Elizabeth G. King

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.

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. Despite the compelling evidence of fluctuating selection from recent studies, there is a disconnect between empirical and theoretical findings concerning the underlying mechanisms due to the limited evidence regarding the scale and processes that generate genome-wide oscillations. This study aims to elucidate how both genetic factors (eg heritability, number of causative loci) and ecological factors (eg season length, the difference in the phenotypic optima between seasons, population size dynamics) drive fluctuating selection and to identify the parameters that produce consistent oscillatory patterns. We developed a modeling framework integrating quantitative and population genetics to simulate a population under various selection regimes. We applied spectral analysis to detect periodicity, indicating cyclical selective environments. Our simulations highlight the conditions sustaining oscillations in allele frequencies over time. Spectral analysis successfully identifies the periodic patterns from allele frequency trajectories, even under highly complex selection regimes. Not only does our study clarify the conditions that yield oscillatory behaviors, but these parameters can also potentially be estimated in natural populations, providing a possibility of empirically testing these models.

All species evolve under selective pressures that emerge from their interactions, often antagonistic, with other species. Phenotypes mediating species interactions manifest as the combined products of the genomes of interacting species; understanding the evolutionary processes acting in one lineage therefore cannot be attained without bridging the genomes of interacting species. Venoms have arisen independently more than 100 times in animals and serve diverse roles in species interactions, including predation and defense. Each venom is evolutionarily entwined with reciprocal phenotypes, such as venom resistance, in often diverse recipient species. Despite extensive work on venoms, the full genetic basis for resistance to whole venoms is largely unknown. Using the venom of the Florida blue centipede (Scolopendra viridis) comprised of 35 toxins and Drosophila melanogaster as model prey, we investigated the genetics of venom resistance for a naive prey through experimental evolution and genetic-mapping approaches. We identified 12 consensus genes across techniques associated with venom resistance, yet individual experiments suggested a genome-wide basis for resistance involving hundreds to thousands of genes, despite the relative simplicity of the venom of S. viridis. We found no evidence for fitness trade-offs associated with the evolution of resistance and revealed a stark contrast in the nature of venom resistance between prey sexes. The disparity in resistance genetics between prey sexes as well as the relative genetic complexity of venom versus resistance may ultimately give venomous predators a coevolutionary advantage over their prey.

To understand the relative importance of cis and trans effects on regulation, we crossed multi-parent recombinant-inbred lines (RILs) to a common tester and measured allele-specific gene expression in the offspring. Testing the difference of allelic imbalance between two RIL × Tester crosses is a test of cis or trans, depending on the RIL alleles compared. The study design also enables to separation of two sources of trans variation, genetic and environmental, detected via interactions with cis effects. We demonstrate the effectiveness of 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 environment are detectable in 31% of loci and trans effects due to genetic background in 19%, with little overlap in sources of trans variation. The genes identified in this study are associated with genes previously reported to exhibit genetic variation in gene expression. Eleven genes 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 the cuticular protein Cpr67B, suggesting a functional role for standing variation in gene expression. This study provides a blueprint for 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.

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.

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. Despite the compelling evidence of fluctuating selection from recent studies, there is a disconnect between empirical and theoretical findings concerning the underlying mechanisms due to the limited evidence regarding the scale and processes that generate genome-wide oscillations. This study aims to elucidate how both genetic factors (e.g. heritability, number of causative loci) and ecological factors (e.g. season length, the difference in the phenotypic optima between seasons, population size dynamics) drive fluctuating selection and to identify the parameters that produce consistent oscillatory patterns. We developed a modeling framework integrating quantitative and population genetics to simulate a population under various selection regimes. We applied spectral analysis to detect periodicity, indicating cyclical selective environments. Our simulations highlight the conditions sustaining oscillations in allele frequencies over time. Spectral analysis successfully identifies the periodic patterns from allele frequency trajectories, even under highly complex selection regimes. Not only does our study clarify the conditions that yield oscillatory behaviors, but these parameters are also relatively easy to predict from natural populations, providing a possibility of empirically testing these models. Significance statement: As genomic data is becoming increasingly available for different species across time, one observation are patterns where alleles oscillate in a seasonal pattern, which has been interpreted as a signature of fluctuating selection. However, the field lacks theoretical models that predict these persistent oscillations in allele frequencies caused by fluctuating selection. We develop such a theoretical model, defining the conditions under which recurrent, strong oscillations in allele frequencies are predicted to occur because of fluctuating selection. In addition, we develop a novel method to detect patterns of fluctuating selection from genomic data using spectral analysis. Our model considers key parameters that are measurable in real populations, which is an added advantage to test them empirically. Our research paves the way for field biologists to test our predictions and brings us closer to reliably forecasting how populations will evolve in environments that are frequently changing.

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.

Meiotic recombination is a prominent force shaping genome evolution, and understanding why recombination rates vary within and between species has remained a central, though challenging, question. Variation in recombination is widely thought to influence the efficacy of selection in purging transposable elements (TEs), prevalent selfish genetic elements, leading to widely observed negative correlations between TE abundance and recombination rates across taxa. However, accumulating evidence suggests that TEs could instead be the cause rather than the consequence of this relationship. To test this prediction, we formally investigated the influence of polymorphic, putatively active TEs on recombination rates. We developed and benchmarked an 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 TE landscape can actively modify recombination, shaping genome evolution within and between species.

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.

Chicken meat is contaminated with Salmonella from the gut of infected chickens during slaughter. Eradication of Salmonella from broiler chickens through hygiene measures and/or vaccination is not cost-effective; complementary approaches are required. A mature gut microbiota obstructs Salmonella infection in chickens, and deliberate fortification of colonization resistance through prebiotic feed formulations would benefit public health and poultry production. Prebiotic galactooligosaccharides hastens Salmonella clearance from the gut of infected chickens. To better understand the role of galactooligosaccharides in colonization resistance, broiler chickens were raised on a wheat-soybean meal-based feed, with or without galactooligosaccharides for the first 24 days of life. Chickens were orally challenged with Salmonella enterica serovar Enteritidis at 20 days and the effect of supplementary galactooligosaccharides characterized by profiling Salmonella colonization, gut microbiota, innate immune response, and cecal short-chain fatty acid concentrations. Exposure to dietary galactooligosaccharides shortened the time to clear S . Enteritidis from the ceca. Differential abundance analysis of the cecal microbiota associated Salmonella challenge with a bacterial taxon belonging to the Acidaminococcaceae family ( P < 0.005). Increased cecal concentrations of the short-chain fatty acids propionate and valerate were measured in Salmonella -challenged chickens sustained on either control or galactooligosaccharide-supplemented feed relative to mock-challenged controls; but far greater concentrations were detected in chickens fed a galactooligosaccharide-supplemented diet in early life. The abundance of the Acidaminococcaceae taxon exhibited a positive correlation with the cecal concentrations of propionate (ρ = 0.724, P = 0.008) and valerate (ρ = 0.71, P = 0.013). The absence of cecal pro-inflammatory transcriptional responses suggest that the rapid Salmonella clearance observed for the galactooligosaccharide-supplemented diet was not linked to innate immune function. IMPORTANCE: Work presented here identifies bacterial taxa responsible for colonization resistance to Salmonella in broiler chickens. Deliberate cultivation of these taxa with prebiotic galactooligosaccharide has potential as a straight-forward, safe, and cost-effective intervention against Salmonella . We hypothesize that catabolism of galactooligosaccharide and its breakdown products by indigenous microorganisms colonizing the chicken gut produce excess levels of propionate. In the absence of gross inflammation, propionate is inimical to Salmonella and hastens intestinal clearance.