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Mol Ecol. Author manuscript; available in PMC 2017 December 18.
Published in final edited form as:
Published online 2016 October 18. doi:  10.1111/mec.13851
PMCID: PMC5734618

Using long-term experimental evolution to uncover the patterns and determinants of molecular evolution of an Escherichia coli natural isolate in the streptomycin treated mouse gut


Though microbial ecology of the gut is now a major focus of interest, little is known about the molecular determinants of microbial adaptation in the gut. Experimental evolution coupled with whole genome sequencing can provide insights of the adaptive process. In vitro experiments have revealed some conserved patterns: intermediate convergence, epistatic interactions between beneficial mutations and mutations in global regulators. To test the relevance of these patterns and to identify the selective pressures acting in vivo, we have performed a long-term adaptation of an E. coli natural isolate, the streptomycin resistant strain 536, in the digestive tract of streptomycin treated mice. After a year of evolution, a clone from 15 replicates was sequenced. Consistently with in vitro observations, the identified mutations revealed a strong pattern of convergence at the mutation, gene, operon and functional levels. Yet, the rate of molecular evolution was lower than in in vitro and no mutations in global regulators were recovered. More specific targets were observed: the dgo operon, involved in the galactonate pathway that improved growth on D-galactonate, and rluD and gidB, implicated in the maturation of the ribosomes, which mutations improved growth only in the presence of streptomycin. As in vitro, the non-random associations of mutations within the same pathways suggested a role of epistasis in shaping the adaptive landscape. Overall, we show that “evolve and sequence” approach coupled to an analysis of convergence, when applied to a natural isolate, can be used to study adaptation in vivo and uncover the specific selective pressures of that environment.

Keywords: Escherichia coli, experimental evolution, gut colonization mouse model, 536, streptomycin resistance, D-galactonate


Bacteria have evolved for billions of years and colonized all available ecological niches. Many studies have been performed to understand how evolution has shaped the genome of these complex organisms. Comparative genomics as unravelled the dynamics of gene acquisition and losses in bacterial genomes (Touchon et al. 2009), and the general constraints acting on genome composition (Hershberg & Petrov 2010). Yet, much less is known about bacterial genomes evolution in the short term in their natural habitat. While we know through the emergence of resistance to antibiotics that bacterial evolution can be caught in the act, most of our evidence of short-term evolution in the wild is linked to drastic selective pressures such as antibiotics (Wong et al. 2012) or escape of the immune system (Flint et al. 2016). Can other selective pressures be strong enough to drive significant changes in bacterial genomes in the sort term? To investigate that possibility, we have decided to study the evolution of a natural isolate of Escherichia coli in the gut of mice.

E. coli is one of the most characterised bacterial species; it has been used as a model organism in the laboratory to unravel the multiple facets of bacterial biology from biochemistry to genetics. Yet, the natural environment of E. coli is not the laboratory. In the wild, E. coli is a versatile species, known as both a widespread gut commensal of the vertebrates and a dangerous pathogen that can also be retrieved in the environment (Tenaillon et al. 2010). As a pathogen E. coli is responsible for about 1 million human deaths yearly due to intra and now mostly extra intestinal diseases (Russo & Johnson 2003). The species is a structured species subdivided in seven phylogenetic groups A, B1, B2, C, D, E and F (Jaureguy et al. 2008). These groups are not randomly distributed. Previous studies have shown an association between the phylogenetic groups and virulence, with most extra intestinal E. coli pathogens (including urinary tract infection and meningitis strains) belonging to phylogenetic group B2 (Bingen et al. 1998; Picard et al. 1999). The prevalence of the different phylogenetic groups as commensals varies largely across host species and even within host populations (Tenaillon et al. 2010). For instance, the B2 phylogenetic group can be frequently retrieved in wild animal’s guts (Gordon & Cowling 2003; Lescat et al. 2013), rarely found (<10%) as a commensal among human tropical populations while it is dominant (>30%) among the populations of industrialized countries (Tenaillon et al. 2010). Indeed, over the last decades, the prevalence of the B2 phylogroup has significantly increased in frequency in some western countries such as France. B2 phylogroup strains being both highly efficient extra intestinal pathogens, and more and more prevalent as commensals in industrialized countries, they are becoming an increasing public health concern. Yet, the molecular determinants explaining the differences in prevalence of the different subgroups of E. coli among different hosts or populations remain largely unknown.

To identify the molecular bases that may lead E. coli to specialize to certain environmental conditions, two main approaches can be used: comparative genomics of natural isolates and experimental evolution. Thanks to the sequencing revolution, more than two hundred complete genomes of E. coli are now accessible and thousands more are underway. We can subsequently compare these genomes in order to identify some genetic characteristics likely to contribute to the phenotypic differentiation of the subgroups. For instance, we compared 130 sequenced genomes to identify the core region that was the most different between B2 to non-B2 strains: the nhaAR operon involved in pH control. Though we could find a contribution of the operon in extra intestinal virulence, we were not able to observe its implication in colonization of the mouse gut (Lescat et al. 2014) nor in any pH linked phenotypes we could measure in the laboratory. This example illustrates the limitation of comparative genomics: though some specific genomic signatures may be found, we have not access to the environmental conditions that shaped them and it is therefore difficult to assess their evolutionary relevance.

An alternative approach relies in evolving bacteria in controlled conditions. In vitro experimental evolution has been used for decades to study bacterial adaptation. Over the last years, the coupling of experimental evolution with whole genome sequencing allowed progresses in the understanding of the genetic bases of adaptation. The most charismatic experiment in the field is the one initiated more than 25 years ago by Richard Lenski. His 12 lineages long-term evolution experiment (LTEE) has now reached more than 60,000 generations. The genomic data as well as targeted sequencing approaches have revealed convergence among the first targets of adaptation involving several global regulators (Maddamsetti et al. 2015). Another large scale study confirmed these results using 115 lineages adapting for 2,000 generations to high temperature: the first target of adaptation was the RNA polymerase one of the most pleiotropic gene in the genome, and a large convergence was observed between replicates (Tenaillon et al. 2012). These studies and others (Herring et al. 2006; Hindré et al. 2012; Le Gac et al. 2013) suggest that the first steps of adaptation may involve global modifications of gene expression rather than a specific small-scale optimization of some specific pathways. Interestingly, global regulators such as rpoS, have also been found to be involved in the diversification that seems to occur during infections (Levert et al. 2010). However, the relevance of these in vitro or in infectio observations in the primary habitat of E. coli, the gut of vertebrates, remains to be tested.

In vivo assays have also been performed to study adaptation to a more relevant environment for E. coli. In the early 2000’s, Giraud et al. tested the implication of mutator alleles of E. coli in the adaptation to the gut of germ free mice and showed that mutator bacteria possessed a short-term advantage and long-term disadvantage in colonizing new environments (Giraud et al. 2001). Later, Giraud et al. also used germ-free mice inoculated with E. coli MG1655 (Giraud et al. 2008) and observed a rapid adaptation by a single point mutation in EnvZ/OmpR, a two component signal transduction system, which controls more than 100 genes. These observations suggest that global regulators could also be the targets of adaptation in the mouse gut. However, these works are entailed by an important limitation: the gut of adult germ free mice is an artificial environment. Mice that have developed without flora have abnormal immune system and gut physiology (Khoury et al. 1969; Welling et al. 1980; Umekasi 2014). Furthermore, in these experiments, E. coli was alone in the gut. In the wild, E. coli is a minority species of the gut microbiota, which is largely dominated by numerous anaerobes (Arumugam et al. 2011). As a consequence, E. coli is facing intense competition for nutrients, and may also have access to other nutrients processed by the rest of the microbiota. More recently, Barroso-Batista et al. performed an evolution experiment using the laboratory strain E. coli K-12 in streptomycin treated mice. Streptomycin treatment allows the colonization of the digestive tract by a streptomycin resistant E. coli, without altering theoretically the anaerobic microbiota (Barroso-Batista et al. 2014). They observed the rapid apparition of mutations in the different lineages leading to convergence in inactivation of the gat operon conferring advantage to the strains bearing the mutations and unable to utilize the galactitol. Here the first target of adaptation was not a global regulator. However, one limitation of these studies is the use of a laboratory strain that has been subject to many genetic alterations and that has not seen a gut for close to a century. E. coli K-12 is a strain evolved in in vitro conditions since 1922, date of its isolation in Palo Alto, from a diphtheria convalescent patient. Genetic modifications, adaptation to oxygen rich media from the laboratory as well as accumulation of mutations favoured by UV irradiations or strong bottlenecks may modify its adaptation to the mouse gut.

In this context, the aims of this study are (i) to identify the basis of molecular adaptation in in vivo commensal conditions using long-term evolution during one year of a natural isolate of E. coli belonging to the phylogenetic group B2 in the digestive tract of streptomycin treated mice and (ii) to test the phenotypic significances of the molecular modifications observed and their possible advantages in this natural environment.

Materials and Methods

Bacterial strains

We used E. coli K-12 BW25113 (phylogenetic group A) from the KEIO collection (Baba et al. 2006) and 536 (phylogenetic group B2, serotype O6:K5:H31) reference strains. The 536 strain has been isolated from a patient suffering from urinary tract infection in the early 1980’s (Berger et al. 1982) and subsequently shown to be a good gut colonizer in streptomycin treated mouse (Diard et al. 2010). This strain was acquired by our laboratory in 1998 from Jörg Hacker who isolated it and then stored at -80°C with glycerol.

Mutant strains of K-12, obtained from the KEIO collection (K-12ΔdgoR:kan, K-12ΔrluD:kan, K-12ΔgidB:kan) and of 536, obtained during the course of this study using the lambda-red disruption method (Datsenko & Wanner 2000) (536ΔdgoR:cm, 536ΔrluD:cm, 536ΔgidB:cm, 536ΔgidBΔrluD:cm), were also used. For the flux cytometry assays, we used K-12 pdgoR issued from the Zaslaver collection (Zaslaver et al. 2006) which beared the plasmid pdgoR which contained the gfp gene placed under the control of the promoter of the dgoRKADT operon. After extraction of this plasmid, we electroporated it in the K-12ΔdgoR:kan mutant strain. We thus obtained K-12ΔdgoR pdgoR strain.

Mouse intestinal colonization assay and sampling strategy

All animal experiments were performed in compliance with the recommendations of the French Ministry of Agriculture and approved by the French Veterinary Services (accreditation A 75-18-05). The mouse gut colonization model was conducted after approval by the Debré-Bichat Ethics Committee for Animal Experimentation (Protocol Number 2012-17/722-0076) in accordance to the European Decret and French law on the protection of animals. Intestinal colonization by 536 E. coli strain was performed using a mouse model as described elsewhere (Diard et al. 2010). Thirty 6-week-old female outbred mice (Charles River CD-1) were housed in 15 cages (numbered from C1 to C15) during one year (two mice per cage named Ma and Mb). Five days before inoculation of the 536 strain, and throughout the experiment, streptomycin was added to the sterile drinking water at a final concentration of 5 g/L. Likewise the mice were fed with sterile food all along the experiment. Mice free of coliform flora (controlled by plating the faeces on Drigalski plates) were inoculated per os with 106 bacteria in 200 μl of physiological water. The day after the inoculation and during the experiment (Days 1, 5, 33, 47, 155, 188, 217, 294, 336 and 363, the intestinal population of E. coli was estimated by plating dilutions of weighted fresh faeces for each mouse on lysogeny broth (LB) agar with streptomycin (5 g/L) and supplementary samples were performed for sequencing assays (Days 61, 124, 155 and 250). Faeces were harvested for each mouse by putting the mouse in a sterile plastic cage until it defecated. The faeces were stored at -80°C with glycerol for further analysis. For sequencing, the frozen faeces were diluted and plated on LB agar with streptomycin. One colony per LB plate was then randomly picked, grown in LB, stored at -80°C with glycerol and thereafter called “clone”. In addition, to study the reproducibility between the sample of the two mice from each cage, 50 colonies were picked from the faeces on the LB plate for each mouse (100 colonies in total) in cage 1 at Day 363. For the polymorphism study, we picked a pool of about 50 colonies from 4 mice (C2Ma, C3Ma, C8Ma and C11Ma) at both the last time point (Days 250, 336, 363 and 363, respectively) and an intermediate time point (Days 124, 155, 155 and 155, respectively).

High throughput sequencing

From the glycerol stocks, the clones were plated on LB plates, and one colony was randomly picked, and DNA was extracted using Wizard® genomic DNA purification kit (Promega). The genomes of the evolved clones, as well as that of the ancestor 536 strain used for inoculation were sequenced on the Illumina HiSeq 2000 (IntegraGen), with one line of single-end 51-bp reads per genome. We also sequenced after DNA extraction the pool of colonies for the reproducibility and polymorphism assays.

Sequence analyses

Point mutations were identified by comparing sequence reads to the genome of the ancestral strain 536, considered as the reference, using breseq 0.23, a pipeline for analyzing resequenced microbial genomes (Deatherage & Barrick 2014).

Reference reconstruction

In a first step, we sequenced the 536 reference genome and compared the reads to the original strain genome (Brzuszkiewicz et al. 2006) from the MicroScope website ( to identify differences using breseq 0.23 (Deatherage & Barrick 2014). This pipeline maps the reads on an annotated reference genome in gbk format to precisely identify the mutations that occurred: the type of the mutation, the genes it fell into, or the closest ones, and in the case of point mutation the changes in amino acid associated. We then used gdtools utility program from breseq 0.27.1 (Deatherage & Barrick 2014) to integrate the identified mutations inside the reference genome. We repeated this operation of comparison and modification of the reference five times until there was no more mutation identified. The iterations are required to correct mutations in close neighbourhood of one another. Using this method, we identified a total of 351 mutations (230 being point mutations, and 121 indels) between our ancestral 536 strain and the one from the database we used, a number compatible with errors due to Sanger sequencing method at the genome level.

Detection of mutations

Once the reference genome was obtained, we compared this reference to the sequences of the different evolved clones using the same approach. The identified mutations were then manually checked. We did the same for the pooled colonies and also obtained the frequencies for each mutation from breseq 0.27.1(Deatherage & Barrick 2014).

Quantification of convergence

To quantify how significant convergence at the gene level is, for each gene of the genome, we computed the expected number of mutations Ei, that would alter this gene if mutations were randomly spread in the chromosome. We compared that expectation to the observed results Oi through the computation of a G statistics for nonsynonymous mutations: in which the sum is made on all genes with some observed mutations. The number of degrees of freedom of G statistics being difficult to compute when many classes are null (genes with no mutations) we compared the observed G value to that of simulated dataset with mutations randomly spread (Tenaillon et al. 2016).

Phenotypic assays

Flux cytometry

The strain K-12 pdgoR and the mutant strain K-12ΔdgoR pdgoR were compared using flux cytometry as described elsewhere (Bleibtreu et al. 2013).

Growth curves

For the comparative growth assays, strains were grown at 37°C in four media, all supplemented with streptomycin at 5 g/L: LB and minimum Davies medium (DM) with glucose, gluconate or galactonate (20 mmol/L for each sugar). Each medium was adjusted at pH 7 and pH 5.7 with MOPS® and TAPS® products, respectively (Sigma). LB is a complex media, whereas DM is a minimal media with only one source of carbon. All the strains studied were grown overnight (O/N) in LB media in deep-weel plates at 37°C with constant stirring at 280 rpm. O/N cultures were prediluted at 1/100 in physiological serum and strains were inoculated each at 1/100 in a Costar® 96 flat-bottomed well plate and recovered by 50μl of mineral oil. Growth was recorded by an Infinite 200 Tecan®, which measured the OD600 in each well every 5 minutes at 37°C, while stirring for 24 hours. Growth assays were repeated 3 times. The maximum growth rate (MGR) was calculated from growth curves obtained by Tecan®. OD600 in nm were collected and Log-transformed. Curves were calculated from a smoothed spline function. The MGR was defined as the time point at which the maximum value of the derivative of the smoothed function was observed. The doubling times (DTs) in mn have then been computed as followed: DT=Log2/(MGR*60).

Statistical analysis

The DTs were compared by strain and by medium using a Welch test. All statistics were computed using R (R Development Core Team, 2009, Vienna, Austria) and statistical significance was determined at a p-value of less than 0.05.


Mouse colonization assay and clone sampling

Thirty mice in 15 independent cages (2 mice per cage) were colonized by the E. coli 536 strain. As it is known that mice are coprophages, we postulated that sampling one clone of one randomly selected mouse per cage should be representative of the population of E. coli colonizing both mice within a single cage. To validate this approach, we sampled in cage 1 at Days 61, 155, 250 and 363,, one clone from each mouse as well as a mix of 50 clones at Day 363 for each mouse and performed sequencing of the individual clones and the mix. We observed a single mutation difference between the two individual clones per cage and this site was the only polymorphic site recovered in the pooled sequencing. These results validate our strategy, sampling a single mouse per cage being relevant.

The 536 strain was maintained in all alive mice throughout the experiment that lasted 363 days. However, ten mice died in the course of the experiment (figure 1), most of these were issued from different cages except for 2 cages (cages 7 and 14) in which the two mice died before day 363 (supplementary table 1). The average population density was 1.4.109 unity forming colony (UFC)/g of faeces along the experiment. More precisely, density first decreased from 3.109 UFC/g of faeces to 2.104 UFC/g of faeces at day 188 and stabilized at 6.107 until the end of experiment at day 363 (figure 1). One clone per cage has been used for sequencing. The sampling points corresponds (i) to the end of the experiment when at least one of the two mice of the cage was still alive (Day 363 for 10 cages: C1, C4, C5, C6, C8, C9, C10, C11, C13, C15), (ii) the time at which the last mouse of the cage died (Day 250 for cage C2, C7 and C14), and for three cages we could not recover E. coli from the frozen faeces and we sampled the last point that produced an E. coli clone (Day 124 for cage C12 and Day 336 for C3) (supplementary table 1).

Figure 1
Population density of Escherichia coli 536 per gram of feces of the streptomycin treated mice in 15 independent cages after oral colonization of the strain during 363 days.

Description of mutations in the 536 evolved clones

Using breseq pipeline (Deatherage & Barrick 2014), we identified 95 independent mutations that occurred during the experiment of mice gut evolution in the 15 genomes as shown in the supplementary table 2. Amongst those mutations, 70 were point mutations, 17 were small indels (less than 50bp), 6 were large indel events (>25bp) and two were IS insertion. For the point mutations, 44 were non-synonymous including 10 stop mutations; 13 were synonymous and 13 intergenic. The synonymous mutations recovered were only transitions and 11 out of 13 were of C-G to T-A type, a bias reported before (Wielgoss et al. 2011). Assuming 18 generations a day in the mice gut (Rang et al. 1999), we could compute a mutation rate of 1.85 10-10 per base-pair per generation using synonymous mutation. This is consistent with previous estimates of mutation rate of 8.9 10-11 found in the LTEE (Wielgoss et al. 2011). The ratio of rate of non-synonymous to the rate of synonymous, Ka/Ks, was 1.90 (Confidence Interval (CI) estimated by bootstrapping mutations: 1.09-4.01). Such a value suggests that about 50% of non-synonymous mutations (including nonsense mutations) are the product of natural selection. Stop codons were even more overrepresented in the dataset compared to the expectation based on synonymous (Kstop/Ks=6.72, CI: 2.78-15.86) or on all non-stop mutations (Kstop/Ka_nonstop=4.51, CI: 1.96-8.39). This suggests a strong selection for gene inactivation.

Among the 17 small indels (<50bp), 13 lead to frameshifts, and none were intergenic. For the 10 lineages that survived over the 363 days, an average of 7 mutations were found per sequenced genome (4 to 11).

Convergence among lineages

Convergence was observed at several levels. First, while 95 mutations were recovered, they corresponded indeed to 76 different mutations. Six mutations were recovered in 2 to 8 lineages. For instance 8 lineages shared the same C to T mutation in an intergenic region between yjiH and kptA, and 5 shared a G to A non-sense mutation in dgoR. While these shared mutations could be a signal of cross contamination, the star like phylogeny of the samples and the mapping of mutations on that phylogeny suggest that these common mutations must have occurred multiple times (figure 2). In other words, using the previous two examples, the lineages having the yjiH/kptA mutations were not fully included in, fully encompassing or mutually exclusive with the lineages having the afore mentioned dgoR mutations: some lineages have both mutations, one, the other or none. This suggests that if the population evolved asexually, in at least one locus the same mutations occurred several times independently. These convergent mutations account for 26% (25/95) of the mutations found.

Figure 2
Mutation pattern among the evolved lineages.

Convergence could be integrated further at the gene level. Nine genes were found to be mutated repeatedly in different cages. Adding a large deletion recovered 3 times, the mutations showing some sign of convergence among lineages at the gene level accounted for 56% (53/95) of the mutations found. With the limited number of mutations found in each genome, this is a clear sign of convergence (see below). This signal is further reinforced by the fact that all genes affected multiple times presented at least 2 different mutations at the origin of the convergence (table 1). The fact that different alleles are found in the different lineages, excludes the possibility that convergence is only due to a single mutation that was by chance at high frequency in the initial stock used to inoculate the different mice. For instance, ECP_4610, a putative transporter is affected in 5 lineages with 5 different mutations.

Table 1
Summary of the mutations that were found in the evolved lineages.

To quantify how significant this convergence at the gene level is, for each gene of the genome, we computed a statistical test for convergence (see method) in the form of a G statistics (Tenaillon et al. 2016). We found G=482.3 that has to be compared to a mean value of 399.2 for simulations, with a Z-score of 8.3. This suggests that the observed value is more than 8 standard deviations away from the simulations mean and results in a p-value lower than 2.10-16. This overwhelming signal of convergence suggests the filtering action of natural selection.

Among this list of genes showing signs of convergence, two clusters of linked genes can be further identified. dgoR, dgoD and dgoT are implicated in the same metabolic pathways. This observation suggests further that convergence may occur at a higher level of integration than the gene. In the same manner, 6 mutations in the rluD gene were found, and 7 in gidB. Both genes are implicated in the maturation of the ribosomes, and both are linked to streptomycin resistance.

Polymorhism within lineages

To have a better insight on the dynamics of adaptation, we sequenced a pool of about 50 colonies from 4 mice (C2Ma, C3Ma, C8Ma and C11Ma) at both the last time point (Days 250, 336, 363 and 363, respectively) and an intermediate time point (Days 124, 155, 155 and 155, respectively). Using an analysis of polymorphism in these data, we can detect mutations with a frequency higher than 5% in most samples. The analysis of the mutations found at the population level confirmed the presence of the mutations of interest we detected in the clone sequenced. Polymorphism data revealed however if mutations were fully fixed (frequency of 100%) in the population or if they had reached intermediate frequencies. Moreover, several mutations affecting the same gene were found simultaneously in some populations. This suggests some competition between these various alleles. Interestingly, all cases of multiple allele affecting the same gene were restricted to the genes we identified as target of selection with the clone sequencing approach (supplementary table 2). This confirms that we have identified the biggest targets of selection.

The loci under selection showed traces of polymorphism in two ways (supplementary table 3). Either a single allele was present in the population but not fixed or several alleles were found simultaneously in the population. gidB showed the strongest sign of polymorphism. In mouse C11Ma, at day 155, the whole population seems to have mutated gidB, but 4 alleles are involved: a 25bp deletion, a frameshift, P79L and Q23* at respective frequencies of 8%, 60.2%, 8.7% and 31.3% (note that the frequencies are based on local coverage statistics so the sum is not supposed to be precisely 1). At day 363, the 25bp deletion overtook the other alleles and reached a frequency of 100%. In mice C2Ma, two alleles of gidB, Q167* and the Δ25bp deletion, are found polymorphic at both sampling time. dgoR also had traces of polymorphism, with C8Ma having two competing alleles (Δ360bp at 75.3% and Q212* at 20.9%) at one time point (Day 155). Out of the 3 mice studied for polymorphism that had mutations in dgoR at the last sampling time point, only in one case fixation was reached, while in the others the frequency of the alleles was of 55.8% and 74.1%. The study of polymorphism revealed also new alleles in some of the locus of interest. A mutation in the promoter of dgoR reached a frequency of 7.4% in C2Ma at day 124 and another allele between yjiH and kptA reached a frequency of 24% in C8Ma at Day 155 before being out competed by another allele. Finally, in mouse C11Ma, while we did not find any mutation in dgoR in the clone sequenced at the last time point, a mutation was recovered at high frequency in that gene at an earlier time point, but disappeared later on.

The comparison of the alleles found polymorphic in the different populations studied revealed another gene with evidence of convergence. Clone sequencing revealed a frameshift in uxuR in mouse C11Ma. The polymorphism analysis revealed that this mutation was fixed in that population. In 2 other mice we found mutations affecting uxuR. These mutations were at low frequencies (Q116* at 16.7% in mouse C2Ma, A213V at 6.6% in mouse C3Ma) which explains why we have not sampled them with the clone approach. UxuR is a transcriptional factor that regulates a cluster of operons involved in transport and degradation of the sugar acids β-D-glucuronides, glucuronate, gluconate and L-galactonate.

We further focused on both dgoRKADT operon and rluD and gidB complex as the other important convergence targets, the intergenic region between yijH and kptA (hit 11 times but with only 3 alleles) or ECP_4610 (hit 5 times) affect proteins of unknown functions. To limit the use of animals, functional tests were made only in vitro.

Galactonate operon

Role of dgoR and dgoRKADT operon

Among the most notable mutated genes, the genes related to the galactonate operon, dgoRKADT, seemed particularly interesting. Seventeen mutations comprising 6 stop codons, 2 frameshifts, and 3 missenses were recovered in dgoR, 4 missenses in dgoD and 2 missenses in dgoT such that in 11 out of the 15 cages the operon was mutated (figure 3). The operon is composed of dgoR (coding for the repressor), dgoK (coding for a kinase), dgoA (coding for a aldolase), dgoD (coding for a dehydratase) and dgoT (coding for the D-galactonate transporter). This operon is responsible for the utilization of the D-galactonate sugar in a pathway that is somehow parallel to the Entner Doudouroff pathway (EDp): contrary to this pathway the input is D-galactonate rather than gluconate but likewise to the EDp the outputs are pyruvate and D-glyceraldehyde 3P (Lamble et al. 2004). We confirmed that dgoR is the repressor of the pathway, which is expressed at low level in the absence of D-galactonate and allows the inhibition of the expression of the operon. We observed this regulator activity using reporter strains carrying the gfp gene under control of the dgoRKADT operon promoter in K-12 strain. In LB, which does not contain galactonate, no fluorescence was observed. The presence of D-galactonate, induced fluorescence that was revealed using flux cytometry. Moreover the strain E. coli K-12ΔdgoR (Baba et al. 2006) using the same construct presented a constitutive induction of fluorescence in both media (figure 4). These results confirmed the repressor activity of DgoR, which had not yet been observed. To test the effects of the dgoR mutations selected for in our experiments, one evolved lineages of 536 obtained at the end of the experiment from the mouse C8Ma and the ancestor 536 were transformed with the reporter construct plasmid pdgoR. It showed a constitutive expression of fluorescence similar to the one found in E. coli K-12ΔdgoR, confirming that inactivation of the repressor has been selected for (data not shown).

Figure 3
Diagrammatic representation of the dgoRKADT operon of the 15 evolved lineages compared to the Escherichia coli 536 ancestor strain.
Figure 4
Comparison of total fluorescence of a reporter plasmid of the promoter activity of the dgoRKADT operon in Escherichia coli K-12 derived strains.

Interestingly, mutations in dgoD always occurred in lineages having mutation in dgoR and dgoT mutations always occurred in lineages having also dgoR and dgoD mutations (p=0.009). For instance lineages 5 and 13 both had the 3 genes modified, using 6 different mutations.

To test for a potential benefit linked to mutations in the dgoRKADT operon, we cultured the 15 evolved lineages in different conditions. Growth curves were assayed in different media (LB and minimum medium with glucose or gluconate as the sole carbon source) at different pH (5.7 and 7) and in minimum medium with D-galactonate at pH 5.7 because the sugar is not soluble at neutral pH (figure 5). We then compared the DTs of the seven evolved strains with mutations in dgoR, the two evolved strains with mutations in dgoR and dgoD and the two evolved strains with mutations in dgoR, dgoD and dgoT and the four evolved lineages unaffected in dgoR as well that of the ancestor strains. A significant difference in growth was found in minimum medium with D-galactonate between dgoR mutant strains and the others using a Welch test (p=0.000114). In more details, the data suggest that dgoR and dgoR-dgoD (p=0.001) mutants have a higher growth rate than strains non mutated in dgo operon, and that strains with the three mutations dgoR, dgoD and dgoT grow even better than the ones with only two (p=0.0014). Of note, truncating (non sense and frame shift) mutations were observed only in the regulator coding gene dgoR. This suggest that despite an induction of the operon of dgoRKADT operon by D-galactonate, inactivation of the repressor of the operon promotes growth in D-galactonate, and that growth is further improved when genes implicated in the pathway of degradation of the D-galactonate are mutated. No difference was observed in LB, minimum medium with glucose and minimum medium with gluconate between the wild type and the mutant strains at both pH 5.7 and 7 (data not shown).

Figure 5
Mutated strains of Escherichia coli 536 in dgoR grew faster than wild type strains in minimum medium D-galactonate.

Focus on rluD and gidB

rluD and gidB, are other genes that attracted our attention. Specifically, 6 mutations, including frame shifts were found in rluD, and 7 mutations, including 25bp deletions were recovered in gidB. rluD encodes for a pseudo uridin synthetase that catalyses the formation of pseudo uridine on the 23S rRNA, in other words rluD is involved in 23S rRNA maturation. Similarly gidB is implicated in the maturation of the 16S subunit of the ribosomes as it encodes for a S-adenoside methionine dependant methyltransferase. In other bacterial species, both genes have been shown to be involved in streptomycin resistance, as streptomycin antibiotic targets the ribosome (Okamoto et al. 2007; Sato & Iino 2010). It is therefore very likely that these mutations have been selected for the adaptation to the antibiotic selective pressure that was maintained during our experiment. Moreover the selected functions of both genes seem to be associated as no evolved lineage presented mutations in both genes. Given the frequency of these mutations (7 and 6 mutations), we could find a significant repulsion between the mutations (test of non random association using a correlation: p<0.001), suggesting that once one is found in one of the genes the acquisition of a mutation in the other is no more selected for.

We first wanted to test if these mutations increased resistance to streptomycin in our strain. First, we tested if E. coli K-12 mutants of those two genes, K-12ΔrluD and K-12ΔgidB had a higher resistance level by determining the minimum inhibition concentrations (MIC) of streptomycin. K-12ΔrluD and K-12ΔgidB had an MIC of 20 and 80 mg/L, compared to 16 mg/L for K-12, confirming that the genes could play a role in resistance. We then measured the MIC of streptomycin of strain 536 and the 15 lineages. The mutation K87R responsible for the resistance to streptomycin has been identified previously by others and is responsible for high resistance to streptomycin (Pelchovich et al. 2013). Indeed, MIC was over 150 g/L (more than 30 times higher than the concentration used in the experiment in the water of mice) and fairly similar across evolved lineages and the ancestor. We decided not to go further with MIC as such an incredibly high level of resistance prevents any accurate and reproducible measurement.

To go further, we then inactivated both genes independently or in combination in strain 536 and performed growth curves in LB supplemented or not with streptomycin at 5 g/L. The underlying idea was that the mutations could compensate the cost of resistance to streptomycin that has been shown to be costly. Wild type strain 536 and 536ΔgidB grew faster than 536ΔrluD and 536ΔrluDΔgidB (p = 0.001) in LB rejecting the idea that the mutations were compensating a general cost of resistance. Yet, the mutant strains grew faster than the wild type strain 536 in LB with streptomycin (figure 6), revealing that these mutations could readily be selected for under the antibiotic selective pressure used. rluD mutants were dominant over gidB mutations in the sense that rluD was sufficient to promote the reducing of the DT; adding gidB to it did not bring any further improvement. Hence, once rluD is inactivated, we do not expect to find any further mutations in gidB, however, under the conditions we tested, if gidB is mutated first, rluD could still be selected for. Hence our experimental test in LB could only partially explain the observed full repulsion between the two mutated genes.

Figure 6
Inactivation of rluD and gidB genes confers an advantage to the Escherichia coli strains in LB with streptomycin.


Experimental evolution coupled to whole genome sequencing is a powerful tool to study bacterial adaptation (Long et al. 2015). Over the last decades, many E. coli populations have been adapted to different media using that approach. Genomics was then used to uncover the molecular determinants of these adaptations and to follow how reproducible adaptation is. Most studies have been done in vitro and have shown some similarities. First, the response to selection is proportional to the initial maladaptation: clones with initial low fitness tend to improve faster (Couce & Tenaillon 2015). Second, intermediate convergence is observed among replicate lineages. Moreover this convergence is involving different mutations that affect the same genes or functional units. Though integration at the gene level seems the most relevant to study convergence, mutations in different genes involved in the same function can also be identified as a source of convergence (Tenaillon et al. 2012). Third, some epistatic interactions may be found between mutations within genes or among genes (Tenaillon et al. 2012). Fourth, global regulators tend to be frequently recruited during the first steps of adaptations (Hindré et al. 2012). As most studies, especially long-term ones have been done in vitro or in vivo with laboratory strains, the relevance of these observations remained to be questioned. We therefore wanted to know what fraction of these results would still hold if we were to adapt a natural isolate of E. coli to a more natural environment, the mouse gut. We therefore performed a 363-day assay of colonization of the digestive tract of 30 streptomycin treated mice in 15 independent cages with the B2 strain E. coli 536, and sequenced one evolved clone from each cage. Assuming 18 generations per day for E. coli in streptomycin treated mice (Poulsen et al. 1995), this represents about 6,534 generations.

Similarities and differences with in vitro lessons of experimental evolution

We found some similarities and differences with the previous experiments done.

- We recovered an important signal of convergence between lineages at the mutation, gene/operon and function levels. Convergence at the genome level was highly significant; it was variable among lineages. For instance, 3 out of 10 affected genes of in lineage 9 were targeted in other lineages, while the 6 genes affected in lineage 6 were at least found in another lineage. On average, 56% of the mutations affected genes that were also found mutated in at least another lineage. For instance, lineages 5 and 13 shared mutations in dgoR, dgoD, dgoT, rluD and in between yjiH-kptA. They have therefore 5 common targets (hit by different mutations for 3 out of 5) out of 9 or 7 affected genes respectively. They have hence a more than 50% convergence at the gene level.

-As found in vitro, this convergence was based on different mutations in the affected genes, and could be integrated into higher functional levels: rluD-gidB modifications of the ribosomal RNA for instance.

-Also some interactions among mutations within genes or among genes could be found. For instance, though 11 mutations were found in dgoR, no lineage was found with a double mutation in dgoR (p<0.004), which suggest that once the gene is inactivated, no further mutations in the gene are selected for. Among genes, mutations in gidB and rluD were mutually exclusive, while mutations in dgoT only occurred in lineages having a mutation in both dgoR and dgoT. These nonrandom associations of mutations suggest an important role of epistasis in shaping the adaptive landscape.

One important difference with previous work was that no global regulator was recovered among the mutations found. In vitro, for instance rpoB is a very frequent target of adaptation (Conrad et al. 2010; Tenaillon et al. 2012). Despite a extremely high level of conservation within the species, with just 4 non-synonymous mutations in rpoB among 128 E. coli isolated genomes for a 4 Kb gene (Lescat et al. 2014), the mutations is one of the most frequent target of adaptation in many systems (Long et al. 2015)). The lack of mutations in global regulators in our system, suggests that the conditions encountered may be less artificial than in vitro conditions. Yet, if no global regulators are hit, we may still question the targeted functions to the mouse gut to uncover how specific they are from our experimental conditions or from our strain?

Functional targets of adaptation

One of the dominant targets of adaptation is the inactivation of dgoR that leads to an enhanced ability to grow on D-galactonate. Other studies performed in chemostats with specific sugar as nutrients allowed the observation of mutation in genes enhancing the increase of utilization of these sugars (Ferenci 2007; Lee & Palsson 2010). However, the specific convergence in the dgoR gene has never been reported so far. Particularly, it was not recovered by Barroso-Batista et al (Barroso-Batista et al. 2014) who used also streptomycin treated mice to study the evolution of the K-12 strain. Many hypotheses could be proposed. These observations could be linked to the strain studied. We used the strain E. coli 536 which was isolated after a urinary tract infection in a human patient (Brzuszkiewicz et al. 2006), whereas in other studies the strain used for the experiment of colonization was E. coli K-12 which is the typical laboratory strain (Blattner et al. 1997). One possibility is that strain 536 could harbor an abnormal genetic background relative to other strains. For instance, Barroso-Batista et al found that the first response of E. coli K-12 to the gut was mutations inactivating gat operon, but this operon is abnormal in E. coli K-12: it is constitutively expressed due to an insertion sequence integration in its repressor (Barroso-Batista et al. 2014). Hence the observed response could be more a specificity of the strain than a signature of the environment. When we looked at dgoR, in E. coli 536, we could not find any clear specificity. Our functional assays showed that dgoR does repress efficiently the operon in the presence of glucose and release the repression in the presence of D-galactonate. We performed further analysis of dgoR gene history using fully sequenced genome of E. coli isolates. We observed that the entire operon dgoRKADT was missing in the strains E. coli from the serotype O157:H7 belonging to the E group. This group is usually responsible for enterohemorragic diarrhea in patients but it is also retrieved as commensal in digestive tracts of cattle (Cray & Moon 1995). This particular observation could be linked to the specific digestive tract of cattle. We further used more than 110 E. coli genome sequences (Lescat et al 2014) to retrieve the mean pairwise rate of synonymous (Ks) and nonsynonymous substitutions (Ka) of dgoR, dgoD and dgoT. For each of these genes, the ratio Ka/Ks was much less than 1 (0.0055, 0.013 and 0.019 respectively) which comforts the idea that the operon is under strong purifying selection.

We then tried to identify the source of D-galactonate in the digestive tract of animals or human individuals, but nothing has been described about this sugar except the big amount produced in specific tissues of patients or mice with a specific genetic disease (Yager et al. 2004; Ficicioglu et al. 2005). However, this sugar can be produced from galactose using the De Ley Doudoroff pathway present in specific aerobic bacteria such as Stenotrophomonas maltophilia (Brechtel et al. 2002). Saffarian et al. recently identified that these bacteria especially S. maltophilia can be retrieved in the crypts of mice (Saffarian et al. 2015). Besides, we know that S. maltophilia can be retrieved in the mucous membranes of patients under antibiotic selective pressure especially with imipenem treatment, this bacteria being naturally resistant to this antibiotic as well as to all aminoglycosides, including streptomycin. We therefore retrospectively searched for the presence of this species in mouse feces stored at -80°C in glycerol, but failed to find it. Surprisingly, in other experiments with CD1 mice used for colonization assay, we identified the presence of this species in the feces but were not able to find if the species could produce D-galactonate (data not shown). We can also hypothesize that dgoRKADT operon may be used to catabolize some alternative sugars found in the digestive tract of mice, and that this sugar is unable to relieve the repression by dgoR and favors therefore its inactivation. Overall, from the data we have, we can say that selection for dgoR inactivation seem not to be a specificity of the strain we used, and must be due to the environmental conditions we evolved the strains in. However, it is hard to know if the selection for dgoR loss is generally selected in the mouse gut or if it is selected here as an indirect consequence of the use of streptomycin that alters the composition of the microbiota as Leatham-Jensen et al observed missenses in other genes of an E. coli K-12 strain evolved model of colonization (Leatham-Jensen et al. 2012). Nevertheless, these convergences illustrate the ability of E. coli to adapt to the specificity of its gut environment, whether they are linked to the host, its diet or E. coli interactions with the gut microbiota (Maltby et al. 2013).

Our second target of adaptation was directly linked to the use of antibiotic in our experimental setting. Thirteen out of the 15 lineages had mutations either in gidB or in rluD, two genes involved in maturation of ribosomal RNAs and linked to the rpsL gene in which mutations may be responsible of resistance to streptomycin. Experiments in which bacteria are evolved in presence of antibiotics represents one of the typical cases of selection that favors the fast emergence of mutations. It is also a case of medical importance due to the drastic increase of resistance among pathogenic bacteria (Andersson & Hughes 2010; Toprak et al. 2011). Two types of mutations are generally selected for in such a regime: mutations that increase resistance to the antibiotic on the one hand and compensatory mutations on the other. The latter are mutations that are selected as they compensate the cost of some previously acquired costly resistance mutations (Andersson & Hughes 2010). In our case, as the concentration of antibiotic used was much lower than the MIC of the strain (5 g/L compared to an MIC of more than 150 g/L), we were therefore not expecting resistance mutations to be selected for. Indeed, in the case of streptomycin, subinhibitory concentrations higher than 1/4 of the MIC can select for resistance mutations (Gullberg et al. 2011), while in the present setting, strains were exposed to concentrations lower than 1/30 of the MIC. We found however that gidB and rluD inactivations were not compensatory mutations, but mutations that improved growth only in the presence of the antibiotic. Interestingly gidB was found to be inactivated in several streptomycin resistant laboratory strains (Jeong et al. 2009) suggesting that the selection of its inactivation may occur quite broadly under streptomycin selective pressure.

Finally, the pattern of convergence suggests that two other sets of genes of unknown functions are involved in adaptation to the streptomycin treated mice gut. ECP4610, a putative transcriptional regulator is affected 8 times and the intergenic region between yjiH and kptA is modified 11 times. It would be interesting to look in more details at the phenotypic effects of these mutations to uncover the specificity of their contribution to adaptation.

Rates of adaptation

Besides the targets of adaptation, our data gave also the rate at which mutations are selected for in the mice gut. In experimental evolution, fast rate of substitutions can be found, with about 11 mutations in 2,000 generations in a thermal adaptation experiment using a very maladapted strain (Tenaillon et al. 2012), 15 mutations after 5,000 generations in a less challenging environment (Barrick et al. 2009) In vivo, Barroso-Batista et al. found a mean of 2.3 mutations per lineage over 400 generations (Barroso-Batista et al. 2014). Here we found 95 mutations over 4,838 days of evolution, a rate 4.4, 2.4 and 4.7 times lower than the one found in the afore mentioned experiments if we assume 18 generations per day (Rang et al. 1999) and no mutation in the global regulators. Hence, the selective pressures may not be as strong as in other setting, reflecting that despite the antibiotic selective pressure, the strain was more adapted to its environment than the ones of the previous experiments. The polymorphism data were suggesting low selective pressure as in mice C3Ma and C2Ma some polymorphic alleles shifted their frequency by less than 40% over more than 125 days of evolution (table S3) and over 4 populations sequenced after more than 125 days of evolution, a single mutation was found fixed (data not shown). The value of Ka/Ks was substantially higher than one (Ka/Ks=1.90, 95% CI 1.09-4.02) but less than the one found in other studies. For instance, a Ka/Ks close to 5 was found in thermal adaptation suggesting that 80% of non-synonymous mutations were the product of selection in that setting. Such high values of Ka/Ks are not rare, for instance in the long-term experimental evolution, the population sequenced (Barrick et al. 2009) had no synonymous mutations fixed after 20,000 generations. The lower value found here is coherent with a much weaker selective pressure in the mouse gut compared to in vitro evolution.

Overall, our data show that an “evolve and sequence” approach (Long et al 2015) coupled to an analysis of convergence can be used to uncover the molecular bases of short-term adaptation in an in vivo study. With that approach, we identified several genes as targets of selection in the gut of streptomycin treated mice and showed that adaptation could be caught in the act in a somehow natural setting with a natural isolate. This suggests that short term adaptation may contribute to some of the patterns of prevalence we observed in the wild (Tenaillon et al 2010). While some of the dominant targets, gidB/rluD and dgo operon do not seem to be specific of the strain we used, others such as ECP4610 and mutations between yjiH and kptA are more enigmatic and could be specific of the strain, especially ECP4610 that is only found in few strains. Further studies with various strains will be required to investigate the fraction of that adaptation that is dependent on the genetic background of the host. Finally, the presence of genetic responses linked to the antibiotic used to maintain the strain, call for the development of some colonization assays without such a selective pressure.

Supplementary Material

Table S1

Characteristics of mice sampling.

The times of sampling as well as the time of death indicated with the symbol † or sacrifice and the quantification of UFC of E. coli per gram of feces are indicated for each mouse. The mice from which is sampled the sequenced genome is indicated with the symbol *.

Table S2

Summary of the mutations that were found in the evolved lineages.

The name of the genes as well as the orientation and flanking genes in case of an intergenic mutation are furthermore displayed, with the mutations, its type, the number of occurrence, and a description of the gene product.

Table S3

Funding information

This work was supported by the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC Grant 310944. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. MG was supported by the grant FDM20150633803 obtained from the « Fondation pour la Recherche Médicale ».


Data accessibility

The date used to produce the figure 1, ,4,4, ,55 and and66 are accessible on the Dryad website : ( with the number doi:10.5061/dryad.4g503 and in the table S1

The date used to produce the figures 2 and and33 are presented in the table S2 and in the MicroScope website (

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