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The predictability of molecular evolution during functional innovation

Significance Understanding the genetic changes that underlie phenotypic functional innovations is a fundamental goal in evolutionary biology, giving insight into species’ past, present, and future evolutionary trajectories. One important unresolved question is whether such genetic changes typically affect protein expression or protein structure. Here we use large-scale laboratory evolution with bacteria to quantify the types of genetic changes that occur during functional innovation. We show that whether these changes affect protein expression or protein structure depends on which cellular functions are being selected upon. We then show that changes affecting protein expression occur in qualitatively different sets of genes from changes affecting protein structure. These results show that using functional knowledge it is possible to predict the course of evolution. Determining the molecular changes that give rise to functional innovations is a major unresolved problem in biology. The paucity of examples has served as a significant hindrance in furthering our understanding of this process. Here we used experimental evolution with the bacterium Escherichia coli to quantify the molecular changes underlying functional innovation in 68 independent instances ranging over 22 different metabolic functions. Using whole-genome sequencing, we show that the relative contribution of regulatory and structural mutations depends on the cellular context of the metabolic function. In addition, we find that regulatory mutations affect genes that act in pathways relevant to the novel function, whereas structural mutations affect genes that act in unrelated pathways. Finally, we use population genetic modeling to show that the relative contributions of regulatory and structural mutations during functional innovation may be affected by population size. These results provide a predictive framework for the molecular basis of evolutionary innovation, which is essential for anticipating future evolutionary trajectories in the face of rapid environmental change.