Selection for early reproduction leads to accelerated aging and extensive metabolic remodeling in Drosophila melanogaster populations.

Experimental evolution studies that feature selection on life-history characters are a proven approach for studying the evolution of aging and variation in rates of senescence. Recently, the incorporation of genomic and transcriptomic approaches into this framework has led to the identification of hundreds of genes associated with different aging patterns. However, our understanding of the specific molecular mechanisms underlying these aging patterns remains limited. Here, we incorporated extensive metabolomic profiling into this framework to generate mechanistic insights into aging patterns in Drosophila melanogaster . Specifically, we characterized metabolomic change over time associated with accelerated aging in populations of D. melanogaster under selection for early reproduction compared to their controls. Using this data we: i) evaluated the evolutionary repeatability across the metabolome; ii) evaluated the value of the metabolome as a predictor of "biological age" in this system; and iii) identified specific metabolic pathways associated with accelerated aging. Generally, our findings suggest that the metabolome is a reliable predictor of age and senescence in populations that share a recent evolutionary history. Metabolomic analysis revealed that generations of selection for early reproduction resulted in highly repeatable alterations to the metabolome. Specifically, changes in carbohydrate, amino acid, and TCA cycle-related metabolite abundances over time point to metabolic remodeling that favors rapid early reproduction with long-term consequences for carbohydrate and protein utilization.

Genomewide architecture of adaptation in experimentally evolved Drosophila characterized by widespread pleiotropy.

Dissecting the molecular basis of adaptation remains elusive despite our ability to sequence genomes and transcriptomes. At present, most genomic research on selection focusses on signatures of selective sweeps in patterns of heterozygosity. Other research has studied changes in patterns of gene expression in evolving populations but has not usually identified the genetic changes causing these shifts in expression. Here we attempt to go beyond these approaches by using machine learning tools to explore interactions between the genome, transcriptome, and life-history phenotypes in two groups of 10 experimentally evolved Drosophila populations subjected to selection for opposing life history patterns. Our findings indicate that genomic and transcriptomic data have comparable power for predicting phenotypic characters. Looking at the relationships between the genome and the transcriptome, we find that the expression of individual transcripts is influenced by many sites across the genome that are differentiated between the two types of populations. We find that single-nucleotide polymorphisms (SNPs), transposable elements, and indels are powerful predictors of gene expression. Collectively, our results suggest that the genomic architecture of adaptation is highly polygenic with extensive pleiotropy.

Building Bridges from Genome to Physiology Using Machine Learning and Drosophila Experimental Evolution.

Drosophila experimental evolution, with its well-defined selection protocols, has long supplied useful genetic material for the analysis of functional physiology. While there is a long tradition of interpreting the effects of large-effect mutants physiologically, identifying and interpreting gene-to-phenotype relationships has been challenging in the genomic era, with many labs not resolving how physiological traits are affected by multiple genes throughout the genome. Drosophila experimental evolution has demonstrated that multiple phenotypes change because of the evolution of many loci across the genome, creating the scientific challenge of sifting out differentiated but noncausal loci for individual characters. The fused lasso additive model method allows us to infer some of the differentiated loci that have relatively greater causal effects on the differentiation of specific phenotypes. The experimental material that we use in the present study comes from 50 populations that have been selected for different life histories and levels of stress resistance. Differentiation of cardiac robustness, starvation resistance, desiccation resistance, lipid content, glycogen content, water content, and body masses was assayed among 40-50 of these experimentally evolved populations. Through the fused lasso additive model, we combined physiological analyses from eight parameters with whole-body pooled-seq genomic data to identify potentially causally linked genomic regions. We have identified approximately 2,176 significantly differentiated 50-kb genomic windows among our 50 populations, with 142 of those identified genomic regions that are highly likely to have a causal effect connecting specific genome sites to specific physiological characters.

The molecular architecture of Drosophila melanogaster defense against Beauveria bassiana explored through evolve and resequence and quantitative trait locus mapping.

Little is known about the genetic architecture of antifungal immunity in natural populations. Using two population genetic approaches, quantitative trait locus (QTL) mapping and evolve and resequence (E&R), we explored D. melanogaster immune defense against infection with the fungus Beauveria bassiana. The immune defense was highly variable both in the recombinant inbred lines from the Drosophila Synthetic Population Resource used for our QTL mapping and in the synthetic outbred populations used in our E&R study. Survivorship of infection improved dramatically over just 10 generations in the E&R study, and continued to increase for an additional nine generations, revealing a trade-off with uninfected longevity. Populations selected for increased defense against B. bassiana evolved cross resistance to a second, distinct B. bassiana strain but not to bacterial pathogens. The QTL mapping study revealed that sexual dimorphism in defense depends on host genotype, and the E&R study indicated that sexual dimorphism also depends on the specific pathogen to which the host is exposed. Both the QTL mapping and E&R experiments generated lists of potentially causal candidate genes, although these lists were nonoverlapping.

Drosophila transcriptomics with and without ageing.

The genomic basis of ageing still remains unknown despite being a topic of study for many years. Here, we present data from 20 experimentally evolved laboratory populations of Drosophila melanogaster that have undergone two different life-history selection regimes. One set of ten populations demonstrates early ageing whereas the other set of ten populations shows postponed ageing. Additionally, both types of populations consist of five long standing populations and five recently derived populations. Our primary goal was to determine which genes exhibit changes in expression levels by comparing the female transcriptome of the two population sets at two different time points. Using three different sets of increasingly restrictive criteria, we found that 2.1-15.7% (82-629 genes) of the expressed genes are associated with differential ageing between population sets. Conversely, a comparison of recently derived populations to long-standing populations reveals little to no transcriptome differentiation, suggesting that the recent selection regime has had a larger impact on the transcriptome than its more distant evolutionary history. In addition, we found very little evidence for significant enrichment for functional attributes regardless of the set of criteria used. Relative to previous ageing studies, we find little overlap with other lists of aging related genes. The disparity between our results and previously published results is likely due to the high replication used in this study coupled with our use of highly differentiated populations. Our results reinforce the notion that the use of genomic, transcriptomic, and phenotypic data to uncover the genetic basis of a complex trait like ageing can benefit from experimental designs that use highly replicated, experimentally-evolved populations.

Effects of evolutionary history on genome wide and phenotypic convergence in Drosophila populations.

BACKGROUND: Studies combining experimental evolution and next-generation sequencing have found that adaptation in sexually reproducing populations is primarily fueled by standing genetic variation. Consequently, the response to selection is rapid and highly repeatable across replicate populations. Some studies suggest that the response to selection is highly repeatable at both the phenotypic and genomic levels, and that evolutionary history has little impact. Other studies suggest that even when the response to selection is repeatable phenotypically, evolutionary history can have significant impacts at the genomic level. Here we test two hypotheses that may explain this discrepancy. Hypothesis 1: Past intense selection reduces evolutionary repeatability at the genomic and phenotypic levels when conditions change. Hypothesis 2: Previous intense selection does not reduce evolutionary repeatability, but other evolutionary mechanisms may. We test these hypotheses using D. melanogaster populations that were subjected to 260 generations of intense selection for desiccation resistance and have since been under relaxed selection for the past 230 generations. RESULTS: We find that, with the exception of longevity and to a lesser extent fecundity, 230 generations of relaxed selection has erased the extreme phenotypic differentiation previously found. We also find no signs of genetic fixation, and only limited evidence of genetic differentiation between previously desiccation resistance selected populations and their controls. CONCLUSION: Our findings suggest that evolution in our system is highly repeatable even when populations have been previously subjected to bouts of extreme selection. We therefore conclude that evolutionary repeatability can overcome past bouts of extreme selection in Drosophila experimental evolution, provided experiments are sufficiently long and populations are not inbred.

Genome-Wide Mapping of Gene-Phenotype Relationships in Experimentally Evolved Populations.

Model organisms subjected to sustained experimental evolution often show levels of phenotypic differentiation that dramatically exceed the phenotypic differences observed in natural populations. Genome-wide sequencing of pooled populations then offers the opportunity to make inferences about the genes that are the cause of these phenotypic differences. We tested, through computer simulations, the efficacy of a statistical learning technique called the "fused lasso additive model" (FLAM). We focused on the ability of FLAM to distinguish between genes which are differentiated and directly affect a phenotype from differentiated genes which have no effect on the phenotype. FLAM can separate these two classes of genes even with relatively small samples (10 populations, in total). The efficacy of FLAM is improved with increased number of populations, reduced environmental phenotypic variation, and increased within-treatment among-replicate variation. FLAM was applied to SNP variation measured in both twenty-population and thirty-population studies of Drosophila subjected to selection for age-at-reproduction, to illustrate the application of the method.

Genome-wide analysis of long-term evolutionary domestication in Drosophila melanogaster.

Experimental evolutionary genomics now allows biologists to test fundamental theories concerning the genetic basis of adaptation. We have conducted one of the longest laboratory evolution experiments with any sexually-reproducing metazoan, Drosophila melanogaster. We used next-generation resequencing data from this experiment to examine genome-wide patterns of genetic variation over an evolutionary time-scale that approaches 1,000 generations. We also compared measures of variation within and differentiation between our populations to simulations based on a variety of evolutionary scenarios. Our analysis yielded no clear evidence of hard selective sweeps, whereby natural selection acts to increase the frequency of a newly-arising mutation in a population until it becomes fixed. We do find evidence for selection acting on standing genetic variation, as independent replicate populations exhibit similar population-genetic dynamics, without obvious fixation of candidate alleles under selection. A hidden-Markov model test for selection also found widespread evidence for selection. We found more genetic variation genome-wide, and less differentiation between replicate populations genome-wide, than arose in any of our simulated evolutionary scenarios.