Gene expression noise can promote the fixation of beneficial mutations in fluctuating environments

Abstract

Nongenetic phenotypic variation can either speed up or slow down adaptive evolution. We show that it can speed up evolution in environments in which available carbon and energy sources change over time. To this end, we use an experimentally validated model of Escherichia coli growth on two alternative carbon sources, glucose and acetate. On the superior carbon source (glucose), all cells achieve high growth rates, while on the inferior carbon source (acetate) only a small fraction of the population manages to initiate growth. Consequently, populations experience a bottleneck when the environment changes from the superior to the inferior carbon source. Growth on the inferior carbon source depends on a circuit under the control of a transcription factor that is repressed in the presence of the superior carbon source. We show that noise in the expression of this transcription factor can increase the probability that cells start growing on the inferior carbon source. In doing so, it can decrease the severity of the bottleneck and increase mean population fitness whenever this fitness is low. A modest amount of noise can also enhance the fitness effects of a beneficial allele. It can accelerate the spreading of such an allele, increase its likelihood of going to fixation, and reduce its fixation time. Central to the adaptation-enhancing principle we identify is the ability of noise to mitigate population bottlenecks. Because such bottlenecks are frequent in fluctuating environments, and because fluctuating environments themselves are ubiquitous, this principle may apply to a broad range of environments and organisms.

Phenotypic noise and the cost of complexity

“…2009), as suggested by recent experimental studies (Bódi et al. 2017; Schmutzer and Wagner 2020). However, this claim is in contradiction with classical theory that states that phenotypic noise always reduces the selection gradient (Lande 1975; Gavrilets and Hastings 1994; Wang and Zhang 2011; Mineta et al.…”

“…Because phenotypic noise increases the genotypic fitness of an organism far from the fitness optimum, it seems logical that it should facilitate adaptive evolution of the mean phenotype by increasing the fitness of beneficial mutations (Zhang et al 2009), as suggested by recent experimental studies (Bódi et al 2017;Schmutzer and Wagner 2020). However, this claim is in contradiction with classical theory that states that phenotypic noise always reduces the selection gradient (Lande 1975;Gavrilets and Hastings 1994;Wang and Zhang 2011;Mineta et al 2015;Tufto 2015).…”

The adaptive potential of non-heritable somatic mutations

“…Second, somatic genotypic exploration allows selection to act on the potential of genotypes to produce non-heritable adaptive phenotypes, eventually rendering those phenotypes heritable. This makes somatic genotypic exploration akin to the genetic assimilation of plastic phenotypes triggered by environmental conditions [89][90][91][92] or by the stochasticity or "noise" of cellular processes [54,[93][94][95]. Within this context, the so-called "look-ahead effect" [54] is particularly relevant; in this model, phenotypic mutations caused by transcription or translation errors create potentially adaptive protein variants, offering a mechanism for channeling populations towards adaptive genotypes, as in our model.…”

The mutability of demographic noise in microbial range expansions

“…As a result, we hypothesize that the plasticity of demographic noise holds more generally in selforganized systems (52), including colonies, biofilms, spatially-structured microbiomes, and solid cancer tumors, which would be interesting avenues for future study. Additionally, other phenotypic traits have been predicted to influence colony patterning and demographic noise and it would be interesting to test their influence on demographic noise in future work, including that of cell-cell and cell-substrate adhesion (53)(54)(55), cell orientations (32), cell elasticity (32), and variation in single cell growth rates (56,57) and lag times (58,59).…”