Laboratory selection experiments are alluring in their simplicity, power, and ability to inform us about how evolution works. A longstanding challenge facing evolution experiments with metazoans is that significant generational turnover takes a long time. In this work, we present data from a unique system of experimentally evolved laboratory populations of Drosophila melanogaster that have experienced three distinct life-history selection regimes. The goal of our study was to determine how quickly populations of a certain selection regime diverge phenotypically from their ancestors, and how quickly they converge with independently derived populations that share a selection regime. Our results indicate that phenotypic divergence from an ancestral population occurs rapidly, within dozens of generations, regardless of that population's evolutionary history. Similarly, populations sharing a selection treatment converge on common phenotypes in this same time frame, regardless of selection pressures those populations may have experienced in the past. These patterns of convergence and divergence emerged much faster than expected, suggesting that intermediate evolutionary history has transient effects in this system. The results we draw from this system are applicable to other experimental evolution projects, and suggest that many relevant questions can be sufficiently tested on shorter timescales than previously thought.
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.
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.
Energy allocation is believed to drive trade-offs in life history evolution. We develop a physiological and genetic model of energy allocation that drives evolution of feeding rate in a well-studied model system. In a variety of stressful environments Drosophila larvae adapt by altering their rate of feeding. Drosophila larvae adapted to high levels of ammonia, urea, and the presence of parasitoids evolve lower feeding rates. Larvae adapted to crowded conditions evolve higher feeding rates. Feeding rates should affect gross food intake, metabolic rates, and efficiency of food utilization. We develop a model of larval net energy intake as a function of feeding rates. We show that when there are toxic compounds in the larval food that require energy for detoxification, larvae can maximize their energy intake by slowing their feeding rates. While the reduction in feeding rates may increase development time and decrease competitive ability, we show that genotypes with lower feeding rates can be favored by natural selection if they have a sufficiently elevated viability in the toxic environment. This work shows how a simple phenotype, larval feeding rates, may be of central importance in adaptation to a wide variety of stressful environments via its role in energy allocation.
The molecular basis of adaptation remains elusive even with the current ease of sequencing the genome and transcriptome. We used experimentally evolved populations of Drosophila in conjunction with statistical learning tools to explore interactions between the genome, the transcriptome, and phenotypes. Our results indicate that transcriptomic measures from adult samples can predict phenotypic characters at many adult ages. Importantly, when comparing the genome and transcriptome in predicting phenotypic characters, we find that the two types of data are comparably useful. When using genome sites as predictors for the expression of the transcriptome, we find that gene expression is influenced by genomic regions across all large chromosome arms. Conversely, we found many genomic regions influencing the expression of numerous genes, which is consistent with widespread pleiotropy. Our results also highlight the power of the combination of experimental evolution, next-generation sequencing, and statistical learning tools in exploring the molecular basis of adaptation.
The biotechnological task of controlling human aging will evidently be complex, given the failure of all simple strategies for accomplishing this task to date. In view of this complexity, a multi-step approach will be necessary. One precedent for a multi-step biotechnological success is the burgeoning control of human infectious diseases from 1840 to 2000. Here we break down progress toward the control of infectious disease into four key steps, each of which have analogs for the control of aging. (1) Agreement about the fundamental nature of the medical problem. (2) Public health measures to mitigate some of the factors that exacerbate the medical problem. (3) Early biotechnological interventions that ward off the more tractable disease etiologies. (4) Deep understanding of the underlying biology of the diseases involved, leading in turn to comprehensive control of the medical problems that they pose. Achievement of all four of these steps has allowed most people who live in Western countries to live largely free of imminent death due to infectious disease. Accomplishing the equivalent feat for aging over this century should lead to a similar outcome for aging-associated disease. Neither infection nor aging will ever be entirely abolished, but they can both be rendered minor causes of death and disability.
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