Abstract

Parallelism is important because it reveals how inherently stochastic adaptation is. Even as we come to better understand evolutionary forces, stochasticity limits how well we can predict evolutionary outcomes. Here we sought to quantify parallelism and some of its underlying causes by adapting a bacteriophage (ID11) with nine different first-step mutations, each with eight-fold replication, for 100 passages. This was followed by whole-genome sequencing five isolates from each endpoint. A large amount of variation arose—281 mutational events occurred representing 112 unique mutations. At least 41% of the mutations and 77% of the events were adaptive. Within wells, populations generally experienced complex interference dynamics. The genome locations and counts of mutations were highly uneven: mutations were concentrated in two regulatory elements and three genes and, while 103 of the 112 (92%) of the mutations were observed in ≤4 wells, a few mutations arose many times. 91% of the wells and 81% of the isolates had a mutation in the D-promoter. Parallelism was moderate compared to previous experiments with this system. On average, wells shared 27% of their mutations at the DNA level and 38% when the definition of parallel change is expanded to include the same regulatory feature or residue. About half of the parallelism came from D-promoter mutations. Background had a small but significant effect on parallelism. Similarly, an analyses of epistasis between mutations and their ancestral background was significant, but the result was mostly driven by four individual mutations. A second analysis of epistasis focused on de novo mutations revealed that no isolate ever had more than one D-promoter mutation and that 56 of the 65 isolates lacking a D-promoter mutation had a mutation in genes D and/or E. We assayed time to lysis in four of these mutually exclusive mutations (the two most frequent D-promoter and two in gene D) across four genetic backgrounds. In all cases lysis was delayed. We postulate that because host cells were generally rare (i.e., high multiplicity of infection conditions developed), selection favored phage that delayed lysis to better exploit their current host (i.e., ‘love the one you’re with’). Thus, the vast majority of wells (at least 64 of 68, or 94%) arrived at the same phenotypic solution, but through a variety of genetic changes. We conclude that answering questions about the range of possible adaptive trajectories, parallelism, and the predictability of evolution requires attention to the many biological levels where the process of adaptation plays out.

Highlights

  • An important question in evolutionary biology is how deterministic and potentially predictable, vs. stochastic, and less predictable, is the process of adaptation

  • When a population is not optimally adapted to its environment, how many different phenotypic solutions are available to it? How different are they and how do their fitness peaks compare? For each of these phenotypic solutions, how many mutational pathways are available at the genetic level and how probable are each of these? How do population dynamics influence which solutions win out? Each of these questions is complicated in itself and they become considerably more complex when we consider other facets of reality such as changing environments, frequency-dependent dynamics and interacting species that are adapting

  • Similar to the bacterial studies cited above, we found that parallelism is dramatically higher at the phenotypic level than the genetic one

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Summary

Introduction

An important question in evolutionary biology is how deterministic and potentially predictable, vs. stochastic, and less predictable, is the process of adaptation. The answer to this depends on many things that we understand poorly. When a population is not optimally adapted to its environment, how many different phenotypic solutions are available to it? For each of these phenotypic solutions, how many mutational pathways are available at the genetic level and how probable are each of these? Each of these questions is complicated in itself and they become considerably more complex when we consider other facets of reality such as changing environments, frequency-dependent dynamics and interacting species that are adapting. Q Transversion 27 q Mutation at background site q Known regulatory q5 q6 q7 q8

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