Abstract
Understanding the forces that drive the dynamics of adaptive evolution is a goal of many sub-fields and applications of evolutionary biology, including in evolutionary computation. The fitness landscape analogy has served as a useful abstraction for addressing these topics across many systems, and recent treatments have revealed how different environments can frame the particulars of adaptive evolution by changing the topography of fitness landscapes. In this study, we examine how the larger, ambient genetic context in which a protein is embedded affects fitness landscape topography and subsequent evolution. Using simulations on empirical fitness landscapes, we discover that the genetic background - genetic variability in regions outside of the locus under study (in this case, an essential bacterial enzyme target of antibiotics) - influences the speed and direction of evolution in several surprising ways. These findings have implications for how we study the evolution of drug resistance in nature, and for presumptions about how biological evolution might be expected to occur in genetically-modified organisms. More generally, the findings speak to theory surrounding how “difference can beget difference” in adaptive evolution (whether biological, computational, or technological): that small differences in environmental or genetic background can greatly alter the specifics of how evolution occurs, which can rapidly drive even slightly diverged populations further apart.
Highlights
IntroductionThe theme across many of these recent breakthroughs is a growth in our understanding of how various contexts can frame our expectations for how evolution will occur, and render it challenging to predict [16]–[19]
The fitness landscape analogy has undergone a subtle makeover in recent years, with larger data sets and improved methods greatly increasing the scope of systems and questions that the analogy can be used to responsibly address
Other examinations have extracted new information out of empirical fitness landscapes, including how landscapes change in shape during adaptive evolution [13], how indirect pathways are traversed during evolution [14], and how features of a landscape determine the speed of some adaptive trajectories relative to others [15]
Summary
The theme across many of these recent breakthroughs is a growth in our understanding of how various contexts can frame our expectations for how evolution will occur, and render it challenging to predict [16]–[19]. This is of particular importance in studies utilizing empirically determined fitness landscapes to understand the evolution of drug resistance, where the hope is to one day understand how the evolution of resistance occurs such that disease can be treated more effectively [20]–[23]
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