Biology subjects

King, E. G.

Publications and source records attributed to King, E. G..

11 recordsLinked to original sources

bioRxiv2026

Complex-trait dissection with Evolve-and-Resequence: Selection for improved flight performance in laboratory fruit fly populations

Evolve-and-Resequence studies combine experimental evolution with molecular sequencing, enabling associations between complex trait phenotypes and evolved genomic changes. For this, a targeted complex trait is subjected to artificial laboratory selection in genetically variable populations. Using populations of fruit flies Drosophila melanogaster from the DSPR background, we have conducted a short-term selection study on a highly complex trait : flight performance. We selected for improved flight performance using a custom-built flight tunnel system, with light as a motivator and airflow as a resistive factor. As a result, flight performance significantly improved in selected populations compared to controls. We also observed signatures of evolution in flight related traits, such as activity level and wing shape, and in life-history related traits such as adult dry mass and fecundity. We used whole genome pooled sequencing on the selected and control populations, revealing significantly differentiated genomic sites. Gene ontology and enrichment analyses revealed the evolution of pathways involved in response to stimuli, growth, and development. We identified several genes of interest (e.g., Sdc, dally). The range of evolved phenotypes and associated genomic changes show that flight performance is an ideal complex trait for future Evolve-and-Resequence studies.

evolutionary biology↗

bioRxiv2025

Multiparent Recombinant Inbred lines crossed to a tester provide novel insights into sources of cis and trans regulation of gene expression

We propose crossing multi-parent recombinant-inbred-lines (RILs) to a common tester and measuring allele specific gene expression in the offspring. Testing whether allelic imbalance between two RIL x Tester crosses is equal, is a test of cis or trans depending on the RIL alleles compared. The study design also enables to separate two sources of trans variation, genetic and environmental, detected via interactions with cis effects. Examining these components of regulatory variation, we demonstrate this approach in a long-read RNA-seq experiment in female abdominal tissue at two time points in Drosophila melanogaster. Among the 40% of all loci that show evidence of genetic variation in cis, trans effects due to the environment are detectable in 31% of loci and trans effects due to genetic background are detectable in 19% of loci with little overlap in sources of trans variation. The loci identified in this study are associated with loci previously reported to exhibit genetic variation in gene expression in a range of tissues and large population samples, suggesting that there is consistent variation for genetic regulation of gene expression. We show that eleven loci in a QTL for thermotolerance, previously shown to differ in expression based on temperature, have evidence for regulation of gene expression regardless of the environment, including Cpr67B, a cuticular protein suggesting a potential functional role for standing variation in gene expression. This study provides a blueprint for efficiently identifying regulatory variation in gene expression, as the tester design maximizes cis variation and enables the efficient assessment of all pairs of RIL alleles relative to the tester, a much smaller study compared to the pairwise direct assessment.

genetics↗

bioRxiv2025

Directional selection failed to produce changes in olfactory Y-maze learning and memory but induced changes in climbing phenotypes

Learning and memory are fundamental complex traits that allow for assessment and response to changes in their environment. Beyond cognition, these high order traits require several subcomponents, from sensory perception to motor output, in order to execute the intended response to a stimulus. Within the population, we see variation among individuals in abilities to perform learning and memory tasks. It is still largely unknown what genetic factors contribute to variability in these phenotypes; therefore, our study aims to gain better insight by utilizing a directional selection paradigm to drive differences in olfactory learning and memory behavior. Directional selection experiments allow for evaluation of the response to selective pressures across multiple biological levels through amplification of phenotype differences between groups. We used a reward based olfactory associative learning and memory assay to train a synthetic population of flies allowing only those who passed both tests to mate across ten generations. Our study shows significant changes in the climbing subcomponent required to perform well on the y-maze assay, however, we did not observe any significant changes to olfactory learning and memory behavior.

genetics↗

bioRxiv2025

Dissecting fluctuating selection: A unified population and quantitative genetics

One of the longstanding debates in evolutionary biology is the effect of fluctuating selection on genetic changes in populations. However, the extent to which these periodic forces influence organisms at both genomic and phenotypic levels remains unclear. Furthermore, despite the compelling evidence of fluctuating selection from recent studies, there is a disconnect between empirical findings and theoretical models concerning the underlying mechanisms due to the limited evidence regarding the scale and processes that generate stable genome-wide oscillations. This study aims to elucidate the genetic and ecological factors driving fluctuating selection and to identify the parameters that produce consistent oscillatory patterns in allele frequencies. To address these longstanding challenges, we developed a modeling framework integrating quantitative and population genetics to simulate a population under various selection regimes. Using SLiM, a forward evolution simulator, we varied genetic (heritability and genomic architecture) and ecological (selection pressure and season length) parameters. Unlike previous models focusing on selection acting directly on loci, our approach evaluates individual fitness based on the shift in the seasonal optimum relative to the mean phenotype. We also applied spectral analysis to detect periodicity, indicating cyclical selective environments. Our simulations shed light on conditions sustaining oscillations in allele frequencies over time. Spectral analysis successfully identifies the periodic patterns from allele frequency, even under highly complex selection regimes. Not only does our study clarify the conditions that yield persistent oscillatory behaviors, but these parameters are also relatively easy to predict from natural population, providing a possibility of empirically testing these models.

evolutionary biology↗

bioRxiv2025

Multiple methods for assessing learning and memory in Drosophila melanogaster demonstrates the highly complex, context-dependent genetic underpinnings of cognitive traits

Learning and memory are fundamental for an individual to be able to respond to changing stimuli in their environment. Between individuals we see variation in their ability to perform learning and memory tasks, however, it is still largely unknown what genetic factors may impact this variability. To gain better insight to the genetic components impacting variation in learning and memory, we use recombinant inbred lines (RILs) from the Drosophila synthetic population resource (DSPR), a multiparent mapping population exhibiting natural variation in many traits. Using a reward based associative learning and memory assay, we trained flies to associate an odor with a sucrose reward under starvation condition and measured olfactory learning and memory ability in y-mazes for 50 DSPR RILs. While we do not find significant QTLs for olfactory learning or memory, we found suggestive regions that may be contributing to variability in performance when trained to different odors. We provide evidence that performance with specific odors should be considered different phenotypes and introduce new methods for analysis for olfactory y-maze assays with multiple decision points. Additionally, we compare our data to previously collected place learning and memory data to show there is limited correlation in performance outcomes.

genetics↗

bioRxiv2024

Varying recombination landscapes between individuals are driven by polymorphic transposable elements

AbstractsMeiotic recombination is a prominent force shaping genome evolution, and understanding the causes for varying recombination landscapes within and between species has remained a central, though challenging, question. Recombination rates are widely observed to negatively associate with the abundance of transposable elements (TEs), selfish genetic elements that move between genomic locations. While such associations are usually interpreted as recombination influencing the efficacy of selection at removing TEs, accumulating findings suggest that TEs could instead be the cause rather than the consequence. To test this prediction, we formally investigated the influence of polymorphic, putatively active TEs on recombination rates. We developed and benchmarked a novel approach that uses PacBio long-read sequencing to efficiently, accurately, and cost-effectively identify crossovers (COs), a key recombination product, among large numbers of pooled recombinant individuals. By applying this approach to Drosophila strains with distinct TE insertion profiles, we found that polymorphic TEs, especially RNA-based TEs and TEs with local enrichment of repressive marks, reduce the occurrence of COs. Such an effect leads to different CO frequencies between homologous sequences with and without TEs, contributing to varying CO maps between individuals. The suppressive effect of TEs on CO is further supported by two orthogonal approaches-analyzing the distributions of COs in panels of recombinant inbred lines in relation to TE polymorphism and applying marker-assisted estimations of CO frequencies to isogenic strains with and without transgenically inserted TEs. Our investigations reveal how the constantly changing mobilome can actively modify recombination landscapes, shaping genome evolution within and between species.

genomics↗

bioRxiv2024

Larval protein restriction interacts with adult diet to increase fitness in an outbred multiparent population.

Developmental conditions including temperature, diet, and parasites can shape adult fitness phenotypes in many species. Studies typically focus on the additive effects of early-life and adult life conditions on life history response in the context of competing models of developmental plasticity (i.e. environmental matching and silver spoon). These models continue to yield mixed results in the same or different species. Here, we characterize interaction effects of larval vs adult diet on lifespan and fecundity in a high diversity outbred population of Drosophila melanogaster. We compare fitness proxies of matched vs mismatched early-to-late nutritional conditions differing in protein content. Diet interactions significantly affected both traits, albeit differently. We find no consistent evidence for either model. Rather, several patterns emerged including age and sex effects, survival differences in the post-median life phase, regime-specific timing of peak fecundity, and substantial fecundities in older post-median flies. Surprisingly, lower adult protein delayed egg-laying by about 2 weeks, compared to treatments with higher protein in adult diet. Our results underscore the extent to which adult life history expression depends significantly on developmental conditions. Results further highlight the need for assessing possible adult life phase patterns which might potentially reveal trade-offs between adult life stages than traits.

developmental biology↗

bioRxiv2023

Naturally segregating genetic variants contribute to thermal tolerance in a D. melanogaster model system.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

evolutionary biology↗

bioRxiv2018

A model-based high throughput method for fecundity estimation in fruit fly studies.

The ability to quantify fecundity is critically important to a wide range of experimental applications, particularly in widely-used model organisms such as Drosophila melanogaster. However, the standard method of manually counting eggs is time consuming and limits the feasibility of large-scale experiments. We develop a predictive model to automate the counting of eggs from images of eggs removed from the media surface and washed onto dark filter paper. A cross-validation approach demonstrates our method performs well, with a correlation between predicted and manually counted values of 0.88. We show how this method can be applied to a large data set where egg densities vary widely.

bioinformatics↗

bioRxiv2018

The evolutionary potential of diet-dependent effects on lifespan and fecundity in a multi-parental population of Drosophila melanogaster

The nutritional conditions experienced by a population play a major role in shaping trait evolution in many taxa. Constraints exerted by nutrient limitation or nutrient imbalance can influence the maximal value that fitness components such as reproduction and lifespan attains, and organisms may shift how resources are allocated to different structures and functions in response to changes in nutrition. Whether the phenotypic changes associated with changes in nutrition represent an adaptive response is largely unknown. Further, it is unclear whether the response of fitness components to diet even has the potential to evolve in most systems. In this study, we use an admixed multiparental population of Drosophila melanogaster reared in three different diet conditions to estimate quantitative genetic parameters for lifespan and fecundity. We find significant genetic variation for both traits in our population and show that lifespan has moderate to high heritabilities within diets. Genetic correlations for lifespan between diets were significantly less than one, demonstrating a strong genotype by diet interaction. These findings demonstrate substantial standing genetic variation in our population that is comparable to natural populations and highlights the potential for adaptation to changing nutritional environments.

evolutionary biology↗

bioRxiv2017

How to get the most bang for your buck: the evolution and physiology of nutrition-dependent resource allocation strategies

All organisms utilize resources to grow, survive, and reproduce. The supply of these resources varies widely across landscapes and time, imposing ultimate constraints on the maximal trait values for allocation-related traits. In this review, we address three key questions fundamental to our understanding of the evolution of allocation strategies and their underlying mechanisms. First, we ask: how diverse are flexible resource allocation strategies among different organisms? We find there are many, varied, examples of flexible strategies that depend on nutrition. However, this diversity is often ignored in some of the best-known cases of resource allocation shifts, such as the commonly observed pattern of lifespan extension under nutrient limitation. A greater appreciation of the wide variety of flexible allocation strategies leads directly to our second major question: what conditions select for different plastic allocation strategies? Here, we highlight the need for additional models that explicitly consider the evolution of phenotypically plastic allocation strategies and empirical tests of the predictions of those models in natural populations. Finally, we consider the question: what are the underlying mechanisms determining resource allocation strategies? Although evolutionary biologists assume differential allocation of resources is a major factor limiting trait evolution, few proximate mechanisms are known that specifically support the model. We argue that an integrated framework can reconcile evolutionary models with proximate mechanisms that appear at first glance to be in conflict with these models. Overall, we encourage future studies to 1) mimic ecological conditions in which those patterns evolve, and 2) take advantage of the omic opportunities to produce multi-level data and analytical models that effectively integrate across physiological and evolutionary theory.

evolutionary biology↗