NetBioSIG2013-KEYNOTE Stefan Schuster

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Keynote presentation for Network Biology SIG 2013 by Stefan Schuster, Head of Dept. of Bioinformatics at Friedrich-Schiller University Jena, Germany

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NetBioSIG2013-KEYNOTE Stefan Schuster

  1. 1. Insights from network analysis of metabolism in four kingdoms of life Stefan Schuster Friedrich Schiller University Jena, Germany Dept. of Bioinformatics
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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? ?
  6. 6. • 1 glycerol + 3 even-chain fatty acids (odd-chain fatty acids are rare) • Glycerol  glucose OK (gluconeogenesis) • (Even-chain) fatty acids  acetyl CoA (β-oxidation) • Acetyl CoA  glucose? Triglycerides
  7. 7. 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
  8. 8. Metabolism is hypergraph due to bimolecular reactions!
  9. 9. 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
  10. 10. 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
  11. 11. Simple example: P1 P2 P3 1S 1 2 3           −110 101 011 Elementary modes:
  12. 12. flux1 flux2 flux3 generating vectors
  13. 13. 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 1S 1 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.
  14. 14. 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:           −110 101 011 N = (1 −1)  dim = 1
  15. 15. 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 elementary mode consuming AcCoA, nor any one producing glucose. Intuitive explanation by regarding oxaloacetate or CO2.
  16. 16. 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. Caenorhabditis elegans
  17. 17. 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
  18. 18. 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.
  19. 19. Going genome-scale • Can humans convert fatty acids into sugar on entangled routes across a larger network? Mentioned in literature on anecdotal basis
  20. 20. 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
  21. 21. Gluconeogenesis from fatty acids • Is likely to be important – in sports physiology – in diets for weight reduction – in hibernating animals – in embryos within eggs
  22. 22. 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
  23. 23. Glucose AcCoA Cit IsoCit OG SucCoA PEP Oxac Mal Fum Succ Gly Pyr CO2 CO2 CO2 CO2 A successful theoretical prediction Red elementary mode: Usual TCA cycle Blue elementary mode: Catabolic pathway predicted in Liao et al. (1996) and Schuster et al. (1999) for E. coli.
  24. 24. 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) 46446–46451
  25. 25. NADP NADPH NADP NADPH NADHNAD ADP ATP ADP ATP CO2 ATP ADP G6P X5P Ru5P R5P S7P GAP GAP 6PG GO6P F6P FP 2 F6P DHAP 1.3BPG 3PG 2PG PEP E4P Optimization: Maximizing molar yields ATP:G6P yield = 3 ATP:G6P yield = 2 Pyr
  26. 26. 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
  27. 27. 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.
  28. 28. 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.
  29. 29. 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)
  30. 30. S. Schuster et al., J. Math. Biol. 45 (2002) 153-181 Some futile cycles are not easy to find
  31. 31. S. Schuster et al., J. Math. Biol. 45 (2002) 153-181 Some futile cycles are not easy to find
  32. 32. 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.
  33. 33. 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.
  34. 34. 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
  35. 35. 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.
  36. 36. Enzyme cascades – only activated component is depicted Signal E1* E2* E4* Target2Target1 E3* Obviously, elementary signalling routes
  37. 37. 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.
  38. 38. 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*
  39. 39. 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
  40. 40. 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
  41. 41. 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?
  42. 42. Dept. of Bioinformatics group at the School of Biology and Pharmaceutics, Jena University
  43. 43. 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)
  44. 44. AMP NA NaAD NAD Nam NaMN NR NAR NMN PRPP ADP-ribosylation ADP-ribosyl-X Pnc1 H2O NH3 Qns1ATP gln H2O glu NAD_pool NAD_ex NAMPT [human] NR_pool NR_ex Nam_pool Nam_ex Npt1 NAR_pool NAR_ex NA_pool NA_ex Nma1,2 ATP 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
  45. 45. 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) 341–348.
  46. 46. 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.
  47. 47. Summer Winter Connections within SCN Bodenstein, Gosak, Schuster, Marhl, Perc: PLoS Comp. Biol. 8 (2012)

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