Model-based identification of drug targets that revert disrupted metabolism and its application to ageing

Nature Communications

Volumn 4, Issue , 2013, Pages

  Yizhak, Keren ^a   Gabay, Orshay ^b   Cohen, Haim ^b   Ruppin, Eytan ^a  

^a TEL AVIV UNIVERSITY   (Israel)

^b BAR ILAN UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

GLUTATHIONE; THIOREDOXIN;

AGING; ALGORITHM; ANTIBIOTICS; CANCER; DATA SET; DISEASE TREATMENT; DRUG; GENE EXPRESSION; GENOME; IDENTIFICATION METHOD; METABOLISM; NUMERICAL MODEL; YEAST;

ACCURACY; ADH2 GENE; AGING; ALGORITHM; ARTICLE; BIOMIMETICS; CALORIC RESTRICTION; CARBON SOURCE; DOWN REGULATION; DRUG TARGETING; ESCHERICHIA COLI; GENE; GENE EXPRESSION; GRE3 GENE; KNOCKOUT GENE; METABOLIC TRANSFORMATION ALGORITHM; METABOLISM; NONHUMAN; NUCLEOTIDE METABOLISM; PENTOSE PHOSPHATE CYCLE; PREDICTION; SACCHAROMYCES CEREVISIAE;

AGING; ALCOHOL DEHYDROGENASE; ALCOHOL OXIDOREDUCTASES; ALGORITHMS; CALORIC RESTRICTION; COMPUTER SIMULATION; EICOSANOIDS; GENE EXPRESSION REGULATION, FUNGAL; GENOME, FUNGAL; GENOME, HUMAN; HORMESIS; HUMANS; LONGEVITY; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; MOLECULAR TARGETED THERAPY; REACTIVE OXYGEN SPECIES; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SIROLIMUS; STILBENES;

EID: 84886670153     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms3632     Document Type: Article

References (66)

1. Butcher, E. C., Berg, E. L. & Kunkel, E. J. Systems biology in drug discovery. Nat. Biotech. 22, 1253-1259 (2004). (Pubitemid 39336778)
2. Chavali, A. K., Dauria, K. M., Hewlett, E. L., Pearson, R. D. & Papin, J. A. A metabolic network approach for the identification and prioritization of antimicrobial drug targets. Trends Microbiol. 20, 113-123 (2012).
3. Folger, O. et al. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011).
4. Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J. & Palsson, B. O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92-96 (2004). (Pubitemid 38620557)
5. Wessely, F. et al. Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs. Mol. Syst. Biol. 7, 515 (2011).
6. Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M. & Sauer, U. Multidimensional optimality of microbial metabolism. Science 336, 601-604 (2012).
7. Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat. Genet. 43, 656-662 (2011).
8. Chandrasekaran, S. & Price, N. D. Probabilistic integrative modeling of genomescale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 107, 17845-17850 (2010).
9. Jensen, P. A. & Papin, J. A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27, 541-547 (2010).
10. Agren, R. et al. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput. Biol. 8, e1002518 (2012).
11. Burgard, A. & Maranas, C. Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol. Bioeng. 82, 670-677 (2003). (Pubitemid 36576230)
12. Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl Acad. Sci. USA 104, 1777-1782 (2007). (Pubitemid 46239855)
13. Ma, H. et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3, 135 (2007).
14. Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B. Ø. & Ruppin, E. Networkbased prediction of human tissue-specific metabolism. Nat. Biotechnol. 26, 1003-1010 (2008).
15. Lewis, N. E. et al. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat. Biotech. 28, 1279-1285 (2010).
16. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotech. 31, 419-425 (2013).
17. Bordbar, A. & Palsson, B. O. Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J. Intern. Med. 271, 131-141 (2012).
18. Oberhardt, M. A., Yizhak, K. & Ruppin, E. Metabolically re-modeling the drug pipeline. Curr. Opin. Pharmacol. 13, 778-785 (2013).
19. Varma, A. & Palsson, B. O. Metabolic flux balancing: basic concepts, scientific and practical use. Biotechnology 12, 994-998 (1994).
20. Segre, D., Vitkup, D. & Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl Acad. Sci. USA 99, 15112-15117 (2002). (Pubitemid 35334618)
21. Lee, C.-K., Klopp, R. G., Weindruch, R. & Prolla, T. A. Gene expression profile of aging and its retardation by caloric restriction. Science 285, 1390-1393 (1999). (Pubitemid 29411668)
22. Cao, S. X., Dhahbi, J. M., Mote, P. L. & Spindler, S. R. Genomic profiling of short-and long-term caloric restriction effects in the liver of aging mice. Proc. Natl Acad. Sci. 98, 10630-10635 (2001). (Pubitemid 32878672)
23. Kenyon, C. The first long-lived mutants: discovery of the insulin/IGF-1 pathway for ageing. Philos. Trans. R. Soc. B Biol. Sci. 366, 9-16 (2011).
24. Donmez, G. & Guarente, L. Aging and disease: connections to sirtuins. Aging Cell 9, 285-290 (2010).
25. Kanfi, Y. et al. The sirtuin SIRT6 regulates lifespan in male mice. Nature 483, 218-221 (2012).
26. Pearson, K. J. et al. Resveratrol delays age-related deterioration and mimics transcriptional aspects of dietary restriction without extending life span. Cell Metab. 8, 157-168 (2008).
27. Kaeberlein, M. Resveratrol and rapamycin: are they anti-aging drugs Bioessays 32, 96-99 (2010).
28. Smith, E. D. et al. Quantitative evidence for conserved longevity pathways between divergent eukaryotic species. Genome Res. 18, 564-570 (2008). (Pubitemid 351482781)
29. Yiu, G. et al. Pathways change in expression during replicative aging in Saccharomyces cerevisiae. J. Gerontol. A Biol. Sci. Med. Sci. 63, 21-34 (2008). (Pubitemid 351738460)
30. Ge, H. et al. Comparative analyses of time-course gene expression profiles of the long-lived sch9D mutant. Nucleic Acids Res. 38, 143-158 (2010).
31. Matecic, M. et al. A microarray-based genetic screen for yeast chronological aging factors. PLoS Genet. 6, e1000921 (2010).
32. Kaeberlein, M., Kirkland, K. T., Fields, S. & Kennedy, B. K. Genes determining yeast replicative life span in a long-lived genetic background. Mech. Ageing Dev. 126, 491-504 (2005). (Pubitemid 40269293)
33. Burtner, C. R., Murakami, C. J., Olsen, B., Kennedy, B. K. & Kaeberlein, M. A genomic analysis of chronological longevity factors in budding yeast. Cell Cycle 10, 1385-1396 (2011).
34. Fabrizio, P. et al. Sir2 blocks extreme life-span extension. Cell 123, 655-667 (2005). (Pubitemid 41608467)
35. Rattan, S. I. S. Hormesis in aging. Ageing Res. Rev. 7, 63-78 (2008).
36. Welle, S., Brooks, A. I., Delehanty, J. M., Needler, N. & Thornton, C. A. Gene expression profile of aging in human muscle. Physiol. Genomics 14, 149-159 (2003). (Pubitemid 37345223)
37. Welle, S. et al. Skeletal muscle gene expression profiles in 20-29 year old and 65-71 year old women. Exp. Gerontol. 39, 369-377 (2004). (Pubitemid 38264367)
38. Zahn, J. M. et al. Transcriptional profiling of aging in human muscle reveals a common aging signature. PLoS Genet. 2, e115 (2006).
39. Lanza, I. R. et al. Endurance exercise as a countermeasure for aging. Diabetes 57, 2933-2942 (2008).
40. Cunningham, J. mTOR controls mitochondrial oxidative function through a YY1-PGC-1alpha transcriptional complex. Nature 450, 736-740 (2007).
41. Hamilton, B. et al. A systematic RNAi screen for longevity genes in C. elegans. Genes Dev. 19, 1544-1555 (2005). (Pubitemid 41002998)
42. Zhou, B. et al. Midlife gene expressions identify modulators of aging through dietary interventions. Proc. Natl Acad. Sci. USA 109, E1201-E1209 (2012).
43. Sutton, G. et al. Biological aging alters circadian mechanisms in murine adipose tissue depots. Age. 35, 533-547 (2013).
44. Alarcón de la Lastra, C. & Villegas, I. Resveratrol as an anti-inflammatory and anti-aging agent: mechanisms and clinical implications. Mol. Nutr. Food Res. 49, 405-430 (2005). (Pubitemid 40780389)
45. Zimmerman, J. A., Malloy, V., Krajcik, R. & Orentreich, N. Nutritional control of aging. Exp. Gerontol. 38, 47-52 (2003). (Pubitemid 36113261)
46. McDonald, R. B. Influence of dietary sucrose on biological aging. Am. J. Clin. Nutr. 62, 284S-292S (1995).
47. Grandison, R. C., Piper, M. D. W. & Partridge, L. Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila. Nature 462, 1061-1064 (2009).
48. Mesquita, A. et al. Caloric restriction or catalase inactivation extends yeast chronological lifespan by inducing H2O2 and superoxide dismutase activity. Proc. Natl Acad. Sci. 107, 15123-15128 (2010).
49. Burtner, C. R., Murakami, C. J., Kennedy, B. K. & Kaeberlein, M. A molecular mechanism of chronological aging in yeast. Cell Cycle 8, 1256-1270 (2009).
50. Timmers, S. et al. Calorie restriction-like effects of 30 days of resveratrol supplementation on energy metabolism and metabolic profile in obese humans. Cell Metab. 14, 612-622 (2011).
51. Park, S.-J. et al. Resveratrol ameliorates aging-related metabolic phenotypes by inhibiting cAMP phosphodiesterases. Cell 148, 421-433 (2012).
52. Edwards, M. et al. Gene expression profiling of aging reveals activation of a p53-mediated transcriptional program. BMC Genomics 8, 80 (2007).
53. Bordel, S., Agren, R. & Nielsen, J. Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput. Biol. 6, e1000859 (2010).
54. Price, N. D., Reed, J. L. & Palsson, B. O. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2, 886-897 (2004). (Pubitemid 39567269)
55. Feist, A. M. et al. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3, 121 (2007).
56. Mo, M., Palsson, B. & Herrgard, M. Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst. Biol. 3, 37 (2009).
57. Burgard, A. P., Nikolaev, E. V., Schilling, C. H. & Maranas, C. D. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 14, 301-312 (2004). (Pubitemid 38219844)
58. Greer, E. L. & Brunet, A. Different dietary restriction regimens extend lifespan by both independent and overlapping genetic pathways in C. elegans. Aging Cell 8, 113-127 (2009).
59. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901-906 (1999). (Pubitemid 29381993)
60. Fabrizio, P. & Longo, V. D. The chronological life span of Saccharomyces cerevisiae. Aging Cell 2, 73-81 (2003).
61. Wei, M., Madia, F. & Longo., V. D. Studying age-dependent genomic instability using the s. cerevisiae chronological lifespan model. J. Vis. Exp. 55, 3030 (2011).
62. Benov, L., Sztejnberg, L. & Fridovich, I. Critical evaluation of the use of hydroethidine as a measure of superoxide anion radical. Free Radic. Biol. Med. 25, 826-831 (1998). (Pubitemid 28512535)
63. Irazusta, V. n., Cabiscol, E., Reverter-Branchat, G., Ros, J. & Tamarit, J. Manganese is the link between frataxin and iron-sulfur deficiency in the yeast model of friedreich ataxia. J. Biol. Chem. 281, 12227-12232 (2006).
64. Myhre, O., Andersen, J. M., Aarnes, H. & Fonnum, F. Evaluation of the probes 2,7-dichlorofluorescin diacetate, luminol, and lucigenin as indicators of reactive species formation. Biochem. Pharmacol. 65, 1575-1582 (2003). (Pubitemid 36581871)
65. Cipak, A., Jaganjac, M., Tehlivets, O., Kohlwein, S. D. & Zarkovic, N. Adaptation to oxidative stress induced by polyunsaturated fatty acids in yeast. Biochim. Biophys. Acta 1781, 283-287 (2008).
66. Kaeberlein, M. Lessons on longevity from budding yeast. Nature 464, 513-519 (2010).