Download metabolic core - Chair of Computational Biology

V21 Current metabolomics
Review:
(1) recent work on metabolic networks required revising the picture of separate
biochemical pathways into a densely-woven metabolic network
(2) The connectivity of substrates in this network follows a power-law.
(3) Constraint-based modeling approaches (FBA) were successful in analyzing the
capabilities of cellular metabolism including
- its capacity to predict deletion phenotypes
- the ability to calculate the relative flux values of metabolic reactions, and
- the capability to identify properties of alternate optimal growth states
in a wide range of simulated environmental conditions
Open questions
- what parts of metabolism are involved in adaptation to environmental conditions?
- is there a central essential metabolic core?
- what role does transcriptional regulation play?
21. Lecture WS 2005/06
Bioinformatics III
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Distribution of fluxes in E.coli
Aim: understand principles that govern
the use of individual reactions under
different growth conditions.
Nature 427, 839 (2004)
Stoichiometric matrix for E.coli strain MG1655 containing 537 metabolites and
739 reactions taken from Palsson et al.
Apply flux balance analysis to characterize solution space
(all possible flux states under a given condition).
d
Ai    Sij v j  0
dt
j
vj is the flux of reaction j and Sij is the stoichiometric coefficient of reaction j.
21. Lecture WS 2005/06
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Optimal states
Denote the mass carried by reaction j producing (consuming) metabolite i by
vˆij  S ij v j
Fluxes vary widely: e.g. dimensionless flux of succinyl coenzyme A synthetase
reaction is 0.185, whereas the flux of the aspartate oxidase reaction is 10.000
times smaller, 2.2  10-5.
Using linear programming and adapting constraints for each reaction flux vi of the
form imin ≤ vi ≤ imax, the flux states were calculated that optimize cell growth on
various substrates.
Plot the flux distribution for active (non-zero flux) reactions of E.coli grown in a
glutamate- or succinate-rich substrate.
21. Lecture WS 2005/06
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Overall flux organization of E.coli metabolic network
a, Flux distribution for optimized biomass production
on succinate (black) and glutamate (red) substrates.
The solid line corresponds to the power-law fit
that a reaction has flux v
P(v)  (v + v0)- , with v0 = 0.0003 and  = 1.5.
d, The distribution of experimentally determined fluxes
from the central metabolism of E. coli shows
power-law behaviour as well, with a best fit to
P(v) v- with  = 1.
Both computed and experimental flux distribution
show wide spectrum of fluxes.
Almaar et al., Nature 427, 839 (2004)
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Response to different environmental conditions
Is the flux distribution independent of environmental conditions?
b, Flux distribution for optimized biomass on succinate (black)
substrate with an additional 10% (red), 50% (green) and 80% (blue)
randomly chosen subsets of the 96 input channels (substrates) turned
on.
The flux distribution was averaged over 5,000 independent random
choices of uptake metabolites.
 the flux distribution is independent of the external conditions.
Is the wide flux distribution also present in non-optimal
conditions?
c, Flux distribution from the non-optimized hit-and-run sampling
method of the E. coli solution space. The solid line is the best fit, with
v0 = 0.003 and  = 2. Inset shows the flux distribution in four
randomly chosen sample points.
Many individual non-optimal states are consistent with an exponent
 = 1.
Almaar et al., Nature 427, 839 (2004)
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Use scaling behavior to determine local connectivity
The observed flux distribution is compatible with two different potential local flux
structures:
(a) a homogenous local organization would imply that all reactions producing
(consuming) a given metabolite have comparable fluxes
(b) a more delocalized „high-flux backbone (HFB)“ is expected if the local flux
organisation is heterogenous such that each metabolite has a dominant source
(consuming) reaction.
Schematic illustration of the hypothetical scenario in which
(a) all fluxes have comparable activity, in which case we expect kY(k)  1 and
(b) the majority of the flux is carried by a single incoming or outgoing reaction,
for which we should have kY(k)  k .
Almaar et al., Nature 427, 839 (2004)
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Measuring the importance of individual reactions
To distinguish between these 2 schemes for each metabolite i produced
(consumed) by k reactions, define

vˆ
k 
ij

Y k , i     k
vˆ 
j 1  
l

1
il 

2
where vij is the mass carried by reaction j which produces (consumes) metabolite i.
If all reactions producing (consuming) metabolite i have comparable vij values,
Y(k,i) scales as 1/k.
If, however, the activity of a single reaction dominates we expect
Y(k,i) 1 (independent of k).
Almaar et al., Nature 427, 839 (2004)
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Characterizing the local inhomogeneity of the flux net
a, Measured kY(k) shown as a function of k for
incoming and outgoing reactions, averaged over
all metabolites, indicates that Y(k)  k-0.27.
Inset shows non-zero mass flows, v^ij, producing
(consuming) FAD on a glutamate-rich substrate.
 an intermediate behavior is found between
the two extreme cases.
 the large-scale inhomogeneity observed in the
overall flux distribution is also increasingly valid at
the level of the individual metabolites.
The more reactions that consume (produce) a
given metabolite, the more likely it is that a single
reaction carries most of the flux, see FAD.
Almaar et al., Nature 427, 839 (2004)
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Clean up metabolic network
Simple algorithm removes for each metabolite systematically all reactions
but the one providing the largest incoming (outgoing) flux distribution.
The algorithm uncovers the „high-flux-backbone“ of the metabolism,
a distinct structure of linked reactions that form a giant component
with a star-like topology.
Almaar et al., Nature 427, 839 (2004)
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Maximal flow networks
glutamate rich
succinate rich substrates
Directed links: Two metabolites (e.g. A and B) are connected with a directed link pointing
from A to B only if the reaction with maximal flux consuming A is the reaction with maximal
flux producing B.
Shown are all metabolites that have at least one neighbour after completing this procedure.
The background colours denote different known biochemical pathways.
Almaar et al., Nature 427, 839 (2004)
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FBA-optimized network on glutamate-rich substrate
High-flux backbone for FBA-optimized metabolic
network of E. coli on a glutamate-rich substrate.
Metabolites (vertices) coloured blue have at least one
neighbour in common in glutamate- and succinate-rich
substrates, and those coloured red have none.
Reactions (lines) are coloured blue if they are identical
in glutamate- and succinate-rich substrates, green if a
different reaction connects the same neighbour pair, and
red if this is a new neighbour pair. Black dotted lines
indicate where the disconnected pathways, for example,
folate biosynthesis, would connect to the cluster through
a link that is not part of the HFB. Thus, the red nodes
and links highlight the predicted changes in the HFB
when shifting E. coli from glutamate- to succinate-rich
media. Dashed lines indicate links to the biomass
growth reaction.
(1) Pentose Phospate
(11) Respiration
(2) Purine Biosynthesis
(12) Glutamate Biosynthesis
(20) Histidine Biosynthesis
(3) Aromatic Amino Acids (13) NAD Biosynthesis
(21) Pyrimidine Biosynthesis
(4) Folate Biosynthesis
(14) Threonine, Lysine and Methionine Biosynthesis
(5) Serine Biosynthesis
(15) Branched Chain Amino Acid Biosynthesis
(6) Cysteine Biosynthesis (16) Spermidine Biosynthesis
(22) Membrane Lipid Biosynthesis
(7) Riboflavin Biosynthesis (17) Salvage Pathways
(23) Arginine Biosynthesis
(8) Vitamin B6 Biosynthesis (18) Murein Biosynthesis
(24) Pyruvate Metabolism
(9) Coenzyme A Biosynthesis (19) Cell Envelope Biosynthesis
(25) Glycolysis
(10) TCA Cycle
Almaar et al., Nature 427, 839 (2004)
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Interpretation
Only a few pathways appear disconnected indicating that although these pathways
are part of the HFB, their end product is only the second-most important source for
another HFB metabolite.
Groups of individual HFB reactions largely overlap with traditional biochemical
partitioning of cellular metabolism.
Almaar et al., Nature 427, 839 (2004)
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How sensitive is the HFB to changes in the environment?
b, Fluxes of individual
reactions for glutamate-rich
and succinate-rich conditions.
Reactions with negligible flux
changes follow the diagonal
(solid line).
Some reactions are turned off
in only one of the conditions
(shown close to the
coordinate axes). Reactions
belonging to the HFB are
indicated by black squares,
the rest are indicated by blue
dots. Reactions in which the
direction of the flux is
reversed are coloured green.
Only the reactions in the high-flux territory
undergo noticeable differences!
Type I: reactions turned on in one conditions and
off in the other (symbols).
Type II: reactions remain active but show an
orders-in-magnitude shift in flux under the two
different growth conditions.
Almaar et al., Nature 427, 839 (2004)
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Flux distributions for individual reactions
Shown is the flux distribution for four selected
E. coli reactions in a 50% random environment.
a Triosphosphate isomerase;
b carbon dioxide transport;
c NAD kinase;
d guanosine kinase.
Reactions on the   v curve (small fluxes)
have unimodal/gaussian distributions (a and
c). Shifts in growth-conditions only lead to small
changes of their flux values.
Reactions off this curve have multimodal
distributions (b and d), showing several
discrete flux values under diverse conditions.
Under different growth conditions they show
several discrete and distinct flux values.
Almaar et al., Nature 427, 839 (2004)
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Summary
Metabolic network use is highly uneven (power-law distribution) at the global level
and at the level of the individual metabolites.
Whereas most metabolic reactions have low fluxes, the overall activity of the
metabolism is dominated by several reactions with very high fluxes.
E. coli responds to changes in growth conditions by reorganizing the rates of
selected fluxes predominantly within this high-flux backbone.
Apart from minor changes, the use of the other pathways remains unaltered.
These reorganizations result in large, discrete changes in the fluxes of the HFB
reactions.
21. Lecture WS 2005/06
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The same authors as before used Flux Balance Analysis to examine utilization
and relative flux rate of each metabolite in a wide range of simulated
environmental conditions for E.coli, H. pylori and S. cerevisae:
consider in each case 30.000 randomly chosen combinations where each uptake
reaction is a assigned a random value between 0 and 20 mmol/g/h.
 adaptation to different conditions occurs by 2 mechanisms:
(a) flux plasticity: changes in the fluxes of already active reactions.
E.g. changing from glucose- to succinate-rich conditions alters the flux of 264
E.coli reactions by more than 20%
(b) less commonly, adaptation includes structural plasticity, turning on
previously zero-flux reactions or switching off active pathways.
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Emergence of the Metabolic Core
The two adaptation method mechanisms allow for the possibility of a group of
reactions not subject to structural plasticity being active under all environmental
conditions.
Assume that active reactions were randomly distributed.
If typically a q fraction of the metabolic reactions are active under a specific
growth condition,
we expect for n distinct conditions an overlap of at least qn reactions.
This converges quickly to 0.
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Emergence of the Metabolic Core
(A–C) The average relative size of
the number of reactions that are
always active as a function of the
number of sampled conditions (black
line) for
(A) H. pylori,
(B) E. coli, and
(C) S. cerevisiae.
(D and E) The number of metabolic
reactions (D) and the number of
metabolic core reactions (E) in the
three studied organisms.
However, as the number of conditions increases, the curve converges to a
constant enoted by the dashed line, identifying the metabolic core of an organism.
Red line : number of reactions that are always active if activity is randomly
distributed in the metabolic network. The fact that it converges to zero indicates
that the real core represents a collective network effect, forcing a group of
reactions to be active in all conditions.
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Metabolic Core of E.coli
The constantly active reactions form
a tightly connected cluster!
All reactions that are found to be active in each
of the 30,000 investigated external conditions
are shown. Metabolites that contribute directly to
biomass formation are colored blue, while core
reactions (links) catalyzed by essential (or
nonessential) enzymes are colored red (or
green).
(Black-colored links denote enzymes with
unknown deletion phenotype.)
Blue dashed lines indicate multiple appearances
of a metabolite, while links with arrows denote
unidirectional reactions.
Note that 20 out of the 51 metabolites necessary
for biomass synthesis are not present in the
core, indicating that they are produced (or
consumed) in a growth-condition-specific
manner.
Blue and brown shading: folate and
peptidoglycan biosynthesis pathways
White numbered arrows denote current
antibiotic targets inhibited by: (1) sulfonamides,
(2) trimethoprim, (3) cycloserine, and (4)
fosfomycin.
A few reactions appear disconnected since we
have omitted the drawing of cofactors.
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Metabolic Core Reactions
The metabolic cores contain 2 types of reactions:
(a) reactions that are essential for biomass production under all environment
conditions (81 of 90 in E.coli)
(b) reactions that assure optimal metabolic performance.
21. Lecture WS 2005/06
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Characterizing the Metabolic Cores
(A) The number of overlapping metabolic reactions in the
metabolic core of H. pylori, E. coli, and S. cerevisiae.
The metabolic cores of simple organisms (H. pylori and
E.coli) overlap to a large extent.
The largest organism (S.cerevisae) has a much larger
reaction network that allows more flexbility  the relative
size of the metabolic core is much lower.
(B) The fraction of metabolic reactions catalyzed by
essential enzymes in the cores (black) and outside the
core in E. coli and S. cerevisiae.
 Reactions of the metabolic core are mostly
essential ones.
(C) One could assume that the core represents a subset
of high-flux reactions. This is apparently not the case.
The distributions of average metabolic fluxes for the
core and the noncore reactions in E. coli are very
similar.
21. Lecture WS 2005/06
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Correlation among E.coli Metabolic Reactions
Pearson correlation using flux values from 30,000
conditions for each reaction pair before grouping
the reactions according to a hierarchical averagelinkage clustering algorithm.
The values of the flux-correlation matrix range from -1 (red)
through 0 (white) to 1 (blue). The horizontal color bar denotes if a
reaction is a member of the core (green), and the vertical color
bar denotes whether the enzymes catalyzing the reaction are
essential (red).

group of highly correlated reactions significantly
overlaps with metabolic core.
(B) Distribution of Pearson correlation in mRNA
copy numbers from 41 experiments.
The correlations of the core reactions are clearly
shifted towards higher values.
21. Lecture WS 2005/06
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Summary
- Adaptation to environmental conditions occurs via structural plasticity and/or flux
plasticity.
Here: identification of a surprisingly stable metabolic core of reactions that are
tightly connected to eachother.
- the reactions belonging to this core represent potential targets for antimicrobial
intervention.
21. Lecture WS 2005/06
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Integrated Analysis of Metabolic and Regulatory Networks
Sofar, studies of large-scale cellular networks have focused on their connectivities.
The emerging picture shows a densely-woven web where almost everything is
connected to everything.
In the cell‘s metabolic network, hundreds of substrates are interconnected through
biochemical reactions.
Although this could in principle lead to the simultaneous flow of substrates in
numerous directions, in practice metabolic fluxes pass through specific pathways
( high flux backbone).
Topological studies sofar did not consider how the modulation of this connectivity
might also determine network properties.
Therefore it is important to correlate the network topology with the expression of
enzymes in the cell.
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Analyze transcriptional control in metabolic networks
Regulatory and metabolic functions of cells are mediated by networks of interacting
biochemical components.
Metabolic flux is optimized to maximize metabolic efficiency under different
conditions.
Control of metabolic flow:
- allosteric interactions
- covalent modifications involving enzymatic activity
- transcription (revealed by genome-wide expression studies)
Here: N. Barkai and colleagues analyzed published experimental expression data of
Saccharomyces cerevisae.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Recurrence signature algorithm
Aim: identify transcription „modules“ (TMs).
 a set of randomly selected genes is unlikely to be identical to the genes of any
TM. Yet many such sets do have some overlap with a specific TM.
In particular, sets of genes that are compiled according to existing knowledge of
their functional (or regulatory) sequence similarity may have a significant overlap
with a transcription module.
Algorithm receives a gene set that partially overlaps a TM and then provides the
complete module as output.
Therefore this algorithm is referred to as „signature algorithm“.
Ihmels et al. Nat Genetics 31, 370 (2002)
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Recurrence signature algorithm
normalization
of data
identify modules
classify genes
into modules
a, The signature algorithm.
b , Recurrence as a reliability measure. The signature algorithm is applied to distinct input
sets containing different subsets of the postulated transcription module. If the different input
sets give rise to the same module, it is considered reliable.
c, General application of the recurrent signature method.
Ihmels et al. Nat Genetics 31, 370 (2002)
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Correlation between genes of the same metabolic pathway
Distribution of the average correlation
between genes assigned to the same
metabolic pathway in the KEGG database.
The distribution corresponding to random
assignment of genes to metabolic
pathways of the same size is shown for
comparison. Importantly, only genes
coding for enzymes were used in the
random control.
Interpretation: pairs of genes associated
with the same metabolic pathway show a
similar expression pattern.
However, typically only a set of the
genes assigned to a given
pathway are coregulated.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Correlation between genes of the same metabolic pathway
Genes of the glycolysis pathway
(according KEGG) were clustered
and ordered based on the correlation
in their expression profiles.
Shown here is the matrix of their
pair-wise correlations.
The cluster of highly correlated
genes (orange frame) corresponds
to genes that encode the central
glycolysis enzymes.
The linear arrangement of these
genes along the pathway is shown at
right.
Of the 46 genes assigned to the
glycolysis pathway in the KEGG
database, only 24 show a correlated
expression pattern.
In general, the coregulated genes
belong to the central pieces of
pathways.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Coexpressed enzymes often catalyze linear chain of reactions
Coregulation between enzymes
associated with central metabolic
pathways. Each branch
corresponds to several enzymes.
In the cases shown, only one of the
branches downstream of the
junction point is coregulated with
upstream genes.
Interpretation: coexpressed
enzymes are often arranged in a
linear order, corresponding to a
metabolic flow that is directed in a
particular direction.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Co-regulation at branch points
To examine more systematically whether coregulation enhances the linearity of
metabolic flow, analyze the coregulation of enzymes at metabolic branch-points.
Search KEGG for metabolic compounds that are involved in exactly 3 reactions.
Only consider reactions that exist in S.cerevisae.
3-junctions can integrate metabolic flow (convergent junction)
or allow the flow to diverge in 2 directions (divergent junction).
In the cases where several reactions are catalyzed by the same enzymes, choose
one representative so that all junctions considered are composed of precisely 3
reactions catalyzed by distinct enzymes.
Each 3-junction is categorized according to the correlation pattern found between
enzymes catalyzing its branches. Correlation coefficients > 0.25 are considered
significant.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Coregulation pattern in three-point junctions
All junctions corresponding to metabolites that participate in exactly 3
reactions (according to KEGG) were identified and the correlations
between the genes associated with each such junction were calculated.
The junctions were grouped according to the directionality of the
reactions, as shown.
Divergent junctions, which allow the flow of metabolites in two
alternative directions, predominantly show a linear coregulation pattern,
where one of the emanating reaction is correlated with the incoming
reaction (linear regulatory pattern) or the two alternative outgoing
reactions are correlated in a context-dependent manner with a distinct
isozyme catalyzing the incoming reaction (linear switch).
By contrast, the linear regulatory pattern is significantly less abundant in
convergent junctions, where the outgoing flow follows a unique
direction, and in conflicting junctions that do not support metabolic flow.
Most of the reversible junctions comply with linear regulatory patterns.
Indeed, similar to divergent junctions, reversible junctions allow
metabolites to flow in two alternative directions. Reactions were
counted as coexpressed if at least two of the associated genes were
significantly correlated (correlation coefficient >0.25).
As a random control, we randomized the identity of all metabolic genes
and repeated the analysis.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
21. Lecture WS 2005/06
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In the majority of divergent
junctions, only one of the
emanating branches is significantly
coregulated with the incoming
reaction that synthesizes the
metabolite.
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Co-regulation at branch points: conclusions
The observed co-regulation patterns correspond to a linear metabolic flow, whose
directionality can be switched in a condition-specific manner.
When analyzing junctions that allow metabolic flow in a larger number of
directions, there also only a few important branches are coregulated with the
incoming branch.
Therefore: transcription regulation is used to enhance the linearity of metabolic
flow, by biasing the flow toward only a few of the possible routes.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Connectivity of metabolites
The connectivity of a given metabolite
is defined as the number of reactions
connecting it to other metabolites.
Shown are the distributions of
connectivity between metabolites in an
unrestricted network () and in a
network where only correlated
reactions are considered ().
In accordance with previous results
(Jeong et al. 2000) , the connectivity
distribution between metabolites
follows a power law (log-log plot).
In contrast, when coexpression is
used as a criterion to distinguish
functional links, the connectivity
distribution becomes exponential
(log-linear plot).
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2004)
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Differential regulation of isozymes
Observe that isozymes at junction points are often preferentially
coexpressed with alternative reactions.
 investigate their role in the metabolic network more systematically.
Two possible functions of isozymes
associated with the same metabolic
reaction.
An isozyme pair could provide redundancy which may be needed for buffering
genetic mutations or for amplifying metabolite production. Redundant isozymes are
expected to be coregulated.
Alternatively, distinct isozymes could be dedicated to separate biochemical
pathways using the associated reaction. Such isozymes are expected to be
differentially expressed with the two alternative processes.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Differential regulation of isozymes in central metabolic PW
Arrows represent metabolic
pathways composed of a sequence
of enzymes.
Coregulation is indicated with the
same color (e.g., the isozyme
represented by the green arrow is
coregulated with the metabolic
pathway represented by the green
arrow).
 Most members of isozyme pairs
are separately coregulated with
alternative processes.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Differential regulation of isozymes
Regulatory pattern of all gene pairs
associated with a common metabolic
reaction (according to KEGG).
All such pairs were classified into several
classes:
(1) parallel, where each gene is
correlated with a distinct connected
reaction (a reaction that shares a
metabolite with the reaction catalyzed by
the respective gene pair);
(2) selective, where only one of the
enzymes shows a significant correlation
with a connected reaction; and
(3) converging, where both enzymes
were correlated with the same reaction.
Correlations coefficients >0.25 were
considered significant. To be
counted as parallel, rather than
converging, we demanded that the
correlation with the alternative
reaction be <80% of the correlation
with the preferred reaction.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Differential regulation of isozymes: interpretation
The primary role of isozyme multiplicity is to allow for differential regulation of
reactions that are shared by separated processes.
Dedicating a specific enzyme to each pathway may offer a way of independently
controlling the associated reaction in response to pathway-specific requirements,
at both the transcriptional and the post-transcriptional levels.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Genes coexpressed with metabolic pathways
Identify the coregulated subparts of each metabolic pathway and identify relevant
experimental conditions that induce or repress the expression of the pathway
genes.
Also associate additional genes showing similar expression profiles with each
pathway using the signature algorithm.
Input: set of genes, some of which are expected to be coregulated.
Output: coregulated part of the input and additional coregulated genes together
with the set of conditions where the coregulation is realized.
Numerous genes were found that are not directly involved in enzymatic steps:
- transporters
- transcription factors
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Co-expression of transporters
Transporter genes are
co-expressed with the relevant
metabolic pathways providing
the pathways with its metabolites.
Co-expression is marked in green.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Co-regulation of transcription factors
Transcription factors are often co-regulated with their regulated pathways. Shown
here are transcription factors which were found to be co-regulated in the analysis.
Co-regulation is shown by color-coding such that the transcription factor and the
associated pathways are of the same color.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Hierarchical modularity in the metabolic network
Sofar: co-expression analysis revealed a strong tendency toward coordinated
regulation of genes involved in individual metabolic pathways.
Does transcription regulation also define a higher-order metabolic organization, by
coordinated expression of distinct metabolic pathways?
Based on observation that feeder pathways (which synthesize metabolites) are
frequently coexpressed with pathways using the synthesized metabolites.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Feeder-pathways/enzymes
Feeder pathways or genes
co-expressed with the
pathways they fuel. The
feeder pathways (light blue)
provide the main pathway
(dark blue) with metabolites
in order to assist the main
pathway, indicating that coexpression extends beyond
the level of individual
pathways.
These results can be
interpreted in the following
way: the organism will
produce those enzymes that
are needed.
21. Lecture WS 2005/06
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
Bioinformatics III
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Hierarchical modularity in the metabolic network
Derive hierarchy by applying an iterative
signature algorithm to the metabolic pathways,
and decreasing the resolution parameter
(coregulation stringency) in small steps.
Each box contains a group of coregulated genes
(transcription module). Strongly associated
genes (left) can be associated with a specific
function, whereas moderately correlated
modules (right) are larger and their function is
less coherent.
The merging of 2 branches indicates that the
associated modules are induced by similar
conditions.
All pathways converge to one of 3 low-resolution
modules: amino acid biosynthesis, protein
synthesis, and stress.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
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Hierarchical modularity in the metabolic network
Although amino acids serve as building blocks for proteins, the expression of genes
mediating these 2 processes is clearly uncoupled!
This may reflect the association of rapid cell growth (which triggers enhanced
protein synthesis) with rich growth conditions, where amino acids are readily
available and do not need to be synthesized.
Amino acid biosynthesis genes are only required when external amino acids are
scarce.
In support of this view, a group of amino acid transporters converged to the protein
synthesis module, together with other pathways required for rapid cell growth
(glucose fermentation, nucleotide synthesis and fatty acid synthesis).
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
21. Lecture WS 2005/06
Bioinformatics III
45
Global network properties
Jeong et al. showed that the structural connectivity between metabolites imposes a
hierarchical organization of the metabolic network. That analysis was based on
connectivity between substrates, considering all potential connections.
Here, analysis is based on coexpression of enzymes.
In both approaches, related metabolic pathways were clustered together!
There are, however, some differences in the particular groupings (not discussed
here),
and importantly, when including expression data the connectivity pattern of
metabolites changes from a power-law dependence to an exponential one
corresponding to a network structure with a defined scale of connectivity.
This reflects the reduction in the complexity of the network.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
21. Lecture WS 2005/06
Bioinformatics III
46
Summary
Transcription regulation is prominently involved in shaping the metabolic network of
S. cerevisae.
1
Transcription leads the metabolic flow toward linearity.
2
Individual isozymes are often separately coregulated with distinct processes,
providing a means of reducing crosstalk between pathways using a common
reaction.
3
Transcription regulation entails a higher-order structure of the metabolic
network.
It exists a hierarchical organization of metabolic pathways into groups of
decreasing expression coherence.
Ihmels, Levy, Barkai, Nat. Biotech 22, 86 (2003)
21. Lecture WS 2005/06
Bioinformatics III
47