Deep epistasis in human metabolism.
We extend and apply a method that we have developed for deriving high-order epistatic relationships in large biochemical networks to a published genome-scale model of human metabolism. In our analysis we compute 33,328 reaction sets whose knockout synergistically disables one or more of 43 important metabolic functions. We also design minimal knockouts that remove flux through fumarase, an enzyme that has previously been shown to play an important role in human cancer. Most of these knockout sets employ more than eight mutually buffering reactions, spanning multiple cellular compartments and metabolic subsystems. These reaction sets suggest that human metabolic pathways possess a striking degree of parallelism, inducing "deep" epistasis between diversely annotated genes. Our results prompt specific chemical and genetic perturbation follow-up experiments that could be used to query in vivo pathway redundancy. They also suggest directions for future statistical studies of epistasis in genetic variation data sets.
Imielinski
Marcin
M
Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA. mimielinski@partners.org
Belta
Calin
C
D000465:Algorithms; D004843:Epistasis, Genetic; D005649:Fumarate Hydratase / Q000235:genetics / Q000378:metabolism; D055786:Gene Knockout Techniques; D015894:Genome, Human; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics; D008954:Models, Biological; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D017711:Nonlinear Dynamics
D005649:Fumarate Hydratase