A review. Network motifs: theory and
experimental approaches1.
1Uri
Vincent Massinon, March 2015
MA1-BINF-4A
Alon.
A review. Network motifs: theory and experimental approaches1.
An article. Network motifs in the transcriptational regulation network of E. coli2.
1Uri
Alon, 2007 by Nature Publishing Group. DOI: 10.1038/nrg2102
2Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan & Uri Alon, 22 April 2002.
Published online by Nature Publishing Group. DOI: 10.1038/ng881
Network motifs in the transcriptational
regulation network of E. coli.
• Research through a big amount of data to make a general view of the
E. coli’s transcription network.
• RegulonDB : a database with 577 transcriptational interactions and
424 operons involving 116 transcription factors.
• Addition of 35 new transcription factors, including alternative sigmafactors (RNA polymerase subunits), after an extensive search through
the litterature
Experimental design and finds
• General network was made up and this network was scanned with algorithms in the aim to detect recurring
pattern.
• Statistical significance by comparison with randomized network having the same characteristics as the real E.
coli network.
• Three motifs found : (1) the feedforward loops (40 effector operons in 22 systems with 10 different general
transcription factors), (2) the single input element (in 24 systems of three or more operons) and (3) the
dense overlapping regulons (6 DORs),
Advances in understanding
• Allowing each global transcription factor to appear multiple times.
This reduces the complexity of the interconnections while preserving
the informations.
• Over 70% of the operons are connected to the DORs, the rest are in
small system of 1 to 3 operons. Some disjoint systems have up to 25
operons and show many SIMs and FFLs.
• Many FFLs occur at the post-transcriptational level.
Network motifs: theory and experimental
approaches.
• Main idea: the interactome can be described in term of mutiple recurring and
well-described more simple network motifs.
• This review emphasises the interactions between transcription factor proteins
and the gene that they regulate. These interactions take part in the regulation
networks.
• Those pattern are extrapolated from experimental studies carried out on simple
orgnamisms like E. coli.
• In E. coli (and other organisms in the aftermath), motifs were discovered because
of their patterns which occured more often in real networks than in random
networks.
Recurring network motifs
I. Simple regulations
A. Simple regulation
• Occurs when factor Y regulates gene X with no
additional regulation.
• Y is usually activated by the signal SY (a binding
or modification protein).
• A steady state level equal to the ratio of
production and degradation rate
• A response time define as the time it takes to
reach halfway between the initial and final level,
equal to the half-life of the gene product.
B. Negative autoregulation (NAR)
• In about half of the repressors in E. coli.
• Occurs when transcription factor represses the transcription of its own gene.
• Speeds up the response time when it uses a strong promoter (rapid initial rise in [X]).
Rapid rise can be followed by an overshoot or damped oscillations.
• Experimentally, speed-up has been shown using a fluorescently tagged repressor, TetR,
that was designed to repressed its own gene. In E. coli, that was demonstrated in the SOS
DNA-repair system with the master regulator LexA.
• NAR can reduce cell-cell variation in protein levels that result from inherent source of
noise. If the NAR feedback contains a long delay, noise can also be amplified.
C. Positive autoregulation (PAR)
• Occurs when a transcription factor enhances its own rate of production.
• Opposite effect : response times are slowed and variations are usually enhanced.
• In early stages, production of X is low because [X] is low and picks up only when [X] approaches
the activation threshold for its own promoter ( S-shape curved)
• PAR tends to increase cell-cell variability. Weak PAR leads to a broad distribution while strong PAR
can lead to a bimodal distribution.
• In the case of bimodal distribution, that can lead to a differentiation-like partitioning of cells into
two populations. In some cases, that acts more as a memory to maintain gene expression. In
other cases, that maintains mixed phenotypes, which is better to respond to stochastics
environmental pertubations.
II. Feedforward loops (FFL)
• Appears in hundreds of gene system of E. coli.
• This motif consist of 3 genes : (1) a regulator X which
regulates (2) Y, and (3) gene Z, which is regulated by
both X and Y. X is activated by the signal SX.
• Because those 3 interactions can be either activation or
repression, there exist 8 possible structural types of FFL.
4 of them are called coherent FFL whereas the others
are called incoherent FFL.
• Two common ‘’input functions’’: (1) ‘’AND gate’’ and
‘’OR gate’’.
A. Coherent type-1 FFL
• It is a ‘sign-sensitive delay’ element and a persistence detector.
• Both X and Y are transcriptational activators.
AND gate
AND gate
• Delay after stimulation but no delay when stimulation stop.
• Difference between the ON-step and the OFF-step (sign-sensitive
delay).
• The delay is determinded by the biochemical parameters of Y.
• The delay allows to filter out brief spurious pulses of SX (persistence
detector).
• The sign-sensitive delay
has been experimentally
demonstrated in the
arabinose-utilization
system of E. coli (cAMP
signal).
OR gate
• Opposite effect to the AND gate : no delay after stimulation, one
delay when stimulation stops.
• Experimentally demonstrated in the flagella system of E. coli where
the FFL has additive functions (from activators FlhDC and FliA).
B. Incoherent type-1 FFL
• It is a pulse generator and response accelerator.
• Two arms act in opposite direction : X activates Z
but also represses Z by activating Y.
• Faster response time than in simple regulation
pattern.
• This pattern acts as a pulse-like dynamic.
• NAR and I1-FFL can both accelarate the response
time. NAR works only on transcription factors (and
genes of the same operon) whereas I1-FFL can
accelerate any target gene Z.
• Experimentally demonstrated
in the galactose utilization
system of E. coli. In absence
of galactose, glucose
starvation leads to a rapid
induction of the galactose
utilization system.
C. Multi-output FFLs
• Combination of FFLs in which X and Y regulate a multiple output
genes Z1, Z2,… Zn.
• Can generate temporal orders of gene activation and inactivation by
means of hierarchy of regulation thresholds for the different
promoters.
III. Single-input modules (SIM)
• A regulator X regulates both
itself and a group of target
genes.
• Main function : expression
coordination of a group of
genes with shared function. In
addition, it can generate a
temporal expression
programme.
• The temporal order seems to
match the functional order of
the gene.
IV. Dense overlapping regulons (DOR)
• Also called multi-input motifs
(MIMs)
• Consists of a set of regulators that
combinatorially control a set of
output genes.
• It is a ‘gate-array’, carrying out
computation by which multiple
input are translated into multiple
outputs.
V. The global organization of network motifs
• The NAR and PAR networks are sometimes integrated into FFLs, usually on
Y.
• FFLs and SIMs are integrated into the DORs.
• The DORs occur in a single layer : there is no DOR at the output of one
DOR.
• Long regulatory cascades are rare. Most genes are regulated one step away
from their activators.
General view of E. coli transcriptional network
(from Shen-Orr, 2002)
VI. Network motifs in developmental networks
• Transduce the signal into cell-fate decisions.
• Contraints : (1) function on the timescale of one or several celle
generations and often (2) need to make irreversible decisions that last
even after the input signal has vanished.
• Use all the network motifs already described as well as other motifs.
A. Feedback loops comprising two
transcriptional interactions
• Positive-feedback loops made up of two transcription factors that
regulates each other.
• Two kind : (1) a double positive-feedback loop and (2) a doublenegative feedback loop.
• In both cases, a transient signal can cause the loop to lock irreversibly
into a steady state. This is a memory of an input signal.
• DPF loops : either both X and Y are
OFF, or both are ON. Useful to decide
a cell-fate.
• DNF loops: either X is ON and Y is OFF,
or the opposite. Act as toggle switch
beetwen the X en Y steady states.
• In regulating loops, the regulators X
and Y jointly regulate downstream Z
genes.
• In regulated loops, X and Y are both
regulated by a upstream regulator Z.
B. Transcription cascade
• Much longer cascades than in sensory transcription networks.
• Low timescale : on the order of one cell generation at each cascade
step or the half-life of the regulator at each step for degradable
regulators.
• Repressor cascades are more robust to noise in protein-production
rates than those of activator cascades.
VII. Interlocked FFL circuit in development
e.g. differenciation in B. subtilis.
VIII. Network motifs in other biological networks
A. Between two proteins
• Composite motif : negative-feedback loop (NFL)
in which one arm is transcriptational interaction
and the other arm is protein-protein interaction.
• Separation of timescale between the two arms
might help to stabilizise the dynamics of NFL.
• Oscillations in biological systems are often
generated by a composite NFL coupled to a PFL.
• The same motif, with different parameters can
lead to stochastic, excitable systems.
B. Networks of proteins modifications
• Signal-transduction networks showing FFLs and
other patterns such as diamond pattern which
can combine in multi-layer perceptrons.
• Generalization of information form partial
signals or graceful degradation of performance
upon loss of components.
• Data limitated.
C. Motifs in synaptic connection network
• Neurons connected by FFLs and particularly by I1-FFLs with an
afferent input as X, an inhibitory neuron as Y, and a relay neuron that
sends connections to other regions as Z.
• The fully mapped synaptic network of C. elegans shows FFLs,
diamonds, multi-layer perceptrons and two-neurons feedback loops.
IX. Convergent evolution of network motifs
• In most cases, similar FFLs come from convergent evolution, not from
homologies inherited from a common ancestor.
• In the opposite, in FFLs with a common-ancestor FFL, X and Y are
rarely homologous.
• An explanation : transcription networks seem to rewire rapidly on
evolutionary timescale (only fews mutation make broad changes).
X. Future directions
• Predictions about the functions of motifs must be tested
experimentally.
• Comprehension of networks interlocking.
• Test network motifs in eukariotic organisms.
• Beginning of investigation in networks motifs at the level of signalling
networks and neuronal network.