Experimental Evolution in a Warming World: The Omics Era.

A comprehensive understanding of the genetic mechanisms that shape species responses to thermal variation is essential for more accurate predictions of the impacts of climate change on biodiversity. Experimental evolution with high-throughput resequencing approaches (evolve and resequence) is a highly effective tool that has been increasingly employed to elucidate the genetic basis of adaptation. The number of thermal evolve and resequence studies is rising, yet there is a dearth of efforts to integrate this new wealth of knowledge. Here, we review this literature showing how these studies have contributed to increase our understanding on the genetic basis of thermal adaptation. We identify two major trends: highly polygenic basis of thermal adaptation and general lack of consistency in candidate targets of selection between studies. These findings indicate that the adaptive responses to specific environments are rather independent. A review of the literature reveals several gaps in the existing research. Firstly, there is a paucity of studies done with organisms of diverse taxa. Secondly, there is a need to apply more dynamic and ecologically relevant thermal environments. Thirdly, there is a lack of studies that integrate genomic changes with changes in life history and behavioral traits. Addressing these issues would allow a more in-depth understanding of the relationship between genotype and phenotype. We highlight key methodological aspects that can address some of the limitations and omissions identified. These include the need for greater standardization of methodologies and the utilization of new technologies focusing on the integration of genomic and phenotypic variation in the context of thermal adaptation.

Experimental evolution reveals the synergistic genomic mechanisms of adaptation to ocean warming and acidification in a marine copepod.

Metazoan adaptation to global change relies on selection of standing genetic variation. Determining the extent to which this variation exists in natural populations, particularly for responses to simultaneous stressors, is essential to make accurate predictions for persistence in future conditions. Here, we identified the genetic variation enabling the copepod Acartia tonsa to adapt to experimental ocean warming, acidification, and combined ocean warming and acidification (OWA) over 25 generations of continual selection. Replicate populations showed a consistent polygenic response to each condition, targeting an array of adaptive mechanisms including cellular homeostasis, development, and stress response. We used a genome-wide covariance approach to partition the allelic changes into three categories: selection, drift and replicate-specific selection, and laboratory adaptation responses. The majority of allele frequency change in warming (57%) and OWA (63%) was driven by shared selection pressures across replicates, but this effect was weaker under acidification alone (20%). OWA and warming shared 37% of their response to selection but OWA and acidification shared just 1%, indicating that warming is the dominant driver of selection in OWA. Despite the dominance of warming, the interaction with acidification was still critical as the OWA selection response was highly synergistic with 47% of the allelic selection response unique from either individual treatment. These results disentangle how genomic targets of selection differ between single and multiple stressors and demonstrate the complexity that nonadditive multiple stressors will contribute to predictions of adaptation to complex environmental shifts caused by global change.

Adaptive Multiple Comparisons With the Best.

Subset selection methods aim to choose a nonempty subset of populations including a best population with some prespecified probability. An example application involves location parameters that quantify yields in agriculture to select the best wheat variety. This is quite different from variable selection problems, for instance, in regression. Unfortunately, subset selection methods can become very conservative when the parameter configuration is not least favorable. This will lead to a selection of many non-best populations, making the set of selected populations less informative. To solve this issue, we propose less conservative adaptive approaches based on estimating the number of best populations. We also discuss variants of our adaptive approaches that are applicable when the sample sizes and/or variances differ between populations. Using simulations, we show that our methods yield a desirable performance. As an illustration of potential gains, we apply them to two real datasets, one on the yield of wheat varieties and the other obtained via genome sequencing of repeated samples.

Haplotype based testing for a better understanding of the selective architecture.

BACKGROUND: The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies. RESULTS: Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits. CONCLUSIONS: Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

Adaptation in Outbred Sexual Yeast is Repeatable, Polygenic and Favors Rare Haplotypes.

We carried out a 200 generation Evolve and Resequence (E&R) experiment initiated from an outbred diploid recombined 18-way synthetic base population. Replicate populations were evolved at large effective population sizes (>105 individuals), exposed to several different chemical challenges over 12 weeks of evolution, and whole-genome resequenced. Weekly forced outcrossing resulted in an average between adjacent-gene per cell division recombination rate of ∼0.0008. Despite attempts to force weekly sex, roughly half of our populations evolved cheaters and appear to be evolving asexually. Focusing on seven chemical stressors and 55 total evolved populations that remained sexual we observed large fitness gains and highly repeatable patterns of genome-wide haplotype change within chemical challenges, with limited levels of repeatability across chemical treatments. Adaptation appears highly polygenic with almost the entire genome showing significant and consistent patterns of haplotype change with little evidence for long-range linkage disequilibrium in a subset of populations for which we sequenced haploid clones. That is, almost the entire genome is under selection or drafting with selected sites. At any given locus adaptation was almost always dominated by one of the 18 founder's alleles, with that allele varying spatially and between treatments, suggesting that selection acts primarily on rare variants private to a founder or haplotype blocks harboring multiple mutations.

Combining Metabolomics and Experimental Evolution Reveals Key Mechanisms Underlying Longevity Differences in Laboratory Evolved Drosophila melanogaster Populations.

Experimental evolution with Drosophila melanogaster has been used extensively for decades to study aging and longevity. In recent years, the addition of DNA and RNA sequencing to this framework has allowed researchers to leverage the statistical power inherent to experimental evolution to study the genetic basis of longevity itself. Here, we incorporated metabolomic data into to this framework to generate even deeper insights into the physiological and genetic mechanisms underlying longevity differences in three groups of experimentally evolved D. melanogaster populations with different aging and longevity patterns. Our metabolomic analysis found that aging alters mitochondrial metabolism through increased consumption of NAD+ and increased usage of the TCA cycle. Combining our genomic and metabolomic data produced a list of biologically relevant candidate genes. Among these candidates, we found significant enrichment for genes and pathways associated with neurological development and function, and carbohydrate metabolism. While we do not explicitly find enrichment for aging canonical genes, neurological dysregulation and carbohydrate metabolism are both known to be associated with accelerated aging and reduced longevity. Taken together, our results provide plausible genetic mechanisms for what might be driving longevity differences in this experimental system. More broadly, our findings demonstrate the value of combining multiple types of omic data with experimental evolution when attempting to dissect mechanisms underlying complex and highly polygenic traits such as aging.

Genetic stability of Aedes aegypti populations following invasion by wMel Wolbachia.

BACKGROUND: Wolbachia wMel is the most commonly used strain in rear and release strategies for Aedes aegypti mosquitoes that aim to inhibit the transmission of arboviruses such as dengue, Zika, Chikungunya and yellow fever. However, the long-term establishment of wMel in natural Ae. aegypti populations raises concerns that interactions between Wolbachia wMel and Ae. aegypti may lead to changes in the host genome, which could affect useful attributes of Wolbachia that allow it to invade and suppress disease transmission. RESULTS: We applied an evolve-and-resequence approach to study genome-wide genetic changes in Ae. aegypti from the Cairns region, Australia, where Wolbachia wMel was first introduced more than 10 years ago. Mosquito samples were collected at three different time points in Gordonvale, Australia, covering the phase before (2010) and after (2013 and 2018) Wolbachia releases. An additional three locations where Wolbachia replacement happened at different times across the last decade were also sampled in 2018. We found that the genomes of mosquito populations mostly remained stable after Wolbachia release, with population differences tending to reflect the geographic location of the populations rather than Wolbachia infection status. However, outlier analysis suggests that Wolbachia may have had an influence on some genes related to immune response, development, recognition and behavior. CONCLUSIONS: Ae. aegypti populations remained geographically distinct after Wolbachia wMel releases in North Australia despite their Wolbachia infection status. At some specific genomic loci, we found signs of selection associated with Wolbachia, suggesting potential evolutionary impacts can happen in the future and further monitoring is warranted.

Long-term gut microbiome dynamics in Drosophila melanogaster reveal environment-specific associations between bacterial taxa at the family level.

The influence of the microbiome on its host is well-documented, but the interplay of its members is not yet well-understood. Even for simple microbiomes, the interaction among members of the microbiome is difficult to study. Longitudinal studies provide a promising approach to studying such interactions through the temporal covariation of different taxonomic units. By contrast to most longitudinal studies, which span only a single host generation, we here present a post hoc analysis of a whole-genome dataset of 81 samples that follows microbiome composition for up to 180 host generations, which cover nearly 10 years. The microbiome diversity remained rather stable in replicated Drosophila melanogaster populations exposed to two different temperature regimes. The composition changed, however, systematically across replicates of the two temperature regimes. Significant associations between families, mostly specific to one temperature regime, indicate functional interdependence of different microbiome components. These associations also involve moderately abundant families, which emphasizes their functional importance, and highlights the importance of looking beyond the common constituents of the Drosophila microbiome.

Genomic signatures of UV resistance evolution in Escherichia coli depend on the growth phase during exposure.

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.

Select and resequence reveals relative fitness of bacteria in symbiotic and free-living environments.

Assays to accurately estimate relative fitness of bacteria growing in multistrain communities can advance our understanding of how selection shapes diversity within a lineage. Here, we present a variant of the "evolve and resequence" approach both to estimate relative fitness and to identify genetic variants responsible for fitness variation of symbiotic bacteria in free-living and host environments. We demonstrate the utility of this approach by characterizing selection by two plant hosts and in two free-living environments (sterilized soil and liquid media) acting on synthetic communities of the facultatively symbiotic bacterium Ensifer meliloti We find (i) selection that hosts exert on rhizobial communities depends on competition among strains, (ii) selection is stronger inside hosts than in either free-living environment, and (iii) a positive host-dependent relationship between relative strain fitness in multistrain communities and host benefits provided by strains in single-strain experiments. The greatest changes in allele frequencies in response to plant hosts are in genes associated with motility, regulation of nitrogen fixation, and host/rhizobia signaling. The approach we present provides a powerful complement to experimental evolution and forward genetic screens for characterizing selection in bacterial populations, identifying gene function, and surveying the functional importance of naturally occurring genomic variation.