Stochastic switching as a survival strategy in fluctuating environments

Abstract

A classic problem in population and evolutionary biology is to understand how a population optimizes its fitness in fluctuating environments. A population might enhance its fitness by allowing individual cells to stochastically transition among multiple phenotypes, thus ensuring that some cells are always prepared for an unforeseen environmental fluctuation. Here we experimentally explore how switching affects population growth by using the galactose utilization network of Saccharomyces cerevisiae. We engineered a strain that randomly transitions between two phenotypes as a result of stochastic gene expression. Each phenotype was designed to confer a growth advantage over the other phenotype in a certain environment. When we compared the growth of two populations with different switching rates, we found that fast-switching populations outgrow slow switchers when the environment fluctuates rapidly, whereas slow-switching phenotypes outgrow fast switchers when the environment changes rarely. These results suggest that cells may tune inter-phenotype switching rates to the frequency of environmental changes.

Unexpected functional similarities between gatekeeper tumour suppressor genes and proto-oncogenes revealed by systems biology

“…56,57 Figure 1a shows that the coding sequence lengths of GKs and POs are similar to each other but significantly shorter than that of CTs, whereas GC and GC3 contents (GC content within the third site of codons) are significantly higher in GKs and POs than in CTs (Figures 1b and c) as revealed by a multiple logistic model (P¼0.0301, P¼0.0003 and P¼0.0001, for length, GC and CG3s, respectively). Gene length may thus be decreasing, whereas GC and GC3 are increasing from CTs to GKs/POs (Kendall trend analysis, t¼À0.14, two-sided P¼0.0540 for length, t¼0.287; two-sided P¼7.605Â10 À5 for GC, t¼0.327, two-sided P¼6.681Â10 À6 ).…”

Phenotype Switching and Mutations in Random Environments

“…Most work (Kimura 1967;Leigh 1970;Ishii et al 1989;Ben-Porath et al 1993;Lachmann and Jablonka 1996;Bürger et al 2006;Acar et al 2008) on evolution in changing environments considers infinite populations and deterministic cycles; Jablonka et al (1995) and Thattai and van Oudenaarden (2004) consider an infinite population and both deterministic and stochastic cycles. King and Masel (2007) consider finite populations governed by a Moran process when phenotype B has zero fitness in environment E and environment F is absorbing; they obtain exact results under the additional assumption that in E, any new-born B's are immediately replaced.…”

“…A particularly interesting mechanism to accelerate adaptation is stochastic phenotype switching (Thattai and van Oudenaarden 2004;Avery 2006;Acar et al 2008): Each cell may switch randomly from one phenotype to another according to genetically encoded switching rates. E.g., genetically identical E. coli cells subject to fluctuating antibiotic treatment switch stochastically between an antibiotic-sensitive type with normal growth and a resistant type with reduced growth (Balaban et al 2004;.…”

“…In our finite population model, reproduction, genetic mutations, phenotype switches, and environmental changes are all stochastic. This distinguishes our framework from most work on evolution in changing environments where infinite populations are considered with deterministic cycles (Kimura 1967;Leigh 1970;Ishii et al 1989;Ben-Porath et al 1993;Lachmann and Jablonka 1996;Bürger et al 2006;Acar et al 2008) or stochastic cycles (Jablonka et al 1995;Thattai and van Oudenaarden 2004). In particular, to determine optimal switching rates, standard arguments for infinite populations based on geometric mean fitness (Stumpf et al 2002) are not suitable for our model (King and Masel 2007).…”

Information processing in the adaptation of Saccharomyces cerevisiae to osmotic stress: an analysis of the phosphorelay system

“…Additionally to this extrinsic stochasticity, cells themselves act and function in a stochastic manner (Shahrezaei and Swain 2008;Acar et al 2008). Efficient strategies for sensing variations in the environment despite intrinsic fluctuations are crucial for the immediate survival as well as a beneficial long-term adaptation.…”