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![The efficacy of selection in SM1 was more than that in SM4. a SM1 consistently experienced a much lower genetic load than SM1 [the error bars represent SEM (eight replicates)]. b The lagging chunk was the major contributor to the Extent of Adaptation (EoA) in SM4 but not in SM1 [the error bars represent SEM (eight replicates)]. This also means that the contribution of the nose to the EoA [which equals (1 − contribution of the lagging chunk)] in SM1 was much more than that of the lagging chunk. c, d Schematic representations of the distribution of efficiency across individuals during adaptation during the initial phases of evolution (before generation 80). Due to the high efficacy of selection in SM1, the majority of individuals were found in the nose (c). On the other hand, a relatively low efficacy of selection due to harsher bottlenecks in SM4 resulted in most individuals being found in the lagging chunk (please refer to the text for more details) (d). e, f During the later phases of evolution (around generation 360), the contributions of the nose to the overall EoA became relatively similar in SM1 and SM4](publication/330100394/figure/fig5/AS:962647923118093@1606524558260/The-efficacy-of-selection-in-SM1-was-more-than-that-in-SM4-a-SM1-consistently.png)
The efficacy of selection in SM1 was more than that in SM4. a SM1 consistently experienced a much lower genetic load than SM1 [the error bars represent SEM (eight replicates)]. b The lagging chunk was the major contributor to the Extent of Adaptation (EoA) in SM4 but not in SM1 [the error bars represent SEM (eight replicates)]. This also means that the contribution of the nose to the EoA [which equals (1 − contribution of the lagging chunk)] in SM1 was much more than that of the lagging chunk. c, d Schematic representations of the distribution of efficiency across individuals during adaptation during the initial phases of evolution (before generation 80). Due to the high efficacy of selection in SM1, the majority of individuals were found in the nose (c). On the other hand, a relatively low efficacy of selection due to harsher bottlenecks in SM4 resulted in most individuals being found in the lagging chunk (please refer to the text for more details) (d). e, f During the later phases of evolution (around generation 360), the contributions of the nose to the overall EoA became relatively similar in SM1 and SM4
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Periodic bottlenecks play a major role in shaping the adaptive dynamics of natural and laboratory populations of asexual microbes. Here we study how they affect the ‘Extent of Adaptation’ (EoA), in such populations. EoA, the average fitness gain relative to the ancestor, is the quantity of interest in a large number of microbial experimental-evolut...
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Citations
... The per generation rate at which mutations with a beneficial effect size s survive drift is NU b s, where N is the population size and U b is the rate of beneficial mutations . Moreover, apart from having better access to rare large-effect beneficial mutations, larger populations also show greater efficiency of natural selection (Chavhan et al., 2019;Neher, 2013). This leads to our first hypothesis that the larger populations should show greater adaptation to the marginal niche. ...
... We found that the extent of marginal niche adaptation was consistently greater in larger populations (Figure 2). This observation can be explained by the relatively greater supply of variation and better efficiency of natural selection in larger populations (Chavhan et al., 2019(Chavhan et al., , 2020. Moreover, the larger populations showed a relatively lower variation in fitness than the smaller populations (Figures 2A, 3, and 4). ...
... Moreover, the larger populations showed a relatively lower variation in fitness than the smaller populations (Figures 2A, 3, and 4). This observation aligns with the theoretical prediction that the stochastic effects of drift would be weaker in larger populations, which would lead to more repeatable fitness changes in them (Chavhan et al., 2019;Sniegowski & Gerrish, 2010). Although contrasting habitual niches could have led to different population sizes at saturation (N f ), we note that the pairwise difference in N f was never greater than an order of magnitude across the four habitual niches (Supplementary Figure S1). ...
How does niche expansion occur when the habitual (high-productivity) and marginal (low-productivity) niches are simultaneously available? Without spatial structuring, such conditions should impose fitness maintenance in the former while adapting to the latter. Hence, adaptation to a given marginal niche should be influenced by the identity of the simultaneouly available habitual niche. This hypothesis remains untested. Similarly, it is unknown if larger populations, which can access greater variation and undergo more efficient selection, are generally better at niche expansion. We tested these hypotheses using a large-scale evolution experiment with Escherichia coli. While we observed widespread niche expansion, larger populations consistently adapted to a greater extent to both marginal and habitual niches. Owing to diverse selection pressures in different habitual niches (constant versus fluctuating environments; environmental fluctuations varying in both predictability and speed), fitness in habitual niches was significantly shaped by their identities. Surprisingly, despite this diversity in habitual selection pressures, adaptation to the marginal niche was unconstrained by the habitual niche’s identity. We show that in terms of fitness, two negatively correlated habitual niches can still have positive correlations with the marginal niche. This allows the marginal niche to dilute fitness trade-offs across habitual niches, thereby allowing costless niche expansion. Our results provide fundamental insights into sympatric niche expansion.
... Despite the wealth of theoretical literature on this topic, there has been very little empirical investigation of the relationship between bottleneck size and adaptation rate. To our knowledge, the only such study is Chavhan et al. (2019), where the authors serially passaged Escherichia coli for 380 generations under different bottleneck severities. They found that all else equal, with a fixed value of τ = 24 hours, a bottleneck size of D = 10 −1 led to faster adaptation than under very severe bottlenecks of D = 10 −6 . ...
Serial passaging is a fundamental technique in experimental evolution. The choice of bottleneck severity and frequency poses a dilemma: longer growth periods allow beneficial mutants to arise and grow over more generations, but simultaneously necessitate more severe bottlenecks with a higher risk of those same mutations being lost. Short growth periods require less severe bottlenecks, but come at the cost of less time between transfers for beneficial mutations to establish. The standard laboratory protocol of 24-hour growth cycles with severe bottlenecking has logistical advantages for the experimenter but limited theoretical justification. Here we demonstrate that contrary to standard practice, the rate of adaptive evolution is maximized when bottlenecks are frequent and small, indeed infinitesimally so in the limit of continuous culture. This result derives from revising key assumptions underpinning previous theoretical work, notably changing the metric of optimization from adaptation per serial transfer to per experiment runtime. We also show that adding resource constraints and clonal interference to the model leaves the qualitative results unchanged. Implementing these findings will require liquid-handling robots to perform frequent bottlenecks, or chemostats for continuous culture. Further innovation in and adoption of these technologies has the potential to accelerate the rate of discovery in experimental evolution.
... Moreover, the expected number of subcultured bacteria was ≥10 4 for all of our experimental populations. Such subcultures with mean ≥10 4 and differences across replicates <10-fold are expected to lead to efficient and repeatable selection in asexual populations 71 . Aligning with this expectation, we also found highly parallel evolution in terms mutated loci (Fig. 4). ...
The evolutionary transition from unicellularity to multicellularity was a key innovation in the history of life. Experimental evolution is an important tool to study the formation of undifferentiated cellular clusters, the likely first step of this transition. Although multicellularity first evolved in bacteria, previous experimental evolution research has primarily used eukaryotes. Moreover, it focuses on mutationally driven (and not environmentally induced) phenotypes. Here we show that both Gram-negative and Gram-positive bacteria exhibit phenotypically plastic (i.e., environmentally induced) cell clustering. Under high salinity, they form elongated clusters of ~ 2 cm. However, under habitual salinity, the clusters disintegrate and grow planktonically. We used experimental evolution with Escherichia coli to show that such clustering can be assimilated genetically: the evolved bacteria inherently grow as macroscopic multicellular clusters, even without environmental induction. Highly parallel mutations in genes linked to cell wall assembly formed the genomic basis of assimilated multicellularity. While the wildtype also showed cell shape plasticity across high versus low salinity, it was either assimilated or reversed after evolution. Interestingly, a single mutation could genetically assimilate multicellularity by modulating plasticity at multiple levels of organization. Taken together, we show that phenotypic plasticity can prime bacteria for evolving undifferentiated macroscopic multicellularity.
... Moreover, the expected number of subcultured bacteria was ≥10 4 for all of our experimental populations. Such subcultures with mean ≥10 4 and differences across replicates <10-fold are expected to lead to efficient and repeatable selection in asexual populations 71 . Aligning with this expectation, we also found highly parallel evolution in terms mutated loci (Fig. 4). ...
The evolutionary transition from unicellular to multicellular life was a key innovation in the history of life. Given scarce fossil evidence, experimental evolution has been an important tool to study the likely first step of this transition, namely the formation of undifferentiated cellular clusters. Although multicellularity first evolved in bacteria, the extant experimental evolution literature on this subject has primarily used eukaryotes. Moreover, it focuses on mutationally driven (and not environmentally induced) phenotypes. Here we show that both Gram-negative and Gram-positive bacteria exhibit phenotypically plastic ( i.e. , environmentally induced) cell clustering. Under high salinity, they grow as elongated ~ 2 cm long clusters (not as individual planktonic cells). However, under habitual salinity, the clusters disintegrate and grow planktonically. We used experimental evolution with Escherichia coli to show that such clustering can be canalized successfully: the evolved bacteria inherently grow as macroscopic multicellular clusters, even without environmental induction. Highly parallel mutations in genes linked to cell wall assembly formed the genomic basis of canalized multicellularity. While the wildtype also showed cell shape plasticity across high versus low salinity, it was either canalized or reversed after evolution. Interestingly, a single mutation could canalize multicellularity by modulating plasticity at multiple levels of organization. Taken together, we show that phenotypic plasticity can prime bacteria for evolving undifferentiated macroscopic multicellularity.
... If we simply added the immigrants to the native population, the total population size would increase with the migration rate, thus resulting in large differences in population sizes across treatments. Previous studies have shown that population size can affect evolutionary outcomes in microbial populations (Chavhan et al., 2019). Therefore, to avoid the confounding effect of population size, we kept the inoculum size constant (~10 7 cells) and defined migration as the percentage of immigrants in the inoculum. ...
... The OD600 was measured every 2 hours for 24 hours at 37 o C and slow continuous shaking using a plate reader (Synergy HT BioTek, Winooski, VT, USA). We used population growth rate and yield as fitness measures (Chavhan et al., 2019, Karve et al., 2015. The growth curve data were analyzed using a custom python script that fits overlapping straight lines over 6-hour windows. ...
Migration, a critical evolutionary force, can have contrasting effects on adaptation. It can aid as well as impede adaptation. The effects of migration on microbial adaptation have been studied primarily in simple constant environments. Very little is known about the effects of migration on adaptation to complex, fluctuating environments. In our study, we subjected replicate populations of Escherichia coli , adapting to complex and unpredictably fluctuating environments to different proportions of clonal ancestral immigrants. Contrary to the results from simple/constant environments, the presence of clonal immigrants reduced all measured proxies of fitness. However, migration from a source population with a greater variance in fitness resulted in no change in fitness w.r.t the no-migration control, except at the highest level of migration. Thus, the presence of variation in the immigrants could counter the adverse effects of migration in complex and unpredictably fluctuating environments. Our study demonstrates that the effects of migration are strongly dependent on the nature of the destination environment and the genetic makeup of immigrants. These results enhance our understanding of the influences of migrating populations, which could help better predict the consequences of migration.
... Several other recent experiments with bacteria have explored the effects of population bottlenecks on adaptive evolution (19)(20)(21)(22)(23). These studies employed different experimental designs from ours and addressed different questions; some imposed single rather than repeated bottlenecks, while others varied the selective environment, final population size, or both. ...
Population bottlenecks are common in nature, and they can impact the rate of adaptation in evolving populations. On the one hand, each bottleneck reduces the genetic variation that fuels adaptation. On the other hand, each founder that survives a bottleneck can undergo more generations and leave more descendants in a resource-limited environment, which allows surviving beneficial mutations to spread more quickly. A theoretical model predicted that the rate of fitness gains should be maximized using ∼8-fold dilutions. Here we investigate the impact of repeated bottlenecks on the dynamics of adaptation using numerical simulations and experimental populations of Escherichia coli . Our simulations confirm the model’s prediction when populations evolve in a regime where beneficial mutations are rare and waiting times between successful mutations are long. However, more extreme dilutions maximize fitness gains in simulations when beneficial mutations are common and clonal interference prevents most of them from fixing. To examine the simulations’ predictions, we propagated 48 E. coli populations with 2-, 8-, 100-, and 1000-fold dilutions for 150 days. Adaptation began earlier and fitness gains were greater with 100- and 1000-fold dilutions than with 8-fold dilutions, consistent with the simulations when beneficial mutations are common. However, the selection pressures in the 2-fold treatment were qualitatively different from the other treatments, violating a critical assumption of the model and simulations. Thus, varying the dilution factor during periodic bottlenecks can have multiple effects on the dynamics of adaptation caused by differential losses of diversity, different numbers of generations, and altered selection.
Significance
Many microorganisms experience population bottlenecks during transmission between hosts or when propagated in the laboratory. These bottlenecks reduce genetic diversity, potentially impeding natural selection. However, bottlenecks can also increase the number of generations over which selection acts, potentially accelerating adaptation. We explored this tension by performing simulations that reflect these opposing factors, and by evolving bacterial populations under several dilution treatments. The simulations show that the dilution factor that maximizes the rate of adaptation depends critically on the rate of beneficial mutations. On balance, the simulations agree well with our experimental results, which imply a high rate of beneficial mutation that generates intense competition between mutant lineages.
... It is likely that this was a consequence of our experimental protocol for culturing the coevolving pathogens. In the maintenance protocol, the coevolving Pe pathogens were under fluctuating selection, as the coevolving bacteria experienced the following three phases of growth followed by bottlenecks every generation: (Chavhan et al., 2019). In this case, g is likely to be comparable (i.e., of the same order of magnitude) for each of the three phases, the estimates of N 0 in LB (~10 7 ) are likely to be several orders of magnitude higher than the estimates of N 0 for growth in the 10 flies that contribute to the next generation's bacteria (~10 4 when infections are performed at an OD 600 of 0.4) (Gupta et al. (2013) and unpublished observations in our lab). ...
Multiple laboratory studies have evolved hosts against a nonevolving pathogen to address questions about evolution of immune responses. However, an ecologically more relevant scenario is one where hosts and pathogens can coevolve. Such coevolution between the antagonists, depending on the mutual selection pressure and additive variance in the respective populations, can potentially lead to a different pattern of evolution in the hosts compared to a situation where the host evolves against a nonevolving pathogen. In the present study, we used Drosophila melanogaster as the host and Pseudomonas entomophila as the pathogen. We let the host populations either evolve against a nonevolving pathogen or coevolve with the same pathogen. We found that the coevolving hosts on average evolved higher survivorship against the coevolving pathogen and ancestral (nonevolving) pathogen relative to the hosts evolving against a nonevolving pathogen. The coevolving pathogens evolved greater ability to induce host mortality even in nonlocal (novel) hosts compared to infection by an ancestral (nonevolving) pathogen. Thus, our results clearly show that the evolved traits in the host and the pathogen under coevolution can be different from one-sided adaptation. In addition, our results also show that the coevolving host–pathogen interactions can involve certain general mechanisms in the pathogen, leading to increased mortality induction in nonlocal or novel hosts.
... The harmonic mean population size for our principal treatment (F) was ~1.01 × 10 8 for FL and ~4.04 × 10 5 for FS. Moreover, the adaptively relevant population sizes for FL and FS were ~9.13 × 10 6 and ~2.28 × 10 3 respectively (Chavhan et al., 2019b). The selection protocol pertaining to the T and G populations has been reported in Chavhan et al., (2020). ...
Theoretical models of ecological specialisation commonly assume that adaptation to one environment leads to fitness reductions (costs) in others. However, experiments often fail to detect such costs. We addressed this conundrum using experimental evolution with Escherichia coli in several constant and fluctuating environments at multiple population sizes. We found that in fluctuating environments, smaller populations paid significant costs, but larger ones avoided them altogether. Contrastingly, in constant environments, larger populations paid more costs than the smaller ones. Overall, large population sizes and fluctuating environments led to cost avoidance only when present together. Mutational frequency distributions obtained from whole‐genome whole‐population sequencing revealed that the primary mechanism of cost avoidance was the enrichment of multiple beneficial mutations within the same lineage. Since the conditions revealed by our study for avoiding costs are widespread, it provides a novel explanation of the conundrum of why the costs expected in theory are rarely detected in experiments. Although fitness costs are supposed to be ubiquitous, in practice they are detected rarely in microbes. We evolved E. coli populations to show that in fluctuating environments, smaller populations paid significant costs, but larger ones avoided them altogether. Contrastingly, in constant environments, larger populations paid more costs than the smaller ones.
... OD600 was measured every 20 minutes for 24 hours at 37 o C and slow continuous shaking, at 17 cycles per second. Following previous studies (Karve et al., 2015;Sprouffske et al., 2018;Chavhan et al., 2019;Rodríguez-Rojas et al., 2020), we computed the growth rate as the maximum slope of the curve over a moving window of 10 readings. Two measurement replicates were obtained by repeating the entire assay twice. ...
Physiological states can determine the ability of organisms to handle stress. Does this mean that the same selection pressure will lead to different evolutionary outcomes, depending on the organisms' physiological state? If yes, what will be the genomic signatures of such adaptation(s)? We used experimental evolution in Escherichia coli followed by whole-genome whole-population sequencing to investigate these questions. The sensitivity of Escherichia coli to ultraviolet (UV) radiation depends on the growth phase during which it experiences the radiation. We evolved replicate E. coli populations under two different conditions of UV exposures, namely exposure during the lag and the exponential growth phases. Initially, the UV sensitivity of the ancestor was greater during the exponential phase than the lag phase. However, at the end of 100 cycles of exposure, UV resistance evolved to similar extents in both treatments. Genome analysis showed that mutations in genes involved in DNA repair, cell membrane structure and RNA polymerase were common in both treatments. However, different functional groups were found mutated in populations experiencing lag and exponential UV treatment. In the former, genes involved in transcriptional and translational regulations and cellular transport were mutated, whereas the latter treatment showed mutations in genes involved in signal transduction and cell adhesion. Interestingly, the treatments showed no phenotypic differences in a number of novel environments. Taken together, these results suggest that selection pressures at different physiological stages can lead to differences in the genomic signatures of adaptation, which need not necessarily translate into observable phenotypic differences.
Migration, a critical evolutionary force, can have contrasting effects on adaptation. It can aid as well as impede adaptation. The effects of migration on microbial adaptation have been studied primarily in simple constant environments. Very little is known about the effects of migration on adaptation to complex, fluctuating environments. In our study, we subjected replicate populations of Escherichia coli, adapting to complex and unpredictably fluctuating environments to different proportions of clonal ancestral immigrants. Contrary to the results from simple/constant environments, the presence of clonal immigrants reduced all measured proxies of fitness. However, migration from a source population with a greater variance in fitness resulted in no change in fitness with respect to the no‐migration control, except at the highest level of migration. Thus, the presence of variation in the immigrants could counter the adverse effects of migration in complex and unpredictably fluctuating environments. Our study demonstrates that the effects of migration are strongly dependent on the nature of the destination environment and the genetic makeup of immigrants. These results enhance our understanding of the influences of migrating populations, which could help better predict the consequences of migration. Effects of migration are strongly dependent on the nature of the destination environment and the genetic makeup of immigrants.
