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.

“Synthetic recombinant” populations have emerged as a useful tool for dissecting the genetics of complex traits. They can be used to derive inbred lines for fine QTL mapping, or the populations themselves can be sampled for experimental evolution. In the latter application, investigators generally value maximizing genetic variation in constructed populations. This is because in evolution experiments initiated from such populations, adaptation is primarily fueled by standing genetic variation. Despite this reality, little has been done to systematically evaluate how different methods of constructing synthetic populations shape initial patterns of variation. Here we seek to address this issue by comparing outcomes in synthetic recombinant Saccharomyces cerevisiae populations created using one of two strategies: pairwise crossing of isogenic strains or simple mixing of strains in equal proportion. We also explore the impact of the varying the number of parental strains. We find that more genetic variation is initially present and maintained when population construction includes a round of pairwise crossing. As perhaps expected, we also observe that increasing the number of parental strains typically increases genetic diversity. In summary, we suggest that when constructing populations for use in evolution experiments, simply mixing founder strains in equal proportion may limit the adaptive potential.

The ability to predict how natural populations will evolve and adapt to major changes in environmental conditions has long been of interest to evolutionary biologists and ecologists alike. The reality of global climate change has also created a pressing need for advancement in this particular area of research, as species are increasingly faced with rapid shifts in abiotic and biotic conditions. Evolutionary genomics has the potential to be incredibly useful as we move forward in addressing this need and in particular, evolve and resequence (E&R) studies—where researchers combine experimental evolution with whole‐genome sequencing—have an important role to play. However, while E&R studies have shown a great deal of promise in tackling fundamental questions regarding the genetics of adaptation (Long et al., 2015; Schlötterer et al., 2014), it is unclear whether results from laboratory experiments can be directly translated to natural populations. In a From the Cover article in this issue of Molecular Ecology, Hsu et al. (Mol Ecol, 29, 2020) explicitly contend with this issue by examining the overlap between genes implicated in thermal adaptation in a Drosophila melanogaster E&R study and genes identified by comparing natural populations from different latitudinal clines. They report significant correlations between the two sets of temperature‐adaptive genes and ultimately conclude that E&R studies can indeed generate insights applicable to populations inhabiting complex natural environments. While more work is needed to assess the generality of these conclusions, Hsu and Belmouaden (Mol Ecol, 29, 2020) contribute an important precedent.

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 adult lifespan in populations of D. melanogaster where selection for early reproduction has led to an accelerated aging phenotype relative to their controls. Using these data, we (i) evaluated evolutionary repeatability across the metabolome; (ii) assessed the value of the metabolome as a predictor of “biological age” in this system; and (iii) identified specific metabolites associated with accelerated aging. Generally, our findings suggest that selection for early reproduction resulted in highly repeatable alterations to the metabolome and the metabolome itself is a reliable predictor of “biological age”. Specifically, we find clusters of metabolites that are associated with the different rates of senescence observed between our accelerated aging population and their controls, adding new insights into the metabolites that may be driving the accelerated aging phenotype.

This study, utilizing SBF-SEM, reveals structural alterations in mitochondria and myofibrils in human heart failure (HF). Mitochondria in HF show changes in structure, while myofibrils exhibit increased cross-sectional area and branching. Metabolomic and lipidomic analyses indicate concomitant dysregulation in key pathways. The findings underscore the need for personalized treatments considering individualized structural changes in HF.