Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment

Abstract

The environmental conditions of microorganisms' habitats may fluctuate in unpredictable ways, in terms of temperature, carbon source, pH, and salinity to name but a few. Such environmental heterogeneity presents a challenge for such microorganisms, as they have to adapt not only to be fit under a specific condition, but they must also be robust across many conditions and be able to deal with the switch between conditions itself. While experimental evolution has been used for decades to gain insight into the adaptive process, this has been largely in unvarying conditions. In cases where changing environments have been investigated, relatively little is known about how such environment influence the dynamics of the adaptive process itself, as well as the genetic and phenotypic outcomes. We designed a systematic series of evolution experiments where we used two conditions with differing time-scales of adaptation and varied the rate of switching between them. We used lineage tracking to follow adaption itself, and whole genome sequenced adaptive clones from each of the experiments. We find that the both the switch rate and the order of the conditions influences adaptation, and that switching can both speed up and slow down adaptation, depending on those parameters. We also find different adaptive outcomes, both at the genetic and phenotypic level, even if populations spent the same amount of total time in the two different conditions, but that the order and/or switch rate differed. Thus, in a variable environment adaptation depends not only on the nature of the conditions and phenotypes under selection, but also on the complexity of the manner in which those conditions are combined to result in a given dynamic environment.

Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment

“…For instance, the stochastic variance in selection coefficients over one generation (or infinitesimal time step in continuous time) can be used to predict evolutionary outcomes over multiple generations, such as probabilities of fixation [27,29] or expected heterozygosities [28], analogously to the influence of effective population size for genetic drift. However, while the demographic consequences of the magnitude and autocorrelation of environmental variations have been experimentally explored [35][36][37][38], and evolutionary experiments have been performed under randomly changing environments [39][40][41][42][43], we are not aware of attempts to measure the stochastic variance of population genetic change under conditions where patterns of random environmental fluctuations have been experimentally manipulated.…”

yEvo: Experimental evolution in high school classrooms selects for novel mutations and epistatic interactions that impact clotrimazole resistance inS. cerevisiae

“…Previous evolution experiments selecting for azole resistance have demonstrated that this paradigm can identify new resistance factors and can validate candidate secondary antifungals that prevent resistance evolution (e.g. Cowen et al 2000, Anderson et al 2003, Selmecki et al 2009, Hill et al 2013, Boyer et al 2021. We anticipated that additional replicates carried out with different culturing protocols would lead to the identification of novel resistance factors.…”

Predicting population genetic change in an experimental stochastic environment

“…We worked with the halotolerant micro-alga Dunaliella salina, which can thrive across a broad range of salinities. Contrary to previous experiments that also focused on random variation in the environment (Boyer et al, 2021;Dey et al, 2016;Wieczynski et al, 2018), we here allowed salinity to vary over a continuous range (rather than switching randomly from low to high), with a controlled distribution over time. We used a liquid-handling robot to control the mean, variance, and autocorrelation of salinity, and tracked the frequency of standing genetic variants through time by Illumina amplicon sequencing.…”