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Compensatory Evolution of Gene Regulation in Response to Stress by Escherichia coli Lacking RpoS

  • Daniel M. Stoebel,

    Affiliation Department of Microbiology and Moyne Institute of Preventive Medicine, School of Genetics and Microbiology, Trinity College, Dublin, Ireland

  • Karsten Hokamp,

    Affiliation Smurfit Institute of Genetics, School of Genetics and Microbiology, Trinity College, Dublin, Ireland

  • Michael S. Last,

    Affiliation UC Toxic Substance Research and Teaching Program, University of California Davis, Davis, California, United States of America

  • Charles J. Dorman

    Affiliation Department of Microbiology and Moyne Institute of Preventive Medicine, School of Genetics and Microbiology, Trinity College, Dublin, Ireland


The RpoS sigma factor protein of Escherichia coli RNA polymerase is the master transcriptional regulator of physiological responses to a variety of stresses. This stress response comes at the expense of scavenging for scarce resources, causing a trade-off between stress tolerance and nutrient acquisition. This trade-off favors non-functional rpoS alleles in nutrient-poor environments. We used experimental evolution to explore how natural selection modifies the regulatory network of strains lacking RpoS when they evolve in an osmotically stressful environment. We found that strains lacking RpoS adapt less variably, in terms of both fitness increase and changes in patterns of transcription, than strains with functional RpoS. This phenotypic uniformity was caused by the same adaptive mutation in every independent population: the insertion of IS10 into the promoter of the otsBA operon. OtsA and OtsB are required to synthesize the osmoprotectant trehalose, and transcription of otsBA requires RpoS in the wild-type genetic background. The evolved IS10 insertion rewires expression of otsBA from RpoS-dependent to RpoS-independent, allowing for partial restoration of wild-type response to osmotic stress. Our results show that the regulatory networks of bacteria can evolve new structures in ways that are both rapid and repeatable.

Author Summary

Escherichia coli, like all bacteria, expresses distinct sets of genes in response to different environmental challenges. One protein, RpoS, is a central part of the cellular response that brings about these changes in gene expression. Despite the importance of this protein in response to some kinds of stresses, strains that lack a functional RpoS protein are found at appreciable frequency in nature. We sought to understand how these strains evolve to compensate for the lack of RpoS function. We evolved E. coli with and without RpoS in a stressful environment in the lab, and found that strains without RpoS evolved in a uniform and repeatable manner. This was true in terms of how much their fitness increased or in terms of how genes were expressed to compensate for the lack of RpoS. These patterns had a simple cause. A mobile genetic element moved position in the genome, allowing for the transcription of a pair of key genes. The same element moved to the same place in each of our replicate experiments, causing the repeatable change in fitness and gene expression. We conclude that E. coli can rapidly compensate for the lack of RpoS by evolving novel mechanisms to control patterns of gene expression.


Bacterial adaptation to environmental stress involves, in part, a modification of transcription patterns, with downstream impacts on physiological function. In Escherichia coli, the RNA polymerase sigma factor RpoS is a global regulator that coordinates the expression of up to 10% of the genome when the bacterium enters stationary phase or experiences stresses such as starvation, acidity or increased osmolarity [1]. Despite the importance of this protein in many environments, a functional RpoS seems to lower the ability of E. coli to scavenge for scarce nutrients [2],[3]. This cost is hypothesized to occur because there is a limiting amount of core RNA polymerase subunits in the cell, meaning that transcription of stress responsive, RpoS-dependent promoters will decrease the transcription from RpoS-independent promoters involved in nutrient acquisition and utilization [2][4].

The hypothesis that the nature of the RpoS regulatory network creates an inherent conflict between stress protection and nutritional competence (SPANC) [3],[4] provides a basis for predicting how natural selection acts on the global regulatory networks of E. coli. The SPANC hypothesis predicts that natural selection will modify the network in favor of nutritional ability at the expense of stress resistance in some environments by decreasing or abolishing RpoS function. Just this type of selection against RpoS activity has been documented in laboratory studies [2],[5],[6]. In addition, strains with low- or null-activity rpoS alleles have been found in natural populations of E. coli and Salmonella enterica [3],[7].

While strains without functional RpoS are favored in some environments, those same strains may do less well in other, more stressful environments where they may be less fit due to an inability to respond to new challenges. While rpoS strains could adapt by recovering or increasing their RpoS function, the mutations that abolished RpoS function may be very unlikely or impossible to reverse. An alternative mechanism involves the selection of mutations that modify the regulatory network to compensate for the loss of RpoS. These compensatory mutations would then increase the fitness of the bacterium in this new, more stressful environment. We sought to understand this type of adaptation by observing the patterns of increased fitness seen in evolving bacterial lines, and by elucidating the molecular basis of the adaptation.

Results from previous experimental studies suggest that compensation for deleterious mutations is a general phenomenon [8][11]. Less is known, however, about the variability of the process of compensation. Will strains that lack RpoS adapt more or less variably to a stressful environment than strains with a fully functional regulatory network? Will this involve larger or smaller increases in fitness? At a molecular level, mutations affecting other global regulators of transcription [1], or local changes at a promoter [12],[13] may permit transcription in the absence of RpoS, but we were interested in discovering which options actually are favored by natural selection. Would only a few key genes be involved in adaptation, or would adaptation involve changes in large parts of the transcriptome? Here we used experimental evolution [14],[15] to answer these questions about adaptation to the loss of RpoS.


E. coli expresses RpoS in response to stresses such as extremes of pH, temperature or osmolarity. If this regulatory pattern is important for fitness, then strains lacking RpoS should be less fit in these stressful environments. To test this hypothesis, we carried out a competition in a high osmolarity environment between a strain with wild-type rpoS and a strain with a deletion of the rpoS locus. As expected, the ΔrpoS strain was less fit than its wild-type ancestor (Table 1). This was not due to another mutation elsewhere in the genome, as other reconstructions of this same strain pairing showed the same cost (i.e. the difference in fitness) (Table 1, ANOVA, p = 0.89). The fitness cost was only slightly larger when the bacteria were grown with 0.44 M sucrose (t-test, p = 0.01), indicating that there was a general cost of osmotic stress and not only a salt-specific cost. Furthermore, the cost was specific to high osmolarity, as there was no fitness cost when the two stains were competed in the absence of NaCl stress (Table 1). Finally, the fitness cost was not due the activity of the kanamycin resistance gene used to knock out rpoS, as the same resistance cassette placed at the melB locus had no fitness cost (Table 1).

To allow strains to adapt to this high-osmolarity environment, we serially cultured five rpoS+ lines (denoted rpoS+1 to rpoS+5) and five ΔrpoS lines (ΔrpoS−1 to ΔrpoS−5) in a high-osmolarity medium for 250 generations, and isolated a single colony from the final population. When competed against their ancestor, all 10 evolved lines showed an increase in fitness (Figure 1). This was due, at least in part, to the fact that each increased its growth rate (Table S1). Importantly, and unexpectedly, there was no significant difference in the average fitness increase of the ΔrpoS lines and the rpoS+ lines (t-test, p = 0.18). In addition, the variance in fitness among the ΔrpoS lines was smaller than the variance among the rpoS+ lines (F-test, p = 0.02).

Figure 1. Fitness of evolved strains compared with their ancestor.

Five rpoS+ lines (filled circles) were more variable in their fitness increase than the five ΔrpoS lines (open circles). The dashed line shows fitness of 1, indicating equal fitness between two competitors. Each competition experiment was replicated four times, and error bars represent the standard error of the mean.

Was the fitness increase of the ΔrpoS lines a compensation for the lack of RpoS function, or simply a general adaptation to this environment? If it was compensation for lack of RpoS function, then adaptation should be epistatic on rpoS. To test this possibility, we restored the ΔrpoS lines to rpoS+. While rpoS+ is favored over ΔrpoS in the ancestral genetic background, all five ΔrpoS lines became less fit when transduced from ΔrpoS to rpoS+ (Figure 2A), indicating that fitness increased by compensating for the lack of RpoS, rather than by general adaptation to the culture conditions. To see if the rpoS+ lines adapted in an RpoS-dependent manner, we transduced these lines from rpoS+ to ΔrpoS. Although ΔrpoS was costly in the ancestral genetic background, it was even more costly in three of four of the evolved rpoS+ genetic backgrounds (p<0.05) (Figure 2B), indicating that adaptation of these lines was via an RpoS-dependent mechanism.

Figure 2. Adaptation was contingent on the status of rpoS.

A functional rpoS allele was transferred into each of the five ΔrpoS lines, and each of ΔrpoS strains was competed against this newly rpoS+ strain (A). In the wild-type background, strains with rpoS+ are more fit than ΔrpoS (filled circle), indicated by fitness greater than 1. All five evolved backgrounds are less fit as rpoS+ than ΔrpoS (open circles), as indicated by fitness less than 1. The dashed line shows fitness of 1, indicating equal fitness between the two competitors. A ΔrpoS line was derived from the rpoS+ lines, and each of these pairs of strains was competed (B). While ΔrpoS is less fit on the wild-type background (filled circle), ΔrpoS is even more detrimental for lines rpoS+2, rpoS+3, and rpoS+5 (open circles). Line rpoS+1 evolved resistance to P1, so the transduction could not be performed.

We used DNA microarrays to explore the patterns of regulatory evolution underlying our observed fitness changes. To assess if our observed patterns of mean change and variance in fitness were mirrored by changes in the transcriptome, we compared the expression pattern of both the rpoS+ and ΔrpoS lines to that of their ancestors. For each of the 4,254 genes represented on the array, we calculated the difference of each of the 10 evolved lines from its ancestor. For each gene, we then asked whether the average difference was larger for the rpoS+ or the ΔrpoS populations. The rpoS+ lines were more different from their ancestor than the ΔrpoS lines were from their ancestor for 61% of genes, significantly more than would be expected by chance (p<10−16, sign test). We used the same approach to assess the variability in expression patterns, and found that the expression of 90.7% of genes was more variable in the rpoS+ lines than in the ΔrpoS lines (p<10−16, sign test). The expression level of most genes was unchanged in most strains (Table 2), so we repeated this analysis with the expression level set to the ancestral value for all measurements that did not pass our statistical threshold (FDR = 0.001). Of those genes that showed change, 91.5% changed more in the rpoS+ lines than in the ΔrpoS lines (p<10−16, sign test). Further, 92.3% of genes were more variable in the rpoS+ lines than the ΔrpoS lines (p<10−16, sign test). These results neatly paralleled our fitness results, showing that highly similar trajectories of fitness increase in ΔrpoS were underlain by similarly parallel changes in patterns of transcription.

Table 2. Number of genes with differing levels of expression.

The fitness results also showed that the ΔrpoS lines evolved to compensate for the lack of RpoS function. Did they do so by returning transcription back towards original wild-type levels, or did adaptation result in the transcriptome becoming even more different from wild type? We found that 331 genes (or 7.8% of the genome) differed significantly in expression between the ancestral wild type and the ancestral ΔrpoS strains during growth in high osmolarity media. The evolved ΔrpoS lines showed significant changes in the level of expression of between 81 and 156 genes from the ΔrpoS ancestor (Table 2). Of these, 37 were changed significantly in all five lines (Table S2). All 37 were genes that also differed between wild type and ΔrpoS. We compared the average expression level of these 37 genes in the five ΔrpoS lines with their level in their ΔrpoS ancestor, and in the rpoS+ progenitor. In 35 of 37 cases, the expression level of the ΔrpoS lines evolved to be more similar to rpoS+ than was their ΔrpoS ancestor (Figure 3). Thus, compensation for the lack of RpoS function involved partial restoration of the wild-type pattern of transcription.

Figure 3. Expression of the 37 genes with changed expression patterns in all five ΔrpoS lines.

For 35 of 37 genes, the mean level of expression in the evolved lines is closer to the rpoS+ level than the ΔrpoS ancestor is. The log-transformed expression levels normalized to the rpoS+ value are shown. otsB and otsA are the two genes with the lowest values in the ΔrpoS strain.

What sorts of mutations brought about these parallel changes in the expression of 37 genes? To address this question, we focused first on the two genes with the most dramatic changes in expression in the evolved lines. These genes, otsB and otsA were upregulated an average of 12.5 and 9.7 fold, respectively, in the ΔrpoS lines. These genes code for the two enzymes required for trehalose biosynthesis and E. coli synthesizes trehalose under osmotic stress in order to achieve internal osmotic balance [16]. otsB and otsA form an operon (otsBA) that requires RpoS for transcription in wild-type E. coli [17], suggesting that the ΔrpoS lines had evolved RpoS-independent expression of this operon.

To determine whether this new expression pattern was via a mutation in the otsBA promoter or a mutation elsewhere in the genome, we sequenced the otsBA promoter of all five lines and found that all five contained identical IS10 insertions. This insertion was located between the +12 and +13 bases relative to the transcriptional start site determined by Becker and Hengge-Aronis [18]. IS10 contains a promoter, POUT, directed outward from IS10 into adjacent DNA [19],[20]. In all five ΔrpoS lines, IS10 was oriented with POUT reading into the otsB gene. IS10 is known to have strong sequence preferences for insertion, and while the site of this insertion at otsB resembles the preferred site, it is not optimal. There is a marked preference for the symmetric site 5′-GCTNAGC-3′, but we found insertion at 5′-GTAAAGC-3′. The presence of a thymine instead of a cytosine at the second position lowers the insertion frequency over 1,000 fold from the preferred site in another tested context [21].

Surprised to find the same mutation in all five lines, we wanted to eliminate the possibility that this mutation fixed in all five lines because it occurred once before the starting culture was split into the five separate lines. If the mutation occurred in all five lines independently, i.e. after the culture was split, then we reasoned that if we started five completely independent lines, each should acquire the same IS10 insertion into PotsBA. To test this hypothesis, we spread an aliquot of the original ΔrpoS frozen culture onto an L agar plate, picked five separate colonies (thus each founded by a single cell) and used each of these to found a new long-term line. Diagnostic PCR of the PotsBA promoter confirmed that each of these lines began with a wild-type promoter. These lines were evolved for 250 generations under identical conditions to those used in the first experiment. After 250 generations, all five of these lines had acquired an IS10 insertion mutation in the same location and with the same orientation as the first five lines, indicating that the repeated evolution of this particular promoter mutation is not due to the mutation having been present in the starting population.

While all five ΔrpoS lines fixed the same mutation, the dynamics of this mutational sweep need not have been uniform. To explore this, we used QPCR to determine the frequency of the IS10 insertion into PotsBA after 80 generations. The frequency of the insertion varied over two orders of magnitude (Table S3), and the frequency in all five cultures was distinct. This could be due to the insertion occurring at distinct time points in each line, or due to the initial rise of the adaptive mutation being dominated by the stochastic dynamics of culture transfer from flask to flask.

To determine if the IS10 insertion alone was sufficient to allow for RpoS-independent expression of otsBA, we cloned the wild-type and evolved PotsBA promoters into the promoterless gfp-fusion plasmid pZep08 [22]. In the rpoS+ ancestor, the wild-type promoter was expressed even in low-osmolarity MOPS minimal medium, and was upregulated upon the addition of NaCl (Figure 4A). The PotsBA promoter remained un-expressed in both the ancestral ΔrpoS background, and in the ΔrpoS−1 line. On the other hand, the evolved (IS10 inserted) PotsBA promoter was expressed in all three strains (Figure 4B). The ancestral ΔrpoS line and the ΔrpoS−1 line expressed at similar levels, suggesting that there was not a second mutation beyond the IS10 insertion that allowed for otsBA expression in the evolved lines. The evolved promoter was expressed at a lower level in the rpoS+ strain than in either ΔrpoS line, which may explain why the evolved lines became less fit when made rpoS+.

Figure 4. Expression of PotsBA measured by a gfp transcriptional fusion.

Cells were transformed with a plasmid expressing gfp either from the wild-type PotsBA (A) or from PotsBA with IS10 inserted (B) into one of three genetic backgrounds: rpoS+ (circles), ΔrpoS (squares) or ΔrpoS−1 (triangles). Cells were grown over-night in MOPS MM, then diluted 1∶100 into fresh media and grown until they reached an OD600 of 0.25. At this point (time 0) a sample was taken, and NaCl was added to a final concentration of 0.3 M. Fluorescence was measured on a flow cytometer. The experiment was repeated three times, and error bars represent the standard error of the mean.

What is the fitness effect of this IS10 insertion? When the IS10 insertion was moved into the ancestral ΔrpoS background, we found that the newly constructed strain had a fitness of 1.25 when competed against the ΔrpoS strain. The fitness advantage due solely to the IS10 insertion was not different from the fitness of the five evolved ΔrpoS lines (ANOVA, p>0.05), suggesting that the IS10 insertion was responsible for all of the adaptation. To complement these experiments, we transduced all five of the ΔrpoS evolved lines to a wild-type otsBA promoter. These strains were then competed against their ΔrpoS ancestor, and four of the five were now found not to be different to their ancestor (Figure 5). Only the ΔrpoS−3 line was significantly fitter than its ancestor when transduced to wild-type otsBA (fitness = 1.04, p = 0.003), suggesting that it has a second mutation beyond the IS10 insertion at that locus. There is no evidence that the other four strains have any other mutation that affects fitness in the high-osmolarity environment.

Figure 5. Fitness effects of the PotsBA::IS10 insertion.

The fitness of a ΔrpoS::kan strain with the PotsBA::IS10 insertion introduced (filled circle) has fitness of 1.25 relative to the ΔrpoS::kan, a value equivalent to the evolved lines. Four of the five evolved lines, when transduced to wild type PotsBA (open circles), have fitness no different from ΔrpoS::kan. The dashed line shows fitness of 1, indicating equal fitness between the two competitors.

Did all five of the lines that contained the same IS10 do so because this was the only way to upregulate otsBA, or do other routes exist? To answer this question, we selected mutants that could upregulate an otsB-lacZY transcriptional fusion in a ΔrpoS background. Of 21 independent mutants, 19 possessed the same IS10 insertion as we recovered from our experimental evolution, while another had an IS10 insertion between bases −11 and −12 in the promoter (Figure 6). Finally, one mutant had a 6-bp deletion overlapping the −35 box of the promoter [18]. While the wild-type −35 (TGGCGA) box of the PotsBA promoter differs strongly from the RpoD −35 consensus (TTGACA) [23], a sequence closer to the consensus (TTGCAA) lies just upstream in the wild-type promoter. The 6-bp deletion moved this other sequence into position to serve as a −35, presumably allowing RpoD-dependent transcription. These results demonstrate that other mutational routes to upregulation exist, but the IS10 insertion observed in our experimental evolution is the most likely to occur.

Figure 6. The position of 21 mutants that upregulate an otsB::lacZY fusion.

The 19 IS10 insertions between +12 and +13 are in the same position as those recovered from the experimental evolution, and all 19 mutants have IS10 inserted with POUT oriented reading into otsB. Likewise, the IS10 inserted between −11 and −12 has POUT oriented reading into otsB. The start of translation is at +56. The transcriptional start site is from [18], and the −10 and −35 sites are inferred from the data in [23].


The nature of the regulatory network in E. coli that governs its response to stress creates trade-offs between fitness in high and low-stress environments. If selection cannot increase the RpoS-dependent response to stress, then novel regulatory mechanisms may evolve to compensate. We have experimentally evolved both rpoS+ and ΔrpoS lines in an osmotically stressful environment and explored how these populations adapted to this stress. We were surprised to find that the ΔrpoS populations evolved via the same mutation in each line, which did not result in a larger increase in fitness than the rpoS+ lines. We had expected that because they were less fit, the ΔrpoS lines would fix mutations causing a larger average fitness increase, as has been found in other cases of experimental evolution for compensation of deleterious mutations [9],[24]. The expectation of larger fitness increases is supported by models of the genetics of adaptation that follow from Fisher [25],[26]. These models also predict that less fit genotypes will have more possible mutations resulting in more variation in fitness increases. We found the opposite: there was less variation in the fitness increase of ΔrpoS lines, due to the fact that the same mutation fixed in all five populations.

One possible explanation for our observation is that the deletion of rpoS causes the strain to cross a fitness valley, and places it on a smaller peak. On this new, smaller peak, adaptive mutations would be of smaller size, and there would be fewer of them, resulting in more parallel evolution. In biological terms, this implies that it is possible that trehalose biosynthesis is so critical that mutations upregulating otsBA will be much more strongly favored than any other adaptive mutation. If this is the case, then recovery of the same mutation in replicate lines may reflect the limited number of possible adaptive mutations that can upregulate otsBA. Alternatively, it may reflect the fact that the site in the promoter is a hotspot for IS10 insertion. This latter possibility is supported by our observation that 90% of the mutants up-regulating otsBA that we recovered in our screen contained the same IS10 insertion as found in our experimental evolution.

All of the evolution in our ΔrpoS lines was mediated by IS10. While this constancy may be unusual, IS elements have been frequently found as the causes of adaptive mutations in experimental evolution [27][29]. In addition, comparative genomics suggests that IS elements play an important role in genome evolution [30][32]. While the specific role of IS elements in regulatory evolution has been less thoroughly explored, a wide range of IS elements are known to activate transcription by insertion into promoters [12]. Further, IS elements are found in almost all strains of E. coli. In their survey of a representative collection of E. coli isolates, Sawyer et al. [33] found that 97% contained at least one of the six IS elements for which they screened. They did not screen for IS10, but genomic DNA sequencing reveals that it is not rare: it is present in 16% of E. coli and Shigella genomes in GenBank. Thus, it is not improbable that IS elements play an important role in the evolution of the RpoS regulon.

The IS10 insertion in PotsBA affected the transcription of a number of genes beyond only otsBA. The process of compensation for a fitness cost might result in two sorts of patterns of change of transcription. In the first, deletion of RpoS changes the level of transcription of a gene, and compensatory adaptation brings the level of transcription back towards the wild-type level. An alternative is that the process of compensation results in more change in the level of transcription. For example, a gene that is somewhat down-regulated with the loss of RpoS becomes even further down-regulated, or a gene that was not initially affected by the loss of RpoS is upregulated to compensate for the lack of transcription of some other gene. We found that of the 35 genes other than otsBA with changed pattern of transcription across all 5 lines, 33 fell into the former category of (partial) restoration of transcription levels. OtsB and OtsA are biosynthetic enzymes, not transcription factors, but the small molecule they synthesize, trehalose, does stimulate expression of genes involved in maltose transport [34], and the malK-lamB-malM operon is up-regulated in the evolved lines. Other changes in transcription may be responses to other restored physiological states, such as growth rate (the gene rmf [35]), or osmotic balance (proP [36], pspABC [37]). A general implication of these observations is that regulatory systems may be structured such that in the absence of reversion at the genetic level, compensatory mutations can lead to restoration of wild-type patterns of transcription.

What are the consequences for future evolution of compensating for the lack of RpoS with an IS10 insertion? The SPANC model put forth by Ferenci and co-workers [3],[4] proposes that selection will favor decreased levels of RpoS activity in nutrient-poor environments, and increased levels in stressful environments. If null rpoS alleles that cannot be reverted are fixed in nutrient-poor environments, then strains may compensate in stressful environments with mutations elsewhere in the regulatory network. Because epistasis is generated in this compensation, subsequent re-acquisition of RpoS function will decrease fitness. Strains that have taken the first step of adapting in an RpoS-independent manner will continue to do so, leading to even further divergence of regulatory networks.

A mechanism to explain the origins of the observed epistasis, sigma factor competition [38],[39], suggests that epistasis is likely to be a general phenomenon. Sigma factor competition is hypothesized to occur because amounts of core RNA polymerase are limiting for transcription. Thus, if an RpoS molecule interacts with a core subunit to promote transcription from one promoter, there is one less core subunit available for any other sigma factor to interact with and promote transcription. Since the POUT promoter of IS10 is not an RpoS-dependent promoter, the presence of functional RpoS protein will reduce the levels of transcription from promoters like POUT. Because this cause of epistasis is embedded in the fundamental process of transcription, it suggests that mutations recruiting non-RpoS-dependent promoters to compensate for the loss of RpoS will generally be epistatic on the absence of RpoS.

The RpoS regulatory network is a major target for selection because it cannot handle environments that are both physically stressful and nutrient-poor. The SPANC model posits that differing levels of RpoS activity will be selected in different environments. Our work has shown an alternative to increasing RpoS levels in a stressful environment. Strains adapt with a repeatable pattern by up-regulating a single pair of genes on the periphery of a regulatory network, suggesting that regulatory networks may evolve novel structures in a rapid and predictable manner.

Materials and Methods

Strains, plasmids, and media

All strains used are listed in Table S4. Text S1 notes the location of the two copies of IS10 in the ancestral strain used for experimental evolution. Long-term evolution and competition experiments were conducted in MOPS minimal medium [40] with 0.2% glucose (hereafter MOPS MM) as a carbon source. In most experiments an additional 0.3 M NaCl added for osmotic stress. For some experiments no additional NaCl was added, or 0.44 M sucrose was used instead of NaCl. L medium was 0.5% yeast extract, 1% tryptone, 0.5% NaCl. Antibiotics were used at 15 mg l−1 tetracycline, 50 mg l−1 kanamycin, 100 mg ml−1 carbenicllin, and 20 mg ml−1 chloramphenicol.

Long-term experimental evolution

Strains were grown in 25 ml of MOPS MM+0.3 M NaCl in 250 ml flasks, shaken at 200 rpm at 37°C. 25 µl of culture was transferred to 25 ml of fresh media every 24 hours. This 1∶1000 dilution results in log2(1000) = 9.96 doublings per day. The long-term experiment was conducted for 25 days, or approximately 250 generations. Cultures were frozen at −80°C by the addition of glycerol to 20%. At the start of the experiment, a single rpoS+ colony (DMS1684) and a single ΔrpoS colony (DMS1688) were chosen from L agar plates and grown overnight in 1 ml MOPS MM. The next day, 250 µl of culture was added to 25 ml of MOPS MM and grown for two hours at 37°C, shaken at 200 rpm. NaCl was then added to a final concentration of 0.3 M, and cultures were grown for another 22 hours. The next day, these cultures were used to found the five rpoS+ and five ΔrpoS lines by inoculating 25 µl into 25 ml fresh medium.

Competition experiments

To compete a pair of strains, each was first inoculated directly from −80°C frozen culture into 1 ml of MOPS MM in a culture tube and grown overnight at 37°C. The next day, 250 µl of culture was added to 25 ml of MOPS MM and grown for two hours at 37°C, shaken at 200 rpm. NaCl or sucrose was added to a final concentration of 0.3 M or 0.44 M as appropriate to the experiment, and cultures were grown for another 22 hours. To initiate the competition, equal volumes of the two strains were mixed, and 25 µl of mixed culture was added to 25 ml of fresh MOPS MM (plus NaCl or sucrose as appropriate). Cells were grown for 24 hours.

Samples of cells at the start and end of the competition were diluted in MOPS MM without glucose or K2HPO4 and then plated on L agar and on a plate that distinguished the two strains. When the competitors differed by an antibiotic resistance marker, this antibiotic was used to distinguish them. If they did not differ in this way, a mutation in fhuA, conferring resistance to phage T5 was used [41]. This mutation has no effect on fitness (data not shown).

Fitness was calculated as the ratio of the growth rates of the two strains, as described [42]. Differences in plating efficiency between two strains do not effect the calculation of fitness [42].

Strain construction

Alleles were moved between strains by P1 transduction [43]. Movement of the rpoS+ allele into a ΔrpoS background was accomplished by co-transduction with a tetRA element inserted into the ygbM locus. tetRA was amplified from strain CAG18642 by primers ygbM_tetRA+ and ygbM_tetRA− (Table S5) and recombined into the ygbM locus via the lambda-red proteins expressed off plasmid pKD46 [44],[45]. The location of the insert was confirmed by sequencing with primers ygbM_sequence+ and ygbM_sequence−. Movement of wild-type and IS10 inserted PotsBA alleles was via a linked tetRA::araH element. To move the wild-type promoter, tetRA amplified by primers araH_tetRA+ and araH_tetRA− and was inserted into araH of DMS1684 via pKD46 recombination as above. To move the PotsBA::IS10 allele, tetRA was first inserted into araH of the evolved strain DMS1745. Then, the entire tetRA element and PotsBA::IS10 was amplified by PCR using primers araH_tetRA+ and otsB_recomb− and recombined into DMS1684. The insert was confirmed by sequencing with primers araH_tetRA verify+ and araH_tetRA verify−.

Construction of a otsB::lacZYcat mutant was a two step process. First, the cat gene was PCR amplified from plasmid pKD3 [44] using primers cat_lacA+ and cat_lacA−, and recombined into the lacA gene of MG1655 via pKD46 mediated recombination to create strain DMS1976. lacZYcat was then PCR amplified with primers otsB_cds_lacZ_fusion+ and otsA_cds_lacZ_fusion−. These primers included an in-frame stop codon and amplified the native lacZ ribosome binding site. The PCR product was inserted into the otsB gene of DMS1874 via pKD46 mediated recombination. This construct was then P1 transduced into DMS1688 (ΔrpoS::kan) to create strain DMS2098.

DNA microarrays

For all RNA work, strains were inoculated directly from −80°C frozen culture into 1 ml of MOPS MM in a culture tube and grown overnight at 37°C shaken at 200 rpm. The next day, the culture was diluted 1∶100 into MOPS MM and grown for two hours at 37°C, shaken at 200 rpm. NaCl was added to a final concentration of 0.3 M and cultures were grown for another 22 hours. The next day, the culture was diluted 1∶100 into each of two 25 ml volumes of MOPS MM+0.3 M NaCl, and grown until the cells reached an OD600 of between 0.25 and 0.3. Growth was stopped by the addition of 5 ml ice-cold phenol∶ethanol (5∶95 by volume), and the cells were left on ice for 20 to 40 minutes. Cells were pelleted by centrifugation, resuspended in Trizol (Sigma-Aldrich) and frozen at −80°C for up to one week before RNA was extracted following the manufacturer's specification. After checking that RNA was not degraded, RNA from both flasks from each of two separate days was pooled. Double-stranded cDNA synthesis followed the instructions of the manufacturer (Invitrogen). Two separate RNA pools were obtained for each strain, resulting in two hybridizations per strain.

cDNA was Cy3 labeled and hybridized to NimbleGen array design 07112, which contains 5 probes per ORF, replicated in two complete blocks. (These were single-color hybridization experiments.) Slides were scanned on a GenePix 4000B scanner and saved as a TIFF file. Data were extracted from the image file and RMA normalized [46] using NimbleScan 2.4 software (NimbleGen). Microarray data are deposited in GEO under accession number GSE13666, and are available as Table S6.

Transcriptomic data analysis

Log-transformed data were analyzed using the linear modeling approach of Smyth [47] as implemented in the package limma, version 2.16.2 [48] for R, version 2.8.0 [49]. Each array contained two complete block of probes, and these duplicates were used to estimate within array variability as described [50]. A false discovery rate [51] of 0.001 was used as a threshold of distinguishing genes with significant changes of expression. The results of all tests are in Table S7.


Genomic DNA was purified from 50 µl of frozen culture of each line at generation 80, and from strain DMS1745, using the PureGene kit (Gentra). Primer pair PotsBA QPCR+ and IS10 out1 were used to specifically amplify the IS10 insertion into PotsBA, and primer rho QPCR+ and rho QPCR− were used to amplify the control gene rho. QPCR was performed with the FastStart SYBR Green Master Mix (Roche) on a RotorGene RG-3000 (Corbett Research). Samples were run in duplicate on three separate occasions. The method of Pfaffl [52] was used to quantify the frequency of the PotsBA::IS10 in each evolving culture. Purified DNA from strain DMS1745, which contains the PotsBA::IS10 insertion, was used the control sample.

GFP reporter fusions

To measure expression of wild-type PotsBA and PotsBA::IS10, both promoters were amplified by PCR using primers otsBA+NotI and otsBA-XbaI and cloned into the promoterless-gfp reporter plasmid pZep08 [22]. These plasmids were then transformed [53] into appropriate strains. To measure expression, strains were cultured overnight in MOPS MM, and then diluted 1∶100 into 25 ml fresh MOPS MM and grown for two hours. After two hours, NaCl was added to a final concentration of 0.3 M. This is time = 0 on the plots. Cells were sampled by dilution into 4% formaldehyde in PBS, and stored at 4°C overnight. Fluorescence of 10,000 cells from each sample was measured by flow cytometery.

Selection of mutants upregulating otsB::lacZY

Strain DMS2098 was struck onto L+kanamycin plates, and individual colonies were picked into 1.5 ml volumes of MOPS MM+0.3 M NaCl+kanamycin. Cultures were grown until they reached turbidity, which took 1 to 4 days. The entire volume was spun down, resuspended in MOPS MM lacking glucose or K2PO4, and spread on MOPS MM plates, with 0.5% lactose instead of glucose, 0.3 M NaCl, kanamycin, and 40 µg ml−1 X-gal. Plates were incubated at 37°C for 3 days, after which time a single colony was randomly chosen off each plate and purified on a plate of the same media. Of the 23 isolated mutants, two were excluded because IS10 was inserted into the CDS of otsB. This insertion upregulated lacZY, but would knockout otsB in a wild-type background.

Supporting Information

Table S1.

Doubling times. Growth rate was measured in MOPS MM+0.3 M NaCl by change in OD600. Linear regression of log2 transformed measurements between OD600 of 0.05 and 1 was used to estimate the doubling time from each of three replicate experiments. R2 was greater than 0.99 for all regressions.

(0.03 MB DOC)

Table S2.

Genes significantly changed in all 5 evolved ΔrpoS lines. The third column gives the change in the ΔrpoS::kan relative to wild type, while the fourth gives the average change in the five ΔrpoS lines relative to the ancestral ΔrpoS::kan line.

(0.03 MB DOC)

Table S3.

Frequency of the IS10 insertion in each ΔrpoS culture at generation 80. QPCR was used to measure the frequency of the PotsBA::IS10 insertion in each sample. Tukey's HSD test on log-transformed data revealed that all cultures had PotsBA::IS10 frequencies significantly different from the all other cultures (p<0.015).

(0.02 MB DOC)

Table S4.

Strains used in this study.

(0.10 MB DOC)

Table S5.

Primers (5′-3′) used in this study.

(0.05 MB DOC)

Table S6.

This file includes the RMA-normalized, log-transformed microarray data used in this study.

(2.89 MB TXT)

Table S7.

The results of the FDR adjusted significance tests for all genes of all strains.

(0.23 MB TXT)

Text S1.

This file documents the location of IS10 in the genome of the strain used for experimental evolution.

(0.07 MB PDF)


We are grateful to Mike Cashel for strains and to Christina Burch, Daniel Dykhuizen, Paul Rainey, and members of the Dorman Lab for helpful discussions.

Author Contributions

Conceived and designed the experiments: DMS CJD. Performed the experiments: DMS. Analyzed the data: DMS KH MSL CJD. Wrote the paper: DMS CJD.


  1. 1. Weber H, Polen T, Heuveling J, Wendisch VF, Hengge R (2005) Genome-wide analysis of the general stress response network in Escherichia coli: sigmaS-dependent genes, promoters, and sigma factor selectivity. J Bacteriol 187: 1591–1603.
  2. 2. Notley-McRobb L, King T, Ferenci T (2002) rpoS mutations and loss of general stress resistance in Escherichia coli populations as a consequence of conflict between competing stress responses. J Bacteriol 184: 806–811.
  3. 3. King T, Ishihama A, Kori A, Ferenci T (2004) A regulatory trade-off as a source of strain variation in the species Escherichia coli. J Bacteriol 186: 5614–5620.
  4. 4. Ferenci T (2005) Maintaining a healthy SPANC balance through regulatory and mutational adaptation. Mol Microbiol 57: 1–8.
  5. 5. King T, Seeto S, Ferenci T (2006) Genotype-by-environment interactions influencing the emergence of rpoS mutations in Escherichia coli populations. Genetics 172: 2071–2079.
  6. 6. Finkel SE (2006) Long-term survival during stationary phase: evolution and the GASP phenotype. Nat Rev Microbiol 4: 113–120.
  7. 7. Robbe-Saule V, Algorta G, Rouilhac I, Norel F (2003) Characterization of the RpoS status of clinical isolates of Salmonella enterica. Appl Environ Microbiol 69: 4352–4358.
  8. 8. Lenski RE (1988) Experimental studies of pleiotropy and epistasis in Escherichia coli. II. Compensation for maladaptive pleiotropic effects associated with resistance to virus T4. Evolution 42: 433–440.
  9. 9. Moore FB, Rozen DE, Lenski RE (2000) Pervasive compensatory adaptation in Escherichia coli. Proc Biol Sci 267: 515–522.
  10. 10. Burch CL, Chao L (1999) Evolution by small steps and rugged landscapes in the RNA virus phi6. Genetics 151: 921–927.
  11. 11. Schrag SJ, Perrot V, Levin BR (1997) Adaptation to the fitness costs of antibiotic resistance in Escherichia coli. Proc Biol Sci 264: 1287–1291.
  12. 12. Galas DJ, Chandler M (1989) Bacterial insertion sequences. In: Berg DE, Howe MM, editors. Mobile DNA. Washington, DC: ASM press. pp. 109–162.
  13. 13. Massey RC, Rainey PB, Sheehan BJ, Keane OM, Dorman CJ (1999) Environmentally constrained mutation and adaptive evolution in Salmonella. Curr Biol 9: 1477–1480.
  14. 14. Feldgarden M, Stoebel DM, Brisson D, Dykhuizen DE (2003) Size doesn't matter: Microbial selection experiments address ecological phenomena. Ecology 84: 1679–1687.
  15. 15. Elena SF, Lenski RE (2003) Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet 4: 457–469.
  16. 16. Csonka LN, Epstein W (1996) Osmoregulation. In: Neidhardt FC, editor. Escherichia coli and Salmonella typhimurium: cellular and molecular biology, 2nd Edition. 2nd ed. Washington, D.C.: ASM Press. pp. 1210–1223.
  17. 17. Hengge-Aronis R, Klein W, Lange R, Rimmele M, Boos W (1991) Trehalose synthesis genes are controlled by the putative sigma factor encoded by rpoS and are involved in stationary-phase thermotolerance in Escherichia coli. J Bacteriol 173: 7918–7924.
  18. 18. Becker G, Hengge-Aronis R (2001) What makes an Escherichia coli promoter sigma(S) dependent? Role of the −13/−14 nucleotide promoter positions and region 2.5 of sigma(S). Mol Microbiol 39: 1153–1165.
  19. 19. Simons RW, Hoopes BC, McClure WR, Kleckner N (1983) Three promoters near the termini of IS10: pIN, pOUT, and pIII. Cell 34: 673–682.
  20. 20. Haniford DB (2002) Transposon Tn10. In: Craig NL, Craigie R, Gellert M, Lambowitz AM, editors. Mobile DNA II. Washington, DC: ASM Press. pp. 457–483.
  21. 21. Bender J, Kleckner N (1992) Tn10 insertion specificity is strongly dependent upon sequences immediately adjacent to the target-site consensus sequence. Proc Natl Acad Sci U S A 89: 7996–8000.
  22. 22. Hautefort I, Proenca MJ, Hinton JC (2003) Single-copy green fluorescent protein gene fusions allow accurate measurement of Salmonella gene expression in vitro and during infection of mammalian cells. Appl Environ Microbiol 69: 7480–7491.
  23. 23. Shultzaberger RK, Chen Z, Lewis KA, Schneider TD (2007) Anatomy of Escherichia coli sigma70 promoters. Nucleic Acids Res 35: 771–788.
  24. 24. Lenski RE, Bennett AF (1993) Evolutionary response of Escherichia coli to thermal stress. Am Nat 142: S47–S64.
  25. 25. Orr HA (1998) The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52: 935–949.
  26. 26. Fisher RA (1930) The genetical theory of natural selection. Oxford: The Clarendon Press.
  27. 27. Zhong S, Khodursky A, Dykhuizen DE, Dean AM (2004) Evolutionary genomics of ecological specialization. Proc Natl Acad Sci USA 101: 11719–11724.
  28. 28. Cooper VS, Schneider D, Blot M, Lenski RE (2001) Mechanisms causing rapid and parallel losses of ribose catabolism in evolving populations of Escherichia coli B. J Bacteriol 183: 2834–2841.
  29. 29. Zinser ER, Kolter R (2004) Escherichia coli evolution during stationary phase. Res Microbiol 155: 328–336.
  30. 30. Moran NA, Plague GR (2004) Genomic changes following host restriction in bacteria. Curr Opin Genet Dev 14: 627–633.
  31. 31. Mira A, Klasson L, Andersson SG (2002) Microbial genome evolution: sources of variability. Curr Opin Microbiol 5: 506–512.
  32. 32. Siguier P, Filee J, Chandler M (2006) Insertion sequences in prokaryotic genomes. Curr Opin Microbiol 9: 526–531.
  33. 33. Sawyer SA, Dykhuizen DE, DuBose RF, Green L, Mutangadura-Mhlanga T, et al. (1987) Distribution and abundance of insertion sequences among natural isolates of Escherichia coli. Genetics 115: 51–63.
  34. 34. Klein W, Boos W (1993) Induction of the lambda receptor is essential for effective uptake of trehalose in Escherichia coli. J Bacteriol 175: 1682–1686.
  35. 35. Yamagishi M, Matsushima H, Wada A, Sakagami M, Fujita N, et al. (1993) Regulation of the Escherichia coli rmf gene encoding the ribosome modulation factor: growth phase- and growth rate-dependent control. EMBO J 12: 625–630.
  36. 36. Mellies J, Wise A, Villarejo M (1995) Two different Escherichia coli proP promoters respond to osmotic and growth phase signals. J Bacteriol 177: 144–151.
  37. 37. Brissette JL, Weiner L, Ripmaster TL, Model P (1991) Characterization and sequence of the Escherichia coli stress-induced psp operon. J Mol Biol 220: 35–48.
  38. 38. Farewell A, Kvint K, Nystrom T (1998) Negative regulation by RpoS: a case of sigma factor competition. Mol Microbiol 29: 1039–1051.
  39. 39. Nystrom T (2004) Growth versus maintenance: a trade-off dictated by RNA polymerase availability and sigma factor competition? Mol Microbiol 54: 855–862.
  40. 40. Neidhardt FC, Bloch PL, Smith DF (1974) Culture medium for enterobacteria. J Bacteriol 119: 736–747.
  41. 41. Dean AM (1989) Selection and neutrality in lactose operons of Escherichia coli. Genetics 123: 441–454.
  42. 42. Lenski RE, Rose MR, Simpson SC, Tadler SC (1991) Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2,000 generations. American Naturalist 138: 1315–1341.
  43. 43. Miller JH (1992) A short course in bacterial genetics. Cold Spring Harbor Press. 456 p.
  44. 44. Datsenko KA, Wanner BL (2000) One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci USA 97: 6640–6645.
  45. 45. Karlinsey JE (2007) lambda-Red genetic engineering in Salmonella enterica serovar Typhimurium. Methods Enzymol 421: 199–209.
  46. 46. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, et al. (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31: e15.
  47. 47. Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3: Article 3.
  48. 48. Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W, editors. Bioinformatics and Computational Biology Solutions using R and Bioconductor. New York: Springer. pp. 397–420.
  49. 49. R Development Core Team (2008) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  50. 50. Smyth GK, Michaud J, Scott H (2005) The use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21: 2067–2075.
  51. 51. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A Practical and powerful approach to multiple testing. J Roy Stat Soc B 57:
  52. 52. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29: e45.
  53. 53. Groth D, Reszka R, Schenk JA (1996) Polyethylene glycol-mediated transformation of Escherichia coli is increased by room temperature incubation. Anal Biochem 240: 302–304.