This document discusses network analysis of metabolism in four kingdoms of life using elementary flux modes. It provides examples of how this analysis can be used to determine optimal pathways, predict engineering effects, and assess enzyme deficiencies. The analysis allows detection of previously unknown pathways and futile cycles. Applying this to human metabolism, studies have found evidence that fatty acids can be converted to glucose through entangled routes, and have identified futile cycles that may play a role in aging.
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Insights from network analysis of metabolism across four kingdoms
1. Insights from network analysis of
metabolism in four kingdoms of life
Stefan Schuster
Friedrich Schiller University Jena, Germany
Dept. of Bioinformatics
2.
3. Introduction
⢠Several specific features of network
analysis of metabolic systems:
â Mass flow and steady-state assumption
ď makes analysis easier due to strict
mathematical equations
â Besides monomolecular reactions, also
many bi- and multimolecular reactions
ď hypergraph, more complex than
graph
4. Introduction (2)
⢠Examples of goals of modelling:
â Determining optimal pathways
â Predicting the effect of engineering these
networks, e.g. by deleting and/or inserting
enzymes
â Assessment of the impact of enzyme
deficiencies
5. Synthetic biology
⢠Design and construction of new biological
functions and systems not found in nature
⢠Minimal genome / Minimal metabolism ď
Knocking out as many metabolic genes as
possible so that all desired metabolic capabilities
remain
6. Example:
Can sugars be produced from lipids
in animals?
⢠Excess sugar in human diet is
converted into storage lipids, mainly
triglycerides
⢠Is reverse transformation feasible?
?
7. ⢠1 glycerol + 3 even-chain fatty acids
(odd-chain fatty acids are rare)
⢠Glycerol ď glucose OK
(gluconeogenesis)
⢠(Even-chain) fatty acids ď acetyl CoA
(β-oxidation)
⢠Acetyl CoA ď glucose?
Triglycerides
10. Schuster und Hilgetag: J. Biol. Syst. 2 (1994) 165-182
Schuster et al., Nature Biotechnol. 18 (2000) 326-332.
non-elementary flux mode
elementary flux
modes
11. An elementary mode is a minimal set of enzymes that
can operate at steady state with all irreversible reactions
used in the appropriate direction
The enzymes are weighted by the relative flux they carry.
The elementary modes are unique up to scaling.
All flux distributions in the living cell are non-negative
linear combinations of elementary modes
14. Mathematical properties of
elementary modes
Any vector representing an elementary mode involves at least
dim(null-space of N) â 1 zero components.
Example:
P1 P2
P3
1S
1 2
3








ďŁ

=
10
01
11
K
dim(null-space of N) = 2
Elementary modes:








ďŁ

â110
101
011
Schuster et al., J. Math. Biol. 2002,
after results in theoretical
chemistry by Milner et al.
15. Mathematical properties of
elementary modes (2)
A flux mode V is elementary if and only if the null-space of
the submatrix of N that only involves the reactions of V is of
dimension one.
Klamt, Gagneur und von Kamp, IEE Proc. Syst. Biol. 2005, after results in
convex analysis by Fukuda et al.
P1 P2
P3
1S
1 2
3
e.g. elementary mode:








ďŁ

â110
101
011
N = (1 â1) ď dim = 1
18. In a limited network of central metabolism, no
gluconeogenesis from fatty acids
⢠Weinman,E.O. et al. (1957) Physiol.
Rev. 37, 252â272.
⢠L.F. de Figueiredo, S. Schuster, C.
Kaleta, D.A. Fell: Can sugars be
produced from fatty acids? A test case
for pathway analysis tools.
Bioinformatics 25 (2009) 152-158.
Luis de Figueiredo
19. Engineering the glyoxylate shunt
into mammals
⢠Dean JT, ⌠Liao JC.: Resistance to diet-
induced obesity in mice with synthetic
glyoxylate shunt. Cell Metab. (2009) 9:
525-536.
20. Going genome-scale
⢠Can humans convert fatty acids into sugar on
entangled routes across a larger network?
Mentioned in literature on anecdotal basis
21. Going genome-scale
⢠Can humans convert fatty acids into sugar on
entangled routes across a larger network?
Mentioned in literature on anecdotal basis
⢠YES, WE CAN! (In principle)
⢠C. Kaleta, L.F. de Figueiredo, S. Werner, R. Guthke,
M. Ristow, S. Schuster: In silico evidence for
gluconeogenesis from fatty acids in humans, PLoS
Comp. Biol. 7 (2011) e1002116
Christoph
Kaleta
22.
23.
24. Gluconeogenesis from fatty acids
⢠Is likely to be important
â in sports physiology
â in diets for weight reduction
â in hibernating animals
â in embryos within eggs
25. How can Inuit live on a practically
carbohydrate-free diet?
C. Kaleta, L.F. de Figueiredo, S. Schuster
Against the stream: Relevance of gluconeogenesis from
fatty acids for natives of the arctic regions
Intern. J. Circumpol. Health 71 (2012) 18436
27. Glucose
AcCoA
Cit
IsoCit
OG
SucCoA
PEP
Oxac
Mal
Fum
Succ
Gly
Pyr
CO2
CO2
CO2
CO2
Red elementary mode: Usual TCA cycle
Blue elementary mode: Catabolic pathway
predicted in Liao et al. (1996) and Schuster
et al. (1999). Experimental hints in Wick et al.
(2001). Experimental proof in:
E. Fischer and U. Sauer:
A novel metabolic cycle catalyzes
glucose oxidation and anaplerosis
in hungry Escherichia coli,
J. Biol. Chem. 278 (2003)
46446â46451
29. Maximization of tryptophan:glucose yield
Model of 65 reactions in the central metabolism of E. coli.
26 elementary modes. 2 modes with highest tryptophan:
glucose yield: 0.451.
Glc
G6P
233
Anthr
Trp
105
PEP
Pyr
3PG
GAP
PrpP
Schuster, Dandekar, Fell,
Trends Biotechnol. 17 (1999) 53
Tryptophan
30. Turning green: plant metabolism
⢠Previously undescribed
pathway of efficient conversion
of carbohydrate to oil in
developing green plant seeds
detected by EFMs (Schwender
J, Goffman F, Ohlrogge JB,
Shachar-Hill Y: Nature 2004,
432: 779-782).
⢠Involves pentose-phosphate
pathway and RUBISCO
enzyme and provides 20%
more acetyl-CoA for fatty acid
synthesis than glycolysis.
31. Example (of Synthetic Biology?)
from fungal metabolism
⢠Engineering of yeast (and E. coli) to
produce polyhydroxy-butyric acid (PHB, a
bioplastic)
⢠20 EFMs in S. cerevisiae strain
engineered to produce PHB, 7 of which
produce PHB with different yields
⢠Adding the natively absent ATP citrate-
lyase to the network, 496 EFMs.
Maximum theoretical PHB-to-carbon yield
thereby increased from 0.67 to 0.83.
PHB
Carlson, R., Fell, D., and Srienc, F. (2002)
Biotechnol. Bioeng. 79, 121â134.
32. ATP ADP
F6P FP2
Futile cycles
One elementary mode: fructose-bisphosphate cycle
Futile cycles perform no net transformation except
hydrolysis of energy-rich compounds (mainly cofactors)
33. S. Schuster et al.,
J. Math. Biol.
45 (2002) 153-181
Some futile cycles are not easy to find
34. S. Schuster et al.,
J. Math. Biol.
45 (2002) 153-181
Some futile cycles are not easy to find
35. Going genome-scale
Gebauer J, Schuster S, de Figueiredo LF, Kaleta C.
Detecting and investigating substrate cycles in a genome-scale human
metabolic network. FEBS J. (2012) 279: 3192-202.
36. Results from analysis of futile cycles
⢠Evolutionary pressure against futile cycles with a
particular high flux.
⢠ATP consumption of the normal, aged and
Alzheimer brain models does not show
statistically significant differences
CA = cytosol of astrocytes
CN = cytosol of neurons
Gebauer et al.
FEBS J. (2012) 279: 3192-202.
37. Applications of EFM analysis
⢠Checking which biotransformations are
stoichiometrically and thermodynamically
feasible
⢠Determining maximal and submaximal molar
yields of wild-type, recombinant strains, and
knock-out mutants
⢠Quantifying robustness to knock-out
⢠Assessing impact of enzyme deficiencies
⢠Detecting futile cycles
⢠Determining minimal media
⢠Functional genomics â gap filling
38.
39. Application to signalling systems
E1 E1
*
E2 E2
*
E3 E3
*
Target
Signal
Calculating elementary modes gives
trivial result that each cycle
corresponds to one mode. Flow of
information is not reflected.
40. Enzyme cascades â only activated
component is depicted
Signal
E1*
E2*
E4*
Target2Target1
E3*
Obviously, elementary
signalling routes
41. How to define
elementary signalling routes?
⢠Signalling systems are not always at
steady state. Propagation of signals is
time-dependent process.
⢠However: Averaged over longer time
spans, also signalling systems must fulfill
steady-state condition because system
must regenerate.
42. Signal amplification
⢠Mass flow not linked with information flow.
⢠However: Signal amplification requires that
each activated enzyme must catalyse at least
one further activation.
⢠Minimum condition: Each activated enzyme
catalyses exactly one further activation.
⢠Thus, operational stoichiometric coupling of
cascade levels.
⢠E1* + E2 ď E1 + E2*
43. The elementary routes thus calculated
exactly give the signalling routes
Signal
E1*
E2*
E4*
Target2Target1
E3*
J. Behre and S. Schuster,
J. Comp. Biol. 16 (2009)
829-844
44. Conclusions
⢠Elementary modes are an appropriate
concept to describe biochemical
pathways; manifold biochemical and
biotechnological applications.
⢠Two tendencies in modelling: large-
scale vs. medium-scale
⢠Analysis of both types of models allows
interesting conclusions
45. Conclusions (2)
⢠Previously unknown pathways have
been found also in medium-scale
networks
⢠Some questions can only be answered
in whole-cell models, for example: Can
some product principally be synthesized
from a given substrate?
47. Futile cycles
⢠ââŚa search for metabolic markers of aging might include
efforts to determine [...] (b) enzymes that catalyze
opposing reactionsâ (Stadtmann, Exp. Gerontol. 23,
1988, 327-347)
⢠ââŚan attractive candidate for the function of the âŚ
energy-dissipating proton cycle [in mitochondria] is to
decrease the production of ⌠reactive oxygen species
(ROS). This could be important in helping to minimise
oxidative damage to DNA and in slowing ageing.â
(Brand, Exp. Gerontol. 35, 2000, 811-820)
49. Elementary flux modes
include all futile cycles
AMP
ribose-P
Prs1-5
ATP
AMP
NaAD NAD
NaMN
PRPP
Qns1
ATP
gln
H2O
glu
NAD_pool
NAD_ex
Nma1,2
ATP
Bna6 QA
CO2
PPi
PPi
PPi
AMP
ribose-P
Prs1-5
ATP
AMP
NA
NaAD NAD
NaMN
Qns1
ATP
gln
H2O
glu
NAD_pool
NAD_ex
Npt1
NA_pool
NA_ex
Nma1,2
ATP
PPi
PRPP
PPi
PPi
ATP
ADP
AMP
ribose-P
Prs1-5
ATP
Nam
NR
NMN
NAMPT
Sdt1, Isn1
Pi
Pnp1 Pi
ribose-P
PRPP
PPi
ATP
ADP
AMP
NaAD NAD
NaMN
NAR
Qns1ATP
gln
H2O
glu
NAD_pool
NAD_ex
NAR_pool
NAR_ex
Nma1,2
ATP
Nrk1
ATP
ADP
PPi
PPi
AMP
ribose-P
Prs1-5
ATP
AMP
NA
NaAD NAD
Nam
NaMN
NR
Pnc1
H2ONH3
Qns1ATP
gln
H2O
glu
NAD_pool
NAD_ex
NR_pool
NR_ex
Npt1
Nma1,2
ATP
PPi
Urh1
H2Oribose
PRPP
PPi
PPi
ATP
ADP
NAD
Nam
NR
NMN
NAD_pool
NAD_ex
NAMPT
NR_pool
NR_ex
Nma1,2
ATP
Pnp1 Pi
ribose-P
PPi
PRPP
PPi
ATP
ADP
AMP
ribose-P
Prs1-5
ATP
AMP
ribose-P
Prs1-5
ATP
AMP
NA
NaAD NAD
Nam
NaMN
ADP-ribosyl transfer
ADP-ribosyl-X
Pnc1
H2ONH3
Qns1ATP
gln
H2O
glu
Npt1
Nma1,2
ATP
PPi
PRPP
PPi
PPi
ATP
ADP
NAD
Nam
NMNADP-ribosyl transfer
NAMPT
Nma1,2
ATP
PPi
PRPP
PPi
ATP
ADP
ADP-ribosyl-X
AMP
ribose-P
Prs1-5
ATPPRPP
L.F. de Figueiredo, T.I. Gossmann, M. Ziegler, S. Schuster: Pathway analysis
of NAD+
metabolism. Biochem. J. 439 (2011) 341â348.
50. Simulating circadian rhythms
⢠Dynamics of circadian rhythms needs to be
adapted to day length changes between summer
and winter.
⢠Hypothesis: Fraction of long-range connections
between cells in Suprachiasmatic nucleus
adjusts phase distribution: dense long-range
connections during winter lead to a narrow
activity phase, while rare long-range connections
during summer lead to a broad activity phase.