Deep epistasis in human metabolism

Abstract

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. © 2010 American Institute of Physics. ͓doi:10.1063/1.3456056͔Small molecule metabolism mediates the most basic biochemical function of living matter: to derive energy and macromolecules from nutrients in the environment. Metabolic pathways are tied in intricate networks which in E. coli and S. cereviseae have been shown to display a great degree of buffering and epistasis. Epistasis has not been previously been examined on a genome-scale in human metabolism. We apply a novel in silico approach to sample the depth of epistasis in the human metabolic network with respect to a variety of metabolic objectives.

Missing heritability of common diseases and treatments outside the protein-coding exome

“…Much research further supports the notion that gene-gene interactions play a pervasive role in evolution, either by mitigating the deleterious effects (stabilizing selection) of a mutation or promoting new favorable traits (de Visser et al 2011; Imielinski and Belta 2010; Lehner 2011; Shinar and Feinberg 2010; Watt 2013; Wittkopp and Kalay 2012). Rather than being selectable on their own, genetic variants appear to evolve under interactive dynamics, evidenced at the level of RNA and protein expression or function (de Visser et al 2011; Khan et al 2013).…”

“…We will argue here that this assumption may well be correct for deleterious variants conveying disease risk. Yet, adaptive processes result in frequent variants as a response to environmental conditions, with dynamic gene-gene interactions (epistasis) a main factor in evolution (de Visser et al 2011; Hemani et al 2013; Imielinski and Belta 2010; Wittkopp and Kalay 2012). Such adaptive variants can turn deleterious under unfavorable conditions but remain in the noise of GWAS results (Gauderman et al 2013; Hu et al 2011; Moore et al 2006; Van Hulle et al 2013).…”

Predicting and characterizing selective multiple drug treatments for metabolicdiseases and cancer

“…The computational method presented in this paper is based on a bilevel optimization which, after reformulation through duality theory, allows the algorithm to efficiently search the interactions between drugs. With respect to the available literature [7-9,17-19], the procedure we are proposing presents at least three important differences: (i) the synergisms are efficiently explored over all drug combinations without limiting only to pairwise combinations but without doing an exhaustive search, thanks to the application of duality theory; (ii) the multiple drug treatments suggested by the method guarantee both the inhibition of the chosen target (efficacy) and a minimal side effect on the other cellular functions (selectivity); (iii) in our procedure, any metabolic process of the network can be chosen as possible disease and phenotype readout, not only cell growth as common in the FBA literature (a more detailed comparison with the current literature is reported in the Additional file 1). Inspired by works such as Refs.…”

Integration of large-scale metabolic, signaling, and gene regulatory networks with application to infection responses

“…The state of each gene is updated with some delay τ , where τ is the number of time steps required for a regulator to change the expression level of a target gene. The time scale H in (9) is then expressed as H = τ h. 6) Update I: I is updated according to (12) for all reactions j ∈ J, and the procedure is reiterated.…”

“…Within the systems and control community, most of the previous works in this area have focused on studying such networks in isolation [8], [9], [10], [11], [12]. However, recent studies show that the integration of mathematical models of different types of biochemical networks, together with environmental conditions, increases the accuracy of the models, as well as their predictive capacity [13], [14].…”