netbiosig2013-keynote stefan schuster
DESCRIPTION
Keynote presentation for Network Biology SIG 2013 by Stefan Schuster, Head of Dept. of Bioinformatics at Friedrich-Schiller University Jena, GermanyTRANSCRIPT
Insights from network analysis of
metabolism in four kingdoms of life
Stefan Schuster
Friedrich Schiller University Jena, Germany
Dept. of Bioinformatics
Introduction
• Several specific features of network analysis of metabolic systems:– Mass flow and steady-state assumption
makes analysis easier due to strict mathematical equations
– Besides monomolecular reactions, also many bi- and multimolecular reactions hypergraph, more complex than graph
Introduction (2)
• Examples of goals of modelling:
– Determining optimal pathways
– Predicting the effect of engineering these networks, e.g. by deleting and/or inserting enzymes
– Assessment of the impact of enzyme deficiencies
Synthetic biology• Design and construction of new biological
functions and systems not found in nature• Minimal genome / Minimal metabolism
Knocking out as many metabolic genes as possible so that all desired metabolic capabilities remain
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?
?
• 1 glycerol + 3 even-chain fatty acids (odd-chain fatty acids are rare)
• Glycerol glucose OK (gluconeogenesis)
• (Even-chain) fatty acids acetyl CoA (-oxidation)
• Acetyl CoA glucose?
Triglycerides
Glucose
AcCoA
Cit
IsoCit
OG
SucCoA
PEP
Oxac
Mal
Fum
Succ
Pyr
CO2
CO2
CO2
CO2
Exact reversal of glucose degradation impossible because pyruvate dehydrogenase is irreversible. Nevertheless, AcCoA is linked with glucose by a chain of reactions.
Fatty acids
Metabolism is hypergraph due to bimolecular reactions!
Schuster und Hilgetag: J. Biol. Syst. 2 (1994) 165-182Schuster et al., Nature Biotechnol. 18 (2000) 326-332.
non-elementary flux mode
elementary flux modes
An elementary mode is a minimal set of enzymes thatcan 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
Simple example:
P1 P2
P3
1S1 2
3
110
101
011Elementary modes:
flux1
flux2
flux3
generating vectors
Mathematical properties of elementary modes
Any vector representing an elementary mode involves at least dim(null-space of N) − 1 zero components.Example:
P1 P2
P3
1S1 2
3
10
01
11
K
dim(null-space of N) = 2 Elementary modes:
110
101
011
Schuster et al., J. Math. Biol. 2002, after results in theoretical chemistry by Milner et al.
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
1S1 2
3
e.g. elementary mode:
110
101
011N = (1 1) dim = 1
Glucose
AcCoA
Cit
IsoCit
OG
SucCoA
PEP
Oxac
Mal
Fum
Succ
Pyr
CO2
CO2
CO2
CO2
If AcCoA, glucose, CO2 and all cofactors are considered external, there is NO elementarymode consuming AcCoA, nor any one producingglucose.
Intuitive explanation byregarding oxaloacetate or CO2.
Glucose
AcCoA
Cit
IsoCit
OG
SucCoA
PEP
Oxac
Mal
Fum
Succ
Gly
Pyr
CO2
CO2
CO2
CO2 IclMas
Green plants, fungi, many bacteria (e.g. E. coli) and – as the only clade of animals – nematodes harbour the glyoxylate shunt. Then, there is an
elementary mode representing conversion of AcCoA into glucose.
Caenorhabditiselegans
In a limited network of central metabolism, no gluconeogenesis from fatty acids
• Weinman,E.O. et al. (1957) Physiol. Rev. 37, 252–272.
• 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
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.
Going genome-scale
• Can humans convert fatty acids into sugar on entangled routes across a larger network? Mentioned in literature on anecdotal basis
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
ChristophKaleta
Gluconeogenesis from fatty acids
• Is likely to be important– in sports physiology– in diets for weight reduction– in hibernating animals– in embryos within eggs
How can Inuit live on a practically carbohydrate-free diet?
C. Kaleta, L.F. de Figueiredo, S. SchusterAgainst the stream: Relevance of gluconeogenesis from fatty acids for natives of the arctic regionsIntern. J. Circumpol. Health 71 (2012) 18436
Glucose
AcCoA
Cit
IsoCit
OG
SucCoA
PEP
Oxac
Mal
Fum
Succ
Gly
Pyr
CO2
CO2
CO2
CO2
A successful theoretical predictionRed elementary mode: Usual TCA cycleBlue elementary mode: Catabolic pathwaypredicted in Liao et al. (1996) and Schuster et al. (1999) for E. coli.
Glucose
AcCoA
Cit
IsoCit
OG
SucCoA
PEP
Oxac
Mal
Fum
Succ
Gly
Pyr
CO2
CO2
CO2
CO2
Red elementary mode: Usual TCA cycleBlue elementary mode: Catabolic pathwaypredicted 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) 46446–46451
NADP
NADPH
NADP
NADPH
NADHNAD
ADP
ATP
ADP
ATP
CO2
ATP ADP
G6P
X5P
Ru5P
R5P
S7P
GAP
GAP
6PG
GO6P
F6P FP2
F6P
DHAP
1.3BPG
3PG
2PG
PEP
E4P
Optimization: Maximizing molar yields
ATP:G6P yield = 3 ATP:G6P yield = 2
Pyr
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
Trp105
PEPPyr
3PGGAP
PrpP
Schuster, Dandekar, Fell,Trends Biotechnol. 17 (1999) 53
Tryptophan
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.
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, 121–134.
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)
S. Schuster et al.,J. Math. Biol. 45 (2002) 153-181
Some futile cycles are not easy to find
S. Schuster et al.,J. Math. Biol. 45 (2002) 153-181
Some futile cycles are not easy to find
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.
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 astrocytesCN = cytosol of neurons
Gebauer et al.FEBS J. (2012) 279: 3192-202.
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 – gap filling
Application to signalling systems
E1 E1*
E2 E2*
E3 E3*
Target
Signal
Calculating elementary modes gives trivial result that each cyclecorresponds to one mode. Flow ofinformation is not reflected.
Enzyme cascades – only activated component is depicted
Signal
E1*
E2*
E4*
Target2Target1
E3*
Obviously, elementary signalling routes
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.
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 E1 + E2*
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
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
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?
Dept. of Bioinformatics group at the School of Biology and
Pharmaceutics, Jena University
Futile cycles
• “…a search for metabolic markers of aging might include efforts to determine [...] (b) enzymes that catalyze opposing reactions” (Stadtmann, Exp. Gerontol. 23, 1988, 327-347)
• „…an attractive candidate for the function of the … 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.“ (Brand, Exp. Gerontol. 35, 2000, 811-820)
AMP
NA
NaAD NAD
Nam
NaMN
NR
NAR
NMN
PRPP
ADP-ribosylation
ADP-ribosyl-X
Pnc1
H2ONH3
Qns1ATP
gln
H2O
gluNAD_pool
NAD_ex
NAMPT [human]
NR_pool
NR_ex
Nam_pool
Nam_ex
Npt1
NAR_pool
NAR_ex
NA_pool
NA_ex
Nma1,2ATP
Nma1,2
ATP
Nrk1
ATP
ADP
Nrk1
ATP
ADP Sdt1, Isn1
Pi
Sdt1, Isn1
Pi
Pnp1 Pi
ribose-1P
Pnp1
Pi
ribose-1P
Bna6 QA
CO2
PPi
PRPP
PPi
PPi
Npy1
AMP
Urh1
H2O
ribose
Urh1
H2O
ribose
H2O
+
+
+
PRPP
PPi
PPi
PPi
Network of NAD metabolism
Elementary flux modes include all futile cycles
AMP
ribose-P
Prs1-5
ATP
AMP
NaAD NAD
NaMNPRPP
Qns1ATP
gln
H2O
gluNAD_pool
NAD_ex
Nma1,2
ATP
Bna6 QA
CO2
PPi
PPi
PPi
AMP
ribose-P
Prs1-5
ATP
AMP
NA
NaAD NAD
NaMN
Qns1ATP
gln
H2O
gluNAD_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
NAMPTSdt1, Isn1
Pi
Pnp1 Pi
ribose-P
PRPP
PPi
ATP
ADP
AMP
NaAD NAD
NaMN
NAR
Qns1ATP
gln
H2O
gluNAD_pool
NAD_ex
NAR_pool
NAR_ex
Nma1,2
ATP
Nrk1
ATP
ADP
PPiPPi
AMP
ribose-P
Prs1-5
ATP
AMP
NA
NaAD NAD
Nam
NaMN
NR
Pnc1
H2ONH3
Qns1ATP
gln
H2O
gluNAD_pool
NAD_ex
NR_pool
NR_ex
Npt1
Nma1,2
ATP
PPi
Urh1H2Oribose
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
ADPADP-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) 341–348.
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.
Summer Winter
Connections within SCN
Bodenstein, Gosak, Schuster, Marhl, Perc: PLoS Comp. Biol. 8 (2012)