Biology subjects

Arnold, K. R.

Publications and source records attributed to Arnold, K. R..

4 recordsLinked to original sources

bioRxiv2026

Signal, noise, and sampling: How pool size and replication shape metabolomic inference

Metabolomics provides a direct readout of physiological state and is increasingly used in evolutionary and systems biology. In small organisms such as Drosophila melanogaster, metabolomic analyses typically require pooling individuals to obtain sufficient material, yet pool sizes vary widely across studies with little justification. How pooling and biological replication influence metabolome characterization and the detection of biological signal remains poorly understood. Here, we evaluate the effects of pool size and biological replication on metabolomic profiles and signal detection using two complementary experimental designs. In the first, we assess how pooling (5, 50, or 100 individuals) influences metabolomic structure and reproducibility in inbred and outbred populations. In the second, we test how pool size interacts with systematic variation in replicate number to affect detection of diet-associated metabolite changes under a high-sugar perturbation. Pool size strongly influenced metabolomic profiles, with samples pooled at five individuals consistently differing from larger pools, while profiles from 50 and 100 individuals were more similar. Larger pools improved reproducibility in a dataset-dependent manner. In the dietary experiment, smaller pool sizes substantially reduced sensitivity, leading to loss of true diet-associated metabolites without increasing false discoveries. Replicate downsampling further revealed that both pool size and biological replication jointly determine signal retention, with smaller pools accelerating the loss of detectable metabolites under reduced replication. Across all analyses, the ability to detect metabolite signals was strongly dependent on effect size and variability. Metabolites with larger and more stable effect estimates were consistently retained, whereas those with smaller or more variable effects were rapidly lost under reduced sampling. Linear mixed-effects modeling confirmed that detection probability is governed by a balance between biological signal strength and measurement variability, with pool size and replication jointly modulating this relationship. More broadly, our results demonstrate that metabolomic inference is governed by the interplay of signal, noise, and sampling design, with pool size and replication jointly shaping the detectability, stability, and interpretation of biological signals.

molecular biology↗

bioRxiv2026

Beyond Fixation: Persistent Genetic Variation Under Intense Selection

Understanding how and why genetic variation is maintained under sustained selection remains a central question in evolutionary genetics. Experimental evolution shows that adaptation in sexually reproducing populations is often highly polygenic, proceeding through coordinated, genome-wide allele frequency shifts from standing variation rather than classic hard sweeps. Recent explanations emphasize highly polygenic architectures, optimizing-selection, and genetic redundancy, which can slow fixation by distributing selection across many loci during adaptation. However, observations from long-term selection experiments reveal a pattern these frameworks do not fully explain: substantial genetic variation persists after hundreds of generations of intense directional selection in constant environments. Here, we use long-term experimental evolution in Drosophila melanogaster to test whether balancing-selection actively maintains genetic variation under strong life-history selection and preserves evolutionary reversibility. Longstanding populations selected for accelerated or delayed reproduction were shifted to the opposing regime, imposing age-structured fitness trade-offs. Notably, selection for early reproduction is associated with substantial loss of genetic variation, providing a stringent test of whether standing variation is truly depleted. Following reciprocal shifts, populations showed rapid phenotypic convergence toward the target regime. At the genomic level, allele-frequency trajectories were strongly antiparallel and highly repeatable across replicates, revealing coordinated polygenic responses. Relaxing long-standing early-life selection produced a pronounced rebound in genome-wide heterozygosity. Deep sequencing uncovered ultra-rare alleles at sites appearing fixed under standard coverage, indicating low-frequency functional variation persists below detection thresholds. These results suggest that substantial genetic variation can persist under intense directional selection and be rapidly redeployed when selection reverses, consistent with widespread balancing-selection.

evolutionary biology↗

bioRxiv2024

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 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. SignificanceWhile experimental evolution studies featuring Drosophila melanogaster have generated significant insights into the forces that shape aging and life history patterns, more recent efforts incorporating genomic and transcriptomic data have had comparatively little success identifying the molecular mechanisms underlying these patterns. Here we work to incorporate molecular phenotyping into this general framework as a way forward. Specifically, we characterize how the metabolome changes with age in populations of D. melanogaster where hundreds of generations of selection for early reproduction have led to enrichment for an accelerated aging phenotype. By comparing to control populations, we show that the metabolome does appear to capture true signals of "biological age" and provides a new avenue for understanding the factors that underlie complex trait variation in real populations.

evolutionary biology↗

bioRxiv2021

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 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 like aging.

evolutionary biology↗