the intestinal microbiota, look in some detail into
the results of stable isotope-aided studies, and review the conclusions drawn
from such studies on gut microbial functionality.
4.1. General Aspects: Cataloguing Intestinal Microbial
Communities
Microbial functionality represents perhaps the greatest unexplored realm
of gastrointestinal biology with respect to our understanding of the effects
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
99
of microbial activity on health and disease. The introduction of molecular
biological techniques into intestinal microbial ecology in recent years has
uncovered the vastness of microbial diversity in the GI tract. Considerable
attention has been given to determine the spatial and temporal microbial
diversity by high-throughput genetic approaches that are mainly based
on analysis of the microbial signatures in 16S ribosomal RNA (rRNA)
sequences (Amann and Ludwig, 2000; Backhed et al., 2005). All three
domains of life have been detected in the GI tract, but the Bacteria are
highly dominant. A total number of 1014 cells and 41000 species have
been reported (Egert et al., 2006). Of the more than 200,000 rRNA gene
sequences currently present in databases, only approximately 1% are
annotated as being derived from the human intestinal bacteria, of which
approximately 80–90% represent uncultured bacteria (Backhed et al., 2005).
The bacterial divisions that dominate are the Cytophaga, Flavobacterium,
Bacteroides, and the Firmicutes, each estimated to make up about 30% of
the bacteria. Only 6 additional divisions (of a total of 55 discovered to date)
have been reported to occur in the human large intestine, which make the
diversity in the GI tract at the division level among the lowest (Hugenholtz
et al., 1998; Backhed et al., 2005). Diversity present in the GI tract is
hypothesized to be the result of strong host selection and coevolution and
reﬂects natural selection at the microbial level and at the host level. At the
microbial level, lifestyle strategies affect the competitiveness of individual
species in a complex mixture. These strategies include, for instance, growth
rate, substrate use (part of which is host derived, such as mucus), and ability
to cope with the hostile environment (such as the intestinal immune system).
At the host level, deleterious effects of bacteria can reduce host ﬁtness,
resulting in fewer hosts and therefore, less habitat for the microorganisms to
grow in. On the other hand, an activity that promotes host ﬁtness will create
more habitats. One such positive interaction alluded to already earlier is for
instance the production of butyrate, which is used as a source of fuel by the
colonocytes (Roediger, 1982). Although this mutualistic coexistence between
microbiota and host is generally accepted to be correct, it is also believed
that the intestinal microbiota is responsible for numerous intestinal diseases,
such as colon cancer and IBD. And even though there generally is a symbiotic relation between microbiota and host, the individual microorganisms
live in constant battle with each other. For instance, they compete for
substrates for growth (from dietary origin, but also mucus and exfoliated
epithelial cells) and adherence sites (receptors), and they produce metabolites that may kill or slow down growth of other microorganisms when
present in high concentrations (e.g. SCFA, lactate, bacteriocins). By contrast, however, these microorganisms also live in symbiosis, where one
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ALBERT A. DE GRAAF AND KOEN VENEMA
species may produce a nutrient/metabolite that is required for growth of
another species, e.g. in the case of acetogenesis or methanogenesis, where H2
produced by some members of the microbiota is used to produce acetate
or methane, respectively, by other members of the community. Normal
colonization by the human intestinal commensal microbes stimulates a
range of important functions, such as postnatal intestinal maturation,
maintenance of the mucosal barrier, protection against pathogens, and
the development and maturation of the immune system (Cummings and
Macfarlane, 1997; Falk et al., 1998; Elson et al., 2005). Over adulthood, the
composition of the microbiota is rather stable, but speciﬁc for each individual. This is in part determined by genetic factors (Zoetendal et al., 1998).
Diet has the potential to inﬂuence the activity and composition of the
microbiota, although that is generally believed to be only a temporary effect,
unless the dietary components responsible for the change in composition
and/or activity are taken on a frequent basis. Although increasing insight
has been obtained into the microbial diversity, there is very limited knowledge of the metabolic function of the human intestinal microbes, the way
the diet affects metabolic ﬂuxes, and how the produced metabolites affect
the health of the host. Even though it has been possible to determine production of microbial metabolites (even in vivo using stable isotope-labeled
substrates, see below) by the collective microbiota, it has until recently not
been possible to determine which microorganisms are primarily responsible
for the production of these metabolites in situ.
4.2. The Microbiome
Our gut microbiota can be pictured as a microbial organ placed within
a host organ. It is believed that the collective microbiota can carry out more
biochemical conversions than the liver, our most metabolically active organ
with respect to the multitude of different biochemical reactions it can effect.
The collective microbial genome, termed metagenome or microbiome, which
contains more than 100 times the number of genes in the human genome,
encodes biochemical pathways that we have not had to evolve ourselves.
In a recent review in Nature Medicine, Sekirov and Finlay (2006) concluded
that ‘Together with our microbes we are a human–bacterial superorganism
with immense metabolic diversity and capacity’ . In September 2006, the
full genome sequence of 279 bacterial and 23 archeal genomes was
sequenced (resource: Comprehensive Microbial Resource Home Page:
http://cmr.tigr.org/tigr-scripts/CMR/CmrHomePage.cgi), and another 17
genomes were in progress. Also, a large number of genomes of bacteria that
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
101
can be found in the human gastrointestinal tract or have been associated
with human disease have been sequenced (Table 3). Most of these microorganisms have been chosen due to either their pathogenic potential or
their potential probiotic (i.e. health beneﬁcial) activity. These sequencing
activities already provide an enormous wealth of data with respect to the
(potential) metabolic activity of these individual microorganisms. However,
metagenomics provides insight into the genetic potential of complex microbial communities. Assuming that each bacterial species within the GI tract
has an average genome size of 3 Mb, the human intestinal microbiome
probably comprises several thousand Mb, and thus in size equals that of the
human genome. However, due to a much higher gene density, it vastly
exceeds the human genome’s coding capacity (Relman and Falkow, 2001;
Backhed et al., 2005). In addition, an estimation of the total microbial
genomic content in an individual should consider the genetic redundancy
within these communities. Also, the total microbiome in a human population should, in addition to this redundancy, consider the individual composition of the microbiota (Egert et al., 2006). Compelling evidence suggests
that disruption of the intestinal microbial ecosystem contributes to
a number of diseases. However, without understanding the interactions
between the human and microbial genomes, it is impossible to obtain
a complete picture of the effects of the intestinal microbiota on health and
disease. Elegant studies (Hooper et al., 1999; Freitas et al., 2001) have indicated a cross-talk between the members of the microbiota and the host.
They have shown that soluble factors of Bacteroides thetaiotaomicron,
a prominent member of the microbiota, results amongst others, in changes
in the expression of glycosyl residues on host membrane-associated glycosylated proteins. In particular, the upregulation of fucosylated glycans
(Hooper et al., 1999) by B. thetaiotaomicron revealed a novel signaling collaboration between host and microbe to produce nutrients for growth for
the microbe. In a follow-up study, the genome-wide response of the host was
carried out (Hooper et al., 2001; Stappenbeck et al., 2002). These studies
used single cultivable microbial species and focused mostly on host-related
genes and functionalities.
The microbiome of the complex gut microbiota has only recently been
identiﬁed as a target for exploration. Novel hydrolase genes were discovered
in uncultured rumen bacteria (Ferrer et al., 2005) and beta-glucanase genes
were identiﬁed from uncultured bacteria that colonize the large bowel of
mice (Walter et al., 2005). Two metagenomic libraries constructed from
DNA from fecal samples of healthy individuals and patients with Crohn’s
disease (CD) (Manichanh et al., 2006) and screened for 16S rRNA genes
revealed a greatly reduced diversity in the Firmicutes in CD patients, which
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ALBERT A. DE GRAAF AND KOEN VENEMA
Table 3 Fully sequenced genomes and those in progress for human gastrointestinal
bacteria
Completely sequenced genomes
Actinomyces naeslundii
Anaplasma phagocytophilum
Bacillus anthracis
Bacillus subtilis
Bacteroides fragilisb
B. fragilisb
Bacteroides thetaiotaomicron
Bartonella henselae
Bartonella quintana
Bifidobacterium longum
Bordetella bronchiseptica
Bordetella parapertussis
Bordetella pertussis
Borrelia burgdorferi
Borrelia garinii
Brucella abortus
Brucella melitensis
Brucella suis
Burkholderia mallei
Burkholderia pseudomallei
Burkholderia thailandensis
Campylobacter jejunib
C. jejunib
Chlamydia abortus
Chlamydia pneumoniae
Clostridium acetobutylicum
Clostridium perfringens
Clostridium tetani
Corynebacterium diphtheriae
Corynebacterium jeikeium
Coxiella burnetii
Desulfovibrio desulfuricans
Ehrlichia chaffeensis
Enterococcus faecalis
Escherichia coli
Francisella tularensis
Fusobacterium nucleatum
Haemophilus ducreyi
Haemophilus influenzae
Helicobacter hepaticus
Helicobacter pylori
Lactobacillus acidophilus
Lactobacillus helveticus
Lactobacillus johnsonii
Genome sizea (Mb)
3.0
1.5
5.2
4.2
5.2
5.3
6.7
1.9
1.6
2.3
5.3
4.8
4.1
1.5
1.0
3.3
3.3
3.3
5.8
7.3
6.7
1.6
1.8
1.1
1.2
4.1
3.1
2.8
2.5
2.5
2.0
3.7
1.2
3.4
4.6
1.9
2.2
1.7
1.9
1.8
1.7
2.0
2.0
2.0
(Continued )
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
103
Table 3 (continued )
Completely sequenced genomes
Lactobacillus plantarum
Lactobacillus sakei
Lactobacillus salivarius
Lactococcus lactis subsp. lactis
Legionella pneumophila
Leptospira interrogans
Listeria innocua
Listeria monocytogenes
Mycobacterium avium paratuberculosis
Mycobacterium bovis subsp. bovis
Mycobacterium leprae
Mycobacterium tuberculosis
Mycoplasma pneumoniae
Mycoplasma pulmonis UAB
Neisseria gonorrhoeae
Neisseria meningitidis
Nocardia farcinica
Pasteurella multocida
Porphyromonas gingivalis
Prevotella intermedia
Propionibacterium acnes
Pseudomonas aeruginosa
Rickettsia conorii
Rickettsia felis
Rickettsia prowazekii
Rickettsia typhi
Salmonella enterica
S. enterica serovar Typhi
Salmonella typhimurium
Shigella boydii
Shigella dysenteriae
Shigella flexneri
Shigella sonnei
Staphylococcus aureus
Staphylococcus epidermidis
Staphylococcus haemolyticus
Staphylococcus saprophyticus
Streptococcus agalactiae
Streptococcus mutans
Streptococcus pneumoniae
Streptococcus pyogenes
Streptococcus thermophilus
Treponema denticola ATCC
Treponema pallidum
Tropheryma whipplei
Genome sizea (Mb)
3.3
1.9
2.1
2.4
3.4
4.6
3.1
3.0
4.8
4.3
3.3
4.4
0.8
1.0
2.2
2.3
6.3
2.3
2.3
2.7
2.6
6.3
1.3
1.6
1.1
1.1
4.6
4.8
5.0
4.6
4.6
4.6
5.0
2.8
2.5
2.7
2.6
2.2
2.0
2.1
1.8
1.8
2.8
1.1
0.9
(Continued )
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ALBERT A. DE GRAAF AND KOEN VENEMA
Table 3 (continued )
Completely sequenced genomes
Ureaplasma urealyticum parvum
Vibrio cholerae
Vibrio parahaemolyticus
Vibrio vulnificus
Wolinella succinogenes
Yersinia pestis
Yersinia pseudotuberculosis
Genomes in progressc
Lactobacillus gasseri
Lactobacillus casei
Lactobacillus rhamnosus
Lactobacillus helveticus
Lactobacillus delbrueckii
Lactobacillus reuteri
Lactobacillus brevis
Leuconostoc mesenteroides
Pediococcus pentosaceus
Propionibacterium freundereichii
Streptococcus mitis
Genome sizea (Mb)
0.8
4.0
5.2
5.1
2.1
4.8
4.8
Approximate genome size
1.8
2.6
2.4
2.4
2.3
2.5
2.0
2.0
2.0
2.6
2.0
a
Rounded to the nearest decimal; average of genome size of all sequenced strains where applicable.
b
Two strains of this species have been sequenced, with different genome sizes.
c
GI tract species listed are those for which we have information that they are being sequenced.
is in agreement with the hypothesis that the intestinal microbiota has an
important role in CD development. The human gut microbiome initiative
(HGMI) has been proposed as an extension of the human genome project.
New and cost-effective approaches now allow fast and reliable highthroughput sequencing of millions of basepairs. The ﬁrst published results
from such a sequencing effort analyzed 78 million bases (Gill et al., 2006)
in 140,000 sequence reads from DNA libraries from two healthy human
adults. The study revealed that metabolic function analyses of identiﬁed
genes of the sequenced microbiome has identiﬁed enrichment of genes
encoding metabolism of carbohydrates, amino acids, and xenobiotics and
methanogenesis compared with other sequenced microbial genomes. At least
81 different glycosyl hydrolases have been found in the microbiome, indicating its capacity to cleave a vast array of different (mostly food derived)
carbohydrate linkages. Also, an overrepresentation of butyrate kinase
(statistically increased with a factor of 9.30 (odds ratio) compared with other
microbial genomes) was found. It was speculated that this corroborated the
important commitment of the gut microbiota to generating this biologically
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
105
important compound, which serves as the principal energy source for
colonocytes. About 50% of the total of 50,164 open reading frames (ORFs)
predicted matched to the database, of which 5700 were present in both
subjects. Of the total number of ORFs, only 25% could be assigned unambiguously to members of the Archeae or Bacteria. The remainder could
not be assigned unambiguously to either of the two, or did not match any
known ORFs. The 78 million bases sequenced would represent approximately 1–3% of the total microbiome. Approximately 40% of the sequence
reads could not be assembled into contigs, most likely because of low
abundance of the microorganisms, from which the sequence originated,
within the specimens studied. It should be mentioned that, even though the
colon is colonized by a myriad of different microorganisms, only a limited
number of species/genera make up the majority of the microbiota. Only
approximately 15 16S rRNA-targeted probes for bacterial genera/phyla are
required to measure approximately 90% of the bacterial cells in fecal samples from human adults (Harmsen et al., 2002). Therefore, any clone library
obtained from an actual sample from the gut will be dominated by genomic
DNA from these dominant species, even without the bias generated by the
cloning procedure itself. Given the extensive sequencing efforts it would take
to sequence the full complement of the intestinal microbiome, the true metabolic potential of the microbiota will not be unraveled in the near future.
Since the major species probably make up the major activities, this may not
have to be our major goal for the moment. Also, similarities and differences
between the microbiota of different individuals (Gill et al., 2006; two subjects were studied) need to be studied in more detail to be able to decipher
the major activities carried out by the human intestinal microbiota. Current
screening approaches of intestinal metagenomic libraries are not fully
established. Screens can be based either on nucleotide sequences or on
enzyme activities but both strategies have limitations. PCR and hybridization techniques require primers or probes based on previously cloned
(i.e. known) genes. Functional analysis enables the discovery of new classes
of genes, but this requires the expression from the cloned inserts of active
enzymes in heterologous hosts (usually Escherichia coli). In addition,
appropriate assays must be available, and most phenotypes of interest, e.g.
butyrate production, might not be suitable for high-throughput selection.
Recently, an elegant screen has been developed that enables the rapid
identiﬁcation of clones with a desired inducible metabolic activity within
large clone libraries (Uchiyama et al., 2005). The method is based on the
commonly observed substrate induction of genes encoding biodegradative
pathways, and relies on a promoter-trap system to trap genes that encode
catabolic pathways in front of a gene that encodes green ﬂuorescent protein
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(GFP). GFP-positive, induced clones are selected by ﬂuorescence-activated
cell sorting. However, one of the constraints of this method, as with activity
screening in a heterologous host, is that it requires the regulatory machinery
of the expression host to recognize both the promoter and the substrate
(Handelsman, 2005), and therefore the method may have limited applicability. Thus, although these studies begin to deﬁne the functional activities
of the human gut microbiome, future in-depth metagenomics studies are
needed to provide deeper coverage of the microbiome, which has been
termed the second human genome, and to study the relationship between
microbiota and health and disease.
4.3. Stable Isotope Probing: Clues to Metabolic Function from
Genomics Data
Even though the picture of the complete microbiome may be incomplete, the
ﬁrst studies toward the metabolic function of individual members within
the collective microbiota are being undertaken, and the ﬁrst clues are
known. These concern studies where stable isotope-labeled substrates
are used to investigate the role and activity of certain members within the
microbiota on speciﬁc substrates. In Section 5, we will describe the metabolomics approaches to this. Here, we focus on the contribution of speciﬁc
microorganisms to the fermentation of the substrates.
We have discussed that molecular DNA technologies allow for a comprehensive and integrated approach to assessing the structure of microbial
communities, providing a perspective in GI tract microbiology. Although
the application of these tools has significantly advanced our understanding
of the gut microbial diversity, it does not provide functional insight on
which microbes are relevant for speciﬁc dietary conversions (de Vos, 2001;
Egert et al., 2006). The real challenge here is to develop and apply methodologies for analyzing the functionality of the microbiome. For this,
it is important to know which microorganisms are responsible for the
observed activities, elucidating dominant microbial functionalities in the
human GI tract, the impact of speciﬁc dietary components on these functionalities, and ultimately the effect on gut health. Stable isotopes can play
an important role in answering these questions.
To couple the microbial diversity to metabolic function, in situ SIP
approaches appear very promising (Egert et al., 2006). Typically, in nucleic
acid-based SIP studies, 13C-labeled compounds that act as substrates in the
food chain are delivered to cultures of (intestinal) bacteria (Fig. 5). Subsequently, the ribosomal DNA or RNA of the microbial community is
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
107
isolated and subjected to density gradient centrifugation to isolate the heavy,
labeled fraction of nucleic acids. These heavier fractions stem from bacteria
that have consumed the substrate and incorporated the isotopic label in
their nucleic acids. Either general or group-speciﬁc PCR ampliﬁcation
allows 16S rDNA fragments to be ampliﬁed (Satokari et al., 2001; Heilig
et al., 2002). These can then be characterized by high-throughput rDNA
sequence analysis providing insight into the microbial diversity of these
fractions. By following the development of the rDNA sequence diversity in
time, the speciﬁc groups of microbes involved in the food chain from,
for example, carbohydrates to SCFA can be reconstructed. In addition, the
spatial diversity can be probed. This approach has been shown to be useful
to determine the substrate utilization in a variety of microbial communities
(Radajewski et al., 2000; MacGregor et al., 2002). The approach has also
been used in the gut, although until recently restricted to earthworms and
larvae of the cockchafer and the cetoniid beetle (Egert et al., 2003, 2004,
2005; Lemke et al., 2003). Recently, we have taken this SIP strategy and
applied it to a human gut microbial community (Egert et al., 2007). In this
study, 16S rRNA-based SIP and NMR spectroscopy-based metabolic proﬁling were used to identify bacteria fermenting glucose (as a model substrate) under conditions simulating the human intestine. An in vitro model
of the human intestine was inoculated with a GI tract microbiota resembling
that of the small intestine and subsequently 40 mM of uniformly labeled
13
C-glucose was added. RNA was extracted from lumen samples after 0
(control), 1, 2, and 4 h of incubation and fractionated by density gradient
ultracentrifugation. Phylogenetic analysis of the 16S rRNA revealed a
microbial community dominated by microorganisms closely resembling
lactic acid bacteria and Clostridium perfringens, not unlike the microbiota in
the terminal ileum. Fingerprints of the most-labeled rRNA fraction identiﬁed Streptococcus bovis and C. perfringens as the most active glucose fermenters in the model. Accordingly, NMR analysis identiﬁed lactate, acetate,
butyrate, and formate as the principal fermentation products, constituting
up to 96% of the 13C-carbon balance. Thus, RNA-SIP combined with metabolic proﬁling allowed the detection of differential utilization of the general
model carbohydrate glucose, indicating that this approach holds great potential to identify bacteria involved in the fermentation of relevant dietary
oligo- and polymeric carbohydrates in the human large intestine as well.
RNA is the most responsive (sensitive) biomarker for SIP analyses because
it occurs in greater cellular copy numbers, has a higher turnover rate than
does DNA and is produced more or less independent of cellular replication
(Maneﬁeld et al., 2002). Owing to fewer variations in its GC content compared with DNA, ribosomal 16S-based RNA-SIP might also be less
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ALBERT A. DE GRAAF AND KOEN VENEMA
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
109
susceptible to GC effects that interfere with the separation of labeled 16S
rRNA. It should be realized though that it is exactly this difference in GC
content between different members of the microbiota which results in the
fact that a density gradient of unlabeled RNA already contains RNA in the
heavier fractions. For instance, biﬁdobacteria can be found in these fractions (our own unpublished results). Therefore, to study properly which
microorganisms contribute to fermentation of certain substrates, molecular
ﬁngerprinting techniques, such as T-RFLP or DGGE, are required to assess
the enrichment of the heavy fractions for those microorganisms actually
using the substrate (Fig. 5; Egert et al., 2007). Human intestinal samples
seem particularly suited to an RNA-SIP approach because these samples
contain large numbers of highly active cells, resulting in quick and sufﬁcient
labeling of RNA. However, in view of these same large numbers of cells in
the human GI tract, together with the nutrient-rich environment and the
broad range of potential substrates (e.g. carbohydrates, proteins, hostderived substrates) that can be fermented, human intestinal samples necessitate a sensitive RNA-analysis approach to cope with label dilution. The
use of in vitro gut models that closely mimic the environmental conditions in
the GI tract and that are easy to sample enables detailed analyses of successive label incorporation into the RNA of different community members
over time. Such cross-feeding effects (i.e. the use by one member of the
microbiota of labeled metabolites derived from the initially added substrate
produced by a different member) will help to identify food chains in intestinal systems. This may lead to the generation of hypotheses that need to
be tested in vivo. Yet, application of SIP in human trials is challenging. It
remains to be shown (i) whether a labeled substrate can be effectively delivered through the intestinal tract into the target region and homogenously
distributed there, and (ii) whether the (singly or pulsed) applied substrate
concentrations can be adjusted in a way that prevents dilution within the
colon, while still enabling sufﬁcient labeling of microbial (16S r)RNA.
Figure 5 Principle of RNA-based stable isotope probing (SIP) for detection and
characterization of microbes that actively metabolize the labeled substrate.
13
C-labeled substrates are incubated in (a) simple in vitro models (test tube or ﬂask),
(b) sophisticated in vitro systems, or (c) in vivo. Samples obtained from these experiments [in the ﬁgure only shown for samples from (a)] are subjected to RNA
isolation and density gradient centrifugation. After separation of the gradient in
fractions, molecular ﬁngerprinting techniques, such as DGGE (Zoetendal et al.,
2004a) or T-RFLP (Egert et al., 2003) can be used to determine the presence (usually
enrichment) in the heavier fractions of those microorganisms that specifically fermented the substrate and this can be compared with the diversity present in an
unlabeled, control sample. (See plate 3 in the color plate section.)
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5. PROTEOMIC ASPECTS OF INTESTINAL MICROBIAL LIFE
5.1. Functions of Intestinal Bacterial Enzymes
Intestinal bacteria produce large amounts of extracellular enzymes, especially for degradation of mucins and dietary carbohydrates. These enzymes
may be freely soluble or remain membrane bound; the latter are generally
found to be more active.
Mucin oligosaccharide chain-degrading bacteria have been isolated from
feces of healthy subjects (e.g. Salyers et al., 1977; Hoskins et al., 1985; Derrien
et al., 2004) and their enzymes studied. It was concluded that certain Bacteroides, Bifidobacterium, and Ruminococcus strains are numerically dominant
populations degrading mucin oligosaccharides in the human colon due to their
constitutive production of the requisite extracellular glycosidases including
blood group antigen-speciﬁc alpha-glycosidases, sialidase, beta-glycosidases,
alpha-galactosidase, and beta-N-acetyl-hexosaminidases. Enterococcus faecalis
produced predominantly cell bound glycosidases (Salyers et al., 1977; Hoskins
et al., 1985).
Oligosaccharide side chains of human colonic mucins contain O-acetylated sialic acids and glycosulfate esters. Although these substituents
are considered to protect the chains against degradation by bacterial
glycosidases, sialate O-acetylesterase, N-acetylneuraminate lyase, arylesterase, and glycosulfatase activities have been found in fecal extracts
(Corﬁeld et al., 1992). Thus, mucin oligosaccharide chains terminating in
O-acetylated sialic acids are unlikely to be protected from degradation by
enteric bacteria.
High levels (2–565 U/g) of amylase activity have been observed in human
feces, with over 92% of amylase activity being of extracellular origin,
whereas only about 9% of activity was associated with particulate material
and washed cells (Macfarlane and Englyst, 1986). Bacterial cell-bound
amylases were considerably more efﬁcient in breaking down starch,
however, than were the soluble enzymes which occurred in cell-free fecal
supernatant ﬂuids.
Other hydrolytic and reductive bacterial enzymes measured in human
colonic contents include beta-glucuronidase (GN), beta-glucosidase (GS),
arylsulfatase (AS), azoreductase (AR), and nitroreductase (NR). These
enzymes can be involved in production of mutagenic or genotoxic metabolites (McBain and Macfarlane, 1998). Cell-associated AS and extracellular GS were found to be approximately twice as high in the distal colon
compared with the proximal bowel, while AR changed little throughout the
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
111
gut. Upon studying 20 pure cultures of intestinal bacteria these authors
found that various bacterial strains were active producers of GS, GN,
NR, and AR. However, only few of the isolated bacteria produced AS in
small amounts.
In recent years, research on bacterial intestinal proteins seems to have
focused more on the dark sides of our commensals, with a particular interest
in the roles of enzymes and toxins in gut diseases.
Infectious enteric Helicobacter pylori have been shown to produce matrix
metalloproteinases (MMPs) that participate in degradation of the extracellular matrix also on HT29 colon epithelial cells, allowing bacteria to invade
(Yanagisawa et al., 2005).
Bacterial ﬂagellin, a speciﬁc microbial ligand of Toll-like receptor-5
(TLR-5), is released by commensal and enteroinvasive microbes. Flagellin
exposure to an in vivo mouse model of injured colon, but not to intact colon,
was found to significantly aggravate colonic inﬂammation, increase mouse
mortality, enhance histopathological damage in the colonic mucosa, and
to cause severe apoptosis in colonic epithelium (Rhee et al., 2005). These
results demonstrated that bacterial ﬂagellin plays an important role in the
development and progress of colitis, via TLR-5 engagement.
Chitinase 3-like-1 (CHI3L1) is a putative key molecule involved in the
dysregulation of host/microbial interactions that appears to play a central
role in the development of IBD. A very recent study (Mizoguchi, 2006)
demonstrated that CHI3L1 is required for the enhancement of adhesion and
internalization of infectious bacteria in colonic epithelial cells. The expression of CHI3L1 protein was found to be clearly detectable in lamina propria
and colonic epithelial cells in several murine colitis models and UC and
Crohn’s disease patients but absent in normal controls (Mizoguchi, 2006).
It was concluded that CHI3L1 contributes to the facilitation of bacterial
invasion into the intestinal mucosa and the development of acute colitis,
presumably by enhancing the adhesion onto and invasion of bacteria into
colonic epithelial cells.
5.2. Proteomic Studies of the Gut Microbiota: A Largely
Unprobed Area?
Proteomic studies of the intestinal microbiota, in principle, could be useful
to study expression patterns of proteins and enzymes in response to dietary
components and thereby provide a rationale for the development of new
active ingredients (e.g. pre- and probiotics). Yet, the full power of proteomic
analysis remains to be demonstrated in this area also.
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ALBERT A. DE GRAAF AND KOEN VENEMA
The number of studies reported in this ﬁeld is strikingly small and most
studies concentrate on cultivated bacteria. This may have to do with the
difﬁculty of accessing the colon; but more likely, the complex fecal nature
of the samples causes severe problems especially with the widely used
two-dimensional gel techniques the performance of which is susceptible to
sample impurities and difﬁculties in reproducibility. It can be anticipated
that less susceptible techniques such as surface-enhanced laser desorption/
ionization (SELDI)-TOF analysis (Barzaghi et al., 2004), a protocol that has
been successfully applied to a wide range of Gram-positive and -negative
bacteria, will accelerate progress of the study of proteomics of the gut
microbiota in the near future.
Nevertheless, technically impressive methodologies have been developed
that allow the characterization of hundreds of microbial proteins from
bacteria relevant to the colon in a single experiment. For instance,
a nano-high-performance liquid chromatography/mass spectrometry
(nano-HPLC/MS) system was established to separate proteins of E. coli in
a two-dimensional manner by combining strong cation exchange (SCX) and
reversed phased (RP) chromatography (Vollmer et al., 2003). Peptides were
eluted online to an iontrap MS instrument and further analyzed by tandem
MS fragmentation for identiﬁcation using the Swiss Prot Database. Differentially expressed proteins on glucose and lactose were identiﬁed. Similarly,
lactic acid bacteria that are widely used in the agro-food industry have been
characterized by proteomic techniques as reviewed in Champomier-Verges
et al. (2002).
More recently, the proteome of biﬁdobacteria has received considerable
attention. Adaptation to and tolerance of bile stress are among the main
limiting factors to ensure survival of biﬁdobacteria in the intestinal environment of humans. Comparing protein patterns of strains grown with
or without bile showed 34 different proteins whose expression was regulated (Sanchez et al., 2005). These proteins included general stress response chaperones, proteins involved in transcription and translation and
in the metabolism of amino acids and nucleotides, and several enzymes of
glycolysis and pyruvate catabolism, indicating that bile salts induce a
complex physiological response rather than a single event in biﬁdobacteria. In a second study, a strong cation exchange-reversed phase-tandem
mass spectrometry strategy was used to catalogue the most abundantly
expressed proteins of a probiotic Bifidobacterium infantis strain (Vitali
et al., 2005). These authors were able to obtain a global view of the
B. infantis proteome with 136 proteins identiﬁed by multidimensional
protein identiﬁcation technology (MudPIT) analysis that were subsequently compared to available genomic information. Yuan et al. (2006)
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
113
very recently published an even more comprehensive proteomic study on
Bifidobacterium longum NCC2705 in which they succeeded in identifying
369 protein entries by MALDI-TOF-MS and/or ESI-MS/MS. The identiﬁed proteins represent 21.4% of the predicted 1727 ORFs in the genome
and correspond to 30% of the predicted proteome. Interestingly, this
study also aimed to characterize cellular pathways related to important
physiological processes. Comparing proteome maps during growth on
glucose and fructose suggested the presence of a speciﬁc transport system
for fructose in B. longum. Interestingly, the proteome of biﬁdobacteria in
the GI tract of the human infant is being studied (Te Biesebeke et al.,
2004). Over a period of 9 weeks, fecal samples were collected from infants
and studied with two-dimensional gel electrophoresis. A change in protein
expression over time was observed. Detailed analyses of these changes
using MS-analyses are in progress (Te Biesebeke et al., 2004).
As was already apparent from enzymatic analysis discussed above, intestinal bacteria express many proteins that deploy their activities outside
the cell, be it freely soluble or, in many cases preferably, attached to the
cell wall. This makes sense because such essential factors as both
the substrate and the opportunities to attach to the gut wall are located
on the outside of the cell. A well-known intestinal bacterium, Clostridium
difficile, has been analyzed for its cell wall-associated proteome recently
(Wright et al., 2005). This bacterium causes disease of the large intestine,
particularly after treatment with antibiotics, due to production of two
toxins (A and B). In addition to these toxins, C. difficile expresses cell
wall-associated virulence factors including cell wall protein Cwp66, highmolecular weight surface layer protein (HMW-SLP), and the ﬂagella.
However, the genome sequence predicted many more cell wall-associated
proteins that could play a role as virulence factors, and indeed the study
found, among 49 different identiﬁed cell wall proteins, a number of paralogs of HMW-SLP that present interesting targets for further research
(Wright et al., 2005).
The application of proteomics to complex microbial assemblages (metaproteomics) still presents considerable challenges (Wilmes and Bond, 2006).
The most extensive metaproteomic study to date combined proteomics with
metagenomics to study a low-complexity natural bioﬁlm (Ram et al., 2005).
2033 individual proteins of the 12,148 predicted proteins (from the
metagenome sequence) were identiﬁed.
Summarizing, proteomic studies of gut microbiota are still very few
which is a pity because they provide fascinating views on how the intestinal
bacteria forage for food, attach to the host, and send out toxins to defend
themselves.
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5.3. Can Stable Isotopes Help in Proteomics?
The reason for the limited number of proteomic studies on the gut microbiota primarily lies in experimental problems especially related to sample
acquisition and preparation. Also, proteomic techniques are less amenable
to large-scale rapid screening protocols than transcriptome and metabolome
technologies, due to required slow separation technology as well as a complex mass spectrometry-based identiﬁcation with associated need for tryptic
digestion of the sample. Faster, chip-based technologies (such as SELDI
(Issaq et al., 2002) and antibody-based protein chips (Binder et al., 2006))
are in continuous development (see Ramachandran et al., 2005 for a recent
review) but their application to map the complete proteome of the intestinal
microbiota, containing many unknown proteins, and many important proteins attached to the bacterial cell walls, is still to be awaited. Stable isotopes
do not seem to have properties that could change that situation.
Nevertheless, stable isotopes play a significant role in proteomics as
a means to provide standards for quantiﬁcation. Isotope-coded afﬁnity tags
(ICAT) (Gygi et al., 1999) is probably the best example; this technique
employs isotopic reagents for labeling two different populations of proteins
that can subsequently be compared against each other quantitatively using
mass spectrometry, allowing e.g. the determination of organelle location of
proteins (Dunkley et al., 2004). SILAC (Stable Isotope-Labeling with
Amino acids in cell Culture) (Ong et al., 2002) provides another, inexpensive
and accurate procedure that can be used as a quantitative proteomic
approach in any cell culture system simply by comparing the protein proﬁles
measured by mass spectrometry from cell cultures grown in unlabeled
culture medium vs. those grown in deuterium-labeled medium.
Stable isotopes can be used to monitor protein synthesis and determine
protein fractional synthesis rates (FSRs), i.e. protein metabolic ﬂuxes,
using the MIDA approach introduced by Hellerstein and Neese (1999).
This technique, because it uses mass spectrometry analysis of peptide
protein fragments, is possibly relatively easy to combine with existing
mass spectrometry-based protein proﬁling approaches. The technique as
originally published is technically involved and therefore requires close
attention to potentially confounding factors and analytic performance for
optimal application. However, a new development of MIDA was recently
described (Busch et al., 2006) that employs 2H2O labeling to permit sensitive, quantitative, and operationally simple measurements of protein
turnover in vivo, especially for proteins with slow constitutive turnover.
While this technique appears best suited to slowly turning-over proteins, it
does bring the prospect of dynamic protein proﬁling closer. It is to be
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115
awaited whether this prospect will turn into reality in the near future, and
whether future developments will be suited also to probe the proteome of the
colonic microbiota.
6. METABOLOMICS
Of the functional genomics toolbox, metabolomics is the most recent addition. The technique involves the non-targeted, holistic analysis of changes
in the total set of metabolites in a sample (the metabolome) in response to
environmental or cellular changes. Only now is the metabolomics approach
technically feasible, due to the enormous improvements made in the past few
years in analytical chemistry and bioinformatics. There have been enormous
improvements in the separation and detection of metabolites. Moreover,
progress in bioinformatics makes it now possible to process and interpret
large sets of biochemical data generated through this non-biased holistic
approach.
Metabolites are low molecular weight organic compounds (o1000 Da)
that participate in general metabolic reactions or are required for the maintenance, growth, and normal functioning of a cell (Beecher, 2003). Metabolites mostly play a role in cellular metabolism and as carriers of energy
and reducing equivalents. The total number of different metabolites that are
present in any given cell is as yet unknown. In total, almost 20,000 microbial
metabolites have been described so far (Vicente et al., 2003). However, many
of these metabolites are only present in relatively few microorganisms. From
the recent annotation of microbial genome sequences, between 241 and 794
metabolites were deduced to be present in microorganisms (Vaidyanathan
and Goodacre, 2003). Since around 40% of the genes present in the microbial genomes have an unknown function, the actual number of metabolites
may be approximately three times more (van der Werf et al., 2005).
There are several reasons why metabolomics is the functional genomics
technology of choice. First of all, the information that can be derived from
the metabolome corresponds to a very different perspective on cellular
functioning than those of the genome, transcriptome, and proteome. While
genomic studies are highly instrumental in uncovering the genetic potential
of the gut microbiome, and the transcriptome reﬂecting the functional
response, the proteome and the metabolome together determine the actual
functionality of a cell. The biochemical level of the metabolome is closest to
that of the function of a cell (the phenotype), and thus the study of the
metabolome (together with the ﬂuxome) is the most relevant in order to
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comprehend biological functioning. This is especially so since changes in the
levels of individual enzymes have, in general, little effect on metabolic ﬂuxes,
but do have an effect on the concentrations of the metabolites (Sanford
et al., 2002; Goodacre and Kell, 2003) produced in the microbial pathway
containing that enzyme.
However, although metabolomics is thus ‘the preferred choice’ of the
currently established ‘-omics’ technologies, from a more technical point of
view, there are still several challenges. Many metabolites, especially signal
molecules, are present only transiently and in very low concentrations. The
sensitivity and dynamic range of analytical instrumentation, when applied in
non-target mode, is still not as high as it should be, and thus these metabolites may not be measured. In addition, since the intestinal microbiota is
composed of 41000 different species, it may be impossible to relate production of certain metabolites to speciﬁc members of the microbiota.
By contrast, interaction of microbial metabolites, from the intestinal microbiota, with the host is basically restricted to the extracellular metabolites,
simplifying matters again. In addition, whereas the microbial composition
may be very different between subjects and even vary considerably with time
(Barcenilla et al., 2000), we must assume that the microbiota as a whole
performs a stable set of activities within a population, given the enormous
functional overlap (redundancy) between microorganisms. Also, the use
of stable isotopes may help to shed light on what microbes are doing, both
on the level of identiﬁcation of microbes that are actively fermenting a given
substrate (using SIP, Section 4), and on the level of metabolite production,
as is discussed below.
6.1. Microbial Products and What They Can Mean to Us
The proximal colon receives food residues and other substrates from the
small intestine and is therefore rich in carbohydrate and protein. It is generally accepted that carbohydrates are the preferred substrate for most
members of the colonic microbiota. The carbohydrates are used to obtain
energy, while any available protein is incorporated into biomass. However, fermentable carbohydrates may become depleted more distally along
the colon, leading to decreased activity of saccharolytic bacteria. Conversely, the proteins and peptides that are present throughout the colon
can be utilized by protein or amino acid fermenting bacteria when the
carbohydrate is depleted. Numerically important proteolytic species
identiﬁed in the large bowel include species belonging to the genera
Bacteroides, Propionibacterium, Clostridium, Fusobacterium, Streptococcus,
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
117
and Lactobacillus (Macfarlane and Cummings, 1991). Studies in sudden
death individuals have shown that concentrations of metabolites from
proteolytic fermentation are higher in the distal colon compared with the
proximal colon (Cummings et al., 1987; Macfarlane et al., 1992a; Smith and
MacFarlane, 1996, 1997): e.g. distal concentrations of phenolic compounds
were four times that detected in proximal regions.
Therefore, the presence of fermentable carbohydrates inﬂuences proteolytic fermentation in the colon, as also recently shown using stable isotopes (De Preter et al., 2004). Although carbohydrate fermentation
predominates in the large intestine as a whole, the fermentation of proteins
becomes quantitatively more important distally. It is also interesting to note
that the majority of colorectal cancers occur in the distal side of the colon
(Hughes et al., 2000; Hope et al., 2005) where the SCFA concentration is at
its lowest, the concentrations of proteolytic metabolites is at its highest, and
contact of the intestinal epithelium with luminal contents is increased due to
the more solid nature of luminal contents and also due to the slower transit
through this segment of the bowel. Therefore, it is speculated that protein
degradation in the colon is relevant for colon cancer (Hughes et al., 2000).
Similarly, it is speculated that there is a correlation between the occurrence
of protein fermentation in the distal colon and the onset of UC (Roediger
et al., 1997; Levine et al., 1998). This is, however, all circumstantial
evidence, and hard proof is lacking. And therefore, little is known about the
biological role in vivo of these potentially toxic metabolites derived from
proteolytic fermentation.
It is believed that carbohydrate fermentation results in the production of
beneﬁcial microbial metabolites such as the SCFAs (primarily acetate, propionate, and butyrate), while protein metabolism may lead to what are
generally considered toxic metabolites, such as hydrogen sulﬁde, ammonia,
and phenolic and indolic compounds. Hydrogen sulﬁde is also produced
by the sulfate-reducing bacteria (SRB), which characteristically couple
oxidative phosphorylation with the reduction of sulfate to sulﬁde.
Butyrate is the principal energy source of colonic epithelial cells. Up to
70% of the energy used by these cells comes from microbially produced
butyrate (Roediger, 1980). Butyrate has been implicated in colorectal
tumorigenesis since it exerts a multitude of anti-tumor effects in transformed
cells in vitro, such as modulation of cell proliferation, differentiation, and
apoptosis (Young and Gibson, 1994). In vivo, luminal butyrate concentrations are inversely correlated with tumor size in experimental colorectal
tumorigenesis, and direct rectal or cecal installation of butyrate reduced the
size and number of tumors in experimental carcinogenesis. It is, therefore,
no surprise that considerable experimental effort is being expended in order
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to deﬁne the effects of butyrate and the mechanisms by which it acts. However, the cellular effects of butyrate are complex, especially since those in
one cell system may be the complete opposite of those in a different but
related cell system. This so-called ‘butyrate paradox’ has been observed in
relation to cell proliferation, differentiation, and apoptosis (Gibson et al.,
1999b). The biological basis for these contrasting effects has not been
deciphered. However, it is hypothesized that the responses of cells to butyrate may depend on the cells’ state of activation, independent of butyrate
oxidation (Gibson et al., 1999b).
The formation of toxic hydrogen sulﬁde (H2S) by human commensal
bacteria, either from protein fermentation or sulfate reduction by SRB, is
assumed to promote the development of inﬂammatory intestinal diseases
(Levine et al., 1998; Ohge et al., 2005), particularly in the distal part of the
human colon. Here, as discussed above, carbohydrate availability is limited
because the quickly fermentable carbohydrates have already been fermented
in the proximal colon. Therefore, the microbiota switches to protein fermentation, with concurrent production of putrefactive metabolites. It is
unknown where SRB display the highest activity, but the presence of electron acceptors throughout the colon (e.g. acetate, H2) suggests that SRB
may be active throughout the whole large intestine, although they have to
compete for H2 with acetogens and methanogens.
As touched upon above, there is speculation that there is a correlation
between putrefaction and the occurrence or start of onset of UC and colon
cancer. Although this is circumstantial evidence, the current belief is that H2S,
in particular, may be responsible for this (Levine et al., 1998; Ohge
et al., 2005) as it blocks oxidation of butyrate in colonic epithelial cells.
Roediger et al. (1993) showed inhibition of butyrate oxidation by H2S
in vitro in both rat and human colonocytes at a concentration of 2 mmol/L.
Using human colon tissue, Christl et al. (1994) showed that 1 mmol sulﬁde/L
significantly increased cell proliferation rates and other changes normally seen
in UC. Studies have shown that dietary protein does contribute to sulﬁde
production in the large intestine (Magee et al., 2000). In general, the higher the
intake of protein, the higher is the production of sulﬁde in the colon, with a
production of 3 mmol after a dietary protein intake of 200 g/day. Other
sources of sulfur present in the colon are from inorganic sulfate and mucin.
Daily intake of inorganic sulfate is estimated to range from 1.5 to 16 mmol/kg.
Estimated amounts of mucin (an unknown part of which is sulfated) excreted
in the lumen of the GI tract are 4100 g/day (Lichtenberger, 1995). Sulfate
from mucin can be liberated by numerous members of the microbiota that
contain sulfatases (e.g. Bacteroides), after which the liberated sulfate may become available for SRB. It is estimated that SRB derive 1.5 to 2.6 mmol
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
119
sulfate/day from sulfated mucin (McGarr et al., 2005). Therefore, it is possible
that a combination of dietary protein, sulfated mucins, and inorganic sulfur
additives in food could result in fecal sulﬁde concentrations that may lead to
pathological processes.
Hydrogen sulﬁde also functions as a neuromodulator, but whether it
modulates visceral perception and pain in humans is currently unknown. A
recent study in rats (Distrutti et al., 2006) investigated the role of H2S in
modulating the nociception (ability to feel pain) to colorectal distension
(CRD), a model that mimics some features of IBS. Treating rats with NaHS
resulted in a dose-dependent attenuation of CRD-induced nociception. It
was concluded that H2S release in the colon might actually be beneficial in
treating painful intestinal disorders. This contrasts with the current belief
that H2S is deleterious to health. Equivalent with the ‘butyrate paradox’,
there seems to be a ‘H2S paradox’ as well. How much of this paradox is
based on differences in dose–response is presently unknown.
The other proteolytic toxic metabolites are also deleterious for health. The
branched chain fatty acids (BCFAs), which are produced by fermentation of
the branched chain amino acids valine, leucine, and isoleucine, can cause
liver problems (Mortensen and Clausen, 1996). Ammonia is toxic to the
colonic epithelium and promotes colon cancer in rats. In addition, it is
a potential liver toxin and has been implicated in the onset of neoplastic
growth (Clinton, 1992; Macfarlane and Macfarlane, 1995). The production
of phenolic and indolic compounds by intestinal bacteria has been associated with a variety of disease states in humans, including schizophrenia
(Macfarlane and Macfarlane, 1995). In addition, they appear to act as
co-promoter in the development of colorectal cancer (Rowland, 1995).
Other metabolites that are produced by the intestinal microbiota but have
not been discussed so far included gases, primarily H2, CO2, CH4, but 4250
other vapors can be detected in expired breath and are assumed to be produced
partly in the colon (Brydon et al., 1986; Levitt et al., 1995; Suarez
et al., 1998). Depending on the speed of production and accumulation
(possibly up to 25 L/day), these gases may cause intestinal symptoms such
as abdominal cramps and urge of defecation, while impaired transit and
tolerance to gas has been implicated in IBS (Suarez, 2000; Serra et al., 2001).
Also, certain minor food components may be fermented into bioactives, such as
the conversion of sinigrin (a glucosinolate) into allyl-isothiocyanate (Krul et al.,
2002), which has been shown to inhibit metastasis of human hepatoma cells
(Hwang and Lee, 2006), or the breakdown of ﬂavonoids into several different
phenolic compounds, such as 3-methoxy-4-hydroxyphenyl acetic acid, 4-hydroxyphenyl acetic acid, 3,4-dihydroxyphenyl acetic acid, 3-(3-hydroxyphenyl)
propionic acid, 2,4,6-trihydroxybenzoic acid, 3-(4-hydroxy-3-methoxyphenyl)
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propionic acid, and 3-hydroxyphenyl acetic acid (Gao et al., 2006). The
biological activity of most of these is unknown, but 3,4-dihydroxyphenyl acetic
acid has been shown to have an anti-proliferative activity (Gao et al., 2006).
In addition, compounds such as nitrosamines may be formed by the reaction of
secondary amines with nitrite at low pH. It goes beyond the scope of this review
to discuss these in any detail.
Microbial metabolites may inﬂuence the metabolic integrity of intestinal
epithelial cells and induce mucosal immune responses. In recent experiments, the effects of the microbial metabolites butyrate, iso-valerate, and
ammonium on CaCO-2 cells was investigated (van Nuenen et al., 2005).
Barrier function was determined by measuring transepithelial electrical resistance. The barrier function of CaCO-2 cells remained intact in this study
after exposure with the type and concentrations of metabolites used. However, addition of phenolic compounds, above a certain threshold value, had
a dramatic effect on the transepithelial electrical resistance (Venema et al.,
unpublished data). These experiments need to be conﬁrmed and extended.
This can be done in in vitro experiments, but also in other models more
closely simulating the real situation.
In the same set of experiments (van Nuenen et al., 2005), the effect of
microbial metabolites on cytokine production by macrophages was tested as
well. In these experiments, the macrophage cell line U937 was cultured alone,
or in combination with CaCO-2 cells. Production of TNF-a and
IL-10 was measured. These experiments showed that CaCO-2 monoculture
cells did not secrete detectable levels of TNF-a or IL-10 after metabolite
exposure (in the presence or absence of stimulation with LPS) (van Nuenen
et al., 2005). In the U937 monoculture experiments, addition of 50 and
100 mM butyrate or iso-valerate, or of 20 and 40 mM ammonia resulted in a
dose-dependent inhibition of TNF-a secretion compared with LPS (positive
control), while lower, more physiological concentrations (4–20 mM for butyrate and iso-valerate; 2 and 4 mM for ammonia) stimulated TNF-a secretion
(dose independently). IL-10 secretion by these macrophages in monoculture
was suppressed by all metabolites in all concentrations compared to LPS (dose
dependently), except for the lowest concentration of ammonia, for which IL-10
secretion by macrophages was almost twice as high as with LPS. This shows
that the immune system underlying the colonic epithelium may be differentially inﬂuenced by the different metabolites, which in turn may also be present
in ﬂuctuating concentrations, and therefore metabolites may have stimulating
or suppressing effects on production of cytokines depending on concentration.
An experiment that still needs to be performed would include a mixture of
these metabolites, to determine whether the effects of one of the metabolites
dominates over that of the others.
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121
The absorption of SCFA and butyrate from the colonic lumen is a very
efﬁcient process as only 5–10% is excreted in the feces. At least 60% of the
SCFA uptake is effected by simple diffusion of protonated SCFA involving
hydration of luminal CO2 (Topping and Clifton, 2001). The remainder
occurs by active transport through two transporters, the monocarboxylate
transporter isoform 1 (MCT1), which is coupled to a transmembrane proton
gradient, and the sodium-coupled monocarboxylate transporter (SMCT1)
(Gupta et al., 2006).
How does butyrate exert such a wide array of effects? The ability of butyrate
to regulate gene expression is often attributed to its induction of histone hyperacetylation through the inhibition of histone deacetylase (HDAC). Hyperacetylation of histones disrupts their association with DNA, resulting in more
‘open’ chromatin structure that facilitates access of transcription factors to
speciﬁc genes. This has been demonstrated by the fact that trichostatin A,
which specifically inhibits HDAC, mimics many of the effects of butyrate
(Gibson, 2000). However, it is likely that butyrate has other intracellular targets. These include the hyperacetylation of non-histone proteins, alteration of
DNA methylation, selective inhibition of histone phosphorylation, and the
modulation of intracellular kinase signaling (Daly and Shirazi-Beechey, 2006).
This multiplicity of effects may underlie the ability of butyrate to modulate
gene expression at several levels including transcription, mRNA stability, and
elongation. The response to butyrate is complex, involving multiple distinct
mechanisms/pathways (Daly and Shirazi-Beechey, 2006), with different pathways operative in different cells. As said before, butyrate is transported across
the membrane by MCT1. The expression of MCT1 is significantly downregulated in the human colon during the transition from normality to malignancy.
This leads to a reduction of butyrate uptake and may contribute to the
development of colonic neoplasia (Daly and Shirazi-Beechey, 2006).
Currently, the mechanism of the effects observed is unknown. In addition
to the observed effect on the inhibition of histone deacetylase, the promoters
of several genes contain a highly conserved sequence, the butyrate response
element. It is likely that effects of butyrate are realized through one
of these mechanisms. However, it is currently unknown what the mechanism
of action is for other microbial metabolites.
6.2. Tracing the Fate of Prebiotics: In Vitro Models and Stable
Isotopes
Prebiotics are deﬁned as non-digestible food ingredients that beneﬁcially
affect the host by selectively stimulating the growth or activity of one or a
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limited number of bacterial species already resident in the colon and presumed to be health promoting (Gibson and Roberfroid, 1995), for example, by increasing numbers of indigenous biﬁdobacteria (Finegold et al.,
1983; Gibson and Roberfroid, 1995; Gibson et al., 1999a). Many carbohydrates are reported to be prebiotic, including fructooligosaccharides,
galactooligosaccharides, isomaltooligosaccharides, and lactulose (Gibson
and Roberfroid, 1995; Gibson et al., 1999a). Apart from their activity on
the composition of the large intestinal microbiota, prebiotics also affect
the microbial metabolite pool in the colon. Most measurements on
microbiota and metabolites in humans are performed in feces, which is
basically the only non-invasive material that can be obtained from healthy
volunteers. A drawback of analysing fecal samples is the fact that they do
not represent quantitatively what happens in the proximal part of the
colon where fermentation of most prebiotics takes place. SCFA from
prebiotic fermentation are predominantly produced in the proximal part
of the colon and will be absorbed by the body to a considerable extent
during subsequent transit of the chyme to the distal colon and rectum,
which may take anywhere from 24 to 472 h depending on the individual.
Consequently, the amount and ratio of SCFA recovered in the feces will
not reﬂect those resulting from fermentation of the prebiotics in the
proximal colon, but rather that of local production in the distal colon.
To be able to properly study the microbial processes occurring in the
proximal colon, various in vitro models simulating the fermentation processes occurring in the lumen of the colon have been developed (McBain
and Macfarlane, 1997; Minekus et al., 1999; De Boever et al., 2000).
Although we acknowledge the existence of other in vitro models, it goes
beyond the scope of this review to discuss these here. Here, we would like
to exemplify the advantages of these models in studying the activity of the
intestinal microbiota on the basis of some of our own results in a dynamic,
computer-controlled in vitro model of the large intestine. This model features, amongst others, peristaltic movements and removal of microbial
metabolites (Minekus et al., 1999). The model allows frequent sampling in
time, such that time series can be prepared for MFA (see below) and SIP
(Egert et al., 2007). In this manner, better insight is obtained in the
chronological order of the processes that underlie fermentation of undigested dietary components, and the microbial pathways involved in the
fermentation of these substrates. The model closely simulates the in vivo
conditions of the GI tract of humans during the passage of food under
average conditions of a population. Validation of the colon model was
done with regard to the composition of the microbiota, the enzymatic
activity of the microbiota, and the production and concentration of
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
123
SCFA using data from sudden death individuals (Cummings et al., 1987;
Macfarlane et al., 1992a). Since the in vitro system offers the possibility of
separating the test substrate from other compounds, the effects of substrates and especially the mechanisms behind this effect can be properly
studied. A mass balance can be made in this system, because in principle
all metabolites that are produced are collected. This is a major advantage
over in vivo studies. Even if in vivo samples from the lumen of the colon
and the blood would be taken, not all produced SCFA would be measured
(Cummings and Macfarlane, 1991). This is because butyrate especially is
used as a substrate by colonocytes (Roediger, 1982) and therefore only
low amounts of this microbial metabolite are found in the bloodstream
(Cummings and Macfarlane, 1991). In the in vitro model all SCFA are
detected, either in the lumen of the model, or in the collected dialysis ﬂuid.
The potential of this system as a tool to study fermentation of dietary
components was demonstrated in experiments with a variety of substrates,
including pectin (Minekus et al., 1999), fructooligosaccharides (Minekus
et al., 1999), inulin (van Nuenen et al., 2003), lactulose (Venema et al.,
2003), tagatose (Venema et al., 2005), resistant starch (Venema et al.,
2004), and lactitol (Minekus et al., 1999). Parameters such as total SCFA
production and the SCFA ratio were determined in time to characterize
the fermentation. This allowed the mechanistic study of the effects of food
components on microbial metabolite production at its most active site, the
proximal colon. For instance, when lactulose was added, a biﬁdogenic
effect, and thus prebiotic effect, was shown in vitro (Venema et al., 2003),
as reported previously in the literature (Terada et al., 1991; Mizota, 1996).
This study showed that, after in vitro addition of lactulose, the microbiota
hardly produced any butyrate any more. Apparently, lactulose changed the
activity and/or composition of the microbiota such that butyrate is no
longer produced. The effects of lactulose on butyrate production would not
have been evident in vivo, because in vivo a multitude of other substrates are
available to the microbiota, such as resistant starch, mucin, desquamated
epithelial cells, etc. Here, the value of being able to separate, in vitro,
the mixture of in vivo substrates (amongst others the test compound, other
dietary components, desquamated cells, and mucin) from the test substrate
(in this case lactulose), was great and made it possible to investigate the
underlying mechanism of speciﬁc stimulation of microorganisms by lactulose. It should be mentioned though that these models have limitations and
cannot mimic all conditions prevailing in vivo, and also may not be able to
allow growth of all microorganisms present in vivo.
There are indications that the administration of prebiotics suppresses the
generation and accumulation of toxic metabolites from protein
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fermentation, and through a suppression of toxic metabolites, the incidence
of colon cancer may decrease. However, substantial evidence supporting
these beneﬁcial effects of prebiotics is currently lacking, mainly due to the
inaccessibility of the colon and the unavailability of reliable tracers. In vitro,
the addition of 10 g/day of inulin, the best studied prebiotic to date,
resulted in a twofold reduction in the production of ammonia and an
undetectable production of BCFA (Van Nuenen et al., 2003). Interestingly,
the addition of a protein fermentative microorganism, C. difficile, increased
the production of these protein-fermentative metabolites. Addition of inulin
to this situation reduced ammonia, BCFA, and production of phenolics
(van Nuenen et al., 2003). A recent study investigated in vivo whether the
administration of a selected prebiotic (lactulose) would result in a reduced
concentration of one or more protein-fermentative metabolites in the colon
(De Preter et al., 2004). Ten grams of lactulose were given at breakfast and
at supper for 2 weeks. Before and after these 2 weeks, a test meal containing
[2H4]tyrosine and lactose-[15N]ureide was consumed. Urinary p-[2H4]cresol
and total 15N were measured. This study showed a significant reduction in
both urinary biomarkers, and provides direct evidence that in vivo also,
colonic protein degradation is reduced by the administration of lactulose as
a fermentable carbohydrate, resulting in a lower concentration of potentially
toxic metabolites.
The full metabolome upon addition of prebiotics can be studied, either
from fecal material or from in vitro derived samples, of which the latter
are more clean, but do not necessarily contain all metabolites observed in
real samples. As far as we are aware, initiatives to measure a full metabolome of fecal matter have not been undertaken. In studies in in vitro
models of the large intestine, a ﬁrst attempt to measure as many metabolites as possible has been made (our own unpublished results). Here,
the holistic approach of metabolomics was taken. Apart from metabolites
that are usually studied and measured in intestinal microbiology, such as
the SCFA, lactate, ammonia, etc., several other extracellular microbial
metabolites have been identiﬁed using this metabolomics approach. These
include ethanol, acetaldehyde, methanethiol, and dimethylsulﬁde. None
of these should raise surprise, as all are known to be microbial metabolites, but they are not generally measured or detected in samples
related to the (lumen of the) GI tract. In these types of experiments,
however, it is unclear which metabolite is produced from which substrate,
emphasizing once more that our knowledge about the activities of the
intestinal microbiota is woefully inadequate. Use of stable isotopes may
ﬁll some of the gaps in this knowledge, as exempliﬁed in the studies
described above (De Preter et al., 2004).
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125
6.3. Evidence of Cross-Feeding
An interesting aspect of the biﬁdogenic nature of certain prebiotics, is that
upon feeding these carbohydrates, the butyrate production is also increased.
Yet, biﬁdobacteria are uncapable of producing butyrate. This indicates that
there is more occurring than just biﬁdobacteria fermenting the prebiotics,
and this is either an indication of other species fermenting the prebiotics
as well, or an indication of cross-feeding. Probably both occur. The gut
microbiota, as we have seen, is a very complex community that comprises
hundreds of different species of microbes from different genera. One of the
great puzzling questions is, how these microbes can work together so well to
perform the functions that the microbiota generally does, and how it is
possible that the microbiota composition can adapt itself to such rapidly
and strongly changing conditions as those in the colon, without significant
disturbance of its overall function. Advancements of science in this area of
gut research are among the most interesting today. Some striking results are
discussed here.
Oxalobacter formigenes, a strictly anaerobic bacterium found in the
human colon, presents a beautiful example of how the beneﬁts of cometabolism extend beyond the gut wall, and intestinal microbial metabolism
really is integrated with host metabolism (Stewart et al., 2004). Oxalate is
ingested in a wide range of foods and beverages and is formed endogenously
as a waste product of metabolism. Bacterial, rather than host, enzymes are
required for the intestinal degradation of oxalate in man and mammals. The
bacterium primarily responsible is O. formigenes (Stewart et al., 2004).
Oxalate is excreted in urine and the loss of O. formigenes may be accompanied by elevated concentrations of urinary oxalate, increasing the risk of
recurrent calcium oxalate kidney stone formation. The interesting points
here are that O. formigenes has an obligate requirement for oxalate (produced by the host) as a source of energy and cell carbon. In return, the host
is saved from kidney stone formation. But colleague microbes would also
beneﬁt. In O. formigenes, the proton motive force needed for energy conservation is generated by the electrogenic antiport of oxalate and formate by
the oxalate–formate exchanger. Thus, O. formigenes produces formic acid
which in turn, as a cross-feeding substrate, may serve as a one-carbon donor
in the metabolism of many other intestinal microbes.
Even when considering a single aspect of the colonic microbiota’s function, butyrate production, the situation is complex and confusing. Many
different species of butyrate-producing bacteria are present. In a study of
seven of those, namely strains of Roseburia sp., Faecalibacterium prausnitzii,
and Coprococcus sp. from the human gut that produce high levels of butyric
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acid in vitro, distinct patterns of available butyrate pathway enzymes and
fermentation patterns were discovered (Duncan et al., 2002). Strains of
Roseburia sp. and F. prausnitzii possessed butyryl coenzyme A (CoA):
acetate-CoA transferase and acetate kinase activities, but no butyrate kinase
activity. Although unable to use acetate as a sole source of energy, these
strains showed net utilization of acetate during growth on glucose, indicating that in the gut also they need a co-substrate for growth. In contrast,
Coprococcus sp. strain L2-50 possessed a complete set of detectable butyrate
synthetic enzyme activities: butyrate kinase, acetate kinase, as well as
butyryl-CoA:acetate-CoA transferase. Yet, this strain was found to be a net
producer of acetate instead of butyrate!
The use of lactate by intestinal bacteria also presents some puzzling
questions. The microbial community of the human colon contains many
bacteria that produce lactic acid, but lactate is normally detected only at
low concentrations (o5 mM) in feces from healthy individuals. To study
which microorganisms are mainly responsible for lactate utilization in
the human colon, bacteria able to utilize lactate and produce butyrate were
isolated from fecal samples (Duncan et al., 2004). Out of nine such strains
identiﬁed, four were found to be related to Eubacterium hallii and two to
Anaerostipes caccae, while the remaining three represented a new species
within a clostridial cluster. Interesting in view of the results discussed above,
no significant ability to utilize lactate was detected in the butyrate-producing
species Roseburia intestinalis, Eubacterium rectale, or F. prausnitzii (raising
the question of which co-substrate of acetate these bacteria employ in the
gut). Whereas E. hallii and A. caccae strains used both D- and L-lactate, the
remaining strains used only the D isomer. Lactate utilization was prevented
by the presence of glucose. However, when grown on starch in separate
co-cultures with a starch-utilizing Bifidobacterium adolescentis isolate, two
E. hallii strains and one A. caccae strain formed butyrate and the lactate
produced by B. adolescentis became undetectable (Duncan et al., 2004).
The effects of changes in the gut environment upon the human colonic
microbiota are poorly understood. Studies of the response of human fecal
microbial communities to alterations in pH (5.5 or 6.5) and peptides (0.6 or
0.1%) yielded surprising results (Walker et al., 2005). SCFA proﬁles differed
markedly between conditions. Moreover, very substantial changes in the
levels of the bacterial groups Bacteroides and Roseburia were monitored by
using ﬂuorescence in situ hybridization with a panel of speciﬁc 16S rRNA
probes. These ﬁndings suggested that a lowering of pH resulting from
substrate fermentation in the colon may boost populations of butyrateproducing bacteria, while at the same time curtailing the growth of
Bacteroides sp. (Walker et al., 2005).
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In a recent study, cross-feeding of microorganisms on acetate and lactate
to form butyrate was investigated using stable isotopes (Morrison et al.,
2006). [U-13C6]Glucose was used to show that M+2 and M+4 isotopomers
were the principal butyrate species produced from glucose fermentation, via
[13C2]acetyl CoA as intermediate (Fig. 6). Lactate conversion to acetate,
propionate, and butyrate were also observed. Conversion of propionate or
butyrate into other SCFA was negligible. The degree of interconversion was
dependent on which individual provided the fecal sample, indicating
some degree of host speciﬁcity in microbial activity between different
individuals. For instance, in only two of the ﬁve fecal samples, lactate to
propionate conversion was found (Morrison et al., 2006). In addition, the
authors studied butyrate production from fructooligosaccharides, a prebiotic stimulating biﬁdobacteria. Biﬁdobacteria produce primarily acetate
and lactate, but not butyrate. Yet, addition of fructooligosaccharides
to fecal batch cultures significantly increased butyrate production, and
the stable isotope data allowed the conclusion that as much as 80% of
this butyrate was derived from interconversion of extracellular acetate
and lactate, with acetate being quantitatively more significant (Morrison
et al., 2006).
7. METABOLIC FLUX ANALYSIS APPLIED TO THE GUT
What are the best parameters to characterize physiology? The end function
of gene expression, protein synthesis, and establishment of metabolite pools
is to maintain organism life. From life in its simplest form, prokaryotes, we
learn that cell physiology foremost serves to maintain maximal growth rate
under the prevailing environmental conditions in the organism’s habitat.
This primarily implicates the directing of appropriate material in the various
biosynthetic pathways, and the supply of sufﬁcient metabolic energy to drive
these processes. Furthermore, transport processes of metabolites and ions
are the basic means by which the cell ensures a closed material balance, and
homeostasis of its inner environment. Therefore, metabolic ﬂuxes of the
primary metabolism, together with membrane transport ﬂuxes, can be considered parameters that are very closely linked with cellular physiology.
Thus, it seems worthwhile to measure and monitor these ﬂuxes in addition
to the genomic, proteomic, and metabolomic characterizations addressed
above. MFA, as this activity is called, has seen a tremendous development in
the past few decades and stable isotopes have played a key role in this
progress, as indicated previously.
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Figure 6 Probing colonic SCFA metabolism with stable isotopes. Using the principle of isotope dilution, the colonic bacterial
synthesis rate of butyrate can be determined after continuous infusion of [1-13C]butyrate in the cecum by measuring the
[1-13C]butyrate tracer–tracee ratio (TTR) in the portal blood (relevant carbon labeling patterns are indicated by squares). In this
case, the bacterial metabolism will be producing unlabeled butyrate. Cross-feeding of butyrate-producing gut bacteria on acetate
may be evidenced by infusing [U-13C2]acetate in the cecum, and measuring the abundance of [1,2-13C2]-, [3,4-13C2]-, and [U-13C4]isotopomers in portal butyrate (relevant 12C/13C carbon isotopomer patterns are indicated by circles). In this case, if butyrateproducing bacteria take up acetate, part of the acetyl-CoA pool will get labeled with [1,2-13C2]acetyl units which leads to the
mentioned butyrate isotopomers.
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The aim of MFA applied to the gut is to identify and quantify significant
metabolic ﬂuxes in vitro and in vivo mediated by the microbial conversion
of substrates in the colonic intraluminal environment. The fermentation of
complex carbohydrates as well as of proteins deserves special attention here,
as SCFA primarily result from carbohydrate fermentation while protein
fermentation yields amongst others sulfur-containing compounds that compromise health (see previous section). The human in vivo approach necessitates non- or minimally invasive investigation techniques. Hence, the
primary investigation tool is that of the use of stable isotopes, which is
increasingly used in dietary interventions (Labayen et al., 2004). One may
anticipate testing 13C-labeled carbohydrate substrates with a varying degree
of polymerization, as well as isotopically labeled proteins. The metabolic
fate of the isotopically labeled atoms can be followed by high-throughput
mass spectroscopic analysis of the various metabolites in body samples
including blood, urine, feces, and epithelial biopsies. These analyses can
be coupled to dedicated gas chromatography-TOF-mass spectrometry
(GC-TOF-MS) for the analysis of volatile organic compounds in the
exhaled air, and their isotopic labeling. In addition, metabolite concentrations can be determined in these samples, and genomic and proteomic expression proﬁles may be recorded from biopsy samples. The resulting data
set provides a basis for the correlation of gut microbial metabolic activity
with host responses, and ultimately human health. Newest developments in
experimentation technology that can be applied in this area include the use
of targeted administration of isotopically labeled substrates to the colon
using e.g. pH-sensitive coated capsules (Tuleu et al., 2002; Oo et al., 2003).
In the following sections, illustrative results pertinent to MFA of the gut
microbiota as well as the host metabolic response will be discussed.
7.1. Insights into Bacterial Metabolic Routes
The ﬁrst important step in developing MFA of the colonic microbiota is
the definition of the metabolic network that is operative. This would seem
a task of unprecedented difﬁculty given that we are dealing with a highly
complex and diverse symbiotic community of microbes that altogether form
a microbiome with a genome coding capacity vastly exceeding that of the
human genome as referred to previously. Moreover, it is well documented
that considerable variations in both intensity and type of microbial metabolic activity occur along the GI tract (Jensen and Jorgensen, 1994;
Metges, 2000). The cecum and proximal colon are the metabolically most
active parts. As has been elegantly demonstrated to be the case for termites
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(see Brune and Friedrich, 2000 and references therein), there very likely
will be distinct structure–localization–function relationships of bacterial
metabolism also in the human gut. One genus of bacteria may supply the
substrate for another, thus leading to a diverse and dynamic yet functionally
stable microbial ecosystem. For instance, lactate produced by lactic acid
bacteria and biﬁdobacteria is rapidly converted to butyric acid by clostridia
and Eubacterium sp. (Bourriaud et al., 2005). Bacteria and Archeae
performing saturation of unsaturated fatty acids, reduction of nitrite to
ammonia, reduction of sulfate to sulﬁde, reduction of CO2 to methane,
and reduction of CO2 to acetate provide possible hydrogen sinks (Jensen
and Jorgensen, 1994) and by their action lower the partial pressure of
intestinal hydrogen gas, thus creating thermodynamic conditions that
allow for increased overall fermentation rates (Backhed et al., 2005). The
different bacterial genera present in the colon play distinct roles in the
metabolic chain of polysaccharide processing (Backhed et al., 2005;
Bourriaud et al., 2005; McGarr et al., 2005), from depolymerization, sugar
utilization, and production of intermediate metabolites such as hydrogen,
lactate or ethanol, and conversion of these intermediates into end products
(SCFA, methane).
How then, given all this complexity, can one think of setting up a metabolic network to perform ﬂux analysis? The approach should be to consider
the microbiota as a whole rather than concentrating on all individual members. One can then proceed in a meaningful manner knowing that the
microbiota performs only a limited number of major metabolic functions
(Backhed et al., 2005; McGarr et al., 2005), including:
breakdown of polysaccharides producing lactate, volatile SCFAs (formic
acid, acetate, propionate, butyrate, valerate) and related metabolites, as
well as gases (carbon dioxide, hydrogen, and methane);
breakdown of dietary peptides to amino acids for incorporation into
biomass or for subsequent fermentation, with concomitant production of
putrefactive metabolites;
breakdown of endogenous (i.e. produced by the host) proteins especially
mucins and other mucosal proteins.
Thus, one may start by ﬁrst accounting for the quantitatively major
processes, after which the network can be reﬁned more and more, so as
to include also the quantitatively minor (but possibly at least as interesting)
pathways such as bile acid metabolism and production of vitamins.
Following this approach, the strategy is now to build the pathway network
by putting together all available pieces of information on gut microbial
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
131
pathways and their approximate metabolic ﬂuxes from the literature. In the
following, relevant available literature data is reviewed.
To quantify carbohydrate digestion in the small intestine or fermentation
in the large intestine in vivo, 13C-labeled carbohydrate substrates such
as starch are administered orally and the appearance of products such as
glucose in blood (Korach-Andre et al., 2004) or carbon dioxide in exhaled
breath (Christian et al., 2002) has been monitored. Using mathematical modeling the relative amounts of the substrate digested in the small
and large intestine can be determined (Christian et al., 2002). Small
intestinal and oro-cecal transit time (OCTT) can be measured with the
lactose-[13C]ureide breath test (Priebe et al., 2004).
Considerable efforts have been made to quantify accurately the microbial
SCFA production. This is not an easy task due to the very active metabolism
of the colonocytes and the liver which interferes as soon as the SCFA are
released in the gut lumen (Fig. 7). In an elegant protocol, Kien et al. (1996)
used [2-2H3]acetate and [1-13C] sugars infused into the colonic lumen of pigs
to determine the rate of microbial acetate synthesis as well as the fraction
of the sugars metabolized to acetate in a single experiment. Using
[1-13C]butyrate infusion in the colon and sampling of portal blood, these
authors later determined microbial butyric acid production in pigs (Kien
et al., 2002) and showed that butyrate is also produced endogenously by
these animals (Kien et al., 2000). Pouteau et al. (2003) have developed
and applied protocols to determine SCFA production in humans, including
intragastric infusion to evaluate ﬁrst-pass splanchnic retention of SCFA.
Isotopic tracers have been highly instrumental for the clariﬁcation of
metabolic pathways involved in biosynthesis of compounds, as described in
an exquisite review by Bacher et al. (1999). By detecting doubly-13C-labeled
acetate produced from [3-13C]glucose, Wolin et al. (1998) could establish
that in a fecal suspension isolated from an infant, the Bifidobacterium pathway was the major glucose fermentation pathway used. These authors also
demonstrated the operation of the Embden–Meyerhof–Parnas as the major
glycolytic pathway leading to SCFA in fecal suspensions of adults (Wolin
et al., 1999). In these experiments, they found that a considerable portion of
microbially produced acetate was formed via the Wood–Ljungdahl pathway
of CO2 reduction.
The analysis of amino acid metabolism by isotope labeling is complicated
by the fact that these compounds are very actively turned over in each and
every organ of the body, and most of them are rapidly de- and reaminated in
transaminase reactions. Thus, all indispensable branched-chain amino acids
become 15N labeled after intravenous application of only 15N-labeled
leucine (Lien et al., 1997), because they are in rapid transaminase
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Figure 7 Assessing colonic SCFA production in vivo is not a straightforward
task. SCFA are mainly produced by bacterial fermentation in the colonic lumen, but
endogenous production may also take place in liver and peripheral organs (as in the
case of acetate). Part of the microbially produced SCFA may ‘disappear’ in the
lumen itself due to uptake and metabolization by other microorganisms (cross-feeding). A large part of SCFA produced in the colonic lumen may be disposed of in
colonocytes and never even reach the bloodstream (as in the case of butyrate). The
liver also has an active metabolism of SCFA, and almost completely prevents butyrate from reaching the peripheral bloodstream. Using the principle of isotope dilution in the experimental conﬁguration depicted in the ﬁgure, the measured TTR of
arterial SCFA reﬂects the sum of SCFA coming from the liver and SCFA produced
in peripheral organs, rather than the true colonic production. To probe the latter
correctly, intraluminal infusion is required. Ra and Rd signify rates of appearance
and disappearance, respectively, due to active metabolism in the various organs.
equilibrium with their respective precursor keto acids. Amino acids from
circulating blood may exchange via enterocytes with the colonic lumen,
causing mixing of endogenous and microbial amino acids. As much of
20–30% of liver-produced ureum may be used by intestinal bacteria for
amino acid and protein synthesis (Moran and Jackson, 1990) (cf. Fig. 3).
A number of studies employing 15N-labeled urea have been performed to
assess this issue. Urea diffuses into the colon where it is hydrolyzed by
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
133
bacterial urease to ammonia before being assimilated. The microbial origin
of a significant fraction of lysine and threonine in body protein has been
established from 15N urea labeling experiments (Lien et al., 1997; Metges,
2000). Studies with combined 15NH4Cl and 14C-polyglucose in pigs (Torrallardona et al., 2003) conﬁrmed this fact and, following sampling at different locations along the GI tract, gave strong indications that absorption
of amino acids from microbial origin mainly occurs in the ileum and not in
the colon. An important implication of these ﬁndings is that, as with vitamins, metabolic requirement cannot be equated with dietary requirement.
Closely related to this issue is the determination of the daily requirement for
essential amino acids (for a recent overview on this subject see Kurpad and
Young (2003), which is currently done via tracer-based protocols such as the
indicator amino acid oxidation technique (for example, threonine (Wilson
et al., 2000) and lysine (Kriengsinyos et al., 2002)).
Dietary fat apparently is a minor substrate for the colon due to the high
efﬁciency of the fat uptake. Also, the bile acid cycle is highly efﬁcient.
Nevertheless, a minor (1–5%) fraction of bile salts reach the colon where
anaerobic bacteria from, for example, the genus Clostridium metabolize
them to secondary bile acids, especially lithocholic acid and deoxycholic acid
(DCA) (McGarr et al., 2005). The latter is partly absorbed but cannot be
reconverted to cholic acid by the liver. Serum levels of DCA in patients
with colon cancer have been shown to be consistently higher than in
healthy subjects (McGarr et al., 2005). The sulfated amino acid taurine is
an important substrate for bile acid conjugation in the liver and a more
highly preferred sulfur source for fecal microbial metabolism (McGarr et al.,
2005). Since taurine conjugation is increased in individuals on a high
animal protein diet, investigation of the colonic metabolism of this amino
acid in relation to colon cancer may be relevant. A ﬁrst taurine turnover study, on whole body level, employing [1,2-13C2]taurine has recently
appeared (Rakotoambinina et al., 2004), showing very low turnover in
healthy adults.
Toxic nitrogen containing and/or aromatic end products of bacterial
fermentation may present a health risk especially on animal protein-rich
diets. Pre- and/or probiotic intakes have various claimed beneﬁcial effects
which generally are difﬁcult to prove. De Preter et al. (2004) and Geboes
et al. (2005) introduced the use of lactose-15N2-ureide and [2H4]tyrosine
as useful quantitative indicators for pre-/probiotics efﬁciency, namely
from the capacity of a product to suppress the generation and accumulation in urine of (i) 15N label derived from toxic bacterial ammonium,
and (ii) toxic p-[2H4]cresol, a bacterial degradation product from tyrosine
fermentation.
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With the help of the information presented above, an overall metabolic
network of gut microbial metabolism can be constructed. To complete the
model, transport routes that account for uptake of substrate and removal of
products (e.g. via epithelial absorption and feces) have to be included.
7.2. Get Quantitative: Mass Balances Reveal a Lot
In a consistent metabolic network, considered at (quasi)steady state, the
possible ﬂuxes are constrained by stoichiometry relations that reﬂect the
mass balances (Schilling et al., 2000). As a consequence, the ranges within
which intracellular metabolic ﬂuxes can vary can be predicted and conclusions can be drawn on how the metabolic network responds under different
conditions of, for example, substrate availability. While this approach has
been extensively and successfully applied with microorganisms (Reed and
Palsson, 2003), it may equally well be used to determine and even predict
metabolic ﬂuxes in mitochondria (Ramakrishna et al., 2001). The next step
in setting up MFA of the colonic microbiota therefore is to gather as much
quantitative experimental data as possible on ﬂuxes that represent the inputs
and outputs of that metabolic network in order to reduce the available ﬂux
solution space (Wiback et al., 2004). In other words, measured rates of, for
example, carbohydrate intake by the gut, SCFA use by the colon, etc.,
contribute to determine the bacterial intracellular metabolite ﬂuxes. Careful
balancing of SCFA production in anaerobic cultures of fecal bacteria
for instance has already provided important insights in their regulation by
carbohydrate availability and growth rate (Macfarlane and Macfarlane,
2003). The potential of this procedure to derive conclusions on important
ﬂuxes of intermediary metabolism such as the activity of the citric acid cycle
(i.e. mitochondrial function) even in a complex organ, has recently been
demonstrated with isolated perfused livers (Arai et al., 2001; Lee et al., 2003;
Yokoyama et al., 2005). For the colon, experimental procedures as discussed
in the previous section can be employed to obtain relevant data.
Of course, to be really successful, MFA of the colonic microbiota has to
be performed in vivo with the colon functionally operative in the entirety of
the functioning body. Indeed there is a true perspective that this can actually
be accomplished. Namely, the ﬂux balancing approach is also possible in
vivo using multi-catheterized blood sampling approaches (Ten Have et al.,
1996). Basically, this method employs measurement of blood ﬂow and sampling of blood upstream and downstream of organs, allowing the set up of
a net material balance across each organ. This approach has been used to
study interorgan amino acid metabolism during acute liver failure in pigs
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
135
and humans, where it led to a much improved insight in the role played by
different organs (muscle, kidney, spleen, liver, portally drained viscera)
during pathogenesis of the disease (Olde Damink et al., 2002; Ytrebo et al.,
2006). When the method is combined with isotopic tracers, the total disposal
rate of a traced compound (e.g. an amino acid) in an organ can be derived
from the loss of tracer across that organ. This allows the calculation of the
true uptake or production of this amino acid in that organ as net balance
plus disposal (Bruins et al., 2002, 2003). The use of suitably chosen isotopically labeled colon substrate tracers will further allow an increase in the
analyzing power of ﬂux analysis, much the same as discussed previously for
single microorganisms.
Therefore, once stable isotopes are included, there is genuine reason to be
optimistic about the prospect of MFA of the gut microbiota also in vivo in
the near future.
7.3. Stable Isotope-Aided Quantification of Pathways:
Functional Genomics
What is the practical route for MFA of the colonic microbiota? Once the
network is deﬁned, the ﬁrst step as we have seen is to construct material and
metabolite balances over the colon. On the experimental side, this will
involve the measurement of the input of non-digested material from the
small intestine into the colon, the measurement of differential metabolite
appearance rates in portal vs. arterial blood, and correcting the results for
material lost in feces as well as for products of digestion in the small
intestine (cf. Fig. 7). This analysis yields the basic input–output analysis of
the colon, which however still has to be completed by taking into account
the material metabolized by the gut wall. Stable isotopes may be employed
at this stage in addition to the net balancing to determine the total metabolite disposal and true production rates as explained previously. This is
an enterprise that may involve elaborate and strongly invasive experiments.
Next comes the probing of the actual bacterial intracellular metabolic
network. While this needs ﬁnally to be done for the in vivo situation in the
intact functioning colon, useful a priori information on the regulation of gut
bacterial metabolism may be derived from carefully planned in vitro experiments such as those on SCFA production discussed above (Macfarlane and
Macfarlane, 2003). These experiments should include product proﬁles,
cross-feeding effects, inﬂuence of thermodynamic constraints and pH,
among others. This work could be performed in validated in vitro colon
model systems such as described in a previous section (Minekus et al., 1999).
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Of course, in vitro conditions will most certainly differ considerably from
those in vivo, which will in turn affect the microbiota composition and hence
the overall metabolic activity pattern. Nevertheless, such in vitro models
may be highly instrumental in model development, i.e. setting up the basic
metabolic network of involved pathways and transport routes that one
expects. Then, this overall network might be decomposed into a limited set
of sub-networks each characteristic for a certain genus of bacteria, much in
the same way as elementary mode balancing (Schuster et al., 2002; Cakir
et al., 2004). Changes in microbiota composition then will only affect the
relative contributions of those modes, and not the pathway network model
as a whole.
FBA will subsequently reveal to which extent the resulting equation
system is under-determined from a mathematical point of view, given the
measured material balance data. Subsequently, intelligent strategies may be
employed (Mollney et al., 1999; Isermann and Wiechert, 2003) to design
stable isotope labeling experiments that will produce the additional data
necessary to completely solve the metabolic ﬂux network thus constructed,
in vitro and also in vivo. Subsequently, after the experiments using the required isotopically labeled substrates are actually conducted, the data
resulting from MS and/or NMR analyses will be used in a non-linear least
squares ﬁtting procedure to yield the full set of ﬂuxes in the metabolic
network model.
The ﬁnal perspective of MFA developed along these lines is a map
of metabolic pathway activities in the colonic microbiota, that can be decomposed into sub-maps of methanogenic, mixed acid, and other typical
microbial fermentation modes, each eventually attributable to a different
genus of microorganisms. One may then proceed to investigate how these
ﬂux maps differ between subjects with differences in microbiota composition, or how these ﬂux maps change upon feeding different prebiotic substrates, or how these ﬂux maps relate to host disease parameters, etc. Ideally,
this information is correlated with data from the various ‘-omics’ platforms,
leading to a true functional genomics application (Hellerstein, 2004).
8. EMERGING PICTURE OF THE ROLE OF
MICROORGANISMS INTEGRATED IN MAN
In recent years, the picture of the role played by the gut microorganisms
within us has become increasingly clear. All evidence points at a truly mutualistic relationship. Our guts provide a habitat for incredible numbers
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
137
of mostly anaerobic bacteria in an otherwise deadly, oxygen-rich environment. What we are getting in return has been largely derived from studies
that compared Germ-Free (GF) rodents with those that have acquired a
microbiota since birth (conventionally raised CONV-R). A recent overview
can be found (Backhed et al., 2005). Important ﬁndings from these studies
show that GF rodents show disturbed bile acid balances which affect cholesterol homeostasis, they show reduced cardiac weight and output, they are
more vulnerable to vitamin deﬁciencies (including vitamin K, B6, B12, biotin,
folic acid, and pantothenate), they extract less energy from their diet, their
immune system development is different (e.g. strongly reduced serum IgM
and IgG levels), and they are unable to metabolize dietary oxalates, leading
to kidney stone formation. Interestingly, GF mice are resistant to IBD and
are less susceptible to arthritis and colitis, indicating that there is also a risk
involved with carrying around our microbiota.
8.1. Energy Balance
Large intestinal fermentation can account for 10% of our daily energy
supply (Bergman, 1990). Thus, the colonic microbiota plays a very significant role in whole body energy supply. Studies with GF and CONV-R
rodents have shown that CONV-R animals were able to extract significantly
more energy from their diets than GF counterparts, as judged from the fact
that they had 40% more total body fat while consuming less food per day
(Backhed et al., 2004). This corroborates ﬁndings from other studies
(Scheppach et al., 1991; Pouteau et al., 2005) that evidenced increased serum
acetate concentrations and turnover, correlating with colonic carbohydrate
fermentation. Microbially produced butyrate is the preferred and most
important energy source for colonocytes (Csordas, 1996). These points
all more or less reﬂect a direct effect, i.e. additional energy produced by
microbial fermentation of substrates entering the colon that would otherwise be useless to the host. There is however reason to believe that indirect
effects may be at least as important. The colon functions within the whole
of the intestine and associated visceral organs in controlling body energy
balance (Badman and Flier, 2005). Gut and organs together play a key
sensing and signaling role in the physiology of energy homeostasis. The gut,
the pancreatic islets of Langerhans, elements in the portal vasculature, and
even visceral adipose tissue communicate via neural and endocrine pathways
with the controllers of energy balance in the brain. Signals reﬂecting energy
stores, recent nutritional state, and other parameters are integrated in the
central nervous system, particularly in the hypothalamus, to coordinate
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energy intake and expenditure (Badman and Flier, 2005). We are only
beginning to uncover all the different features of this complex regulatory
network, and expectations are high that understanding of the mechanisms
that control energy balance will provide clues for therapies to ﬁght the
metabolic syndrome.
8.2. Innate Immune System
Only a thin layer of epithelial cells known as enterocytes separates the host
from the intestinal lumen. These cells must form an effective barrier against
incursions and introgressions by the intestinal microbiota. Interestingly,
part of this barrier function appears to be carried out by intestinal bacteria
themselves: the puriﬁed adhesin of a B. adolescentis strain was found
to inhibit the adhesion of enteropathogenic E. coli and C. difficile to an
intestinal epithelial cell line (Zhong et al., 2004). However, to offer protection in case that the barrier function becomes impaired, the bulk of cells
aligned below the layer of enterocytes are immune cells (Chin, 2004). The
intestinal immune response and the mucosal layer therefore are both very
important for human host defence and can be affected by the gut microbial
fermentation products of carbohydrates and proteins, of which notably
SCFA and sulfur-containing compounds have been studied in most detail.
Of the SCFA, butyrate has best been studied. Butyrate was found to
decrease colitis in animal models. Moreover, butyrate resulted in an increase
of IgA-producing cells and mucosal IgA concentrations, the secretion of
anti-inﬂammatory cytokines and decreased myeloperoxidase (MPO) activity. Most of these parameters have been studied using cell lines or animal
models. However, in patients with UC, sodium butyrate enemas are
found to improve inﬂammatory scores, clinical symptoms, and intestinal
permeability. Apart from possible anti-inﬂammatory effects, butyrate also
inﬂuences the intestinal mucus production (Cassidy et al., 1982; Finnie et al.,
1995; Barcelo et al., 2000). In vivo, the number of goblet cells was found to
increase and dose-dependent increases in mucus secretion were observed
(Barcelo et al., 2000) upon addition of butyrate. However, intestinal mucus
also serves as substrate for bacterial fermentation (degradation of proteins
and saccharide-side chains). In addition, intestinal mucus is a source
of sulfur for SRB, which are able to use the sulfate liberated from mucins
for the production of hydrogen sulﬁde. This sulﬁde can inhibit butyrate
oxidation by the epithelial cell (Roediger et al., 1993) and is associated with
apoptosis, loss of goblet cells, and distortion of the crypt cell architecture.
Limited information is available about the effects of sulfur-rich diets on
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139
SRB and their effect on intestinal inﬂammatory parameters and mucus
production in humans. Furthermore, the interactive effects of sulfur-rich
diets (i.e. increased sulﬁde production) and carbohydrate consumption in
humans are still largely unknown. In view of the rising number of clinical
cases with intestinal complaints, there is a need for increased research efforts
in this area. For example, IBS, a functional bowel disorder, affects a large
number of subjects. Prevalence data show a frequency ranging from 3 to
20% in the general population of which a large part does not seek medical
attention but has complaints of pain, diarrhea, and/or constipation (Verne,
2004). Moreover, the prevalence of IBS is found to increase. A disturbed
inﬂammatory response, role of mast cells, and an abnormal colonic fermentation are all observed in IBS and beneﬁcial effects have been reported
using pre- and probiotics. Because of the magnitude of the population
affected by this disorder, it is worthwhile to study the mechanisms behind
this. Needless to point out, further insight into the health effects of SCFAs
and sulﬁde with regard to major intestinal mucosal functions is important
also for healthy subjects.
8.3. Intestinal Microbiota: Is There a Link With Obesity?
As pointed out above, the colon functions within the whole of the intestine
and associated visceral organs in maintaining the host energy balance. An
important aspect is the role that intestinal microorganisms play in cholesterol and bile acid metabolism, performing deconjugation and metabolism
of bile salts. Disturbed bile acid metabolism was observed in GF rodents,
but it has also been found that the SCFA acetate can interfere directly with
lipid metabolism. By contrast, acetate production (presumably microbial)
after lactulose ingestion in overweight subjects was recently shown to result
in short-term decrease in free fatty acid level and glycerol turnover related to
a decrease of lipolysis (Ferchaud-Roucher et al., 2005), both factors believed
to help in preventing insulin resistance. By contrast, however, acetate may
also stimulate lipid synthesis (Wolever et al., 1995), and it remains to be
settled whether acetate has a long-term beneﬁcial effect. Interestingly, the
latter study also showed that another SCFA, propionate, inhibited lipid
synthesis from acetate. Backhed et al. (2004) found that conventionalization
of GF mice with a microbiota harvested from cecum of CONV animals
produced a 60% increase in body fat content and insulin resistance despite
reduced food intake. An increased absorption of monosaccharides from
the intestine was detected in conventionalized mice, resulting in de novo
lipogenesis in the liver. In addition, they showed that Fiaf, a lipase inhibitor,
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was suppressed in the intestinal epithelium of conventionalized mice and
that this suppression was essential for the microbiota-induced deposition of
fat in adipose tissue. These ﬁndings suggest that, at least in the mouse, the
gut microbiota affects energy harvest from the diet and energy storage
in adipose tissue. Whether this is also the case in humans remains to be
determined. Thus, the effects of colonic SCFA production or microbial
activity in general on lipid metabolism is difﬁcult to predict. The true role of
the colon in regulation of lipid metabolism is very likely to be an even more
complex one, involving multiple neural and endocrine pathways. The recent
ﬁnding that ingestion of dietary fat stimulates cholecystokinin (CCK)
receptors, but at the same time leads to attenuation of the inﬂammatory
response by way of the efferent vagus nerve and nicotinic receptors, may
be an interesting foretaste of the type of regulatory hardwiring that can
be expected. Here, we have a novel neuro-immunologic pathway, controlled
by nutrition, that may help to explain the intestinal hyporesponsiveness to
dietary antigens.
Our intestine developed during evolution for optimal survival on natural
diets. The recent rise to epidemic dimensions of obesity-linked diseases correlates, in a timely manner, with a shift in dietary habits toward a reduced
intake of dietary ﬁber, an increased intake of simple sugars, a high intake of
reﬁned grain products, an altered fat composition of the diet, and a dietary
pattern characterized by a high glycemic load (Suter, 2005). Recent epidemiological research (Maskarinec et al., 2006) of a large ethnically diverse
population showed that on an individual level, fat and protein consumption
predicted a higher BMI, and dietary ﬁber intake predicted a lower BMI.
Similarly, a higher consumption of meat, poultry, and ﬁsh was related to
excess weight, whereas fruit and vegetable intake were inversely associated
with excess weight. There is growing evidence of the high impact of dietary
ﬁber and foods with a low glycemic index on single risk factors (e.g. lipid
pattern, diabetes, inﬂammation, endothelial function, etc.) as well as the
development of the endpoints of atherosclerosis (especially coronary heart
disease; Suter, 2005). A recent review (Hyman, 2006) pointed out that it is
the glycemic load, rather than the glycemic index, that affects the neuroendocrine–immune signaling. Dietary ﬁber is one of the main factors lowering the glycemic load, whence its beneﬁcial effects in reducing weight. This
author also points out that bacterially produced fatty acids lower cholesterol
production in the liver. In line with this observation, it is tempting to
hypothesize that obesity may, at least in part, be associated with deprivation
of proper substrate for colonic microbial fermentation. Obviously, in obesity, the normal regulatory response to high leptin levels is blunted by another, as yet unknown, regulatory component. If this blunting is associated
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
141
with substrate limitation of the microbiota, this would hint at a colon-linked
regulator. There is an interesting parallel with polyphosphate metabolism in
bacteria that could support this view: many bacteria in carbon-rich media,
when confronted with certain dietary component limitations, shift to accumulation of large amounts of polyphosphate, probably for energy storage
(Ault-Riche et al., 1998). Physiological studies could be instrumental in
ﬁnding which aberrations in metabolic, proteomic, and/or genomic proﬁles
linked to the colon can be found in obesity, after which subsequent research
may uncover the relevant regulatory consequences of these aberrations.
Such studies may proﬁt considerably from the integration of enteric neurobiological approaches (Grundy, 2004; Grundy and Schemann, 2005), a
fascinating and rapidly developing ﬁeld.
8.4. Role of Stable Isotopes
Can stable isotopes contribute to knowledge in the ﬁeld of host–microbe
interaction? Yes, they can. The key advantage of stable isotope methods is
that they are very potent in tracing the fate of substrates entering the colon
on the metabolic level, and therefore allow for a speciﬁc correlation of host
responses to colon-derived metabolic events. This helps in discriminating
colonic microbiota-related effects against those having an endogenous
origin, and therefore allow a clearer picture of host–microbe interaction
to emerge.
For instance, supplementation with resistant starch (RS) has been shown
to improve colonic lesions in a dextran sulfate sodium (DSS)-induced colitis
model in rats. To ﬁnd out whether it is the increased colonic butyrate production that accelerates the healing process, Moreau et al. (2004) measured
the ceco-colonic uptake of butyrate and its oxidation into CO2 and
ketone bodies in control and DSS-treated rats fed a ﬁber-free basal diet or
a RS-supplemented diet. After cecal infusion of [1-13C]butyrate, concentrations and 13C-enrichment of butyrate, ketone bodies, and CO2 were quantiﬁed in the abdominal aorta and portal vein, and portal blood ﬂow was
measured. These measurements allowed the authors to determine the utilization of butyrate specifically by the colonic mucosa, and to conclude that
increased utilization of butyrate by the mucosa is subsequent to evidence
of healing, and appears to be a consequence rather than a cause of the
RS healing effect (Moreau et al., 2004).
In another study (Pouteau et al., 2005), it was tested whether acetate
from colonic fermentation of inulin would stimulate peripheral acetate
turnover in dogs. Dogs were administered with simultaneous infusions of
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[1-13C]acetate i.v. and [1,2-13C2]acetate intrarectally. After switching from
a control diet to a 3% inulin-enriched diet, initially no changes in whole
body acetate concentration and turnover were seen after 4 days, but after
21 days the whole body acetate turnover had increased significantly
by 31%. While it was determined that a significant acetate production
occurred in the colon, no [1,2-13C2]acetate tracer was recovered in the
peripheral circulation. This led to the conclusion that the occurring
colonic fermentation of inulin indirectly stimulated whole body acetate
turnover (Pouteau et al., 2005).
While these examples concern the effect of microbial processes on the
host, the effects of host metabolism on microbial processes may also
be probed by stable isotopes. One example obviously is the measurement of
SCFA synthesis on various diets (a direct effect of host behavior), a nice
example of a more indirect interaction concerns mucin. Mucus and mucosal
proteins represent an important substrate for intestinal bacteria. Faure
et al. (2002) have developed a method to measure intestinal mucoprotein
FSR (%/day) in vivo by using the ﬂooding dose method with the stable
isotope L-[1-13C]valine. Free L-[1-13C]valine enrichments in the intracellular
pool were determined by GC-MS, whereas L-[1-13C]valine enrichments in
puriﬁed mucoproteins or intestinal mucosal proteins were measured by gas
chromatography-combustion-isotope ratio mass spectrometry. Using this
method, Faure et al. (2005) compared rats fed isonitrogenous diets (12.5%
protein) containing 30% (group 30) and 100% (control group) of the theoretical threonine requirement for growth. The mucin FSR was significantly
lower in the duodenum, ileum, and colon of group 30 compared with controls. Because mucin mRNA levels did not differ between these two groups,
mucin production in group 30 probably was impaired at the translational
level. These results clearly indicated that restriction of dietary threonine
significantly and specifically impairs intestinal mucin synthesis. In clinical
situations associated with increased systemic threonine utilization, threonine
availability may limit intestinal mucin synthesis and consequently reduce gut
barrier function in the absence of adequate dietary threonine intake.
In another study, glutamine was found to stimulate gut mucosal protein
synthesis (Coefﬁer et al., 2003).
While the above examples pertain to in vivo studies, stable isotope-based
methods are equally potent in sorting out regulatory effects at the cellular
level, as e.g. evidenced in HT29 cells where it was found that, upon butyrate
supplementation, these colon cells replace glucose for butyrate as an energy
substrate (Boren et al., 2003). Therefore, stable isotope methods offer prospects for rapid screening protocols such as stable isotope-based dynamic
metabolic proﬁling (SIDMAP) (Boros et al., 2003).
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143
9. NEW ASPECTS IN THE STUDY OF INTESTINAL BACTERIAL
PHYSIOLOGY
9.1. Microbes at War: Population Competition Models
Some of the species of adherent intestinal microorganisms in the intestine
have exploited and adapted to particular microniches in different compartments of the colon with its extremely large surface area created by the
complex involution of crypts and villi. These bacteria are continuously
competing for survival. The ability to persist and propagate or be ultimately
eliminated, is dependent to a large extent upon the armory of each combatant (Chin, 2004). Susceptibility or immunity of each strain to the arsenal
of bacteriocins or quorum sensing factors produced by another constitutes
a community at war. Yet, seemingly in stark contradiction, this scenario
may be vital for their very existence, as we will see shortly.
The weaponry of intestinal microbes is diverse and sometimes ingenious.
In addition to ‘fair’ weapons such as bacteriocins, they can use less direct
but potentially even more powerful tricks. For instance, very active excretion of acetate may induce growth limitation of a competitor who is susceptible to acetate uncoupling, as, for example, described for a Clostridium
sp. (Baronofsky et al., 1984). As another example, some Bifidobacterium
strains produce adhesins that competitively inhibit adherence of E. coli
and C. difficile to intestinal epithelial cells, providing themselves with
an increased resistance against being washed out of the colon (Zhong
et al., 2004), while stimulating washout of their competitors. Similarly,
Lactobacillus plantarum is able to produce a protein that may prevent
adhesion of E. coli carrying type-1 ﬁmbriae to bind to mannose-containing
glycans (Pretzer et al., 2005).
Competition experiments of gut microbial strains (Kato et al., 2005) have
shed light on the mechanisms that allow stable coexistence of enemies in
bacterial cultures. A cellulose-degrading deﬁned mixed culture consisting of
ﬁve intestinal bacterial strains was established that showed no change in
cellulose-degrading efﬁciency, while all members stably coexisted through 20
sub-cultures. The mechanisms responsible for the observed stability were
investigated by constructing ‘knockout communities’ in which one of the
members was eliminated. Thereafter, the roles played by each eliminated
member and its impact on the other members of the community were evaluated from measured dynamics of the community structure and the cellulose
degradation proﬁles of these mixed cultures. Integration of the results
showed different synergistic and detrimental relationships between different
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sets of the ﬁve bacterial strains. An obvious synergistic effect was that
the aerobic bacteria introduced anaerobic conditions, which permitted
anaerobic Clostridium to supply metabolites (acetate and glucose) for their
growth in return. A detrimental effect was the inhibition of cellulose
degradation due to excessive acetate production by another Clostridium
sp. As an important conclusion, the balance of the various types of
relationships (both positive and detrimental) is apparently essential for the
stable coexistence of the members of this mixed culture (Kato et al., 2005).
In this type of investigation, stable isotope analysis may be helpful. They
may offer powerful tools to interpret the results of cross-feeding experiments
in mixed cultures by their ability to trace back speciﬁc metabolic transitions
(cf. Fig. 6). This has, for instance, allowed the unequivocal detection of the
presence of biﬁdobacteria in a human colonic microbiota (Wolin et al.,
1998) by establishing 13C-labeling patterns that are speciﬁc for the unique
Bifidobacterium pathway of hexose catabolism.
To better understand the mechanisms governing the stable coexistence of
different competing bacterial strains, the approach of building a predictive
theoretical model is worth serious consideration. This requires us to be
philosophical to a certain extent. The outcome of the struggle for life
of bacteria in the human colon to some level will reﬂect the results of coevolution. At ﬁrst, one might be inclined to expect that one or another single
species (let’s take us, humans, for a moment) may have such enormous
competitional advantages that it will outgrow all others. However, this is
obviously never the case in reality. If there is one thing that research on the
colon shows, it is that even we as humans cannot live a healthy life without
the help of those seemingly insignificant microbes inside us. Apparently,
there is much evolutionary advantage in sharing resources and surviving as
a consortium, rather than alone (Pfeiffer et al., 2001). The fact that the
colonic microbiota is able to vary considerably in composition and time,
while remaining able to perform a rather stable overall metabolic function,
must reﬂect intrinsic principles or ‘laws’ governing their dynamic yet persistent coexistence. It is the task of biological modelers to sort out those
principles. Indeed, they are already on the job and interesting parallels with
such seemingly far-off ﬁelds as game theory have already been discovered.
The rationale here is that by evolving toward optimal properties, organisms
change their environment, which in turn alters the optimum. Evolutionary
game theory provides an appropriate framework for analyzing evolution in
such ‘dynamic ﬁtness landscapes’ (Pfeiffer and Schuster, 2005). Indeed,
theoretical simulations correctly predicted that an ensemble of toxinproducing, toxin-sensitive, and toxin-resistant strains of E. coli is able
to coexist when living in spatially structured, non-transitive interaction
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
145
(Kerr et al., 2002; Kirkup and Riley, 2004). A corollary of this observation is
that bacteriocins promote, rather than eliminate, microbial diversity in the
gut too. This in fact corroborates the experimental ﬁndings (Kato et al.,
2005) discussed above. Further interesting progress in this area is certain to
be awaited in coming years.
10. CONCLUSIONS AND FUTURE PROSPECTS
10.1. Toward a Systems Biology of the Gut
The combination of high-throughput methods of molecular biology with
advanced mathematical and computational techniques has propelled the
emergent ﬁeld of systems biology into a position of prominence. Unthinkable a decade ago, it has now become possible to screen and analyze the
expression of entire genomes, simultaneously assess large numbers of proteins and their prevalence, and characterize in detail the metabolic state of
a cell (population). Because of these general advances in life sciences,
research on the physiology of the intestinal microbiota as it functions within
and in interaction with the host is rapidly growing in intensity also. The
literature covered in this review bears testimony to the fact that our knowledge on a wide range of issues related to gut microbes and the role they play
in the mutualistic relationship with their host has expanded enormously in
recent years. Having said this, it must however be admitted that all this
knowledge in fact is still fragmentary and that a fully integrated picture of
host–microbe interactions has yet to be established. Notably, the mechanisms by which the metabolic activity of our intestinal microbiota inﬂuence
processes leading to disease, are still very far from being understood. Their
elucidation requires an understanding of metabolic regulation that so far has
been limited by a failure to consider regulation within the context of the
whole network (Sweetlove and Fernie, 2005), in this case of microbial and
host metabolic and signaling pathways. Several approaches that provide
tools for the required integration of data, models, and thinking (Davis and
Hord, 2005) are now appearing in the literature. Lee et al. (2005) discuss
how, for the design of cells that have improved metabolic properties for
industrial applications, informative high-throughput analysis and predictive
computational modeling or simulation must be combined to generate new
knowledge through iterative modiﬁcation of an in silico model and experimental design. Such new modeling approaches should aim to take full
advantage of genome sequence data, transcription proﬁling, proteomics and
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metabolite proﬁling, and integrate global metabolic models with genetic and
regulatory models for strain improvement (Smid et al., 2005).
The applicability of this approach has already been demonstrated in
a study that describes the in-depth analysis of the intracellular metabolite
concentrations, metabolic ﬂuxes, and gene expression (metabolome, ﬂuxome, and transcriptome, respectively) of lysine-producing C. glutamicum
at different stages of batch culture revealing distinct phases of growth and
lysine production (Kromer et al., 2004). The integrated approach was
valuable for the identiﬁcation of correlations between gene expression and
in vivo activity for numerous enzymes, and allowed, for the ﬁrst time,
an integrated overview of the regulation of C. glutamicum intermediary
metabolism when this organism switches to L-lysine production.
The way to go that is emerging from the literature (Hellerstein, 2004;
Boros, 2005) is obviously to link up stable isotope-aided MFA with the
existing ‘-omics’ technologies. Innovative modeling frameworks that are
able to integrate data from all these four platforms are required for this
purpose. Such models should allow the determination of regulatory properties of the studied organism from the experimental data by incorporating
such diverse information as pathway structures, ﬂux balance constraints,
isotopic labeling routes, thermodynamic constraints, enzyme kinetic properties, statistical correlations, and employing suitable minimization criteria.
This challenge is formidable but methods are in very rapid development,
and are rapidly gaining predictive power for metabolic regulation (Wiback
et al., 2004). Recent developments in this ﬁeld include the introduction
of ‘scale-free’ networks (Barabasi and Albert, 1999), the implementation of
constraints imposed by kinetic and equilibrium constants in the isotopomer
distribution analysis (Selivanov et al., 2005), and hybrid cooperations
between kinetics-based dynamic models and FBA-based static models (Yugi
et al., 2005; Kitayama et al., 2006), while the importance of including new
levels of the metabolic regulatory hierarchy (such as protein–protein interaction) has also been pointed out (Sweetlove and Fernie, 2005). With these
recent advances in theoretical aspects of network thinking and a postgenomic landscape in which our ability to quantify molecular changes at
a systems level is unsurpassed, the time is ripe for the development of this
new level of understanding of metabolic network regulation in the world of
our intestinal microbiota, with its immensely complex network of intricate
microbe–microbe and microbe–host interactions.
One of the more simple but invaluable lessons to be learned pertaining to
this ﬁeld is that, together with a ﬁber-rich diet, a good breakfast is the best
guarantee to make that network do what is was designed for – keep us lean
and healthy (Hyman, 2006).
MICROBIAL PHYSIOLOGY IN THE LARGE INTESTINE
147
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intestinal lumen
enterocytes
serosa
(blood)
from
arterial
diet
urea
microbial
compartment
NH3
urea
His, Lys, Thr
AA
AA
Val, Ile, Leu
microbial
Protein
endogenous
Protein
NH3
AAdisp
Protein
NH3
to portal
vein
Plate 2 Schematic representation of gut-associated nitrogen metabolism, compiled
from information in Metges (2000) and references therein. Colored circles symbolize
15
N isotope label originating from urea (red) or ammonia (blue), respectively, with
fading color intensity indicating isotope dilution. Urea diffuses from the blood through
the enterocytes into the intestinal lumen, where it is hydrolyzed by bacterial urease into
ammonia and carbon dioxide. Ammonium is the preferred non-speciﬁc microbial nitrogen substrate for synthesis of e.g. amino acids. Microbially synthesized amino acids
may partially be released into the gut lumen and taken up by ileal enterocytes (in the
colon, bacterial cell densities are so high that microbially synthesized amino acids
probably never reach colonocytes). Therefore, bacteria may supply a signiﬁcant portion of the body’s requirement for indispensable amino acids. 15N label appearance in
histidine, lysine, and threonine upon 15N-urea or 15N-ammonia administration is proof
of microbial activity since these amino acids cannot be endogenously transaminated.
However, after administration of e.g. the 15N-labeled indispensable amino acid leucine,
15
N label will appear in other branched-chain indispensable amino acids as well as in
dispensable amino acids (AAdisp) since the body is able to transaminate leucine, valine,
and isoleucine. Due to extensive amino acid exchange between blood, enterocytes, and
intestinal microbiota, interpretation of 15N labeling experiments is often ambiguous.
Combining nitrogen-15 labeling with carbon-13 or carbon-14 labeling as done e.g. in
Torrallardona et al. (2003), therefore, may constitute a useful approach to arrive at
unequivocal conclusions (For b/w version, see page 92 in this volume).
Plate 3 Principle of RNA-based stable isotope probing (SIP) for detection and
characterization of microbes that actively metabolize the labeled substrate. 13C-labeled substrates are incubated in (a) simple in vitro models (test tube or ﬂask), (b)
sophisticated in vitro systems, or (c) in vivo. Samples obtained from these experiments
[in the ﬁgure only shown for samples from (a)] are subjected to RNA isolation and
density gradient centrifugation. After separation of the gradient in fractions, molecular ﬁngerprinting techniques, such as DGGE (Zoetendal et al., 2004a) or T-RFLP
(Egert et al., 2003) can be used to determine the presence (usually enrichment) in the
heavier fractions of those microorganisms that speciﬁcally fermented the substrate
and this can be compared with the diversity present in an unlabeled, control sample
(For b/w version, see page 108 in this volume).
13
C-labeled substrate
a
b
c
incubation
density
gradient
centrifugation
Density
RNA
extraction
gradient
fractionation
H2O
labeled RNA
molecular
comparison of
fractions
unlabeled RNA
Bacterial Physiology, Regulation and
Mutational Adaptation in a Chemostat
Environment
Thomas Ferenci
School of Molecular and Microbial Biosciences G08, The University of Sydney, NSW 2006,
Australia
ABSTRACT
The chemostat was devised over 50 years ago and rapidly adopted for
studies of bacterial physiology and mutation. Despite the long history
and earlier analyses, the complexity of events in continuous cultures is
only now beginning to be resolved. The application of techniques for
following regulatory and mutational changes and the identiﬁcation of
mutated genes in chemostat populations has provided new insights into
bacterial behaviour. Inoculation of bacteria into a chemostat culture
results in a population competing for a limiting amount of a particular
resource. Any utilizable carbon source or ion can be a limiting nutrient
and bacteria respond to limitation through a regulated nutrient-speciﬁc
hunger response. In addition to transcriptional responses to nutrient
limitation, a second regulatory inﬂuence in a chemostat culture is the
reduced growth rate ﬁxed by the dilution rate in individual experiments.
Sub-maximal growth rates and hunger result in regulation involving
sigma factors and alarmones like cAMP and ppGpp. Reduced growth
rate also results in increased mutation frequencies. The combination of a
strongly selective environment (where mutants able to compete for
limiting nutrient have a major ﬁtness advantage) and elevated mutation
ADVANCES IN MICROBIAL PHYSIOLOGY, VOL.53
ISBN 978-0-12-373713-7
DOI: 10.1016/S0065-2911(07)53003-1
Copyright r 2008 by Elsevier Ltd.
All rights reserved
170
THOMAS FERENCI
rates (both endogenous and through the secondary enrichment of
mutators) results in a population that changes rapidly and persistently
over many generations. Contrary to common belief, the chemostat
environment is never in ‘‘steady state’’ with ﬁxed bacterial characteristics
usable for clean comparisons of physiological or regulatory states.
Adding to the complexity, chemostat populations do not simply exhibit
a succession of mutational sweeps leading to a dominant winner clone.
Instead, within 100 generations large populations become heterogeneous
and evolving bacteria adopt alternative, parallel ﬁtness strategies.
Transport physiology, metabolism and respiration, as well as growth
yields, are highly diverse in chemostat-evolved bacteria. The rich
assortment of changes in an evolving chemostat provides an excellent
experimental system for understanding bacterial evolution. The adaptive
radiation or divergence of populations into a collection of individuals
with alternative solutions to the challenge of chemostat existence
provides an ideal model system for testing evolutionary and ecological
theories on adaptive radiations and the generation of bacterial diversity.
1. General introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. The chemostat environment and its applications to studies of bacteria .
3. The physiological changes in an organism inoculated into a chemostat:
The example of glucose-limited Escherichia coli . . . . . . . . . . . . . . . . .
3.1. Transport and Membrane Permeability. . . . . . . . . . . . . . . . . . . . .
3.2. Metabolism and Energetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3. Stress Regulation and Gene Expression . . . . . . . . . . . . . . . . . . .
3.4. Antibiotic Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5. Quorum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Variations in responses within and between species . . . . . . . . . . . . . .
5. Steady state or constant change in a chemostat population? . . . . . . . .
6. Mutation rates and mutators in chemostat populations . . . . . . . . . . . . .
7. Mutational takeovers and population changes . . . . . . . . . . . . . . . . . . .
8. A mutational sweep in detail: The physiological advantage and
spread of mgl mutations in glucose-limited E. coli . . . . . . . . . . . . . . . .
9. Other mutations in chemostat populations and their physiological
effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1. Changes in the lac System . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2. Outer Membrane Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.3. rpoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.4. mlc and malT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.5. ptsG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.6. Metabolic Changes and Cross-Feeding . . . . . . . . . . . . . . . . . . . .
9.7. Amplification and Other Genomic Rearrangements . . . . . . . . . . . .
10. Emerging diversity in chemostat populations . . . . . . . . . . . . . . . . . . . .
10.1. Diversity in Regulatory Strategies . . . . . . . . . . . . . . . . . . . . . . .
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CHEMOSTAT ENVIRONMENT
10.2. Diversity in Transport Strategies . . . . . . . . . . . . .
10.3. Diversity in Metabolic and Bioenergetic Strategies.
11. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. GENERAL INTRODUCTION
Unusually for microbiology, a science in which empirical observations predominate, continuous culture methods originated from conceptual and formal analyses of the growth properties of bacteria. In 1950, two milestone
papers proposed and demonstrated that bacterial cultures can be grown
indeﬁnitely by pumping fresh medium at a ﬁxed rate into a culture vessel
with a constant volume (Monod, 1950; Novick and Szilard, 1950a). The
medium controls growth in that one component becomes limiting, preventing growth beyond a particular density. In synthetic media containing
a single component such as glucose, amino acid or ion at a concentration
lower than generally used in batch culture, growth is capped by a single
nutrient and is characteristically called a nutrient-limited chemostat. The
behaviour of bacteria in a chemostat more or less follows the kinetic formulations derived and reﬁned in several studies (Monod, 1950; Novick and
Szilard, 1950a; Herbert et al., 1956; Pirt, 1975; Dykhuizen and Hartl, 1983;
Panikov, 1995). Nevertheless, derived growth equations rely on empirical
observations describing bacterial growth with sub-saturating concentrations
of individual nutrients (Monod, 1949). Inherent assumptions in the description of chemostat cultures are saturable growth kinetics and a stable halfsaturation constant for the given substrate/organism pairing. Another
assumption is the constancy of biomass yield from substrate for the studied
organism. This review will not deal with growth kinetics in detail, but the
concept of absolute constants in bacterial growth relevant to chemostat
culture has been questioned and discussed elsewhere (Ferenci, 1999a).
At several points in this review, entrenched views on the properties
of chemostats will be reassessed in view of the plasticity in bacterial
characteristics due to physiological and mutational adaptations in chemostat-grown populations. My approach will be more empirical than formal
and I will mainly focus on describing the complexities of events in chemostats rather than forcing bacterial behaviour into equations or models.
There are more than 5000 papers dealing with chemostat cultures so some
added focus was needed to keep this review to manageable proportions.
172
THOMAS FERENCI
Several reviews and books deal with some aspects of chemostat culture
(Kubitschek, 1970; Pirt, 1975; Dykhuizen, 1993; Kovarova-Kovar and Egli,
1998). This discussion will concentrate on basic principles in bacterial physiology and mutational adaptation arising from studies of chemostats limited
by a single nutrient. Many lessons can be learned from single organismsingle limitation experiments so I will not consider multi-stage chemostats
(Novick, 1959; Lovitt and Wimpenny, 1981) or other ingenious-stat variations. Neither chemostats inoculated with more than one type of organism
(e.g. phage and bacteria, Horne, 1970; or inter-species competitions, Veldkamp and Jannasch, 1972) nor continuous cultures with more than one
limiting nutrient (e.g. multiple sugars; Egli et al., 1993; Dykhuizen and
Dean, 2004) will be discussed. Also not considered are numerous studies
involving chemostats for the improvement of strains, directed gains in
function, processes or plasmid stability.
The motivation for this review is actually no different to that in a
50-year-old paper on a similar topic (Moser, 1957). It is sobering to
re-quote some of the opening words of Moser: ‘‘population dynamics has
been studied in bacteria for many years, but today this ﬁeld has become an
attractive and promising subject of experimental and theoretical investigation because of two factors. First, the astounding advances made in our
knowledge of the genetic mechanisms of bacteria. Second, the development
of devices for the continuous growth of large bacterial populations y’’.
These ideas can be re-stated after 50 years but there is certainly a resurgence
of interest in the area of chemostat research for three reasons. These
are, ﬁrstly, the revival in the use of chemostats for gene expression
comparisons under stable physiological conditions (Hoskisson and
Hobbs, 2005 and references therein); secondly, the mushrooming interest in experimental evolution using a variety of selection environments
including chemostats (Dykhuizen, 1990; Watt and Dean, 2000; Elena and
Lenski, 2003; Adams, 2004); and thirdly the better understanding of the
chemostat environment in eliciting physiological responses (Ferenci,
1999b, 2001). The last point impacts on the other two, because a better
understanding of the physiological state of nutrient-limited bacteria is half
the battle in interpreting gene expression studies and predicting the kind
of beneﬁcial mutations that occur in evolving populations. Regulatory
and mutational changes giving rise to physiological and metabolic adaptations go hand-in-hand. Hence, the ﬁrst aim of the review is to describe
the rapid regulatory transitions in one well-studied system (glucose limitation in E. coli chemostat cultures) and then to proceed to discussion of
‘‘steady states’’, mutational processes and what mysterious assemblages
constitute a long-term chemostat population.
CHEMOSTAT ENVIRONMENT
173
2. THE CHEMOSTAT ENVIRONMENT AND ITS APPLICATIONS
TO STUDIES OF BACTERIA
Not all chemostats are equal. There are numerous chemostat designs
adopted in research besides those produced commercially. Two major hurdles in adopting chemostats for the usual microbiology laboratory are cost
and space considerations. These costs are particularly problematic when
multiple parallel cultures are needed as in most physiological and evolutionary studies; comparisons of gene expression under replicate or manipulated conditions, replicate parallel evolution experiments or testing
competitive ﬁtness of several strains all need more than one culture.
In the author’s laboratory, simpliﬁed chemostats with less rigorous control
shown in Fig. 1 are used, made of modiﬁed Schott bottles. Four such cheap
chemostats with reduced working volumes can be run simultaneously in
a small area. The level of control of environmental parameters such as pH,
dissolved O2, biomass is less than in the most sophisticated commercial
equipment, but a well-buffered medium and relatively low biomass levels
prevent secondary limitations. The dangers of secondary limitations have
been documented (Ihssen and Egli, 2004) so an awareness of the carrying
capacity of home-made chemostats is needed. Further miniaturization
of chemostat cultures to 10 mL working volumes has been reported recently
for metabolic comparisons (Nanchen et al., 2006). Even more exciting
developments are on the horizon with the description of continuous ﬂow
microﬂuidic devices in which the properties of individual cells can be
observed (Balagadde et al., 2005; Groisman et al., 2005; Zhang et al., 2006).
Once generally available, these will provide a new impetus for chemostat
studies with the added advantage of on-line monitoring of expression and
visualization of individual microbes under controlled conditions. Other
recent technical advances for particular applications are in the tricky control
of oxygen availability in studying transitions between aerobiosis and
anaerobiosis (Alexeeva et al., 2002) and in the prevention of wall growth
(change of cells from planktonic to bioﬁlm forms adhering to the chemostat
vessel) (de Crecy-Lagard et al., 2001; Kashiwagi et al., 2001).
Whatever the design of the chemostat used, the experimental choices
available to the researcher are:
(a)
the choice of organism. The inoculum is of course determined by the
interests of the experimenter, but when a choice of strain within a species
or between laboratory strains is available, several factors can inﬂuence
the course of experiments. The strain variations and polymorphisms that
inﬂuence the behaviour in chemostats are considered in Section 4.
174
THOMAS FERENCI
medium inflow controlled by a
Watson-Marlow 302S pump
air inlet from a
Hy-Flo air pump
air inlet through
medium break
overflow outlet
sampling port
Screw-cap 100 ml
Schott bottle sealed
with silicone
Water bath
80 ml working
volume
sparger
stirrer
Figure 1 A simple positive-pressure chemostat. Four such chemostats can be
concurrently operated on a multi-place stirrer base at the same dilution rate using the
multi-channel peristaltic pump and individual air pumps attached to each inlet. The
working volume is set by the positioning of the overﬂow outlet. The positive-pressure
medium break prevents back-contamination of medium.
(b)
the choice of limiting nutrient. Limitation with carbon source, nitrogen
source, phosphorus source, etc. can all give the same growth rate in
a chemostat, but the gene expression patterns are very different
(Hua et al., 2004). The hunger responses to speciﬁc limitations are considered in Section 3. The cell volumes of E. coli also differ under different
forms of limitation (Shehata and Marr, 1971), reinforcing differences
CHEMOSTAT ENVIRONMENT
(c)
(d)
(e)
175
in growth and physiology. Of course, different limitations also result in
very different selection pressures on cultures (Section 9.4) and were
reported to give different mutation rates (Novick and Szilard, 1951).
the choice of medium and concentration of limiting nutrient. The
concentration of nutrient pumped into a chemostat determines the cell
density achieved. The density of a culture not only inﬂuences quorum
sensing aspects (Liu et al., 2000), but high densities also can bring on
secondary limitations such as low oxygen availability or trace metal
requirement. Unless high cell yields are absolutely needed, cultures
at around 108 cells/mL will avoid most of these secondary effects of
high density.
the dilution rate for running the chemostat. The dilution rate, D, theoretically sets the growth rate in continuous culture (Monod, 1950).
Any dilution rate that does not result in washout of bacteria results in
nutrient limitation, but the growth rate and concentration of regulatory molecules (s factors, cAMP, ppGpp) are highly dilution rate dependent (Notley and Ferenci, 1996; Notley-McRobb et al., 1997; Teich
et al., 1999). The production of particular enzymes and pattern of gene
expression is thus very sensitive to dilution rate (Matin, 1979; Ferenci,
1999b). The regulatory differences brought on by differences in D are
discussed in Section 3. Mutation frequency and ﬁtness effects of
mutations are also dependent on the dilution rate (Notley-McRobb
et al., 2003) and are discussed in Sections 6 and 9. It also follows that
no arbitrary dilution rate represents a state ‘‘characteristic’’ of a
nutrient-limited condition.
sampling times for particular tests of the culture, be they for gene
expression or sampling for mutations. The time-course of events in
chemostats after inoculation with a batch-cultured inoculum is more
complex than simply a transition between two states. The incorrect but
widespread belief that chemostats reach a steady state with constant
properties is discussed in Section 5. The implications for reproducible
sampling are also described in Section 5 and the time-scale of mutational take-overs and evolution experiments is discussed in Section 7.
So what can and cannot be studied with chemostats? Broadly speaking,
two research applications were foreseen by the inventors of chemostat culture and these are still the most widely studied. Firstly, chemostats permit
bacteria to be grown in one growth phase, with a ﬁxed doubling time and
with better control of the environment and cell density. In chemostats,
studies of physiological and regulatory phenomena should be less affected
by growth transitions, metabolic shifts or quorum sensing. The medium and
176
THOMAS FERENCI
atmosphere can also be controlled to look at the effect of individual environmental changes without modifying doubling times. An excellent recent
example of this is the study of the effects of temperature on cellular composition without changes in growth rate (Cotner et al., 2006). Many other
such studies are in the literature and Section 3 will include examples in
various areas of bacterial physiology. Nevertheless, as noted above, the
properties of cells in a nutrient-limited state are a function of D and several
other factors noted above, so it is important not to lose sight of the effect of
limitation and growth rate on gene expression, metabolism and physiology
in the interpretation of chemostat experiments.
The second fundamental area of application of chemostats is in the study
of mutational adaptation in populations grown continuously under a more
or less constant selection pressure. The strong competition between members of chemostat populations under nutrient limitation is itself a strong
selection condition where mutants with better ability to utilize nutrients
have a ﬁtness advantage (Harder et al., 1977; Dykhuizen and Hartl, 1983).
As shown in Sections 6–9, recent data suggest that mutational changes are
evident very shortly after the start of chemostat cultivation. The ability to
propagate chemostat populations for extended periods (weeks to months)
further allows the study of successions of mutations and the gains in new
characteristics. Results of the past 10 years also suggest that chemostat
populations become highly heterogeneous in remarkably short time periods.
This also makes chemostats good systems for the study of the generation of
bacterial diversity, as discussed in Section 10. The nature of alternative
beneﬁcial mutations permitting ﬁtness under nutrient limitation also reveals
much about the redundancy of metabolic and physiological responses to the
same environment. The emergence of diversity in chemostats further sees
the appearance of intra-population interactions aside from competition,
such as cross-feeding (Treves et al., 1998). Section 10 describes how the
chemostat environment itself evolves over time, as a consequence of the
changes in populations.
In reality, there are still very limited examples of studies with chemostats in
which the total complexity of changes in gene expression, physiology,
metabolism, as well as mutational adaptations are well analysed. As far as I
am aware, lactose- and glucose-limited cultures of E. coli are the closest to this
breadth of coverage. The lactose studies have been reviewed recently
(Watt and Dean, 2000) so much of the subsequent discussion will focus on
glucose-limited chemostats. With E. coli, there is also the possibility of comparisons between different types of limitation, permitting identiﬁcation of
responses that are nutrient speciﬁc. Most importantly, E. coli is the only
bacterium in which the regulatory and mutational adaptations can be
CHEMOSTAT ENVIRONMENT
177
considered in enough detail as two parts of the same story, namely the
acquisition of ﬁtness in a new environment, as considered in the later sections.
3. THE PHYSIOLOGICAL CHANGES IN AN ORGANISM
INOCULATED INTO A CHEMOSTAT: THE EXAMPLE OF
GLUCOSE-LIMITED ESCHERICHIA COLI
So what happens when batch-cultured bacteria are inoculated into a
chemostat? The initial transitions are similar in many ways to the set
of changes in bacteria approaching stationary phase. The regulatory signals
emanating from reduced growth rates (effects on ppGpp and RpoS levels)
and reduced availability of carbon source in the case of glucose limitation
(effects on cAMP levels) determine the global shifts in gene expression
and physiological responses (Ferenci, 1999b). A major difference is that
stationary phase bacteria rapidly deplete remaining carbon source on the
way to starvation whereas the chemostat environment maintains a low
but biologically signiﬁcant level of nutrition and low growth rates. Batchcultured bacteria go through transient peaks of alarmones like cAMP in
approaching starvation, but as shown in Fig. 2, chemostat cultures continue
to maintain higher levels of cAMP when the bacteria reduce glucose levels to
the micromolar range and begin to grow at the rate determined by the
dilution rate. It is worth noting that the residual glucose level is different
with particular D values; the higher the D and growth rate, the higher the
residual glucose (Senn et al., 1994). In turn, this means that cAMP, RpoS
and ppGpp levels are also distinct at different dilution rates (see Section 3.3),
which is why the choice of D is signiﬁcant in studies of gene expression.
Metabolic regulation is also a function of D and the balance of glucose
converted to CO2 or acetate is different at low and high dilution rates. The
detailed effects of D on transport, regulation, metabolism and physiology
are discussed below.
3.1. Transport and Membrane Permeability
The residual glucose concentration in a chemostat is a function of D, but is
of the order of micromolar or below (Senn et al., 1994). The most obvious
physiological response in bacteria to such low nutrient levels is to improve
scavenging ability for limiting nutrient (Harder and Dijkhuizen, 1983).
Bacterial afﬁnity for nutrients is far from constant. Afﬁnity differs between
178
THOMAS FERENCI
Growth (A=580 nm)
res
idu
al
0.20
ssion
al expre
mgl, m
glu
os
AMP
internal c
e
0.8
0.10
0.4
0.00
Glucose concn. (mM)
1.2
0.30
ity
al dens
bacteri
2
4
6
8
20
0.0
25 30
Time (h)
Figure 2 Transitions in E. coli upon acclimatization to a glucose-limited environment. In a chemostat inoculated with E. coli and established at a dilution rate of
0.3 h1 and 1 mM glucose in the feed medium, most of the major transitions occur
within the ﬁrst 8 h. Bacterial density stabilizes at the carrying capacity determined by
the limiting medium, glucose levels drop markedly to the mM range, global regulatory circuits involving cAMP are activated and gene expression responsive to nutrient limitation (mgl, mal genes) are highly expressed. The curves are based on data
in Notley-McRobb et al. (1997).
oligotrophic bacteria and bacteria like E. coli which needs to adapt to more
than one habitat (Button, 1985), but neither is the afﬁnity for limiting
nutrient constant in the same bacterium under different environmental conditions (Ferenci, 1999a). The importance of transport to chemostat behaviour was recognized long ago (Hansen and Hubbell, 1980; Harder and
Dijkhuizen, 1983) and several regulatory adaptations ensure the expression
of multiple sets of genes responsible for high-afﬁnity transport systems
under nutrient limitation. Depending on the substrate, the expression of
particular cytoplasmic membrane transporters as well as outer membrane
porins in Gram-negatives is modiﬁed by nutrient limitation. This is true not
just for glucose limitation; for example, phosphate limitation induces PhoE
(involved in outer membrane permeability of anions; Overbeeke and
Lugtenberg, 1980) and Pst proteins (involved in high-afﬁnity, binding protein-dependent transport of phosphate; Medveczky and Rosenberg, 1971).
Typical of many such limitational adaptations, PhoE complements the
general porins and the Pst system complements a lower afﬁnity system
(Pit; Rosenberg et al., 1977) that bacteria use when phosphate is not at low
concentration.
CHEMOSTAT ENVIRONMENT
179
A common theme in all kinds of nutrient limitation is that outer membrane composition is modiﬁed, including in organisms other than E. coli
(Hancock et al., 1982; Sterkenburg et al., 1984). The compositional changes
mainly affect the porin proteins responsible for outer membrane permeability (Nikaido and Vaara, 1985). In E. coli, the relative proportion of the
less selective porins OmpF and OmpC is controlled by nutrient availability;
the larger-channel, more permeable OmpF protein is preferentially expressed at low nutrient levels (sub-micromolar glucose at D ¼ 0.3–0.4 h1),
whereas OmpC is more prevalent with excess nutrient (Liu and Ferenci,
1998). Interestingly, OmpC, which imparts reduced permeability and greater
levels of antibiotic and detergent resistance on E. coli, is present in preference to OmpF at even lower dilution rates (D ¼ 0.1 h1) involving further
reduced residual glucose levels. In other words, closer to starvation, the
response of E. coli is towards self-protection and reduced permeability
(Ferenci, 1999b). The complex relationship between dilution rate and porin
expression involves control by several global transcriptional regulators
(Liu and Ferenci, 2001).
Another outer membrane adaptation peaks at intermediate dilution rates
in glucose-limited chemostats. The level of LamB protein is 20-fold elevated
at D ¼ 0.5–0.6 h1. As for OmpF, LamB is also lowered in amounts with
nutrient excess or starvation conditions (Death et al., 1993). LamB is
a sugar-selective porin (Death et al., 1993) produced from the lamB gene
regulated as part of the mal regulon (Boos and Shuman, 1998). LamB and
the mal regulon are rapidly induced on transition to glucose limitation
(Fig. 2). The porin encoded by lamB improves glucose scavenging at
micromolar glucose levels and is induced by the combined elevation of intracellular cAMP and endogenous inducers under glucose limitation (Notley
and Ferenci, 1995). The maltotriose produced under glucose limitation is the
result of multiple intracellular metabolite pool changes in chemostat-grown
E. coli (Tweeddale et al., 1998). The combined effect of OmpF and LamB
changes under glucose limitation at intermediate dilution rates is to increase
permeability to limiting nutrients. The importance of these regulatory
adaptations is apparent from the results that mutants lacking one or both of
these porins are less ﬁt in chemostat culture (Death et al., 1993; Liu and
Ferenci, 1998).
As well as the outer membrane changes described above, periplasmic and
cytoplasmic membrane components also change under glucose limitation.
In most forms of carbon source limitation, the concentration of cAMP is
a major factor in the control of high-afﬁnity cytoplasmic membrane transporters. cAMP levels in carbon-limited chemostats are much higher than
in exponential batch culture, as discussed in Section 3.3. The cAMP effect is
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THOMAS FERENCI
very broad and contributes to the elevated expression of many binding
protein-dependent transport systems in E. coli, as shown in proteomic analyses (Wick et al., 2001; Ihssen and Egli, 2005). Transport systems involving
periplasmic binding proteins generally have higher substrate afﬁnities than
facilitators, symporters or phosphotransferase-type transporters (Furlong,
1986; Ferenci, 1996). The high-afﬁnity systems provide scavenging ability
for amino acids, inorganic ions and sugars and allow bacteria to utilize
micromolar concentrations of substrates. Speciﬁcally for glucose, limitation
results in the induction of the high-afﬁnity MglBAC system, involving the
periplasmic glucose–galactose binding protein, which is half-saturated at
sub-micromolar glucose levels (Death and Ferenci, 1993). The rapid increase
in mgl expression on entry into glucose limitation is shown in Fig. 2. The
induction of the Mgl system is in stark contrast to nutrient-excess situations,
where glucose down-regulates transporters for other substrates and
represses systems like the mal regulon and mglBAC by cAMP-mediated
catabolite repression (Death and Ferenci, 1994; see Section 3.3).
The usage of binding protein-dependent transport systems entails an afﬁnity
advantage but also a cost in terms of energetics. Binding protein-dependent
systems are more expensive in ATP input than alternative transporters
with lower afﬁnity such as proton-coupled symporters or the phosphotransferase system (Muir et al., 1985; Driessen et al., 1987). As noted
in Fig. 3, glucose can be recognized by several alternative cytoplasmic
membrane transport systems besides Mgl (Lengeler, 1993), but these are of
lesser importance under glucose-limiting conditions (Ferenci, 1996). In particular, the afﬁnity advantage of transport at low substrate concentrations
makes binding protein-dependent transport a ﬁtness beneﬁt in chemostats;
with limiting glucose, mutants lacking the glucose–galactose binding
protein-dependent Mgl system are much less competitive than wild-type
bacteria or those lacking the glucose phosphotransferase system (Death and
Ferenci, 1993). Despite the cost, active transport also contributes to the ﬁtness
of yeast in sugar-limited chemostats compared with transport
dependent on facilitated diffusion (Weusthuis et al., 1994). A broad generalization in microbial physiology based on the examples above is that
there is an in-built redundancy and energetic cost–beneﬁt trade-off inherent in
adapting nutrient transport to different nutrient levels.
The importance of transport to growth in chemostats is also supported by
theoretical studies (Hansen and Hubbell, 1980; Shoemaker et al., 2003).
Additional strong evidence that elevated scavenging ability for limiting nutrients in chemostats is important in ﬁtness comes from analysis of longer
term, mutational adaptations that occur in continuous cultures. As noted
from the ﬁrst studies onwards, chemostat-evolved mutant bacteria are better
CHEMOSTAT ENVIRONMENT
181
O2
14
Glucose
Slow growth rate
Nutrient limitation
1
2
3
4
5
6
7
Glucose
6-P
19
20
21
8
9
10
cAMP
RpoS
ppGpp
Pyruvate
11
12
13
[H++e]
15
16
17
Anabolism/
Biomass
18
H2O
CO2
Acetate
Figure 3 Redundancies in glucose transport and metabolism in E. coli. Through
the outer membrane, glucose can pass through porins OmpC (1), OmpF (2) and
LamB (3). Glucose is a substrate of the PEP:glucose phosphotransferase system (4),
PEP:mannose phosphotransferase system (5), the glucose–galactose binding proteindependent Mgl system (6) and the GalP symporter (7) in the cytoplasmic membrane.
When phosphorylated either with the PTS or glucokinase, glucose-6-phosphate can
be converted to pyruvate using three alternative reactions sequences (glycolysis (8),
the Entner–Doudoroff pathway (9) or the pentose phosphate pathway (10)). Reduced cofactors in glucose oxidation are oxidized by alternative enzymes (NDH-1
and NDH-2 (11 and 12)) which in turn can feed into alternative respiratory chains
(cytO (13) or cytD (14)). Pyruvate can be oxidized to CO2 via the tricarboxylic acid
cycle (15) or a variation using glyoxylate cycle enzymes (16). Carbon for biosynthesis
is taken from both carbohydrate metabolism and pyruvate metabolism (17). Acetate
production from pyruvate (18) can also take place depending on environmental
conditions. Nutritional and growth rate-dependent factors in chemostats also inﬂuence at least three global regulatory circuits involving cAMP (19), RpoS (20) and
ppGpp (21) under glucose-limited conditions. See text for discussion and references.
at uptake of limiting nutrient (Novick and Szilard, 1951), as described in
more detail in Sections 8–10.
3.2. Metabolism and Energetics
The range of metabolic ﬂexibility of E. coli is even more extensive than seen
with transport adaptations. Growth on glucose can involve alternative metabolic pathways and result in non-identical outputs (yields, metabolic products) in different situations. As summarized in Fig. 3, three known pathways
can convert hexose phosphate to pyruvate, alternative pathways take
pyruvate to CO2; there are alternative respiratory chain components, as well
as non-constant fermentation balances or products in E. coli (see references
182
THOMAS FERENCI
in Neidhardt, 1996). The choice between alternatives is highly sensitive to
the environment, including the chemostat environment. Recent studies have
conﬁrmed that the dilution rate setting under glucose limitation can strongly
inﬂuence metabolic processes (Kayser et al., 2005; Nanchen et al., 2006).
The proportion of glucose metabolized via the pentose phosphate pathway,
the glyoxylate pathway and indeed almost all ﬂuxes were non-linearly
dependent on the dilution rate setting (Nanchen et al., 2006). Transcription
of metabolic genes is also differentially regulated at different growth rates
and also with different forms of limitation (Hua et al., 2004). Consistent
with likely shifts in metabolism, metabolite pools are also strongly affected
by dilution rate, as shown by metabolome analysis and comparisons
between bacteria grown at different dilution rates. Slow dilution rates
(at 0.1 h1) induced changes characteristic of stressed bacteria, such as
trehalose accumulation (Tweeddale et al., 1998).
An unexpected recent ﬁnding was that the glyoxylate cycle, normally
associated with acetate incorporation and not with glucose utilization, was
expressed in glucose-limited bacteria (Fischer and Sauer, 2003; Maharjan
et al., 2005). The switch to the glyoxylate cycle can also be partly observed at
the transcriptional level (Franchini and Egli, 2006). There is also increased
production of isocitrate lyase (AceA) after prolonged glucose limitation
(Wick et al., 2001). There are alternative views on why the glyoxylate cycle
may be beneﬁcial under glucose limitation (Wick et al., 2001; Fischer and
Sauer, 2003). Also, the presence of the pathway is not universal in glucoselimited cultures; some E. coli laboratory strains such as the W3110 lineage
does not use the glyoxylate cycle under glucose limitation (Fischer and
Sauer, 2003). This is one of many strain-variable properties discussed in
Section 4 that fog a uniﬁed view of even a single species.
Respiration rates are also dilution rate dependent (Kayser et al., 2005).
Although it is outside the scope of this review to consider the control of
the respiration/fermentation balance and the related ﬁeld of aerobiosis/
anerobiosis regulation, these are active ﬁelds of research using chemostats.
Amongst the topics addressed, the regulation of acetate production due to
NADH/NAD ratio (Vemuri et al., 2006), the role of global transcriptional
regulators (Alexeeva et al., 2003) and the effect of alternative electron
acceptors (Wang and Gunsalus, 2003) have all been investigated using
chemostats. On the energetics side, it was also shown that transcription of
ATPase genes decreases moderately with increasing growth rate (Kasimoglu
et al., 1996) and the metabolic balance changes due to mutations in ATPase
(Noda et al., 2006).
Chemostats also allow analysis of a fundamental question in bioenergetics, namely whether there is evolutionary selection for optimal efﬁciency or
CHEMOSTAT ENVIRONMENT
183
rate in bacterial metabolism. There is an assumption that in general, metabolic efﬁciency is dependent on the trade-off between the rate and yield
of energy metabolism (Pfeiffer et al., 2001). Relevant to the above question
is that, perhaps paradoxically, a characteristic of bacteria is that microbial
growth yields are often 50% less than optimal (Westerhoff et al., 1983).
Energetic efﬁciency has been tested in chemostat culture and it was concluded that rate and not efﬁciency is optimized under nutrient limitation
(Demattos and Neijssel, 1997). Bacteria in populations may not have the
luxury of evolving in an energy-efﬁcient way and in situations where there is
kinetic growth competition amongst members of the same culture, perhaps
there is more of a selection for rate than yield (Fong et al., 2003). A recent
chemostat study has also looked at competition between energetically efﬁcient and inefﬁcient yeasts with temporal and spatial variation (MacLean
and Gudelj, 2006). How prolonged nutrient limitation in an unstructured
environment with a single resource or niche inﬂuences metabolic strategies
shifts the yield-rate trade-off is discussed in Section 11.
A long-observed feature of chemostat growth that still cries out for a
metabolic explanation is the concept of maintenance energy (Pirt, 1965;
Tempest and Neijssel, 1984). Numerous studies have documented the
decreasing efﬁciency of biomass production at slow dilution rates and extrapolating energy demands to zero growth rates suggested that bacteria need
some energy for maintaining viability at slow or zero growth rates. A comparison of measured maintenance coefﬁcients was assembled recently and
also shows inconsistencies between different strains (Nanchen et al., 2006).
A molecular explanation of maintenance energy is still lacking however, and
it needs testing whether elevated stress metabolism such as the synthesis of
trehalose and many stress resistance proteins by starvation is responsible for
the maintenance energy effect in starving or close-to-starving cells.
3.3. Stress Regulation and Gene Expression
Several features of the chemostat environment contribute to altered gene
expression relative to nutrient-excess bacteria and the extent of the transcriptional changes is evident from microarray comparisons (Hua et al.,
2004; Franchini and Egli, 2006). Undoubtedly many regulators are involved
in the adaptation to the chemostat environment and the sections below
focus on the role of obviously important global controllers. This is not to
say that the ones considered are totally responsible for chemostat adaptation. Many other transcriptional regulators change signiﬁcantly in concentration in the chemostat environment, but have not yet been fully
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THOMAS FERENCI
investigated in this context. For example, hns transcription and H-NS protein level, with major inﬂuences on DNA structure and transcription
(McLeod and Johnson, 2001), both increase signiﬁcantly with increasing D
in glucose- and ammonia-limited chemostats (X. Liu and T. Ferenci,
unpublished results) (Liu et al., 2000). In contrast, himA gene transcription
and IHF, also involved in the regulation of many genes (Goosen and Van
De Putte, 1995), increase greatly with decreasing D (Liu, 2001). The full
implications of these changes have not been studied yet and it needs to be
kept in mind that the overall patterns of regulation will be even more complicated than is apparent from the examples described below.
3.3.1. Starvation and Stress Signals and RpoS
E. coli senses reduced growth rate as an indicator of stress and elevates
RpoS protein levels (Hengge-Aronis, 2000) even when nutrient limitation
is the sole problem in the environment. Consequently, the general stress
response controlled by RpoS, with its hundreds of coregulated genes (Patten
et al., 2004; Weber et al., 2005), is highly expressed in chemostats (Notley
and Ferenci, 1996). The level of RpoS rises with decreasing dilution rate and
especially sharply at slow growth rates near or below D ¼ 0.1 h1. This
trend is seen both with glucose- as well as ammonia-limitation, and RpoS
levels are if anything even higher under N-limitation (Liu and Ferenci,
2001). Paradoxically, almost none of the genes controlled by RpoS are of
physiological beneﬁt in directly overcoming nutrient limitation. Is the seemingly unnecessary induction of RpoS in chemostats at low dilution rates
a failure of regulation or an artefact of the chemostat system?
Several lines of argument could be used to support the RpoS response
in chemostats as a sensible reaction to environmental signals. Chemostats
are sometimes criticized as unnatural environments and it may be argued
that low levels of nutrient without other stresses is not commonly met in the
habitats of bacteria like E. coli. In more natural situations, a mix of simultaneous stresses such as osmotic, pH or oxidative stress is present. An
alternative explanation is that even when temporarily present on its own,
nutrient limitation is ecologically sensed as a useful signal for hard times
to come. Despite some merit in these arguments, there is accumulating
evidence that the induction of the general stress response in chemostats may
be an indication of a fault in the sensing and processing of stress responses.
The observation that rpoS polymorphisms and rpoS null mutants are
common in natural populations does suggest that E. coli and Salmonella in
nature can beneﬁt from losing RpoS function (Ferenci, 2003). This indicates
that expression of the general stress response is sometimes a burden even in
CHEMOSTAT ENVIRONMENT
185
natural habitats, as it is in chemostat culture. Consistent with this view, rpoS
mutants are selected in continuous culture under glucose limitation to avoid
the RpoS cost (Notley-McRobb et al., 2002a). Recent studies suggest that
mutational loss of RpoS is a consequence of the trade-off between stress
responses and nutritional responses to hunger in general (Ferenci, 2005).
This balancing of stress/nutrition capabilities occurs not just in chemostats,
but also inﬂuences growth with poorer carbon sources like acetate that
occur in natural habitats (King et al., 2004). The sigma factor-dependent
competition in transcription documented in other studies (Farewell et al.,
1998; Nystrom, 2004) imposes two alternative and mutually exclusive
choices on E. coli, namely either to optimize stress responses based on RpoS,
or to utilize nutrients dependent on RpoD (Ferenci, 2005). Because of s
factor competition, differences in RpoS levels affect both stress responses
(controlled by RpoS) as well as vegetative gene expression due to RpoD
(or s70) (Jishage et al., 1996; Jishage and Ishihama, 1997; Nystrom, 2004).
The latter controls genes involved in nutrient utilization so ﬁtness in
chemostats is inversely proportional to RpoS protein level within a strain
(King et al., 2004). Hence many polymorphisms affecting stress response
and nutritional properties are seen within the species E. coli. As noted in
Section 6, polymorphisms and strain variation in RpoS protein levels found
in natural isolates can strongly inﬂuence regulation and ﬁtness properties in
a chemostat culture.
3.3.2. Nutritional Status and cAMP
The extracellular levels of all essential nutrients are signals sensed by bacteria like E. coli. Depleting levels of phosphorus, nitrogen or sulphur sources
have their own sensing and response mechanisms (Wanner, 1993; Ikeda
et al., 1996; Gyaneshwar et al., 2005). In considering glucose limitation, here
too, major shifts in gene expression occur with reduced levels of extracellular
carbon source (Hua et al., 2004; Franchini and Egli, 2006). Indeed, the scale
of the changes in switching from glucose-excess to glucose-limited environments is extensive because many of the changes are controlled by cAMP and
Crp protein, which represent truly ‘‘global’’ regulators of E. coli (Saier,
1996). cAMP and Crp together control expression of about 200 operons and
many more genes (Gosset et al., 2004; Zheng et al., 2004).
As demonstrated in chemostats, cAMP levels (both intracellular and
excreted) are much elevated under glucose but not nitrogen limitation
(Harman and Botsford, 1979; Matin and Matin, 1982; Notley-McRobb
et al., 1997). The control of cAMP levels occurs through regulation of
adenylate cyclase, which is in turn is regulated by glucose availability
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THOMAS FERENCI
through components of the PEP:glucose phosphotransferase system and
Crp (Takahashi et al., 1998; Park et al., 2006). Reduced extracellular glucose
levels trigger an increase in cAMP synthesis (Fig. 2). After a major jump in
cAMP concentration when glucose drops below about 0.3 mM (NotleyMcRobb et al., 1997), cAMP levels continue to rise more moderately with
decreasing dilution rates and further reduction of extracellular glucose
levels. The precise shape of the dilution rate–cAMP response curve is strainspeciﬁc however, particularly at low dilution rates, due to unidentiﬁed
variations even between E. coli K-12 strains (Notley-McRobb et al., 1997).
Relevant to the chemostat environment, the physiological response to
high cAMP levels is the much-elevated expression of a large number of
nutrient transport proteins (Section 3.2; Wick et al., 2001; Hua et al., 2004;
Franchini and Egli, 2006). These include many binding protein-dependent
transporters besides the mglBAC and the mal-lamB gene products speciﬁcally useful in scavenging glucose. Extrapolating to natural environments,
the expression of systems speciﬁc for other carbon sources has been interpreted as a response that can broaden the possibilities and prepare E. coli to
utilize any available substrate in a nutrient-limiting environment (Ihssen and
Egli, 2005).
3.3.3. Growth Rate and ppGpp
The growth rate of E. coli has a major effect on the rate of macromolecular
synthesis (Maaloe and Kjeldgaard, 1966). The ribosomal content of E. coli in
glucose-limited chemostats rises linearly with dilution rate in the measured
interval between D ¼ 0.2 and 0.7 h1 (Yun et al., 1996). The concentration
of the alarmone ppGpp, produced by RelA and SpoT, controls many of the
growth rate-related changes in bacteria (Cashel et al., 1996) although changes
to nucleotide concentrations (Schneider and Gourse, 2003) and DNA
supercoiling may also contribute (Travers and Muskhelishvili, 2005). The
intracellular concentration of ppGpp increases with decreasing dilution rate
in glucose-limited chemostats, particularly sharply at Do0.1 h1 (Teich
et al., 1999).
The multitude of ppGpp-dependent phenotypes in bacteria has been
reviewed recently (Braeken et al., 2006). Signiﬁcant to regulation in chemostats are the interaction of ppGpp with RNA polymerase (Artsimovitch
et al., 2004) and the role of ppGpp in diverting transcription to promoters
differentially regulated by sigma factors (Jishage et al., 2002). Both ppGpp
and RpoS are elevated and function synergistically to provide a general
repositioning of transcription in stressed cells (Nystrom, 2004).
CHEMOSTAT ENVIRONMENT
187
Table 1 Examples of physiological responses to environmental stresses studied with
chemostat cultures
Organism
E. coli
E. coli
E. coli
E. coli
E. coli
Bacillus cereus
Streptococcus mutans
Streptococcus mutans
Xanthomonas campestris
Lactococcus lactis
Stress
Oxidative stress:
paraquat effects on
metabolomes
Nitric oxide stress:
transcriptome
Water activity
UV, sunlight killing
effects
Heavy metal effects (Zn):
transcriptome
Acid tolerance: effects of
growth rate
Acid effects on bioﬁlms
Acid effect on membrane
lipids
Acid stress on enzyme
activities
Acid stress and
energetics
Reference
Tweeddale et al. (1999)
Flatley et al. (2005)
Roller and
Anagnostopoulos (1982)
Berney et al. (2006)
Lee et al. (2005)
Thomassin et al. (2006)
Li et al. (2001)
Quivey et al. (2000)
Esgalhado and Roseiro
(1998)
O’Sullivan and Condon
(1999)
3.3.4. Other Studies of Regulation in Chemostats
Chemostats can also be used to impose controlled changes to the environment and to study transcriptional and physiological responses aside from
the general stress response. As summarized in Table 1, virtually any stress
can be imposed on bacterial chemostat cultures by manipulating culture
conditions or modiﬁcations such as illumination of the culture. As also
included in this non-exhaustive list, Table 1 shows environmental conditions
such as the effect of low pH that can be applied to studies of many different
types of bacteria.
3.4. Antibiotic Sensitivity
A number of chemostat studies have reached the conclusion that growth
rate is an important factor in antibiotic susceptibility and bacteria growing
at slow dilution rates are not as sensitive as fast-growing organisms (Brown
et al., 1990 and references therein). This generalization does not hold for all
forms of limitation, and interestingly different limitations affected
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THOMAS FERENCI
chlorhexidine susceptibility in opposite ways; susceptibility increased with
growth rate for carbon-limited chemostats but decreased with phosphorus
limitation (Brown et al., 1990). In some cases, as with polymyxin sensitivity,
the difference in susceptibility was ascribed to changes in lipopolysaccharide
content at different dilution rates (Wright and Gilbert, 1987). Undoubtedly,
changes in porin content from OmpF to OmpC described in Section 3.1 and
responses associated with the general stress response in Section 3.3 also
contribute to altered access and effects of antibiotics at slow growth rates.
There is also some evidence that antibiotic resistance through efﬂux is also
a function of growth rate. The expression of the E. coli acrAB efﬂux system
was increased at low D and was higher under glucose limitation (Rand et al.,
2002), possibly contributing to resistance at slow growth rates. Furthermore,
decreased susceptibility to ciproﬂoxacin and tetracycline at slow dilution
rates was associated with a higher proportion of persister cells at low D
(Sufya et al., 2003). Persister cells, that are not killed by antibiotics as readily
as the rest of a population, have been recently implicated in the emergence
of treatment-resistant organisms in clinical settings (Balaban et al., 2004;
Kussell et al., 2005). Chemostats potentially offer an excellent system to
study the factors governing the generation and population distribution of
persisters in much more detail.
3.5. Quorum Sensing
Population density and the circulation of extracellular signal molecules
(autoinducers) are important factors in bacterial behaviour (Reading and
Sperandio, 2006). Several classes of autoinducer molecules are produced by
bacteria and regulate multiple physiological responses (Camilli and Bassler,
2006). Chemostats offer an ideal way of studying the production and effects
of autoinducers because population density in chemostats is directly controlled by the medium composition (Section 2). Early studies of autoinduction of bioluminescence indeed used chemostats to demonstrate density
effects (Rosson and Nealson, 1981). The effect of growth rates at constant
densities can also be studied and the production of one autoinducer, AI-2,
has been studied in this way (DeLisa et al., 2001). AI-2 production is
strongly elevated at high growth rates and was subject to stress-induced
perturbations (DeLisa et al., 2001).
Other physiological effects of density on gene expression and metabolism
can also be measured in chemostats by changing the population density
(Liu et al., 2000). For example, the expression of porin genes is controlled by
E. coli population characteristics and ompF transcription is strongly repressed
CHEMOSTAT ENVIRONMENT
189
at high density (Liu et al., 2000). The regulation of the stress regulator RpoS
by density is more controversial, and different results were obtained in
different laboratories (Liu et al., 2000; Ihssen and Egli, 2004). It remains
to be established whether a secondary limitation or strain differences
(see Section 4) explain the different population density results obtained.
Quorum sensing is an important factor in bioﬁlm formation by different
types of bacteria (Parsek and Greenberg, 2005). To remove variables from
bioﬁlm formation, chemostats have been modiﬁed to study the colonization
of surfaces like glass rods inserted into continuous cultures (Keevil, 2001;
Li et al., 2001). Of course, the bacteria in bioﬁlms are not subject to the same
thoroughly mixed local environment that planktonic bacteria enjoy, but at
least some environmental aspects can be controlled in this way. Indeed
recent evidence suggests the bioﬁlm bacteria in a continuous culture grow
faster than planktonic bacteria and can survive washout at fast dilution rates
(Bester et al., 2005). The effect of dilution rate on bioﬁlm buildup and the
effect of antibiotics can be effectively studied in such systems (Molin et al.,
1982; Anwar et al., 1992).
4. VARIATIONS IN RESPONSES WITHIN AND BETWEEN
SPECIES
The choice of organism in chemostat studies is of course determined by the
aims of the experiment, but several strain characteristics have been shown
to affect behaviour in continuous culture. A comparison of more than
70 isolates of E. coli taken straight from natural habitats resulted in an
approximately threefold range of maximal growth rates in glucose minimal
medium (Mikkola and Kurland, 1991). Chemostat cultivation resulted
in the slower isolates rapidly adapting to the laboratory environment with
altered growth kinetics. Ribosomal function was one property that changes
after chemostat culture; translational kinetics is highly variable in natural
isolates but converges on that of laboratory K-12 strains after growth
in glucose-limited chemostats (Mikkola and Kurland, 1992; Rang et al.,
1997). The maximal growth rate of such isolates changed within 200 generations and other more subtle changes possibly occurred even earlier.
Hence care is needed in studying the growth and ecological behaviour of
natural isolates in chemostats to avoid pitfalls due to acclimatization in
laboratory culture.
Even when strains well adapted to the laboratory, such as E. coli K-12, are
available, at least three strain characteristics can change the course of
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THOMAS FERENCI
chemostat experiments. Firstly, the propensity for wall growth (bioﬁlm formation) and the rapidity of appearance of adherent mutants is species- and
strain-variable (Larsen and Dimmick, 1964). The formation of a bioﬁlm in a
culture vessel alters the environment of a chemostat sub-population and can
locally affect gene expression and conditions for selecting further mutations.
Fortuitously rather than by design, the strain used in my laboratory for
long-term evolution experiments does not appear to readily mutate to wall
growth. Other strains of E. coli readily produce curli adhesins (Vidal et al.,
1998; Prigent-Combaret et al., 2000) and rapidly change the characteristics
of the chemostat culture away from a homogeneous environment. The
genetic basis of the differences in adhesiveness between strains is not always
known, so some empirical testing is advisable. There could well be a range of
genetic pathogenicity elements that affect adhesion and it may be relevant
that pathogenic O157:H7 bacteria show high adhesion in chemostat culture
(James and Keevil, 1999).
A second source of variation in chemostat behaviour between strains is
subtle but with signiﬁcant consequences. As introduced in Section 3.3, strain
differences affecting the RpoS (or sS) s factor can inﬂuence gene expression,
stress responses, competitive behaviour and the selection of beneﬁcial
mutations in long-term cultures (Ferenci, 2005). Such global differences are
common and many surveyed strains within the species E. coli contain characteristic and distinct levels of RpoS protein even when grown under identical conditions (King et al., 2004). As discussed in Section 7, the level
of RpoS inﬂuences which mutations are enriched and the evolutionary
pathway with prolonged continuous culture.
The third area of strain variation is in metabolism, as introduced in Section 3.2. Even in the well-studied utilization of glucose by E. coli, many
discrepancies are present in the literature. To give one example of differences
due to metabolic strain variation, acetate is produced from glucose in most
E. coli strains under aerobic conditions when glucose is in excess (or in
chemostats at high dilution rates; el-Mansi and Holms, 1989). Still,
for unknown reasons, the amount of glucose converted to acetate by different E. coli lab strains is highly uneven (Luli and Strohl, 1990). A more
comprehensive view of metabolic variation is available from metabolome
analysis and comparative proﬁling of strains across the species E. coli. Most
strikingly, the metabolome proﬁles of separate E. coli isolates are highly
distinct and less than 30% of metabolite pools are conserved in all strains
(Maharjan and Ferenci, 2005). There were even metabolome differences
between laboratory strains of E. coli K-12. Metabolomic divergence into A,
B1, B2 and D subgroups resembles the divergence seen in taxonomic trees
within the species derived from gene sequence comparisons (Pupo et al.,
CHEMOSTAT ENVIRONMENT
191
1997; Maharjan and Ferenci, 2005). Hence, chemostat studies of metabolism
with a particular strain need to be interpreted in this wider context.
Finally and more broadly problematic in microbiological studies, the
possibility of non-constancy of properties does exist even with the same
labelled strain of the same species (O’Keefe et al., 2006). Differences
in storage conditions, errors in handling and subculturing can readily create
divergence in stocks (Johnson et al., 2001) and hence differences in strain
characteristics. A good example came from comparison of the variations
in stocks of the E. coli W3110 strain used in different laboratories in Japan
(Jishage and Ishihama, 1997).
5. STEADY STATE OR CONSTANT CHANGE IN A
CHEMOSTAT POPULATION?
For applications needing stable and reproducible levels of gene expression, an important consideration is the timing of samples after chemostats
are inoculated. When is the most reproducible time for stable comparisons? As shown in Fig. 2, within 24 h of inoculation, bacterial cell
densities approach a constant value and the initial hunger responses also
plateau. Of course, this is true only if the inoculum does not contain high
levels of limiting nutrient which need to be depleted or washed out to
attain limitation. With high volumes of inocula, an overshoot of bacterial
density is observed before limitation sets in. An attainment of a constant
bacterial density or yield is not however a clear-cut indicator of a steady
state. As shown in independent studies with different organisms, the
concentration of limiting nutrient continues to drop for many generations
in chemostats even after a steady biomass level is reached, indicating a
lack of a genuine steady state (Rutgers et al., 1987; Kovarova-Kovar and
Egli, 1998). Nevertheless, classic chemostat texts recommend, and recent
studies frequently use, 5–8 vol. of medium passing through a chemostat
vessel before a ‘‘steady state’’ is reached (Hua et al., 2004; Nanchen et al.,
2006). In one study with Saccharomyces cerevisiae, even 10–14 vol.
changes were used to ‘‘avoid strain adaptation’’ for expression studies
(Boer et al., 2003). It is highly unlikely that a steady residual nutrient level
is reached in most of these studies. Moreover, at D ¼ 0.1 h1, or 0.1 vol./h,
5 vol. is equivalent to 50 h. With E. coli at least, this length of time
is sufﬁcient to see almost a complete replacement of a population by
mutants and is even sufﬁcient to see the initiation of a second round of
mutational sweeps (Notley-McRobb et al., 2003; Section 7).
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THOMAS FERENCI
So what is more problematic, the lack of attainment of a steady state
or the danger of mutational changes? My personal view is that the latter
is more likely to lead to misleading results in measuring expression or other
physiological properties of chemostat-inoculated bacteria. Sampling for
expression studies is therefore advisable soon after the initial rapid depletion
of limiting nutrient and the attainment of a constant cell density. As shown
in Fig. 2, the time-course of expression changes in genes susceptible
to glucose limitation reach a fairly stable, elevated level within 24 h. The
threshold concentration for glucose limitation in eliciting the hunger
response is below approximately 0.3 mM, which occurs (with E. coli and
glucose limitation) within the ﬁrst 6–10 h (Notley-McRobb et al., 1997).
The entrenched idea of steady states and chemostats as constant environments is probably due to two factors. The ﬁrst is the easy but misleading
description of chemostats in terms of simple equations. Starting with Monod
but continuing to this day in many text-books, a basic assumption is that
bacteria have ﬁxed properties (Pirt, 1975; Panikov, 1995) with deﬁnable
enzyme-like characteristics. This assumption is untenable except when a gross
simpliﬁcation is sufﬁcient. The maximal growth rate, or mmax, of bacteria in
chemostats changes with prolonged culture (Mikkola and Kurland, 1992) and
the afﬁnity, or Ks is also non-constant. Indeed, this has been demonstrated
directly as a change of Ks at different dilution rates (Wick et al., 2002). The
molecular explanation is probably distinct for different limiting substrates, but
for E. coli/glucose, changes in apparent Ks are due to shifting expression
of transporter genes at different dilution rates (Ferenci, 1996, 2001). These
molecular differences probably explain the deviations from simple Monod
saturation kinetics noted in numerous studies (e.g. Shehata and Marr, 1971).
The second contributing factor in historically viewing chemostat populations as being in steady state was the unrecognized rapidity and extent of
mutational changes. In the absence of gross morphological differences and
lack of techniques for the analysis of genetic changes in populations, it was
easy to overlook the impact of mutations on chemostat behaviour. Given
the mutational adaptations considered below, there is no truly safe extended
phase in which cultures exhibit constant properties.
6. MUTATION RATES AND MUTATORS IN CHEMOSTAT
POPULATIONS
In all life-forms, evolution is dependent on the availability of mutations in a
population. Mutation supply and the exploration of a wide range of
CHEMOSTAT ENVIRONMENT
193
mutational variations is dependent on both the population size and the
mutation rates within populations (de Visser et al., 1999). Most chemostat
populations involve high numbers (usually1010–1012) of bacteria, so the
generation of a large number of spontaneous mutations is inevitable. Given
that loss-of-function mutations occur in 1 in 105 or 106 newly divided cells,
and even rarer gain-of-function mutations occur in 1 in 109–1010 bacteria,
the possibility of beneﬁcial mutations leading to organisms ﬁtter than the
inoculated strain is ever-present. The ﬁrst chemostat studies already demonstrated that in tryptophan-limited chemostats, E. coli mutants with better
tryptophan transport take over the population although the mutations were
not characterized (Novick and Szilard, 1950b). Similar trends in transport
improvement occur in glucose-limited populations of E. coli or yeast
(Dykhuizen and Hartl, 1981; Helling et al., 1987; Dunham et al., 2002) and
probably all other chemostat populations. Detailed studies with E. coli
populations of different sizes indicated that mutation availability is not a
problem with commonly used (41010) populations, but does affect the rate
and shape of mutational take-overs in small populations (Wick et al., 2002).
The effect of limiting population size was demonstrated in the progression of
glucose-limited E. coli cultures towards better glucose scavenging ability. By
measuring residual glucose levels in chemostats over 500 h (Wick et al.,
2002), a small population (107 bacteria) was found to exhibit a step-wise
improvement in afﬁnity whereas a continuum of increased afﬁnities was
observed when 1011 cells were evolving. Evolution towards low residual
glucose levels was also more reproducible between parallel cultures with
large populations, consistent with the expectation that the availability of
beneﬁcial mutations was more subject to chance in small populations.
The constancy of mutation rates was not experimentally measured in
Wick et al. (2002), but several studies measured mutation rates in chemostats (Novick and Szilard, 1951; Kubitschek and Bendigkeit, 1964). Mutation
rates vary with the type of limiting nutrient (Novick and Szilard, 1950b).
Different dilution rates also change mutation rates, with low dilution rates
giving a 30-fold increase in the rate of accumulation of mutations with
no selective advantage (e.g. resistance to phage T5 causes no beneﬁt or loss
of ﬁtness under glucose limitation; Notley-McRobb et al., 2003). The mutational differences can be rationalized as a manifestation of the broader
phenomenon of stress-induced mutagenesis, which is more often studied in
stationary phase cultures and in colonies on plates (Bjedov et al., 2003).
It should be emphasized that there has been no detailed study on the mechanism of mutation rate regulation in chemostats, although continuous cultures would be ideal for controlling the level of stress and analysis of the
cellular systems that regulate mutation rates (Tenaillon et al., 2004; Foster,
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THOMAS FERENCI
2005). For example, the study of the expression of the SOS response and
error-prone DNA polymerases would add greatly to our understanding
of mutation supply under nutrient limitation and the regulation of stressinduced mutagenesis (Tippin et al., 2004).
Mutation rates in chemostats could also increase if DNA repair processes
such as mismatch repair were less highly expressed under nutrient stress.
This kind of regulation has been proposed for stationary phase changes in
mutation rates (Feng et al., 1996). There has been one study on the effect
of chemostat culture on the regulation of DNA repair involving mutY
(Notley-McRobb et al., 2002b), but chemostat culture could also add much
to understanding the contribution of other pathways of DNA repair in
controlling mutation supply. For example, mutS expression is affected
in stationary phase in many E. coli strains (Li et al., 2003), so is also likely to
be altered in chemostat culture.
The availability of mutations is also strongly inﬂuenced by the occurrence
of mutator mutations in natural and chemostat populations of E. coli.
Amongst environmental and especially clinical isolates of E. coli, individual
strains are present with orders of magnitude higher mutation rates than seen
in ‘‘normal’’ E. coli (Leclerc et al., 1996). Actually, surveys of mutation
frequencies in large strain collections show that even ‘‘normal’’ mutation
rates are subject to a wide range of variation (Bjedov et al., 2003) as well as
are subject to different levels of regulation (Li et al., 2003). The isolates with
greatly elevated mutation rates often contain mutator mutations, such as in
mutS with over 100-fold increase in mutation rates (Li et al., 2003) or other
defects in DNA repair (Cox, 1976; Denamur and Matic, 2006). Mutators
were also present in some long-term experimental lineages (Sniegowski et al.,
1997) as well as in signiﬁcant numbers in 6 of 11 chemostat populations
under prolonged glucose limitation (Notley-McRobb et al., 2002c).
As shown in pioneering studies by Cox and others, mutator strains are
associated with a ﬁtness advantage in chemostat populations and outcompete
bacteria with normal mutation rates (Gibson et al., 1970; Cox and Gibson,
1974; Trobner and Piechocki, 1984). The ﬁtness beneﬁt comes not from the
mutator mutation itself but the ability to hitchhike with a greater number of
beneﬁcial mutations that arise in the mutator sub-population (Miller et al.,
2000; Tenaillon et al., 2001; Denamur and Matic, 2006). This process of secondary selection has been demonstrated in evolving chemostat populations
and both the mutator mutation and the linked beneﬁcial mutation identiﬁed
(Notley-McRobb and Ferenci, 2000a; Notley-McRobb et al., 2002b, c). In
Fig. 4, the mutS mutation hitchhiked with beneﬁcial mgl mutations. For
reasons not yet explained, mutY and mutS mutations were the predominant
mutators and occurred in over 30% abundance in the chemostat populations
CHEMOSTAT ENVIRONMENT
195
>99% mgl mutS+
<1% mgl mutS
66% mgl mutS+
33% mgl mutS
% of mutants in population
100
2
90
80
70
mgl
mutS
mlc
1
60
50
40
30
20
10
0
0
100
200
300
400
Time (h)
Figure 4 Changes in chemostat populations resulting from mutations in mgl,
mutS and mlc. Glucose-limited conditions in chemostats at dilution rate 0.3 h1 were
sampled to test individual isolates for three characteristics at different time points.
For details of the mgl, mutS and mlc assays, see Notley-McRobb and Ferenci
(2000a).
studied (Notley-McRobb et al., 2002b). The appearance and even predominance of mutators was followed by eventual elimination of mutators through
the appearance and spread of beneﬁcial mutations in the non-mutator subpopulation (Notley-McRobb et al., 2002c). The elimination of mutators was
not predicted in earlier models of mutator spread in populations (Tenaillon
et al., 1999), probably because the inherent heterogeneity of evolving chemostat populations was not recognized at this time (see Section 10). Nevertheless,
the transient enrichment of mutators has been demonstrated in environments
where mutational adaptation is paramount, and which explains the natural
occurrence of mutators in hostile surroundings and amongst antibiotic-resistant bacteria (Oliver et al., 2000; Denamur and Matic, 2006).
7. MUTATIONAL TAKEOVERS AND POPULATION CHANGES
Fitness increases resulting from natural selection require mutations that
confer an advantage in a particular environment. As noted already by
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THOMAS FERENCI
Novick and Szilard (1950b), chemostats provide a controlled environment in
which it should be possible to follow how mutations spread in populations.
Are the same mutations always present in an evolving chemostat population? Do they sweep in a particular sequence? What are beneﬁcial mutations
in a molecular and physiological context? How rapidly do mutations arise
and spread? How does the course of evolution change with small variations
of growth rate or environment? Do chemostats result in a single winner
clone? Or diversity? All these questions and others can be usefully tackled
with chemostats, and the sections below report on progress on these topics.
Before discussing these however, a broader perspective needs introducing,
because many of the neo-Darwinian ideas on mutation-selection came from
outside the bacterial world and because a series of classic studies on
the spread of mutations in bacterial populations form the background to
subsequent discussions. An excellent review on the early work in this area
is by Dykhuizen (1990).
Although experimental evolution studies have a long history (references in
Kassen and Rainey, 2004), the ﬁrst inﬂuential studies of bacterial population
changes in experimental cultures was by Ryan (Atwood et al., 1951) using
serial transfer. The evidence that mutations swept through populations was
indirect and relied on following the proportion of easily followed mutations
(with neutral ﬁtness effects) in populations. The ﬂuctuation in the proportion of bacteria with spontaneously arising phage T5 resistance mutations
was observed in several independent chemostat lines (Novick and Szilard,
1950b; Kubitschek, 1970; Helling et al., 1987). The occasional transitions in
the proportions of such neutral mutations, called periodic selection events,
were classically attributed to the displacement of the original population
by a mutant with a growth advantage; the reduction in the proportion of
T5-resistant mutants during such sweeps was explained by the likelihood
that the beneﬁcial mutation arises in the predominant T5-sensitive members
of the population. The kinetics of such mutational sweeps was formalized by
Moser (1957), who assumed the sweeping mutant is of a single ‘‘predominant’’ form and other mutants are a minor proportion of the population. In
this model, mutations swept to ﬁxation and new rounds of mutations arose
successively from the predominant type. A discussion of the consequences of
this model are presented in Kubitschek (1974) and Dykhuizen (1990). But in
the absence of identiﬁed beneﬁcial mutations, the real behaviour of populations could not be easily analysed. Identiﬁed changes in glucose-limited
populations have now permitted testing of how mutations really sweep
populations and Section 8 will deal with one example in detail.
A second historically important aspect of mutational sweeps is the
question of time-scale and how mutations and ﬁtness effects of mutations
CHEMOSTAT ENVIRONMENT
197
distribute themselves over time. There has been a general assumption that
experimental evolution studies require hundreds or thousands of generations. A well-known example of long-term studies is the Lenski experiments on serially subcultured E. coli (Lenski et al., 1991), now being
analysed to 20,000 generations (Woods et al., 2006). In chemostats,
glucose-limited E. coli has been grown for over 1800 generations at
D ¼ 0.2 h1 (Helling et al., 1987). In none of these long-term cultures is
there evidence of a bacterium reaching a constant, optimal state and
population changes were still occurring. In replicate serial-dilution cultures, the ﬁtness achieved by different populations after 10,000 generations was not identical. The greatest step-wise increases in ﬁtness accrued
in the ﬁrst 2000 generations, and ﬁtness increases slowed over subsequent
generations (Lenski and Travisano, 1994; Elena and Lenski, 2003).
In chemostats also, the rapid sweeps with large ﬁtness effects occur early
in the culture period (within the ﬁrst two weeks at D ¼ 0.1 h1; Maharjan
et al., 2006). Beyond that, incomplete, slower sweeps with weaker beneﬁts
are apparent after three weeks and beyond (see Fig. 1 in Maharjan et al.,
2006).
A crucial question that can be answered with chemostat cultures
is whether a bacterial population stays uniform after many generations
in the same environment. Does a population acquiring a succession of
beneﬁcial mutations become a super-ﬁt clone or does it diverge in the
selection environment? This question is important in distinguishing
between two historically inﬂuential models of how mutations contribute
to ﬁtness. As discussed by Dykhuizen in a bacterial context, the end result
distinguishes between the Fischer and Wright views of evolution, towards
either single or multiple ﬁtness solutions in evolving populations (Dykhuizen, 1990). The experimental evidence is beginning to favour the divergence model. Coexistence of distinct clones has been found in long-term
experimental evolution studies (Rosenzweig et al., 1994; Elena and Lenski, 1997; Rozen and Lenski, 2000), although some of these coexistences
were explained by the formation of cross-feeding consortia partitioning
metabolism in the population (Treves et al., 1998; Porcher et al., 2001).
Nevertheless, population heterogeneity was evident in various other
studies of experimental evolution in E. coli (Finkel and Kolter, 1999;
Papadopoulos et al., 1999; Notley-McRobb and Ferenci, 1999a; Schneider
et al., 2000; Friesen et al., 2004). In chemostats, the level of divergence
is much greater than previously expected (Maharjan et al., 2006). We need
to therefore consider how mutational events occur and contribute to
this level of divergence in chemostat populations, as discussed in
Section 10.
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THOMAS FERENCI
8. A MUTATIONAL SWEEP IN DETAIL: THE PHYSIOLOGICAL
ADVANTAGE AND SPREAD OF MGL MUTATIONS IN
GLUCOSE-LIMITED E. COLI
In every aerobic glucose-limited culture investigated, regulatory mutations
affecting mgl expression spread through the population (Notley-McRobb
and Ferenci, 1999a; Notley-McRobb et al., 2003). Much-elevated levels of
MglB protein were found in proteomics studies of long-term glucose-limited
chemostat cultures (Wick et al., 2001). The mgl mutations are also genetically well characterized. The strong beneﬁt of mglD (also called galS;
Weickert and Adhya, 1993) inactivation or changes in the mgl operator
results in constitutive mglBAC expression and consequent improvement of
glucose uptake through the cytoplasmic membrane (Death and Ferenci,
1993). The Mgl system also catalyses galactose uptake, so it is possible to
follow Mgl expression changes speciﬁcally using galactose transport assays.
As shown in Fig. 4, the proportion of bacteria with Mgl changes rapidly
increases over a very short period in a glucose-limited chemostat, due to the
strong ﬁtness advantage of mgl-constitutive bacteria. This mutational sweep
reproducibly appeared within four days or 15 generations at slow dilution
rates (0.1 h1) but after 13–23 days or over 260 generations at fast dilution
rates (0.6 h1) in the same aerobic medium (Notley-McRobb et al., 2003).
Several aspects of mgl mutation spread in chemostat populations are instructive of how the interaction between cellular physiology, ﬁtness effects
and the environment shape a mutational sweep.
The unexpected complexity of sweeps is illustrated by the events in the
population shown in Fig. 4. The ancestral population was not simply replaced
by a new population with a mutated mgl allele. Three concurrent events were
taking place at the ﬁrst point arrowed in Fig. 4. Firstly, multiple alleles at the
mgl locus were responsible for the sweep, with up to 13 different sequence
changes detected in the same culture (Notley-McRobb and Ferenci, 2000a).
Secondly, some mgl mutations occurred in cells that also acquired a mutS
mutation (or vice versa), but others were mutS+. Thirdly, between the ﬁrst and
second arrows, the mgl mutations associated with mutS were largely eliminated
and the mgl mutants that won out were essentially all mutS+. To explain this,
it is necessary to postulate yet another beneﬁcial mutation, which provides
additional ﬁtness to the mgl mutS+ bacteria. This third mutation did not arise
in mgl mutS types, leading to their elimination. The mutS mutation, as noted
in Section 6, does not have a negative ﬁtness effect that could explain the rapid
purging. Hence over two days or so, what appears to look like a smooth
takeover by mgl bacteria in Fig. 4 is actually a complex process, with
mutations in at least three genes changing frequencies in the population.
CHEMOSTAT ENVIRONMENT
199
Yet another lesson from Fig. 4 is on the constancy of growth rates in
chemostat populations. As is evident from the speed of the sweeps, subpopulations with beneﬁcial mutations are growing considerably faster and
growing through many more generations than the ancestral bacteria over
the same period. The dilution rate clearly does not equate to speciﬁc growth
rate for sweeping mutant populations. This makes it misleading to plot
generations as a time-axis as is often done for long-term chemostat studies.
Isolates with multiple beneﬁcial mutations will have undertaken many more
generations than can be calculated from chemostat theory.
The multiplicity of mutant alleles involved in mgl sweeps in chemostats is
not conﬁned to mutations in this gene. The spread of rpoS mutations (Notley-McRobb et al., 2002a), malT mutations (Notley-McRobb and Ferenci,
1999b) as well as the mlc mutations shown in Fig. 4 are all due to concurrent
increases in the frequencies of different alleles. In the mlc sweep, 15 different
mlc sequence changes appeared during the sweep (Notley-McRobb and
Ferenci, 2000a). Although the physiological effect on phenotype is similar
and all mlc mutations in chemostats result in a loss of function of the
regulator Mlc, the genetic effect is to partition the population into bacteria
with distinct chromosomal histories. The hitchhiking of other mutations in
separate chromosomes (such as mutS in Fig. 4) results in the seeds of genetic
heterogeneity that arises with prolonged chemostat culture.
From the gradients of the lines in Fig. 4, it is evident that mgl mutations
exhibit a faster rate of population take-over than mlc mutations. This is
of course because the ﬁtness increase imparted by the mutations is not
equivalent. The ﬁtness effects can themselves be compared in chemostats by
competing strains carrying a mutation against either ancestor or a strain in
which the mutated gene has been replaced by a wild-type allele (see examples
in Dean, 1989; Notley-McRobb et al., 2003; King et al., 2004). The greatest
ﬁtness increases are reproducibly associated with the earliest recognized
sweeps and involve mgl and rpoS mutations in glucose-limited chemostats of
E. coli (Notley-McRobb et al., 2003). The observed gradients of individual
sweeps decreases after about two weeks of culture and population transitions do not result in a complete take-over (Maharjan et al., 2006). In
general, this pattern of mutational change in chemostats ﬁts with the view
that the biggest ﬁtness increases occur early in evolving populations (Lenski
and Travisano, 1994).
The mgl mutations evident in chemostat populations are a mixture of
mglD mutations inactivating the repressor of the mglBAC genes (Weickert
et al., 1991) and operator mutations upstream of mglBAC (Notley-McRobb
and Ferenci, 1999a). Initially, mglD mutations predominate, but after several weeks of chemostat culture, the ratio of mglO to mglD mutations
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THOMAS FERENCI
increases (Notley-McRobb and Ferenci, 1999a; Notley-McRobb et al.,
2003). The trend towards mglO-containing bacteria is explained by a slight
difference in Mgl expression. mglO mutants have a measurably higher
transport activity at low sugar concentrations than mglD bacteria. The likely
explanation for the increased ﬁtness of mglO mutations is that operator
mutations escape repression not only by MglD, but also other repressors
in the LacI/GalR family, that recognize operators resembling the mglO
sequence (Notley-McRobb and Ferenci, 1999a).
Another curious aspect of the mgl mutations in chemostats was the
strong bias in the type and position of DNA changes found by sequencing
a large number of mutations. Particularly in the operator, speciﬁc hotspots for transition mutations were noted at symmetrical positions on
opposite strands of the operator sequence (Notley-McRobb and Ferenci,
1999a). A possible explanation of the targets is that the mutations arise at
positions made sensitive to chemical change under stress (Wright, 2004).
Wright proposed that stretches of DNA with the potential to form stemloop structures of high stability, such as at mglO, lead to instability of the
exposed bases in this region. The unpaired bases are particularly susceptible to change under stress, such as at slow growth rates in chemostats
(Wright, 2004). This kind of mechanism may explain the bias in mgl
mutations, as one of a number of adaptive mutational mechanisms in
stressed cells (Foster, 2005).
Although mgl mutations universally appear in aerobic chemostat cultures of E. coli, no such mutations appeared in anaerobic glucose-limited
populations (Manche et al., 1999). Under fermentative conditions, ptsG
mutations were the main source of transport-improving mutations. The
ptsG mutations do not increase afﬁnity anywhere as much as mgl mutations so why the absence of mgl mutations? This unexpected result does
have a physiological basis; anaerobic conditions do not permit functioning of the Mgl system. The lack of anaerobic Mgl activity is not due
to transcriptional regulation, but occurs at the protein function level.
How this occurs is still unknown, but may be linked to the inability of
E. coli to utilize several sugars under anaerobic conditions (Mat-Jan et al.,
1991). A rationalization can be offered for the absence of the Mgl system
under fermentative conditions; the expensive ATP usage (two ATPs in
transport and glucose phosphorylation) of binding protein-dependent
transporters results in an energetic impasse when only two ATPs are
produced in fermentation pathways (Muir et al., 1985). Whatever the
explanation, this example of differences between anaerobic/aerobic mutational adaptations demonstrates how sensitive evolutionary pathways
are to physiological effects on ﬁtness.
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201
9. OTHER MUTATIONS IN CHEMOSTAT POPULATIONS AND
THEIR PHYSIOLOGICAL EFFECTS
Attempts to identify beneﬁcial mutations occurring in chemostat cultures
have been documented with several E. coli/substrate limitation combinations, although surprisingly few changes have been characterized in both
genetic and physiological detail. The sections below deal with such better
understood examples.
9.1. Changes in the lac System
The historically important studies with the lactose–E. coli combination
provided the ﬁrst deﬁned examples of chemostat mutations (Novick and
Horiuchi, 1961). Many historical aspects of mutational adaptations and
polymorphisms in the lac system were discussed in a review (Watt and Dean,
2000) so only a brief synthesis is offered here.
When lactose-limited chemostats are initiated, the most obvious observed
change is the signiﬁcant increase in b-galactosidase production and the accumulation of mutants with constitutive expression of the lac operon (Novick and Horiuchi, 1961). Later in the life of the chemostat, further increases
in expression result from ampliﬁcation of the lac region of the chromosome
(Horiuchi et al., 1962), and the extent of chemostat-selected lac ampliﬁcations were recently characterized using a lactose analog (lactulose) as limiting substrate (Zhong et al., 2004). The accumulation of b-galactosidase is
not however the major gain in ﬁtness in utilizing lactose in chemostats
(Dykhuizen et al., 1987; Dean, 1989); the ‘‘cell wall’’ components (Dean,
1989), namely LacY in the cytoplasmic membrane and porins in the outer
membrane, exert a greater inﬂuence on adaptation in the chemostat environment. This idea is supported by LacY being a site of positive selection in
natural isolates (Wagner and Riley, 1996) and, more conclusively, that
chemostats select for lacY mutants with increased afﬁnity for lactose (Tsen
et al., 1996). Lactose limitation in continuous culture also selects for both
regulatory and structural gene changes in porins (Zhang and Ferenci, 1999),
as discussed below.
9.2. Outer Membrane Changes
Except for some bulky or anionic nutrients, outer membrane permeability is
dependent on passive diffusion through porin pores in E. coli and other
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THOMAS FERENCI
Gram-negative bacteria (Nikaido, 2003). For a disaccharide like lactose, the
channel accessibility is more restricted than for monosaccharides; there is a
greater than 10-fold difference in permeation rates through OmpF between
glucose and lactose (Nikaido and Vaara, 1985). Lactose uptake at low
nutrient levels is indeed limited by outer membrane permeability in E. coli
and lactose-limited chemostats most commonly enrich mutants with structural gene changes that remove the channel constriction (L3 loop) in the
OmpF porin (Zhang and Ferenci, 1999). Increased expression of OmpF was
also noted, but the mutations causing this increase were not identiﬁed.
But what about smaller substrates like monosaccharides? There has been
relatively little effort in identifying mutational outer membrane changes
until recently, but the evidence suggests that, even for glucose, extensive
outer membrane differences contribute to ﬁtness (Maharjan et al., 2006).
Glucose can pass through more than one porin channel across the outer
membrane, as shown in Fig. 3. In glucose-limited chemostats, mutations are
present that affect one or more of these three paths so all three can be
improved by selection. The levels of OmpC, OmpF and LamB are elevated
in one or more classes of isolate after 26 days of glucose limitation. Some
isolates have more than one protein in increased amounts; Class 4 isolates
(Maharjan et al., 2006) increased both LamB and OmpF, whereas Class 2
isolates have all three at a higher level. Altogether, ﬁve different combinations of outer membrane changes were noted with altered expression of one
or more porins. Genetic analysis suggested that, even in individual protein
pattern classes, several mutational causes could give rise to the same observed changes. The overall conclusion is that outer membrane permeability
is subject to strong selection in chemostats even for monosaccharide substrates and multiple mutations are accumulated to improve permeability at
low concentrations.
The causes of LamB protein elevation are due to mutations in mlc and
malT (see Section 9.2) but OmpC regulation changes cannot yet be
explained. The alteration in OmpF levels in some classes is also largely
unsolved, but could result from mutations in any of numerous inputs into
the control of porin levels (Pratt et al., 1996; Liu and Ferenci, 2001). However, OmpF increases in some isolates can be ascribed to rpoS mutations
(Section 9.3), because RpoS negatively regulates ompF transcription
(Pratt et al., 1996; Liu and Ferenci, 2001).
The selection condition in chemostats, driving bacterial outer membranes
to ever-increasing ﬁtness under glucose limitation, involves a high level of
evolutionary specialization and a loss of some properties that make E. coli
ﬁt in its normal habitat. The outer membrane changes in chemostats provide
a good example of this type of antagonistic pleiotropy. All the outer
CHEMOSTAT ENVIRONMENT
203
membrane changes noted above increased permeation not just to nutrients,
but also to antibiotics and detergents (Zhang and Ferenci, 1999; Maharjan
et al., 2006). Isolates from chemostats with porin changes are much more
sensitive to several antibiotics including large b-lactams like cloxacillin and
detergents like SDS or bile salts, which make chemostat isolates sensitive
to their normal intestinal habitat. Indeed, it is possible to use chemostats to
explore the level of antagonistic pleiotropy between selection for better nutrition and resistance to antibiotics by incorporating low levels of antibiotic
into a glucose-limited chemostat. The competitive ﬁtness of bacteria in such
an environment is very much dependent on the level of outer membrane
permeability and the concentration of antibiotic (Maharjan and Ferenci,
unpublished results).
9.3. rpoS
As discussed in Section 3.3, mutational sweeps affecting rpoS can be readily
studied in chemostat cultures. In some strains of E. coli with high endogenous RpoS protein levels (including our usual MC4100 derivative used
in experimental evolution studies; Notley-McRobb and Ferenci, 1999a),
mutational loss of RpoS is a major ﬁtness gain and indeed the earliest
identiﬁed major sweep. In glucose-limited cultures at D ¼ 0.1 h1, rpoS null
mutants constitute 420% of the population by the second day (4–5 vol. of
medium) and in chemostats limited by diluted Luria broth, mutants are
already evident within three culture volumes (King et al., 2006). Populations
of other E. coli strains such as the commonly used MG1655 strain, on the
other hand, show few detectable rpoS mutants in the ﬁrst four days of
culture but exhibit attenuated rpoS phenotypes after hundreds of generations of cultivation (unpublished observations cited in Ihssen and Egli,
2004). The differences in rpoS selection between strains has been extensively
discussed recently (Ferenci, 2003, 2005).
An exploitable advantage of studying the rpoS sweep in chemostats is that
it can be adopted as a model system for the analysis of what makes mutations sweep populations. The interplay between the environment and the
beneﬁt derived from mutations in rpoS can be easily followed using simple
population-level screens. The spread of rpoS mutations is detectable by either the staining of colonies with iodine (measuring the RpoS-dependent
level of glycogen; Hengge-Aronis and Fischer, 1992) or by growth on minimal acetate plates (measuring the RpoS-dependent negative effect on use of
poor substrates; King et al., 2004). Changes in the proportions of wild-type
and mutant bacteria can hence be readily followed in chemostat samples.
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THOMAS FERENCI
The trade-off between the need for RpoS-dependent stress resistance and the
selection pressure to lose RpoS for improved ﬁtness can be studied by varying the chemostat environment and looking at the speed of mutational
sweeps under different conditions (King et al., 2006). An unexpected ﬁnding
from such studies was that there is a signiﬁcant difference in the ﬁtness
properties of rpoS mutants in aerobic and anaerobic conditions (King et al.,
2006). In anaerobic chemostats under glucose limitation, RpoS has a role
not needed under aerobic conditions (King and Ferenci, 2005). Hence studying the beneﬁt of mutations in different environments in chemostats has the
additional advantage of revealing subtleties in cellular physiology.
9.4. mlc and malT
Mutations in malT and mlc arise in glucose-limited chemostat cultures
grown with slow dilution rates (D ¼ 0.1–0.3 h1). As noted in Section 8, Mlc
is a repressor and MalT an activator of the mal regulon (Boos and Shuman,
1998). One physiological target of mlc and malT mutations in chemostats is
the outer membrane LamB protein, whose expression is controlled as part of
the mal regulon (Boos and Shuman, 1998). As discussed in Section 3.1,
LamB facilitates glucose entry across the outer membrane at low concentrations. The timing of appearance of mlc and malT mutations in replicate
glucose-limited populations is always after rpoS and mgl mutations become
common (Notley-McRobb et al., 2003). A possible physiological rationale
for this sequence is that outer membrane permeability becomes a signiﬁcant
bottleneck after the mgl mutations have greatly increased ﬂux through the
cytoplasmic membrane.
At low dilution rates, different populations often have either mlc or malT
mutations, but sometimes both (Notley-McRobb et al., 2003). The increase
in mal expression resulting from mlc mutations is less than from malT
changes, so there may be some additional advantage in acquiring malT
mutations after mlc has spread (Notley-McRobb and Ferenci, 1999b). By
contrast, mlc mutations do have a property not shared by malT in that mlc
mutations also increase expression of ptsG (Tanaka et al., 1999; Plumbridge,
2002; Seeto et al., 2004). The ptsG-encoded glucose phosphotransferase
transporter is not as important as Mgl under glucose limitation, but could
potentially increase ﬁtness by a smaller amount. Indeed, in a recently
analysed population under glucose limitation, mutants with increased ptsG
expressions were present (Maharjan et al., 2006).
The mlc mutations in chemostats include a wide range of base changes
and insertions resulting in loss of repressor activity (Notley-McRobb and
CHEMOSTAT ENVIRONMENT
205
Ferenci, 1999b). The malT mutations are different in that they are all point
mutations affecting a few regions in MalT protein resulting in active
activator even in the absence of inducer (Schlegel et al., 2002).
Interestingly, the beneﬁt conferred by high expression of the mal regulon
under glucose limitation is evident only at slow dilution rates
(D ¼ 0.1–0.3 h1). At D ¼ 0.6 h1, malT mutations do not appear in
chemostat populations (Notley-McRobb et al., 2003). As noted in Section
3.1, mal and lamB expression is already optimal at fast dilution rates so
a mutation to mal constitutivity is not needed. Perhaps surprisingly, a malTconstitutivity mutation confers a competitive disadvantage at faster growth
rates, for unknown reasons (Notley-McRobb et al., 2003). Hence as seen
also for rpoS mutations, the mutational sweeps in populations are highly
sensitive to growth rate and environmental factors.
9.5. ptsG
Improvements in glucose transport across the cytoplasmic membrane are not
solely due to Mgl changes under glucose limitation. As noted in Section 8,
the PEP:glucose phosphotransferase system is a major player in glucose
uptake under all conditions, but has an especially important role in glucoselimited chemostats with low O2 availability. In the absence of Mgl-dependent
glucose transport, PtsG overexpression and gains in afﬁnity were found in
isolates under these conditions. Interestingly, the same base change within
ptsG was responsible for both the regulation and afﬁnity change, and
occurred at just two positions in ptsG in parallel populations (Manche et al.,
1999; Notley-McRobb and Ferenci, 2000b). In aerobic chemostats, isolates
with mutations affecting ptsG were present, but occurred after mutations
affecting mgl and mal regulation (see Section 9.4) and are distinct from the
mutations in anaerobic cultures. The mutations affecting PtsG levels in aerobic chemostats (Maharjan et al., 2006) are outside ptsG or mlc and have not
yet been fully characterized.
9.6. Metabolic Changes and Cross-Feeding
The landmark ﬁnding of the Adams group, that bacterial isolates from
glucose-limited chemostats have evolved alternative metabolic strategies
(Helling et al., 1987; Rosenzweig and Adams, 1994; Treves et al., 1998;
Adams, 2004), has greatly extended our understanding of the scope of what
may be happening in chemostat populations. Six of the 12 glucose-limited
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THOMAS FERENCI
populations after 4400 generations contained acetate-scavenging bacteria
with mutations up-regulating acetyl-CoA synthetase (Treves et al., 1998).
The mutations were of different types but were all upstream of the acs gene.
The important characteristic of these acetate scavengers is that they crossfeed off other chemostat strains that produce elevated levels of acetate when
in the glucose-limited environment (Helling et al., 1987). The acetate producers and users have different growth efﬁciencies when using glucose and
also compete against ancestor and each other in a way indicative of intercellular interactions. The signiﬁcance of this ﬁnding is that an environment
which should, in the simplest view, result in a ﬁt strain efﬁcient for glucose
utilization actually supports more than one adaptational strategy.
The conclusion that evolved long-term chemostat bacteria do not converge on a unique metabolic strategy is strongly supported by recent data.
In analysis of the highly diverged population discussed in Section 10, it was
apparent that the growth yields on glucose were very heterogeneous within
a population. The same culture contained competing strains with either
reduced or elevated growth yields, or those unchanged from the ancestor.
The mutation(s) responsible for these changes still need to be identiﬁed.
9.7. Amplification and Other Genomic Rearrangements
At the genetic level, the examples from glucose-limited chemostats above
involved mutations affecting single genes through base changes or inactivation through insertion sequences. Another genetic solution to increased
ﬁtness, as seen within the lactose chemostats, is for bacteria to increase gene
copy number through ampliﬁcation of the lac region (Horiuchi et al., 1962).
Ampliﬁcation of genomic material (Romero and Palacios, 1997) and rearrangements due to mobile genetic elements are of considerable importance
in bacterial evolution (Arber, 2000). Moreover, there is recent discussion as
to whether transient ampliﬁcation is of signiﬁcance in adaptive or stressinduced mutations (Pettersson et al., 2005). The latter could also be signiﬁcant in evolution in the nutrient-stressed chemostat environment. So how
important is ampliﬁcation in chemostat culture?
Strong evidence for the frequent occurrence of adaptive duplications of
ﬁtness-conferring genes came from studies of Salmonella typhimurium grown
in chemostats limited by arabinose, sorbitol or melibiose (Sonti and Roth,
1989). All cultures showed a duplication of a similar large region of the
chromosome, covering the genes responsible for transport and metabolism
of these sugars. Bacteria with chromosomal duplications rose to over 90%
of the population within 50–100 h of limitation. Interestingly, the proportion
CHEMOSTAT ENVIRONMENT
207
of bacteria with duplications dropped sharply after about 100 h (Sonti and
Roth, 1989). Presumably, other types of mutations had bigger ﬁtness
advantages and bacteria with these can displace bacteria with duplications.
Whether the further mutations are to some extent dependent on the duplications is not known. Nevertheless, duplications are common in populations
of E. coli (Haack and Roth, 1995) and the lactose and arabinose examples
do illustrate that ampliﬁcations can contribute to overcoming nutrient limitation by increasing gene copy number. A similar conclusion was found
with Saccharomyces cultures; ampliﬁcations of transporter genes and genomic rearrangements were common in glucose-limited chemostats (Dunham
et al., 2002). In glucose-limited cultures of E. coli, evidence is less extensive
but some mgl mutations do show instability in phenotypes, possibly due to
unstable ampliﬁcation events (Maharjan et al., 2006).
The stability of the genome with respect to mobile elements has not been
well analysed with chemostat cultures, but the serial dilution regime of
E. coli in glucose medium does result in major reassortment of insertion
sequences after 10,000 generations (Papadopoulos et al., 1999). It would be
surprising if similar rearrangements did not also occur in chemostats and
there is data from the analysis of mutations in rpoS and mgl genes that
IS elements were responsible for some of the loss of function mutations
(Notley-McRobb and Ferenci, 1999a; Notley-McRobb et al., 2003). The
contribution of mobile elements to chemostat diversity is worthy of further
study.
10. EMERGING DIVERSITY IN CHEMOSTAT POPULATIONS
This section should be prefaced by the comment that the results discussed
below are strain dependent and selection condition dependent. There are
replicate populations that exhibit the properties outlined below; there is also
accumulating published and unpublished evidence that changes of strain,
limiting nutrient or dilution rate results in a different mix of evolved strains.
This should not come as a surprise, considering the data above showing that
rpoS, mgl and malT mutations have different ﬁtness advantages in different
environments and different backgrounds. So this discussion is speciﬁc to one
E. coli laboratory strain in a glucose-limited chemostat at D ¼ 0.1 h1 but is
instructive of the huge diversity that one clonal population can achieve.
Also, in this section, only heterogeneity based on mutational change
is considered. Recent studies in a number of systems indicate that stable
genetic change is not the only source of variation in populations; epigenetic
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variation, leading to differences in gene expression within the same genetically identical population, is also evident in bacterial cultures (Laurent
et al., 2005; Smits et al., 2006). These phenomena are undoubtedly evident
in chemostat populations and add to the complexity within the chemostat
community, but is not further discussed here.
The genetic and phenotypic diversity apparent in chemostats does not have
a strong theoretical foundation. It has often been argued that evolutionary
diversiﬁcation follows from selection for specialization on alternative resources
(Kassen and Rainey, 2004; MacLean, 2005). In a chemostat limited by a single
resource, divergence should not happen, if the niche exclusion principle is
strictly followed (Hardin, 1960). Also, from the perspective of chemostat
operation, the open ﬂow system in chemostats was considered not to be useful
in the development or study of inter-cellular interactions (Jannasch and Egli,
1993). Nevertheless, beginning with the work of the Adams group (reviewed in
Adams, 2004), analysis of individuals within a chemostat population has
demonstrated that intra-population interactions do take place and involves the
establishment of a new community environment in the chemostat (Kurlandzka
et al., 1991; Treves et al., 1998). In other words, even with a single, constantly
applied limitation, bacterial populations establish a new ecological framework
through mutational adaptation. This section focuses on how easily bacteria
by-pass the competitive exclusion principle and establish complex interacting
societies within a single chemostat culture.
The extent of divergence in chemostats has been largely underestimated
because limited numbers of isolates from any population were analysed in
earlier studies and few differentiating phenotypes were recognized. The
identiﬁed mutational changes described above do offer means of following
shifts in populations. For example, including a transcriptional lacZ fusion
into the ancestor allows detection of several levels of mal expression resulting from different mutations (Notley-McRobb and Ferenci, 1999b). The
heterogeneity in populations also becomes visibly obvious on agar plates
when indicator plates are used to show differences in lac fusion expression.
The differences on lactose–EMB (eosin–methylene blue) indicator media
shown in Fig. 5 are the result of mlc and malT mutations as well as smaller
changes due to rpoS and other unidentiﬁed mutations. The colony size
differences on the plates are also heritable and small colony types are often
associated with inefﬁcient glucose throughput (see Section 10.3). Figure 5
depicts the extensive heterogeneity after 30 days of culture at D ¼ 0.1 h1 in
a glucose-limited chemostat. Other simple plate assays can detect mutational
changes in chemostats; diversity can also be seen using staining methods for
rpoS and by testing sensitivity to inhibitors such as a-methyl glucoside,
3-amino-1,2,4-triazole (AT) or antibiotics (Maharjan et al., 2006).
CHEMOSTAT ENVIRONMENT
209
Figure 5 Heterogeneity in evolving chemostat populations, as detected with indicator plates. A glucose-limited E. coli culture in chemostats with a dilution rate
0.1 h1 was diluted after 30 days and spread on eosin–methylene blue (EMB) indicator plates containing lactose as fermentable substrate. The ancestor strain used
(BW2952; Notley-McRobb and Ferenci, 1999a, b) has a deletion of chromosomal lac
genes but has a lacZY transcriptional fusion to malG, which is regulated as part of
the mal regulon. As discussed in the text (Section 9), several different beneﬁcial
mutations change mal regulation in evolving chemostat populations. The colony
colour and colony size are both heritable characteristics. (See plate 4 in the color
plate section.)
When 40 or more isolates were recently compared for multiple characteristics, several different phenotypic groupings within one glucose-limited population were noted (Maharjan et al., 2006). A similar level of diversity was
found in eight chemostat populations also analysed with 40 isolates each after
20–26 days, although the observed combination of mutations was not identical
in each culture (Seeto and Ferenci, unpublished results). As shown in Table 2,
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Table 2 Differences between co-existing isolates in a long-term (26-day) chemostat
culture of E. coli described in Maharjan et al. (2006) and Maharjan et al. (2007)
Examples from one
population
Class 1
BW4003 BW4005
BW4021 BW4023
BW4036 BW4039
Class 2
BW4029
Class 3
BW4001
BW4006–BW4012
BW4020
Class 4a
BW4002 BW4035
Class 4b
BW4030
Class 4c
BW3767 BW4004
BW4022
BW4024–BW4028
Properties
a. Unchanged Glc transport
b. Unchanged regulation
c. Use of alternative
resources
a. Unchanged Glc transport
b. Unchanged regulation
c. Increased metabolic
efﬁciency
d. Reduced respiration
a. Increased transport
(through PtsG)
b. Global regulation: reduced
ppGpp
c. Reduced metabolic
efﬁciency
d. Increased respiration
a. Very high Glc transport
(through Mgl)
b. Global regulation: rpoS
mutation (L247stop)
c. Unchanged or increased
metabolic efﬁciency
d. Increased respiration
a. Very high Glc transport
(through Mgl)
b. Global regulation: rpoS
mutation (H191P)
c. Unchanged or increased
metabolic efﬁciency
d. Increased respiration
a. Very high Glc transport
(through Mgl)
b. Global regulation: rpoS
mutation (E315 stop)
c. Unchanged or increased
metabolic efﬁciency
d. Increased respiration
Fitness
strategy
Different
ecotype
K strategist
r strategist
Mixed
Mixed
Mixed
regulatory, physiological and metabolic divergence was observed amongst
coexisting isolates. The different combination of properties gives rise to the
conclusion that members of the same population gain ﬁtness through alternative survival strategies. Class 1 isolates in Table 2 have persisted in the
CHEMOSTAT ENVIRONMENT
211
chemostat environment but do not take up or metabolize glucose better than
the ancestor, and indeed one of these Class 1 isolates is less ﬁt under glucose
limitation than the ancestor (Maharjan et al., 2006). The simplest explanation
for the behaviour of BW4005 and Class 1 is that it is competing for an
alternative niche present in the chemostat, just as the acetate scavengers did in
the Adams studies (Adams, 2004). The BW4005 does not specialize in utilizing
acetate however, and its niche was not identiﬁed. Still, these ﬁndings extend the
Adams studies that a chemostat develops into a new environment when a
population evolves.
The Class 2 isolate is interesting in that it conserves glucose rather than
transports it effectively or quickly (Maharjan et al., 2007). Indeed, its characteristic is to increase growth yield on glucose and decrease wasteful
respiratory metabolism. This class is a K strategist from an ecological point
of view (MacArthur and Wilson, 1967) and utilizes a limiting resource with
increased efﬁciency. The Class 2 is minor numerically, but shows that kinetic
adaptation is not the only answer to resource limitation.
Class 3 isolates have the opposite approach to ﬁtness and use glucose with
elevated transport rates and also elevated respiration (Maharjan et al., 2007).
The combined effect of increased transport and metabolism is a signiﬁcant
20% decrease in the growth yield on glucose. The numerically signiﬁcant Class
3 isolates used a high rate/low efﬁciency approach to ﬁtness so are r strategists
from an ecological viewpoint (MacArthur and Wilson, 1967).
The Class 4 isolates have similar properties and are numerically the most
common. They are all the best-evolved from the glucose transport point
of view and strongly outcompete ancestor when placed in a reconstituted
glucose-limited environment. Members of this group have heterogeneous
growth yields, though most isolates are more efﬁcient than ancestor in
turning glucose into biomass (Maharjan et al., 2007). Class 4 isolates have
a mixed approach, increasing scavenging rates, but not at the cost of
metabolic efﬁciency. The difference between the Class 4 sub-classes is in the
allele of rpoS present; the different alleles indicate separate parallel evolutionary routes and acquisition of similar characteristics in three separate
lines. The contribution of regulatory, transport and metabolic differences
within one population is discussed below.
10.1. Diversity in Regulatory Strategies
The majority of the identiﬁed mutations arising in chemostat populations,
discussed in Section 9, alter regulation. This predominance of regulatory
mutations is no different to results with other experimental evolution
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populations, in which changes in regulation are the most obvious differences
(Elena and Lenski, 2003). What is interesting though is that the regulatory
mutations are not universally distributed in different isolates from the same
population. The coexistence of bacteria with different combinations of regulatory changes is good evidence that there are redundant solutions to
ﬁtness under glucose limitation.
One clear example of regulatory redundancy is the change of mal
regulation through two alternative mutations (see Section 9.4 on mlc and
malT). Both mutations overcome the bottleneck of lamB expression and
hence increase transport across the outer membrane. Isolates with mlc
or malT mutations can coexist and contribute to overall divergence in populations; further beneﬁcial mutations in malT or mlc backgrounds increase
the possibility of different phenotypes through epistatic effects. The role of
mlc in ptsG regulation also distinguishes mlc mutants from malT organisms
and possibly the path to further ﬁtness through yet additional mutations.
Another regulatory change, and an early one in glucose-limited bacteria, is
the functional loss of a major sigma factor (see Section 9.3 on RpoS).
The rapid sweep by rpoS mutants is not however to complete ﬁxation. In all
populations studied, close to 1% of bacteria remain rpoS+ for many generations. It is possible that this persisting sub-population differs from the ancestor
and acquired an alternative ﬁtness property, but the nature of this is unknown.
Nevertheless, continued presence of the rpoS+ sub-population allows further
changes to occur in different regulatory backgrounds and can result in entirely
different ﬁtness solutions. In all eight studied populations studied, rpoS+ bacteria eventually recover in frequency and constitute a signiﬁcant proportion of
the population after about four weeks (Maharjan et al., 2006; S. Seeto, unpublished results). Contributing to diversity, three types of adaptation were
found amongst rpoS+ bacteria (Classes 1, 2 and 3 in Table 2).
Class 3 bacteria have another characteristic phenotype indicative of regulatory change. When tested for sensitivity to AT, Class 3 isolates were
all highly sensitive. The sensitivity to AT is dependent on the level of his
gene expression, as AT inhibits histidine biosynthesis (Rudd et al., 1985).
In turn, his expression is dependent on ppGpp so AT sensitivity was used as
a possible screen for changes in ppGpp levels. The increased sensitivity in
Class 3 isolates may therefore indicate yet another change in global gene
regulation, if indeed the Class 3 isolates can be conﬁrmed to contain lowered
ppGpp levels.
The examples above are probably a small fraction of the regulatory
changes in chemostat populations, because only a minority of the
phenotypic changes were explained up to now. The metabolic and transport changes below cannot be explained by the effects of the so far identiﬁed
CHEMOSTAT ENVIRONMENT
213
regulatory mutations, so must be due to yet unrecognized changes.
Nevertheless, it is clear that no single regulatory solution provides a
winning combination for success in a glucose-limited chemostat.
10.2. Diversity in Transport Strategies
Changes in outer membrane and cytoplasmic transporters are common in
chemostat-adapted mutants. Diversity in populations arises because different combinations of outer and inner membrane changes can provide equivalent ﬁtness contributions (Maharjan et al., 2006). As discussed in Section
9.2, a single population can accommodate at least ﬁve different combinations of outer membrane changes, due to up-regulation of one or more
porins in individual isolates. The redundancy shown in Fig. 3, with three
parallel pathways for glucose permeation across the outer membrane, allows
any or all of these pathways to be increased in amount or structurally altered
to overcome glucose limitation. So it is perhaps not so surprising that
coexisting long-term chemostat isolates exhibited changes affecting one or
more of these components. The relative ﬁtness contribution of each outer
membrane change still needs investigation, but it would be surprising if the
ﬁtness changes were not cumulative in small increments.
The same level of redundancy in cytoplasmic membrane transport, with
the alternatives shown in Fig. 3, also permitted the selection of isolates with
increases in PtsG or Mgl transporters or both (Classes 3, 4 in Table 2;
Maharjan et al., 2007). We do not know the full physiological consequences
of improving one transport path as against another, but one impact is predictable. The PtsG system is energized by phosphoenol pyruvate, so an
increased PtsG function demands higher usage of this glycolytic product. By
contrast, the operation of the Mgl system needs more ATP, so evolution
of high Mgl activity requires an increased ATP supply. It would not be
surprising that the evolution of increased glycolytic ﬂux helped the former
whereas increased ATP production (possibly through respiration) the latter.
Although this is pure speculation, it is these sorts of secondary effects that
could drive the selection of further divergent metabolic adaptations
described in the next section.
10.3. Diversity in Metabolic and Bioenergetic Strategies
A remarkable feature of long-term chemostat populations is that bacteria
adopt ecological survival strategies covering the gamut of metabolic rate or
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THOMAS FERENCI
yield strategies. As summarized in Table 2, glucose conversion into biomass
is faster but less efﬁcient in some ﬁt strains whereas others rely on metabolic
efﬁciency for surviving in the competitive chemostat environment. Recent
bioenergetic and metabolomic results (Maharjan et al., 2007) are indicative
of more extensive and rapid metabolic variegation than previously recognized in experimental populations (Rosenzweig et al., 1994; Fong et al.,
2005). Yeast chemostat cultures were also changed in metabolic properties
within 250 generations of glucose-limited growth (Dunham et al., 2002) but
the scope of in-population divergence and the rapidity of accumulation
of such changes was unrecognized. In an ecological context, these results
obviate the postulated need for multiple niches or resources in bacterial
diversiﬁcation (Kassen and Rainey, 2004). Also, contrary to formal predictions (Pfeiffer, et al., 2001), derivatives of a single parental strain in a constant environment diverge to take both sides of what was proposed to be
a fundamental rate/yield trade-off affecting growth energetics. The high
plasticity of the yield/rate balance suggests that metabolic properties are
under frequent re-adjustment in bacterial evolution and not a fundamental
property of organisms (Ferenci, 2005). The rapid divergence in bacteria
under sub-optimal nutritional conditions needs to be recognized as a
potential source of biodiversity and intra-species heterogeneity (Feil, 2004).
The details of the mutations causing shifts in yields and rates are not yet
known. In line with the discussion of transport and regulation above,
it would not be surprising if the redundancy in metabolic capabilities shown
in Fig. 3 allowed beneﬁcial mutations to change pathways or respiratory
chains individually, resulting in diversity. It is known that mutational
changes to the parallel but energetically different respiratory chains of
E. coli can increase growth yields (Calhoun et al., 1993), and this is a possible explanation for events in glucose-limited chemostats. Metabolic differentiation could also explain the evolution of cross-feeding when high-ﬂux,
low-yield bacteria produce a new resource (acetate or other fermentation
product). Even low levels of these products could support a low number of
other organisms beginning to specialize on the alternate resource.
11. CONCLUSIONS
Chemostats are excellent experimental systems for controlling the environmental state of bacteria in a reproducible manner. The set environment is at
the same time a stimulus for changes in gene expression as well as a selection
condition for ﬁtter mutants. The culture conditions allow many variables to
CHEMOSTAT ENVIRONMENT
215
be studied, but it is important to understand the effects imposed by settings
such as dilution rate, culture density, limiting substrate and timing of samples. Despite the experimental constancy, it is impossible to keep biological
variation out of consideration when dealing with chemostat cultures as with
any other living system.
Perhaps the most exciting application of chemostats is the ability to follow
evolution almost in real time. Another beauty of following bacterial populations in chemostats is that it reveals so much about the physiological basis of
ﬁtness. Each identiﬁed mutation discussed above deﬁnes a property in the
ancestor bacteria that acts as a bottleneck under a particular, set environmental
condition. For this reason alone, there is no better experimental system for
studying the interaction of bacteria with an environmental situation. Many
of the changes observed were unexpected, which is one of the excitements
in experimental evolution studies. It is particularly the unforeseen results that
lead to a better understanding of natural phenomena, such as the limitation of
global regulatory circuits in reacting to the environment. The identiﬁcation
of the yet unknown mutations will no doubt lead to further surprises.
The most recent conclusion from studies of glucose-limited bacteria is that
there are many alternative ways of becoming ﬁt in even a relatively constant
selective environment. In evolutionary terms, these alternatives are indications of the multiple ﬁtness peaks on an evolutionary landscape (Wright,
1932, 1980). Indeed, there is good indication that mutations in chemostats
can reveal for the ﬁrst time what these multiple ﬁtness peaks look like in
physiological rather than conceptual terms.
With hindsight, the multiplicity of parallel cellular capabilities in bacteria
(as in Fig. 3) makes the alternative paths to ﬁtness inevitable. As discussed by
others, redundancy in capabilities leads to robustness in biological systems
(Kitano, 2004; Wagner, 2005a). Robustness in avoiding total damage from
deleterious mutations has been the main grounds for suggesting robustness as
of signiﬁcance in biology (Wagner, 2005b). Our results with chemostat populations suggest that redundancy in cellular functions also opens up a greater
repertoire for beneﬁcial change in bacterial populations (Maharjan et al., 2006).
It will be interesting in the future to see whether the results with the E. coli/
glucose system holds true for other organisms and other forms of limitation.
ACKNOWLEDGEMENTS
This review is dedicated to the two highly committed assistants, Lucinda
Notley-McRobb and Shona Seeto, who rapidly learned that chemostats do
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not stop over weekends and holidays and whose dedication contributed to
virtually all our chemostat results in the past 10 years. I also thank Beny
Spira and Katherine Phan for reading the manuscript.
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Plate 4 Heterogeneity in evolving chemostat populations, as detected with indicator plates. A glucose-limited E. coli culture in chemostats with a dilution rate
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Metallosensors, The Ups and Downs
of Gene Regulation
Amanda J. Bird
Division of Hematology, Department of Internal Medicine, University of Utah Health
Sciences Center, Salt Lake City, UT 84132, USA
ABSTRACT
In fungal cells, transcriptional regulatory mechanisms play a central
role in both the homeostatic regulation of the essential metals iron,
copper and zinc and in the detoxiﬁcation of heavy metal ions such as
cadmium. Fungi detect changes in metal ion levels using unique
metallo-regulatory factors whose activity is responsive to the cellular
metal ion status. New studies have revealed that these factors not only
regulate the expression of genes required for metal ion acquisition,
storage or detoxiﬁcation but also globally remodel metabolism to
conserve metal ions or protect against metal toxicity. This review
focuses on the mechanisms metallo-regulators use to up- and
down-regulate gene expression.
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Iron-Responsive Transcriptional Regulation: An Overview .
2.2. Iron-Responsive Gene Activation in S. cerevisiae . . . . . . .
2.3. Iron-Responsive Gene Repression in S. cerevisiae. . . . . .
ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53
ISBN 978-0-12-373713-7
DOI: 10.1016/S0065-2911(07)53004-3
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All rights reserved
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AMANDA J. BIRD
2.4. GATA Factors and Iron-Responsive Transcriptional Regulation .
2.5. Iron-Responsive Transcription in the Absence of Oxygen . . . . .
2.6. The Iron Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Copper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1. Copper-Responsive Gene Activation . . . . . . . . . . . . . . . . . . . .
3.2. The Copper Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zinc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1. Zinc-Responsive Transcriptional Regulation: An Overview. . . . .
4.2. Zap1-Dependent Activation of Gene Expression . . . . . . . . . . . .
4.3. Zap1-Dependent Repression of Gene Expression. . . . . . . . . . .
4.4. Zap1 and Zinc Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cadmium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1. Cadmium: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2. Cadmium-Responsive Activation of Gene Expression . . . . . . . .
5.3. The Cadmium Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. INTRODUCTION
A number of transition metal ions are essential for growth but are
potently toxic when in excess. For example, copper is an essential cofactor of
the cytochrome oxidase complex that allows cells to utilize a non-fermentable carbon source. However, excessive copper levels can catalyse the
generation of hydroxy radicals that cause damage to DNA, protein and
lipids. Consequently, it is important that essential metals ions such as
copper are maintained at a level that is sufﬁcient but not toxic to cell growth.
Other transition metals such as cadmium are toxic and have no known
cellular function. Cells therefore need systems to rapidly detoxify these
heavy metal ions.
In fungi, transcriptional regulation of gene expression plays a central
role in metal ion homeostasis and in the detoxiﬁcation of non-essential
metals. Metallo-regulatory factors are a class of transcription factors
whose activity is regulated by cellular metal ion status. By regulating the
expression of genes involved in metal ion uptake, compartmentalization,
reutilization, sequestration and/or conservation, these factors ensure
metal levels are maintained at an optimal level for cell growth. This
article reviews the most recent advances that have been made in understanding how fungi sense and globally change metabolism in response
to the essential metals iron, copper and zinc and the non-essential metal
cadmium at the transcriptional level.
THE UPS AND DOWNS OF GENE REGULATION
233
2. IRON
2.1. Iron-Responsive Transcriptional Regulation: An Overview
Iron is an essential redox active metal that serves as a catalytic cofactor for
a wide variety of enzymes. Although iron is an abundant element in the
Earth’s crust, it is generally found as insoluble ferric oxides. As a consequence, most microorganisms strive to obtain iron from the environment.
Conversely, the redox active nature of iron allows it to react with hydrogen
peroxide to form hydroxy-radicals, a free radical species that can damage
many cellular components. In fungi, transcriptional regulatory mechanisms
play a central role in maintaining the balance between iron deﬁciency and
iron toxicity (Rutherford and Bird, 2004). In the yeast Saccharomyces
cerevisiae, two paralogous iron-responsive transcriptional activators, Aft1
and Aft2, increase target gene expression during iron deﬁciency. In other
fungal systems, an iron-responsive subset of the GATA family of transcription factors mediates gene repression during iron sufﬁciency (Rutherford
and Bird, 2004). Recent studies have shown that these factors not only
regulate the expression of genes involved in iron acquisition but also have
roles in the global remodelling of metabolic pathways to reduce the cellular
requirement for iron during periods of iron starvation. Here, I have focused
on some of the newer roles these factors play in both the up-regulation and
down-regulation of various cellular processes.
2.2. Iron-Responsive Gene Activation in S. cerevisiae
In S. cerevisiae, Aft1 regulates the expression of genes that are involved in
iron acquisition, release of iron from intracellular stores and iron conservation. The most widely studied genes are those that are involved in iron
acquisition or mobilization (Fig. 1). These include genes that are involved in
iron uptake (FTR1, FET3, FET4) reduction of ferric iron (FRE1-2), delivery
of copper to the Fet3 ferrioxidase (ATX1, CCC2), iron-siderophore uptake
(ARN1-4 and FIT1-3), release of iron from stores in the vacuole (FET5,
FTH1, SMF3) and degradation of haem (HMX1) (Yamaguchi-Iwai et al.,
1996; Lin et al., 1997; Yun et al., 2000; Protchenko et al., 2001; Rutherford
et al., 2001; Jensen and Culotta, 2002; Portnoy et al., 2002; Protchenko and
Philpott, 2003). A number of recent studies have shown that an alternative
way of surviving iron starvation is to decrease the cells, reliance on
iron. Transcriptional regulation of gene expression plays a major role in this
234
AMANDA J. BIRD
Figure 1 Schematic representation of proteins that are involved in the iron acquisition or iron sparing response during iron starvation. Shown are the protein
products of genes that are transcriptionally up-regulated during iron deﬁciency in S.
cerevisiae. The topology and total number of transmembrane domains of membrane
proteins was based on the following studies, Arn1 (Kim et al., 2005), Ftr1 (Severance
et al., 2004), Fet3 (De Silva et al., 1995), Smf3 (Courville et al., 2004), Fth1/Fet5
(Urbanowski and Piper, 1999), Vht1 (Stolz et al., 1999), Mrs4 (Pebay-Peyroula et al.,
2003) and Ccc2 (Payne and Gitlin, 1998). A dotted line has been used to separate
proteins that are involved in iron acquisition or iron sparing/conservation. Circling
arrows indicate proteins whose cellular localization is regulated by iron (Aft1)
(Yamaguchi-Iwai et al., 2002) or iron-bound siderophores (Arn1) (Kim et al., 2002)
while grey arrows indicate the direction of transport of copper (Cu), iron (Fe) or
biotin. Iron in black circles represents siderophore-bound iron. Cth2-dependent posttranscriptional degradation of mRNA (wavy lines) has been shown for a limited
number of genes and cellular processes (respiration/haem synthesis). See Puig et al.
(2005) and text for further mRNA targets of Cth2.
THE UPS AND DOWNS OF GENE REGULATION
235
iron-sparing response (Fig. 1). Aft1 mediates iron conservation through the
activation of CTH2 expression and the subsequent Cth2-dependent mRNA
decay (See Section 2.2). Aft1 also ensures that alternative iron-independent
biosynthetic pathways are induced during iron starvation. For example,
biotin synthesis requires three enzymes, Bio2, Bio3 and Bio4 to synthesize
biotin from the precursor 7-keto, 8-amino-pelargonic acid. Bio2 (biotin
synthase) contains a 4Fe–4S cluster and catalyses the rate limiting step in
biotin synthesis. During iron deprivation, cells rely on Aft1-dependent induction of VHT1 biotin transporter, since BIO2, BIO3 and BIO4 transcripts
are less abundant under these conditions (Shakoury-Elizeh et al., 2004). The
induction of the Vht1 iron-independent pathway by Aft1 therefore ensures
cells obtain sufﬁcient amounts of the essential nutrient biotin while sparing
iron that would be incorporated into Bio2.
While the functional role of Aft1 in iron acquisition has been well documented, the function of its paralogue Aft2 it still unclear. The high conservation of the Aft1 and Aft2 DNA-binding domains allows both factors to
bind to a core CACCC promoter element (FeRE). However, differences in
the ﬂanking nucleotides allow the preferential binding of Aft1 or Aft2 at
some promoters. Aft1 preferentially binds to FeREs that are primarily
found in the promoters of genes involved in iron acquisition (e.g. FET3,
ARN1) (Rutherford et al., 2003; Courel et al., 2005). FeREs that Aft2 speciﬁcally or preferentially binds to are generally located in the promoter of
genes involved in organelle iron transport or use (e.g. SMF3, ISU1, MRS4)
(Rutherford et al., 2003; Courel et al., 2005). Thus, Aft1 and Aft2 respond
to the same cellular signal (low iron), have a signiﬁcant overlap in target
gene expression but have diverged far enough that they each regulate a
distinct set of genes involved in iron homeostasis.
So why have cells maintained Aft2? Despite Aft2 speciﬁcally regulating a
number of genes involved in iron homeostasis, a strain that lacks Aft2
displays no mutant phenotype under iron-limiting conditions (Blaiseau
et al., 2001; Rutherford et al., 2001). A clue to Aft2 function might be
obtained by understanding why speciﬁc genes are primarily Aft2 targets.
For example, Aft2 increases the expression of genes that are required for
Fe–S cluster formation/mitochondrial Fe transport during iron deﬁciency.
Fe–S cluster synthesis is a vital cellular process (Kispal et al., 2005) and yet,
during iron starvation, a number of mRNA transcripts (NFS1 ISA1 and
YAH1) that encode proteins involved in Fe–S synthesis are down-regulated
in a Cth2-dependent manner (see Section 2.3; Puig et al., 2005). Similar
examples of differential regulation of Fe–S synthesis/transport genes are
observed in other yeast. For example, the levels of ISU1 and ATM1 transcripts increase while NFS1, ISA1 and YAH1 transcripts decrease during
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iron deﬁciency in Candida albicans (Lan et al., 2004b). Isu1 could be a key
determinant in the maintenance of Fe–S cluster synthesis when iron is limiting. One potential hypothesis is that Aft2-dependent regulation channels
iron into essential Fe–S cluster synthesis allowing cell survival during conditions of severe iron deﬁciency. Consistent with this model, an aft1/aft2
mutant strain is more sensitive to the lack of iron than an aft1 mutant
(Blaiseau et al., 2001; Rutherford et al., 2001). A second phenotype of the
aft1/aft2 mutant strain is hypersensitivity to hydroxyurea (HU) (Dubacq
et al., 2006). HU is a speciﬁc inhibitor of the iron-binding protein ribonucleotide reductase that catalyses the rate limiting step in dNTP synthesis.
Aft2-dependent mobilization of iron stores may therefore be of importance
under conditions of high iron demand, such as when iron-binding proteins
like ribonucleotide reductase are being rapidly synthesized. Finally, it
remains possible that Aft2 is induced or functions under a yet to be identiﬁed condition. A genome wide screen has identiﬁed aft2 to be synthetically
lethal with hsp82 (Zhao et al., 2005). Hsp82 is a heat shock protein (one
isoform of the S. cerevisiae Hsp90) that is required for the folding of a subset
of ‘difﬁcult-to-fold’ proteins, such as the transcription factors Hap1 and
Gcn2 (Burnie et al., 2006). Although it remains to be tested whether this
result was a false positive, this result would suggest that Aft2 functions in
a pathway that is distinct from Aft1. Further studies will have to decide
whether Aft2 is a major player in iron homeostasis or whether it is simply
the weak counterpart of Aft1.
2.3. Iron-Responsive Gene Repression in S. cerevisiae
An alternative way of conserving iron is to alter metabolism such that nonessential iron binding proteins are no longer synthesized. One way in which
this is globally achieved in S. cerevisiae is through the Aft1- and Aft2dependent induction of the CTH2 gene (Puig et al., 2005). CTH2 encodes an
mRNA-binding protein that shares homology with the mammalian tandem
zinc ﬁnger protein tristetraprolin (TTP). In mammalian cells, TTP binds to
AU-rich elements (AREs) within the 30 untranslated regions (30 -UTRs) of
mRNAs, promoting deadenylation and increased rates of mRNA turnover
(Lai et al., 2003). Similarly, in S. cerevisiae Cth2 decreases mRNA transcript
stability by binding to AREs that are located in the 30 -UTRs of a large
number of transcripts. Many of the transcripts encode proteins that bind
iron or are found in iron-dependent processes (Puig et al., 2005). For example, mRNAs involved in haem and Fe–S cluster synthesis, mitochondrial
respiration, sterol metabolism and fatty acid metabolism are targeted for
THE UPS AND DOWNS OF GENE REGULATION
237
degradation in a Cth2-dependent manner. Thus, Cth2-mediated degradation
plays a central role in the iron-sparing response. However, the transcriptional regulation of CTH2 expression by Aft1 is the key determinant that
ensures that this mechanism only occurs in low iron.
Apart from the extensive Cth2-mediated post-transcriptional control,
other complementary mechanisms exist at the transcriptional level. For
example, the GLT1 gene is transcriptionally down-regulated in response to
iron starvation (Shakoury-Elizeh et al., 2004). GLT1 encodes the Fe–S
containing enzyme glutamate synthase that catalyses the formation of
glutamate from glutamine and a-ketoglutarate. Repression of GLT1 in
response to iron starvation maps to a CGG palindrome promoter sequence
indicative of a binding site for a bi-nuclear Zn-cluster family member.
Currently, it is unknown which member of the bi-nuclear Zn-cluster family
binds to this site.
The identiﬁcation of these regulatory pathways reveals how multiple
mechanisms function in concert. For example, the remodelling of biotin
metabolism in response to iron deﬁciency requires both the Aft1-dependent
induction of VHT1 and CTH2 gene (Shakoury-Elizeh et al., 2004; Puig
et al., 2005). The increased expression of CTH2 results in the increased
Cth2-mediated degradation of BIO2 and BIO3 mRNA. This concurrent
regulation by Aft1 therefore precisely coordinates the activation of biotin
uptake pathway while down-regulating biotin synthesis in response to the
same low iron environmental signal.
2.4. GATA Factors and Iron-Responsive Transcriptional
Regulation
The majority of fungi lack Aft1 and Aft2 homologues and instead regulate
transcription in response to iron using a GATA-type transcription factor.
GATA factors are a large group of regulatory proteins that use zinc ﬁnger
domains to bind to a promoter element containing the core sequence
50 -GATA-30 . The iron-responsive GATA factors include Fep1 from Schizosaccharomyces pombe, Sfu1 from C. albicans, SRE from Neurospora crassa,
SREA from Aspergillus nidulans, SreP from Penicillium chrysogenum and
Urbs1 from Ustilago maydis (Voisard et al., 1993; Haas et al., 1997, 1999;
Zhou et al., 1998; Pelletier et al., 2002; Lan et al., 2004b). All of these factors
function by repressing gene expression under iron-replete conditions. Analogous to Aft1 from S. cerevisiae, these factors regulate genes required
for iron homeostasis including high-afﬁnity iron uptake and siderophore
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uptake (Haas et al., 1999; Oberegger et al., 2002; Pelletier et al., 2002, 2003;
Lan et al., 2004b).
Iron-responsive GATA factors regulate the expression of additional genes
required for specialized functions. It is well known that genes involved in
iron acquisition are essential for virulence of pathogenic fungi (Ramanan
and Wang, 2000; Knight et al., 2005). Genomic proﬁling in C. albicans has
revealed that a broad range of genes required for pathogenicity are regulated
either directly or indirectly in response to changing iron levels. These include
genes encoding secreted hydrolytic enzymes, cell wall/surface proteins and
genes required for blastospore to hyphal transitions (Lan et al., 2004b).
With a few exceptions (e.g. S. cerevisiae), the majority of fungi possess genes
required for siderophore biosynthesis. Genes required for siderophore
synthesis are frequently up-regulated in response to iron depletion allowing
fungi to efﬁciently compete with other microorganisms for limited iron
(reviewed by Philpott, 2006).
In S. cerevisiae, four proteins Hap2, Hap3, Hap4 and Hap5 form the
CCAAT-binding factor that activates the transcription of a range of genes
required for respiration in response to glucose depletion. Hap2, Hap3 and
Hap5 form a heterotrimeric DNA-binding domain while the Hap4 subunit
contains the transactivation domain function. The levels of Hap4 limit the
activity of the CCAAT-binding factor (Kwast et al., 1998). In the presence
of glucose, HAP4 is repressed. As a consequence, the CCAAT-binding
factor is only active in the absence of glucose. In S. pombe, the Php2, Php3
and Php5 homologues of Hap2, Hap3 and Hap5 play an important role in
mediating gene repression in response to iron deprivation (Mercier et al.,
2006). In iron-replete media, the Php2, Php3 and Php5 complex activates
the expression of the genes pcl1+, sdh4+ and isa1+ (Mercier et al., 2006).
Pcl1, Sdh4 and Isa1 are all iron-binding proteins. During iron starvation
recruitment of a fourth protein, Php4 to the Php2/3/5 complex leads to the
down-regulation of pcl1+, sdh4+ and isa1+ gene expression. Php4 shares
little homology with Hap4, with the exception of a small motif that is
required for Hap4 to associated with the Hap2/3/5 complex. The key to the
iron-responsiveness of these genes lies in the regulation of php4+ expression.
php4+ is a target of iron-responsive GATA factor Fep1 (Mercier et al.,
2006). Consequently, php4+ is only expressed during iron starvation. The
differences between the regulatory mechanism used by S. cerevisiae and
S. pombe to conserve iron have been summarized in Fig. 2.
It is likely that other fungi use a similar mechanism to mediate ironresponsive gene repression. Microarray analysis of C. albicans identiﬁed
over 1100 mRNA transcripts whose abundance changed in response to iron
status (Lan et al., 2004b). Comparable to S. cerevisiae, mRNA transcripts
THE UPS AND DOWNS OF GENE REGULATION
239
Figure 2 Regulatory mechanisms used by S. pombe and S. cerevisiae to regulate
gene expression/transcript abundance in response to iron. Mechanisms involved in
the up-regulation of gene expression during iron deﬁciency (1) or the down-regulation of gene expression/transcript abundance (2). See text for further details.
encoding proteins involved in many iron-dependent processes such as Fe–S
cluster synthesis, haem synthesis and mitochondrial respiration were
less abundant during iron starvation. Notably, mRNA transcripts of the
C. albicans PHP4 homologue (orf 19.8298/19.861) but not the CTH2
homologue were more abundant during iron starvation in an Sfu1-dependent manner. Genomic proﬁling also suggests Sfu1 acts as a transcriptional
activator at some promoters, which could be consistent with Sfu1 regulating
a repressor. It therefore seems likely that both C. albicans and S. pombe
use a transcriptional regulatory network to repress gene expression in
response to iron depletion. These iron-responsive regulatory networks
emphasize mechanisms that up- and down-regulate gene expression are
important in metal homeostasis.
2.5. Iron-Responsive Transcription in the Absence of Oxygen
Many iron-dependent pathways require oxygen and will not function in its
absence. For example, haem synthesis requires oxygen. As a consequence,
high-afﬁnity iron uptake that requires a haem-containing reductase cannot
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occur under anaerobic conditions. In the absence of oxygen, cells therefore
retain the need for essential iron-dependent processes such as Fe–S cluster
synthesis but do not need oxygen-requiring iron metabolic pathways such as
high-afﬁnity iron uptake and haem synthesis. Here, I have focused primarily
on the transcriptional regulatory mechanisms used in S. cerevisiae to alter
metabolism under these conditions. A more detailed picture of the cellular
remodelling of iron metabolism and other metabolic pathways in response
to cellular oxygen tension can be found in a number of excellent reviews
(Kwast et al., 1998; Kaplan et al., 2006).
In the absence of haem/oxygen, cells up-regulate the expression of genes
required for iron-siderophore and low-afﬁnity iron uptake and downregulate the expression of genes required for high-afﬁnity iron uptake. These
changes in gene expression are mediated by a number of factors. Cells sense
oxygen tension through the haem-binding regulator Hap1 (reviewed by
Kwast et al., 1998; Mense and Zhang, 2006). In the presence of haem, Hap1
induces the expression of a wide range of genes involved in mitochondrial
respiration and the oxidative stress response (Ter Linde and Steensma,
2002). One of the most important targets of Hap1 is the repressor of hypoxic
genes Rox1 (Lowry and Zitomer, 1984). Rox1 represses genes required
for growth under anaerobic conditions and genes required for oxygendependent functions. When oxygen levels are low, the derepression of these
latter genes becomes important for using the limited oxygen more efﬁciently.
Four Rox1 target genes that are important for iron homeostasis are FET4,
SMF3 and potentially FIT2 and FIT3 (Jensen and Culotta, 2002; Ter Linde
and Steensma, 2002). Fet4 is a low-afﬁnity iron, zinc and copper transporter, Smf3 helps mobilize iron from the vacuolar store, while Fit2 and Fit3
are glycosylphosphatidylinositol anchored cell wall proteins that facilitate
iron-siderophore uptake (Portnoy et al., 2000; Protchenko et al., 2001;
Jensen and Culotta, 2002; Waters and Eide, 2002). The derepression of these
genes during low oxygen or anaerobic conditions provides an entry route for
iron in the absence of the high-afﬁnity iron uptake system. ISU2, which
encodes a scaffold protein for Fe–S cluster formation (Lill and Muhlenhoff,
2006), is also a putative target gene of Rox1 (Ter Linde and Steensma,
2002). Analogous to the up-regulation of ISU1 in response to low iron, the
increased expression of ISU2 in the absence of oxygen could be a mechanism
to drive Fe–S cluster synthesis during iron-limitation (see Section 2.2).
In the absence of oxygen or haem, cells need only to induce the expression
of a subset of the iron regulon (Crisp et al., 2003; Kaplan et al., 2006). For
example, the Aft1 target genes involved in high-afﬁnity iron uptake (e.g.
FTR1, FET3) are not required while genes involved in siderophore mediated
iron uptake (e.g. ARN1-4) are retained. A recent study in S. cerevisiae has
THE UPS AND DOWNS OF GENE REGULATION
241
shown how several regulatory factors allow the differential expression of
Aft1 target genes under conditions of haem deﬁciency (Crisp et al., 2006;
Fig. 3). Analysis of two Aft1 target promoters, FET3 and ARN1 revealed
Aft1, Tup1 and Cti6 are central to this process. Tup1 is a general repressor
of transcription that acts in a complex with Cyc8 (previously Ssn6) (Keleher
et al., 1992). Cti6 is a PHD ﬁnger-containing protein that binds to the Tup1/
Cyc8 complex and relieves repression by recruiting the SAGA complex
(Papamichos-Chronakis et al., 2002). When cells are iron starved, Aft1 binds
to both the FET3 and ARN1 promoters where it recruits Tup1 and Cti6
(Crisp et al., 2006). Both FET3 and ARN1 are induced under these conditions. Aft1-dependent activation of FRE2 also requires the recruitment of
the Tup1/Cyc8 complex suggesting that Tup1 and Cyc8 might have a global
role in Aft1-target gene activation (Fragiadakis et al., 2004). During haem
deﬁciency, Cti6 is lost from the FET3 promoter but is retained at ARN1
(Crisp et al., 2006). Under these conditions, the Tup1/Cyc8 complex acts as
a repressor at the FET3 promoter. It is not known how the Tup1/Cyc8
complex functions as a co-activator in iron-limiting/haem-rich media but as
a repressor in the absence of iron and haem. Importantly, it is the retention
of Cti6 at the ARN1 promoter that is both necessary and essential for the
activation of ARN1 during haem deﬁciency. Retention of Cti6 at the ARN1
promoter requires a 14-bp promoter sequence that is absent from the FET3
promoter. In addition to a role in the absence of haem and iron, Cti6 is
required for growth under iron-limiting conditions and a number of Aft1
Figure 3 Differential regulation of gene expression in response to haem deﬁciency. Shown are regulatory factors recruited to speciﬁc promoters under the listed
conditions. For further details and precise stoichiometries of factors that interact
with Hap1, see Lan et al. (2004a) and Mense and Zhang (2006). It is currently
unknown if Cti6 and Tup1 play a role in the regulation of FET4 expression.
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AMANDA J. BIRD
target genes are up-regulated in cti6 null strains (Puig et al., 2004). Thus,
there is still much to learn about the relationship between Aft1 and Cti6 at
various promoters. So far, most studies have focused on the role of a single
metallo-regulator in the activation or repression of a target gene in response
to one metal. These studies elegantly demonstrate how combinations of
regulatory factors can allow differential gene expression in response to
multiple environmental signals.
2.6. The Iron Sensors
An important part of metal homeostasis is the ability of metallo-regulators
to be able to ‘sense’ the intracellular levels of a speciﬁc metal ion. Studies of
iron responsive factors have revealed very different mechanisms in the way
Aft1- and GATA-like proteins sense iron. The regulation of Aft1 by iron is
based on its cellular localization. When iron is limiting Aft1 accumulates in
the nucleus, while in iron-rich media Aft1 is preferentially found in the
cytoplasm. Mutation of the nuclear export signal (NES) results in constitutive Aft1 activity conﬁrming the importance of nuclear accumulation of
Aft1 for maximal target gene expression (Yamaguchi-Iwai et al., 2002). The
signal controlling nucleocytoplasmic shuttling is not iron directly but a signal that stems from the mitochondrial Fe–S machinery. In cells that are
defective in mitochondrial Fe–S cluster biogenesis, Aft1 activates target gene
expression even in the presence of high cytosolic iron levels (Chen et al.,
2004). These data suggest that the binding of a Fe–S cluster or a Fe–S cluster
containing protein could inactivate Aft1. Notably, a conserved Cys–X–Cys
motif that potentially could form a metal-binding domain is located next to
the DNA-binding domain and is essential for Aft1 iron sensing (YamaguchiIwai et al., 1995). Nuclear and cytosolic Fe–S cluster synthesis requires the
export of an unknown Fe–S precursor from the mitochondria through the
Atm1 transporter. Three additional cytosolic proteins Nar1, Cfd1 and
Nbp35 are then required for the assembly of mature cytosolic Fe–S cluster
proteins (Rouault and Tong, 2005; Lill and Muhlenhoff, 2006). Although
Aft1 activity is constitutive in the absence of Atm1, it is regulated in cells
lacking Nar1, Cfd1 and Nbp35 (Rutherford et al., 2005). Therefore, Aft1 is
not directly sensing the maturation of a cytoplasmic/nuclear Fe–S cluster
protein but instead senses the unknown compound that is exported from the
mitochondria via Atm1. It is currently unknown if Aft1 directly senses this
intermediate or whether an intermediary protein senses the mitochondrial
Fe–S signal and in turn regulates Aft1 activity.
THE UPS AND DOWNS OF GENE REGULATION
243
A number of proteins have been identiﬁed that directly interact with
Aft1. Grx3 and Grx4 are nuclear monothiol glutaredoxins. The only
other monothiol glutaredoxin in S. cerevisiae is Grx5 (Wheeler and Grant,
2004). Grx5 is found in mitochondria and is required for the activity of
enzymes containing Fe–S clusters (Rodriguez-Manzaneque et al., 2002).
It is thought that Grx5 acts as a thiol reductase by potentially reducing
mixed disulphides formed between proteins and glutathione (Tamarit et al.,
2003). The cellular role of Grx3 and Grx4 is unknown. Cells lacking both
Grx3 and Grx4 have constitutive Aft1 activity while over-expression of
GRX4 leads to the reduced expression of Aft1 target genes (Ojeda et al.,
2006). The conserved Cys–X–Cys motif required for Aft1 iron sensing is
necessary for the interaction with both Grx3 and Grx4. While the activity
of Aft1 is clearly affected by Grx3 and Grx4, it remains unclear what
precise role they play in iron sensing. The interaction of Grx3 and Grx4
with Aft1 was shown by two-hybrid analysis to be independent of iron
levels. Under normal growth conditions, Grx3 and Grx4 are nuclear
localized while Aft1 cycles between the nucleus and cytosol. Grx3 and Grx4
are therefore only in the same compartment as Aft1 during iron deﬁciency.
A simple model where glutathione is the iron-signalling molecule and the
Grx3/Grx4-dependent removal of a glutathione adduct from Aft1 leads to
its inactivation seems unlikely. A glutathione-deﬁcient gsh1 strain has constitutive Aft1 activity and so far there is no evidence that Aft1 forms a
mixed disulphide with glutathione (Rutherford et al., 2005; Ojeda et al.,
2006). Further studies will therefore be needed to understand why
Aft1-dependent activation of gene expression requires two monothiol
glutaredoxins.
Finally, in addition to iron-dependent regulation Aft1 regulates the
expression of its own gene and a number of signalling pathways alter Aft1
activity in response to various environmental signals (Lee et al., 2002;
Haurie et al., 2003). It is unknown if Aft2 is regulated in a similar manner to
Aft1.
The majority of studies to date have examined the role of Aft1 during iron
starvation where it is maximally active. Although Aft1 is preferentially
found in the cytosol under iron-replete conditions a small amount of Aft1
still resides in the nucleus. Could Aft1 have a functional role in the nucleus
under iron-replete conditions? A recent study has demonstrated that an aft1
mutant strain has increased rates of chromosomal loss and non-disjunction
events suggesting it may be required for the proper segregation of chromosomes (Measday et al., 2005). Chromosome spread analysis localized Aft1
to discrete foci, a number of which overlap with the centromeric protein Ndc10. Aft1 also interacts with the centromere binding factor Cbf1 in
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AMANDA J. BIRD
two-hybrid analysis and an aft1 mutant strain displays a synthetic growth
defect with a cbf1 mutant (Measday et al., 2005). It is currently unknown
whether any of these growth or chromosomal defects can be rescued by high
iron or exacerbated by growth under severely iron-limiting conditions.
Whether these affects are a result of the induction of an unknown Aft1
target gene that is required for maintaining chromosomal stability or
whether Aft1 has a more direct role at the centromere, they provide an
exciting new cellular role for Aft1.
Iron-responsive GATA family members bind to GATA elements
located in the promoter regions of target genes and repress gene expression in response to iron. In S. pombe, repression is mediated by the
Fep1-dependent recruitment of the Tup11/Tup12 repression complex
(Znaidi et al., 2004). Tup11 is a homologue of Tup1 from S. cerevisiae.
Similarly, in C. albicans, Tup1 is required for iron-dependent repression of
Sfu1 target genes (Knight et al., 2002). A number of studies have begun to
address how these factors sense iron. Studies with Fep1 have shown that it
is stable and localized to the nucleus in the presence or absence of iron
suggesting that iron regulates DNA-binding activity and/or the interaction
with Tup11/12 (Pelletier et al., 2005). The iron-responsive GATA family
members contain an atypical DNA-binding domain that contains two
zinc ﬁnger domains that surround a region containing four conserved
cysteine residues. The four conserved cysteine residues are necessary for
DNA-binding activity in all family members while the requirement of one
or both of the zinc ﬁnger domains in this process is dependent on the family
member (Rutherford and Bird, 2004). In Fep1, mutagenesis of the conserved cysteine residues leads to the loss of iron-responsive gene repression
in vivo (Pelletier et al., 2005). However, mutagenesis of the equivalent
cysteines in the GATA factor Sre leads the constitutive repression of siderophore synthesis (a process regulated by Sre) (Harrison and Marzluf,
2002). It is currently unknown if this difference is a result of additional
Sre-independent iron-responsive regulation in N. crassa or if it results from
an intrinsic disparity in the regulation of Sre and Fep1 by iron. An obvious
model is that iron directly binds to the DNA-binding domain allowing
these factors to bind to DNA and/or interact with the Tup1 repressor
complex. In vitro studies suggest Sre binds iron while recombinant Fep1 can
only be puriﬁed from cells in the presence of iron (Harrison and Marzluf,
2002; Pelletier et al., 2002). Thus, the iron-responsive GATA family members may directly sense iron while Aft1 senses iron deﬁciency indirectly
through Fe–S cluster biosynthesis, a process that is affected by cellular iron
content.
THE UPS AND DOWNS OF GENE REGULATION
245
3. COPPER
3.1. Copper-Responsive Gene Activation
Copper is an essential nutrient that is found in a variety of enzymes including Cu,Zn superoxide dismutase, cytochrome c oxidase and multicopper
oxidases. When in excess, copper can catalyse the generation of reactive
hydroxy radicals that damage DNA, proteins and lipids. As a consequence,
copper homeostasis is stringently maintained by multiple complementary
mechanisms. Copper-responsive transcriptional regulation is of particular
importance in fungi where a variety of factors control genes required for
copper acquisition and detoxiﬁcation.
In S. cerevisiae, copper levels are controlled through the actions of Mac1
and Ace1. Mac1 is active during copper starvation and induces the expression of genes required for high-afﬁnity copper uptake (CTR1, CTR3) and
reduction of Cu2+ to Cu+ (FRE1, FRE7) (Gross et al., 2000). Ace1 protects
cells from copper toxicity by inducing the expression of copper-chelating
metallothioneins (CUP1, CRS5) and the antioxidant superoxide dismutase
(SOD1) (Gralla et al., 1991; Gross et al., 2000). In addition to these classical
target genes, extensive genomic proﬁling has identiﬁed new Mac1 copperregulated target genes (e.g. YFR055w, CRR1) (De Freitas et al., 2004; van
Bakel et al., 2005). However, the roles of these newly identiﬁed Mac1 target
genes in copper homeostasis are currently unknown. Both microarray analysis and individual studies have reiterated the tight relationship between
copper and iron homeostasis. For example, both high-afﬁnity iron and
copper uptake rely on a haem-binding oxidoreductase of the FRE family.
Akin to the regulation of high-afﬁnity iron uptake by Aft1, Mac1-dependent
activation of CTR1 expression is repressed in a Tup1-dependent manner in
the absence of haem (Crisp et al., 2006). Thus, copper-responsive regulators
play a primary role in copper acquisition and copper detoxiﬁcation.
In other fungi, copper-responsive regulators regulate a number of additional genes. In Podospora anserina, the Mac1 orthologue GRISEA remodels metabolic pathways to spare copper. In response to copper limitation,
P. anserina uses an iron-dependent respiration pathway with an alternative
terminal oxidase (Borghouts et al., 2001). Coincident with this change in
respiratory pathways, the Mac1 ortholog GRISEA induces the expression of
a manganese-requiring superoxide dismutase gene (SOD2) (Borghouts et al.,
2001). Thus, copper is not needed for cytochrome c oxidase and Cu,Zn
superoxide dismutase production and cells are protected from reactive
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AMANDA J. BIRD
oxygen species by SOD2. In S. pombe, Cuf1 is functionally comparable to
Mac1 but contains a DNA-binding domain that is homologous to Ace1
(Beaudoin and Labbe, 2001; Beaudoin et al., 2003). Cuf1 activates genes
required for copper uptake and release from the vacuolar stores while
represses the expression of genes required for high-afﬁnity iron uptake
(Labbe et al., 1999; Bellemare et al., 2002). This dual regulation by Cuf1
ensures genes that are ineffective in the absence of copper are not expressed.
The ability of fungi to undergo dimorphic transitions to ﬁlamentous
forms is essential for virulence (Lengeler et al., 2000). A number of recent
studies have implicated copper-responsive transcription factors to be important in this transition. For example, ectopic expression of the C. albicans
Mac1 in S. cerevisiae promotes ﬁlamentous growth in diploids and invasive
growth in haploids (Huang et al., 2006). In the opportunistic pathogen
Cryptococcus neoformans, the blue copper oxidase laccase is essential for
virulence. Copper-responsive transcriptional regulation is important for
laccase production at a number of levels. In an oxy2 mutant, laccase production is decreased (Nyhus and Jacobson, 2004). oxy2 is thought to be
allelic to the C. neoformans MAC1. Regulation of genes required for copper
uptake therefore plays an important role in copper delivery to laccase. In
the presence of copper, laccase gene expression (CNLAC1) is induced (Zhu
et al., 2003). Although not directly tested, this response is likely mediated by
the C. neoformans Ace1 homologue. Thus, copper regulators are important
under both copper-limiting and copper-replete conditions. Phenotypic
switching is a mechanism by which spontaneous variants are generated
within infecting population of microorganisms. Differences in phenotypes of
the variants provide a mechanism for the rapid adaptation to environment
changes. In Candida glabrata, phenotypic switching is associated with
increased expressions of Mac1, Amt1 (C. glabrata Ace1 homologue) and a
number of their target genes (Srikantha et al., 2005). This switch is independent of copper levels and suggests that these factors and their target
genes may have important unknown roles in colonization and virulence.
3.2. The Copper Sensors
The copper sensing domains of Mac1 and Ace1 have been well characterized
(reviewed by Rutherford and Bird, 2004). Mac1 contains two
Cys–X–Cys–X4–Cys–X–Cys–X2–Cys–X2–His domains designated C1 and
C2. Each of these domains can bind four Cu+ ions in a polycopper cluster
and is found within a transactivation domain. Inactivation of Mac1 is mediated by a copper-dependent intramolecular interaction between the Mac1
THE UPS AND DOWNS OF GENE REGULATION
247
DNA-binding domain and the C2 transactivation domain. The copper-detoxifying factor Ace1 is constitutively expressed and resides in the nucleus in
an inactive form. When exposed to copper, Ace1 binds Cu+ cooperatively
to form a polycopper cluster. This in turn leads to a conformational switch
that converts Ace1 into a DNA-binding active form. The metallo-regulation
of Mac1 and Ace1 occurs independently suggesting that no common pathway signals the changes in copper status to both factors (Keller et al., 2005).
These two copper-sensing domains are highly conserved in other fungal
copper sensors. However, different fungal species have evolved various ways
in which they use these same domains to control activity. For example,
inactivation of Cuf1 by copper is mediated by a copper-dependent interaction between the DNA-binding domain and N-terminal Cys-rich region
(C1) (Beaudoin and Labbe, 2006). This interaction in Cuf1 masks the nuclear localization signal (NLS), sequestering Cuf1 in the cytosol in the
presence of copper (Beaudoin and Labbe, 2006) in contrast to the constitutive nuclear localization of Mac1 (Jensen and Winge, 1998). In Yarrowia
lipolytica, Crf1 (an Ace1 ortholog) is localized to the nucleus in the presence
of copper while having a cytosolic residence during copper limitation
(Garcia et al., 2002). It is therefore possible that the formation of the
N-terminal polycopper cluster in Crf1 may expose a NLS or mask an NES,
which leads to copper-dependent nuclear retention. It is currently unknown
if there is any advantage or reasons for the differences in the regulation of
copper sensing domains in different fungi. Studies in S. cerevisiae have
demonstrated that a speciﬁc subset of copper-binding proteins, copper
chaperones, bind and deliver copper to intracellular compartments or copper-requiring proteins (Field et al., 2002). No copper chaperone has been
identiﬁed for Mac1. Cellular signalling pathways may also be important for
regulation since Mac1 needs to be phosphorylated before it can bind to
DNA (Heredia et al., 2001). The differences in cellular localization may
therefore arise from differences in copper sensing or regulation by signalling
pathways in different species.
4. ZINC
4.1. Zinc-Responsive Transcriptional Regulation: An
Overview
Zinc is an essential nutrient that is found in a wide range of enzymes and in
many regulatory proteins. Unlike copper and iron, zinc is not redox active.
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However, when in excess, zinc is toxic to cell growth. Although the precise
reason for this toxicity is unknown, it may arise from zinc binding to
improper sites in some proteins. Cells therefore need to employ a variety of
homeostatic mechanisms to maintain an optimal level of zinc. The only
characterized fungal factor that regulates gene expression in response to zinc
is Zap1 from S. cerevisiae (Zhao and Eide, 1997). This review has therefore
focused on the importance of zinc-responsive transcriptional control for cell
survival from studies with Zap1. Zap1 homologues are not present in every
fungal genome. For example, S. pombe contains no known zinc-responsive
transcription factor and yet the transcript abundance of the ZYM1 metallothionein increases under zinc-replete conditions (Borrelly et al., 2002).
Although it is unknown whether this response is transcriptional or posttranscriptional, it suggests that very distinct factors, such as the ironresponsive regulators Aft1 and Fep1, may control gene expression in
response to zinc in different fungi.
4.2. Zap1-Dependent Activation of Gene Expression
Zap1 activates gene expression in response to zinc starvation by binding in
a site-speciﬁc manner to 11 bp zinc responsive elements (ZREs) that are
located in the promoter regions of all known target genes (Zhao et al., 1998).
The current predicted number of target genes is 49 of which 14 have been
conﬁrmed by direct experimental analyses. The characterized targets of
Zap1 are illustrated in Fig. 4. Not surprisingly, a number of these genes
mediate zinc uptake (ZRT1, ZRT2 and FET4) or zinc release from the
vacuole stores (ZRT3) (Zhao and Eide, 1996a, 1996b; Lyons et al., 2000;
Waters and Eide, 2002). Zap1 also activates the expression of two additional
zinc permease genes, ZRC1 and ZRG17, that encode proteins involved in
zinc inﬂux into the vacuole and endoplasmic reticulum (ER), respectively
(Yuan, 2000; MacDiarmid et al., 2003; Ellis et al., 2005). Although counterintuitive, the increased expression of ZRC1 during zinc deﬁciency is a
proactive mechanism to protect zinc-starved cells from sudden exposure to
high levels of zinc (MacDiarmid et al., 2003). The regulation of ZRG17
expression is potentially a mechanism to control zinc inﬂux into the ER.
Zrg17 transports zinc into the ER as a heterodimeric complex with Msc2
(Ellis et al., 2004, 2005). If neither protein functions as a homodimer then
zinc transport would depend upon increased ZRG17 expression and the
corresponding increase in Zrg17 protein levels. Thus, Zap1 plays a critical
role in maintaining cytoplasmic and possibly ER zinc levels, during zinc
deﬁciency.
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249
Figure 4 The protein products of genes that are transcriptionally regulated by
Zap1. Proteins have been separated according to their cellular roles in either zinc
acquisition or zinc sparing response. Proteins that fall into neither of these categories
or whose role zinc homeostasis has yet to be determined, are in their own class
(other). The topology and total number of transmembrane domains of membrane
proteins was based on the following studies, Zrt1, Zrt2, Zrt3 (Zhao and Eide, 1996a),
Zrc1, Zrg17 (Palmiter and Findley, 1995), Dpp1 (Han et al., 2004) and Izh1/2
(Yamauchi et al., 2003). Grey arrows indicate the direction of zinc transport and
black arrows indicate whether a gene is up-regulated (m upright arrow) or downregulated (k inverted arrow) by Zap1.
Analysis of a subset of the remaining target genes has provided further
incite into areas of metabolism that alter during zinc deﬁciency. One unpredicted correlation was the tight coordination of phospholipid metabolism
with zinc metabolism. During zinc deﬁciency, Zap1 induces the expression
of the diacylglycerol pyrophosphate phosphatase gene DPP1, the phosphatidylinositol synthase gene PIS1 and the ethanolamine kinase gene EKI1
(Han et al., 2001, 2005; Kersting and Carman, 2006). The regulation of these
genes by Zap1 has substantial effects on the levels of both major and minor
phospholipids during zinc deﬁciency (Iwanyshyn et al., 2004). For example,
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AMANDA J. BIRD
the increased levels of vacuolar membrane-associated Dpp1 results in the
reduction of the vacuole membrane phospholipids diacylglycerol pyrophosphate and phosphatidate while the induction of PIS1 leads to increased
levels of phosphatidylinositol. Zinc depletion additionally leads to a
decrease in the levels of phosphatidylethanolamine, which is partly attributed to decreased expression of the CHO1 phosphatidylserine synthase
(Iwanyshyn et al., 2004). Notably, this decrease does not require Zap1, but is
instead mediated through an UASINO promoter element by an unknown
mechanism. Phospholipids have a wide range of functions including altering
the efﬁciency of membrane permeases and acting as secondary messengers.
Therefore, one of the most interesting questions remaining to be answered is
why is there the remodelling of phospholipid metabolism in response to zinc
starvation? Other Zap1 target genes include IZH1 and IZH2 (also known
as PHO36) that encode proteins with homology to vertebrate membrane
steroid receptors (Lyons et al., 2004). Recent studies have revealed that Izh2
is localized to the plasma membrane where it acts as the receptor for the
plant defensin, osmotin (Narasimhan et al., 2005). On binding osmotin, Izh2
induces apoptosis via the Ras2 signalling pathway. In humans, the two Izh
homologues function as receptors for the hormone adiponectin. Binding of
adiponectin to the receptors stimulates increased glucose uptake and fatty
acid catabolism (Kadowaki and Yamauchi, 2005). In yeast, deletion of
IZH2 leads to multiple defects in lipid and phosphate metabolism and zinc
sensitivity while over-expression of IZH1 or IZH2 led to decreased activity
of a Zap1 reporter construct (Karpichev et al., 2002; Lyons et al., 2004).
It therefore appears that the Izh receptors may control signalling cascades
that can affect lipid, phosphate and zinc homeostasis. It remains unknown
if any other ligand, apart from osmotin mediates Izh signal transduction.
Finally, the cellular roles of a number of conﬁrmed Zap1 target genes (e.g.
ZPS1) have yet to be identiﬁed.
4.3. Zap1-Dependent Repression of Gene Expression
An emerging theme amongst fungal transcription activators is their role in
mediating gene repression. Studies of three Zap1 target genes (ZRT2, ADH1
and ADH3) have demonstrated how Zap1, a transcriptional activator, can
lower gene expression (Fig. 5). The role of Zap1 in mediating gene repression was ﬁrst revealed when the expression of ZRT2 was examined over a
range of zinc levels (Bird et al., 2004). ZRT2 expression increased, as cells
become zinc starved but decreased upon severe zinc limitation. This atypical
proﬁle results from the arrangement and afﬁnity of ZREs located within the
THE UPS AND DOWNS OF GENE REGULATION
251
Figure 5 Down-regulation of gene expression by Zap1. Schematic representation
of the mechanisms Zap1 uses to repress gene expression. TATA elements (stars),
ZREs (black boxes) major RNA transcripts (thick wavy line) or minor RNA transcripts (thin wavy line) are shown.
ZRT2 promoter. When cells become starved of zinc, Zap1 binds to two
high-afﬁnity ZREs within the ZRT2 promoter and activates gene expression. As cells become severely zinc starved, transcription is blocked as a
result of Zap1 binding to a third low-afﬁnity ZRE element that maps to the
ZRT2 transcriptional initiation site. This orderly expression pattern ensures
that the Zrt2 low-afﬁnity zinc uptake system is only produced when it has
the capacity to transport zinc.
ADH1 encodes the major zinc-dependent alcohol dehydrogenase utilized
by yeast during fermentation while ADH3 encodes the major mitochondrial
zinc-dependent alcohol dehydrogenase. Zap1 mediates the repression of
ADH1 by binding to the ADH1 promoter and inducing the expression
of the ZRR1 RNA transcript (Bird et al., 2006a). ZRR1 acts in cis by
transcriptional interfering with the binding of the two major activators of
ADH1 expression, Rap1 and Gcr1. A similar mechanism is thought to
occur at the ADH3 promoter through induction of the intergenic transcript
ZRR2. The decrease in ADH1 and ADH3 gene expression is paralleled by
the Zap1-dependent activation of ADH4 (Lyons et al., 2000). ADH4
encodes a mitochondrial alcohol dehydrogenase that resembles the ironbinding ADHII from Zymomonas mobilis. Despite the homology to the
iron-binding alcohol dehydrogenases, Adh4 is thought to be a zincmetalloprotein (Drewke and Ciriacy, 1988). So why would cells spare zinc
by decreasing ADH1 and ADH3 expression only to then incorporate
zinc into Adh4? One possibility is that there is still a saving in zinc. Adh1
and Adh3 form tetramers where each subunit binds two zinc atoms while
Adh4 is predicted to be a dimer with one zinc atom per subunit. A second
possibility is that some property of Adh4 is advantageous during zinc starvation. Whatever the reason turns out to be, the use of intergenic transcripts
to repress gene expression reveals yet another mechanism by which cells can
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AMANDA J. BIRD
coordinate both the increased and decreased expression of genes in parallel
pathways with one factor.
4.4. Zap1 and Zinc Sensing
As zinc levels increase due to increased expression zinc transporters, such as
ZRT1, or decreased expression of genes like ADH1, cells no longer
require Zap1. Thus, to avoid zinc overload, one of the most important
properties of Zap1 is its ability to be inactivated by zinc. Like many
transcription factors, Zap1 autoregulates the expression of its own gene to
maximize the levels of Zap1 protein under conditions where it is
active. However, the most novel regulatory attribute of Zap1 is its ability
to be inactivated by zinc at a post-translational level (Bird et al., 2000).
Zap1 contains two transactivation domains that are independently regulated by zinc. Activation domain 1 (AD1) is located at the N-terminus of
the protein and is surrounded by two regions that are essential for zinc
responsiveness (Herbig et al., 2005). The Zap1 DNA-binding domain is
also necessary for inactivation of AD1 by zinc. AD1 contains no known
zinc-binding motif but binds multiple zinc ions in vitro, while mutations to
cysteine and/or histidine residues that surround AD1 lead to constitutive
AD1 function. These results have led to a model in which zinc binding to
AD1 stimulates an intramolecular interaction between AD1 and the DNAbinding domain thereby masking AD1 function (Herbig et al., 2005). The
second activation domain (AD2) of Zap1 is autonomously regulated by
zinc and uses a novel regulatory zinc ﬁnger pair motif to sense zinc (Bird
et al., 2003, 2006b). The distinguishing features of zinc ﬁnger pairs are
hydrophobic residues located within each ﬁnger that mediate an interﬁnger
protein–protein interaction (Wang et al., 2006). Two features of the
Zap1 AD2 pair are unusual. First, an activation domain maps precisely to
the one of the zinc ﬁngers and second the zinc bound to the pair is highly
labile in nature (Bird et al., 2003; Wang et al., 2006). It is thought
that under zinc-replete conditions, the increased occupancy of this pair
with zinc results in the formation of the zinc ﬁnger pair masking residues
critical for activation domain function. It remains unknown why Zap1
would need two independent zinc sensors. Possible reasons include either
differential regulation of these activation domains in response to various
environmental signals, the ability of them to sense different cellular zinc
levels giving a graded response to zinc or allowing differential target gene
expression.
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253
5. CADMIUM
5.1. Cadmium: An Overview
Cadmium is a non-essential, toxic, heavy metal ion. Its toxicity arises from
its ability to block sulphdryl groups in enzymes, compete with zinc in proteins and generate oxidative stress (McMurray and Tainer, 2003; Waisberg
et al., 2003). Thiol-containing compounds, such as glutathione and phytochelatins (PC), play an important role in protecting fungi from cadmium
toxicity (reviewed by Mendoza-Cozatl et al., 2005). For example, in S.
pombe cadmium–glutathione and cadmium–PC complexes are transported
to the vacuole where they can be stored in stable, high molecular weight
complexes. Given the high sulphur content of glutathione and PC, it is not
surprising that cadmium detoxiﬁcation is intricately linked to sulphur metabolism. Here, I have highlighted what is known about transcriptional
regulation of gene expression in response to cadmium in S. cerevisiae and
S. pombe.
5.2. Cadmium-Responsive Activation of Gene Expression
In S. cerevisiae, cadmium leads to the activation of the transcriptional activators Yap1, Yap2 and Met4. Yap1 and Yap2 are two basic leucine zipper
(bZIP) transcription factors that, like Aft1 and Aft2, have overlapping as
well as unique functions (Fernandes et al., 1997; Cohen et al., 2002). Yap1
preferentially regulates genes required for the detoxiﬁcation of reactive oxygen species. Notably, Yap1 activates the expression of YCF1 (required for
vacuolar import of cadmium–glutathione complexes) and GSH1 (required
for glutathione biosynthesis) (Wemmie et al., 1994; Wu and Moye-Rowley,
1994; Cohen et al., 2002). Yap2 preferentially controls genes required for
protein turnover and protein folding (Cohen et al., 2002). The induction of
these latter genes may be important in the removal and replacement of
damaged proteins. Met4 plays a pivotal role in cadmium detoxiﬁcation.
Met4 remodels metabolism such that sulphur-depleted isoforms of several
carbohydrate metabolism enzymes are used instead of sulphur-rich forms
(Fauchon et al., 2002). For example, under normal conditions, cells use the
Pdc1 pyruvate decarboxylase. Pdc1 contains 12 Met and 4 Cys residues. In
the presence of cadmium, cells repress PDC1 expression and induce PDC6.
The Pdc6 isoform is a relatively sulphur-poor enzyme containing 4 Met and
1 Cys residues (Fauchon et al., 2002). This decrease in sulphur-containing
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AMANDA J. BIRD
protein synthesis allows the sulphur that is saved to be rerouted into antioxidant glutathione (Lafaye et al., 2005). This remodelling therefore serves
two purposes: it limits damage to speciﬁc pathways while sparing sulphur
for glutathione production. Finally, a Met4-dependent pathway may be one
mechanism by which cells delay the G1–S transition of the cell cycle in
response to cadmium. This delay/arrest ensures that cell division does not
occur in the presence of damaged proteins (Yen et al., 2005). The bZIP
regulator, ZIP1, in S. pombe mediates many similar responses. Zip1 activates the expression of a subset of genes required for sulphur uptake and
metabolism in response to cadmium and mediates growth arrest of cells
when exposed to high levels of cadmium (Harrison et al., 2005). It is noteworthy that a strain lacking Pap1 (the S. pombe homologue of Yap1) is
sensitive to cadmium suggesting that the protective role against cadmium of
these bZIP proteins is conserved in other yeast (Toone et al., 1998). Thus,
transcriptional control of genes involved in the control of the sulphur metabolism, cadmium chelation, protein turnover and cell cycle progression all
contribute to cadmium detoxiﬁcation.
5.3. The Cadmium Sensors
How do transcriptional regulators sense a toxic metal such as cadmium?
Studies with Yap1 and Met4 have revealed two very different regulatory
mechanisms (Fig. 6). Yap1 has two different redox centres that control the
nuclear accumulation of Yap1 in response to speciﬁc oxidative stresses
(Azevedo et al., 2003). When exposed to peroxides, the thiol peroxidase
Hyr1 (previously Gpx3) mediates the formation of an intramolecular disulphide bond between Cys303 and Cys598 in Yap1. This disulphide bond is
thought to induce a conformational change that masks the NES leading to
the nuclear accumulation of Yap1 and target gene activation (Delaunay
et al., 2000, 2002). Nuclear retention of Yap1 can also be induced through
chemical modiﬁcation of reactive cysteine residues (Cys598, Cys620 and
Cys629) that lie close to the NES (Azevedo et al., 2003). Furthermore, a
minimal domain which included the residues Cys598, Cys620 and Cys629
from Yap1 accumulated in the nucleus only after exposure to cadmium
(Azevedo et al., 2003). Thus, cadmium most likely controls Yap1 activity by
directly binding to cysteine residues in the C-terminal redox centre instigating Yap1 nuclear retention.
Ubiquitylation plays an important role in controlling the activity and
stability of Met4. Polyubiquitylation is a general mechanism by which a
protein is marked for degradation by the 26S proteasome. The covalent
THE UPS AND DOWNS OF GENE REGULATION
255
Figure 6 Cadmium-responsive transcriptional control. Factors involved in the
regulation of gene expression in the presence or absence of cadmium. A ‘U’ in a circle
represents ubiquitin, the proteasome has been indicated by the letter ‘P’ and UBP
represents the unknown cadmium inducible/responsive deubiquitinylating enzyme.
Shown are the different mechanisms of Met4 regulation in minimal media+methionine (MM+Met) and in rich media.
attachment of a polyubiquitin chain to a protein requires a cascade of E1,
E2 and E3 enzymes to activate and transfer ubiquitin. A common class of E3
enzymes (or ubiquitin ligases) is the SCF (Skp1-Cullin/Cdc52-F box protein)
complex. The variable member of the ubiquitin ligase complex is the F-box
protein that determines substrate speciﬁcity. When methionine is added in
excess to minimal media, Met4 is polyubiquitylated and degraded by the
proteasome (Rouillon et al., 2000). The F-box protein Met30 is speciﬁc for
Met4 and mediates this process. An alternative regulatory mechanism exists
in rich media. Here, Met4 is oligo-ubiquitylated but is not degraded (Kaiser
et al., 2000; Flick et al., 2004, 2006). Under these conditions, Met4 remains
in the nucleus and activates a small subset of its target genes (Kuras et al.,
2002). Cadmium has the ability to override both regulatory mechanisms
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AMANDA J. BIRD
leading to the constitutive activation of Met4. Exposure of cells to cadmium
in minimal media in the presence of high methionine leads to the rapid
dissociation of Met30 (Barbey et al., 2005). This, in turn, prevents Met4
ubiquitylation and subsequent degradation. In vitro, cadmium does not
inhibit the ability of Met4 to be ubiquitylated suggesting that the dissociation of Met30 is not simply mediated by direct cadmium binding (Barbey
et al., 2005). In rich media, cadmium leads to the deubiquitylation of
Met4 returning it to its active form (Barbey et al., 2005; Yen et al., 2005).
The cadmium inducible/responsive deubiquitinylating enzyme is currently
unknown. In S. pombe, cadmium responsive regulation of Zip1 also occurs
through ubiquitin-mediated degradation (Harrison et al., 2005). In the
absence of cadmium the F-box protein Pof1, which is speciﬁc for Zip1,
interacts with a phosphorylated form of Zip1 leading to its polyubiquitylation and degradation. In the presence of cadmium, Zip1 is stabilized
suggesting that the interaction between the transcription factor and F-box
protein is key to cadmium responsiveness.
6. CONCLUSIONS
The study of fungal metallo-regulatory factors has greatly advanced our
understanding of the importance of metals in biology. Many mechanisms
are used to sense different metal ions and to globally remodel metabolism
according to metal ion status. It has now become clear that during metal ion
deﬁciencies, metallo-regulatory proteins play central roles in both metal
acquisition and in metal ion conservation.
Transcription factors that respond to metals are found in all eukaryotes.
For example, in humans MTF-1 is activated by zinc, the hypoxia-inducible
factor 1 (HIF-1) is activated by copper while Nrf2, a bZIP factor that is
subject to ubiquitin-mediated degradation, is stabilized in the presence of
cadmium (Andrews, 2001; Giedroc et al., 2001; Stewart et al., 2003; Martin
et al., 2005). In Chlamydomonas, the copper-responsive factor, Crr1, is
required for both the activation and repression of genes involved in copper
homeostasis (Quinn et al., 2000; Moseley et al., 2002; Kropat et al., 2005). In
Arabidopsis, FIT1 regulates gene expression in response to iron deﬁciency
(Colangelo and Guerinot, 2004) while the transcript abundance of many
genes is regulated by iron, copper and zinc levels (Petit et al., 2001; Connolly
et al., 2002; Tarantino et al., 2003; Mukherjee et al., 2006; Talke et al., 2006).
Metallo-regulation at the transcriptional level is observed in other multicellular model systems. For example, in Drosophila, MTF-1 activates
THE UPS AND DOWNS OF GENE REGULATION
257
metallothionein gene expression in response to copper and activates the
expression of the CTR1B copper importer during conditions of copper
deﬁciency (Balamurugan and Schaffner, 2006) while in Caenorhabditis
elegans, genes encoding ferritin and aconitase are transcriptionally regulated
by iron (Gourley et al., 2003). Lessons learned from studies of metalloregulators in fungi therefore provide a foundation for understanding how
factors sense metals and how metal ion availability affects basic cellular
metabolism. With the increasing number of metallo-regulatory factors being
identiﬁed, the future with transcriptional regulators looks up and not down.
ACKNOWLEDGEMENTS
I would like to thank Dennis Winge, Paul Cobine, Aaron Atkinson and Oleh
Khalimonchuk for helpful comments and discussion on the manuscript.
A.B. is a member of the Winge lab, whose work is supported by grant CA
61286 from the National Cancer Institute, National Institutes of Health.
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Author Index
Abe, T., 105
Abee, T., 98
Abt, B., 238
Abul-Hassan, K.S., 45
Adams, J., 172, 175, 192, 195–196,
205–208, 213
Adams, W.B., 40
Adhya, S., 197, 199
Adkins, E.M., 241, 245
Adriano, J.M., 39
Aebersold, R., 114
Afghani, B., 9
Agabian, N., 236–238
Aguilar Netz, D.J., 235
Agustines, M., 110
Aharonowitz, Y., 19–20, 28
Ahmad, M.S., 79
Ahmed, S., 82, 89, 91, 142
Ahn, K., 45
Aiba, H., 185, 204
Aikawa, K., 9
Ailabouni, A., 131
Ajami, A., 89
Akimenko, V.K., 28, 32, 43
Akkermans, A.D., 97, 100, 107
Akotia, V., 81
Akpolat, O.M., 30, 48
Alam, J., 256
Alam, M.T., 173
Alam, S., 256
Albeck, A., 47
Albeck, M., 47
Albendea, C., 45
Albert, R., 146
Alder, N.P., 256
Alexandraki, D., 241
Alexeeva, S., 173, 181
Allison, D.G., 37, 186
Allue, J.L., 45
Alonso, G., 16, 20
Alric, M., 82, 122–123, 135
Al-Soud, W.A., 111
Altman, E., 181
Amann, R., 99, 107
Amarri, S., 83, 131
Anagnostopoulos, G.D., 187
Anderson, R.P., 76–77
Anderson, T.F., 28
Andersson, D.I., 206
Andersson, L., 174, 185
Andoh, A., 79, 81
Andrews, B., 243–244
Andrews, G.K., 256
Angermayr, K., 237–238
Anglade, P., 112
Anisimova, L.A., 28, 32, 43
Antoine, J.M., 101
Antonelli, E., 119
Antoniewicz, M.R., 93
Anwar, H., 188
Aposhian, H.V., 8
Appleman, M.D., 28
Apuy, J.L., 256
Arai, K., 134
Araya, M.A., 20–22, 48
Arber, W., 197, 206
Aries, V., 76
Arigoni, D., 88, 90, 131
Armes, L.G., 50
Arnold, F.H., 173
Arshad, M., 12
Artsimovitch, I., 186
Asakura, T., 7, 182
Aschmann, S.M., 12
Ash, J.S., 256
Askeland, E., 234–237
Askwith, C.C., 234
Asmuss, M., 45
270
Athenstaedt, K., 249
Atkinson, R., 12
Atwood, K.C., 195
Aude, J.C., 253
Augenlicht, L.H., 78
Ault-Riche, D., 140
Ausubel, F.M., 36
Avazeri, C., 21–22, 26, 28, 42–43, 48–49
Awada, M., 89, 114
Azadi, P., 34
Azevedo, D., 254
Azpiroz, F., 119
Babidge, W., 117
Baca, B.E., 20
Bacher, A., 88, 90, 131
Bachofen, R., 41
Backhed, F., 98–99, 101, 130, 136–137,
139
Badman, M.K., 137
Badran, A.M., 133
Badrane, H., 192, 206, 213
Badry, E.A., 28, 30–34, 37–38
Baetz, K., 243–244
Bahler, J., 254, 256
Bailey, L.B., 89
Bailey, M.J., 91, 107
Baker, B.J., 113
Balaban, N.Q., 37, 187
Balagadde, F.K., 173
Balamurugan, K., 257
Balk, J., 235, 242–243
Ball, R.O., 89, 133
Ballevre, O., 84, 90–91, 131, 142
Bamba, T., 79
Bammens, B., 133
Banﬁeld, J.F., 113
Bank, S., 90, 131, 144
Banta, S., 134
Baquero, F., 194
Barabasi, A.L., 146
Baraige, F., 112
Barbara, G., 81
Barbey, R., 255–256
AUTHOR INDEX
Barcelo, A., 138
Barceloux, D.G., 23
Barcenilla, A., 116, 126
Bard, M., 242
Bar-Joseph, Z., 243
Barkla, D.H., 118
Barnoud, D., 131
Baronofsky, J.J., 143
Barraco, P., 256
Barrault, M.B., 254
Barrett, J.A., 129, 187
Barrnett, R.J., 28
Barth, S., 256
Bartram, P., 79
Barzaghi, D., 112
Basilio, A., 115
Basit, A.W., 129
Baskunov, B.P., 28, 32, 43
Basnayake, R.S.T., 30, 48
Bassilian, S., 82, 89, 91, 142
Bassin, C., 187
Bassler, B.L., 187
Bassler, J., 235
Basu, P., 38, 40
Bates, D., 36
Baudouin-Cornu, P., 256
Baumgart, D.C., 81
Baxa, U., 8
Baylor, M.R., 15
Bazett-Jones, D.P., 14
Beatty, J.T., 11, 28
Beaudoin, J., 237–238, 244, 246–247
Bebien, M., 25, 33, 39, 46
Becker, K., 36
Bedekovics, T., 235
Bederman, I.R., 90
Beecher, C.W., 115
Begum, S., 113
Bell, C.J., 79
Bellalou, J., 173
Bellemare, D.R., 246
Belli, G., 243
Belliveau, B.H., 13
Ben-Amitai, D., 7
AUTHOR INDEX
Ben-Amor, K., 98
Bendigkeit, H.E., 192
Bengert, G.A., 30
Benoit, G., 190
Bensoussan, L., 120
Bentley, R., 29–30
Bentley, W.E., 188
Benz, R., 177
Berg, O.G., 206
Bergman, E.N., 75, 78, 137
Bergomi, M., 23
Berkman, O., 93
Berks, B.C., 32
Bernalier, A., 82, 122–123, 135
Berney, M., 187
Berthiaume, F., 93, 134
Bertrand, K.P., 37
Besser, T.E., 15
Bester, E., 188
Bevins, C.L., 81
Beyersmann, D., 253
Beysen, C., 89, 114
Bhattacharjee, H., 50
Biddanda, B.A., 175
Bienenstock, J., 80
Bilge, S.S., 15
Binder, S.R., 114
Bindi, A.B., 40
Bingham, S.A., 79–80, 117
Bird, A.J., 231, 233, 244, 246, 250–252
Birkenbihl, R.P., 256
Birkett, A., 77
Birringer, M., 38
Bisognano, C., 36
Bius, J.H., 30, 48
Bjedov, I., 193
Bjornstedt, M., 25
Black, D.S., 37
Blackadder, E.S., 27
Blackshear, P.J., 236
Blagoev, B., 114
Blaiseau, P.L., 235–236
Blake, R.C., 113
Blanchard, J.L., 192
271
Blankman, E., 250, 252
Blattner, F.R., 9, 14–15
Blaut, M., 97
Blazquez, J., 194
Bledsoe, T.L., 13
Blessing, H., 45
Blitchington, R.B., 97
Bloemberg, G.V., 35–36
Blot, M., 197, 206
Boccazzi, P., 173
Bochner, B.R., 36, 212
Bo¨ck, A., 3, 7, 38, 41
Bock, H.D., 82
Bocker, U., 79
Boelens, P.G., 90
Boer, V.M., 191
Boesten, R., 113
Bohannan, B.J., 145
Bohmig, G.A., 79, 81
Boivin, M.A., 81
Boles, B.B., 36
Boles, J.O., 8, 46
Bond, P.L., 113
Bonhoeffer, S., 144, 182, 213
Bonilla, I., 36
Bonnaud, E., 101
Bonneau, L., 46
Bonnet, R., 97
Booijink, C.C., 113
Boone, C., 236, 243–244
Boos, W., 178, 203–204
Borek, V., 27
Boren, J., 82, 89, 91, 142
Borghese, R., 33, 43, 47
Borghouts, C., 245
Borgmann, E., 82
Born, T.L., 112
Boronin, A.M., 28, 32, 43
Boros, L.G., 82, 89, 91, 142, 146
Borrelly, G.P., 248
Borriello, G., 32
Borsetti, F., 1, 28, 33, 42–44, 47–48
Bost, M.C., 134
Botsford, J.L., 185
272
Botstein, D., 192, 206, 213, 233, 235,
237, 240, 248, 251
Boucherie, H., 243
Bouige, D., 46
Boulding, E.T., 110
Bountra, C., 81
Bourriaud, C., 130
Bowman, R.H., 13
Boyle, J.D., 32
Boyle, M.A., 79
Boza, J., 91, 142
Bracken, W.M., 45
Brackett, D.J., 142
Bradley, D.E., 18
Braeken, K., 186
Bragnall, K.W., 5
Branch, W.J., 80, 117, 123
Branda, S.S., 34
Brandolin, G., 234
Breslin, N.P., 81
Bressan, R.A., 250
Breuille, D., 91, 142
Briat, J.F., 256
Brigidi, P., 112
Brink, E.J., 123
Brobech Mortensen, P., 80
Broderick, S., 37
Bron, P.A., 143
Brooun, A., 37
Brown, G.E., 7
Brown, K., 113
Brown, M.R.W., 186
Brown, N.L., 16, 28, 44
Brown, P.O., 192, 206, 213, 233,
235–237, 240, 245, 248, 251
Brown, T.A., 39
Brown, W.C., 40
Bruchert, V., 107
Bruins, M.J., 82, 135
Bruland, K.W., 30
Brune, A., 107, 109, 129
Brunengraber, H., 89–90, 131
Bruun, L.D., 107
Bryant, R.D., 39
AUTHOR INDEX
Bryden, L.J., 15
Brydon, W.G., 119
Buchanan, B.B., 41
Buchczyk, D.P., 7
Budisa, N., 8, 46
Bueno, L., 81
Bugnicourt, A., 32
Bunnett, N.W., 81
Burgard, A.P., 93
Burian, J., 15, 22, 44
Burk, R.F., 8
Burke, P.V., 238, 240
Burkle, A., 45
Burland, N.T., 9, 14–15
Burnett, S., 33
Burnie, J.P., 236
Burtscher, H., 79, 81
Busch, R., 89, 114
Butler, E., 248
Button, D.K., 177
Butzner, J.D., 79
Buzzelli, J., 40
Byron, K., 118
Cabello, A., 115
Cabiscol, E., 243
Cagney, G., 236
Cakir, I., 9
Cakir, T., 136
Calderon, I.L., 20, 48
Caldwell, D.E., 32
Calhoun, M.W., 214
Camadro, J.M., 235–236
Camenisch, G., 256
Camilli, A., 187
Campbell, J.K., 173
Campbell, N.H., 256
Campbell, R.G., 89
Campo, P., 194
Canovas, D., 20
Cantaﬁo, A.W., 13
Canton, R., 194
Capati, C., 255
Capdevila, S., 36
AUTHOR INDEX
Carlin, L., 140
Carlino, U.B., 190
Carlson, D.E., 41
Carlson, M., 241
Carman, G.M., 249–250
Carnevali, M., 5, 11, 43
Carter, T.L., 236
Cascante, M., 82, 89, 91, 142, 146
Cases, I., 20
Cashel, M., 185, 212
Cassidy, M.M., 138
Castaneda, M., 20
Castermant, J., 46
Castillo, L., 89
Cave, D.R., 125
Cayuela, C., 98, 101
Cebula, T.A., 193
Cellier, M.F., 234
Centelles, J.J., 82, 89, 91, 142
Ceri, H., 28, 30–34, 36–38, 45, 48
Chadwick, V.S., 76–77
Chai, D., 46
Chait, R., 37, 187
Chakraborty, S., 234
Challenger, F., 30
Chaloupka, R., 234
Champ, M.M., 141
Champomier-Verges, M.C., 112
Chandra, J., 38
Chandramouli, V., 89
Chang, C.-H., 18
Chang, J.C., 131
Chapman, M.A., 79
Chapman, P.A., 9, 27
Chardon, P., 101
Charlebois, R., 40
Charvin, G., 207
Chasteen, T.G., 21–22, 29–30, 41, 48
Chatham, J.C., 90
Chau, Y.K., 30
Chaudier, J., 45
Chauvin, J.P., 39
Chayvialle, J.A., 138
Chegwidden, K., 177
273
Chen, C.M., 50
Chen, D., 254, 256
Chen, J.Y., 45, 246
Chen, O.S., 242
Chen, X., 45, 249, 256
Cheng, B., 97–98, 109, 243–244
Cherbut, C., 79, 130
Chernikova, T.N., 101
Cheung, B.P.K., 38
Chevalier, A., 236
Chich, J.F., 112
Chikhale, P.J., 80
Child, M.W., 80, 126
Chin, J., 138, 143
Chinkes, D.L., 89
Cho, H., 173
Choi, E.J., 45
Choi, H., 173
Choi, Y., 45
Chotani, G., 116
Christensen, B., 87
Christian, M.T., 83, 131
Christl, S.U., 79, 118–119
Chu, F., 34
Chung, J.,, 13
Chung, Y.M., 45
Church, G.M., 93, 253
Ciriacy, M., 251
Cirino, G., 119
Claeyssens, S., 91, 142
Clamp, J.R., 110
Clark, D.P., 200
Clark, S., 248
Clausen, M.R., 119
Claustre, J., 138
Clayton, N., 81
Clendenin, W.M., 193
Clifton, P.M., 121
Clinton, S.K., 119
Coats, B.S., 89
Coca, M.A., 250
Coefﬁer, M., 91, 142
Cohen, B.A., 253
Cohen, D.P., 113
274
Cohen, G., 20
Colangelo, E.P., 256
Cole, S.P., 18
Colleran, E., 15
Collier, C.T., 97–98
Collier, P.J., 186
Collins, M.D., 89, 97, 114
Collins, S.M., 81
Combourieu, B., 80
Combs, G.F., 41, 46
Combs, S.B., 46
Comte, B., 90
Condon, S., 187
Cong, Y., 100
Connolly, E.L., 256
Conrads, T.P., 114
Constantinidou, C., 185
Conway, P.L., 189
Cooke, T.D., 30
Cooper, J.R., 131
Cooper, K., 87
Cooper, W.C., 5–6
Corﬁeld, A.P., 110
Corinaldesi, R., 81
Corner, G.A., 78
Cornivelli, L., 250
Cortese, M.S., 27
Cosenza, V., 79
Costerton, J.W., 30, 32, 188
Cotgreave, I.A., 46
Cotner, J.B., 175
Cottrell, G.S., 81
Courbeyrette, R., 236
Courel, M., 235
Cournoyer, B., 10, 19, 21
Coursange, E., 197
Courtin, O., 45
Courville, P., 234
Cox, E.C., 193
Cox, G.B., 44
Cox, S.G., 248
Crawford, R.L., 27–29
Cremon, C., 81
Crenn, P., 133
AUTHOR INDEX
Criddle, R.S., 41, 49
Crisp, R.J., 240–242, 245
Crooks, M., 247
Crossett, B., 113
Csordas, A., 137
Csotonyi, J.T., 11, 42
Cuber, J.C., 138
Cuevas, W.A., 185
Cui, Z.J., 143–145
Culotta, V.C., 233, 240, 247
Cummings, J.H., 75, 77–80, 100,
117–118, 123, 137
Cunnane, S.C., 139
Curle, C., 26
Curry, S.C., 27
Cvitkovitch, D.G., 187–188
Dahlmans, V.E., 88
Dahout-Gonzalez, C., 234
Dalal, V., 79
Daldal, F., 33, 47
Dalen, H., 81
Daly, K., 121
Damsz, B., 250
Danby, S.G., 186
Dancis, A., 233, 238, 242, 244
Dandekar, T., 93
Daniels, K., 246
Daniels, R., 186
Danot, O., 204
D’Argenio, G., 79
Darmaun, D., 133
Das, S.K., 84, 90
Daum, G., 249
Dauner, M., 87
Davey, M., 236
Daviaud, D., 79
David, F., 90
David, M., 7
Davidson, R.C., 246
Davies, D.G., 30
Davies, J.A., 36
Davies, N.A., 134
Davies, S., 9
AUTHOR INDEX
Davis, C.D., 145
Davis, H.M., 87
Davis, R.W., 236–238
Davis, S.R., 89
De Boer, E., 9
De Boever, P., 122
De Castro, C.L., 90
De Crecy-Lagard, V., 173
De Freitas, J.M., 245
De Giorgio, R., 81
de Graaf, A.A., 73, 84, 87, 89, 93–96,
99, 101, 106–107, 109, 122, 136
de Graaf, I.A., 80
de Jager, M.H., 80
de Ligt, R.A., 120
De Lorenzo, V., 11, 20
de Los Reyes-Gavilan, C.G., 112
De Mattos, M.J.T., 173, 181, 214
De Moll-Decker, H., 26
De Preter, V., 117, 124, 133
De Silva, D.M., 234
De Visser, J.A.G.M., 192
De Vos, W.M., 73, 93, 97–101, 106–107,
109–110, 113, 122, 143, 146
de Vries, M.C., 113
De Vuyst, L., 117, 124, 133
de Waard, P., 107, 109, 122
De Winde, J.H., 191
Dean, A.M., 169, 172, 176, 198, 200
Death, A., 174, 178–179, 185, 191, 197
Deboy, R.T., 104–105
Dechelotte, P., 91, 142
Deeley, R.G., 18
Defago, G., 36
Defer, N., 78
Degano, R., 247
Degener, J.E., 105
Dejong, C.H., 90
Delaunay, A., 254
Delavari, P., 190
Delisa, M.P., 188
Delle Cave, M., 79
Delle Valle, N., 79
Delort, A.M., 80
275
Demange, P., 8, 46
Demattos, M.J.T., 182
DeMeio, R.H., 7
Demeter, J., 235, 237
DeMoll-Decker, H., 13
Denamur, E., 193–194
Denis-Pouxviel, C., 79
Denton, M.B., 8
Depege, N., 256
Deplancke, B., 122
Derrien, M., 110, 113
Des Rosiers, C., 90
Deuticke, B., 47
Deutz, N.E.P., 82, 87, 89–90, 97, 107,
109, 122, 134–135
Dey, M., 193
Dhut, S., 254, 256
Di Gregorio, S., 12, 23
Di Tomaso, G., 5, 43
Diakos, C., 79, 81
Diaz-Orejas, R., 11
Diez, N., 129
Dignass, A.U., 81
Dijkema, C., 90, 107, 109, 122
Dijkhuizen, L., 177
Dimmick, R.L., 189
Dimmitt, R.A., 100
Ding, H., 137, 139, 243–244
Diop, L., 98
Distrutti, E., 119
Dockery, J., 35
Dodson, B., 83, 127, 131
Doebeli, M., 197
Dogan, H.B., 9
Dolnikowski, G.G., 84, 90
Dominguez, A., 247
Dominguez-Abascal, F., 79
Dong, H., 112
Donohue, J., 28
Donovan, S.M., 81
Donovan, T.J., 9, 27
Doornbos, R.P., 120
Dore´, J., 97–98, 101
Dorel, C., 189
276
Dorr, A., 129
Dowdle, P.R., 40
Drahovska, H., 15
Drenkard, E., 36
Drewke, C., 251
Driessen, M., 179
D’Souza, C., 246
Dubacq, C., 236
Ducrotte, P., 91, 142
Duda, V.I., 28, 32, 43
Dudley, R.E., 45
Duesterhoeft, S., 248
Duffy, B.K., 36
Dufour, Y.S., 173
Dugan, M.E., 91, 131–132
Dumon, H.J., 137, 141–142
Dumont, M.G., 91
Duncan, A.,, 118, 138
Duncan, S.H., 80, 116, 125–126
Dungan, J., 236–238
Dunham, M.J., 192, 206, 213
Dunkley, T.P., 114
Dunlap, B., 8
Dunlap, R.B., 8, 46
Duperchy, E., 197
Dupree, P., 114
Dusel, G., 79
Dwarakanath, A.D., 138
Dykhuizen, D.E., 169, 171–172, 175,
192, 194, 196, 200
Dyllick-Brenzinger, M., 17
Eastburn, K.K., 81
Eastwood, M.A., 119
Eckburg, P.B., 104–105
Eckerskorn, C., 8, 46
Eckhardt, K., 256
Edwards, C.A., 83, 127, 131
Edwards, J.S., 93, 134
Efendic, S., 89
Egert, M., 93, 99, 101, 106–107, 109,
122
Eggeling, L., 89, 96
AUTHOR INDEX
Egli, T., 172–173, 176–177, 179–180,
183–185, 187–188, 190–192, 197, 203,
207
Ehrlich, G.D., 32
Eichinger, D., 88, 90, 131
Eide, D.J., 234, 240, 248–249, 250–252
Eikmanns, B.J., 96
Eisenreich, W., 88, 90, 131
Eisner, H.D., 118
Eiteman, M.A., 181
Ekberg, K., 89
Elborough, K., 101
El-Demerdash, F.M., 45
Elena, S.F., 172, 196, 211
Elia, M., 137
Elkins, J.G., 33
Ell, P.J., 129
Ellen, R.P., 187–188
Ellis, C.D., 248
Ellis, C.J., 77, 117–118
Ellwood, D.C., 77
El-Mansi, E.M., 189
El-Meleigy, M., 46
El-Omar, E.M., 117
Elson, C.O., 100
Elsworth, R., 171
Elthon, T., 245
Elzinga, H., 87, 131
Emili, A., 236
Enfors, S.O., 174, 185
Engelberg-Kulka, H., 41
Engelen, M.P., 89–90, 97
England, R.R., 186
Englyst, H.N., 110
Engman, L., 46
Enss, M.L., 81
Epping, E., 34
Eriksson, M., 256
Ernstberger, H., 190
Escanero, J.F., 45
Esgalhado, M.E., 187
Espitalier-Noel, G., 80
Eubel, J.K., 7
Evans-Galea, M., 252
AUTHOR INDEX
Evenepoel, P., 133
Ezan, E., 254
Fahey, R.C., 40
Fairweather, N., 113
Falk, P.G., 100–101
Falkow, S., 101
Fan, A.M., 45–46
Fanning, A., 80
Farewell, A., 184
Fauchon, M., 253
Faure, M., 91, 142
Faustoferri, R., 187
Fava, F., 11
Fay, L.B., 91, 142
Fedi, S., 5, 11, 43, 47
Feil, E.J., 213
Fejdi, P., 15, 22, 44
Fekete, Z., 235
Feldman, M.W., 145
Fell, D.A., 93, 136
Felschow, D., 114
Feng, E., 112
Feng, G., 193
Feng, L.S., 252
Ferchaud-Roucher, V., 139
Ferea, T., 192, 206, 213, 233, 235, 237,
240
Ferenci, T., 169, 171–172, 174, 176–180,
183–185, 187–206, 208–213, 215
Fernandes, A.R., 246
Fernandes, L., 253
Fernandez-Banares, F., 79
Fernie, A.R., 145–146
Ferrer, M., 101
Fett, J.P., 256
Field, J.A., 90
Field, L.S., 247
Figurski, D.H., 18
Findlay, K., 32
Findley, S.D., 249
Finegold, S.M., 98, 122
Finkel, S.E., 197
Finlay, B.B., 80, 100
277
Finnie, I.A., 138
Finot, P.A., 91, 142
Fioramonti, J., 81
Fiorucci, S., 119
Fischer, D., 203
Fischer, E., 90, 180–181
Fischer, W., 4
Flamme, I., 256
Flatley, J., 187
Fleischer, S., 107
Fleming, A., 7
Fleming, J., 7
Flick, K., 255
Flier, J.S., 137
Flint, H.J., 80, 116, 126
Flohe, L., 38
Foglia, G.R., 36
Foladori, P., 43
Foley, C.S., 81
Folino, M., 79
Fomenko, D.E., 38–39
Fong, S.S., 182, 213
Fonty, G., 82, 122–123, 135
Forchhammer, K., 3, 7
Foster, P.L., 193, 199
Foster, S.J., 20
Fox, T., 245
Fraenkel, E., 243
Fragiadakis, G.S., 241
Fraley, C.D., 140
Franchini, A.G., 180, 183–185
Franchini, F., 83, 131
Francia, F.,, 28, 42–43, 48
Francois, P., 36
Frangeul, L., 101
Franke, K.W., 23
Frankenberger, W.T., 7, 12, 23, 29
Franklin, M.J., 32
Fraser-Liggett, C.M., 104–105
Fredericks, J., 33
Freitas, M., 98, 101
Frenais, R., 137, 141–142
Friedman, L., 34
Friedrich, M., 129
278
Friedrich, M.W., 107, 109
Friesen, M.L., 197
Froguel, P., 249
Fuchs, G., 88, 90, 131
Fuchs, J.A., 50
Fuentes, D.E., 20, 22, 48
Fuentes, L., 45
Fujimoto, M., 79
Fujimura, S., 9
Fujisawa, T., 9
Fujiyama, D., 9
Fujiyama, Y., 79, 81
Fukunaka, A., 234, 242
Fuller, M.F., 92, 132
Funchain, P., 193
Furet, J.P., 80
Furlong, C.E., 179
Furne, J.K., 77, 117–119
Fusunyan, R.D., 79
Gadd, G.M., 26, 34
Gaertner, K., 81
Gailer, J., 8
Gaither, L.A., 248, 251
Galea, C., 240
Gamet, L., 79
Ganapathy, V., 121
Ganessunker, D., 81
Ganther, H.E., 25
Gao, K., 120
Garber, E.A., 50
Garberg, P., 46
Garbisu, C., 41
Garcia, E., 235, 237
Garcia, J.J., 45
Garcia, S., 247
Garcia-Puges, A., 79
Gardner, J.L., 89, 114
Gardner, W.L., 41
Garin, J., 25, 33, 46
Garny, K., 188
Gasch, A.P., 248, 251
Gaskins, H.R., 81, 97–98
Gassull, M.A., 79
AUTHOR INDEX
Gatti, D.L., 50
Ge, K., 45
Geboes, K.P., 117, 124, 133
Geironson, L., 111
Gelb, M.H., 114
Gennis, R.B., 214
Gentry, D.R., 185
George, G.N., 8, 41
Gerard, B., 193
Gerber, G.K., 243
Gerber, S.A., 114
Gerdes, R.G., 46, 177
Gerhardt, M.B., 13
Germida, J.J., 19
Gerrard, T.L., 23
Gerrish, P.J., 192–193
Ghannoum, M.J., 38
Gharat, L., 80
Ghoos, Y., 133
Ghose, A., 7
Giardina, C., 79
Gibbons, J., 246
Gibson, G.R., 77, 80, 97, 117, 119,
122–123
Gibson, K.E., 202
Gibson, P.R., 79, 117–118, 121
Gibson, T.C., 193
Gidrol, X., 236
Giedroc, D.P., 256
Gifford, D.K., 243
Gilbert, P., 37, 186
Gill, S.R., 104–105
Gine, J.J., 79
Giordano, G., 21–22, 26, 28, 42, 48–49
Gitlin, J.D., 234
Gladyshev, V.N., 38–39
Glass, R.S., 8
Glenn, A.R., 39, 49
Glossenger, J., 114
Gloux, K., 101
Godelle, B., 194, 196
Godon, J.J., 97
Goebel, B.M., 97–99
Goering, P.L., 45
AUTHOR INDEX
Goldhaber, S.B., 45
Golyshin, P.N., 101
Golyshina, O.V., 101
Gomes, C., 20
Gomollon, F., 79
Goncharoff, P., 18
Gonzalez, A., 129
Gonzalez, C., 20
Gonzalez-Huix, F., 79
Gonzalez-Lara, V., 79
Goodacre, R., 115–116
Goodman, M.F., 193
Goodman, M.T., 140
Goodman, S.H., 28
Goosen, N., 183
Gordon, D.B., 243
Gordon, J.I., 98–101, 104–105, 130,
136–137, 139
Gordon, M., 251
Gosset, G., 185
Goulle´, J.P., 46
Goupry, S.M., 141
Gourley, B.L., 257
Gourse, R.L., 185
Grady, E.F., 81
Grahn, M.F., 79
Gralla, E.B., 245
Gramet, G., 97–98
Grant, C.M., 243
Green, F.B., 13
Green, J., 187
Greenberg, E.P., 30, 33, 188
Greenblatt, J., 236
Greenway, D.L.A., 186
Gregory, J.F., 89
Grifﬁths, R.I., 107
Groisman, A., 173
Groisman, I., 41
Gromer, S., 7
Groothuis, G.M., 80
Gross, C., 245
Grosse, S., 39, 42
Grundy, D., 141
Guant, L., 45
279
Gudelj, I., 182
Guerinot, M.L., 256
Guespin-Michel, J., 207
Guest, I., 7
Guilhaus, M., 112
Guller, L., 15, 22, 44
Gun, H., 9
Gunsalus, R.P., 181–182
Gupta, N., 45, 47, 121
Gupta, S., 180
Gu¨rleyu¨k, H., 41
Gutman, N., 17
Guyoneaud, R., 34
Guzzo, J., 243–244
Gyaneshwar, P., 184
Gygi, S.P., 114
Haack, K.R., 206
Haas, H., 237–238
Habeeb, R.L., 15
Hagen, K.D., 13
Hainsworth, E., 114
Hale, B., 253
Halestrap, A.P., 79
Halkman, A.K., 9
Hall-Stoodley, L., 30
Hamer, G., 176–177
Han, G.S., 249–250
Han, S.H., 249
Hancock, R.E.W., 177
Handelsman, J., 106
Hanikenne, M., 256
Hankemeier, T., 115
Hanna, M.N., 187–188
Hannett, N.M., 243
Hanselmann, K.W., 25–26, 46
Hansen, C.L., 173
Hansen, S.R., 177, 180
Hansson, L., 101
Hara, A., 123
Hara, K., 249
Harashima, T., 246
Harbison, C.T., 243
Harder, W., 175, 177
280
Hardin, G., 207
Harig, J.M., 79
Harman, I., 81
Harman, J.G., 185
Harmsen, H.J., 105
Harrigan, G.G., 142
Harris, C.I., 92, 132
Harris, R.M., 44
Harrison, C., 254, 256
Harrison, G., 26
Harrison, J.J., 1, 28, 30–34, 36–38, 45,
48
Harrison, K.A., 244
Harrison, M.D., 248
Harrison, P.R., 7
Hartl, D., 192
Hartl, D.E., 171, 175, 200
Hartwig, A., 45
Haruta, S., 143–145
Hasegawa, D., 12
Hasegawa, P.M., 250
Hassan, H., 80
Hassett, D.J., 33
Hatada, M., 8
Hatﬁeld, D.L., 8
Ha¨uXler, S., 35
Haurie, V., 243
Havekes, L.M., 88
Havenaar, R., 82, 119, 122–123, 135
Hayes, P.C., 134
Hayhurst, E.J., 20
Hazelton, G.A., 45
He, K., 46
He, T., 105
Heber, D., 120
Hecht, V., 180–181
Hecketsweiler, B., 91, 142
Heider, J., 3, 7, 38, 41
Heilig, H., 98
Heilig, H.G., 107
Heiner, A.M., 131
Heinzle, E., 146
Heitman, J., 246
Hellerstein, M.K., 78, 89, 114, 136, 146
AUTHOR INDEX
Helling, R.B., 192, 195–196, 205
Hellingwerf, K.J., 173, 181–182
Henderson, C., 116
Henderson, T.A., 37
Hengge, R., 183
Hengge-Aronis, R., 183, 203
Hennig, U., 82
Henning, S.M., 120
Hensel, R., 30
Herbert, D., 171
Herbig, A., 252
Heredia, J., 247
Hernandez, V.J., 185
Hernandez-Navarro, A., 253
Herrero, E., 243
Herweck, F., 79
Hettich, R.L., 113
Heulin, T., 23, 28, 35
Heuveling, J., 183
Heuvelink, A.E., 9
Heydorn, A., 32
Hieter, P., 243–244
Hilgetag, C., 136
Hill, M.J, 75–77
Hill, S.M., 15
Hill, T.M., 37
Hinojosa, J., 79
Hinton, J.C., 32
Hippler, M., 256
Hiramatsu, R., 9
Hirayama, H., 40
Hirkala, D.L., 19
Hirner, A.V., 30
Hixson, C., 114
Hjelle, J.D., 45
Hoa, N.T., 81
Hoac, T., 243–244
Hobbs, G., 172
Hobman, J.L., 16, 28, 44, 185
Hockin, S.L., 26, 34
Hodges, S., 134
Hodgetts, S.J., 236
Hofer, I., 10
Hoffman, P.S., 15
AUTHOR INDEX
Hoffmann, S., 45
Hogg, R.W., 199
Hoja, U., 234
Hold, G.L., 117
Hollibaugh, J.T., 40
Hollins, J.G., 27
Holman, P., 245
Holmgren, A., 25
Holms, W.H., 189
Holstege, F.C., 245
Holt, L., 79
Holzhutter, H.G., 93
Hong, J., 185
Hooper, L.V., 98, 100–101, 137, 139
Hooreman, M., 189
Hope, M.E., 117
Hord, N.G., 145
Horikoshi, K., 40
Horiuchi, T., 200, 205
Horl, W.H., 79, 81
Horne, M.T., 172
Horth, P., 112
Hosaka, T., 186
Hoskins, L.C., 110
Hoskisson, P.A., 172
Hou, Y., 14, 16, 44, 50
Houry, W.A., 236
Hove, H., 80
Howard, P.H., 3
Howitt, S.M., 44
Hoyer, L.L., 38
Hoyle, B.D., 32
Hsing, W.H., 202
Hsu, Y.C., 236
Hua, Q., 174, 180, 183–185, 190
Huang, C., 32–33, 112
Huang, G.H., 246
Huang, L., 112
Huang, N., 79
Huang, P., 112
Hubbard, A.K., 79
Hubbell, S.P., 177, 180
Huber, H., 42, 49
Huber, R., 8, 42, 46, 49
281
Huber, R.E., 39, 41, 49
Hudman, J.F., 39, 49
Hugenholtz, J., 146
Hugenholtz, P., 97–99
Hughes, M.N., 187
Hughes, R., 117–118
Huh, S.H., 45
Hui, D., 10
Huijser, P., 256
Huis in’t Veld, J.H., 82, 122–123, 135
Humblot, C., 80, 119
Humm, A., 46
Hurd-Karrer, A.M., 23
Hutton, M., 79
Huys, G., 117, 124, 133
Hwang, E.S., 119
Hylemon, P.B., 119, 130, 133
Hyman, M.A., 140, 146
Igarashi, Y., 143–145
Iglewski, B.H., 33
Igliki, F., 133
Ihssen, J., 173, 179, 185, 187–188, 203
Ikeda, I., 81
Ikeda, T.P., 184
Ikemura, T., 105
Im, E., 111
Imaizumi, K., 81
Inada, T., 185, 204
Inagaki, F., 40
Inan, M.S., 79
Ineson, P., 107
Innis-Whitehouse, W., 81
Iovino, P., 79
Irwin, B., 37
Isbister, J.D., 112
Isermann, N., 94–95, 136
Ishihama, A., 184, 189–190, 198, 203
Ishii, M., 143–145
Isnard, A.D., 254
Issaq, H.J., 114
Ito, Y., 249–250
Iwamoto, G.K., 81
Iwanaga, T., 79
282
Iwanyshyn, W.M., 249–250
Iwata, A., 184
Iyer, V.R., 245
Izaki, K., 14
Ize, B., 32
Jackson, A.A., 84, 132
Jacob, J., 7
Jacobs, J.L., 7
Jacobson, E.S., 246
Jacoby, G.A., 6, 14
Jalan, R., 134
James, B.W., 189
Janaky, T., 235
Jannasch, H.W., 172, 207
Jansson, K., 83
Jappe`, J., 12, 43
Jaron, S., 233, 235–236, 240
Jarrin, C., 101
Jarvis, G., 101
Jelacic, S., 15
Jellema, R.H., 115
Jennings, E.G., 243
Jensen, B.B., 82–83, 129–130
Jensen, K.F., 173
Jensen, L.T., 233, 240, 247, 252
Jeong, D.W., 45
Jerchel, D., 28
Jernelov, A., 3
Jhanke, G., 45
Ji, G., 50
Jickling, G., 17
Jin, J., 250
Jishage, M., 184, 186, 190
Jobin, C., 79
Jobin, M.P., 187
Jobling, M.G., 15
Johnson, A.D., 241
Johnson, J.R., 190
Johnson, R.C., 183
Johnston, C.N., 249
Jones, B.J., 257
Jones, G., 77
Jones, N., 254, 256
AUTHOR INDEX
Jones, T., 236–238
Jordan, M.I., 184
Jorgensen, H., 82–83, 129–130
Jorgensen, J., 82, 91
Joseph, P., 253
Joubert, L., 188
Joyce, A.R., 213
Ju, H.R., 81
Jung, Y.K., 45
Junot, C., 254
Juste, C., 80
Kadowaki, T., 249–250
Kagelari, O., 79
Kahn, R., 234
Kain, R., 117
Kaiser, I.I., 40
Kaiser, P., 254–256
Kaldalu, N., 37
Kamon, J., 249
Kanauchi, O., 79
Kapaniris, O., 118, 138
Kaplan, J., 234, 240–242, 245
Kaplan, S., 28, 42
Kaplanek, P., 236
Karaolis, D.K.R., 190
Karlson, U., 29
Karnbrock, W., 46
Karpichev, I.V., 250
Kashiwagi, A., 173
Kashket, E.R., 143
Kasimoglu, E., 182
Kasper, H., 79, 118
Kassen, R., 194, 207, 213
Kasumov, T., 90
Kataoka, M., 123
Katayama, S., 254, 256
Kato, S., 143–145
Katschinski, D.M., 256
Katsuno, M., 173
Kay, W.W., 44
Kayser, A., 180–181
Kearns, D.B., 34
Keelan, M., 9, 14–15
AUTHOR INDEX
Keevil, C.W., 188–189
Keilbaugh, S.A., 79
Keleher, C.A., 241
Keles, S., 245
Kell, D.B., 116
Kelleher, J.K., 84–85, 90, 93
Kelleher, M., 245
Keller, G., 243, 247
Kellermann, J., 8, 46
Kelley, W.L., 36
Kelly, A.J., 37
Kennington, E.A., 236
Kepner, J., 131
Keren, I., 37
Kerr, B., 145
Kersting, M.C., 249
Kessi, J., 25–26, 46
Keston, A.S., 85
Keyser, R., 34
Khachane, A.N., 101
Khalili, A., 34
Khandelwal, S., 45
Khodursky, A., 169, 200
Khodursky, A.B., 181
Kick, L.S., 89
Kidd, M., 80
Kien, C.L., 131
Killeen, E., 256
Kim, A.M., 32
Kim, I.Y., 45
Kim, J.H., 245
Kim, K.K., 250
Kim, K.-S., 18
Kim, M.S., 45
Kim, N., 45
Kim, T.S., 45
Kim, T.Y., 145
Kim, Y.K., 45, 89, 114, 234
Kimata, K., 204
Kimmel, E., 241, 245
King, T., 184, 189, 198, 202–203
Kingston, D.G., 80
Kinoshita, A., 146
Kirchhof, M., 183
283
Kirchner, T., 79
Kirdar, B., 136
Kirisits, M.J., 35
Kirkup, B.C., 145
Kishony, R., 187
Kispal, G., 235
Kita, S., 249
Kitamura, T., 249
Kitano, H., 215
Kitayama, T., 146
Kjeldgaard, N.O., 185
Klaassens, E.S., 113
Klapper, I., 35
Klassen, C.D., 45
Klausner, R.D., 233, 242, 244
Kleerebezem, M., 113, 143
Klein, E.A., 8
Klett, A., 27
Klonowska, A., 23, 28, 35
Klopprogge, K., 146
Klotz, L.O., 7
Klucar, L., 9, 15, 22, 44
Knight, S.A., 238, 244
Kochetov, G.A., 146
Koh, G.Y., 137, 139
Koike, S., 97–98, 109
Kokavec, J., 15
Kokkotou, E., 111
Kolonel, L.N., 140
Kolter, R., 30, 32, 34, 197
Komorowski, R.A., 79
Korach-Andre, M., 131
Korber, D.R., 32
Korch, S.B., 37
Kori, A., 184, 189, 198, 203
Kormutakova, R., 9, 15
Kornberg, A., 140
Kosman, D.J., 234
Kovarova-Kovar, K., 172, 190
Kowalik, L., 37, 187
Kownatzki, D., 94, 136
Koyasu, S., 249
Kozlowski, F., 130
Krafft, T., 42
284
Kramer, M., 252
Kramer, U., 256
Kratchmarova, I., 114
Kreienbring, F., 82
Krempf, M., 84, 90, 131, 139, 141
Kriaris, M.S., 110
Kriengsinyos, W., 133
Krishnan, S., 79
Kristensen, D.B., 114
Krogan, N., 236
Kromer, J.O., 146
Kroncke, K.D., 7
Kropat, J., 256
Kropﬂ, K., 12
Kruh, J., 78
Krul, C., 119–120
Kryukov, G.V., 39
Ktistaki, E., 241
Kubitschek, H.E., 172, 192,
195–196
Kuchel, P.W., 146
Kuenen, J.G., 175
Kuge, S., 254
Kuhl, M., 34
Kuhn, D.M., 38
Kuhn, R., 28
Kuipers, E.J., 120
Kuipers, O.P., 207
Kuleasan, H., 9
Kull, F.J., 39
Kumar, S., 25
Kunkle, M., 8
Kupchak, B.R., 250
Kuras, L., 255
Kurland, C.G., 188–189, 191
Kurlandzka, A., 207
Kuroda, M., 39
Kurpad, A.V., 133
Kussell, E., 187
Kustu, S., 184
Kusunoki, M., 79
Kvint, K., 184, 186
Kwast, K.E., 238, 240
Kwok, T., 243–244
AUTHOR INDEX
LaBaer, J., 114
Labarre, J., 25, 33, 46, 253–254
Labayen, I., 129
Labbe, S., 231, 237–238, 244, 246–247
Lacroix, C., 46
Laden, B.P., 47
Lafaye, A., 254
Lagniel, G., 25, 33, 46, 253–254
Lai, S.C., 201
Lai, W.S., 236
Lai, Z.S., 138, 143
Laine´, G., 46
Laishley, E.J., 26, 39
Laity, J.H., 252
Lallet, S., 235
Lambert, J., 143
Lamers, W.H., 135
Lampert, S.M., 234
Lampis, S., 12, 23
Lan, C.Y., 236–238, 241
Lan, R.T., 190
Landau, B.R., 89
Lange, H., 235
Langlois, R., 246
Lappin-Scott, H.M., 32
Larauche, M., 81
Larner, A.J., 29
Larsen, D.H., 189
Larson, D.N., 114
Lau, M., 242
Lau, T., 89
Lauer, K.P., 112
Lauquin, G.J., 234
Laurent, M., 207
Lavoinne, A., 91, 142
Lawrence, J.R., 32
Lawson, S., 13
Le Bizec, B.J., 141
Le Bourhis, A.-G., 97–98
Le Marchand, L., 140
Le, N.H., 194
Le, T.T.T., 189
Lear, W., 40
Lebioda, L., 8, 46
AUTHOR INDEX
Lecannu, G., 79
Leclaire, J., 45
Leclerc, J.E., 193
Lederer, H.M., 79
Lee, B.C., 41
Lee, B.L., 7
Lee, B.R., 185
Lee, D.Y., 145
Lee, H.C., 241
Lee, H.J., 119
Lee, J.I., 201
Lee, K., 93, 134
Lee, L.J., 187
Lee, P.W., 146
Lee, S.H., 45
Lee, S.Y., 45, 145
Lee, T.A., 255–256
Lee, T.I., 243
Lee, W.N., 82, 89, 91, 142
Leene, W., 28
Lehnert, B.E., 45
Lei, X.-H., 36
Leibler, S., 37, 187
Leibold, E.A., 257
Leighton, T., 41
Leinfelder, W., 3, 7
Lejeune, P., 189
Lemke, T., 107, 109
Lendenmann, U., 172, 176–177
Lengeler, J.W., 179
Lengeler, K.B., 246
Lens, P.N., 90
Lenski, R.E., 172, 192–193, 196–197,
199, 206, 211
Leong, S.A., 237
Lesuisse, E., 235–236, 238, 244
Letscher, D., 134
Lettinga, G., 90
Levchenko, A., 173
Leverve, X., 131
Levine, J., 77, 117–118
Levitt, M.D., 77, 117–119
Levy, R.D., 80
Lew, D.P., 36
285
Lewandowski, Z., 32
Lewinski, K., 8
Lewis, G.E., 13
Lewis, K., 30–31, 37
Lewis, T.A., 27
Ley, R.E., 98–99, 101, 130, 136
Li, B.G., 193
Li, J., 50
Li, Q., 112
Li, T., 112
Li, Y.H., 187–188
Liang, T.W., 81
Lichtenberger, L.M., 118
Lide, D.R., 5–6
Lien, K.A., 91, 131–132
Lightfoot, F.G., 138
Lill, R., 235, 240, 242–243
Lilley, K.S., 114
Lim, H.C., 185
Lim, S., 82, 89, 91, 142
Lin, H.Y., 174, 185
Lin, S.J., 233
Lindblow-Kull, C., 39
Linden, T., 256
Lithgow, J.K., 20
Liu, B., 129
Liu, M., 17
Liu, S., 37
Liu, X., 112
Liu, X.F., 240
Liu, X.Q., 174, 177–178, 183, 188, 202
Liu, Y., 120
Liu, Z., 39, 41
Ljungh, S., 111
Ljungqvist, O., 83
Lloyd, B.H., 15
Lloyd, S., 90
Lloyd-Jones, G., 11, 15–16, 22, 28, 44
Lobo, C., 173
Lobreaux, S., 256
Lodola, C., 20
Loguinov, A., 245
Lohmeier-Vogel, E.M., 48–49
Lombardi, G., 79
286
Lombardia, L., 253
Longin, R., 189
Lorenz, R.G., 100
Lorusso, L., 40
Losick, R., 34
Louis, P., 126
Lovitt, R.W., 172
Lowry, C.V., 240
Loyola, C.A., 20
Loza-Tavera, H., 253
Lu¨tkemeier, P., 47
Lu, J., 120
Lucchini, S., 32
Ludwig, W., 99
Lugtenberg, B., 177
Lugtenberg, B.J., 35–36
Luiking, Y.C., 90
Luis Moroder, L., 46
Luk, E., 247
Luli, G.W., 189
Lundquist, T., 13
Lundqvist, H., 46
Luo, H., 252
Luxon, P.L., 30
Luypaerts, A., 133
Lyons, A., 80
Lyons, J., 89
Lyons, T.J., 248, 250–251
Ma, J.F., 33
Ma, T.Y., 81
Maaloe, O., 185
Maathuis, A., 107, 109, 122
Macarthur, R.H., 208, 210
MacDermott, R.P., 79
MacDiarmid, C.W., 248
Macfarlane, G.T., 75, 77–78, 80, 100,
110, 117, 119, 122–123, 134–135
Macfarlane, S., 77, 119, 134–135
MacGregor, B.J., 107
Mack, C., 96
Mackay, W.G.,, 127
Mackie, R.I., 97–98, 109
Maclean, R.C., 182, 207
AUTHOR INDEX
Macsharry, J., 80
Macy, J.M., 13, 26, 42
Madara, J.L., 81
Madden, S., 8
Madoff, R.D., 118
Maeda, Y., 50
Maest, A.S., 40
Magee, E.A., 117–118
Mages, M., 12
Magnuson, A., 83
Maguin, E., 112
Magyarosy, A.C., 41
Mahadevan, R., 134, 146
Maharjan, R., 196–197, 199, 201–202,
204–206, 208, 210–212, 215
Maharjan, R.P., 180, 190, 208–209,
211–213
Mahieu, L., 46
Mai, D., 236
Makino, W., 175
Malagelada, J.-R., 119
Malagoli, C., 23
Malek, J., 182
Manche, K., 199, 204
Manchester, J.K., 101
Manderson, WG., 27
Maneﬁeld, M.,, 91, 107
Manegatti, M., 5, 43
Mangold, M., 101
Manichanh, C., 101
Manley, S.A., 8
Mann, C., 236
Mann, M., 114
Manning, S., 175, 196, 205, 207
Mao, E., 193
Maranas, C.D., 93
Marano, M.A., 89
Marciniak, J.Y., 182
Mardis, M.J., 37
Margolles, A., 112
Marguerie, G., 253
Marhan, S., 107
Mariadason, J.M., 78, 118
Marino, M.E., 89, 114
AUTHOR INDEX
Marks, L., 133
Marliere, P., 173
Marol-Bonnin, S., 82, 122–123, 135
Marques, L.L.R., 30–31, 36
Marquis, R., 187
Marr, A.G., 174, 191
Marsh, J.B., 84, 90
Marteau, P., 82, 101, 107, 122–123, 135
Martin, A.L., 3
Martin, F., 256
Martin, I., 9, 14–15
Martin, J.C., 116
Martin, L.,
Martin, L.J., 130, 137, 141–142
Martin, M., 36
Martin, P.M., 121
Martinez, J.A., 129
Martinez-Ballarin, E., 45
Martinez-Granero, F., 36
Martinez-Salmeron, J.F., 79
Marx, A., 94
Marzluf, G.A., 237, 244
Maskarinec, G., 140
Mason, C., 9
Mathan, M., 79
Mathisen, G.E., 98, 122
Matic, I., 193–194
Matin, A., 174–175, 185
Matin, M.K., 185
Mat-Jan, F., 200
Matskevich, V., 252
Matsumoto, M., 9
Matsunaga, T., 12
Matsushita, K., 182
Matthews, D.E., 87, 89
Matthews, R.C., 236
Mattick, J.S., 34
Mazzacca, G., 79
Mcauliffe, J., 184
McBain, A.J., 110, 122
McCabe, B.J., 84, 90
McCall, K., 252
McCormick, T., 38
McCracken, V.J., 100
287
McCulloch, A., 93, 134
McDermott, T.R., 33
McDonald, P., 50
McDonald, W.H., 255
McEvoy, J.L., 237
McFeters, G.A., 32–33
McGarr, S.E., 119, 130, 133
McIntosh, T.S., 87
McIntyre, A., 79
McKay, L.F., 119
McKee, W.B., 110
McLaren, A., 81
Mcleod, S.M., 183
McMurray, C.T., 253
McNamara, P.J., 36
McVey Ward, D., 240
McWilliam Leitch, E.C., 80, 126
Measday, V., 243–244
Medveczky, N., 177
Mehring, M., 30
Meier, S., 234
Meier-Eiss, J., 197, 206
Meijer, D.K., 80
Mencarelli, A., 119
Mendoza-Cozatl, D., 253
Meng, Y.L., 39
Mense, S.M., 240–241
Merchant, S., 256
Mercier, A., 238, 246
Merrin, J., 37, 187
Meshalkina, L.E., 146
Messing, B., 133
Metges, C.C., 91–92, 129, 132
Mettraux, C., 91, 142
Meyer, P.D., 82, 123–124
Meyer, S., 174, 185
Michalke, K., 30
Michel, C., 79, 130
Michelacci, F., 33, 47
Michiels, J., 186
Midtvedt, T., 98, 100–101
Mikkola, R., 188–189, 191
Milanick, M.A., 248
Millan-Plano, S., 45
288
Millard, S., 118, 138
Miller, J.H., 193
Miller, L.G., 40
Miller, S., 8
Miller, T.L., 90, 131, 144
Milne, J.B., 40
Mimouni, D., 7
Min, B.M., 45
Minchin, S.D., 185
Minekus, M., 82, 122–123, 135
Miner, P.B., 81
Misell, L.M., 89, 114
Mishina, M., 9
Mistou, M.Y., 112
Mitra, B., 39
Mitra, R.D., 253
Mitsuoka, T., 123
Mitsuyama, K., 79
Miwa, Y., 9
Miyagishi, M., 249
Miyagishima, N., 12
Miyazaki, Y., 9
Mizoguchi, E., 111
Mizota, T., 123
Mizutani, T., 182
Moeller, I., 79
Moennoz, D., 91, 142
Molenaar, D., 143, 146
Moles, J., 79
Molin, G., 188
Molin, S., 32, 189
Mollney, M., 89, 94, 96, 136
Momoshima, N., 50
Monahan, K., 187
Monod, J., 171, 174
Montigon, F., 91, 142
Mooney, M., 252
Moore, J., 117
Moore, M.D., 28, 42
Moore, R.E., 233
Morales, N.M., 190
Moran, B.J., 84, 132
Morano, K.A., 244, 246
Moreau, N.M., 141
AUTHOR INDEX
Morell, P., 47
Morelli, A., 119
Moreno-Sanchez, R., 253
Morgan, B.A., 254
Mori, H., 174, 180, 183–185, 190
Moris, M., 186
Morita, T., 79
Moro, F., 138
Moroder, L., 8
Morrison, D.J., 83, 87, 127, 131
Morselli-Labate, A.M., 81
Morshed, M.G., 9
Mortensen, P.B., 82, 91, 119
Morton, H.E., 28
Morton, R., 183
Moscoso, H., 20
Moseley, J.L., 256
Moser, H., 172, 195
Mostafa, M.E., 38
Mougel, C., 10
Moyed, H.S., 37
Moye-Rowley, W.S., 253
Mravec, J., 15
Muhlenhoff, U., 240, 242–243
Muir, J., 77
Muir, M., 179, 200
Mukai, Y., 231, 237–238, 244
Mukherjee, I., 256
Mukherjee, P.K., 38
Mukhopadhyay, C.K., 256
Mukhopadhyay, R., 50
Mukopadhyay, R., 5
Munshi, M.M., 9
Murakami, K., 249
Murat, J.C., 79
Murgia, I., 256
Murillo, L.A., 236–238
Murray, H.L., 243
Murray, R.D., 131
Murrell, J.C., 91, 107
Murthy, S.N., 79
Muskhelishvili, G., 185
Mutzel, R., 173
Myneni, S.C.B., 7
AUTHOR INDEX
Nagai, R., 249
Nagele, E., 112
Nagrath, D., 134
Nagy, A., 137, 139
Nagy, J., 113
Nakayama, Y., 146
Nalin, R., 101
Nanchen, A., 173, 180, 182, 191
Naquin, R., 256
Narang, A., 180
Narasimhan, M.L., 250
Navarro, E., 79
Naylor, C.P., 80, 117, 123
Nayyar, S.N., 185
Nealson, K.H., 187
Nebe, T., 79
Nedredal, G.I., 134
Neef, A., 101
Neese, R.A., 89, 114
Neidhardt, F.C., 180
Neijssel, O.M., 182, 214
Nelson, K.E., 104–105
Nerenberg, R., 13
Nesme, X., 10
Neubauer, P., 174, 185
Neuefeind, T., 46
Neve, J., 8
Neveu, N., 46
Newman, R.A., 44
Newman, R.D., 13
Newport, G., 236–238
Newton, J.M., 129
Ng, C., 174, 183, 188
Ng, L.K., 9, 14–15
Nguyen, P.G., 84, 90, 131, 137, 141–142
Nie, X.Y., 246
Niedermeyer, G., 110
Nielsen, J., 87
Nikaido, H., 177, 201
Nikolaev, E.V., 93
Nilsson, I., 188
Nishiumi, E., 182
Nishizono, S., 81
Niu, Y., 120
289
Niwano, K., 9
Noah, L., 137, 141–142
Noda, S., 182
Noh, K., 95
Nomura, A.M., 140
Norgren, L., 83
Notley, L., 174, 178, 183
Notley-Mcrobb, L., 174, 178, 180,
184–185, 187, 191–199, 201–206,
208–212, 215
Noumachi, W., 173
Novick, A., 171–172, 174, 180, 192,
194–195, 200, 205
Noviski, N., 89
Nunan, K.M., 13
Nusrat, A., 81
Nygren, J., 83
Nyhus, K., 246
Nystrom, T., 184, 186
Oberegger, H., 238
O’Brien, M., 111
Ochi, K., 186
Ochsner, U.A., 33
Oddie, K.M., 13
O’Dea, K., 77
Oden, K.L., 214
Odom, D.T., 243
Odom, J.D., 8, 46
Oehme, F., 256
O’Gara, F., 36
O’Halloran, T.V., 233
O’Hara, A.M., 80
Ohge, H., 118
Ohno, Y., 79
Ohteki, T., 249
Ojeda, L., 242–243
Okamoto, T., 79
O’Keefe, K.J., 190
O’Keefe, S.J., 80
Olde Damink, S.W., 134
Oleke, B.C., 23
Oliver, A., 194
Olson, M.E., 36
290
O’Mahony, C., 80
O’Mahony, L., 80
O’Morain, C.A., 81
Ong, S.E., 114
Onoe, K., 182
Oo, C., 129
O’Regan, P., 80
Oremland, R.S., 38, 40
Orlandi, S., 119
Osaki, S., 50
Osborn, A.M., 16, 28, 44
Osborn, S., 44
Oshima, T., 174, 180, 183–185, 190
Osiewacz, H.D., 245
Osterreicher, C.H., 79, 81
O’Sullivan, E., 187
O’Sullivan, M., 81
Oswald, W.J., 13
O’Toole, G.A., 32, 34
Ouni, I., 255
Ourania, R., 118
Ovari, M., 12
Overbeeke, N., 177
Owen, W., 89
Owira, P., 80
Pace, N.R., 97–99
Paganelli, G.M., 79
Pagano, I., 140
Page, M.D., 256
Painter, E.P., 23–24, 40
Palacios, R., 206
Palade, G.E., 28
Paliy, O., 184
Palmer, T., 32
Palmiter, R.D., 249
Palsson, B.O., 93, 134, 146, 182, 213
Pan, L.J., 138, 143
Pan, X., 246
Pandey, A., 114
Pang, C.P., 201
Panikov, N.S., 171, 191
Panja, A., 79
Papadopoulos, D., 197, 206
AUTHOR INDEX
Papamichos-Chronakis, M., 241
Parahitiyawa, N.B., 38
Paraskeva, C., 79
Parasuram, P., 252
Pardo, J.M., 250
Parekh, N.R., 107
Park, E., 45
Park, H.S., 45
Park, I.S., 45
Park, S.J., 182
Park, Y.C., 8
Park, Y.H., 185
Parker, S.B., 257
Parkos, C.A., 81
Parmar, R., 79
Parolini, O., 79, 81
Parra, M.D., 129
Parsek, M., 35
Parsek, M.R., 30–31, 33–34, 188
Parson, W., 238
Parsons, A.B., 236
Pasquinelli, G., 81
Passador, L., 33
Paszczynski, A.J., 27–29
Patlan, V., 186
Patten, C., 183
Patti, J.M., 8
Patton, E.E., 255
Paulo, P.L., 90
Payne, A.S., 234
Payne, W.L., 193
Pean, M., 131
Pebay-Peyroula, E., 234
Pedram, A., 81
Pelaez, F., 115
Pelletier, B., 231, 237–238, 244
Pelletier, E., 101
Pelzer, A., 45
Pena, M.M., 246
Pencharz, P.B., 89, 133, 139
Pereira, Y., 254
Perez, J.M., 20, 48
Perna, N.T., 9, 14–15
Peronnet, F., 131
AUTHOR INDEX
Perozziello, G., 173
Petat, C., 236, 253
Peterkofsky, A., 185
Peters, G., 36
Petersen, S., 89, 96
Peterson, D.A., 98–99, 101, 130, 136
Petit, J.M., 256
Petrakis, T., 241
Pettersson, M.E., 206
Pfeiffer, T., 144, 182, 213
Pﬂieger, D., 254
Pham, P., 193
Philippe, C., 119
Phillips, J., 77
Philpott, C.C., 233–235, 237–238, 240
Phung, L.T., 3, 13, 50–51
Pichuantes, S., 20
Pickering, I.J., 8, 41
Piechocki, R., 193
Piel, J., 10
Pierru, B., 42
Pignol, D., 42–43, 49
Pijl, H., 88
Pilawa, S., 38
Piller, F., 101
Piloquet, H., 139
Pilpel, Y., 253
Pilyugin, S.S., 180
Pinto, R., 193–194
Piper, M.D.W., 191
Piper, R.C., 234
Pirt, S.J., 171–172, 182, 191
Pitts, B., 32
Plaisancie, P., 138
Plishker, M.F., 21–22
Plugge, C.M., 107, 109–110, 122
Plumbridge, J., 204
Polen, T., 183
Polli, J., 80
Pollington, A., 240
Pomare, E.W., 80, 117, 123, 137
Pommerenke, B., 107
Pommier, J., 21–22, 26, 28, 42, 48–49
Pool, W., 82, 123
291
Poole, R.K., 177, 187
Pop, M., 104–105
Popham, D.L., 184
Porcelli, I., 32
Porcher, E., 196
Porter, T.D., 45, 47
Portnoy, M.E., 233, 240
Poser, B., 47
Postma, P.W., 179, 185, 190
Pot, B., 117, 124, 133
Pot, I., 243–244
Pothoulakis, C., 111
Potrykus, J., 34
Pouteau, E., 84, 90, 131, 137, 139,
141–142
Powers, L., 131
Powers, P., 131
Poynton, H., 245
Poyton, R.O., 238, 240
Prade, L., 46
Prado, M., 247
Prasad, P.D., 121
Prats, G., 190
Pratt, L.A., 202
Prenner, E.J., 8
Prensier, G., 189
Presser, T.S., 40
Preston, T., 83, 87, 127, 131
Pretzer, G., 143
Previs, S.F., 84, 90
Priebe, M.G., 131
Prigentcombaret, C., 189
Prince, R.C., 8, 41
Pringault, O., 34
Proctor, R.A., 36
Pronk, J.T., 179, 191
Prost, L., 35
Protchenko, O., 233, 240
Pryde, S.E., 116, 126
Pufahl, R.A., 233
Puig, S., 234–237, 242
Pulimood, A.B., 79
Pullan, S.T., 187
Pupo, G.M., 190
292
Qiao, W., 252
Quadroni, M., 179–180, 185, 197
Quake, S.R., 173
Quinlivan, E.P., 89
Quinn, J.J., 79, 256
Quinn, J.M., 256
Quivey, R.G., 187
Raangs, G.C., 105
Raasi, S., 255
Rabenstein, D.L., 25
Rabiei, M., 38
Rabot, S., 80, 119
Radajewski, S., 107
Radke, J., 246
Radman, M., 193
Raﬁi, M., 133
Ragab, A.M., 46
Ragas, P.C., 34
Rahaman, M.M., 9
Rainey, P.B., 194, 207, 213
Rakotoambinina, B., 133
Ram, R.J., 113
Ramachandran, N., 114
Ramadan, S.E., 46
Ramakrishna, B.S., 79
Ramakrishna, R., 93, 134
Ramanan, N., 238
Ramirez, A., 20
Ramirez-Solis, A., 5
Ramos-Montoya, A., 146
Rand, J.D., 186
Randi, M.R., 43
Rang, C.U., 189
Rashford, J., 233, 235, 237, 240
Rasoulpour, R.J., 79
Rastall, R.A., 122
Ratcliffe, R.G., 84
Rathgeber, C., 11, 28
Ratner, S.,, 85
Ray, E., 233, 235–236
Razak, A.A., 46
Reading, N.C., 187
Reamer, D.C., 30
AUTHOR INDEX
Rech, S., 42
Reches, M., 41
Rechkemmer, G., 79
Redd, M.J., 241
Redhead, D.N., 134
Redler, B., 83
Reed, J.L., 134
Reed, S.I., 255
Reeves, G.T., 180
Reeves, P.R., 190
Regalla, L.M., 250
Rege, B., 80
Reilly, M.P., 7
Relman, D.A., 101, 104–105
Remesy, C., 79
Ren, B., 243
Ren, Y.X., 138, 143
Renga, B., 119
Rensen, P.C., 88
Rensing, C., 39
Rerat, A.A., 82
Resch, A., 41
Reszko, A.E., 90
Revhaug, A., 134
Reyes-Duarte, D., 101
Rhee, S.H., 111
Rhodes, J.M., 138
Richardson, C.J., 118
Richet, E., 204
Richter, F., 79
Rickard, K., 118
Ridlon, J.M., 119, 130, 133
Riedel, C., 96
Rieder, C., 88, 90, 131
Riedesel, H., 81
Riegler, M., 111
Riera, J., 79
Rigottier-Gois, L., 97–98, 101
Riley, M.A., 145, 196, 201
Rinaldi, N.J., 243
Rinas, U., 180–181
Rist, B., 114
Ritchie, D.A., 15–16, 28, 44
Rittenberg, D.,, 85
AUTHOR INDEX
Rittmann, B.E.,, 13
Rivilla, R., 36
Roberfroid, M.B., 122
Robert, F., 243
Roberts, I.S., 34
Robins, R.J., 130
Robinson, A.K., 248
Robinson, N.J., 248
Roca, J., 101
Rochet, V., 97–98
Rodarte, G., 236–238
Rodgers, J., 248
Rodrigues-Pousada, C., 253–254
Rodriguez Lemoine, V., 16, 20
Rodriguez-Manzaneque, M.T., 243
Roe, F., 32
Roediger, W.E., 78, 99, 117–118, 123,
138
Rogers, J., 79
Rojas, D.M., 33
Rolfes, R., 235, 237
Roller, M., 79
Roller, S.D., 187
Rombeau, J.L., 79
Romero, D., 206
Romero-Gomez, M., 134
Romijn, J.A., 88
Rooker, M., 9, 14–15
Rooyackers, O., 83
Roper, N.J., 28, 30–34, 37
Ros, J., 243
Rose, C., 134
Rose, D., 42, 49
Rose, M.R., 196
Roseiro, J.C., 187
Rosella, O., 79
Rosen, B.P., 5, 39, 50
Rosenberg, D.W., 79
Rosenberg, H., 177
Rosenzweig, F., 192, 206, 213
Rosenzweig, R.F., 196, 205, 207, 213
Rossol, S., 79
Rosson, R.A., 187
Roth, H., 131
293
Roth, J.R., 206, 212
Rothenberger, D.A., 118
Rother, M., 41
Rothman, D.L., 84, 90
Rotte, C., 235
Rouault, T.A., 242
Rouch, D.A., 16, 28, 44
Rouillon, A., 255–256
Roviezzo, F., 119
Rowland, I.R., 77, 119
Rozen, D.E., 196
Rudd, K.E., 212
Ruppin, E., 93
Russo, T.A., 190
Rutgeerts, P., 133
Rutgers, M., 190
Rutherford, J.C., 233, 235–236,
242–244, 246
Rutten, E.P., 90
Ryan, F.J., 195
Rydzynski, K., 81
Rye Clausen, M., 80
Saadi, S., 18
Saadia, R., 80
Saarela, M., 107
Saavedra, C.P., 20–22, 48
Sabaty, M., 42–43, 49
Saber, S.M., 38
Sacher, M., 42, 49
Saemann, M.D., 79, 81
Saetre, A., 17
Saftic, S., 188
Sagliocco, F., 243
Sahm, H., 89, 96
Said, H.M., 81
Saier, M.H., 185
Saito, M., 9
Sakazaki, R., 9
Sakihama, Y., 40
Sakono, M., 81
Saleh, F.A., 9
Saltman, L.H., 18
Salyers, A.A., 110
294
Samaranayake, L.P., 38
Samaranayake, Y.H., 38
Samuel, B.S., 104–105
Samuels, M., 254
Sanchez, B., 112
Sanchez-Lombrana, J.L., 79
Sanchez-Romero, J.M., 11
Sanders, O.I., 39
Sanderson, I.R., 79
Sanford, K., 116
Sangurdekar, D.P., 181
Santini, D., 81
Santini, J.M., 38
Santos, V.A., 101
Sapin, C., 98, 101
Sartor, R.B., 79
Sasaki, M., 79
Sasaki, R., 234, 242
Sata, S., 9
Satokari, R.M., 107
Sauer, K., 30
Sauer, N., 234
Sauer, U., 90, 173, 180–182, 191
Sauer, W.C., 91, 131–132
Sawers, G., 3, 7
Saxena, J., 3
Saxer, G., 197
Schade-Serin, V., 89, 114
Schaffner, W., 257
Schellhorn, H., 183
Schemann, M., 141
Scheppach, W., 78–79, 118, 137
Scheppe, M.L., 193
Scherrer, R., 37
Schertzberg, M., 183
Scheu, S., 107
Schicker, A., 173, 180, 182, 191
Schilling, C.H., 93, 134
Schlegel, A., 204
Schmitt, P., 187
Schneider, D.A., 185, 196–197, 206
Schneider, L.K., 195
Schoenheimer, R., 85
Schoeser, M., 238
AUTHOR INDEX
Schols, A.M., 89–90, 97
Schro¨der, I., 42
Schreiner, O., 28
Schrenzel, J., 36
Schreurs, W.J., 143
Schuldiner, S., 17
Schultz, J., 241
Schuren, F., 113
Schuster, S., 93, 136, 144, 182, 213
Schweizer, E., 234
Sebat, J.L., 27
Seckler, R., 8
Sediari, L., 119
Seeram, N.P., 120
Seeto, S., 174, 180, 191–194, 196–199,
201–206, 208–213, 215
Segel, I.H., 41, 49, 80
Segre, D., 93
Sekine, S.I., 186
Sekirov, I., 80, 100
Selivanov, V.A., 146
Semenkovich, C.F., 137, 139
Sen, S., 134
Senn, H., 176–177
Sentenac, A., 253
Seok, Y.J., 185
Serra, J., 119
Severance, S., 234
Shachar-Hill, Y., 84
Shah, D., 37
Shah, M., 113
Shakoury-Elizeh, M., 235, 237
Shamberger, R.J., 24, 40
Shanahan, F., 80
Shane, B., 89
Shaner, L., 246
Sharp, R.R., 196, 213
Shauger, A.E., 184
Shehata, T.E., 174, 191
Sheikh, B., 243–244
Shen, W.C., 246
Sherburne, R., 14
Sherburne, R.K., 15
Shibata, Y., 249
AUTHOR INDEX
Shibutani, Y., 7
Shim, J., 45
Shimada, T., 9
Shimizu, K., 174, 180, 183–185, 190
Shimizu, T., 249
Shingler, V., 186
Shirazi-Beechey, S.P., 121
Shlomi, T., 93
Shlyapnikov, M.G., 28, 32, 43
Shoemaker, J., 180
Shohat, B., 7
Shohat, M., 7
Shrift, A., 39
Shu, Y.Z., 80
Shulman, R.G., 84, 90
Shuman, H., 178, 203
Siddik, Z.H., 44
Siddiqui, A.A., 81
Siddons, C.A., 9, 27
Sidique, T., 23
Siefke, C., 94
Siekel, P., 15, 22, 44
Sies, H., 7
Silar, P., 245
Silhavy, T.J., 202
Silver, S., 3, 13, 50–51
Silverberg, B.A., 30
Simon, I., 243
Simpson, S.C., 196
Singer, M.V., 79
Singh, B., 79
Singh, P.K., 36
Sinskey, A.J., 173
Sipos, K., 235
Sisson, G., 15
Slater, C., 83, 87
Slootweg, T., 11
Slupska, M.M., 193
Small, G.M., 250
Smeets-Peeters, M., 82, 122–123, 135
Smid, E.J., 146
Smidt, H., 93, 98–99, 101, 106–107, 109,
122
Smith, E.A., 117
295
Smith, N.R., 41
Smits, M.A., 143
Smits, W.K., 207
Sa´nchez-Contreras, M., 36
Snel, J., 143
Snell, P., 129
Sniegowski, P.D., 193
Snozzi, M., 172, 176–177
Soave, C., 256
Soergel, K.H., 79
Soeters, P.B., 82, 134–135
Soll, D.R., 246
Solovjeva, O.N., 146
Sommer, H., 79
Song, L.X., 50
Sonnenburg, J.L., 98–99, 101, 130, 136
Sonti, R.V., 206
Sorgenfrei, O., 146
Sosa, A., 20
Soto, F., 256
Soucaille, P., 116
Soularue, P., 253
Souza, V., 193
Spadafora, P.J., 139
Sperandio, V., 187
Spizzo, T., 248
Spoering, A., 30, 37
Springer, S.E., 39
Springﬁeld, J.R., 77, 117–119
Sproule, K.M., 30–31, 34, 37–38
Sredni, B., 7, 47
Sredni, D., 7
Srikantha, T., 246
Srinivasan, C., 252
Stackebrandt, E., 11, 28, 42
Stacpoole, P.W., 89
Stadtman, T.C., 39, 41
Stams, A.J., 90
Stanghellini, V., 81
Stanley, N.R., 32
Stappenbeck, T.S., 101
Stark, P.R., 114
Starkey, M., 34–35
Stearman, R., 233, 244
296
Steen, H., 114
Steensma, H.Y., 240
Steinbacher, S., 8, 46
Steinberg, N.A., 40
Steiner-Mordoch, S., 17
Steipe, B., 8, 46
Stell, A.L., 190
Stellaard, F., 87, 89, 131
Stemmler, T.L., 5
Stensonholst, L., 188
Stephanopoulos, G.N., 93, 134
Sterkenburg, A., 177
Stevens, A.M., 173
Stewart, C.S., 116, 125–126
Stewart, D., 256
Stewart, J., 193
Stewart, P.S., 32–35
Stillman, D.J., 250
Stingl, U., 107
Stockl, J., 79, 81
Stofﬂer, G., 237–238
Stolz, J.F., 38, 40, 234
Stoodley, P., 30, 32
Storey, D.G., 36
Strand, I., 83
Strap, J.L., 188
Stremick, C.A., 30–31, 33, 36, 45, 48
Strengman, E., 245
Strike, P., 15–16, 28, 44
Strohl, W.R., 189
Strompl, C., 101
Stroobant, P., 96
Struhl, K., 253
Stuchlik, S., 15, 22, 44
Su, N.Y., 254, 256
Su, T., 245
Suarez, F.L., 119
Suau, A., 97
Sufya, N., 37, 186
Sugano, M., 81
Sugimoto, M., 146
Sugiyama, K., 79
Sugiyama, T., 249
Sullivan, M.X., 28
AUTHOR INDEX
Sultanul Aziz, K.M., 9
Summers, A.O., 3, 6, 13–15
Suter, P.M., 140
Sutherland, I.W., 34
Sutren, M., 97
Sutter, V.L., 98, 122
Suyk-Wierts, J.C., 134
Suzina, N.E., 28, 32, 43
Suzuki, Y., 9
Svensater, G., 187–188
Swearingen, J.W., 21–22
Sweetlove, L.J., 145–146
Swierczek, S., 252
Swings, J., 117, 124, 133
Switzer-Blum, J., 40
Szczypka, M.S., 253
Szentkuti, L., 81
Szilard, L., 171, 174, 180, 192,
194–195
Szomolay, B., 35
Szyperski, T., 89
Tabet, J.C., 254
Tacnet, F., 254
Taddei, C., 5, 43
Taddei, F., 193–194
Tadler, S.C., 196
Tagami, H., 204
Tagne, J.B., 243
Tainer, J.A., 253
Taira, K., 249
Takahashi, H., 185
Takahashi, K., 79
Takahashi, T., 9
Takai, K., 40
Takata, Y., 140
Takekawa, S., 249
Takezawa, Y., 182
Takizawa, K., 9
Talke, I.N., 256
Tamarit, J., 243
Tan, K.-S., 25
Tanabe, H., 79
Tanaka, Y., 204
AUTHOR INDEX
Taneja, R., 80
Tang, S., 241
Tannock, G.W., 101
Tantalean, J.C., 20, 48
Tapiero, H., 7
Tarantino, D., 256
Tari, L.W., 17
Tarr, P.I., 15
Tavan, E., 98
Taylor, A., 27, 46
Taylor, B.A., 138
Taylor, D.E., 3, 9, 12, 14–19, 21, 23–24,
27–28, 38, 44, 46, 48, 50
Te Biesebeke, R., 113
Teich, A., 174, 185
Teitzel, G.M., 30–31
Teixeira De Mattos, M.J., 190
Telford, J.N., 23
Telling, R.C., 171
Tempest, D.W., 182
Ten Have, G.A., 134
Tenailleau, E., 130
Tenaillon, O., 193–194, 196
Ter Linde, J.J., 240
Terada, A., 123
Terauchi, Y., 249
Teusink, B., 88, 146
Tew, K.D., 7
Theg, S.M., 256
Thelen, M.P., 113
Thelin, A., 101
Theondel, M., 36
Thiele, D.J., 234–237, 242, 245–246, 253
Thomas, D., 255–256
Thomas, J.W., 28
Thomassin, S., 187
Thompson, C.M., 243
Thompson-Eagle, E.T., 12, 29
Thorell, A., 83
Thuillier, F., 133
Tichonicky, L., 78
Tiedeman, J.S., 233, 235, 237, 240
Timmis, K.N., 101
Tippin, B., 193
297
Tobe, K., 249
Toda, T., 254, 256
Toews, A.D., 47
Tokunaga, T.K., 7
Toledano, M.B., 254
Tolker-Neilsen, T., 34
Tolmachev, V., 46
Tomas, J.M., 44
Tomita, F., 182
Tomita, M., 146
Tomizawa, J., 200, 205
Tomlin, K.L., 28, 31–34
Tompkins, R.G., 134
Tong, A., 236, 243–244
Tong, W.H., 242
Toninello, A., 44
Toone, W.M., 254
Topalidou, I., 241
Topping, D.L., 121
Toptchieva, A., 15
Torrallardona, D., 92, 132
Tottey, S., 256
Touati, D., 25, 33, 46
Toupance, B., 194
Townsend, D.M., 7
Toyonaga, A., 79
Travers, A., 185
Travisano, M., 196–197, 199
Tremaroli, V., 33, 47
Tresan, R.B., 13
Treves, D.S., 175, 196, 205, 207, 213
Trevors, J.T., 13
Trezeguet, V., 234
Trobner, W., 193
Troger, J., 256
Trott, A., 244
Trugnan, G., 98, 101
Trutko, S.M., 28, 32, 43
Tsang, P.W.K., 38
Tsay, R., 89
Tsen, S.D., 201
Tseng, C.P., 182
Tsuchida, A., 249
Tsui, H.C.T., 193
298
Tsujikawa, T., 79, 81
Tsuno, N.H., 249
Tsunoda, M., 249
Tu, N., 15, 22, 44
Tucker, F.L., 28
Tuleu, C., 129
Tumpling, W., 12
Turecek, F., 114
Turna, J., 9, 15, 22, 44
Turnbaugh, P.J., 104–105
Turner, P.E., 190
Turner, R.J., 1, 3, 12, 14, 16–19, 21–24,
26, 28, 30–34, 36–38, 42–46, 48–50
Tweeddale, H., 178, 180, 187
Tyers, M., 255–256
Tyson, G.W., 113
Tzamarias, D., 241
Tzeng, C.M., 140
Uchida, S., 249
Uchiyama, T., 105
Ueda, S., 184
Ueta, R., 234, 242–244
Uetrecht, J., 7
Ulgen, K.O., 136
Ung, S., 48–49
Ungerstedt, U., 83
Urabe, I., 173
Urbanowski, J.L., 234
Vaara, M., 177, 201
Vagstad, A., 250
Vahouny, G.V., 138
Vaidyanathan, S., 115
Vaisanen, M.L., 80
Valachovic, M., 242
Valdes, J.J., 188
Valentine, J.S., 245
Valkova, D., 15
Vallini, G., 12, 23
Vallino, J.J., 93
van Bakel, H., 245
Van Dam, K., 179, 182, 190
van de Kerkhof, E.G., 80
AUTHOR INDEX
Van De Putte, P., 183
van den Bogaard, A.E., 134
Van den Broek, D., 35–36
van den Heuvel, E.G., 82, 123
van der Greef, J., 96
van der Heijden, R., 96
van der Laan, M., 245
van der Meer, R., 143
van der Vossen, J.M.B.M., 82, 123
van der Werf, M.J., 115
van der Woude, J.C., 120
van Eijk, H.M., 87
Van Fleet-Stalder, V., 30, 41
Van Iterson, W., 28
van Leeuwen, P.A., 90
van Lier, J.B., 90
van Netten, P., 9, 27
van Nuenen, M.H., 119–120
van Nuenen, M.H.M.C., 82, 123–124
Van Tassell, R.L., 80
Van Veen, H.W., 44
Vandaux, P., 36
Vandenbroek, P.J.A., 179
Vanderleyden, J., 186
Vandijken, J.P., 179
Vanhoutte, T., 117, 124, 133
Vargas, C.N., 192, 195–196, 205
Vary, J.C., 15
Vasquez, C., 20
Vasquez, C.C., 20–22, 33, 48
Vassylyev, D.G., 186
Vassylyeva, M.N., 186
Vaughan, E.E., 97–98, 107, 110, 113
Vavrova, S., 15
Veening, J.W., 207
Veenstra, T.D., 114
Velazquez, O.C., 79
Veldkamp, H., 172
Vemuri, G.N., 181
Vendeland, S., 8
Venema, K., 73, 82, 93, 99, 101,
106–107, 109, 120, 122–124
Venkataraman, K., 252
Veprek, B., 3, 7
AUTHOR INDEX
Verbeke, K., 117, 124, 133
Verberkmoes, N.C., 113
Verbrugghe, K., 117, 124, 133
Vercellotti, J.R., 110
Verkade, P., 81
Vermeglio, A., 12, 21–23, 25–26, 28, 33,
35, 39, 42–43, 46, 48–49
Vermeulen, M., 119
Vermunt, S.H., 123
Verne, G.N., 139
Verstraete, W., 122
Veyrier, F., 234
Vicente, M.F., 115
Vidal, O., 189
Vik, S., 34
Vilaire, G., 238
Vilchez, G., 16, 20
Villa, N.Y., 250
Villacieros, M., 36
Vinceti, M., 23
Vinella, D., 185
Vinh, J., 254
Vitali, B., 112
Vitkup, D., 93
Vivian, A., 19, 21
Vivoli, G., 23
Vlegels, E., 177
Voisard, C., 237
Volkert, T.L., 243
Vollmann, A., 42, 49
Vollmer, M., 112
Von Eiff, C., 36
Von Engelhardt, W., 81
von Lieres, E., 96
Vonk, R.J., 131
Voshol, P.J., 88
Vulpe, C., 245
Wachters-Hagedoorn, R.E., 131
Wada, M., 182
Waddington, W.A., 129
Wagner, A., 215
Wagner, B., 107, 109
Wagner, M., 47
299
Wagner, R.R., 201
Wagner, S.A., 110
Wagner-Recio, M., 47
Wahl, S.A., 87
Wahren, J., 89
Waisberg, M., 253
Wait, R., 113
Wajngot, A., 89
Waki, H., 249
Waldron, S., 87
Walker, A.W., 80, 126
Wallace, J.L., 119
Walmsley, A.R., 50
Walmsley, R., 45
Walper, J.F., 28
Walter, E.G., 3, 14–16, 18
Walter, J., 101
Walters, M.C., 32
Wang, B., 112
Wang, F., 248
Wang, H., 112
Wang, H.N., 181
Wang, J., 112, 237
Wang, J.D., 138, 143
Wang, P., 246
Wang, Q.Y., 138, 143
Wang, T., 137, 139
Wang, Y., 238
Wang, Z., 252
Wanner, B.L., 184
Ward, T.R., 15
Wasinger, V., 112
Wasserman, D.H., 90
Watanabe, C., 8
Watanabe, K., 105
Watanabe, S., 19, 21
Waters, B.M., 240, 248
Watson, J.H.P., 77
Watson, R.B., 114
Watt, W.B., 172, 176, 200
Watzl, B., 79
Waugh, M., 246
Weaver, C.T., 100
Weaver, G.A., 131
300
Weaver, L.T., 83, 87, 127, 131
Webb, D.C., 44
Weber, H., 183
Weber, J., 180–181
Weerapreeyakul, N., 80
Wegrzyn, G., 34
Wei, E.T., 23
Wei, K., 112
Weickert, M.J., 197, 199
Weilenmann, H., 191–192
Weilenmann, H.U., 187
Weiner, J.H., 3, 12, 14, 16–19, 21–24,
26, 28, 38, 42, 44, 46, 48–50
Weintraub, A., 8
Weitman, H., 47
Welling, G.W., 105
Welty, F.K., 84, 90
Wemmie, J.A., 253
Wen, X., 79
Wen, Z., 45
Wendisch, V.F., 183
Wenger, R.H., 256
Werner, A., 245
Werner, E., 32
Werner, M., 253
West, S.E., 33, 110
Westerhoff, H.V., 182
Weusthuis, R.A., 179
Whanger, P., 8
Wheeler, G.L., 243
Whelan, K.F., 15
Whitchurch, C.B., 34
White, K.N., 50
Whited, G., 116
Whitehall, S.K., 248
Whiteley, A.S., 91, 107
Whitﬁeld, C., 34
Whitham, G.H., 4
Whitman, W.B., 41
Whittaker, S., 9
Wiback, S.J., 134, 146
Wick, L.M., 179–180, 185, 191–192, 197
Wickenheiser, E.B., 30
Wiechert, W., 87, 89–90, 94–96, 136
AUTHOR INDEX
Wiersma, A., 143
Wijmenga, C., 245
Wildgust, M.A., 50
Wilding, I., 129
Wilkens, L.R., 140
Wilkins, T.D., 80, 110
Williams, C.R., 200
Williams, H.H., 23
Williams, L., 179, 200
Williams, N.S., 79
Williams, R., 134
Williamson, J., 89
Williamson, P.R., 246
Williamson, W.M., 11
Wilmes, P., 113
Wilson, A.J., 118
Wilson, D.C., 133
Wilson, E.O., 208, 210
Wilson, K.H., 97
Wilson, T.H., 201
Wimpenny, J.W., 172
Winge, D.R., 233, 235–236, 242–243,
245, 247, 250–252
Winkler, M.E., 193
Winkworth, C.L., 196
Winstone, T.L., 17
Winterstein, C., 33, 47
Wintz, H., 245
Winzerling, J.J., 8
Wittenberg, C., 255
Wittmann, C., 146
Wohlschlegel, J.A., 255
Wolever, T.M., 139
Wolfaardt, G., 188
Wolfe, R.R., 84, 88–89
Wolin, M.J., 90, 131, 144
Wong, M.H., 101
Wong, P.T.S., 30
Wood, C.M., 79
Woods, J.H., 136
Woods, R., 196
Worsham, M.B., 40
Wouters, E.F., 89–90, 97
Wouters, J.T.M., 177
AUTHOR INDEX
Wozniak, D.J., 34
Wright, A., 113
Wright, B.E., 199
Wright, N.E., 186
Wright, S., 214
Wright, W.F., 13
Wu, A.L., 253
Wu, C.Y., 252
Wu, G.D., 79
Wu, H.Y., 81
Wu, Q., 8
Wunsche, J., 82
Wurzel, M., 94–95
Wycoff, T.O., 34
Wykes, L.J., 133
Wyrick, J.J., 243
Xia, F., 32
Xia, Y., 8
Xiqui, M.L., 20
Xu, A., 120
Xu, J., 101
Xu, K.D., 32
Xu, P., 237
Yakimov, M.M., 101
Yamada, A., 12
Yamaguchi-Iwai, Y., 233–234, 240,
242–244
Yamai, S., 9
Yamato, Y., 40
Yamauchi, T., 249–250
Yamdagni, R., 8
Yanagisawa, N., 111
Yang, C., 46, 174, 180, 183–185, 190
Yang, G., 45
Yang, H., 79
Yarema, M.C., 27
Yarmush, M.L., 93, 134
Yates, J.R., 255
Yau, J.Y.Y., 38
Ye, J., 38
Yee, A., 41
Yen, J.L., 254–256
301
Yerry, S., 90, 131, 144
Yeung, A., 193
Yeung, S.K.W., 38
Yilmaz, A., 9
Yilmaz, H., 9
Yin, L., 79
Ying, T., 112
Yokomizo, T., 249
Yokota, A., 182
Yokoyama, S., 186
Yokoyama, T., 134
Yomo, T., 173
You, L.C., 173
Youderian, P., 20, 48
Young, G.P., 79, 117
Young, M.Y., 7
Young, P.A., 40
Young, R.A., 243
Young, V.R., 89, 133
Younis, H.S., 8
Ytrebo, L.M., 134
Yu, E.Y., 8
Yu, H.N., 8
Yu, L., 46
Yu, P.L., 180
Yu, Y.M., 89
Yuan, D.S., 248
Yuan, J., 112
Yuen, K., 243–244
Yugi, K., 146
Yun, C.W., 233–234
Yun, D.J., 250
Yun, H.S., 185
Yurkov, V., 11–12, 28, 42–43
Yurkova, N., 11, 28
Zadic, P.M., 9, 27
Zadra, I., 237–238
Zagorec, M., 112
Zahir, Z.A., 7, 12
Zair, Y., 139
Zanaroli, G., 11
Zannoni, D., 1, 5, 11, 28, 33, 42–44,
47–48
302
Zarzov, P., 256
Zawadzka, A.M., 27–29
Zeitlinger, J., 243
Zello, G.A., 89
Zeng, M., 112
Zeyl, C.W., 192
Zhang, E., 201–202
Zhang, H., 255
Zhang, L., 240–241
Zhang, S., 246
Zhang, X.J., 89, 112
Zhang, Y., 7, 12, 23, 38–39, 90, 112,
131, 144
Zhang, Z.G., 173, 185
Zhang, Z.S., 138, 143
Zhao, H., 248–249, 252
Zhao, R., 236, 246
Zheng, D.L., 185
Zheng, O., 46
AUTHOR INDEX
Zhong, S., 200
Zhong, S.S., 138, 143
Zhou, L.W., 237
Zhou, T., 50
Zhu, L., 112
Zhu, X., 246
Zhu, Z., 247
Ziglio, G., 43
Ziman, M., 36
Zinoni, F., 3, 7, 41
Zitomer, R.S., 240
Zlabinger, G.J., 79, 81
Znaidi, S., 244
Zoetendal, E.G., 97–98, 100, 107, 109
Zohri, A.A., 38
Zoller, W.H., 30
Zuckermann, F.A., 81
Zumbrennen, K.B., 257
Zurakowski, D., 89
Subject Index
Note: The page numbers taken from ﬁgures and tables are given in italics.
acetate-scavenging bacteria, 206
adenylate cyclase, 185–186
ADH1 gene, 250–252
ADH3 gene, 250–251
ADH4 gene, 251
alarmone ppGpp, 186
amino acids
branched chain, 119
generation by gut bacteria, 91–92
metabolism by isotope labeling,
131–134
in humans, 91
3-amino-1,2,4-triazole (AT), 208
ammonia, 119
anaerobic bacteria, 76
Anaerostipes caccae, 126
anaplerosis, 96
antagonistic pleiotropy, 202–203
anti-inﬂammatory cytokines, 79
anti-inﬂammatory effect, 79
Arabidopsis, 256
arginine metabolism in humans, 89
ArsAB, 50
ArsC, 50
arsenate reductase, 50
Aspergillus nidulans, 237
Aspergillus parasiticus Var. globosus,
38
Astragalus bisulcatus, 12
autoinducers, 188
Bacillus, 23
Bacillus mycoides, 12
bacterial ﬂagellin, 111
bacteriocins, 143, 145
Bacteroides, 118
Bacteroides thetaiotaomicron, 101
biﬁdobacteria, 122, 125–127
Bifidobacterium, 131, 143
Bifidobacterium adolescentis, 126
bioﬁlms, see microbial bioﬁlms
biomethylation of tellurium,
27–28
branched chain amino acids, 119
branched chain fatty acids (BCFAs),
119
butyrate, 78–79, 117–118, 121, 123, 126,
131
C. albicans, 38, 237–239, 244, 246
iron deﬁciency in, 236
C. difficile, 124
C. diphtheriae, 29
C. glutamicum, 96, 146
C. neoformans, 246
C. pasteurianum, 26, 39
C. tropicalis, 38
cadmium
responsive
activation of gene expression,
253–254
transcriptional control, 253–254
sensors, 254–256
toxicity, 253
Caenorhabditis elegans, 257
cAMP, 179–180
Candida glabrata, 246
Candida spp, 37–38
carbohydrate
fermentation, 77, 79
metabolism, 76, 83, 253
ceﬁxime-tellurite media, 9
304
cell-cell signaling, 33
Cfd1 cytosolic protein, 242
chalcogenides
comparison among, 4
glasses, 4
chalcogens, see also polonium;
selenium; tellurium
bacterial exposure, 5
bacterial physiology and,
38–45
biological reduction, 22–29
biochemical pathways for, 24
compounds, 4
efﬂux transporter, 44–45
mechanism of toxicity,
45–49
metabolic intermediates, 5
methylation in bacteria,
29–30
microbial processing of metalloid,
22–29
reactions, 4–5
sequestration by bioﬁlm matrix,
34–35
use in selective bacterial growth
media, 9–10
challenger mechanism, 30
chemostat, physiological changes in
organisms inoculated into
metabolism and energetics,
181–183
stress regulation and gene expression,
183–187
transport and membrane
permeability, 177–181
chemostat-adapted mutants, diversity in
transport strategies, 213
chemostat cultures, organisms in,
response to environmental stress,
187
chemostat environment
applications to bacterial studies,
173–177
SUBJECT INDEX
changes resulting from mutations,
195
mutational takeovers and population
changes, 195–197
mutation rates and mutators in,
192–195
nutrient-limited, 171
variation in behavior among various
species, 189–191
chemostats, divergence in, 207–211
heterogeneity detected in indicator
plates, 209
in metabolic and bioenergetic
strategies, 213–214
in regulatory strategies, 211–213
in transport strategies, 213
Chitinase 3-like-1 (CHI3L1), 111
Chlamydomonas, 256
chlorhexidine, 188
cholecystokinin (CCK) receptors, 140
Citrobacter, 23
13
C-labeled carbohydrate substrates,
131
Clostridium, 133, 144
Clostridium perfringens, 107
cloxacillin, 203
colanic acid, 34
colon cancer, 77, 80, 118, 133
colorectal cancer, 117, 119
colorectal distension (CRD), 119
colorectal tumorigenesis, 117
copper homeostasis, 245, 256
copper-responsive
gene activation, 245–246
sensors, 246–247
Coprococcus sp., 125
CRD-induced nociception, 119
Crohn’s disease (CD), 101
patients, 111
Crp protein, 185
CTH2 gene, 236–237
Cu, Zn superoxide dismutase, 245
cytochrome c oxidase (COX), 43, 245
cytochrome oxidase complex, 232
SUBJECT INDEX
cytoplasmic nitrate reductases, 26
cytosolic Fe–S cluster synthesis, 242
cytosolic glutathione peroxidase
(GSHpx), 46
deuterium labeling, 89
diacylglycerol pyrophosphate
phosphatase gene (DPP1),
249–250
dietary fat, 133
dipicolinic acid, 27
Drosophila, 256
E. coli, 26, 39, 44, 105, 176, 193
adaptive stress responses, 35
bacterial persistence, 37
bioﬁlms formation by, 34
divergence amongst coexisting
isolates in a chemostat culture,
209, 210, 211
DSS640, bioﬁlms of, 32–33
nitrate reductases (NR) from, 43
selenite toxicity, 45–46
strains, 32, 144–145
under aerobic conditions, 190
thiol oxidation in, 49
thioredoxin reductase (TR) from, 25
transport and membrane
permeability, 177–181
E. coli, in glucose-limited chemostats
antibiotic sensitivity, 187–188
metabolism and energetics, 181–183
mgl mutations in aerobic glucoselimited, 198–200, 207
quorum sensing, 188–189
stress regulation and gene expression,
183
growth rate and ppGpp, 186
nutritional status and cAMP,
185–186
physiological responses to
environmental stresses, 187
starvation and stress signals and
RpoS, 184–185
305
Embden–Meyerhof–Parnas, 131
enterobacter, 23
Enterococcus faecalis, 110
environmental bioinorganic chemistry, 3
environmental stress, response of
organisms to, 187
eosin–methylene blue (EMB), 209
ethanolamine kinase gene (EKI1),
249
Eubacterium hallii, 126
Eubacterium rectale, 126
Eubacterium sp., 130
F. prausnitzii, 125, 126
FBA, 136
Fe–S cluster synthesis, 235–236, 240,
242, 244
Fiaf, 139–140
ﬁngerprinting techniques, 91
ﬂagellin, 111
ﬂuxome, 78
formic acid, 125
Fourier transform (FT) mass
spectrometers, 87
fractional synthesis rates (FSRs), 114
fructooligosaccharides, 122, 123, 127
fungi, metalloregulators in, 232–257
G. stearothermophilus, 22
V, 21, 48
galactooligosaccharides, 122
galS, 198
GATA factors, iron-responsive
transcriptional regulation and,
237–239
GC-combustion-isotope ratio mass
spectrometry (GC-C-IRMS), 87
gene expression
cadmium responsive activation of,
253–254
in response to haem deﬁciency, 241
Zap1-dependent
activation of, 248–250
repression of, 250–252
306
gene repression
iron-responsive, 236–237
in S. cerevisiae, 236–237
Zap1-dependent, 250–252
germ-free (GF) rodents, 137
GlpF, 39
GLT1 gene, 237
gluconeogenesis, 96
in humans, 89
glucose–galactose binding protein, 180
glutamine, 142
glutathione reductase (GR), 25
green ﬂuorescent protein (GFP),
105–106
Grx3 monothiol glutaredoxin, 243
Grx4 monothiol glutaredoxin, 243
Grx5 monothiol glutaredoxin, 243
gut
metabolic ﬂux analysis applied to,
127–136
pH 79–80
gut-associated nitrogen metabolism, 92
gut bacteria
amino acid generation by, 91–92
effect on immune system, 81
metabolic activation of drugs by,
80–81
gut microbial ecosystem, importance of
butyrate, 78–79
gut microorganisms, role of, 136–137
energy balance, 137–138
innate immune system, 138–139
in obesity, 139–141
haem synthesis, 239
Hap2 protein, 238
Hap3 protein, 238
Hap4 protein, 238
Hap5 protein, 238
Helicobacter pylori, 111
histone deacetylase (HDAC), 121
homocysteine remethylation
metabolism in humans, 89
Hsp82 protein, 236
SUBJECT INDEX
humans
amino acids metabolism by isotope
labeling in, 91
arginine metabolism in, 89
gastro-intestinal (GI) tract, 75–77, see
also gut; intestinal bacteria
gluconeogenesis in, 89
homocysteine remethylation
metabolism in, 89
hydrogen sulﬁde (H2S), 118, 119
hydroxyurea (HU), 236
hypocholesterolaemic effects, 29
IBD, see inﬂammatory bowel disease
IBS, see irritable bowel syndrome
IL-10, 120
inﬂammatory bowel disease (IBD), 77,
137
in situ SIP approach, 106–109
intestinal bacteria, genomic inventories,
97–98
microbial communities, 98–100
microbiome, 100–106
stable isotope probing, 106–109
intestinal bacterial enzymes, functions
of, 110–111
intestinal bacterial metabolism, 75– 78
and gut health, 77– 78
metabolic activation of drugs by,
80–81
MFA in, see metabolic ﬂux analysis
role of stable isotopes in, 84–85
intestinal bacterial physiology, 143–145
methods to study, 81–84
intestinal diseases, 77
intestinal microbiota, proteomic studies
of, 111–113
intestinal symptoms, from gaseous
metabolites, 119
inulin, 123, 124, 141–142
iron
homeostasis, 235–236, 245
sensors, 242–244
starvation, 234, 239
SUBJECT INDEX
iron-responsive gene activation in S.
cerevisiae, 233–236
iron-responsive gene repression in S.
cerevisiae, 236–237
iron-responsive transcriptional
regulation, 233
and GATA factors, 237–239
iron-responsive transcription in absence
of oxygen, 239–242
irritable bowel syndrome (IBS), 77, 81,
119
symptoms, 119
Isa1+ gene, 238
Isa1 protein, 238
iscS gene, 48
isomaltooligosaccharides, 122
isotope-coded afﬁnity tags (ICAT),
114
isotopomers, 87, 94
IZH1 gene, 250
IZH2 gene, 250
Kaschin-Beck disease, 45
Keshan’s disease, 45
kilAtelAB, 11, 12, 18–19, 23
b-lactams, 203
lactitol, 123
Lactobacillus plantarum, 143
lactose-limited chemostats, 201
lactulose, 122, 123
ingestion, 139
lamB gene, 179
LamB protein, 179, 202, 204
leucine, 131
macrophage cell line, 120
maltotriose, 179
MalT protein, 205
marine purple non-sulfur bacteria, 12
mass isotopomer analysis, 89
mass isotopomer distribution analysis
(MIDA), 89
mass spectrometry (MS), 87
307
mast cell activity for intestinal function,
81
matrix metalloproteinases (MMPs), 111
metabolic ﬂux analysis (MFA), 78,
93–95
applied to gut, 127–136
of colonic microbiota, 134–135
in detecting microbial metabolic
stress, 95–97
metabolites, 115, 120
gaseous, 119
toxic, 117
metabolomics, 115–116
metal ion homeostasis, 232, 242
metalloids, 50
methicillin-resistant Staphylococcus
aureus (MSRA), 9, see also S.
aureus
methylation of chalcogens, 29–30
methyl cobalamin, 29–30
a-methyl glucoside, 208
mglD, 198
mgl mutations in aerobic glucoselimited E.Coli, 198–200
microbial bioﬁlms, 13, 30–32
adaptive stress responses, 35
formation, 189
fungal bioﬁlms, 37–38
genetic diversity and colony
morphology variants, 35–36
matrix, sequestration in, 34–35
persister cells, 36–37
physiology, 33
structure and susceptibility, 32–33
minimization of metabolic adjustment
(MOMA), 93
mitochondrial Fe–S cluster biogenesis,
242
mlc and malT mutations in chemostats,
204–205
monocarboxylate transporter isoform 1
(MCT1), 121
mucins, 98, 110, 118, 119, 142
multiple tracer techniques, 89
308
mutational takeovers and population
changes, 195–197
mutation rates and mutators in
chemostat populations, 192–195
mutations in chemostat populations
and their physiological effects
ampliﬁcation and other genomic
rearrangements, 206–207
changes in lac system, 201
metabolic changes and cross-feeding,
205–206
mlc and malT, 204–205
outer membrane changes, 201–203
ptsG, 205
rpoS, 203–204
mutators, 194
Mycobacterium avium complex, 9
myeloperoxidase, 79
N. crassa, 237, 244
NapA, 26
Nar1 cytosolic protein, 242
NarGHIJ, 26
NarZUWV, 26
Nbp35 cytosolic protein, 242
neuromodulator, 119
nitrate reductases (NR), 42
periplasmic and membrane-bound, 43
nitrosamines, 120
NMR spectroscopy, 87
non-essential metals, detoxiﬁcation of,
232
nutrient-limited chemostat, 171
OmpC, 179
OmpF protein, 179
organochalcogen compounds, 29
oro-cecal transit time (OCTT), 131
oxalate, 125
Oxalobacter formigenes, 125
oxygen, 239–242
P. aeruginosa, 10–11, 31, 33, 43
ATCC 27853 bioﬁlms, 33
SUBJECT INDEX
PA14, SCV cells of, 36
selenite toxicity range, 45
P. anserina, 245
P. chlororaphis O6, SCV cells of,
36
P. denitrificans, 43
P. fluorescens
bioﬁlms of, 36
K27, 30
phenotypic variation in, 36
tellurate addition, 48
P. pseudoalcaligenes, 43
KF707, 47
P. putida, 27
Paracoccus pantotrophus, 43
Paralvinella sulfincola, 11
Pcl1+ gene, 238
Pcl1 protein, 238
PCR techniques, 91
pectin, 123
Penicillium chrysogenum, 237
periodic selection events, 196
periplasmic nitrate reductase, 26
persister cells, 31, 188
PhoE, 178
phosphatidylinositol synthase gene
(PIS1), 249–250
phosphoenol pyruvate, 213
phosphotransferase system, 180
planktonic cultures, 33
plant rhizoﬁltration, 12
polonium, 49–50, see also chalcogens
polychlorinated biphenyl (PCB)
degradation, 11
polyubiquitylation, 254, 256
porin(s), 188, 201–202
genes, 188–189
proteins, 179
potassium tellurite, 27
prebiotic carbohydrates, 122
prebiotics, effects on intestinal
microbiota, 121–124
proteins, involved in iron acquisition,
234
SUBJECT INDEX
protein fermentation, 77, 80
microorganisms for, 124
proteomic studies of intestinal
microbiota, 111–113
proton-coupled symporters, 180
Pseudomonas, 23
Pseudomonas pseudoalcaligenes, 11
Pseudomonas putida, 11
Pseudomonas spp, 35, 36
Pseudomonas stutzeri, 27
Pst proteins, 178
ptsG mutations, 200
PtsG system, 213
pyridine-2,6-bisthiocarboxylic acid
(PDTC), 24
quorum-sensing (QS), 33, 143, 188–189
R. capsulatus, 26, 28, 43, 47, 48
R. sphaeroides, 30, 39, 43
radioactive tracers, 86
Ralstonia eutropha, 43
Ras2 signalling pathway, 250
reactive oxygen species (ROS), 25
reduced thiol (RSH), 48
regulatory on/off minimization
(ROOM), 93
resistant starch (RS), 123
healing effect, 141–142
rhizosphere bacteria, 23
Rhodocyclus tenuis, 30
Rhodospirillum rubrum, 30
ribonucleotide reductase protein,
236
RNA as biomarker, 107
Roseburia intestinalis, 126
Roseburia sp., 125, 126
RpoS, 189, 202
protein, 184, 190
rpoS, 203–204
S. aureus, 9, 31
ATCC 29213, planktonic cultures of,
33
309
SCV cells of, 36
selenite toxicity range, 45
S. cerevisiae, 191, 233, 236, 238–240,
243–248, 253, 256
iron-responsive gene
activation in, 233–236
repression in, 236–237
S. pombe, 237–239, 244, 246, 248, 253–254
Saccharomyces, 207
S-adenosylmethione (SAM), 29
Salmonella typhimurium, 39, 206
SCFA, 75, 77, 78, 79, 91, 107, 117, 123,
131
acetate, 139
concentration in case of death, 79–80
metabolism, effect of pH on, 80
metabolism with stable isotopes, 128
from prebiotic fermentation, 122
production, colonic, 132
production in anaerobic cultures of
fecal bacteria, 134
uptake in colonic lumen, 121
schizophrenia, 119
SCV cells of S. aureus, 36
Sdh4+ gene, 238
Sdh4 protein, 238
selenate toxicity, 49
selenite/selenate resistance genes, 21–22
selenite toxicity, 49
selenium
allotropic forms, 6
applications in biotechnology/
industry/ bioremediation, 11–13
in bioﬁlms, 34
biological reduction, 23–27
biological uses, 7–8
biomethylation, 29–30
chemical properties, 7
compounds, 6
in diet, 45–46
occurrence, 6
oxyanions, nitrate reductase (NR)
activity on, 42–43
oxyanions, resistance towards, 13–14
310
precipitation in SRB bioﬁlms, 34–35
toxicity in bacteria, 49
toxicity in eukaryotic cells, 45–46
use in structural biochemistry, 8
selenocysteine, 8
selenodiglutathione, 25
selenomethionine, 29
Selenomonas ruminatum, 39
selenopersulﬁde, 26
selenosis, 45
sensors
cadmium, 254–256
copper-responsive, 246–247
iron-responsive, 242–244
zinc-responsive, 252
Shewanella oneidensis, 34–35
Shiga toxin-producing E. coli (STEC)
O26, 9
short-chain fatty acids, see SCFA
siderophores, 27
biosynthesis, 238
SILAC (stable isotope-labeling with
amino acids in cell culture), 114
silicates, 4
sinigrin, 119
SIP techniques, 93
small colony variant (SCV) cells, 35
sodium-coupled monocarboxylate
transporter (SMCT1), 121
sodium selenite, 31
stable isotopes, 85–86
applications in biomedicine, 90–93
for cross-feeding of microorganisms
on acetate and lactate, 127
detection, 87
metabolic ﬂux analysis using,
93–95
probing of intestinal bacteria,
106–109
role in host–microbe interaction,
141–142
role in proteomic study of gut
microbiota, 114–115
SUBJECT INDEX
role in study of biosynthetic pathway,
88–89
SCFA metabolism with, 128
stable isotope-aided quantiﬁcation of
metabolic pathways, 135–136
stable isotope-aided studies, 89
on nucleic acids, 91
of proteins, 91
stable isotope-based dynamic metabolic
proﬁling (SIDMAP), 142
stable isotope-labeling
amino acid metabolism by,
131–134
techniques, 84
stable isotope probing (SIP), 91
Stenotrophomonas maltophilia, 12
Streptococcus bovis, 107
stress-induced mutagenesis, 193–194
sulfatases, 118
sulfate-reducing bacteria (SRB), 26, 34,
117, 118–119
Sulfurospirillum, 23
T. selenatis, 26
tagatose, 123
taurine, 133
tellurate, 9
resistance genes, 21
toxicity, 48
tellurides, methylated, 29
tellurite, 9–10, 10–11, 29, 42, 45
resistance determinants, 11,
12
toxicity, 46–48
use in selective bacterial growth
media, 9–10
tellurium
appearance, 5
applications in biotechnology/
industry/ bioremediation,
11–13
bioﬁlms, 34
biological reduction, 27–29
biological uses, 7–8
SUBJECT INDEX
biomethylation, 27–28
combination with Pt, 6
environmental forms, 6
methylation, 29–30
occurrence in earth’s crust, 5
oxyanions, resistance towards,
13–14
recovery, 5
structural biochemistry, 8
Ter determinants and, 14–21
toxicity in bacteria, see tellurate,
toxicity; tellurite, toxicity
telluromethionine, 8, 29
Thauera selenatis, 13
Thaurea, 23
thioredoxin reductase (TR) from
E. coli, 25
thioredoxin (Trx), 25
time-of-ﬂight (TOF) mass
spectrometers, 87
tracer–tracee ratio (TTR), 88
tristetraprolin (TTP) protein, 236
U937, 120
ubiE gene, 21, 22
ubiquitylation, 254, 256
urea, 132–133
ureum, 132
Ustilago maydis, 237
VHT1 gene, 237
311
Wolinella, 23
Wood–Ljungdahl pathway,
131
Yarrowia lipolytica, 247
Zap1-dependent activation of gene
expression, 248–250
Zap1-dependent repression of gene
expression, 250–252
Zap1-dependent sensing, 252
zinc
homeostasis, 249
responsive transcriptional regulation,
247–248
sensing, 252
starvation, 250
Zap1-dependent
activation of gene expression,
248–250
repression of gene expression,
250–252
sensing, 252
zinc responsive elements (ZREs), 248,
251
ZRC1 zinc permease gene, 248
ZRG17 zinc permease gene, 248
ZRT1 gene, 252
ZRT2 gene, 250–251
Zymomonas mobilis, 251