Experimental Studies of Evolutionary Dynamics in Microbes

Abstract

Evolutionary dynamics in laboratory microbial evolution experiments can be surprisingly complex. In the past two decades, observations of these dynamics have challenged simple models of adaptation and have shown that clonal interference, hitchhiking, ecological diversification, and contingency are widespread. In recent years, advances in high-throughput strain maintenance and phenotypic assays, the dramatically reduced cost of genome sequencing, and emerging methods for lineage barcoding have made it possible to observe evolutionary dynamics at unprecedented resolution. These new methods can now begin to provide detailed measurements of key aspects of fitness landscapes and of evolutionary outcomes across a range of systems. These measurements can highlight challenges to existing theoretical models and guide new theoretical work towards the complications that are most widely important.

Keywords: clonal interference; contingency; ecological diversification; epistasis; pleiotropy.

Figure 1 (Key Figure):

(A) Simulated evolutionary dynamics in an asexually evolving population, with parameter values typical in a laboratory evolution experiment. Mutations arise often enough that they cannot be selected on individually. Instead, between the appearance of a new mutation in a population and its eventual extinction or fixation, many other mutations arise in the population, either on the same genetic background or in a competing lineage. As a result, the fate of each mutation is not determined only on its own merits, but is intertwined with all other mutations in the population. Most beneficial mutations are outcompeted by fitter clones before they are able to rise to substantial frequencies. We note that these evolutionary dynamics have never been directly observed at the resolution shown here. (B) These evolutionary dynamics can be studied in laboratory settings using a range of methods: (i) Fitness assays. The relative increase in fitness of the evolving population compared to the ancestor offers a coarse view of the underlying evolutionary dynamics. (ii) The frequencies of pre-introduced genetic markers through time. As with fitness assays, changes in marker frequencies reflect the aggregate effects of multiple evolutionary events. These methods cannot resolve the effects of individual mutations. (iii) Population metagenomic sequencing offers a view of individual mutations that arise during evolution. However, only mutations that reach substantial frequencies (typically at least ~5% or more) are observable. Thus only a tiny and biased subset of all the mutations occurring in the population is visible. (iv) Newer barcoding methods make it possible to observe lineage dynamics at much higher resolution. Up to the resolution limits imposed by the evolutionary process itself (i.e. genetic drift), these lineage dynamics can be used to infer when beneficial mutations occur and their effects on fitness. However, because barcode diversity is lost as the population evolves, these methods are currently limited to studying short timescales.