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Journal of Microbiological Methods 53 (2003) 211 – 219
www.elsevier.com/locate/jmicmeth
Review article
Analysis of environmental microbial communities by
reverse sample genome probing
E. Anne Greene, Gerrit Voordouw *
Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada T2N 1N4
Abstract
Development of fast and accurate methods for monitoring environmental microbial diversity is one of the great challenges in
microbiology today. Oligonucleotide probes based on 16S rRNA sequences are widely used to identify bacteria in the
environment. However, the successful development of a chip of immobilized 16S rRNA probes for identification of large
numbers of species in a single hybridization step has not yet been reported. In reverse sample genome probing (RSGP), labelled
total community DNA is hybridized to arrays in which genomes of cultured microorganisms are spotted on a solid support in
denatured form. This method has provided useful information on changes in composition of the cultured component of
microbial communities in oil fields, the soil rhizhosphere, hydrocarbon-contaminated soils and acid mine drainage sites.
Applications and limitations of the method, as well as the prospects of extending RSGP to cover also the as yet uncultured
component of microbial communities, are evaluated.
D 2003 Elsevier Science B.V. All rights reserved.
Keywords: Environmental microbial communities; Reverse sample genome probing; 16S rRNA
1. Introduction
Environmental microbial communities can contain
large numbers of different microorganisms. For instance, Torsvik et al. (1996) have concluded from an
analysis of Cot curves that soil microbial communities
contain of the order of 103 to 104 genome equivalents.
Recently the term ‘‘metagenome’’ has been coined for
the total chromosomal DNA that can be extracted from
an environmental microbial community (Rondon et
al., 2000). Assuming the average microbial genome to
consist of 3 106 basepairs (bp), it follows that the soil
* Corresponding author. Tel.: +1-403-220-6388; fax: +1-403289-9311.
E-mail address: [email protected] (G. Voordouw).
metagenome may have a combined length of 3 109
to 3 1010 bp of distinct DNA sequence, equal to or
surpassing the human genome. A shotgun strategy
towards completely sequencing the soil metagenome
would be frustrated by the fact that the component
genomes are present in variable amounts: enormous
numbers of clones would have to be sequenced to
achieve multi-fold coverage of the rarest contributing
components of the soil metagenome. A successful
shotgun sequencing strategy towards characterization
of environmental metagenomes has not yet been
reported. Instead, libraries of BAC clones with large
inserts (typically 100 kb) have been constructed for
soil (Rondon et al., 2000) and marine surface water
(Beja et al., 2000) and selected clones with interesting
phylogenetic or functional associations have been
sequenced (Beja et al., 2002).
0167-7012/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved.
doi:10.1016/S0167-7012(03)00024-1
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E.A. Greene, G. Voordouw / Journal of Microbiological Methods 53 (2003) 211–219
The objective of the current review is to evaluate
how reverse sample genome probing (RSGP) can
contribute to determine the composition and variation
in composition of the metagenome. RSGP was developed in our laboratory to determine the composition
of the culturable component of microbial communities
in oil fields (Voordouw et al., 1991, 1992, 1993;
Telang et al., 1997) and in soils (Shen et al., 1998;
Hubert et al., 1999; Greene et al., 2000, 2002). The
method can in principle be used to analyze microbial
communities of medical relevance, e.g. as found in the
human colon. RSGP is a method to characterize
community composition, focusing on the most prevalent species. Differently from PCR very minor community components cannot be analyzed with the
technique.
1.1. Steps required for implementing RSGP analysis
Implementation of RSGP involves four steps: (i)
Isolation of chromosomal DNA from pure cultures
from the selected environment. In addition to species
of special interest to the target environment (hydrocarbon-oxidizing bacteria in a soil subject to bioremediation) it is desirable to include a broad range of
other culturable bacteria. (ii) Cross-hybridization testing to define species with limited genomic crosshybridization. Genomes that show in excess of 70%
cross-hybridization can be regarded as representing
the same species (Wayne et al., 1987; Cho and Tiedje,
2001; Kisand et al., 2002). Bacteria with limited
genomic cross-hybridization have been referred to as
standards, because it is not always clear if genomes
resolved by RSGP are separated at the genus or
species level. (iii) Preparation of genome arrays by
spotting known amounts of denatured genomic DNAs
from all identified standards on a solid support.
Macroarrays have primarily been used for this purpose. However, Wu et al. (2002) recently reported the
construction of microarrays to which they referred as
community genome arrays (CGAs) and which they
planned to use for assessing microbial community
composition. One of the advantages of macroarrays is
that more denatured DNA can be spotted, increasing
detection sensitivity. An internal standard (e.g. denatured bacteriophage E DNA) should also be spotted on
the array. (iv) Random labelling of a defined mixture
of total community and internal standard DNA,
hybridization of the labelled probe with the genome
array and detection and analysis of the individual dot
hybridization data.
2. Limits to RSGP detection
The hybridization intensity Ix, observed following
hybridization of a labelled, E-spiked community DNA
containing a fraction fx (wt/wt) of genome x with a
genome array containing cx of immobilized denatured
genome x, increases with cx and fx:
Ix ¼ kx cx fx
ð1Þ
where kx is a proportionality (hybridization) constant.
Thus, detection sensitivity can be improved by
increasing cx, the amount of denatured DNA (in pg
or ng) spotted on the filter, although the relationship
between Ix and cx is not linear at higher concentrations
(Fig. 1). A modified equation, taking the time depend-
Fig. 1. Dependence of hybridization intensity (Ix or IE; DPSL,
relative units) on amount of DNA (cx or cE; ng) spotted on a filter.
Data are shown for the Desulfovibrio vulgaris Hildenborough
genome (Ix, cx, open squares; 3500 kb) and for the bacteriophage E
genome (IE, cE, filled squares; 48.5 kb). A filter containing the
indicated amounts of denatured D. vulgaris and E DNA was
hybridized with a randomly labelled probe of 100 ng D. vulgaris
and 1 ng E DNA. The dependence between I and c is linear up to
cx = 50 ng for the D. vulgaris (Ix = 47cx + 126; r2 = 0.983; dashed
line) and up to cE = 12.5 ng for the E genome (IE = 20.3cE + 26.1;
r2 = 0.998; dashed line) in this experiment.
E.A. Greene, G. Voordouw / Journal of Microbiological Methods 53 (2003) 211–219
ence of filter hybridization into account has been used
by Cho and Tiedje (2002). Eq. (1) is valid, when the
fraction of probe bound to the filter is small relative to
the fraction that remains in solution, a condition that is
generally satisfied (Voordouw et al., 1993). Spotting
genomic fragments, instead of whole genomes, further
reduces the fraction fx by which the immobilized
fragment is represented in the labelled probe. Thus,
when a 3000 kb genome, that comprises 1% of a
labelled sample, were to be represented on an array by
a 3 kb PCR fragment, fx is reduced a further 1000fold. Fortunately, Ix decreases by a much smaller
factor, typically only 10- to 20-fold, when equal
weights of the fragment or the whole chromosome
are spotted, because the hybridization constant kx
increases with decreasing size of the hybridizing
DNA (Voordouw et al., 1993). When hybridizing a
single labelled DNA with different amounts of the
same immobilized target, the detection limit is determined solely by the lowest values of Ix that can be
reliably determined. However, when hybridizing a
labelled mixture of DNAs with an array, the detection
limit is determined also by the degree of cross-hybridization. The average value for all 2162 cross-hybridizations (Telang et al., 1997) for 47 spots of a genome
array representing the microbial community in an oil
field was 2.6%. Thus, only fx values well in excess of
0.026 may be significant in view of this degree of
cross-hybridization. It thus appears that spotting
whole genomes increases the detection limit by
increasing target size ( fx), but decreases the detection
limit through high cross-hybridization. Immobilizing
a more limited target (an oligonucleotide or restriction
fragment) can increase specificity at the expense of
detection sensitivity. These considerations are not
unique to the RSGP method but apply equally to
other, e.g. gene array, hybridization methods. Crosshybridization will cause a low level of hybridization
to be observed to a spotted gene even when the gene
in question is deleted and is likely the primary reason
why very low levels of gene expression cannot be
quantitated through array hybridization. A systematic
analysis of cross-hybridization between all genes
represented on an array is not generally presented
and may indeed be prohibitively expensive when
thousands of genes need to be analyzed. There are
thus advantages and disadvantages to replacement of
genome arrays by genome fragment (or oligonucleo-
213
tide) arrays from a hybridization point of view. However, the most significant reason for trying this is that
it would allow coverage of the uncultured component
of environmental microbial communities. Although
possible in principle, the problem of linking a specific
DNA fragment to a particular strain is formidable and
requires extensive characterization of an environmental metagenome through cloning and sequencing.
Dividing environmental, microbial communities
arbitrarily into communities that are very complex
(1000 species, average fx = 0.001), complex (100
species, average fx = 0.01), of limited diversity (10
species, average fx = 0.1) and single strain (1 species, fx = 1), it follows that the cross-hybridization
problem will generally preclude RSGP analysis of
very complex communities (e.g. soil), because the
average degree of cross-hybridization between species genomes exceeds most of the fx values that
may be expected. The method is suitable for
analysis of dominant members of complex communities ( fx>0.05) found in oil fields or bioreactors
and in analysis of enrichment cultures or synthetic
consortia which are of limited diversity. Although
the soil metagenome can thus not be analyzed
directly by RSGP, enrichment cultures derived from
soils can often be successfully analyzed, because
enrichment changes the community from one that is
very complex to one that is of limited diversity.
RSGP is also particularly suitable for analyzing
enrichment cultures, because enrichment biases the
community towards organisms that can be cultured.
When a sample DNA is spiked with E and then
labelled, Eq. (1) applies to all genomes x, as well as to
the E DNA on the filter. Hence, the fractions fx of
genomes x can be estimated as (Voordouw et al., 1993):
fx ¼ ðkE =kx ÞðIx =cx ÞðIE =cE Þ1 ðfE Þ
ð2Þ
As discussed above, calculated fx values will, in general, contain contributions due to cross-hybridization.
The relative hybridization constant kE/kx can be determined by hybridizing a labelled mixture containing fx
and fE of genomes x and E to the filter containing cx and
cE of genomes x and E:
kE =kx ¼ ðfx =fE ÞðIE =cE ÞðIx =cx Þ1
ð3Þ
e.g. from the data in Fig. 1, kE/kx = 55 can be calculated.
Because kE/kx is not a universal constant for all bacte-
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E.A. Greene, G. Voordouw / Journal of Microbiological Methods 53 (2003) 211–219
rial chromosomes, it must be determined for each
genome on the array. Values for fx calculated with
Eq. (2) should satisfy 0 < fx < 1. However, sometimes
values fx>1 are calculated, perhaps due to preferential
label allocation (more label incorporated into genomes
x than into the internal standard genome E). If this is a
frequently occurring problem one is forced to report
relative fx values (%). Except for synthetic consortia,
the fx values calculated with Eqs. (1) and (2) cannot be
corrected for contributions due to cross-hybridization.
Cross-hybridization will cause every reported fx value
to be overestimated. If all genomes in a metagenome
are represented on the filter, this will cause the sum,
Sfx, of all fx values to exceed 1. One can report data by
setting Sfx = 1. The resulting relative fx values are best
reported as percentages, not as fractions. A drawback
of this is that relative fx values (%) only represent the
fractions of standards in the portion of the community
spotted on the master filter.
3. Experimental details
3.1. Isolation of DNA from pure bacterial strains
An informative genome array, to which we have
referred as a master filter in previous work, contains
the genomes of the largest possible number of
genomically distinct bacteria (standards), that can
be obtained from the target environment, usually
through culturing. Culture methods will of course
vary depending on the target environment and are
outside the scope of this review. DNA is isolated
from approximately 0.5 g (wet weight) of cells with
the method of Marmur (1961) or variations thereof.
The final DNA preparation is dissolved in 10 mM
Tris –HCl, 0.1 mM EDTA, pH 7.4 (TE), ideally at a
concentration of 50 ng/Al. It does not need to be of
high molecular weight, but must be free of RNA. Its
concentration must be carefully determined, by UV
spectroscopy or by fluorometric methods, as
described elsewhere (Voordouw et al., 1993). The
need to culture is a drawback of the RSGP method.
As indicated in the Introduction, uncultured bacteria
can in principle be represented by cloned DNA
fragments, provided relationships between uncultured
strains and cloned DNA fragments can be established.
3.2. Cross-hybridization testing
Solutions of genomic DNAs in TE are placed in a
boiling water bath for 3 min and then placed on ice.
Following centrifugation, 2 Al volumes for all genomes
are spotted on a set of membrane filters. Spotting can
be done manually with a PB600 repeating dispenser
(Hamilton, Reno, NV), fitted with a 100 Al #710
Hamilton syringe. The syringe accepts micropipet tips
and the dispenser delivers 2 Al per click. Although this
may seem archaeic, it is actually quite accurate (Fig. 1)
and doable for arrays of up to 50 genomes. Use of a
robotic device is of course preferable. Following spotting, the filters are dried and in the case of Hybond-N
(Amersham), UV irradiated to covalently link the
denatured DNAs to the membrane (Voordouw et al.,
1991). Probes are prepared by labelling a mixture of
genomes x and E using Klenow polymerase, random
hexamers and [a-32P]dCTP (Voordouw et al., 1992).
Following hybridization of each probe with a filter
under stringent conditions (6 SSC at 68 jC; Sambrook et al., 1989; Voordouw et al., 1989), washing and
drying, the filters are exposed to a phosphoimager plate
(e.g. of a Fuji Bas1000 Bioimaging Analyzer). Hybridization intensities Ix for all dots are evaluated with
MacBas software (Fuji Photo Film). Intensities corrected for differences in the immobilized DNA concentration (Ix/cx) are then plotted for all genomes on the
filter, setting the value for the probe genome to 100. An
example of two representative cross-hybridization
plots is shown in Fig. 2. Genomes with (Ix/cx)>80
can be combined to represent the same standard.
3.3. Preparation of genome arrays
Once a set of genomes with limited cross-hybridization has been identified genome arrays (master
filters), containing spots for genomes x and E, are
prepared as outlined in the previous section. Because
the goal is to determine (Ix/cx), i.e. the slope of the
plots in Fig. 1, accuracy is improved by spotting each
genome x at multiple concentrations. We have not
usually done that, except for the E standard. Once a
set of genome arrays has been obtained, they are
hybridized with labelled mixtures of each genome x
and E (Fig. 2), to measure cross-hybridization for the
set of filters made and to determine kE/kx. The 16S
rRNA genes of all standard genomes are PCR ampli-
E.A. Greene, G. Voordouw / Journal of Microbiological Methods 53 (2003) 211–219
Fig. 2. Cross-hybridization analysis. (A) Genome 2 and (B) genome
27 were used as probes against a genome array from which strongly
cross-hybridizing genomes had already been excluded. The
observed hybridization intensities corrected for differences in
amounts spotted (Ix/cx) are plotted against genome number x. The
(Ix/cx) value for the probe genome was set at 100%. Average percent
cross-hybridization with the other 47 standard genomes was (A)
1.9% and (B) 0.9%.
fied and sequenced to define the phylogenetic affiliation of the standards (Telang et al., 1997; Greene at
al., 2000).
3.4. Hybridization of community DNA probes with
genome arrays
The ease with which community DNA can be
isolated depends on the environment. In the case of
produced water from oil fields a sample (e.g. 0.5 l) is
centrifuged (16,000 g; 20 min; 4 jC) and DNA is
directly isolated from the pellet with a Marmur type
procedure (Marmur, 1961) that includes proteinase K
digestion (Voordouw et al., 1991). The final DNA
preparation is suitable for labelling and hybridization
to the genome array. In the case of soil, cells must
215
first be separated from particulates by extraction with
0.1% Na4P2O710H2O in the presence of acid-washed
polyvinylpolypyrollidone (Holben et al., 1988). Cells
are pelleted from the combined extracts and DNA is
extracted with a Marmur-type procedure. Humic
acids are removed from the DNA preparation (Jackson et al., 1997) and the purified DNA is dissolved in
TE. Following concentration determination, known
amounts of community and E DNA are labelled. It
should be pointed out that purification of a representative community DNA sample from soil is difficult.
A recent experimental review aimed at obtaining
DNA from the largest possible portion of the soil
microbial community has been presented by Frostega˚rd et al. (1999).
We routinely use 32P-labelled probes, prepared as
follows. To a microfuge tube are added 100 ng of
community DNA and sterile, distilled water to 15 Al,
followed by 5 Al of freshly prepared 0.5 ng/Al E DNA,
6 Al of primer extension (PE) mix, 2 Al of DNA
polymerase I Klenow fragment (1 U/Al) and 2 Al of
[a32P]dCTP (10 mCi/ml, 3000 Ci/mmol). The mixture
is incubated at room temperature for 3 h. PE mix is 44
Al of 0.9 M HEPES, 0.1 M MgCl2, pH 6.6; 25 Al of 1
M Tris –HCl pH 7.4; 10 Al of 0.1 M dithiothreitol; 4 Al
each of 50 mM dATP, dGTP and dTTP; 10 Al of 10
mg/ml random hexanucleotides (Voordouw et al.,
1992). PE mix can be stored indefinitely at 20 jC.
A filter (genome array) is placed in a polypropylene bag and prehybridized with a minimal volume
(125 Al per DNA spot) of prehybridization solution
(Voordouw et al., 1990) at 68 jC. After completion of
labelling, the probe is placed in a boiling water bath
for 3 min and added to the bag. Following overnight
hybridization at 68 jC, the filters are washed, dried
and exposed to phosphoimager plates as described in
Section 3.2. Hybridization intensities Ix are evaluated
for all dots and used to calculate fx values (the fraction
of each genome x in the community DNA), using Eq.
(2) or relative fx values (by setting Sfx = 1) as indicated in Section 2.
Filters can be reused many times. In the case of
radioactive probes, we prefer to simply let the label
decay, i.e. to reuse the filters after 6 months (12 32Phalf lives). Very little probe hybridizes to the filter. As
a result the concentration of single stranded DNA in
the dots remains constant throughout the hybridization
procedure and from one procedure to the next. Instead
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of using 32P-labelled probes, non-radioactive probes,
e.g. biotinylated or fluorescent probes can in principle
also be used. The use of Cy3- and Cy5-labelled
fluorescent probes is discussed in Section 5 and offers
the possibility to combine standard and reference
genomes (x and E) in a single spot. Microarrays are
then hybridized to a mixture of Cy3-labelled sample
and Cy5-labelled E DNA. Because non-radioactive
probes do not decay, reuse of solid supports (micro- or
macro-arrays) can be problematic.
4. Results of RSGP analysis of community DNAs
4.1. Microbial communities in oil fields
The RSGP method was initially developed to
analyze microbial communities in oil fields. Two main
concerns with respect to the activities of microbes in
oil fields are the occurrence of souring, which is the
microbial production of H2S, and of metal corrosion.
Sulfate-reducing bacteria (SRB) are thought to contribute to both of these deteriorating activities. Souring accelerates when oil is produced by water
injection, yielding a mixture of produced water and
oil, especially if the injection water is rich in sulfate
(e.g. sea water). Microbially influenced corrosion
(MIC) is the ability of SRB and other anaerobic oil
field bacteria to use metallic iron (Fe0) as electron
donor for sulfate reduction. Above ground treatment
facilities are frequently treated with biocides to combat these deteriorating processes. In initial studies, the
question was addressed whether a specific microbial
community develops on corrosion plugs, circular
pieces of metal with an iron surface of several cm2
that can be installed into above ground oil field
installations (separating tanks, pipelines). Following
a proof of principle and the demonstration that distinct
communities of SRB in oil fields could be distinguished by RSGP (Voordouw et al., 1991, 1992),
quantitative RSGP as defined by Eqs. (1) –(3) was
first used to analyze microbial communities in produced waters and on corrosion coupons of several
Western Canadian oil fields (Voordouw et al., 1993).
A filter with distinct genomes of 16 SRB and four
heterotrophs, and four different concentrations of E
DNA was used in these analyses. It was clear that the
microbial diversity in this environment was rather
limited, allowing clear hybridizations to be observed.
SRB were more prevalent than the heterotrophs on
corrosion plug metal surfaces, in agreement with the
idea that SRB may be main contributors to corrosion.
Values for fx as high as 10 were calculated with Eq.
(2). These high values were likely caused by nonlinearity of I versus c, since very high cx and cE were
spotted in these initial studies. Introduction of phosphoimaging technology allowed cx and cE to be
lowered to 50 and 10 ng, respectively (Fig. 1),
allowing calculation of fx values that were more
consistently in the range 0 < fx < 1. Reevaluation of
microbial community composition on corrosion coupons gave fx = 0.1– 0.4 for the most prominent Desulfovibrio spp., indicating that these SRB might
contribute to corrosion (Nemati and Voordouw,
2000). Interestingly, it could be shown by RSGP
analysis of SRB enrichments that the organisms
present on corrosion coupons are often more resistant
to the biocides used for their control than other
community members (Telang et al., 1998).
With respect to monitoring of souring, the RSGP
method was used to study the effect of nitrate addition
to oil field injection waters. This was known to boost
nitrate-reducing, sulfide-oxidizing bacteria (NR-SOB)
that are part of the resident community. Injection of
nitrate greatly boosted Thiomicrospira sp. strain CVO
from fx = 0.01 – 0.03 to fx = 0.16 – 1.2 during nitrate
injection (Telang et al., 1997). Another NR-SOB,
Arcobacter sp. strain FWKO B, was not increased
by nitrate addition (Telang et al., 1999).
4.2. Microbial communities in soils
Bagwell and Lovell (2000) used RSGP to monitor
communities of diazotrophs in Spartina alterniflora
salt marshes. In particular, community changes due
to long-term fertilization were monitored. Genomes
for their array were isolated from the short-form
Spartina, the long-form Spartina, or the Juncus
rhizosphere. Genomes for a number of reference
strains obtained from culture collections were also
included. Peculiarly, abundances measured for these
reference strains were not very different from abundances measured for diazotrophs isolated from the
target environment. A possible explanation for this
may be that the diversity in this environment is really
too large to be able to measure fx values in com-
E.A. Greene, G. Voordouw / Journal of Microbiological Methods 53 (2003) 211–219
munity DNA without some form of enrichment to
limit diversity. Thus, the observed hybridizations
may be caused largely or in part by cross-hybridization, as explained in Section 2. Bagwell and
Lovell (2000) did not use an internal standard
DNA and it is thus hard to estimate the diversity
in this rhizosphere environment in terms of fx, but the
diversity is likely to be large. The authors found a
general decrease in diazotroph population upon fertilization. Chao et al. (1997) similarly found that the
effects of different kinds of fertilizer on soil microbial communities could not easily be analyzed with
RSGP. In contrast, microbial communities in acid
mine drainage sediments could be analyzed directly
by RSGP, indicating limited diversity in this extreme,
low pH environment (Leveille et al., 2001).
Effects of organic pollutants on microbial community structure in soil were reported by Shen et al.
(1998), Hubert et al. (1999) and Greene et al. (2000,
2002). These authors were not successful in directly
analyzing changes in soil community composition
caused by the presence of contaminants. Using arrays
of 55 genomes, of which 36 represented a diversity
of aerobic hydrocarbon oxidizers, it was found that
hybridization with directly isolated, labelled soil
DNAs rarely gave features that could be meaningfully interpreted. Instead they analyzed the effect of
prolonged exposure of soil enrichment cultures to
well-defined concentrations of BTEX-type hydrocarbons referred to as C5+. Dilutions of the C5+
components in vacuum pump oil were placed in
dessicators to allow growth of the enrichment cultures at constant, reduced hydrocarbon vapor pressure. Interestingly, a succession was found in which
communities in these enrichment cultures were first
dominated by Pseudomonas spp. and then by Alcaligenes spp. The most interesting, but also somewhat
disappointing, result was that this succession was
hardly influenced by the nature of the hydrocarbon
input (e.g. only benzene, only styrene or the entire
C5+ mixture), suggesting the presence of a highly
interactive food web (Greene et al., 2002). This
means that community composition, as determined
by RSGP, cannot really define which hydrocarbons
are being metabolized, although major community
shifts are still expected when the carbon input is
changed from hydrocarbons to, for example, carbohydrates.
217
Kisand et al. (2002) took a somewhat different
approach towards characterizing culturable estuarine
bacteria in the Baltic sea, while still relying mainly on
chromosomal DNA hybridization. These authors first
determined which cultured strains hybridized most
prominently to labelled total community DNA. They
then extracted chromosomal DNAs from these abundant species to be used as probes in community
analysis in a conventional hybridization format
(immobilized community DNAs hybridized with
genome probes derived from abundant species). Five
of six dominant community members were in the
Cytophaga – Flexibacter – Bacteroides (CFB) group.
The average degree of cross-hybridization among
these five was rather high: (12 F 2%; Kisand et al.,
2002). There is no fundamental advantage in terms of
hybridization theory or practice to this approach over
the RSGP method. The relatively high degree of
cross-hybridization in the study by Kisand et al.
precludes accurate determination of most fractions
fx < 0.1. From a time management point of view,
hybridization of n genome probes with N environmental DNA samples, consisting of a mixture of
genomes, is best done according to Kisand et al.
(2002) when n < N and is best done in RSGP mode
when n>N. In many projects, samples trickle in over
time, hence N is small at any given moment. Yet one
wants a description of the community in these samples
in terms of a relatively large number of genomes (n)
making RSGP the preferred approach.
5. Prospects for further development
Efforts have been made to use gene rather than
genome arrays for analysis of environmental communities. The advantages of this approach are manifold. Genes can be propagated in plasmids or can be
continually amplified as PCR products, obviating the
need to grow and maintain a large number of standard
organisms. As explained already, genes or other
suitable genome fragments may hybridize more specifically. They can be derived through cloning and
sequence analysis of the metagenome from both
cultured and as yet uncultured organisms of the
community. The construction of arrays of genes of
interesting functionalities, e.g. dissimilatory sulfite
reductase (dsr), nitrogenase (nif), nitrate reductase
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E.A. Greene, G. Voordouw / Journal of Microbiological Methods 53 (2003) 211–219
(nirS), and naphthalene dioxygenase (nahA) to name
just a few, has been advocated (Greer et al., 2001; Cho
and Tiedje, 2002). Such arrays could in principle
directly yield information on the potential of an
environment to use sulfate as the electron acceptor,
fix nitrogen, reduce nitrate or remove naphthalene
contamination. Of course generic use of such a gene
array is only possible if the genes used are highly
conserved.
The disadvantages of using gene arrays for analysis
of environmental metagenomes have already been
discussed. The expected low fx values call for highly
sensitive detection methods to determine Ix. Use of
fluorescent probes offers sensitive detection. However, in a microarray format, the cx values are 1000fold lower than for the macroarrays discussed here (pg
rather than ng). Increasing cx, by improving linkage
chemistry, was considered a primary route for increasing the applicability of gene microarrays (Cho and
Tiedje, 2002). An advantage of the use of fluorescent
detection is that it allows simultaneous hybridization
with two differently decorated probes. Cho and Tiedje
(2002), recommended spotting 1:1 mixtures of the
chosen gene and E DNA. Hybridization with a mixed
probe of Cy3-labelled model community DNA and
Cy5-labelled E DNA allowed easy quantification of
all Cy3 signals through comparison with the Cy5 E
reference hybridization intensities.
Development of a genome fragment array that
efficiently covers an entire environmental microbial
community requires linkage of the fragments to the
strains from which they were derived. Assuming that
sequencing of the entire metagenome is not yet an
option, a possible strategy towards this goal is to (i)
generate a BAC library, (ii) identify and sequence all
BAC clones containing 16S rRNA genes, allowing
definition of their phylogenetic affiliation, and (iii)
define unique sequences elsewhere in the 100 kb
BAC sequences to construct unique gene or genome
fragment arrays, which can then be used to track
community dynamics. Basing the array on 16S rRNA
oligonucleotide probes may be difficult for very large
numbers of strains, because the 16S rRNA sequence
is rather conserved (Cho and Tiedje, 2001; Kisand et
al., 2002), offering too little sequence diversity for
successful analysis of a very complex metagenome or
a very complex community 16S rRNA. However,
progress along this route with systems of limited
diversity has been reported recently (Koizumi et al.,
2002).
Acknowledgements
EAG was supported by a postdoctoral fellowship
from the Natural Sciences and Engineering Research
Council of Canada. This work was supported by an
NSERC Strategic Grant to GV.
References
Bagwell, C.E., Lovell, C.R., 2000. Persistence of selected Spartina
alterniflora rhizoplane diazotrophs exposed to natural and manipulated environmental variability. Appl. Environ. Microbiol.
66, 4625 – 4633.
Beja, O., Suzuki, M.T., Koonin, E.V., Aravind, L., Hadd, A., Nguyen, L.P., Villacorta, R., Amjadi, M., Garrigues, C., Jovanovich,
S.B., Feldman, R.A., DeLong, E.F., 2000. Construction and
analysis of bacterial artificial chromosome libraries from a marine microbial assemblage. Environ. Microbiol. 2, 516 – 529.
Beja, O., Suzuki, M.T., Heidelberg, J.F., Nelson, W.C., Preston,
C.M., Hamada, T., Eisen, J.A., Fraser, C.M., DeLong, E.F.,
2002. Unsuspected diversity among marine aerobic anoxygenic
phototrophs. Nature 415, 630 – 633.
Chao, W.-L., Tien, C.-C., Chao, C.-C., 1997. Investigation of the
effect of different kinds of fertilizers on the compositions of a
soil microbial community using the molecular biology technique. J. Chin. Agric. Chem. Soc. 35, 252 – 262.
Cho, J.-C., Tiedje, J.M., 2001. Bacterial species determination from
DNA – DNA hybridization by using genome fragments and
DNA microarrays. Appl. Environ. Microbiol. 67, 3677 – 3682.
Cho, J.-C., Tiedje, J.M., 2002. Quantitative detection of microbial
genes by using DNA microarrays. Appl. Environ. Microbiol. 68,
1425 – 1430.
˚ ., Courteois, S., Ramisse, V., Clerc, S., Bernillon, D.,
Frostega˚rd, A
Le Gall, F., Jeannin, P., Nesme, X., Simonet, P., 1999. Qantification of bias related to the extraction of DNA directly from
soils. Appl. Environ. Microbiol. 65, 5409 – 5420.
Greene, E.A., Kay, J.G., Jaber, K., Stehmeier, L.G., Voordouw, G.,
2000. Composition of soil microbial communities enriched on a
mixture of aromatic hydrocarbons. Appl. Environ. Microbiol.
66, 5282 – 5289.
Greene, E.A., Kay, J.G., Stehmeier, L.G., Voordouw, G., 2002.
Microbial community composition at an ethane pyrolysis plant
site at different hydrocarbon inputs. FEMS Microbiol. Ecol. 40,
233 – 241.
Greer, C.W., Whyte, L.G., Lawrence, J.R., Masson, L., Brousseau,
R., 2001. New molecular tools are creating innovative ways to
address environmental challenges. Environ. Sci. Technol. 35,
364 A – 370 A.
Holben, W.E., Jansson, J.K., Chelm, B.K., Tiedje, J.M., 1988.
DNA probe method for the detection of specific microorgan-
E.A. Greene, G. Voordouw / Journal of Microbiological Methods 53 (2003) 211–219
isms in the soil bacterial community. Appl. Environ. Microbiol.
54, 703 – 711.
Hubert, C., Shen, Y., Voordouw, G., 1999. Composition of toluenedegrading microbial communities from soil at different concentrations of toluene. Appl. Environ. Microbiol. 65, 3064 – 3070.
Jackson, C.R., Harper, J.P., Willoughby, D., Roden, E.E., Churchill,
P.E., 1997. A simple, efficient method for the separation of
humic substances and DNA from environmental samples. Appl.
Environ. Microbiol. 63, 4993 – 4995.
Kisand, V., Cuadros, R., Wikner, J., 2002. Phylogeny of culturable
estuarine bacteria catabolizing riverine organic matter in the
Northern Baltic sea. Appl. Environ. Microbiol. 68, 379 – 388.
Koizumi, Y., Kelly, J.J., Nakagawa, T., Urakawa, H., El-Fantroussi,
S., Al-Muzaini, S., Fukui, M., Urushigawa, Y., Stahl, D.A., 2002.
Parallel characterization of anaerobic toluene- and ethylbenzenedegrading consortia by PCR-denaturing gradient gel electrophoresis, RNA – DNA membrane hybridization, and DNA microarray technology. Appl. Environ. Microbiol. 68, 3215 – 3225.
Leveille, S.A., Leduc, L.G., Ferroni, G.D., Telang, A.J., Voordouw,
G., 2001. Monitoring of bacteria in acid mine environments by
reverse sample genome probing. Can. J. Microbiol. 47, 431 – 442.
Marmur, J., 1961. A procedure for the isolation of deoxyribonucleic
acid from micro-organisms. J. Mol. Biol. 3, 208 – 218.
Nemati, M., Voordouw, G., 2000. Identification and characterization of sulfate-reducing bacteria involved in microbially
influenced corrosion in oil fields. Corrosion 2000. NACE
International, Houston, TX, pp. 00126-1 – 00126-9.
Rondon, M.R., August, P.R., Bettermann, A.D., Brady, S.F., Grossman, T.H., Liles, M.R., Loiacono, K.A., Lynch, B.A., MacNeil,
I.A., Minor, C., Tiong, C.L., Gilman, M., Osburne, M.S.,
Clardy, J., Handelsman, J., Goodman, R.M., 2000. Cloning
the soil metagenome: a strategy for accessing the genetic and
functional diversity of uncultured microorganisms. Appl. Environ. Microbiol. 66, 2541 – 2547.
Sambrook, J., Fritsch, E.F., Maniatis, T., 1989. Molecular Cloning:
A Laboratory Manual, 2nd ed. Cold Spring Harbor Laboratory
Press, Cold Spring Harbor, NY.
Shen, Y., Stehmeier, L.G., Voordouw, G., 1998. Identification of
hydrocarbon-degrading bacteria in soil by reverse sample genome probing. Appl. Environ. Microbiol. 64, 637 – 645.
Telang, A.J., Ebert, S., Foght, J.M., Westlake, D.W.S., Jenneman,
G.E., Gevertz, D., Voordouw, G., 1997. The effect of nitrate
injection on the microbial community in an oil field as moni-
219
tored by reverse sample genome probing. Appl. Environ. Microbiol. 63, 1785 – 1793.
Telang, A.J., Ebert, S., Foght, J.M., Westlake, D.W.S., Voordouw,
G., 1998. Effects of two diamine biocides on the microbial
community from an oil field. Can. J. Microbiol. 44, 1060 – 1065.
Telang, A.J., Jenneman, G.E., Voordouw, G., 1999. Sulfur cycling
in mixed cultures of sulfide-oxidizing and sulfate- or sulfurreducing bacteria. Can. J. Microbiol. 45, 905 – 913.
Torsvik, V., Sørheim, R., Goksøyr, J., 1996. Total bacterial diversity
in soil and sediment communities—a review. J. Ind. Microbiol.
17, 170 – 178.
Voordouw, G., Strang, J.D., Wilson, F.R., 1989. Organization of
genes encoding [Fe] hydrogenase in Desulfovibrio vulgaris
subsp. oxamicus Monticello. J. Bacteriol. 171, 3881 – 3889.
Voordouw, G., Niviere, V., Ferris, F.G., Fedorak, P.M., Westlake,
D.W.S., 1990. The distribution of hydrogenase genes in Desulfovibrio and their use in identification of species from the oil
field environment. Appl. Environ. Microbiol. 56, 3748 – 3754.
Voordouw, G., Voordouw, J.K., Karkhoff-Schweizer, R.R., Fedorak, P.M., Westlake, D.W.S., 1991. Reverse sample genome
probing, a new technique for identification of bacteria in environmental samples by DNA hybridization, and its application to
the identification of sulfate-reducing bacteria in oil field samples. Appl. Environ. Microbiol. 57, 3070 – 3078.
Voordouw, G., Voordouw, J.K., Jack, T.R., Foght, J., Fedorak, P.M.,
Westlake, D.W.S., 1992. Identification of distinct communities
of sulfate-reducing bacteria in oil fields by reverse sample genome probing. Appl. Environ. Microbiol. 58, 3542 – 3552.
Voordouw, G., Shen, Y., Harrington, C.S., Telang, A.J., Jack, T.R.,
Westlake, D.W.S., 1993. Quantitative reverse sample genome
probing of microbial communities and its application to oil field
production waters. Appl. Environ. Microbiol. 59, 4101 – 4114.
Wayne, L.G., Brenner, D.J., Colwell, R.R., Grimont, P.A.D., Kandler, O., Krichevsky, M.I., Moore, L.H., Moore, W.E.C., Murray, R.G.E., Stackebrandt, E., Starr, M.P., Tru¨per, H.G., 1987.
Report of the ad-hoc-committee on reconciliation of approaches
to bacterial systematics. Int. J. Syst. Bacteriol. 37, 463 – 464.
Wu, L., Thompson, D.K., Bagwell, C.E., Tiedje, J.M., Zhou, J.,
2002. Community genome arrays: an approach for detection
and quantification of selected bacterial taxa in environmental
samples. Proceedings 102nd General Meeting of the American
Society of Microbiology, Salt Lake City, UT, Abstract Q-50.