Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations

Abstract

Evolution is an on-going process, and it can be studied experimentally in organisms with rapid generations. My team has maintained 12 populations of Escherichia coli in a simple laboratory environment for >25 years and 60 000 generations. We have quantified the dynamics of adaptation by natural selection, seen some of the populations diverge into stably coexisting ecotypes, described changes in the bacteria's mutation rate, observed the new ability to exploit a previously untapped carbon source, characterized the dynamics of genome evolution and used parallel evolution to identify the genetic targets of selection. I discuss what the future might hold for this particular experiment, briefly highlight some other microbial evolution experiments and suggest how the fields of experimental evolution and microbial ecology might intersect going forward.

Figure 1

Fitness trajectories of evolving E. coli populations. (a) Fitness trajectory for one population, Ara–1, relative to its ancestor over the first 2000 generations of the LTEE. Error bars are 95% confidence intervals based on replicated assays. The line segments show the fit of a step model to the data. Modified from Lenski and Travisano (1994). (b) Trajectory for the grand-mean fitness across the LTEE populations over 50 000 generations. Error bars are 95% confidence limits based on replicate populations. The curve shows the fit of a power-law model. Modified from Wiser et al. (2013).

Figure 2

Muller plot showing the relative abundances of 42 mutations found in population Ara–1 during its first 20 000 generations. The labels are names of the mutated genes; dots before the gene name indicate mutations that eventually fixed in the population. Note the period between ~7000 and ~14 000 generations, when two major clades—one shown in shades of green, and the other in yellows and reds—transiently coexisted before the latter eventually drove the former extinct. Coexistence was supported by a negative frequency-dependent interaction, with the fluctuations evidently caused by beneficial mutations that gave one lineage or the other a temporary advantage. Modified from Maddamsetti et al. (2015).

Figure 3

Changing mutation rate in population Ara–1. This population maintained the low ancestral mutation rate for over 20 000 generations. It then evolved hypermutability that increased its point mutation rate, with partial compensation later occurring in parallel in two lineages within that population. Modified from Wielgoss et al. (2013).

Figure 4

Growth curves of two Cit^– clones, each with and without a plasmid carrying the evolved citrate-utilization module, on the medium used in the LTEE. OD, optical density, shown on a natural-logarithmic scale. (a) The ancestral strain of the LTEE. (b) A 32 000-generation clone closely related to the Cit⁺ lineage that evolved in population Ara–3. The red and dark blue curves show average growth trajectories for the parent clone and its plasmid-bearing transformant, respectively; the light blue curves show replicates for the transformant. The ancestral strain’s transformant experiences a long and variable delay between depletion of glucose and growth on citrate, whereas the evolved transformant with the same plasmid switches over quickly and efficiently. Modified from Blount et al. (2012).

Figure 5

Accumulation of nonsynonymous mutations relative to the neutral expectation based on synonymous mutations, after adjusting for the numbers of genomic sites at risk for the two classes. Each point shows the average of two clones from one population that retained the ancestral mutation rate throughout or had not yet evolved hypermutability. The black line shows the mean trajectory. Modified from Tenaillon et al. (2016).