Bistability in a Metabolic Network Underpins the De Novo Evolution of Colony Switching in Pseudomonas fluorescens

Abstract

Phenotype switching is commonly observed in nature. This prevalence has allowed the elucidation of a number of underlying molecular mechanisms. However, little is known about how phenotypic switches arise and function in their early evolutionary stages. The first opportunity to provide empirical insight was delivered by an experiment in which populations of the bacterium Pseudomonas fluorescens SBW25 evolved, de novo, the ability to switch between two colony phenotypes. Here we unravel the molecular mechanism behind colony switching, revealing how a single nucleotide change in a gene enmeshed in central metabolism (carB) generates such a striking phenotype. We show that colony switching is underpinned by ON/OFF expression of capsules consisting of a colanic acid-like polymer. We use molecular genetics, biochemical analyses, and experimental evolution to establish that capsule switching results from perturbation of the pyrimidine biosynthetic pathway. Of central importance is a bifurcation point at which uracil triphosphate is partitioned towards either nucleotide metabolism or polymer production. This bifurcation marks a cell-fate decision point whereby cells with relatively high pyrimidine levels favour nucleotide metabolism (capsule OFF), while cells with lower pyrimidine levels divert resources towards polymer biosynthesis (capsule ON). This decision point is present and functional in the wild-type strain. Finally, we present a simple mathematical model demonstrating that the molecular components of the decision point are capable of producing switching. Despite its simple mutational cause, the connection between genotype and phenotype is complex and multidimensional, offering a rare glimpse of how noise in regulatory networks can provide opportunity for evolution.

Transcription factor levels enable metabolic diversification of single cells of environmental bacteria

“…Our study highlights the importance of cell physiology and internal composition and its impact on phenotypic variability. Also, the model presented here emphasizes the need to understand regulatory complexity under the light of population behaviour (Silva-Rocha et al, 2013) and community function (Ackermann, 2013;Kotte et al, 2014;Gallie et al, 2015;Zimmermann et al, 2015) rather than just individual benefit.…”

“…In this scenario, there is a transition from a single gene expression state to two different stable states driven by a change in substrate. In all these cases, the presence of positive feedback is required to generate and maintain the different expression states (Acar et al, 2005;Maamar and Dubnau, 2005;Suel et al, 2006;Veening et al, 2008;Kotte et al, 2014;Gallie et al, 2015;Venturelli et al, 2015), while molecular noise may enable phenotype switching. The existence of two simultaneous expression states without positive feedback is a much less-frequent phenomenon, and to the best of our knowledge has never been observed in this context.…”

“…This originates large fluctuations in this transcription factor, which is required to activate the TOL network response, inducing long-lasting random episodes where the Pu promoter remains inactive. This mechanism of phenotypic diversification adds to the repertoire of genetic, regulatory and biochemical devices through which bacteria cope with nutritionally changing environments (Kotte et al, 2014;Ackermann, 2015;Gallie et al, 2015).…”

The evolutionary advantage of heritable phenotypic heterogeneity

“…Phenotypic variability is often caused by switches between different regulatory states that produce bi-or multi-stability, due to fluctuations in levels of methylation at CpG sites, for example in mRNA transcription, or protein translation 37 . Our focus here, however, is not on the specific mechanisms or bio-physical forces that govern these phenotypic state transitions 9,22,38,39 . Instead, we aim to elucidate the population-level consequences of increased phenotypic availability from a given genotype and how this non-genetic variability determines the long-term evolutionary outlook of a population 6,[40][41][42] .…”

“…Classic examples include the bifurcation of a genotypically monomorphic population into two phenotypically distinct bistable subpopulations 1 , or phase variation, a reversible switch between different phenotypic states driven by differences in gene expression 3,4,9 . In order to motivate our modeling study, we first describe and compare four clinically relevant examples of phenotypic bet-hedging.…”