The dynamics of molecular evolution over 60,000 generations

Abstract

The outcomes of evolution are determined by a stochastic dynamical process that governs how mutations arise and spread through a population. Here, we analyze the dynamics of molecular evolution in twelve experimental populations of Escherichia coli, using whole-genome metagenomic sequencing at 500-generation intervals through 60,000 generations. Despite a declining rate of fitness gain, molecular evolution continues to be characterized by signatures of rapid adaptation, with multiple beneficial variants simultaneously competing for dominance in each population. Interactions between ecological and evolutionary processes play an important role, as long-term quasi-stable coexistence arises spontaneously in most populations, and evolution continues within each clade. We also present new evidence that the targets of natural selection change over time, as epistasis and historical contingency alter the strength of selection on different genes. Together, these results show that long-term adaptation to a constant environment can be a more complex and dynamic process than is often assumed.

Different mechanisms drive the maintenance of polymorphism at loci subject to strong versus weak fluctuating selection

“…; Good et al. ; Charlesworth ). In a particularly striking example, sequencing studies in temperate Drosophila populations have found hundreds of polymorphic loci that undergo seasonal oscillations in allele frequency (Bergland et al.…”

Microbial Experimental Evolution – a proving ground for evolutionary theory and a tool for discovery

“…Twenty years ago, an evolutionary biologist might have predicted that these populations of E. coli would have reached optimal fitness after a few thousand generations. However, we now know that each population continues to adapt after 61,500 generations . A key discovery has been the evolution of the utilisation of citrate (cit+ phenotype), a carbon source used as a buffer in the growth media.…”

Embodied Evolution in Collective Robotics: A Review

“…It is now possible, for instance, to study evolutionary mechanisms through methods such as gene sequencing (Blount et al, 2012;Wiser et al, 2013). However, in vitro experimental evolution has its limitations: with evolution in "real" substrates, the time scales involved limit the applicability to relatively simple organisms such as Escherichia coli (Good et al, 2017). From a theoretical point of view, population genetics (see Charlesworth and Charlesworth (2010) for a recent introduction) provides a set of mathematically grounded tools for understanding evolution dynamics, at the cost of many simplifying assumptions.…”