Repeatability of gene expression evolution in experimental environmental adaptation

Domain: neuroscience
Published: 2026-01-26
Editorial outcome: minor_revision

Abstract
Assessing the relative roles of chance and necessity in evolution is of wide interest, but it requires evolving the same population under the same environment multiple times—a virtually impossible task in nature that has been repeatedly accomplished in the laboratory. Capitalizing on the transcriptome data collected in 10 laboratory evolution studies conducted in 22 distinct environments, we investigate the evolutionary repeatability of a total of 182,103 gene expression traits in a prokaryotic and five eukaryotic species. The number of gene expression traits exhibiting concordant changes in direction and magnitude between two replicate populations typically exceeds the chance expectation by 10 to 100 standard deviations. Contrasting replicate evolution in the same environment with that in different environments suggests that the concordance in expression evolution is largely attributable to environment-specific selections, a finding that is further supported by comparing with the outcome of mutation accumulation experiments where the efficacy of selection is minimized. Additionally, genes controlled by more transcription factors tend to show more repeatable expression evolution, likely due to a higher certainty in the occurrence of mutations altering their expressions. In conclusion, contrary to almost unrepeatable genotypic evolution, phenotypic evolution during environmental adaptation is quite repeatable and deterministic, at least for gene expressions.

Introduction
The relative importance of chance and necessity in evolution has long been a favorite topic of biologists1–11. On the one hand, even for the same population evolving under the same environment, the outcome can vary due to several stochastic processes. First, mutations occur randomly. Second, the fixation or loss of a mutant allele is influenced by genetic drift, another stochastic process. Third, the appearance and fixation of a mutation could influence the selection coefficients of future mutations and future evolution, a phenomenon known as contingency9,12. While contingency itself is not stochastic, the outcome is, because of the randomness of the initial mutation and possibly its fixation. Hence, in a fitness landscape13, which maps genotypes to fitness, two identical populations starting from the same location in the landscape could evolve to different locations due to the stochastic processes mentioned, especially when the landscape is rugged14. On the other hand, there are also deterministic factors in evolution, such as the selection imposed by the environment and the prediction that the population fitness generally increases through evolution15. The relative importance of chance and necessity in evolution is an empirical question. Furthermore, because fitness is directly determined by the phenotype and because one phenotype can be realized by many genotypes, the relative importance of chance and necessity in evolution likely depends on whether genotypic or phenotypic evolution is concerned and what phenotypic traits are concerned5,16.

Because repeatability implies necessity, comparative biology has addressed the empirical question of the relative roles of chance and necessity in genotypic and phenotypic evolution by looking for convergence, which refers to evolution to the same state in different lineages17,18. While cases of convergence are known at genotypic and phenotypic levels, divergent evolution is far more common than convergent evolution9,19–21, which might lead one to conclude that evolutionary repeatability is generally low and thereby chance plays a bigger role than necessity in evolution. However, comparative biology is perhaps not an ideal approach to this question, because there may simply be too few opportunities for evolution to repeat itself due to the variation in genotype and/or environment among different evolutionary lineages.

To better evaluate the relative importance of chance and necessity in evolution, Gould famously introduced the metaphor of “replaying life’s tape” in his book Wonderful Life12. Gould described his gedankenexperiment as replays from an identical starting condition, but he lamented that such experiments can never be performed12. (Note that Gould also asked how differences in starting conditions would influence evolutionary outcomes12, but we will not address this question here.) While it is indeed extremely difficult to evolve the same population under the same environment multiple times in nature, such experiments have been conducted many times in the laboratory by the experimental evolution approach22–24. In experimental evolution, one can evolve the same genotype or population under the same laboratory conditions multiple times and record the genotypic and phenotypic evolution that ensues. It is therefore an ideal approach to gauging evolutionary repeatability and understanding the relative roles of chance and necessity in evolution9.

In fact, experimental evolution has convincingly demonstrated that genotypic evolution is generally unrepeatable—the same mutation is very rarely fixed in replicate populations evolving under the same environment, even though different mutations in the same gene or in different genes of the same biological process/pathway are often fixed in replicate populations25,26. The low repeatability of genotypic evolution supports the fitness landscape-based theoretical prediction. At the phenotypic level, one trait—fitness (or its proxy)—almost always increases in experimental evolution25,27, but because this trend is virtually independent of the genotype and environment, it is irrelevant to the type of evolutionary repeatability concerned here. For other phenotypic traits, results from experimental evolution are mixed9,28–31. For example, the evolution of antibiotic resistance appears to be highly repeatable, yet the evolution of the capacity to grow aerobically on citrate occurred in only one of 12 replicate populations of Lenski’s long-term evolution experiment (LTEE) of Escherichia coli in a low glucose medium9. Notwithstanding, because the number of phenotypic traits analyzed per study has been small, we lack a general understanding of the repeatability of phenotypic evolution.

An opportunity has now arrived to fill the above gap, thanks to a number of experimental evolution studies that collected transcriptome data. Because the expression level of every gene is a phenotypic trait, these transcriptome data allow probing thousands to tens of thousands of traits per study32. Here we investigate the evolutionary repeatability of a total of 182,103 gene expression traits from 10 experimental studies of six species in 22 environments. Our results paint a general picture that, during environmental adaptation, evolution of gene expression is quite repeatable and deterministic and that the concordant expression changes across replicate populations are driven largely by environment-specific selections with additional contributions from mutation bias (i.e., the rate of mutation affecting the expression of a gene varies across genes).

Results
Experimentalevolutionandassociatedtranscriptomedata
To study the evolutionary repeatability of gene expression traits, we identified from the literature 10 experimental evolution studies that included transcriptome profiling (Table 1). These studies involved the adaptation of a bacterium (E. coli), a fungus (yeast S. cerevisiae), two invertebrates (fly Drosophila simulans and copepod Acartia tonsa), a vertebrate (guppy Poecilia reticulata), and a plant (morning glory Ipomoea purpurea) in a total of 22 environments. In each environment, two or more replicate strains/populations evolved for an equal number of generations. Gene expressions were measured for each replicate strain/population when it first entered the environment concerned (i.e., ancestral expressions) and at the end of the experimental evolution in that same environment (i.e., evolved expressions). Hence, we assume that any gene expression changes detected were due to genetic changes (i.e., evolution) rather than plastic changes33, although we cannot definitively exclude the involvement of phenotypic plasticity (see Discussion).

We addressed the following questions when analyzing the transcriptome data. First, did the number of genes that showed an expression change in two or more replicate populations during experimental evolution exceed the chance expectation, which is the number under the null hypothesis of independent phenotypic evolution among replicates? Second, did the number of genes that showed the same direction of expression change in two or more replicates exceed the chance expectation? Third, did the number of genes that showed the same direction and magnitude of expression change in two or more replicates exceed the chance expectation? Fourth, if an experimental evolution study involved two or more different environments, we further asked whether the evolutionary repeatability of gene expression was higher in the same environment than in different environments. A positive answer would suggest that the observed evolutionary repeatability among replicates in the same environment was at least in part attributable to the selection imposed by the common environment. Otherwise, the observed evolutionary repeatability might have other causes, such as mutation bias. For one species (E. coli), we additionally had access to transcriptome data from a mutation accumulation (MA) experiment where the populations went through repeated severe bottlenecks such that the efficacy of selection was minimized. We thus addressed the fifth question: how repeatable was gene expression evolution in the virtual absence of selection? Sixth, the inclusion of many more than two replicate populations in some of the experimental evolution studies allowed quantifying the level of expression evolution repeatability for each gene, enabling us to address whether different gene expression traits have different levels of evolutionary repeatability and why. We start by describing our analysis of the first experimental evolution study in detail, which is followed by a summary of the results from all 10 studies.

E.coliadaptationtoaglycerolmediumandalactatemedium
Fong et al. evolved an E. coli K-12 strain that had been adapted to a glucose medium in a glycerol medium for 44 days (~600 generations), with seven replicates28. They similarly evolved the same ancestral strain in a lactate medium for 60 days (~1000 generations), with seven replicates28. Adaptation was evident in the two evolution experiments, because for each of the 14 E. coli populations, the growth rate more than doubled at the end of the evolution compared with that at the beginning. The authors used microarrays to quantify gene expressions of the ancestral line and the seven glycerol-evolved lines in the glycerol medium, and the ancestral line and the seven lactate-evolved lines in the lactate medium, respectively. Biological replicates were included in the microarray experiment, permitting identifying from each evolved line genes that showed a significant expression change from the ancestral level at a false discovery rate (FDR) of 0.05. These genes are referred to as differentially expressed genes (DEGs).

The number of DEGs identified from the seven replicate populations in glycerol varied between 1182 and 2778. For each pair of replicate populations, we computed Dice’s coefficient of similarity between their DEGs, which is the number of DEGs shared by the two replicates divided by the mean number of DEGs in the two replicates (see Methods). Dice’s coefficient is quite high, with an average of 0.781 for the 21 replicate pairs in glycerol (left-most blue bar in Fig. 1a). For comparison, for each replicate pair, we estimated the chance expectation of Dice’s coefficient of similarity in DEGs, which is the coefficient when the DEGs of one replicate are independent from those of the other replicate under the assumption that all genes are equally likely to be DEGs (see Methods). We found that this expectation is on average 0.446 for the 21 replicate pairs (left-most gray bar in Fig. 1a). For each replicate pair, we additionally estimated the mean and standard deviation (SD) of the number of shared DEGs by chance (see Methods). These values allowed calculating a Z-score, which is the number of SDs by which the observed number of shared DEGs exceeds the mean chance expected number of shared DEGs; a Z-score of 1.96 corresponds to a P-value of 0.05 in a two-tailed Z-test. The Z-score ranged from 29.1 to 63.7 for the 21 pairs of replicates (left-most column of blue dots in Fig. 1a), indicating that the number of shared DEGs significantly exceeds the chance expectation for every replicate pair.

Similar analyses of Dice’s coefficient of similarity in DEGs were performed for all groups of three replicate populations in glycerol, all groups of four replicate populations, and so on, and the results obtained match those from replicate pairs qualitatively (Fig. 1a). When all seven replicates are simultaneously considered, the randomly expected Dice’s coefficient is only 0.0076, yet the observed value is 0.562 (i.e., on average 56.2% of DEGs in a replicate are also DEGs in all other replicates), demonstrating a substantial level of evolutionary repeatability of gene expression in E. coli’s adaptation to the glycerol medium. Similarly, for each group of three or more replicates, we measured the number of shared DEGs by a Z-score. These Z-scores ranged from 46.8 to 297 (Fig. 1a).

We similarly analyzed the seven replicate populations adapted to the lactate medium and obtained qualitatively comparable results (Fig. 1b). Z-scores are generally even greater in lactate (Fig. 1b) than in glycerol (Fig. 1a).

The relatively high evolutionary repeatability of gene expression in each of the two environments raises the question of whether DEGs in glycerol overlap with those in lactate. There are a few reasons why this could be true. First, there may be a mutation bias that causes unequal expression evolution among genes regardless of the specific selection imposed by the environment. For example, expression variation created by mutation may be particularly large for certain genes, which could create shared DEGs between populations evolving in different environments. Second, some shared DEGs may reflect adaptation to the shared components of the glycerol and lactate media or, to a lesser degree, reflect similar intensities of purifying selection on a gene expression trait in the two media for most expression traits. It may also reflect other common aspects of the evolution experiments, such as temperature and serial batch culture conditions. Third, E. coli cells may employ a general response involving the same set of genes to a broad spectrum of stress, such that even though glycerol and lactate impose different stresses, the gene expression responses could be similar. Finally, in both media, E. coli fitness rose by adaptation; some gene expression changes may be consequences of a fitness increase, so could be shared between populations adapting to different environments. Indeed, Dice’s coefficients and Z-scores both reveal that the sharing of DEGs between two populations respectively adapting to the glycerol and lactate media is greater than the chance expectation (Fig. 1c). Nonetheless, Dice’s coefficients are significantly lower between the two environments (blue bar in Fig. 1c) than within each environment (two horizontal lines in Fig. 1c). Further, Z-scores for the number of shared DEGs between environments (Fig. 1c) are smaller than those within each environment (Fig. 1a, b). These observations suggest that the similarity in DEGs between replicate populations evolving in the same environment is, in a large part, attributable to the environment-specific selection imposed on these populations.

In the above, we considered gene expression evolution without specifying whether the expression level has increased or decreased. Next, we assigned a DEG to one of two categories that respectively included expression increases and decreases. We considered the expression change of a gene repeated if it is a DEG and has the same direction of expression change in all populations compared. Under the new definition, we re-estimated Dice’s coefficients and Z-scores (Fig. 1d–f). As expected, Dice’s coefficients are lower now than in Fig. 1a–c, but they remain higher than the chance expectations. Similarly, Z-scores tend to be lower than those in Fig. 1a–c but remain greater than 1.96 in all cases. These results reveal evolutionary repeatability of gene expression even under a more stringent definition of repeatability.

We further increased the stringency in the definition of expression evolution repeatability by considering not only the direction but also the magnitude of expression evolution. If a DEG shows the same direction of expression change in each population concerned and if the difference in log2(expression ratio) between any two populations concerned is no greater than 0.5, where expression ratio is the evolved expression level divided by the ancestral level, we consider the gene to have shown repeated expression changes. We then reperformed all earlier analyses. Again, we observed qualitatively similar results (Fig. 1g–i).

Hence, by all three definitions of repeatability with different levels of stringency, we found gene expression trait evolution to be quite repeatable in E. coli’s adaptation in glycerol and lactate, respectively. Furthermore, this evolutionary repeatability is significantly greater between populations evolving in the same environment than those evolving in different environments, confirming a major role of the selection imposed by a common environment in the observed evolutionary repeatability.

Analysesofnineadditionalexperimentalevolutionstudies
Below, we briefly describe the nine additional experimental evolution studies considered (Table 1), followed by a summary of the results from the analyses of all 10 studies. In the second experimental evolution study, Sandberg et al. evolved an E. coli K-12 strain that had been adapted to 37°C in 42°C for ~1500 generations, with 10 replicates34. Adaptation to the high temperature was evident from increased population growth rates. RNA-seq transcriptomic data were collected from the ancestral line and 10 evolved lines at 42°C.

In the third study, Rodríguez-Verdugo et al. evolved a strain of E. coli B (REL1206) that had been adapted to 37°C in 42°C for ~2000 generations, with two replicates35. They reported increased cell density for the evolved strains relative to the ancestor at high temperature. RNA-seq was used to profile the transcriptome of the ancestral strain and the two evolved lines at 42°C. Although both the second and third studies investigated E. coli adaptation to a high temperature, they used different growth media (in addition to different strains and different numbers of generations of evolution). Hence, the two evolution experiments took place in different environments.

In the fourth study, Horinouchi et al. evolved an E. coli strain that had been adapted to the M9 minimal medium in each of 11 different harsh environments for 350–800 generations, with five replicates per harsh environment36. The authors observed drastically increased growth rates of these lines in the harsh environments after the experimental evolution. Transcriptomes were microarray-profiled for the ancestral line in each harsh environment and for all evolved lines in the respective environments where they evolved. Note that, although one of the harsh environments contained lactate, as in the first study28, the lactate concentration differed in the two studies; hence we treated the two as different environments.

In the E. coli LTEE, Lenski established 12 parallel E. coli lines from a single clone and allowed them to continuously evolve in a low glucose medium37. These lines have been evolving for over 80,000 generations, and their fitness has been continuously rising27. In the fifth study, transcriptomes from the ancestor and the 12 parallel lines at ~50,000 generations were collected in the low glucose medium by RNA-seq38. Because one transcriptome (Ara+6) was contaminated38, we analyzed the remaining 11 replicate lines.

In the sixth study, Dhar et al. respectively evolved S. cerevisiae that had been adapted to a benign environment in salt stress (NaCl) and in oxidative stress (H2O2) for 300 generations, each with three replicates39. They observed a significant increase in yeast fitness in each stressful environment after the experimental evolution. Microarray-based transcriptome profiling was performed for the ancestral strain under the two stresses and each evolved line under the respective stress.

In the seventh study, Mallard et al. exposed five replicate populations of D. simulans to a hot environment with fluctuating temperature between 18 and 28°C for 64 generations and another set of five replicate populations to a cold environment with fluctuating temperature between 10 and 20°C for 39 generations, respectively40,41. They collected eggs from each population and reared them at 23°C in a common garden experiment. Transcriptomes of five cold-evolved populations in 23°C and five hot-evolved populations in 23°C were profiled by RNA-seq. As previously demonstrated, the cold-evolved populations can be considered ancestral in their gene expression levels42. Hence, we compared gene expressions at 23°C between the cold-evolved and hot-evolved populations.

To investigate how climate change might affect the marine copepod A. tonsa, in the eighth study, Brennan et al. experimentally evolved copepods in an environment reflecting today’s oceanic pH and temperature and in a potential future ocean environment with a lower pH and a higher temperature for 20 generations, each with four replicates43. Copepods showed rapid adaptation to the stressful future environment by recovering egg production and hatching success in 20 generations. Each population was then separated into two, one of which was maintained in the same environment as the previous 20 generations for three more generations, while the other was transplanted to the alternative environment for three generations. We compared RNA-seq transcriptomic data from two groups of populations collected after 23 generations: group 1 populations were continuously maintained in the future environment for 23 generations (reflecting evolved expressions), while group 2 populations were transplanted to the future environment after being maintained in today’s environment for 20 generations (reflecting ancestral expressions).

In the ninth study, Ghalambor et al. investigated the adaptation of the Trinidadian guppy P. reticulata by introducing individuals sampled from a high-predation ancestral environment to two low-predation streams44. Evidence for adaptation to the low-predation environment was observed after a year. They reared guppies caught from the ancestral environment and those caught from the low-predation streams in an artificial low-predation environment. RNA-seq was performed using the brain tissue from these guppies.

In the tenth study, Van Etten and colleagues selected the common morning glory I. purpurea for herbicide resistance over three generations, with eight replicates45,46. They also maintained a control line without herbicide for the same number of generations. Then, they applied herbicide on both the resistant lines and the control line. RNA-seq transcriptomes were collected from leaf tissues at 8 and 32 h after herbicide treatment. We analyzed the transcriptomes collected at 32 h post-treatment, by which point the gene expression response was expected to be fully induced.

In total, the 10 studies included the evolution of 117 populations of one prokaryote and five eukaryotes in 22 distinct environments. Following the analysis in Fig. 1, we examined the evolutionary repeatability of gene expression in an environment using Dice’s coefficients of similarity and Z-scores, by considering the three definitions of repeatability introduced earlier. Below, we describe the findings from the analyses of replicate pairs (Fig. 2), because results from the analyses of three or more replicates are qualitatively similar (Figs. S1–S7). First, when sharing a DEG between replicates is considered repeatable expression evolution, the average Dice’s coefficient is greater than the chance expectation in each experiment, although a substantial variation in Dice’s coefficient exists among the 22 experiments (Fig. 2a). Almost all Z-scores are greater than 1.96, with the median being 26.1 (Fig. 2b). Second, when sharing a DEG with the same direction of expression change between replicates is considered repeated evolution, the average Dice’s coefficient is again greater than the chance expectation in each of the 22 experiments (Fig. 2c) and almost all Z-scores are greater than 1.96, with the median being 30.5 (Fig. 2d). Third, when sharing a DEG with the same direction and magnitude of expression change between replicates is considered repeated evolution, the mean Dice’s coefficient is again greater than the chance expectation in each of the 22 experiments (Fig. 2e) and almost all Z-scores exceed 1.96 (median=23.6) (Fig. 2f). These results demonstrate that gene expression evolution is generally quite repeatable.

In addition to the first experimental evolution study analyzed (Fig. 1), two other studies respectively performed experimental evolution in more than one environment (Table 1). We thus similarly examined evolutionary repeatability between populations evolving in different environments (Figs. S8 and S9). The general trend observed is similar to that found from the first study (Fig. 1c, f, i). That is, the similarity in gene expression evolution between populations evolving in different environments is greater than the chance expectation, yet is lower than that between populations evolving in the same environment.

Of the 10 studies, only two28,38 contained biological replicates when profiling the transcriptome of each strain. For these two studies, we identified DEGs using an adjusted P-value of 0.05 after FDR corrections as the cutoff. For the remaining studies, we computed the expression ratio by dividing the evolved expression level of a gene by its ancestral expression level and used a cutoff of |log2(expression ratio)| > 0.5 for microarray data and > 2 for RNA-seq data to identify DEGs (see Methods). We used different cutoffs for the two types of data because, compared with RNA-seq, microarray has a smaller dynamic range and is less powerful in detecting expression differences47,48. Regardless, because we always contrast observed evolutionary repeatability with its chance expectation under the same cutoff, the specific cutoff used is not expected to affect our results qualitatively. To confirm this prediction, we employed the |log2(expression ratio)| cutoff appropriate for the respective transcriptomic data type on the two datasets with biological replicates in transcriptome profiling28,38. Overall, we found that |log2(expression ratio)| and adjusted P-value cutoffs yielded qualitatively similar results (Figs. S10 and S11), suggesting that our results from the eight datasets without biological replicates in transcriptome profiling are likely reliable.

Mutationbiascontributestotheevolutionaryrepeatabilityofgeneexpression
Our analyses not only revealed a similarity in gene expression evolution among replicate populations adapting to the same environment but also discovered a significant albeit lower level of similarity between populations adapting to different environments (Fig. 1c, f, i; Figs. S8 and S9). These observations suggest the possibility that evolutionary repeatability is in part due to mutation bias49, which makes the rate of mutation influencing the expression of a gene different for different genes. To test this hypothesis, we analyzed transcriptomic data collected from an E. coli MA experiment (Table 1). Specifically, Tsuru and Furusawa established two founder strains (A and B) from an E. coli mutator strain (K-12 MG1655) lacking the dnaQ gene50. They then propagated three parallel MA lines per founder strain for 30 rounds of single-cell bottlenecks on LB plates and collected the transcriptomes of the two founder strains and six MA lines using microarrays. We analyzed these data using the three different definitions of evolutionary repeatability.

For the three MA lines derived from Founder A, under each definition of evolutionary repeatability, the mean Dice’s coefficient is greater than the corresponding chance expectation, and every Z-score is greater than 1.96 for replicate pairs and replicate triplets (Fig. 3a–c). Similar results were obtained for the three MA lines derived from Founder B, although some Z-scores are below 1.96 (Fig. 3d–f). These results demonstrate that expression evolution is somewhat repeatable even in the virtual absence of selection. This said, we note that the median Z-scores observed here for replicate pairs are much lower than those in Fig. 2. Similarly, the mean Dice’s coefficients observed here are smaller than those of most datasets in Fig. 2. Furthermore, the evolutionary repeatability of gene expression due to mutation bias is probably lower in experimental evolution than observed here because of the use of a mutator in the MA experiment. Hence, our results suggest that mutation bias is a contributor to the repeatability of gene expression evolution observed in experimental evolution, yet its contribution is minor. Furthermore, because the mean Dice’s coefficients observed here (Fig. 3) are similar to those observed between populations adapting to different environments (Fig. 1c, f, i; Figs. S8 and S9), the similarity in expression evolution in different environments may be largely due to mutation bias.

Expressionevolutionismorerepeatableforgenescontrolledbymoretranscriptionfactors
Because gene expression evolution is not 100% repeatable (Fig. 2), one wonders whether different genes show different levels of repeatability. To this end, we focused on the E. coli LTEE dataset38 because of its large number of replicate populations. The relative level of expression repeatability of a gene is measured by the number of replicates in which a gene is found to be a DEG. If all genes have equal repeatability, the number of replicates in which a gene is a DEG should follow a Poisson distribution, whose variance equals the mean. The variance of the observed distribution of the number of replicates in which a gene is a DEG is much greater than that of the Poisson expectation (P < 0.001, one-tailed chi-squared test comparing the observed variance with the Poisson variance), with overrepresentations of genes that show repeatability in 0 and in at least 4 replicates (Fig. 4a). This result indicates that different genes vary significantly in their expression evolution repeatability.

What types of genes tend to have higher repeatability? Genes controlled by more TFs are expected to have larger targets for expression-altering mutations51. Hence, there is a higher certainty for genes controlled by more TFs to have genetic expression variants in replicate populations, which will translate to a higher evolutionary repeatability. To confirm this prediction, we focused on three large datasets from E. coli experimental evolution34,36,38 because (i) the information about the number of TFs controlling each gene is readily available in E. coli52, and (ii) two of the three large datasets each have ≥10 replicates per environment34,38 while the remaining one has 11 environments each with five replicates, which may be considered to have 55 approximate replicates36. Indeed, in each of these datasets, we observed a significant positive correlation between the number of TFs controlling a gene and its repeatability in expression evolution (i.e., the number of replicates in which the gene is a DEG) (Fig. 4b–e). Notwithstanding, the correlations were relatively small, suggesting that the number of TFs controlling a gene is but one of potentially many factors influencing the expression evolution repeatability of the gene. It would be interesting to tease apart the contributions of these factors when more data become available in the future.

Methods
E.colitranscriptomicdata
Six different E. coli transcriptomic datasets were used in this study.
The first dataset is from the experimental evolution performed by Fong et al.28. Specifically, the authors evolved an E. coli K-12 MG1655 strain that had been adapted to a glucose medium in a glycerol medium and in a lactate medium, respectively, each with seven replicates. They used microarray to quantify gene expressions of (i) the ancestral line evolving in the glycerol medium on day 1 (five biological replicates) and the same ancestral line evolving in the lactate medium on day 1 (six biological replicates), and (ii) seven parallel lines that had evolved in the glycerol medium for 44 days (three biological replicates per line) and seven parallel lines that had evolved in the lactate medium for 60 days (three biological replicates per line), respectively. In total, expressions of 4392 genes were analyzed in each environment. This study investigated the repeatability of gene expression evolution but concluded that the repeatability was low, apparently because the authors required a gene to be a DEG in the vast majority of replicate populations for it to be considered a case of repeated evolution and because they did not compare the observed number of cases of repeated evolution with that expected by chance28.

The second dataset is from the experimental evolution of E. coli strain K-12 MG1655 that had been adapted to 37°C in glucose M9 minimal medium at 42°C, with 10 replicates34. After approximately 1500 generations of growth, the authors performed RNA-seq on (i) the ancestral line of E. coli at 42°C with 10 biological replicates (the mean expression level from the 10 replicates was considered for each gene), and (ii) 10 evolved lines (with one replicate per line) at 42°C. In total, the expressions of 3815 genes were considered.

The third dataset is from the experimental evolution of E. coli strain B REL1206 that had been adapted to 37°C in 42°C, with two replicates35. After 2000 generations, the authors performed RNA-seq on (i) the ancestral strain at 42°C and (ii) two evolved lines at 42°C. In total, the expressions of 4180 genes were analyzed.

The fourth dataset is from the experimental evolution of E. coli strain MDS42 preadapted to the M9 minimal medium with glucose in 11 different harsh environments for 350–800 generations36. The 11 harsh environments are M9 medium with 400mM NaCl (NaCl), 210mM potassium chloride (KCl), 16μM cobalt chloride (CoCl2), 32.5mM sodium carbonate (Na2CO3), 40mM L-lactate (Lac), 30mM L-malate (Mal), 8.75mM methacrylate (MCL), 50mM crotonate (Cro), 350μM methylglyoxal (MG), 1.25% n-butanol (BuOH), and 4.8μM cetylpyridinium chloride (CPC), respectively. The authors used microarray to measure the gene expressions of (i) the ancestral strain in the 11 harsh environments, and (ii) five parallel lines that evolved in each harsh environment in the respective harsh environment. In total, the expressions of 4492 genes were considered.

The fifth dataset is from the E coli LTEE37. Briefly, 12 populations were founded from a single clone of E coli strain B in 1988 and allowed to continuously evolve in a low-glucose medium. Favate et al. performed RNA-seq on a single clone (with two biological replicates) from each of the 12 parallelly evolving lines61. Because one of the samples (Ara+6) was contaminated, we excluded it from our analysis. In total, the expressions of 4133 genes were considered. Gene expression data are available as an output file from DESeq261. This study also investigated the repeatability of gene expression evolution and concluded that the repeatability was high61.

The sixth dataset is from an MA experiment of E. coli K-12 MG165550. The authors established two founder strains (A and B) from an E. coli mutator strain lacking the dnaQ gene. Single colonies were randomly picked and transferred daily to fresh LB agar plates for 30 rounds. Transcriptomes were collected using microarray for (i) two biological replicates of each founder strain, and (ii) three parallel MA lines diverged from each founder strain after 10 rounds and 30 rounds (one biological replicate per line). We analyzed the transcriptomes at the later time point and used the mean expression level of the ancestral line from the two biological replicates.

Yeasttranscriptomicdata
Dhar et al. respectively evolved the haploid yeast strain BY4741 in salt stress (NaCl) and oxidative stress (H2O2) for 300 generations39. After the experimental evolution, they profiled the transcriptomes using a microarray on (i) the ancestral strain in NaCl and H2O2, respectively, and (ii) three parallel lines evolved in NaCl (in NaCl) and three parallel lines evolved in H2O2 (in H2O2), respectively. The microarray experiment had one biological replicate per evolved line and two biological replicates for the ancestral line (the mean from the two biological replicates was used in the analysis). In total, the expressions of 5814 genes were considered.

Flytranscriptomicdata
Mallard et al. collected Drosophila simulans in Northern Portugal in 200840,41. They evolved five replicate populations in a hot environment with 12 h at 18°C (dark) and 12 h at 28°C (light) for 64 generations. They evolved another five replicate populations in a cold environment with 12 h at 10°C (dark) and 12 h at 20°C (light) for 39 generations (cold). They performed RNA-seq on (i) the five cold-evolved populations after rearing them at 23°C and (ii) five hot-evolved populations after rearing them at 23°C. In total, expressions of 11,870 genes were considered.

Copepodtranscriptomicdata
Brennan et al. collected marine copepods Acartia tonsa from Esker Point Beach in June 2016 and evolved them for 20 generations in an ambient environment (AM) (18°C, pH ~8.2, and pCO2 ~400μatm) and a potential future environment with ocean warming and acidification (OWA) (22°C, pH ~7.5, and pCO2 ~2000μatm)43. In the 21st generation, they started to reciprocally transplant some copepods between AM and OWA, while others remained in their respective original environments as control populations. The reciprocal transplant and the controls continued for three more generations (22nd, 23rd, and 24th). RNA-seq libraries were prepared with a pool of copepods after each reciprocal transplant for each condition. We analyzed the transcriptomes from the 24th generation of (i) the four parallel populations of copepods in AM that were transplanted to OWA at the 21st generation, and (ii) the four parallel populations of copepods in OWA since the beginning of the experiment. In total, expressions of 25,218 genes were considered.

Guppytranscriptomicdata
Ghalambor et al.44, introduced Trinidadian guppies (P. reticulata) sampled from a high-predation natural environment to two low-predation environments in March 2008. After 1 year of experimental evolution (about three to four generations), Ghalambor et al. performed RNA-seq on the brain tissues of guppies from the two introduced populations after acclimation in an artificial low-predation environment. They also performed RNA-seq on the brain tissues of guppies that were sampled from the high-predation environment and acclimated in the same artificial low-predation environment. In total, expressions of 37,444 genes were considered.

Morningglorytranscriptomicdata
Van Etten and colleagues selected common morning glories (I. purpurea) for herbicide resistance and performed RNA-seq on leaf tissues after the experimental evolution45,46. Seeds from an ancestral population were sampled from the University of Georgia Plant Sciences Farm in Oconee, GA in 2000. The offspring of this population were screened in a greenhouse for high resistance to the herbicide glyphosate, and the top 20% resistant lines were selected for herbicide resistance for three generations. Control lines were also developed by randomly choosing 20% of the ancestral population and growing them for three generations in the same greenhouse with no herbicide. Seeds from both the resistant lines and the control lines were planted into two blocks with a randomized treatment design (herbicide or no herbicide). After 6 weeks of growth, the authors applied herbicide and collected leaf tissues 8 and 32 h after the treatment, respectively. In this study, we used leaf transcriptomic data from (i) 16 inbred individuals from the control lines 32 h after herbicide treatment, and (ii) 8 inbred individuals from the resistant lines 32 h after herbicide treatment. In total, expressions of 25,619 genes were considered.

IdentificationofDEGs
Biological replicates were included in transcriptome profiling of both ancestral and evolved lines in two datasets28,38. We identified DEGs using the R package limma62 at an FDR of 0.05 in each test for microarray data provided by Fong et al. For RNA-seq data from Favate et al. (available as DESeq2 output), we similarly used an FDR cutoff of 0.05 to identify DEGs. For the rest of the datasets, where biological replicates were not included in transcriptome profiling of both ancestral and evolved lines, DEGs were identified using |log2(evolved expression/ancestral expression)| cutoffs. For microarray data, the cutoff of 0.5 was applied. For RNA-seq data, the cutoff of 2 was applied.

Dice’scoefficientofsimilarity
We followed a previous study25 to employ Dice’s coefficient of similarity to measure the similarity between two gene sets. Dice’s coefficient equals 2|X1 \cap X2| / (|X1| + |X2|), where X1 and X2 refer to two gene sets, and |X1| and |X2| represent the numbers of genes in X1 and X2, respectively, and |X1 \cap X2| represents the number of genes in the intersection of X1 and X2. Similarly, Dice’s coefficient of similarity among n gene sets equals n|X1 \cap X2 \cap ... \cap Xn| / \sum_{i=1}^{n} |Xi|. Dice’s coefficient ranges from 0 (no gene appears in all sets) to 1 (all sets are the same).

Similarityingeneexpressionevolutionbetweenreplicatepopulationsbychance
Let us assume that n evolved populations each have the same m genes. In each population, we marked a subset of genes that showed significant expression changes in evolution, which allowed computing the number (N) of genes marked in all n populations as well as Dice’s coefficient of similarity among the n sets of marked genes. To assess the number of genes marked in all n populations simply by chance, we performed the following analysis. First, for each population, we randomly marked the same number of genes as originally marked. Second, we counted the number of genes now marked in all n populations. We repeated this process 1000 times to obtain the mean (M) and SD of the number of genes marked in all populations by chance. We then computed Z-score = (N-M)/SD. Based on the same 1000 sets of randomly marked genes of the n populations, we computed 1000 Dice’s coefficients of similarity among the n populations by chance and their mean. Similar analyses were performed when we also considered the direction of a gene expression change or considered both the direction and magnitude of a gene expression change in defining evolutionary repeatability.

Transcriptionfactors
The number of transcription factors controlling each E. coli gene was downloaded from RegulonDB version 13.6.0 (released 03/19/2025)52. In total, there are 241 TFs that control 2432 genes via 5732 interactions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Unlike many natural phenomena, evolution involves a tension between chance and necessity. Natural selection necessarily drives increases in fitness, but the variation it works upon is generated by stochastic mutation, and any variants can be lost to random genetic drift. At the same time, the environment exerting selection is highly changeable. Stephen Jay Gould famously argued that the involvement of chance makes evolution fundamentally contingent. Gould was wobbly on exactly what he meant, but philosophers have come to define contingency in an evolutionary context as meaning that evolutionary outcomes depend on exact historical sequences of events that could have occurred differently, and that different histories can lead to different outcomes. Gould’s work has spurred many studies aiming to test the contingency of evolution, with many being based in the use of experimental evolution with microbes and other fast-growing organisms. The backbone of these studies have been efforts to instantiate his thought experiment of “replaying life’s tape”, in which he suggested that, were one to restart evolution in the past and then have it proceed again to the present day, each replay would yield a different result owing to chance differences in history. These studies have made use of experiments in which initially identical replicate populations are allowed to evolve under the same conditions, and then the divergence or convergence of their evolution is examined at different levels\nThis manuscript details a large-scale study of the evolution of transcriptional changes in experimental populations of a variety of organisms during adaptation to 22 different environmental conditions in the course of many published experiments. It thus examines remarkably broad diversity of organisms and conditions for convergent evolution at the level of gene expression. Under these conditions, divergence between replicates with and among experiments and organisms would be consistent with gene expression evolution being contingent on the evolutionary histories underlying them. By contrast, repeated, convergent evolution would suggest more deterministic evolution driven by natural selection regardless of historical differences. The authors found a remarkable level of evolutionary repetition in gene expression, both between and among the evolving populations examined that suggests a previously unexpected degree of determinism in phenotypic evolution at that level. They propose that the findings may reflect the many-to-on nature of the genotype-phenotype-fitness map, such that the mutational targets are large enough that phenotypic evolution can be highly repeatable, even when genotypic evolution is not. They conclude that these findings indicate that the scope for contingency in evolution may be more limited than thought.\nOverall, this is a remarkable and impressive piece of work. The scope of the research is astonishing, honestly. The paper is also clear and well-written. The methods, data collection, and analyses make sense to me, and I have no real suggestions there. I really only have some relatively minor, pedantic quibbles that I think the authors should take into consideration in interpreting their results, and which suggest that their conclusions should be toned down a bit to be less categorical. I want to stress, though, that my issues are genuinely minor, and go to the complexity of the topic. Please do not take them as major criticisms of the work itself, which, again, is spectacular, but simply the take of someone who has spent far too much time thinking about historical contingency in evolution than is likely healthy.\nI’ll start on a pedantic point about the description of Gould’s thought experiment in the manuscript. Gould was not especially clear on contingency, even when describing his thought experiment. He actually presents and conflates two alternatives. The first is, as the authors say, that different outcomes would results from replays from the exact same initial conditions. But he then suggests that different outcomes would stem from differences in the initial conditions. Which he meant is still arguable. The existence of some difference in the history of the replicates, whenever that difference comes in, and no matter its source, is the important thing. This interpretation is consistent with the current understanding of contingency as elaborated by philosophers of science, John Beatty and Eric Desjardins, that it is a property of historical causal systems in which the past influences what can happen (path dependence) and different histories or event paths are possible, with at least some of those different histories leading to different outcomes. (See: https://doi.org/10.1163/187226311X599916 and https://doi.org/10.1007/s10539-011-9256-4) Contingency is not, as it is put in the introduction, simply that a mutation can influence future evolution.\nChance is often improperly conflated with contingency, but there are not the same. Chance is necessary for contingency, but it is not contingency itself. Chance enables contingency by injecting unpredictability at nodal points in event paths, thus allowing evolution to go down one path versus another depending on which of various possible events by chance occur at the nodes. (Chance brings in a form of probabilistic causal dependence, which, along with path dependence, is necessary for contingency, but contingency is an effect of how those two things interact, and is not synonymous with either one alone.) Inputs of chance include those that are intrinsic to the core evolutionary processes, those coming from the stochasticity of mutation and genetic drift, for instance. However, they also include chance coming from outside processes, including coincidental, sometimes stochastic environmental shifts and the evolution of other populations making up the biotic environment. Lab evolution experiments generally insulate study populations from such environmental sources of chance. It is important to take that insulation into account when interpreting results because environmental sources of chance are likely the most important in evolution. Where lab experiments are most important, typically, is in assessing the scope for contingency in evolution owing to the intrinsic sources of chance within the core evolutionary processes. Hence, the following part of discussion is a bit of a problem:\n“The relatively high evolutionary repeatability of gene expression discovered in the present work contrasts the prior observations from comparative studies, suggesting that the seemingly low evolutionary repeatability of gene expression in natural evolution is likely du to the variation in genotype and/or environment across evolutionary lineages (i.e., a lack of opportunity for repeated evolution) rather than a genuinely low repeatability. In other words, the relative importance of chance in evolution may have been overestimated.”\nThis statement is problematic because it basically says that chance (here seemingly standing in for contingency) has an overstated importance in evolution because evolution is likely highly repeatable if you exclude major sources of chance. It is interesting that one of Gould’s points was that evolution’s causal structure might be such that there are regularities that create repetition and predictability, but random environmental shifts and things like asteroid impacts disrupt them, making it all unpredictable again. This subtlety needs to be taken into consideration. I think a good argument can be made that the results do suggest that there is more predictability intrinsic to the core evolutionary processes than previously considered, but that conclusion should carry the caveat that the results do not take into consideration environmental sources of chance. Even so, one of the lessons from the Lenski experiment, especially the citrate work, is that, when only the core-most evolutionary processes are operating, history may not have much of an effect on how evolution plays out most of the time, because natural selection drives populations along evolutionary paths that generally lead to similar places. However, sometimes history matters a great deal because evolution happens to take a path that leads to a very different and consequential place/outcome, as occurred with the LTEE population in which aerobic growth on citrate evolved.\nAnother facet of contingency that needs to be taken into consideration is the meaning of “outcome”. Evolution in nature does not end. Outcomes are just intermediate points to later evolution. That facile observation is relevant because natural selection may lead to the repeated evolution of similar phenotypes via different histories and based in different genotypes, but those underlying differences might affect subsequent evolution in unpredictable ways. I suppose a way of thinking of this is that evolutionary repetition could be transient, or that the apparent repetitions are not repetitions at all, but merely rhymes. I think that this possibility might be particularly relevant when interpreting the results here, or at least considered as a caveat.\nThe above dealing with fundamental concepts about contingency and how they might alter the implications of the findings here is my principal issue. I am just laying them out there and merely asking that the authors consider being a bit more precise in how they deal with contingency in the text, and then to reconsider just how categorical their presented conclusions are. Beyond the above, I have a few small, minor issues, which I list below.\nMinor Issues:\nThe title is a bit over-ambitious, and the paper does not actually provide a full answer to the question. Indeed, for as amazing as this work it, it really can’t answer the grand question of just how repeatable phenotypic evolution really is. I would urge that the authors consider a more restrained title.\nWhat was the point of analyzing parallelism in transcriptional changes per se without considering whether those changes were increases or decreases?\nIntroduction:\nParagraph 1, sentence 9:\nThe logic here does not follow. The relative importance of chance and necessity in evolution is an empirical question because it is an empirically judiciable question, not because contributions of stochastic and deterministic processes in evolution depend on the genotype, mutation rate, etc. Consider rephrasing or striking. Honestly, I think it’s fine to just say:\n“The relative importance of chance and necessity in evolution is an empirical question.”\nParagraph 3:\nAgain, Gould’s thought experiment is not well described. (The issue with describing it well lies with Gould, of course.)\nParagraph 5:\nWhether or not fitness is a phenotype of something else that is a consequence of an organism’s phenotype is a big and contentious issue, so I would suggest striking the statement that it is a phenotype in and of itself. Moreover, while fitness almost always increases in experimental evolution, the rate at which it does can persistently vary across populations. Lenski’s LTEE shows this clearly, as each of the twelve populations has different fitness trajectories.\nParagraph 5, sentence 3:\nShould be “Long-Term Evolution Experiment (LTEE) with Escherichia coli….”\nResults\nSection: E. coli adaptation to a glycerol medium and a lactate medium\nParagraph 5, sentence 4:\nYou should also consider other common aspects of the evolution conditions, including the temperature, serial batch culture conditions, planktonic growth, etc, and not just the media themselves. You give a broader description of adaptation to the general environment later in the paper, so you could port it over to here.\nSentence 6:\nI’m not sure what you mean here. Could you explain this a bit more more clearly?\nSection: Mutation bias contributes to the evolutionary repeatability of gene expression\nI guess this isn’t really an issue, so much as personal puzzlement. These results are very intriguing and I’m not quite sure what or how to think about them. There would seem to be something going on that isn't being considered. Very interesting…\nI hope the authors have follow up work planned to look more deeply into this finding and its origins.\nDiscussion:\nParagraph 4:\nIt is odd to reference Wiser et al 2013 in sentence 3, and then to follow it with “…it is highly likely that the gene expression levels at the end of the experimental evolution in each study do not represent those when the populations are fully adapted to the environment” given that a key finding of that work is that “fully adapted to the environment” is likely an impossible condition to reach over any timescale over which life is likely to exist. Over time, in the highly unlikely situation of a perfectly stable environment, a population may get progressively closer to the top of a fitness peak, it will never actually get there.", "reviewer_2": "The paper “How repeatable is phenotypic evolution?” by Jiachen Li and Jianzhi Zhang present a meta-analysis across 10 studies on gene expression changes during experimental evolution, mostly adaptive evolution, across different systems and conditions. The paper is well written and presented and the questions posed are interesting. I have two major comments:\n- The authors indicate that the gene expression changes are not attributable to gene expression plasticity but instead to mutations during the adaptive evolution process. However, the gene expressions changes are in the order of 1000’s . If there is not a gene expression due to the plasticity of adaptation to the selective environment how the large number of genes affected can be explained. Do the authors think that mutations in few transcription factors will change the expression of 1000’s genes. Are there evidences supporting this (e.g. from the transcript sequence?) Or can expression plasticity take longer to take place and therefore not present in the ancestral expression? If this is the case, will strongly affect the results by contributing toward the changes repeatability.\n- How novel are the presented findings compare to the reanalyses studies? Have the authors of the 10 studies reanalyzed posed similar questions. I understand that here the finding are more generalizable given the meta analysis across systems and conditions but have the original studies come up with similar conclusions?"}, "author_response": "We thank the two reviewers for their valuable comments, which have helped improve the clarity and rigor of our manuscript. Below please find our point-to-point response in blue.\nReviewer #1\n[...Reviewer 1 comments repeated...]\nWe thank the reviewer for the very positive evaluation of our manuscript.\n[...Reviewer 1 comments repeated...]\nWe thank the reviewer for pointing out the two different interpretations of Gould’s thought experiment. To avoid confusion, we have briefly mentioned this distinction and clarified our focal question (lines 95-97).\n[...Reviewer 1 comments repeated...]\nWe agree with the reviewer about the distinction between chance and contingency and agree that chance in evolution also includes random shifts in the environment. Because the goal of our study is to investigate evolutionary repeatability under strict replay experiments, we analyzed data from laboratory evolution experiments in which the environmental conditions were generally well controlled. In such cases, chance in evolution primarily arises from intrinsic sources such as mutation and drift. If environmental shifts are considered when assessing evolutionary repeatability, it is not surprising that evolution may follow different paths. This said, we would like to stress that some of the studies we analyzed were conducted in the wild, where conditions are difficult to control. For instance, Ghalambor et al. introduced Trinidadian guppies from a high-predation ancestral stream into two low-predation streams, which likely differed to some extent due to random environmental shifts. Therefore, in studies conducted in nature, the effects of random environmental shifts in evolution were already included in the assessment of repeatability. We have incorporated the above points in the revised manuscript (lines 512-523).\n[...Reviewer 1 comments repeated...]\nWe agree with the reviewer that evolution in nature has no endpoint. Repeatability observed from relatively short evolution experiments only represents snapshots of longer evolution. Gene expression evolution is quite repeatable across all 10 studies considered, despite variation in the duration of the evolution experiment measured in hours or the number of generations. Hence, if replicate populations in each study were allowed to continue their evolution, we expect to observe qualitatively similar results. We have added a brief discussion of this point (lines 486-497).\n[...Reviewer 1 comments repeated...]\nWe agree that we are studying only one group of phenotypic traits—gene expression levels. So, one idea is to make the title more specific such as “How repeatable is phenotypic evolution: An analysis of gene expression traits in evolution experiments”. However, we feel that this revised title is not as arresting as the original one. Furthermore, the question we are interested in is the repeatability of phenotypic evolution in general, although we address it here using gene expression traits (see lines 524-536). Hence, unless the editor strongly suggests that we adopt the more specific title, we would like to keep the original title.\n[...Reviewer 1 comments repeated...]\nBecause only a minority of genes changed expression during experimental evolution, a gene that altered its expression in multiple replicate populations—even in opposite directions—still indicates a certain degree of repetition.\nIntroduction:\nParagraph 1, sentence 9:\n[...Reviewer 1 comments repeated...]\nWe have revised the sentence as suggested (lines 78-79).\nParagraph 3:\n[...Reviewer 1 comments repeated...]\nWe have added a sentence to clarify it (lines 95-97).\nParagraph 5:\n[...Reviewer 1 comments repeated...]\nWe believe that fitness itself is a phenotypic trait, even though it can also be considered a composite trait that is made up of other traits. There are many such composite traits. For example, the lifespan of a person is a trait, but it can also be considered a composite trait determined by many health-related traits. We agree that the rate with which fitness increases in evolution can vary across populations, but this point seems unrelated to our statement that fitness generally increases in experimental evolution.\nParagraph 5, sentence 3:\n[...Reviewer 1 comments repeated...]\nChanged as suggested.\nResults\nSection: E. coli adaptation to a glycerol medium and a lactate medium\nParagraph 5, sentence 4:\n[...Reviewer 1 comments repeated...]\nWe agree and have added a sentence to reflect this point (lines 214-216).\nSentence 6:\n[...Reviewer 1 comments repeated...]\nWe meant that E. coli cells may employ a general response involving the same set of genes to a broad spectrum of stress. We have clarified this point in the revised manuscript (lines 216-218).\nSection: Mutation bias contributes to the evolutionary repeatability of gene expression\n[...Reviewer 1 comments repeated...]\nIn this analysis, we observed greater repeatability than expected by chance even in the absence of selection. We interpreted this result as mutation bias that underlies evolutionary repeatability. For example, if the expression of a gene is frequently altered by mutation (i.e., the gene expression trait has a large mutation target), the gene expression will likely change repeatedly in evolution. This phenomenon is related to the subsequent finding that genes regulated by more TFs tend to evolve in expression (Fig. 4). The idea of mutation bias underlying evolutionary repeatability was explained in lines 208-212.\nDiscussion:\nParagraph 4:\n[...Reviewer 1 comments repeated...]\nWe completely agree and have revised that section to better reflect our intended meaning (lines 486-497).\nReviewer #2\n[...Reviewer 2 comments repeated...]\nWe thank the reviewer for the overall positive evaluation of our manuscript.\n- The authors indicate that the gene expression changes are not attributable to gene expression plasticity but instead to mutations during the adaptive evolution process. However, the gene expressions changes are in the order of 1000’s. If there is not a gene expression due to the plasticity of adaptation to the selective environment how the large number of genes affected can be explained. Do the authors think that mutations in few transcription factors will change the expression of 1000’s genes. Are there evidences supporting this (e.g. from the transcript sequence?) Or can expression plasticity take longer to take place and therefore not present in the ancestral expression? If this is the case, will strongly affect the results by contributing toward the changes repeatability.\n[...Reviewer 2 comments repeated...]\nThe reviewer asked how the large number of gene expression changes observed in some experimental evolution studies could be explained by mutations. In fact, a previous study addressed this question explicitly and showed that, by adding one mutation that was observed in E. coli experimental evolution to the ancestral strain, over a thousand genes changed their expressions1. Hence, accumulated mutations in experimental evolution can cause expression changes of hundreds to thousands of genes.\nThe reviewer wondered whether some of the gene expression changes observed in experimental evolution could be caused by phenotypic plasticity. We note that all studies profiled gene expressions in the new environment after some time of acclimation presumably till the plastic changes are induced, because all studies attempted to quantify gene expression evolution in the new environment. For instance, in the study aforementioned1, the E. coli population was expression-profiled after growing for 1 day in the new environment, likely sufficient for a full induction of plastic gene expression changes. When a study contained several acclimation times, we always used the transcriptome after the longest acclimation to allow the fullest induction of plastic changes. While we cannot guarantee that the acclimation time used is sufficient in every study, we found high repeatability of gene expression evolution across all studies—including those that clearly had a sufficient time for acclimation such as the study mentioned earlier1. Hence, we believe that our results are better explained by effects of mutation and selection instead of plasticity. Notwithstanding, we have included this caveat in a paragraph of extensive discussion (lines 461-485).\n- How novel are the presented findings compare to the reanalyses studies? Have the authors of the 10 studies reanalyzed posed similar questions. I understand that here the finding are more generalizable given the meta analysis across systems and conditions but have the original studies come up with similar conclusions?\n[...Reviewer 2 comments repeated...]\nOnly two of the 10 studies analyzed here explicitly investigated the repeatability of gene expression evolution2,3. One of them reached the same conclusion as ours3 whereas the other reached a different conclusion because it defined repeated evolution by repetition across almost all replicates and did not compare the observed repetition with the randomly expected repetition2. We have added to the manuscript the above information when introducing these studies and datasets (lines 550-554 and 583-584).\nReferences cited"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate that the authors took the points I made in my review seriously, and I believe they have satisfied my concerns. The original manuscript was very strong, and the care that the authors put into address both my concerns and those of the other reviewer made it even stronger. I still think the title is over-ambitious, but I understand why the authors are wedded to it. If the editor doesn't have a problem with it, I'm fine with running with it, too. Again, this is an excellent piece of work.", "reviewer_2": "Thank you for your reply. I still perplexed about my comment 1 on how many genes are due to plasticity rather than mutations. Maybe a better design to address this question would be to add an expression analysis after a period of relax selection (e.g. after the mutation are fixed or on individual mutated clones). I guess this might have the potential to disentangle the two sources of expression variation."}, "author_response": "Response to reviewers\nWe thank the two reviewers for re-reviewing our manuscript. Below please find our point-to-point response in blue.\nReviewer #1\n[...Reviewer 1 comments repeated...]\nAfter consultation with the editor, we have changed the title of our manuscript to “Repeatability of gene expression evolution in experimental environmental adaptation”.\nReviewer #2\n[...Reviewer 2 comments repeated...]\nThe proposed design would not solve the potential problem of including plastic changes in the measured gene expression changes, because the problem exists so long as the organisms are not fully acclimated to the new environment at the time of the first transcriptome profiling. We have thus acknowledged this possibility at the beginning of Results (added to lines 141 to 143).\nNonetheless, because genetic expression changes tend to reverse plastic expression changes, our measured expression changes are unlikely to be greater in magnitude than genetic expression changes; consequently, evolutionary repeatability is unlikely to have been overestimated (added to lines 483-486)."}]

Decision letter