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Journal of Bacteriology, December 2003, p. 7031-7035, Vol. 185, No. 24
0021-9193/03/$08.00+0 DOI: 10.1128/JB.185.24.7031-7035.2003
Copyright © 2003, American Society for Microbiology. All Rights Reserved.
It Is All about Metabolic Fluxes
Jens Nielsen*
Center
for Process Biotechnology, BioCentrum-DTU, Technical University of
Denmark, DK-2800 Kgs. Lyngby,
Denmark
 |
INTRODUCTION
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|---|
In the field of genomics there has been a move towards the development
of novel experimental techniques that enable analysis of all components
of a certain kind in a biological system, and this has resulted in the
appearance of new methods for analyzing the omes.Obviously, in a given cellular system it is attractive to measure all
the mRNAs, all the proteins, a large number of the metabolites, a large
fraction of protein-protein or protein-DNA interactions, and so on, but
a fundamental problem in functional genomics is integration of the
information obtained, i.e., how this information can be integrated and
lead to new insights into the functioning of cellular processes.
Bioinformatics and advanced computer models are continuously supplying
new methods for integration of data, and surely progress in the field
of systems biology will eventually result in an ability to describe
cellular functions in silico. In the race to obtain large amounts of
data for phenotypic characterization of different cellular systems, a
relatively simple experimental technique for quantitative determination
of metabolic fluxes has escaped the attention of a large part of the
biological research community; this technique has been used primarily
by researchers in the field of metabolic engineering
(1). The technique is
based on relatively old principles from biochemistry, namely, feeding
of specifically 13C-labeled substrates to the cell for
characterization of the metabolism. However, with the development of
the necessary mathematical framework for analysis of data obtained from
this type of analysis it has become possible to obtain estimates for
the fluxes in the different parts of the central carbon metabolism.
This information is obviously interesting in connection with improving
metabolite production by a given microbial cell, but as demonstrated in
a paper in this issue of Journal of Bacteriology
(11), it also provides a
very powerful tool for functional analysis of different mutant cells.
In this short commentary the use of this technique for functional
analysis and the advantages and limitations of different techniques for
flux quantification are discussed, and some of the underlying methods
are reviewed, Finally, some future perspectives are
given.
|
METABOLIC NETWORKS
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Cellular metabolism is
represented by a large number of metabolic reactions that are involved
in the conversion of the carbon source into building blocks needed for
macromolecular biosynthesis. Furthermore, there are specific reactions
that ensure the constant supply of Gibbs free energy via ATP and redox
equivalents (generally in the form of the cofactor NADPH) needed for
biosynthesis of macromolecules. This large number of metabolic
reactions forms a so-called metabolic network inside the cells, and as
a result of reconstruction of the complete metabolic networks in
different bacteria (6,
17,
18) and in the yeast
Saccharomyces cerevisiae
(8), more insight into the
function of complete metabolic networks has been obtained. These
reconstructed metabolic networks can be used for detailed studies of
metabolic functions (4,
16) and the effect of
gene deletions (6,
7,
9), and in the context of
flux analysis there are two key lessons that can be learned.
The
first lesson is that the fraction of open reading frames (ORFs) in a
given genome directly involved in cellular metabolism is relatively
low. Table
1 lists some statistics on the metabolic networks in four different
microorganisms, and it is interesting that a higher percentage of the
ORFs encode enzymes involved in metabolism in bacteria with small
genomes, like Helicobacter pylori and Haemophilus
influenzae (16 to 18%), than in Escherichia coli
(15%) and the yeast S. cerevisiae (12%). In
E. coli, which has relatively complex regulatory systems, and
in eukaryotic cells a larger fraction of the ORFs code for proteins
involved in regulation, and the fraction is even larger in higher
eukaryotes. However, despite the relatively low fraction of ORFs
involved directly in cellular metabolism, many more ORFs do have an
impact on cellular metabolism via regulation of gene expression and
enzyme activities. Thus, in the MIPS database
(http://mips.gsf.de/proj/yeast/index.jsp)
there are about twice as many ORFs grouped into carbon and energy
metabolism as there are ORFs involved in this part of the metabolism in
the reconstructed metabolic network
(8), and the majority of
the additional ORFs encode proteins involved in regulation.
The
second lesson is that the reconstructed networks clearly illustrate how
the different parts of the cellular metabolism are interconnected,
particularly due to usage of common cofactors, like ATP, ADP, NADH, and
NADPH. These cofactors are produced in the cellular energy metabolism
and are used in a large number of biosynthetic reactions. However, it
is not only these cofactors that ensure a tight connection among the
different branches of the metabolic network; e.g., in the network for
S. cerevisiae there are 86 metabolites (corresponding to
15% of all metabolites in the metabolic network) which are
involved in 10 or more reactions. This tight connection of reactions in
the cellular metabolism through sharing of metabolites is illustrated
in Fig.
1 for the four reconstructed metabolic networks mentioned above. The
tight connection of the different parts of the metabolism means that
changes in fluxes in one part of the metabolism disseminate to many
other parts of the metabolism, resulting in a global response. Thus,
measurement of even a few metabolic fluxes may provide valuable
information about the function of the complete metabolic
network.
View larger version (25K):
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FIG. 1. Frequency
plot of the number of reactions that each metabolite
appears in for four different reconstructed metabolic networks. For
each metabolic network the 10 metabolites that appear in the most
reactions are listed. PP, pyrophosphate; COA, coenzyme A. The numbers
in the box specify the numbers of reactions the 10 most frequently used
metabolites participate in for the four different
microorganisms.
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|
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FLUXES AND PHENOTYPE
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As a result of
evolution, the function of the central carbon metabolism has been
fine-tuned to exactly meet the needs for building blocks and Gibbs free
energy in conjunction with cell growth. There is therefore
tight regulation of the fluxes through the central carbon metabolism.
However, when a given cell experiences a change in its environment, the
metabolism has to be adjusted. For example, when the carbon source
changes from glucose to acetate, the cells need to down-regulate
certain parts of the metabolic network (glycolysis) and activate other
parts of the metabolic network (gluconeogenesis). Clearly, a large
number of ORFs are involved in this regulation, and modifying the
activity of the gene products of these ORFs also influences the fluxes
in the metabolic network.
In order to understand the complex
regulation of metabolic fluxes, one can specify the flux through a
given biochemical reaction as a function of three factors: (i) the
activity level of the enzyme catalyzing the reaction; (ii) the
properties of the enzyme (i.e., its affinities for the substrates and
possible affectors (inhibitors, activators, etc.); and (iii) the
concentrations of the metabolites affecting the enzyme activity,
including the reactants and products of the enzyme-catalyzed
reaction.
The activity level is a function of gene expression,
translation, and posttranslational protein modifications. The
properties of the enzyme are generally fixed for the biological system
under study, but in cases in which heterologous enzymes are inserted in
order to redirect carbon fluxes, it is relevant to consider the
properties of the heterologous enzyme compared with those of other
enzymes interacting with the same metabolite pools. The concentrations
of the metabolites are themselves functions of the fluxes in the
metabolic network and the properties of the enzymes, and thus there is
important feedback regulation imposed on the system.
From the
information discussed above it is clear that the metabolic fluxes
represent the final outcome of cellular regulation at many different
levels, and hence they are an ultimate representation of the cellular
phenotype expressed under certain conditions. Analysis of metabolic
fluxes is therefore an interesting approach to functional analysis of
cells, as illustrated by Hua et al.
(11), who analyzed two
different knockout mutants of E. coli by quantifying the
metabolic fluxes.
|
FLUXES AND GENOTYPE
|
|---|
As
discussed above, metabolic fluxes represent integrative information;
i.e., the metabolic fluxes are a function of gene expression,
translation, posttranslational protein modifications, and
protein-metabolite interactions. In biotechnology it is interesting to
obtain integrative information, as one is primarily interested in
whether a specific modification results in a higher flux (or a lower
flux if this is desirable), whereas understanding the exact molecular
mechanisms underlying the change in the flux may be less important.
However, for functional analysis of, for example, orphan gene function
it is difficult to apply integrative information alone, and in this
research area metabolic flux analysis, or fluxome analysis, has to be
combined with analysis of other omes (e.g., the transcriptome, the
proteome, the interactome, and the metabolome). Thus, it is only
through analysis of several omes that it is possible to decode the
functions of ORFs involved in overall regulation of cellular metabolism
(15). Despite the
drawback of representing integrative information, fluxome analysis does
represent a method that is attractive for initial screening to
determine the functions of orphan genes, as it is a simple method that
allows rapid determination of whether deletion of an orphan gene
results in modification of the fluxes. Furthermore, fluxome analysis
may be used to obtain further insight into the functions of genes with
known functions, as illustrated in studies of knockout mutants of
S. cerevisiae (5,
10).
|
HOW TO MEASURE FLUXES
|
|---|
There are no direct methods for analysis of
metabolic fluxes. However, based on one key assumption, it is possible
to impose a large number of constraints on the fluxes in a given
metabolic network. This assumption is as follows: all fluxes into a
given intracellular metabolite pool balance all fluxes out of the pool.
Basically, this assumption implies that the intracellular concentration
of all metabolites is constant at all times, and obviously this is not
the case. However, due to the rapid turnover of metabolite pools the
intracellular metabolite concentrations can be adjusted rapidly to new
levels, and in fact it has been observed that even after drastic
changes in the environment the level of intracellular metabolites is
adjusted to a new constant value within 1 to 2 min
(20).
The key
assumption mentioned above means that for a given metabolic network the
balances around each metabolite impose a number of constraints on the
system. In general, if there are J fluxes and K
metabolites, then the degrees of freedom is F =
J - K, and through measurement of only
F fluxes the remaining fluxes can be calculated. Some fluxes
can be measured directly (e.g., the fluxes of substrates into the cells
and the fluxes of metabolites that are secreted from the cells), but
even though some studies have relied only on measurement of these
so-called exchange fluxes
(12,
22), it is normally not
possible to measure sufficient fluxes to calculate the remaining fluxes
with good precision (24).
However, if one feeds the cells 13C-labeled glucose (e.g.,
glucose with enriched 13C in the first position) and
subsequently analyzes the 13C enrichment pattern in
different intracellular metabolites, one obtains additional
experimental data that can be used to obtain solid flux estimates.
However, one needs to combine these data with information about the
carbon transitions in all biochemical reactions, and the mathematical
complexity therefore increases substantially. In recent years solid
mathematical frameworks for analysis of this kind of experimental data
have been developed (23,
25,
26), and this has
resulted in computer algorithms for calculation of the metabolic fluxes
from this kind of 13C enrichment data
(24). It should, however,
be mentioned that currently it is only possible to quantify the fluxes
in the central carbon metabolism, but as indicated above, this part of
the metabolism is tightly connected to most other parts of the cellular
metabolism and it is therefore also the part of the complete metabolic
network that is most interesting to study.
Several experimental
techniques for analysis of the enrichment patterns in intracellular
metabolites have been developed, but all these techniques are currently
based on using nuclear magnetic resonance (NMR)
(13,
14) or gas
chromatography-mass spectrometry (GC-MS)
(1). In all methods the
enrichment patterns are not measured directly with the intermediates of
central carbon metabolism (e.g., pyruvate and oxaloacetate), but rather
they are measured with the corresponding amino acids (e.g., alanine and
aspartate), as the amino acids are present at much higher levels in the
cell both as free amino acids and integrated into proteins. The
information content is somewhat different from the information content
resulting from an analysis of the enrichment patterns by NMR or GC-MS,
but the underlying principle is the same. In order to avoid the
relatively complex data analysis required for estimating the metabolic
fluxes, a simpler method of estimating flux ratios has been developed
based on cofeeding unlabeled and uniformly 13C-labeled
[6-13C]glucose
(19). The resulting
13C labeling patterns of metabolic intermediates are
analyzed by two-dimensional NMR spectroscopy of the amino acids. Since
different pathways leading to the same metabolite yield different
intact fragments, it is possible to easily calculate flux ratios. In
the study of Hua et al.
(11) the authors
performed a flux ratio analysis, but they also estimated all of the
fluxes; this study was the first study in which both methods were used.
Both of the methods provide the same results for fluxes at key branch
points and therefore basically provide the same kind of information.
Determination of the flux ratios is simpler and may therefore seem more
attractive, but estimation of all the fluxes provides a better
visualization of the results and provides a more complete set of data.
However, one should be aware of the fact that not all fluxes may be
estimated with the same precision
(2).
|
IDENTIFICATION OF METABOLIC NETWORK TOPOLOGIES
|
|---|
Besides allowing quantification
of the metabolic fluxes, the use of 13C-enriched carbon
sources is a powerful approach for identifying the metabolic network
topology (i.e., which pathways are active under different growth
conditions). From analysis of the enrichment patterns in intracellular
metabolites one can deduce which pathways are active. This is
illustrated in Fig.
2, which shows the enrichment pattern in pyruvate when glucose with
13C enrichment in the first position is metabolized via
three different pathways, the Embden-Meyerhof-Parnas pathway, the
pentose phosphate pathway, and the Entner-Doudoroff pathway. If glucose
is metabolized via the Embden-Meyerhof-Parnas pathway, one-half of the
pyruvate molecules are enriched in the third position, whereas one-half
of the pyruvate molecules are enriched in the first position if glucose
is metabolized via the Entner-Doudoroff pathway. However, if glucose is
metabolized via the pentose phosphate pathway, then there is no
enrichment of pyruvate as the 13C is lost as carbon
dioxide.
View larger version (20K):
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|
FIG. 2. Illustration
of how measurement of the 13C enrichment patterns can be
used to identify active pathways. EMP, Embden-Meyerhof-Parnas; ED,
Entner-Doudoroff; PP, pentose
phosphate.
|
|
The principles illustrated above can be taken much
further, and it may even be possible to locate specific biochemical
reactions in different compartments in eukaryotic cells
(5,
21). Hua et al.
(11) identified the
topology of the metabolic network in wild-type E. coli and in
mutants with disruptions of phosphoglucomutase and glucose-6-phosphate
dehydrogenase and subsequently quantified the metabolic fluxes in the
networks identified. Of particular interest, they identified some
Entner-Doudoroff pathway activity in a phosphoglucose isomerase
knockout strain and also activity in the glyoxylate
shunt.
|
A NEW TOOL IN FUNCTIONAL GENOMICS?
|
|---|
As
mentioned above, so far metabolic flux analysis has primarily been used
for quantification of fluxes in connection with metabolic engineering
of microbial overproducing strains, but as discussed here, it is
obvious that this technique offers some interesting possibilities for
performing functional analyses of different mutants in the field of
functional genomics, as illustrated by Hua et al.
(11) and other workers
(5). In order for the
technique to gain wider application in functional genomics, however, it
is necessary to develop the technique further, and among other things
this may involve ease of experimentation, direct analysis of
metabolites, and high-throughput analysis.
Ease
of experimentation.
The
experimental technique that has been developed is relatively easy to
perform, and in particular, the introduction of simple GC-MS methods
has allowed analyses to be performed in many laboratories. However,
interpretation of the experimental data is relatively complicated, and
in particular, identification of the metabolic network requires
substantial insight into cellular metabolism. This problem may be
solved in the future with better computer algorithms for rapid testing
of different metabolic networks and at the same time quantification of
the metabolic fluxes.
Direct analysis of
metabolites.
The technique of
measuring the enrichment pattern in amino acids rather than in
intracellular metabolites facilitates the analysis substantially, but
it would be interesting to use novel methods for direct analysis of the
enrichment patterns in intracellular metabolites, like pyruvate and
oxaloacetate. This would enable analysis of the fluxes during rapid
transients, something that is not possible with the current
techniques (due to the slow dynamics in turnover of the protein pool).
However, introduction of new analytical techniques will also require
more advanced models for data interpretation as issues related to
turnover of amino acids will become
relevant.
High-throughput
analysis.
In principle, there
is nothing that prevents the use of the current techniques for
high-throughput analysis; these techniques include, e.g., using
microtiter plates for growth of different cells and subsequent analysis
of a large number of mutants. Such techniques should enable screening
of a large number of mutants and thus enable the development of large
databases that can be used for more detailed functional
analysis.
There have been recent developments in all three areas
described above, and it is therefore predicted that metabolic flux
analysis will be used much more widely for functional analysis in the
future.
|
ACKNOWLEDGMENTS
|
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I acknowledge Jochen
Förster (Fluxome Sciences A/S), Thomas Grotkjær (DTU),
and Mats Åkesson (DTU) for fruitful comments. Iman Famili
(University of California at San Diego) is acknowledged for putting
Fig. 1 together. I also
acknowledge a good and friendly collaboration with Bernhard Palsson
(University of California at San Diego) in modeling of cellular
metabolism.
|
FOOTNOTES
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* Mailing
address: Center for Process Biotechnology, BioCentrum-DTU, Building
223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
Phone: 45 4525 2696. Fax: 45 4588 4148. E-mail:
jn{at}biocentrum.dtu.dk. 
The
views expressed in this Commentary do not necessarily reflect the views
of the journal or of ASM.
|
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Journal of Bacteriology, December 2003, p. 7031-7035, Vol. 185, No. 24
0021-9193/03/$08.00+0 DOI: 10.1128/JB.185.24.7031-7035.2003
Copyright © 2003, American Society for Microbiology. All Rights Reserved.
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Top
Introduction
Metabolic networks
