Biology subjects

Ayroles, J. F.

Publications and source records attributed to Ayroles, J. F..

18 recordsLinked to original sources

bioRxiv2026

Multiple host species maintain genetic diversity in herbivore populations

How natural populations maintain genetic variation for fitness remains a central problem in evolutionary biology. Theory predicts that opposing selective pressures across environments can preserve fitness-associated polymorphism, but how interactions among species contribute to this process remains poorly understood. Here we show that host diversity preserves standing genetic variation within sexual populations of the leafmining fly Scaptomyza flava. We evolved twelve populations for ten generations on either of two naturally co-occurring host plants or on both, pooling survivors from the two hosts before mating. Single-host populations evolved reciprocal fitness trade-offs, whereas two-host populations maintained relatively even performance across hosts. Whole-genome time series dynamics identified a linked region in which alleles increased on one host and decreased on the other, while showing little net change under two-host conditions. Wild flies reared from the two hosts showed corresponding allele-frequency differences within this region. Across the genome, two-host populations retained more rare variants than populations evolving on either host alone. These findings show that selection imposed by different host species in sympatry can maintain standing genetic diversity within an herbivore population.

evolutionary biology↗

bioRxiv2026

Mapping the mammalian dark metabolome by in vivo isotope tracing

Despite decades of biochemical study, a comprehensive map of the mammalian metabolome remains elusive. Mass spectrometry-based metabolomics detects thousands of small molecule-associated signals in mammalian tissues, but it is currently unclear how many of these reflect products of endogenous metabolism. Here, we leverage systematic in vivo isotope tracing to infer the biosynthetic origins of unidentified metabolites. We administered 26 different isotopically labelled nutrients to mice, measured circulating and tissue metabolite labelling by mass spectrometry, and developed a statistical framework to infer the number of carbon atoms incorporated from each of these precursors into more than 4,000 putative metabolites. We show this information can be harnessed for biosynthesis-aware structure elucidation using a multimodal AI model that co-embeds isotopic labelling patterns with chemical structures. This approach revealed several previously unrecognized families of mammalian metabolites, including cysteine-derived alkylthiazolidines, dithioacetal mercapturic acid derivatives, short-chain N-acyltaurines, acylglycyltaurines, and N-oxidized taurines. It further uncovered a family of mevalonate-derived isoprenoid metabolites that includes 2,3-dihydrofarnesoic acid, which is markedly depleted in both mouse and human aging. Age-related depletion of these isoprenoids is driven by impaired coenzyme A synthesis. Our work establishes the biosynthetic precursors for thousands of unidentified metabolites and reveals multiple previously unrecognized branches of mammalian metabolism.

biochemistry↗

bioRxiv2026

Divergent Biological Consequences of APOE Isoforms Across Industrialized and Non-Industrial Environments

The apolipoprotein {varepsilon}4 (APOE {varepsilon}4) isoform directly alters cholesterol and immune biology and is associated with an increased risk of neurodegenerative and cardiometabolic disease in industrialized settings; nevertheless, APOE {varepsilon}4--which is ancestral in humans--has persisted over evolutionary time. One potential explanation is that the costs and benefits of APOE {varepsilon}4 were significantly different in the environments in which humans evolved compared to those we experience today. In support, previous work has suggested that living in a high pathogen environment, engaging in high levels of physical activity, or eating a low fat diet can dampen the detrimental effects of APOE {varepsilon}4, and has revealed positive effects for fertility. However, direct tests of whether APOE isoforms are associated with different biological outcomes in non-industrial versus industrialized contexts are lacking. Working with the Turkana of Kenya and the Orang Asli of Peninsular Malaysia--two Indigenous groups in which individuals of shared ancestry span a continuum of subsistence, non-industrial to urban, industrialized lifestyles--we investigated how APOE genotypes impact cholesterol, immunological, and reproductive traits and tested for genotype x environment (GxE) interactions. First, we confirmed established genotype effects across lifestyles, showing that more APOE {varepsilon}4 alleles are associated with higher total cholesterol, higher LDL cholesterol, and lower HDL cholesterol. Second, we tested for lifestyle interactions, finding lifestyle-dependent effects of genotype on innate immune biomarkers in the Orang Asli but not Turkana. Finally, we show that more APOE {varepsilon}4 alleles are correlated with an extended reproductive lifespan, however this effect is relatively weak, is not consistent across populations, and does not correspond with a higher reproductive output. Together, our study provides evidence that industrialized environments can modify the biology of APOE {varepsilon}4; however, we find that APOE {varepsilon}4 is not universally beneficial in non-industrial contexts, highlighting the role of local environmental variation in determining its specific costs and benefits.

evolutionary biology↗

bioRxiv2026

Environmental perturbation increases gene expression variability and unmasks genetic regulation for transcriptional robustness

Environmental perturbation reshapes phenotypes not only by shifting trait means but also by modulating phenotypic variability. Whether environmentally induced changes in variability -environmental robustness- are genetically regulated remains unclear. To address this question, we collected nearly 2,000 matched genomes and transcriptomes from an outbred population of Drosophila melanogaster before and after exposure to a high-sugar diet, and quantified how dietary stress remodels expression variability and its genetic architecture. We find that environmental perturbation induces a transcriptome-wide increase in expression variability, with the notable exception of key developmental pathways. At the same time, stress unmasks pervasive genetic regulation for expression variability (veQTL). These loci are mostly environment-dependent, show distinct functional and evolutionary constraints from mean-regulating loci, and appeared detrimental when they confer to much robustness to dietary perturbation. Together, our findings establish gene expression variability as a central dimension of the organismal response to environmental changes, and uncover its genetic basis as a distinct, evolutionary constrained layer of phenotypic regulation beyond mean effects.

evolutionary biology↗

bioRxiv2024

Longitudinal sequencing reveals polygenic andepistatic nature of genomic response to selection

Evolutionary adaptation to new environments likely results from a combination of selective sweeps and polygenic shifts, depending on the genetic architecture of traits under selection. While selective sweeps have been widely studied, polygenic responses are considered more prevalent but challenging to quantify. The infinitesimal model makes explicit the hypothesis about the dynamics of changes in allele frequencies under selection, where only allelic effect sizes, frequencies, linkage, and gametic disequilibrium matter. Departures from this, like long-range correlations of allele frequency changes, could be a signal of epistasis in polygenic response. We performed an Evolve & Resequence experiment in Drosophila melanogaster exposing flies to a high-sugar diet as a source of environmental stress for over 100 generations. We tracked allele frequency changes in >3000 individually sequenced flies as well as population pools and searched for loci under selection by identifying sites with allele frequency trajectories that differentiated selection regimes consistently across replicates. We estimate that at least 4% of the genome was under positive selection, the result of a highly polygenic response. Most of this response was seen as small but consistent allele frequency changes over time, and there were only a few large allele-frequency changes (selective sweeps). We then searched for signatures of selection on pairwise combinations of alleles in the new environment and found several strong signals of putative epistatic interactions across unlinked loci that were consistent across selected populations. Finally, we measured differentially expressed genes (DEGs) across treatments and show that DEGs are enriched for selected SNPs, suggesting a regulatory basis for the selective response. Our results suggest that epistatic contributions to polygenic selective response are common and lead to detectable signatures.

evolutionary biology↗

bioRxiv2024

Sex differences in immune function and disease risk are not easily explained by an evolutionary mismatch

The "Pregnancy Compensation Hypothesis" (PCH) posits that sex differences in the mammalian immune system reflect the effects of selection on female immunity during pregnancy, during which increased immunomodulation is required to reduce immune responses to the fetus, while maintaining the ability to respond to pathogens. In humans, fewer pathogens and lower parity in urban, industrialized environments has been suggested to leave female immune systems under-stimulated, creating an "evolutionary mismatch" which exacerbates sex differences in immunity and increases female autoimmune disease risk. Yet, robust tests of this mismatch hypothesis have not been conducted. Here, we first confirmed a sex-bias in autoimmune disease prevalence in a large dataset of individuals from the United Kingdom (UK Biobank). Second, we asked whether sex differences in immune function are affected by shifts toward urban, lower-parity lifestyles in a single population--the Turkana people of northwest Kenya. We found that lifestyle alters sex differences in immune cell type proportions, but not in gene expression levels. Contrary to expectations from the PCH, parity did not predict immune physiology. We then tested for generalizable trends across natural fertility mammalian populations versus urban humans (data from Turkana, NHANES, GTEx, Batwa and Bakiga, yellow baboons, and macaques). We did not find consistent relationships between lifestyle and sex-biases in immune biomarkers or between parity and the same immune outcomes. Indeed, we found that parity predicts autoimmune disease risk in the opposite direction than expected from the PCH, with higher parity associated with higher autoimmune disease risk while adjusting for relevant socioeconomic variables in the UK Biobank. Taken together, our work suggests that while sex clearly influences immune physiology and disease risk in urban settings, these effects are not easily explained by an evolutionary mismatch. Future work addressing how sex interacts with lifestyle change to generate disease is needed.

evolutionary biology↗

bioRxiv2023

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

genomics↗

bioRxiv2023

Saturating the eQTL map in Drosophila melanogaster: genome-wide patterns of cis and trans regulation of transcriptional variation in outbred populations

Decades of genome-wide mapping have shown that most genetic polymorphisms associated with complex traits are found in non-coding regions of the genome. Characterizing the effect of such genetic variation presents a formidable challenge, and eQTL mapping has been a key approach to understand the non-coding genome. However, comprehensive eQTL maps are available only for a few species like yeast and humans. With the aim of understanding the genetic landscape that regulates transcriptional variation in Drosophila melanogaster, we developed an outbred mapping panel in this species, the Drosophila Outbred Synthetic Panel (Dros-OSP). Using this community resource, we collected transcriptomic and genomic data for 1800 individual flies and were able to map cis and trans eQTLs for 98% of the genes expressed in D. melanogaster, increasing by thousands the number of genes for which regulatory loci are known in this species. We described, for the first time in the context of an outbred population, the properties of local and distal regulation of gene expression in terms of genetic diversity, heritability, connectivity, and pleiotropy. We uncovered that, contrary to long-standing assumptions, a significant part of gene co-expression networks is organized in a non-modular fashion. These results bring the fruit fly to the level of understanding that was only available for a few other organisms, and offer a new mapping resource that will expand the possibilities currently available to the Drosophila community. This data is available at Drosophilaeqtl.org.

genomics↗

bioRxiv2023

From GWAS to signal validation: An approach for estimating SNP genetic effects while preserving genomic context

Validating associations between genotypic and phenotypic variation remains a challenge, despite advancements in association studies. Common approaches for signal validation rely on gene-level perturbations, such as loss-of-function mutations or RNAi, which test the effect of genetic modifications usually not observed in nature. CRISPR-based methods can validate associations at the SNP level, but have significant drawbacks, including resulting off-target effects and being both time-consuming and expensive. Both approaches usually modify the genome of a single genetic background, limiting the generalizability of experiments. To address these challenges, we present a simple, low-cost experimental scheme for validating genetic associations at the SNP level in outbred populations. The approach involves genotyping live outbred individuals at a focal SNP, crossing homozygous individuals with the same genotype at that locus, and contrasting phenotypes across resulting synthetic outbred populations. We tested this method in Drosophila melanogaster, measuring the longevity effects of a polymorphism at a naturally-segregating cis-eQTL for the midway gene. Our results demonstrate the utility of this method in SNP-level validation of naturally occurring genetic variation regulating complex traits. This method provides a bridge between the statistical discovery of genotype-phenotype associations and their validation in the natural context of heterogeneous genomic contexts.

genetics↗

bioRxiv2023

Adaptations to water stress and pastoralism in the Turkana of northwest Kenya

The Turkana people inhabit arid regions of east Africa--where temperatures are high and water is scarce--and they practice subsistence pastoralism, such that their diet is primarily composed of animal products. Working with Turkana communities, we sequenced 367 genomes and identified 8 regions putatively involved in adaptation to water stress and pastoralism. One of these regions includes a putative enhancer for STC1--a kidney-expressed gene involved in the response to dehydration and the metabolism of purine-rich foods such as red meat. We show that STC1 is induced by antidiuretic hormone in humans, is associated with urea levels in the Turkana themselves, and is under strong selection in this population (s[~]0.041). This work highlights that partnerships with subsistence-level groups can lead to new models of human physiology with biomedical relevance.

evolutionary biology↗

bioRxiv2022

Characterizing the landscape of gene expression variance in humans

Gene expression variance has been linked to organismal function and fitness but remains a commonly ne-glected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease. Author SummaryGene expression variance, or the variation in the level of gene expression within a population, can have significant impacts on physiology, disease, and evolutionary adaptations. While the average level of gene expression is typically the focus of research, the variation around this average level (i.e., gene expression variance) can also be important for understanding complex traits and disease. Here, we investigate the landscape of transcriptional variance across tissues, populations, and studies. Using large publicly available RNA-seq data sets, we were able to identify the general properties associated with high- and low-variance genes, as well as factors driving variation in variance across genes. Specifically, we uncovered gene expression variance was significantly associated with gene length, nucleotide diversity, the degree of connectivity and the presence of non-coding RNA. Our results suggest that the mechanisms responsible for maintaining optimal levels of variation in high-versus low-variance differ, and that this variability is the result of different patterns of selection.

genomics↗

bioRxiv2022

Relative abundance data can misrepresent heritability of the microbiome

Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this question can be addressed by estimating the heritability (h2) of the microbiome - the proportion of variance in the abundance of each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample. We derived an analytical approximation for the heritability that one obtains when using such relative abundances and we uncovered three problems: 1) The interdependency between taxa leads to imprecise heritability estimates. 2) Large sample size leads to high false discovery rates, overestimating the number of heritable taxa. 3) Microbial co-abundances lead to biased heritability estimates. We conclude that caution must be taken when interpreting heritability estimates and comparing values across studies.

evolutionary biology↗

bioRxiv2020

Diet unmasks genetic variants that regulate lifespan in outbred Drosophila

Evolutionary theory suggests that lifespan-associated alleles should be purged from the gene pool, and yet decades of GWAS and model organism studies have shown they persist. Here, we address one potential explanation, the idea that the alleles that regulate lifespan do so only in certain contexts. We exposed thousands of outbred Drosophila to a standard and a high sugar diet. We then sequenced over 10,000 individuals and track genome-wide allele frequency changes over time, as these populations aged. We mapped thousands of lifespan-altering alleles, some associated with early vs late life tradeoffs, late-onset effects, and genotype-by-environment interactions. We find that lifespan-reducing alleles are most likely to be recently derived, have stronger effects on a high-sugar diet, consistent with the hypothesis that historically neutral or beneficial alleles can become detrimental in novel conditions. We also show that the gene midway, a regulator of lipid storage and ortholog of the lifespan-associated gene DGAT1 in mice, also regulates lifespan in Drosophila. Our results provide insight into the highly polygenic and context-dependent genetic architecture of lifespan, as well as the evolutionary processes that shape this key trait.

evolutionary biology↗

bioRxiv2020

GxGxE interactions and adaptive potential of the microbiome in Drosophila melanogaster

Can the microbiome serve as a reservoir of adaptive potential for hosts? To address this question, we leveraged [~]150 generations of experimental evolution in Drosophila melanogaster in a stressful, high-sugar (HS) diet. We performed a fully reciprocal transplant experiment using the control and evolved HS bacteria. If the microbiome confers benefits to hosts, then transplant recipients should gain fitness benefits compared to controls. Interestingly, we found that benefits do exist, but mismatches between fly evolution and microbiome exerted fitness costs by slowing development and reducing fecundity, especially in the stressful HS diet. The dominant HS bacteria (Acetobacter pasteurianus) uniquely encoded several genes to enable uric acid degradation, mediating the toxic effects of uric acid accumulation due to the HS diet for flies. Our study demonstrates that host genotype x microbiome x environment interactions have substantial effects on host phenotype, highlighting how host evolution and ecological context together shape the adaptive potential of the microbiome.

evolutionary biology↗

bioRxiv2020

When the microbiome defines the host phenotype: selection on vertical transmission in varying environments

The microbiome can contribute to variation in fitness-related traits of their hosts, and thus to host evolution. Hosts are therefore expected to be under selection to control their microbiome, for instance through controlling microbe transmission from parents to offspring. Current models have mostly focused on microbes that either increase or decrease fitness. In that case, host-level selection is relatively straightforward, favouring either complete or no inheritance. In natural systems, however, vertical transmission fidelity varies widely, and microbiome composition is often shaped by a combination of vertical and horizontal transmission modes. We propose that such mixed transmission could optimize host fitness under fluctuating environments. Using a general model, we illustrate that decreasing vertical transmission fidelity increases the amount of microbiome variation, and thus potentially phenotypic variation, across hosts. Whether or not this is advantageous depends on environmental conditions, how much the microbiome changes during host development, and the contribution of other factors to trait variation. We discuss how environmentally-dependent microbial effects can favor intermediate transmission, review examples from natural systems, and suggest research avenues to empirically test our predictions. Overall, we show that imperfect transmission may be adaptive by allowing individuals to ensure phenotypic variability in their offspring in contexts where varying environments mean that this strategy increases long-term fitness.

evolutionary biology↗

bioRxiv2020

The microbiome responds to Evolve and Resequence experiments in Drosophila melanogaster

BackgroundExperimental evolution has a long history of uncovering fundamental insights into evolutionary processes, but has largely neglected one underappreciated component--the microbiome. As eukaryotic hosts evolve, the microbiome may also evolve. However, the microbial contribution to host evolution remains poorly understood. Here, we re-analyzed genomic data to characterize the metagenomes from ten Evolve and Resequence (E&R) experiment in Drosophila melanogaster to determine how the microbiome changed in response to host selection. ResultsBacterial diversity was significantly different in 5/10 studies, primarily in traits associated with metabolism or immunity. Duration of selection did not significantly influence bacterial diversity, highlighting the importance of associations with specific host traits. Additionally, we find that excluding reads from a facultative symbiont, Wolbachia, in the analysis of bacterial diversity changes the inference, raising important questions for future E&R experiments in the D. melanogaster microbiome. ConclusionsOur genomic re-analysis suggests the microbiome often responds to host selection; thus, the microbiome may contribute to the response of Drosophila in E&R experiments. We outline important considerations for incorporating the microbiome into E&R experiments. The E&R approach may provide critical insights into host-microbiome interactions and fundamental insight into the genomic basis of adaptation.

evolutionary biology↗

bioRxiv2019

TM3’seq: a tagmentation-mediated 3’ sequencing approach for improving scalability of RNA-seq experiments

RNA-seq has become the standard tool for collecting genome-wide expression data in diverse fields, from quantitative genetics and medical genomics to ecology and developmental biology. However, RNA-seq library preparation is still prohibitive for many laboratories. Recently, the field of single-cell transcriptomics has reduced costs and increased throughput by adopting early barcoding and pooling of individual samples --producing a single final library containing all samples. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many projects depend on individual library generation to preserve important samples or for follow-up re-sequencing experiments. Improving on currently available RNA-seq methods we have developed TM3seq, a 3-enriched library preparation protocol that uses Tn5 transposase and preserves sample identity at each step. TM3seq is designed for high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample). The protocol was tested in a range of human and Drosophila melanogaster RNA samples, recovering transcriptomes of the same quality and reliability than the commercial NEBNext(R) kit. We expect that the cost-and time-efficient features of TM3seq make large-scale RNA-seq experiments more permissive for the entire scientific community.

genomics↗

bioRxiv2018

Existence and implications of population variance structure

Identifying the genetic and environmental factors underlying phenotypic differences between populations is fundamental to multiple research communities. To date, studies have focused on the relationship between population and phenotypic mean. Here we consider the relationship between population and phenotypic variance, i.e., \"population variance structure.\" In addition to gene-gene and gene-environment interaction, we show that population variance structure is a direct consequence of natural selection. We develop the ancestry double generalized linear model (ADGLM), a statistical framework to jointly model population mean and variance effects. We apply ADGLM to several deeply phenotyped datasets and observe ancestry-variance associations with 12 of 44 tested traits in ~113K British individuals and 3 of 14 tested traits in ~3K Mexican, Puerto Rican, and African-American individuals. We show through extensive simulations that population variance structure can both bias and reduce the power of genetic association studies, even when principal components or linear mixed models are used. ADGLM corrects this bias and improves power relative to previous methods in both simulated and real datasets. Additionally, ADGLM identifies 17 novel genotype-variance associations across six phenotypes.

genetics↗