Microbial populations founded by a single clone and propagated under resource limitation can become polymorphic. We sought to elucidate genetic mechanisms whereby a polymorphism evolved in Escherichia coli under glucose limitation and persisted because of cross-feeding among multiple adaptive clones. Apart from a 29 kb deletion in the dominant clone, no large-scale genomic changes distinguished evolved clones from their common ancestor. Using transcriptional profiling on co-evolved clones cultured separately under glucose-limitation we identified 180 genes significantly altered in expression relative to the common ancestor grown under similar conditions. Ninety of these were similarly expressed in all clones, and many of the genes affected (e. g. , mglBAC, mglD, and lamB) are in operons coordinately regulated by CRP and/or rpoS. While the remaining significant expression differences were clone-specific, 93% were exhibited by the majority clone, many of which are controlled by global regulators, CRP and CpxR. When transcriptional profiling was performed on adaptive clones cultured together, many expression differences that distinguished the majority clone cultured in isolation were absent, suggesting that CpxR may be activated by overflow metabolites removed by cross-feeding strains in co-culture. Relative to their common ancestor, shared expression differences among adaptive clones were partly attributable to early-arising shared mutations in the trans-acting global regulator, rpoS, and the cis-acting regulator, mglO. Gene expression differences that distinguished clones may in part be explained by mutations in trans-acting regulators malT and glpK, and in cis-acting sequences of acs. In the founder, a cis-regulatory mutation in acs (acetyl CoA synthetase) and a structural mutation in glpR (glycerol-3-phosphate repressor) likely favored evolution of specialists that thrive on overflow metabolites. Later-arising mutations that led to specialization emphasize the importance of compensatory rather than gain-of-function mutations in this system. Taken together, these findings underscore the importance of regulatory change, founder genotype, and the biotic environment in the adaptive evolution of microbes.
Evolutionary biologists have long sought to understand mechanistically how adaptive genetic variation arises and persists. Experimental studies using model organisms such as Drosophila [1]–[3] and C. elegans [4]–[6] transformed the search for such mechanisms from a retrospective to a prospective endeavor. But, long generation times, sexual recombination and practical limits on lab population size make higher eukaryotes imperfectly suited to study the tempo, trajectory and mechanisms by which evolution occurs in asexual species and in the somatic cells of sexual organisms. There, new genetic variation is limited by the rate of mutation supply and, in bacteria, also by the incidence of horizontal gene transfer. Fortunately, evolution in asexual species and cells can be studied using microbial models [7], [8]. Early microbial studies helped lead to two generalizations concerning the emergence and persistence of genetic variation in large, asexual populations. First, over ecological time and in the absence of spatial structure and differential predation, competition for the same limiting resource selects for one fittest variant, an insight that came to be known as the “competitive exclusion principle” [9], [10]. Second, over evolutionary time variation arising by mutation is subject to “periodic selection” leading to a succession of genotypes each more fit than its immediate predecessor [11]–[13]. These generalizations led to the expectation that large, clonal populations evolving under resource limitation should exhibit limited genetic variation. Experimental evidence now suggests otherwise. Multiple genotypes which arise from a single ancestral clone can coexist over evolutionary time; in other words, out of one comes many (e unum pluribus). This phenomenon has been documented in spatially and temporally unstructured chemostats [14], [15], in temporally-structured batch cultures [16]–[20], and in spatially-structured microcosms [21]. In each setting, the emergence and persistence of polymorphism in the absence of sexual recombination seems to require that cohabitants exploit alternative ecological opportunities (i. e. , unoccupied niche space), and/or accept trade-offs between being a specialist and a generalist (as reviewed in [22], also see [23]. In serial dilution batch culture multiple growth parameters can come under selection [reviewed in 24]. Different clones may arise that have reduced lag time, increased maximum specific growth rate, or enhanced capacity to survive at high cell densities in the presence of low nutrients. Periodic changes in population density and nutrient levels may bring balancing selection to bear on these different phenotypes, especially if antagonistic pleiotropy precludes evolution of one fittest genotype having all of these advantageous traits. In spatially structured environments selection may favor mutants better adapted to particular regions or better able to colonize microhabitats formed at the boundaries between such regions. In continuous nutrient-limited environments (e. g. , chemostats), theory [25]–[27] predicts that selection will favor clones better able to scavenge the limiting resource or more efficiently convert that resource to progeny. Ultimately, the outcome of the “evolutionary play” in any of these “ecological theaters” will depend on founder genotype, the complexity of genetic pathways which lead to different adaptive strategies, as well as the propensity of key steps along those pathways to undergo mutation and to act pleiotropically. Only recently have we begun to discover genetic mechanisms that explain how balanced polymorphisms arise and persist in large, asexual populations. In serial batch culture, differences in the activity of the global regulator RpoS help explain co-existence of two E. coli isolates with different propensities to survive extended stationary phase [28]; the precise genetic basis for these activity differences, however, remains obscure. In a spatially structured microcosm founded by a single clone of Pseudomonas fluorescens, a methylesterase structural mutant arose and persisted because the resulting change in exopolysaccharide production enabled the mutant to colonize the air-broth interface [29]. Finally, in glucose-limited chemostats polymorphic E. coli populations repeatedly evolved, in part owing to local regulatory mutations that affect expression of a single operon (acs-actP-yjcH) [30]. When adaptive clones from one such population were grown in monoculture, strain-specific differences in ca. 20% of identifiable proteins expressed suggested the presence of other mutations with highly pleiotropic effects [31]. Thus, regardless of experimental system, uncertainty remains as to whether either regulatory or structural mutations consistently deliver greater fitness increments, which category of mutation better explains the maintenance of diversity, and whether one type is more likely to precede the other in an evolutionary sequence leading to balanced polymorphism. Theoretical considerations have led some to argue that the major phenotypic changes which underlie adaptive radiation are more likely due to regulatory than to structural mutations [32], [33 and refs. therein]. This argument is based on the perception that changes in coding sequences are more likely to have large pleiotropic effects than changes in the expression of those sequences, in particular changes that arise from the mutation of cis-regulatory elements affecting single genes. In effect, this type of regulatory mutation enables selection more easily to “tinker” (sensu Jacob, 1977), as it provides a mechanism to alter functionality in one process while still preserving the role of pleiotropic genes in others [34]. Also, and this fact too often goes unappreciated, a discrete cis-regulatory mutation preserves the capacity to restore the ancestral pattern of expression via compensatory or back-mutations. The proposition that regulatory mutations play a greater role in adaptive diversification has been criticized on empirical and theoretical grounds by Hoekstra and Coyne who point out the vastly greater number of examples where adaptation is attributable to structural rather regulatory mutations, as well as the facts that cis-acting elements offer much smaller targets for mutation than ORFs, and that in many species pleiotropic effects arising from structural mutations may be buffered by gene duplication [35]. While the “cis-regulatory hypothesis” is based largely on a consideration of multicellular eukaryotes, tends to be focused on events that transpire during plant and animal development, and requires what we view to be an artificial distinction between physiological and morphological adaptation, it nevertheless provides a useful framework in which to make predictions about how adaptive diversification might occur in ‘simpler’ species. In bacteria, both types of mutations can be evolutionarily significant. Structural mutations in the HopZ family of Type III secreted effector (T3SE) proteins play a major role in pathoadaptation by Pseudomonas syringae to its plant hosts [36]; likewise, T3SE mutations underlie host immune suppression by Yersinia and Xanthomonas [37]. On the other hand, pathoadaptation leading to an intracellular lifestyle in Samonella enterica results from a cis-regulatory mutation, specifically, acquisition of a binding site for pathogenicity island-2 regulator SsrB [38]. We sought to address the issue of structural versus regulatory change in Escherichia coli by investigating an experimental population first described by Helling et al. [14]. This population was founded by a single clone and evolved in an aerobic, glucose-limited chemostat at constant dilution rate (D = 0. 2 h−1) and constant temperature (30°C). Helling et al. inferred from fluctuations in a neutral marker that adaptive mutations occurred about once every 100 generations. At the time they concluded their experiment (765 generations) they could distinguish four strains on the basis of colony size and ampicillin sensitivity (see Table 1). Three of these phenotypes were shown to stably co-exist in reconstruction experiments, wherein the majority clone strain, CV103, was followed in rank order of abundance by CV116 and CV101 [15]. Each strain exhibited a characteristic pattern of protein expression, as determined by 2D protein gel electrophoresis, when grown by itself in glucose-limited chemostats; as a group, evolved clones significantly differed from their common ancestor at ∼160 expressed proteins of ∼700 that could be resolved [31]. Relative to their common ancestor, all evolved clones showed enhanced uptake of the glucose analogue 14C-α-methylglucoside (αMG), and CV103 accumulated significantly more α–MG than any other clone [14], even though its yield was less than the other adaptive clones. Equilibrium glucose concentration (the amount detectable in a culture of actively dividing cells at steady state) was an order of magnitude less in CV103 than in CV101 chemostats and less than half that observed in CV116 (see Table 1). Unlike CV101 and CV116, however, CV103 left metabolizable carbon in the chemostat, effectively creating niches conducive to the evolution of cross-feeding. The other strains filled those niches, efficiently scavenging overflow metabolites below detection limit [15]. Acetate-scavenging strains were subsequently observed in 6 out of 12 independent evolutionary populations founded by cells of similar genetic background grown under similar conditions [39]. The Helling and Adams population is a classic example of how adaptive evolution occurs in the context of niche diversification. Because the approximate number of fixed adaptive mutations is few and the number of significant changes in protein expression is many, this population is well-suited for identifying mutations that exert large effects, and determining whether those mutations occur at loci that encode enzymes in metabolism, at trans-acting loci that encode proteins which regulate expression of multiple enzymes, or at cis-acting sequences that control how activators and repressors act on single genes. We tested the hypothesis that enhanced uptake and assimilation of the primary resource, glucose, results from one (or few) early-arising mutations in trans-acting global regulators, and that specialization on secondary resources arises from later-arising mutations in key structural loci or in their cis-acting sequences. Lastly, we anticipated that in comparing the consortium' s expression profile to that of individual members grown in monoculture, we would discover emergent properties of the system not apparent using a purely reductionist approach. Transcriptional profiling of evolved strains in monoculture reveals ∼180 genes significantly altered in expression. Many shared increases and decreases are attributable to shared mutations in rpoS and the maltose operon operator mglO. Expression differences that distinguish isolates occur mainly in the majority clone, CV103. Many of these genes are regulated (or are predicted to be regulated) by cAMP receptor protein (CRP) and/or the global stress regulator CpxR. The “community” expression profile is strikingly similar to the monoculture profiles of the three sub-dominant clones, suggesting that biochemical interactions among clones alter CRP-CpxR regulation. We identified in the founder regulatory mutations in genes required for acetate and glycerol catabolism that likely predispose this system to the evolution of cross-feeding. Among adaptive clones, we found shared mutations in rpoS and mglO, and mutations that distinguish clones from one another at pacs, malT and glpK. Taken together, our results suggest that both cis- and trans-regulatory changes underlie adaptive diversification in a simple, unstructured, resource-limited environment, and that founder genotype and chemical interactions among clones not only facilitate co-evolution, but also strongly impact their respective patterns of gene expression.
Table 1 summarizes previously published phenotypic data on the Helling et al. strains which are germane to interpretation of the expression and sequencing Results presented below [14], [15]. To assess the level of large-scale genetic variation between the ancestor and the evolved clones, we performed rep-PCR fingerprinting and array-CGH. BoxA1R rep-PCR fingerprints were indistinguishable (see Figure S1). However, a-CGH revealed a deletion of ∼30 Kb in the majority clone, CV103 (Figure 1). A total of 27 genes were lost by the deletion, 12 of which have no known function. Of the remaining 15,3 have a predicted function based on homology to previously characterized genes and 12 are involved in a variety of cellular processes including transcription, arginine biosynthesis, anaerobic respiration, nitrogen metabolism and glycoprotein biosynthesis. We used DNA microarrays to assess global transcriptional patterns of individual strains in glucose-limited chemostats. Evolved clones were grown to steady state (∼14 generations) under conditions identical to those under which they evolved (D = 0. 2 h−1,30°C). In each case, steady state transcript levels were estimated in relation to the ancestral strain JA122 grown in parallel under identical conditions. Relative to the common ancestor, expression of 6. 8% (∼279 genes) of the measurable transcriptome was at least 2-fold increased or decreased in the evolved isolates (Figure S2). This number favorably compares with an early proteomic analysis of these strains grown in chemostat monoculture wherein Kurlandzka et al. [31] found that ∼160 expressed proteins of the ∼700 they were able to resolve differed between evolved clones and their common ancestor. Using 1-class SAM, we identified 90 genes whose expression was significantly up- or down-regulated in all clones when grown in chemostat monoculture (Figure 2, Table S1). The 21 up-regulated genes, representing 9 unique transcription units, are primarily involved in carbon catabolism. The remaining 69 down-regulated genes from 58 transcription units belonged to a variety of MultiFun classes including carbon metabolism, building block/macromolecule biosynthesis, transport and adaptation to osmotic stress. To ascertain how the transcriptional profiles of evolved clones differ from one another we performed a 4-class SAM analysis (Figure 3, Table 2, Table S2). Aside from the anticipated overexpression of acs-yjcHG in CV101, the transcription patterns of CV101, CV115 and CV116 appear remarkably similar. By contrast, CV103 differs from the other three at a number of loci, and accounts for the great majority (∼93%) of the significant differences that distinguish adaptive clones. When we adjusted δ (a tuning parameter that can be manually adjusted) to reflect a natural break in the data, we found that a total of 91 genes from 64 transcription units significantly differ in steady state expression levels in at least one isolate at a false discovery rate of 0%. These genes tend to fall into three MultiFun classes: metabolism, cell structure and transport. Under the category of metabolism, forty-four genes from twenty-seven transcription units vary in their relative expression patterns. The metabolism-building block biosynthesis subclass contained the most independent transcription units (8/27), including acs-yjcHG (acetyl CoA synthetase). Conspicuously absent is mRNA transcribed from the NRZ operon (narZYWV), which is deleted in CV103. This operon is normally induced during stationary phase and appears to be actively transcribed in the ancestor but slightly down-regulated in CV101, CV115 and CV116 (Figure 3, [49]. However, the fitness effect of this deletion is currently unknown (Figure 1). Reconstruction experiments demonstrated that three of the evolved strains could stably coexist in continuous culture as a consortium, and that their coexistence was made stable by cross-feeding [15]. When limited on 0. 0125% glucose, the consortium reproducibly apportioned as ∼70% CV103,20% CV116 and 10% CV101 at steady state. To better understand mechanisms underlying stable coexistence we interrogated the consortium transcriptome. In general, we observe that genes significantly up or down in the 1-class SAM monoculture analysis behave similarly when clones are co-cultured (Figure 4). Furthermore, consortium profiling extends the results of our monoculture analyses to include other members of operons previously identified by 1-class SAM. For example, malK and malM (which are co-transcribed with lamB), as well as malF, G and S from two separate, but similarly regulated transcription units each show increased expression when cells are cultured as a consortium (Figure 4D). Several transcripts significantly up-regulated in the consortium, including genes for a second glycerol-3-phosphate transporter/phosphodiesterase, glpTQ, part of the G3P-dehydrogenase, glpA, and fumarase genes, fumA and fumC, were not scored as significantly up-regulated in the monoculture 1-class SAM using the highly stringent method of hand-tuning δ (Figure 4D, Figure S3). However, the majority of these were considered significant when a strict 0% FDR cutoff was applied. Those that do not meet this criterion are marked with a “†” in Figure 4. When we compared the consortium' s transcriptional profile to the 4-class SAM (Figure 5) we were surprised to find that many of the transcripts that distinguished CV103 from the other evolved clones in monoculture had expression patterns similar to CV101, CV115 and CV116, even though reconstruction experiments show that CV103 always emerges as the numerically dominant consortium member [14], [15]. To ascertain whether this phenomenon was a general feature of the dataset, we looked at transcript levels across all samples for genes that were either (A) significant in the consortium analysis but not in the monoculture experiments, or (B) significant in the monoculture experiments but not in the consortium profile. For this comparison, we used the highly stringent method of hand tuning δ to determine significance cutoff. In both cases, the vast majority of genes that were differentially regulated in CV103 monoculture (and thus distinguished this isolate from the other clones) again had transcript levels that closely matched CV101, CV115 and CV116. While this analysis is limited by the fact that the individual contributions of isolates cannot be dissected from the consortium RNA pool, the sheer number of transcripts that follow this trend strongly suggests that CV103 has a different gene expression profile in the shared metabolic environment of the consortium than when it is grown in isolation. Three genes (lamB, acs and flgB) with different relative expression levels were selected for quantitative reverse transcriptase PCR on RNA isolated from chemostat monocultures. PCR results for all three closely approximated array results with correlation coefficients ranging from 0. 78–0. 99 (see Figure S4). To place our results in the context of previously published work and to uncover mutations which may contribute to the transcriptional profiles of the adaptive clones, we sequenced 13 candidate genes and their corresponding regulatory elements (Table 3, for primers see Table S3). Selection of candidate genes was guided by the observation that members of the evolved polymorphism had differentiated from one other and their common ancestor with respect to glucose, acetate and glycerol metabolism [15].
Our results show that all evolved clones share a common regulatory response to long-term glucose limitation. In general, genes involved in the phosphotransferase system, glycolysis, the pentose-phosphate pathway and mixed acid fermentation are down-regulated whereas TCA cycle genes are up-regulated (Figure S3). At first glance it may seem that reduced expression of glycolytic genes would be disadvantageous under glucose limitation. However, consistent with the energy conservation hypothesis [e. g. see 90], it may be economical for chemostat-grown cells to synthesize the minimum level of enzymes needed to process a limiting nutrient whose residual concentration has become vanishingly low. Strikingly similar changes in central metabolic gene expression have been reported for E. coli in batch culture as well as for Baker' s yeast following adaptive evolution in long-term, aerobic, glucose-limited chemostat culture [18], [91], [92]. The generality of this phenomenon across replicate experiments within the same species, as well as across Domains, suggests that microbes may have limited options for increasing fitness in environments where glucose is the sole carbon source. However, new evolutionary opportunities may arise in the form of other carbon sources released during glucose metabolism. Our 1-class microarray analysis and sequencing results indicate that changes in levels of the stationary-phase sigma factor, σS, expected from the shared C→T transition at nucleotide 97, account for many genes being significantly down-regulated in all strains. Most of these changes are consistent with the expression profiles of an rpoS knockout batch-cultured in rich medium: there, relative to wild type, all central metabolic pathways including the TCA cycle were down-regulated during early stationary phase, while the TCA cycle was strongly up-regulated during exponential phase [44]. At steady state under continuous nutrient limitation, bacterial growth approximates late exponential/early stationary phase in batch culture [26]. It is tempting to speculate that the pattern of expression we observe for genes in central metabolism is what might observe if an rpoS knockout were grown under our experimental conditions. And indeed, experiments to test this hypothesis are planned. Alternatively, up-regulation of TCA cycle genes in our strains may result from altered σs activity caused by incomplete suppression of the rpoSAm mutation, translation of truncated σS, or the effect of yet-to-be identified regulatory mutation (s). In addition to shared global expression patterns for central metabolic genes, our microarray results show that evolved isolates also up-regulate genes involved in moving glucose across the outer and inner membranes. Increased transcription of the inner membrane Mgl galactose ABC-transporter (which also transports glucose) is common response to continuous glucose limitation [42], [93], and our experimental system is no exception. This regulatory adjustment is easily accounted for by a mutation present in all of the evolved isolates in the mgl operator sequence that presumably interferes with GalS-mediated suppression of mgl transcription [42], [93]. Similarly, increased movement of glucose into the periplasm in the evolved isolates is undoubtedly due in part to overexpression of the LamB glycoporin, another hallmark feature of E. coli adaptation to glucose limitation [41], [94]. In Ferenci and colleagues' experiments, adaptive overexpression of LamB (which is part of the malT regulon) results from mutations in the mal repressor Mlc and/or its activator MalT [41], [93]. Sequencing of mlc and its associated regulatory region failed to uncover mutations in any of our evolved clones. We did find a mutation in the gene encoding MalT, but its distribution was limited to CV101, CV115 and CV116 and its location was unique relative to other MalT mutations characterized as constitutive. It is surprising that this mutation does not occur in CV103 considering that, on average, CV103 has 3–6 fold higher transcript levels of lamB than the other three strains (significant in a between-subjects t-test, p = 0. 0007). While the superior glucose scavenging ability of CV103 may be attributed to its increased LamB expression relative to other evolved clones, this increase cannot be explained by inactivation of Mlc or by a constitutive mutation in MalT, as neither occurs in this strain. We also failed to recover mutations at ptsG, and we did not detect increased OmpF expression, both of which have been observed to enhance glucose uptake in other evolution experiments [57]. While increased LamB expression in all the evolved isolates is almost certainly due to defective rpoS, the rpoS mutation is shared and cannot account for among-strain differences [47]. CV103 does lack the Ala→Glu substitution at aa 53 in MalT (total length, 901 aa), a positive regulator of lamB. Based on the distribution of mutations in rpoS, galS, acs and glpK, it is highly probable that this mutation occurred in the common ancestor of CV101 and CV116 prior to specialization of CV101 on acetate, but after the divergence of CV103 (see Figure 6). Other mutations in the N-terminal portion of MalT which have been reported to arise in glucose-limited chemostats result in its constitutive expression [41]. However, despite the relatively large number of such mutations which have been characterized (at least 16), none is in the same position or motif as the one we report here [41], [68]. Interestingly, adaptation to long-term glucose limitation in batch culture can select for mutations that partially or fully inactivate MalT, one of which does occur in the same helix as our mutation [95]. If the malT mutation shared by CV101 and CV116 results in a weakened activator, and consequently less LamB, there exists the intriguing (although highly speculative) possibility that in our experiment, down-regulation of glucose influx through LamB could provide an advantage to minority clones that specialize on excess excreted carbon. Additional experiments will be needed to determine whether the malT mutation shared by CV101 and CV116 explains their diminished lamB expression, relative to CV103. Alternatively, it may be that CV103 has higher levels of endogenous maltotriose inducer, or harbors as yet unidentified mutations that affect lamB transcription and/or glucose uptake via other routes. Whether physiological or genetic, the mechanism underlying two-fold differences in the expression of this key transporter promises to be unique and interesting, and will be the subject of future investigations. The constitutive overexpression of acetyl-CoA synthetase that enables CV101 to capture overflow acetate from the dominant clone has a clearly documented mutational basis that has been re-confirmed by our microarray and sequencing results. This mutation is selectively favored because the dominant clone, CV103, poorly recovers acetate produced via glycolysis [14], a phenotype that manifests as high equilibrium acetate concentration when CV103 is grown in chemostat monoculture (see Table 1) and absence of Acs activity when CV103 is grown in either batch or chemostat culture [15]. Given that the ancestor, JA122, has a weakened acs promoter, and that acetate is scavenged at low concentrations almost exclusively via the acs pathway, the acetate defect in CV103 could be explained by this genetic predisposition compounded by increased catabolite repression of acs arising from increased glucose transport. The rate of glucose uptake, equilibrium acetate concentration, and acetyl CoA synthetase measurements of CV116 under glucose limitation support this contention since all are intermediate between JA122 and CV103 (see Table 1). Moreover, when cells are grown in the presence of acetate and glycerol CV116 exhibits ancestral levels of Acs specific activity while CV103 Acs activity is negligible [15]. Thus, acs is neither appropriately activated nor repressed in CV103. Regulation of acs expression in E. coli is quite complex, integrating signals from the TCA cycle, glyoxylate bypass pathway, and phosphotransacetylase/acetate kinase (pta/ackA) acetate dissimilation pathway [71], [73], [96]. acs expression can also be regulated by growth phase via Fis [97] or via the PTS system by means of cAMP-CRP [72]. Previous work indicated no defect in the regulation or structure of ackA [15]. In the present study, we sequenced the promoter and full structural gene for acs as well as the other enzyme in the dissimilation pathway, pta. With the exception of the first 17 base pairs of pta (which were not sequenced), we found no mutations. Thus, the genetic basis for loss of acs activity in CV103 remains obscure. Increased glycerol uptake coupled with the observation that addition of glycerol increases the equilibrium frequency of CV116 co-cultured with CV103 led to the conclusion that CV116' s success in the chemostat was due, at least in part, to glycerol cross-feeding [15]. Sequencing of the glycerol kinase gene (the rate limiting step in extracellular glycerol metabolism) identified a mutation in CV116 not found in the other isolates. However, given that this was a silent substitution resulting in a codon change from an abundant to a rare tRNA, and given that the surrounding sequence bears little similarity to a glycerol repressor (glpR) binding site, it is difficult to argue that that this mutation has adaptive significance. We therefore next examined glpR and were surprised to find a mutation that was not only present in the ancestor but was present in the E. coli progenitor strain from which JA122 was derived. This mutation has been characterized by other groups, and results in constitutive expression of the glycerol regulon [76]. Many GlpR-regulated genes did not show appreciable expression differences on our microarrays, as would be expected if they were also upregulated in the ancestor. But three genes did show modestly increased transcript levels across all evolved isolates at the 0% FDR level: the glycerol-3-phosphate transporter (glpT), the glycerophosphoryl diester phosphodiesterase (glpQ), and the anaerobic glycerol-3-phosphate dehydrogenase (glpA). These genes are partially under the control of GlpR, but they also have additional regulators not shared by other genes in the glycerol regulon. It appears likely that the superior ability of CV116 to recover and metabolize extracellular glycerol-3-phosphate is related to the increased expression of glpT, but the reason that it is able to scavenge glycerol better than CV101 and CV103 is unresolved. Catabolite repression, and/or glycolytic intermediate feedback due to increased glucose consumption may modulate GlpT activity post-transcriptionally in CV103. Transcriptional profiling of the consortium RNA pool led to the unexpected observation that, in monoculture, CV103 has a different pattern of gene expression than when co-cultured with CV101 and CV116. The genes primarily affected are those that distinguish CV103 from the other clones in the 4-class SAM analysis, suggesting that a global regulatory mechanism is responsible for the shift in expression. Two global regulators dominate the 4-class SAM analysis, CRP and CpxR; together these explain expression patterns for nearly half the transcription units which distinguish CV103. CRP is known or predicted to influence the expression of 23% of CV103-specific transcription units, though none of these are under the exclusive control of CRP. While CpxR controls a smaller proportion of CV103-specifc transcription units, (19%), most of these are solely regulated by CpxR. Thus, CpxR regulation underlies much of CV103' s expression pattern in monoculture; this effect is reversed when CV103 is co-cultured with the subdominant clones. One dramatic environmental difference between the glucose-limited CV103 monoculture environment and the consortium environment is the concentration of extracellular acetate. When CV101 is present, acetate is efficiently scavenged and cannot accumulate. CpxR in its phosphorylated form mediates a global response to extracytoplasmic stressors such as high osmolarity, misfolded outer membrane protein, or alkaline pH (as reviewed in [98]). CpxR is normally activated by its sensor kinase CpxA, but it can also be phosphorylated in a CpxA-independent manner, albeit at a rate of phosphotransfer much lower than that which occurs between the sensor kinase and its response regulator. Although there have been no reports of a direct connection between extracellular acetate concentration and CpxR activation, CpxR can be phosphorylated by acetyl-P, the high-energy intermediate of the Pta/AckA pathway that accumulates during exponential phase growth on glucose and/or a proposed sensor kinase (SKx) that is connected to the Pta-AckA pathway [99]–[102]. Regardless of the precise molecular nature of the interaction, it seems clear that CpxR activation is intimately connected to acetate dissimilation. We previously reported that the Km for acetate kinase in CV103 and CV116 was lower than that of JA122 and CV101 [15]. Given the low equilibrium acetate concentration in the chemostat, it was concluded that this decrease in Km should not significantly affect acetate uptake or secretion. However, alterations in acetate kinase activity, increased acetate secretion, or reduced acetate uptake could conceivably affect the overall performance of the Pta-AckA pathway and thus influence intracellular levels of acetyl-P and/or some yet-to-be-identified effector molecule [102]. Such interactions could be reasonably postulated to elicit a CpxR-mediated transcriptional response when extracellular acetate concentrations increase (as in CV103 monoculture). Shared mutations in rpoS and mglD strongly support the hypothesis that competition for the limiting nutrient, glucose, was the primary selective force operating in the chemostat prior to metabolic divergence of CV101 and CV116 [15]. Increased glucose consumption coupled with acetate and glycerol secretion by CV103 created a favorable environment for the evolution of clones that could efficiently consume these two overflow metabolites. While screening for mutations that contributed to the emergence of cross-feeding populations, we unexpectedly encountered ancestral regulatory mutations in both the acetate and glycerol metabolic pathways that affect the induction of acetyl CoA synthestase (the primary acetate scavenging pathway) and the glycerol regulon repressor GlpR. As a result, it appears that the ancestor is unable to efficiently recover excreted acetate and constitutively overexpresses the glycerol regulon. We believe that these two mutations in the ancestor profoundly influenced the evolutionary outcome of these experiments (as well as the replicate evolution experiments reported in [39], which showed similar qualitative results). Impaired acetate scavenging by the progenitor of CV103 undoubtedly accelerated or predisposed the evolution of a strain that could efficiently utilize this substrate. We cannot argue that acetate scavenging clones would not have eventually arisen from a purely “wild-type” inoculum, but the repeatability of their emergence as well as the precise way in which they were invariably generated (activation of acs by reversion of the ancestral mutation or IS element insertion) suggests that there was strong selective pressure for changes at the acs locus. The influence of the ancestral GlpR mutation is less clear: Overexpression of the glycerol dissimilation pathway could affect the excretion of glycerol-3-phosphate by CV103 or enhance the ability CV116 to recover it. In either case, it seems unlikely that the presence of the GlpR mutation is mere coincidence. Overall, the influence of mutations with global and small-scale regulatory effects on the evolution of the consortium is clear (see Figure 6). The first steps in adaptation to limiting glucose occurred via mutations that increase glucose consumption: inactivation of the stationary-phase sigma factor σS and modification of the glucose/galactose transporter MglBAC repressor binding site. Mutations at these same trans- and cis-acting elements have been previously shown to confer fitness advantages under glucose-limitation, and rpoS mutants are commonly found in natural E. coli populations [46]. Subsequently, mutation of the maltose operon activator (MalT) and deletion of the chromosomal region that contains the NarZ nitrate reductase resulted in two distinct lineages: CV103 and the progenitor of CV101, CV115 and CV116. Strain CV101 acquired the ability to scavenge excreted acetate via the insertion of an IS30 element in the promoter of the acetyl CoA synthetase gene. Two other mutations of unknown effect, the loss of the plasmid pBR322 and a silent mutation in the glycerol kinase gene glpK, further delineated the glycerol-scavenging strain CV116. The founder effect is generally disregarded in microbial evolution experiments because immense population sizes enable a pool of variants to be rapidly generated by mutation and also buffer against severe genetic bottlenecks. The results presented here suggest that microbial evolution experiments are influenced by founder genotype and that such influences can promote evolution of stable polymorphisms. At least one mutation instrumental in the evolution and maintenance of cross-feeding (the acs IS30 insertion) was compensatory rather than neomorphic. Thus, the exploration of new biochemical opportunities required recovery of old functions, in addition to the development of novel traits. These observations may not be confined strictly to experimental systems as many natural microbial populations (such as those that cause nosocomial or chronic infections) are also founded by clones. For example, chronic Pseudomonas aeruginosa infection of the lungs of cystic fibrosis patients frequently originates from one or a few isolates that undergo clonal expansion over the course of many years [103], [104]. Common targets of selection during adaptation of these clones to the CF lung environment are regulatory: mutations in the aminoglycoside efflux pump regulator mexZ can enhance antibiotic resistance and mutations in lasR, a regulator of quorum sensing, may influence biofilm formation during infection. Similarly, Helicobacter pylori infections, the cause of most gastric ulcers, are often initiated in early childhood and persist throughout the lifetime of an untreated individual [105]. In both cases, mechanistic understanding of microbial adaptation is essential to successful implementation of novel therapeutic regimens. Transcriptional profiling and targeted gene sequencing expanded and confirmed certain aspects of our understanding of the mechanisms that drive adaptation and diversification. All identified nonsynonymous mutations were regulatory in nature, but not strictly confined to global regulators. Initial selection in the chemostat favored mutations that enhance competitive acquisition of the limiting resource (such as those in rpoS and mgl), but ancestral regulatory mutations like those in acs and perhaps glpR explain much of the unique behavior of this system. The transcriptional effect of some adaptations was apparent even when consortium members were grown in isolation, while the expression of others appeared to depend on the metabolic activity of sibling clones. Finally, even under strong selection, at least one of the most beneficial mutations served to restore a lost function, thereby creating a stable cross-feeding interaction between adaptive clones. The advantages of E. coli as a model organism for experimental evolution lie in its ease of cultivation, large population sizes, rich history of investigation, and perceived simplicity of adaptive response. An attempt to understand how one E. coli clone adapts to a single environmental factor led to the unexpected discovery that out of one can come many (e unum pluribus), and that biological diversity can evolve and endure even under the simplest conditions. The mutations which we have so far discovered that help to explain this phenomenon localize to transcription factors or cis-regulatory regions, emphasizing the profound influence of differential gene regulation on adaptive evolution. Out of necessity, previous efforts to analyze this microbiological consortium relied upon the assumption that the sum of the individual units was mechanistically equal to the behavior of the whole. And indeed, detailed analysis of each member in isolation provided useful information about both their shared evolutionary history and individual adaptive strategies. However, treating the intact consortium as a single unit revealed a transcriptomic behavior that was clearly different from a simple aggregation of its “atomized” parts (sensu Gould and Lewontin, [106]). Future experiments which rely on advances in whole genome sequencing, cell labeling and cell sorting will enable us to dissect the consortium into its individual components prior to analysis, and precisely identify the characteristics that define each clone' s adaptive strategy. The challenge of deconvoluting individual metabolic responses in this system underscores the complexity of even a simple three-membered “community. ” Our finding that the sum activities of the community do not strictly equal its parts makes clear that experimental microbial evolution is a powerful tool to study the evolution of emergent properties in complex biological systems.
Escherichia coli JA122, CV101, CV103, CV115 and CV116 were stored at −80°C in 20% glycerol (See Table 1). Davis minimal media was used for all liquid cultures with 0. 025% glucose added for batch cultures and 0. 0125% for chemostats [107]. Inocula for chemostat cultures were prepared by growing isolated colonies from Tryptone Agar (TA) plates in Davis medium for 16–20 hours at 30°C, pelleting the cells at 2000× g and resuspending the pellet in fresh medium. A portion of this suspension was used to inoculate chemostats to a density that approximated the expected steady-state density. Chemostats contained Davis minimal media with 0. 0125% glucose and were maintained at 30°C at a dilution rate of ≈0. 2/hr for 70 hours (∼14 generations). A600 readings and spread plate cell counts were taken at regular intervals to monitor growth and cell densities at 70 hours were between 1. 5 and 2. 5×108 cells mL−1. At the end of each chemostat run, three aliquots of 40 mL of culture were rapidly filtered onto 0. 2 µm nylon membranes, flash-frozen in liquid nitrogen and stored at −80°C for RNA extraction. For transcriptional profiling, each strain was grown in triplicate on three different occasions with independently prepared batches of media. To reduce the effect of variation in media preparation, cultures of ancestral JA122 were grown concomitantly, such that each experimental chemostat had a corresponding reference control fed off of the same media reservoir. Genomic DNA was extracted from cells grown in batch culture using a modification of methods described [108]. Subsequent to DNA precipitation, spun pellets were re-suspended in 1XTE (10 mM Tris, 1 mM EDTA, pH 8. 0) containing 50µg/mL DNAse-free RNAse A and incubated at 37°C for 30 minutes. Samples were re-extracted once with phenol∶chloroform (3∶1), once with phenol∶chloroform (1∶1) and twice with chloroform and then precipitated with EtOH using standard techniques. Following re-precipitation, the DNA was dissolved in TE. Total RNA was extracted using an SDS lysis/hot phenol method developed by the Dunham lab http: //www. genomics. princeton. edu/dunham/MDyeastRNA. htm. Briefly, frozen filters were mixed with 4 mL lysis solution (10 mM EDTA, 0. 5% SDS, 10 mM Tris pH 7. 4) and vortexed to remove cells. An equal volume of acid phenol (pH 4. 5) was added and the mixture was incubated at 65°C for 1 hour with frequent mixing. The entire extraction was transferred to a phase-lock gel tube (5Prime Inc. , Gaithersburg, MD) and centrifuged according to the manufacturer' s instructions. The aqueous layer was extracted twice more with chloroform∶isoamyl alcohol (24∶1) and precipitated with ethanol. Pellets were dried and dissolved in RNase free water, treated with 0. 1U/µl RQ1 RNase-free DNase at 37°C for 1 hour (Promega, Madison WI), then further purified using the Qiagen RNeasy Mini kit. RNA quality was assessed on agarose denaturing gels as well as using a Bioanalyzer (Agilent Technologies) and quantified spectrophotometrically. Microarrays were produced using full-length open reading frame PCR products generated with the Sigma-Genosys ORFmers primer set and reaction conditions and cycling parameters recommended by the manufacturer (Sigma-Genosys, The Woodlands, TX). This set contains primer pairs for all 4290 known and hypothetical ORFs in E. coli K12 MG1655. PCR reactions were repeated and pooled as necessary to obtain at least 3 µg of DNA and pooled reactions were ethanol precipitated, resuspended and further purified using a Qiagen MinElute96 UF PCR purification kit. Purified PCR products were run on agarose gels for quantification and to verify PCR product size. 192 PCR products were excluded because they were either the wrong size, produced multiple product bands, or failed to amplify after repeated attempts. An additional 19 ORFs amplified poorly and consequently were spotted at lower levels on the arrays, but were retained in the analyses (see Table S4). Products were standardized to each contain 2 µg (except as noted in Table S4), dried, and dissolved in 10 µl 3× SSC for printing. Arrays were printed onto Corning Gaps II aminosilane-coated slides using a 48-pin Stanford-UCSF style arrayer at the Stanford Functional Genomics Facility (Stanford, CA). Microarray expression profiling and CGH were performed using protocols developed at the J. Craig Venter Institute (http: //pfgrc. jcvi. org/index. php/microarray/protocols. html) with the following modifications. For a-CGH, 5 µg of genomic DNA was sonicated to an average fragment length of 2–5 kb using a Branson Digital Sonifier at 11% amplitude for 1. 1 seconds and a final concentration of 0. 5 mM, and 1∶1 aa-dUTP∶dTTP labeling mixture was used in the Klenow reaction. For expression profiling, 20 µg of total RNA was reverse transcribed using 9 µg of random hexamer and 0. 83 mM 1: 1 aa-dUTP: dTTP labeling mixture. Slides were blocked (using 5× SSC, 0. 1% SDS, 1% Roche Blocking Reagent) prior to hybridization as described (http: //www. genomics. princeton. edu/dunham/MDhomemadeDNA. pdf) (Roche Applied Science, Mannheim, Germany). Hybridized arrays were scanned using an Axon 4000B scanner (Molecular Devices, Sunnyvale, CA). qRT-PCR was performed using the Step-One Plus Real-Time PCR System (Applied Biosystems (ABI), Foster City, CA). Primers and probes were designed using the default parameters with Primer Express 3. 0 and purchased from Integrated DNA Technologies (IDT, Coralville, IA). A 2 µg aliquot of total RNA was treated with RNAse-free DNAse to remove residual DNA and subsequently reverse transcribed using the ABI High Capacity cDNA Reverse Transcription Kit, after which 1 µl of cDNA was added to 1X TaqMan Gene Expression Master Mix containing 900 nM each primer and 250 nM probe and cycled using the universal cycling program for the StepOne system. Relative amounts of each transcript were calculated using the ΔΔCt method using mdaB as an endogenous control [109]. The sequences of the primers and probes used are shown in Table S5. a-CGH images were processed using a combination of GenePix Pro 6. 0, the TIGR TM4 software suite available at (www. tm4. org), and Microsoft Excel [110]. Image analysis and spot filtering was done in GenePix and a-CGH spots were considered acceptable if they: (1) passed the default flag conditions imposed by the software during spot finding; (2) had an intensity∶background ratio >1. 5 and overall intensity >350 in the reference channel; and (3) had an intensity∶background ratio >1. 0 in the experimental channel. GenePix files were converted to TIGR MEV format using Express Converter. Ratios were normalized using total intensity normalization and replicate spots were averaged using TIGR MIDAS software. Results were viewed using Caryoscope 3. 0. 9 (caryoscope. stanford. edu). One a-CGH comparison was performed for each experimental isolate using the ancestor JA122 as the reference genome. For transcriptional profiling, spots were considered acceptable if the regression R2 was >0. 6, or the sum of the median intensities for each channel minus the median background was >500. Spots that contained saturated pixels in both channels were excluded from the analysis, but spots that were saturated in only one channel were flagged and retained. Again, GenePix results were converted to TIGR MEV format using Express Converter and ratios normalized and averaged using TIGR MIDAS. Results were viewed and analyzed using TIGR MeV. Three comparisons, including one dye-flip pair, were performed for each biological replicate for a total of nine comparisons for each strain with the exception of CV116 which only had eight comparisons due to a technical failure. Genes that did not have acceptable spots for 2 out of the 3 biological replicates were excluded from downstream analyses. For each biological replicate, reference RNA was prepared from independent JA122 as described above. Significance Analysis of Microarrays [111] (SAM) was used to examine expression differences between strains using a multi-class comparison consisting of four groups. Similarities among strains were identified using one-class SAM and differences between the strains were examined using a 4-class SAM. δ cutoffs were either (1) assigned visually, a strategy in which the tuning parameter (δ) was adjusted manually to reflect a natural break in the plot of observed vs. expected d-values from a line with slope = 1 (which resulted in a FDR of 0%), or (2) set at the 0% FDR threshold (i. e. the highest δ value that gave a median false discovery rate of 0%). In all cases, these settings resulted in q-values of 0. The default settings for all other parameters were retained. The average (mean) log2 ratios for biological and technical replicates were calculated after SAM analysis using Microsoft Excel. Pair-wise Pearson correlation coefficients between array and qRT-PCR expression data were calculated as in [112] using Microsoft Excel. Trancription unit, regulon and operon information was collated from the EcoCyc Database at http: //www. ecocyc. org [63]. Predicted regulatory binding site information was obtained via TractorDB (http: //www. tractor. lncc. br) [64]. Data are available through the NIH GEO database under accession number GSE17314.
Experimental evolution of asexual species has shown that multiple genotypes can arise from a single ancestor and stably coexist (e unibus plurum). Although facilitated by environmental heterogeneity, this phenomenon also occurs in simple, homogeneous environments provisioned with a single limiting nutrient. We sought to discover genetic mechanisms that enabled an E. coli population founded by a single clone to become an interacting community composed of multiple clones. The founder of this population contained mutations that impair regulation of acetate and glycerol metabolism and likely favored the evolution of cross-feeding. Adaptive clones share cis- and trans-regulatory mutations shown elsewhere to enhance fitness under glucose limitation. Certain mutations that distinguish adaptive clones and underlie evolution of specialists were compensatory rather than gain-of-function, and all that we detected resulted in gene expression changes rather than protein structure changes. Evolved clones exhibited both common and clone-specific gene expression changes relative to their common ancestor; the pattern of gene expression in the dominant clone cultured alone differed from the pattern observed when it was cultured with variants feeding on its overflow metabolites. These findings illuminate the roles played by founder genotype, differential gene regulation, and the biotic environment in the adaptive evolution of bacteria.
genetics and genomics/microbial evolution and genomics
genetics and genomics/gene expression
genetics and genomics/functional genomics
microbiology/microbial evolution and genomics
evolutionary biology/genomics
evolutionary biology
microbiology/microbial physiology and metabolism
genetics and genomics/population genetics
E Unibus Plurum: Genomic Analysis of an Experimentally Evolved Polymorphism in Escherichia coli