SUMMARY Enzyme function and evolution are influenced by the larger context of a metabolic pathway. Deleterious mutations or perturbations in one enzyme can often be compensated by mutations to others. We used comparative genomics and experiments to examine evolutionary interactions with the essential metabolic enzyme dihydrofolate reductase (DHFR). Analyses of synteny and co-occurrence across bacterial species indicate that DHFR is coupled to thymidylate synthase (TYMS) but relatively independent from the rest of folate metabolism. Using quantitative growth rate measurements and forward evolution in Escherichia coli, we demonstrate that the two enzymes adapt as a relatively independent unit in response to antibiotic stress. Metabolomic profiling revealed that TYMS activity must not exceed DHFR activity to prevent the depletion of reduced folates and the accumulation of the intermediate dihydrofolate. Comparative genomics analyses identified >200 gene pairs with similar statistical signatures of modular co-evolution, suggesting that cellular pathways may be decomposable into small adaptive units.

2The ability to predict cell behavior is complicated by an unknown pattern of functional interdependence among genes. Here, we use the conservation of gene proximity across species (synteny) to infer functional couplings between genes. For the folate metabolic pathway, we observe a sparse, modular architecture of interactions, with two small groups of genes coevolving in the midst of others that evolve independently. For one such module -dihydrofolate reductase and thymidylate synthase -we use epistasis measurements and forward evolution to demonstrate both internal functional coupling and independence from the remainder of the genome. Mechanistically, the coupling is driven by a constraint on their relative activities, which must be balanced to prevent accumulation of a metabolic intermediate. The results indicate an organization of cellular systems not apparent from inspection of biochemical pathways or physical complexes, and support the strategy of using evolutionary information to decompose cellular systems into functional units. Keywords:co-evolution, statistical genomics, folate metabolism, dihydrofolate reductase (DHFR), epistasis, experimental evolution . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/120006 doi: bioRxiv preprint first posted online Mar. 23, 2017; 3 IntroductionThe activity of one gene is often modified by the activity of other genes in the genome.This functional coupling between genes makes it difficult to predict cellular behavior as a whole from measurements of each gene (or protein) taken independently. As a consequence, our ability to rationally engineer new metabolic systems (Kim and Copley, 2012; Michener et al., 2014a; Michener et al., 2014b), and quantify the relationship between mutations and disease (Kondrashov et al., 2002; Zuk et al., 2012) is limited. Further, this interdependency amongst genes makes it non-trivial to understand how complex cellular systems are possible through an evolutionary process of stepwise variation with selection (Breen et al., 2012; Wagner and Altenberg, 1996; Weinreich et al., 2013). Thus, an ability to globally map functional couplings between genes and subsequently decompose cellular systems into quasi-independent moduleseach module consisting of several genes engaged in cooperative function -would help render biological systems tractable and predictable.However, it remains unclear if such a modular decomposition is possible, and if so, what the general strategy should be for finding it. A fundamental aspect of this problem is to distinguish functional couplings associated with core, conserved processes from those couplings that reflect species and/or environment specific adaptations. In this sense, we seek a general description of genetic interactions that can serve as a basis for guiding targeted experiments and modeling cellular systems. Here, we develop a map of pairwise gene interactions through ...