title: Model-based identification of drug targets that revert disrupted metabolism and its application to ageing.
abstract: The growing availability of 'omics' data and high-quality in silico genome-scale metabolic models (GSMMs) provide a golden opportunity for the systematic identification of new metabolic drug targets. Extant GSMM-based methods aim at identifying drug targets that would kill the target cell, focusing on antibiotics or cancer treatments. However, normal human metabolism is altered in many diseases and the therapeutic goal is fundamentally different--to retrieve the healthy state. Here we present a generic metabolic transformation algorithm (MTA) addressing this issue. First, the prediction accuracy of MTA is comprehensively validated using data sets of known perturbations. Second, two predicted yeast lifespan-extending genes, GRE3 and ADH2, are experimentally validated, together with their associated hormetic effect. Third, we show that MTA predicts new drug targets for human ageing that are enriched with orthologs of known lifespan-extending genes and with genes downregulated following caloric restriction mimetic treatments. MTA offers a promising new approach for the identification of drug targets in metabolically related disorders.
authors.affiliation: The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel.
authors.forename: Keren
authors.initials: K
authors.lastname: Yizhak
authors.forename: Orshay
authors.initials: O
authors.lastname: Gabay
authors.forename: Haim
authors.initials: H
authors.lastname: Cohen
authors.forename: Eytan
authors.initials: E
authors.lastname: Ruppin
mesh_terms: D000375:Aging / Q000187:drug effects / Q000235:genetics / Q000378:metabolism*; D000426:Alcohol Dehydrogenase / Q000172:deficiency / Q000235:genetics; D000429:Alcohol Oxidoreductases / Q000172:deficiency / Q000235:genetics; D000465:Algorithms; D031204:Caloric Restriction; D003198:Computer Simulation; D015777:Eicosanoids / Q000378:metabolism; D015966:Gene Expression Regulation, Fungal; D016681:Genome, Fungal; D015894:Genome, Human; D059165:Hormesis; D006801:Humans; D008136:Longevity / Q000187:drug effects / Q000235:genetics; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D058990:Molecular Targeted Therapy; D017382:Reactive Oxygen Species / Q000037:antagonists & inhibitors / Q000378:metabolism; D000077185:Resveratrol; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D020123:Sirolimus / Q000494:pharmacology; D013267:Stilbenes / Q000494:pharmacology
chemical_list: D015777:Eicosanoids; D017382:Reactive Oxygen Species; D029701:Saccharomyces cerevisiae Proteins; D013267:Stilbenes; D000429:Alcohol Oxidoreductases; C509911:ADH2 protein, S cerevisiae; D000426:Alcohol Dehydrogenase; C019642:alcohol dehydrogenase (NADP+); D000077185:Resveratrol; D020123:Sirolimus