Adaptive Divergence in Experimental Populations of Pseudomonas fluorescens. IV. Genetic Constraints Guide Evolutionary Trajectories in a Parallel Adaptive Radiation

The capacity for phenotypic evolution is dependent upon complex webs of functional interactions that connect genotype and phenotype. Wrinkly spreader (WS) genotypes arise repeatedly during the course of a model Pseudomonas adaptive radiation. Previous work showed that the evolution of WS variation was explained in part by spontaneous mutations in wspF, a component of the Wsp-signaling module, but also drew attention to the existence of unknown mutational causes. Here, we identify two new mutational pathways (Aws and Mws) that allow realization of the WS phenotype: in common with the Wsp module these pathways contain a di-guanylate cyclase-encoding gene subject to negative regulation. Together, mutations in the Wsp, Aws, and Mws regulatory modules account for the spectrum of WS phenotype-generating mutations found among a collection of 26 spontaneously arising WS genotypes obtained from independent adaptive radiations. Despite a large number of potential mutational pathways, the repeated discovery of mutations in a small number of loci (parallel evolution) prompted the construction of an ancestral genotype devoid of known (Wsp, Aws, and Mws) regulatory modules to see whether the types derived from this genotype could converge upon the WS phenotype via a novel route. Such types—with equivalent fitness effects—did emerge, although they took significantly longer to do so. Together our data provide an explanation for why WS evolution follows a limited number of mutational pathways and show how genetic architecture can bias the molecular variation presented to selection.UNDERSTANDING—and importantly, predicting—phenotypic evolution requires knowledge of the factors that affect the translation of mutation into phenotypic variation—the raw material of adaptive evolution. While much is known about mutation rate (e.g., Drake et al. 1998; Hudson et al. 2002), knowledge of the processes affecting the translation of DNA sequence variation into phenotypic variation is minimal.Advances in knowledge on at least two fronts suggest that progress in understanding the rules governing the generation of phenotypic variation is possible (Stern and Orgogozo 2009). The first stems from increased awareness of the genetic architecture underlying specific adaptive phenotypes and recognition of the fact that the capacity for evolutionary change is likely to be constrained by this architecture (Schlichting and Murren 2004; Hansen 2006). The second is the growing number of reports of parallel evolution (e.g., Pigeon et al. 1997; ffrench-Constant et al. 1998; Allender et al. 2003; Colosimo et al. 2004; Zhong et al. 2004; Boughman et al. 2005; Shindo et al. 2005; Kronforst et al. 2006; Woods et al. 2006; Zhang 2006; Bantinaki et al. 2007; McGregor et al. 2007; Ostrowski et al. 2008)—that is, the independent evolution of similar or identical features in two or more lineages—which suggests the possibility that evolution may follow a limited number of pathways (Schluter 1996). Indeed, giving substance to this idea are studies that show that mutations underlying parallel phenotypic evolution are nonrandomly distributed and typically clustered in homologous genes (Stern and Orgogozo 2008).While the nonrandom distribution of mutations during parallel genetic evolution may reflect constraints due to genetic architecture, some have argued that the primary cause is strong selection (e.g., Wichman et al. 1999; Woods et al. 2006). A means of disentangling the roles of population processes (selection) from genetic architecture is necessary for progress (Maynard Smith et al. 1985; Brakefield 2006); also necessary is insight into precisely how genetic architecture might bias the production of mutations presented to selection.Despite their relative simplicity, microbial populations offer opportunities to advance knowledge. The wrinkly spreader (WS) morphotype is one of many different niche specialist genotypes that emerge when experimental populations of Pseudomonas fluorescens are propagated in spatially structured microcosms (Rainey and Travisano 1998). Previous studies defined, via gene inactivation, the essential phenotypic and genetic traits that define a single WS genotype known as LSWS (Spiers et al. 2002, 2003) (Figure 1). LSWS differs from the ancestral SM genotype by a single nonsynonymous nucleotide change in wspF. Functionally (see Figure 2), WspF is a methyl esterase and negative regulator of the WspR di-guanylate cyclase (DGC) (Goymer et al. 2006) that is responsible for the biosynthesis of c-di-GMP (Malone et al. 2007), the allosteric activator of cellulose synthesis enzymes (Ross et al. 1987). The net effect of the wspF mutation is to promote physiological changes that lead to the formation of a microbial mat at the air–liquid interface of static broth microcosms (Rainey and Rainey 2003).Open in a separate windowFigure 1.—Outline of experimental strategy for elucidation of WS-generating mutations and their subsequent identity and distribution among a collection of independently evolved, spontaneously arising WS genotypes. The strategy involves, first, the genetic analysis of a specific WS genotype (e.g., LSWS) to identify the causal mutation, and second, a survey of DNA sequence variation at specific loci known to harbor causal mutations among a collection of spontaneously arising WS genotypes. For example, suppressor analysis of LSWS using a transposon to inactivate genes necessary for expression of the wrinkly morphology delivered a large number of candidate genes (top left) (Spiers et al. 2002). Genetic and functional analysis of these candidate genes (e.g., Goymer et al. 2006) led eventually to the identity of the spontaneous mutation (in wspF) responsible for the evolution of LSWS from the ancestral SM genotype (Bantinaki et al. 2007). Subsequent analysis of the wspF sequence among 26 independent WS genotypes (bottom) showed that 50% harbored spontaneous mutations (of different kinds; see Open in a separate windowFigure 2.—Network diagram of DGC-encoding pathways underpinning the evolution of the WS phenotype and their regulation. Overproduction of c-di-GMP results in overproduction of cellulose and other adhesive factors that determine the WS phenotype. The ancestral SBW25 genome contains 39 putative DGCs, each in principle capable of synthesizing the production of c-di-GMP, and yet WS genotypes arise most commonly as a consequence of mutations in just three DGC-containing pathways: Wsp, Aws, and Mws. In each instance, the causal mutations are most commonly in the negative regulatory component: wspF, awsX, and the phosphodiesterase domain of mwsR (see text).To determine whether spontaneous mutations in wspF are a common cause of the WS phenotype, the nucleotide sequence of this gene was obtained from a collection of 26 spontaneously arising WS genotypes (WS_A-Z) taken from 26 independent adaptive radiations, each founded by the same ancestral SM genotype (Figure 1): 13 contained mutations in wspF (Bantinaki et al. 2007). The existence of additional mutational pathways to WS provided the initial motivation for this study.

TABLE 1

Mutational causes of WS

WS genotype	Gene	Nucleotide change	Amino acid change	Source/reference
LSWS	wspF	A901C	S301R	Bantinaki et al. (2007)
AWS	awsX	Δ100-138	ΔPDPADLADQRAQA	This study
MWS	mwsR	G3247A	E1083K	This study
WSA	wspF	T14G	I5S	Bantinaki et al. (2007)
WSB	wspF	Δ620-674	P206Δ (8)a	Bantinaki et al. (2007)
WSC	wspF	G823T	G275C	Bantinaki et al. (2007)
WSD	wspE	A1916G	D638G	This study
WSE	wspF	G658T	V220L	Bantinaki et al. (2007)
WSF	wspF	C821T	T274I	Bantinaki et al. (2007)
WSG	wspF	C556T	H186Y	Bantinaki et al. (2007)
WSH	wspE	A2202C	K734N	This study
WSI	wspE	G1915T	D638Y	This study
WSJ	wspF	Δ865-868	R288Δ (3)a	Bantinaki et al. (2007)
WSK	awsO	G125T	G41V	This study
WSL	wspF	G482A	G161D	Bantinaki et al. (2007)
WSM	awsR	C164T	S54F	This study
WSN	wspF	A901C	S301R	Bantinaki et al. (2007)
WSO	wspF	Δ235-249	V79Δ (6)a	Bantinaki et al. (2007)
WSP	awsR	222insGCCACCGAA	74insATE	This study
WSQ	mwsR	3270insGACGTG	1089insDV	This study
WSR	mwsR	T2183C	V272A	This study
WSS	awsX	C472T	Q158STOP	This study
WST	awsX	Δ229-261	ΔYTDDLIKGTTQ	This study
WSU	wspF	Δ823-824	T274Δ (13)a	Bantinaki et al. (2007)
WSV	awsX	T74G	L24R	This study
WSW	wspF	Δ149	L49Δ (1)a	Bantinaki et al. (2007)
WSXb	?	?	?	This study
WSY	wspF	Δ166-180	Δ(L51-I55)	Bantinaki et al. (2007)
WSZ	mwsR	G3055A	A1018T	This study

Open in a separate window^aP206Δ(8) indicates a frameshift; the number of new residues before a stop codon is reached is in parentheses.^bSuppressor analysis implicates the wsp locus (17 transposon insertions were found in this locus). However, repeated sequencing failed to identify a mutation.Here we define and characterize two new mutational routes (Aws and Mws) that together with the Wsp pathway account for the evolution of 26 spontaneously arising WS genotypes. Each pathway offers approximately equal opportunity for WS evolution; nonetheless, additional, less readily realized genetic routes producing WS genotypes with equivalent fitness effects exist. Together our data show that regulatory pathways with specific functionalities and interactions bias the molecular variation presented to selection.