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bioRxiv2023

A ticket to ride: genetic structure and kdr mutations in Aedes aegypti populations along a road crossing the Amazon Rainforest in Amapa State, Brazil

Insecticide resistance in Aedes aegypti is a major threat to the control of this mosquito species that transmits viruses that cause diseases like dengue, Zika, and chikungunya. One type of resistance is caused by alterations in the Nav gene, known as kdr mutations. In Brazil, different kdr haplotypes are present in Ae. aegypti and they may impede vector control operations based on pyrethroids. Although natural populations tend to accumulate genetic differences among isolated localities, this mosquito can actively and passively disperse, hitchhiked with human transportation. In this study, we investigated the genetic structure and kdr dispersion in Ae. aegypti populations in six localities of the Amapa State, Brazil located along a north-south transect of the Amazonian Forest. Genetic structure was assessed using 12 microsatellite loci in a fragment analysis sequencing procedure, and qPCR methods were used to detect the presence and frequency of three well known kdr mutations (V410L, V1016I and F1534C). We found a high prevalence of kdr alleles in all localities, indicating that kdr is spreading in the Amapa State. The microsatellite analyses suggested a certain level of differentiation among the mosquito populations, dividing them into two well-defined clusters, as evidenced by Bayesian and DAPC analyses. The population from Oiapoque (located in the north along the border with French Guiana) had the highest kdr frequencies and the highest genetic differentiation compared to the other localities. Our findings suggest that there is genetic structure among Ae. aegypti from the Amapa State, but with some level of passive gene flow between population clusters. The study highlights the importance of continued surveillance of Ae. aegypti populations to monitor the spread of insecticide resistance and inform on vector control strategies. Author summaryAmapa, a State in northern Brazil, is crucial as a gateway for diseases due to its border with French Guiana. One notable example was chikungunya, transmitted by the Aedes aegypti mosquito, also responsible for dengue, Zika, and yellow fever viruses. Chemical insecticides have been the primary means of control, however, resistance to these compounds is spreading among Ae. aegypti populations, compromising their effectiveness. Mutations known as "kdr" contribute to resistance to pyrethroid insecticides. Our study focused on these mutations and genetic differentiation in Ae. aegypti populations from six cities along the road nestled amidst the Amazonian Forest, linking the capital, Macapa, to Oiapoque on the Brazil-French Guiana border. The results revealed high frequencies of kdr mutations in all populations, indicating probable resistance to pyrethroids. Genetic analysis showed two distinct groups among the mosquitoes, with evidence of mosquito flow, particularly between Macapa and Oiapoque, facilitated by human transportation. Monitoring insecticide resistance and mosquito migration is essential for effective vector control strategies. Understanding these factors enables us to combat mosquito-borne diseases and safeguard public health.

evolutionary biology↗

bioRxiv2023

How Veeries vary: Whole genome sequencing resolves fine-scale genetic structure in a long-distance migratory bird, Catharus fuscescens

Fine-scale resolution of spatial genetic structure is important for understanding a species evolutionary history and contemporary genetic diversity. For high-latitude species with high dispersal ability, such as long-distance migratory birds, populations typically exhibit little genetic structure due to high gene flow and recent postglacial expansion. Some migratory birds, however, show high breeding site fidelity, which might reduce gene flow such that population genetic structure could be detectable with sufficient genomic data. We sequenced over 120 low-coverage whole genomes from across the breeding range of a long-distance migratory bird, the Veery (Catharus fuscescens). As this species breeding range extends across both historically glaciated and unglaciated regions in North America, we evaluated whether contemporary patterns of structure and genetic diversity are consistent with historical population isolation in glacial refugia. We found strong evidence for isolation by distance across the breeding range, as well as significant population structure between southern Appalachian and northern populations. However, patterns of genetic diversity did not support southern Appalachia as a glacial refugium. Resolution of isolation by distance across the breeding range was sufficient to assign likely breeding origins of individuals sampled in this species poorly understood South American nonbreeding range, demonstrating the potential to assess migratory connectivity in this species using genomic data. Overall, our findings suggest that isolation by distance yields subtle associations between genetic structure and geography across the breeding range even in the absence of obvious historical vicariance or contemporary barriers to dispersal.

evolutionary biology↗

bioRxiv2023

Tools for Genetic Engineering and Gene Expression Control in Novosphingobium aromaticivorans and Rhodobacter sphaeroides

Alphaproteobacteria have a variety of cellular and metabolic features that provide important insights into biological systems and enable biotechnologies. For example, some species are capable of converting plant biomass into valuable biofuels and bioproducts have the potential to form the backbone of the sustainable bioeconomy. Among the Alphaproteobacteria, Novosphingobium aromaticivorans, Rhodobacter sphaeroides, and Zymomonas mobilis, show particular promise as organisms that can be engineered to convert extracted plant lignin or sugars into bioproducts and biofuels. Genetic manipulation of these bacteria is needed to introduce engineered pathways and modulate expression of native genes with the goal of enhancing bioproduct output. Although recent work has expanded the genetic toolkit for Z. mobilis, N. aromaticivorans and R. sphaeroides still need facile, reliable approaches to deliver genetic payloads to the genome and to control gene expression. Here, we expand the platform of genetic tools for N. aromaticivorans and R. sphaeroides to address these issues. We demonstrate that Tn7 transposition is an effective approach for introducing engineered DNA into the chromosome of N. aromaticivorans and R. sphaeroides. We screen a synthetic promoter library to identify inducible promoters with strong, regulated activity in both organisms. Combining Tn7 integration with promoters from our library, we establish CRISPR interference systems for N. aromaticivorans and R. sphaeroides that can target essential genes and modulate engineered pathways. We anticipate that these systems will greatly facilitate both genetic engineering and gene function discovery efforts in these industrially important species and other Alphaproteobacteria. IMPORTANCEIt is important to increase our understanding of the microbial world to improve health, agriculture, the environment and biotechnology. For example, building a sustainable bioeconomy depends on the efficient conversion of plant material to valuable biofuels and bioproducts by microbes. One limitation in this conversion process is that microbes with otherwise excellent properties for conversion are challenging to genetically engineer. Here, we report systems to overcome that barrier in the Alphaproteobacteria, Novosphingobium aromaticivorans and Rhodobacter sphaeroides, by producing genetic tools that allow easy insertion of engineered pathways into their genomes and to precisely control gene expression by inducing genes with synthetic promoters or repressing genes using CRISPR interference. These tools can be used in future work to gain additional insight into these and other Alphaproteobacteria and to optimize yield of biofuels and bioproducts.

microbiology↗

bioRxiv2023

Converging Topographic Axes of Connectivity and Genetic Architectures Scaffold Cortical Patterning

Genetic information is involved in the gradual emergence of cortical areas since the neural tube begins to form, shaping the heterogeneous functions of neural circuits in the human brain. Informed by invasive tract-tracing measurements, the cortex exhibits marked interareal variation in connectivity profiles to reveal the heterogeneity across cortical areas. However, it remains unclear about the organizing principles possibly shared by genetics and cortical wiring to manifest the spatial heterogeneity across the cortex. Instead of considering a complex one-to-one mapping between genetic coding and interareal connectivity, we hypothesized the existence of a more efficient way that the organizing principles are embedded in genetic profiles to underpin the cortical wiring space. Leveraging on the vertex-wise tractography in diffusion-weighted MRI, we derived the global connectopies to reliably index the organizing principles of interareal connectivity variation in a low-dimensional space, which specifically captured three dominant topographic patterns along the dorsoventral, rostrocaudal, and mediolateral axes of the cortex. More importantly, we demonstrated that the global connectopies converge to the gradients of vertex-by-vertex genetic correlation matrix on the phenotype of cortical morphology and the cortex-wide spatiomolecular gradients. By diving into the genetic profiles, we found the critical role of genes scaffolding the global connectopies were related to brain morphogenesis and enriched in radial glial cells before birth and excitatory neurons after birth. Taken together, our findings demonstrated the existence of a genetically determined space to encode the interareal connectivity variation, which may give new insights into the links between cortical connections and arealization.

neuroscience↗

bioRxiv2023

Relating Ecological Diversity to Genetic Discontinuity across Bacterial Species

Bacterial genetic discontinuity, representing abrupt breaks in genomic identity among species, is crucial for grasping microbial diversity and evolution. Advances in genomic sequencing have enhanced our ability to track and characterize genetic discontinuity in bacterial populations. However, exploring systematically the degree to which bacterial diversity exists as a continuum or is sorted into discrete and readily defined species remains a challenge in microbial ecology. Here, we aimed to quantify the genetic discontinuity ({delta}) and investigate how this metric is related to ecology. We harnessed a dataset comprising 210,129 genomes to systematically explore genetic discontinuity patterns across several distantly related species, finding clear breakpoints which varied depending on the taxa in question. By delving into pangenome characteristics, we uncovered a significant association between pangenome saturation and genetic discontinuity. Closed pangenomes were associated with more pronounced breaks, exemplified by Mycobacterium tuberculosis. Additionally, through a machine learning approach, we detected key features that impact genetic discontinuity prediction. Our study enhances the understanding of bacterial genetic patterns and their ecological implications, offering insights into species boundaries for prokaryotes.

genomics↗

bioRxiv2023

The maintenance of genetic polymorphism in sexually antagonistic traits

AbstractSelection often favours different trait values in males and females, leading to genetic conflicts between the sexes when traits have a shared genetic basis. Such sexual antagonism has been proposed to maintain genetic polymorphism. However, this notion is based on insights from population genetic models of single loci with fixed fitness effects. It is thus unclear how readily polymorphism emerges from sex-specific selection acting on continuous traits, where fitness effects arise from the genotype-phenotype map and the fitness landscape. Here we model the evolution of a continuous trait that has a shared genetic basis but different optima in males and females, considering a wide variety of genetic architectures and fitness landscapes. For autosomal loci, the long-term maintenance of polymorphism requires strong conflict between males and females that generates uncharacteristic sex-specific fitness patterns. Instead, more plausible sex-specific fitness landscapes typically generate stabilising selection leading to an evolutionarily stable state that consists of a single homozygous genotype. Except for sites tightly linked to the sex determining region, our results indicate that genetic variation due to sexual antagonism should arise only rarely and often be transient, making these signatures challenging to detect in genomic data.

evolutionary biology↗

bioRxiv2023

Evaluating ChatGPT as an Agent for Providing Genetic Education

Genetic disorders are complex and can greatly impact an individuals health and well-being. In this study, we assess the ability of ChatGPT, a language model developed by OpenAI, to answer questions related to three specific genetic disorders: BRCA1, MLH1, and HFE. ChatGPT has shown it can supply articulate answers to a wide spectrum of questions. However, its ability to answer questions related to genetic disorders has yet to be evaluated. The aim of this study is to perform both quantitative and qualitative assessments of ChatGPTs performance in this area. The ability of ChatGPT to provide accurate and useful information to patients was assessed by genetic experts. Here we show that ChatGPT answered 64.7% of the 68 genetic questions asked and was able to respond coherently to complex questions related to the three genes/conditions. Our results reveal that ChatGPT can provide valuable information to individuals seeking information about genetic disorders, however, it still has some limitations and inaccuracies, particularly in understanding human inheritance patterns. The results of this study have implications for both genomics and medicine and can inform future developments in this area. AI platforms, like ChatGPT, have significant potential in the field of genomics. As these technologies become integrated into consumer-facing products, appropriate oversight is required to ensure accurate and safe delivery of medical information. With such oversight and training specifically for genetic information, these platforms could have the potential to augment some clinical interactions.

genomics↗

bioRxiv2023

Utilizing Non-Invasive Prenatal Test Sequencing Data Resource for Human Genetic Investigation

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. With exceptional sensitivity, specificity, and safety, NIPT has gained global adoption, exceeding ten million tests, establishing it as one of the largest human genetic resources. This resource holds immense potential for exploring population genetic variations and their correlations with phenotypes. Here, we present comprehensive methods tailored for analyzing large, low-depth NIPT genetic datasets, involving customized algorithms and software for genetic variation detection, genotype imputation, and genome-wide association analysis. Through evaluations, we demonstrate that, when integrated with appropriate probabilistic models and population-specific haplotype reference panels, accurate allele frequency estimation and high genotype imputation accuracy (0.8 to 0.9) are achievable for genetic variants with alternative allele frequencies between 0.01 and 0.05, at sequencing depths of 0.1x to 0.25x. Additionally, we attained an R-square exceeding 0.9 for estimating genetic effect sizes across various sequencing platforms. These findings establish a robust theoretical and practical foundation for leveraging NIPT data in advancing medical genetic studies, not only in realms of maternal and child health, but also for long-term health outcomes. HighlightsO_LIIntroduction of probabilistic model integration for analyzing large-scale, low-pass non-invasive prenatal test (NIPT) sequencing data C_LIO_LIEvaluation of protocols for variant detection, genotype imputation, and genome-wide association analyses with NIPT data C_LI

bioinformatics↗

bioRxiv2024

Evolution of genetic (co)variances during the worldwide invasion of Drosophila suzukii

The extent to which phenotypic evolution can be constrained by genetic correlations is an important question in evolutionary biology. To address this question, biological invasions are opportune models where derived, invasive populations can be compared to their extant ancestors, allowing to track the evolution of genetic correlations from the ancestor, throughout the invasion process. In this paper, we focused on the worldwide invasion of Drosophila suzukii (Matsumara, 1931), and investigated the evolution of the genetic covariance matrix G of wing shape between ancestral native, and derived invasive populations. Leveraging demographic history resolved by population genetics approaches, we tested whether G remained stable during the invasion. Using a multivariate QST-FST approach, we further tested whether or not the observed phenotypic divergence in wing shape aligned with a neutral scenario of evolution. Our results show moderate yet significant quantitative genetic differentiation of wing shape among D. suzukii populations and a relative stability in the structure of G, presenting a roughly spherical shape but slightly different volumes. These characteristics likely reflect the demographic history of populations and suggest a low level of genetic constraint on wing shape evolution. The divergence between populations was greater than expected under a purely neutral model of evolution, compatible with an effect of divergent selection among them. Overall, our study suggests that selection and drift, but not ancestral genetic constraints, affected the early stages of wing shape evolution during D. suzukii invasion.

evolutionary biology↗

bioRxiv2024

Secreted Particle Information Transfer (SPIT) - A Cellular Platform For In Vivo Genetic Engineering

A multitude of tools now exist that allow us to precisely manipulate the human genome in a myriad of different ways. However, successful delivery of these tools to the cells of human patients remains a major barrier to their clinical implementation. Here we introduce a new cellular approach for in vivo genetic engineering, Secreted Particle Information Transfer (SPIT) that utilizes human cells as delivery vectors for in vivo genetic engineering. We demonstrate the application of SPIT for cell-cell delivery of Cre recombinase and CRISPR-Cas9 enzymes, we show that genetic logic can be incorporated into SPIT and present the first demonstration of human cells as a delivery platform for in vivo genetic engineering in immunocompetent mice. We successfully applied SPIT to genetically modify multiple organs and tissue stem cells in vivo including the liver, spleen, intestines, peripheral blood, and bone marrow. We anticipate that by harnessing the large packaging capacity of a human cells nucleus, the ability of human cells to engraft into patients long term and the capacity of human cells for complex genetic programming, that SPIT will become a paradigm shifting approach for in vivo genetic engineering.

synthetic biology↗

bioRxiv2024

Genetic designs for stochastic and probabilistic biocomputing

The programming of computations in living cells can be done by manipulating information flows within genetic networks. Typically, a single bit of information is encoded by a single genes steady state expression. Expression is discretized into high and low levels that correspond to 0 and 1 logic values, analogous to the high and low voltages in electronic logic circuits. However, the processes of molecular signaling and computation in living systems challenge this computational paradigm with their dynamic, stochastic and continuous operation. Although there is a good understanding of these phenomena in genetic networks, and there are already stochastic and probabilistic models of computation which can take on these challenges, there is currently a lack of work which puts both together to implement computations tailored to these features of living matter. Here, we design genetic networks for stochastic and probabilistic computing paradigms and develop the theory behind their operation. Moving beyond the digital abstraction, we explore the concepts of bit-streams (sequences of pulses acting as time-based signals) and probabilistic-bits or p-bits (values that can be either 1 or 0 with an assigned probability), as more suitable candidates for the encoding and processing of information in genetic networks. Specifically, the conceptualization of signals as stochastic bit-streams allows for encoding information in the frequency of random expression pulses, offering advantages such as robustness to noise. Additionally, the notion of p-bit enables the design of genetic circuits with capabilities surpassing those of current genetic logic gates, including invertibility. We design several circuits to illustrate these advantages and provide mathematical models and computational simulations that demonstrate their functionality. Our approach to stochastic and probabilistic computing in living cells not only enhances and reflects understanding of information processing in biological systems but also presents promising avenues for designing genetic circuits with advanced functionalities.

synthetic biology↗

bioRxiv2024

Genetic-based inference of densities, effective and census sizes of expanding riverine meta-populations of an invasive large-bodied freshwater fish (Silurus glanis L.).

Effective (Ne) and census (Nc) population sizes are key eco-evolutionary parameters. Jointly estimating them have an important practical value for efficient conservation and wildlife monitoring and management. Assessing Ne and Nc remains however challenging for elusive, rare species or species inhabiting in complex habitats like large rivers. Genetic-based Ne estimations could help resolve complex situations, as only a handful of genotyped individuals are needed to estimate Ne, and then NC can be subsequently using an Ne/NC ratio. However, most Ne estimation methods are based on restrictive assumptions (e.g. Wright-Fisher model) making them inappropriate for inferring Ne and Nc for populations and species exhibiting complex dynamics. Here, we aimed at estimating Ne, NC and densities for meta-populations of a large invasive freshwater fish (the European catfish Silurus glanis) that has been introduced in the Garonne-Dordogne river basin (Southwestern France), using a framework that combines multiple data sources and approaches. First, we characterized spatial patterns of genetic variation using microsatellite genotype data, revealing a significant isolation by distance pattern informing about the species dispersal capacities. We then detected four genetically-distinct clusters of individuals coexisting in the river basin that might be the result of multiple introductions from different genetic sources. Further, we characterized the demographic expansion of the species at the river basin scale by analyzing data from a multidecadal demographic monitoring survey, and estimated a specific Ne/Nc ratio for this species. We finally combined all the gathered information to design four competing demo-genetic models accounting for all the complexity of S. glanis meta-populations inhabiting the river basin. We simulated data under these models and then inferred Ne, Nc and densities through approximate Bayesian computation and random forest procedures. We show how multiple genetic and non-genetic approaches can be combined to estimate Ne and Nc in hard-to-monitor meta-populations exhibiting complex demo-evolutionary dynamics.

ecology↗

bioRxiv2024

Genetic Dissection of Heat Stress Tolerance in Soybean through Genome-Wide Association Studies and the Use of Genomic Prediction to Enhance Breeding Applications

Rising temperatures and associated heat stress pose an increasing threat to soybean [Glycine max L. (Merr.)] productivity. Due to a limited choice of mitigation strategies, the primary arsenal in crop protection comes from improved genetic stress tolerance. Despite this current and looming threat to soybean production, limited studies have examined the genetics of heat stress tolerance. There is a need to conduct large-scale germplasm screening and genetic studies, including genome-wide association mapping and genomic prediction, to identify genomic regions and useful markers associated with heat tolerance traits that can be utilized in soybean breeding programs. We screened a diverse panel of 450 soybean accessions from MG 0-IV to dissect the genetic architecture of physiological and growth-related traits under optimal and heat stress temperatures and study trait relationships and predictive ability. The genetic architecture information of the response to heat revealed in this study provides insights into the genetics of heat stress tolerance. Thirty-seven significant SNPs were detected, with 20 unique SNPs detected in optimal, 16 detected in heat stress, and a single SNP detected for a heat tolerance index. Only one significant SNP was identified across temperature treatments indicating a genetic divergence in soybean responses to temperature. The genomic prediction worked well for biomass traits, but physiological traits associated with heat stress had poor model accuracy. Through our phenotyping efforts, we identified heat tolerant soybean accessions. The identification of heat tolerant accessions and significant SNPs are useful in heat tolerant variety development through marker-assisted and genomic selection. Core ideasO_LISoybean exhibit phenotypic diversity in response to heat stress. C_LIO_LILarge scale phenotypic screening identified heat tolerant accessions. C_LIO_LIPreviously unreported QTL and SNP associated with biomass and physiological parameters under heat stress are reported. C_LIO_LIGenomic prediction shows promise in abiotic stress breeding applications. C_LI

genomics↗

bioRxiv2024

Monitoring biodiversity in the global change era: The importance of herbaria and genetic diversity

Climate change is having far-reaching consequences on all living beings, altering ecosystems, habitats, and biodiversity worldwide. Species distributions are shifting or decreasing, with alpine plant species being particularly threatened. Natural population monitoring allows the assessment of the impact of human-induced global changes. However, traditional monitoring strategies based on individual counts may produce delayed signals of biodiversity loss. These approaches overlook the fact that genetic diversity is the fundamental basis for evolutionary processes, as it enables populations to adapt to environmental changes, including those caused by climate change. Here, we draw attention to the use of genetic diversity in monitoring schemes to anticipate negative trends in biodiversity and propose two fundamental methodologies: genomics and the use of herbarium specimens. Firstly, in contrast to genetic markers conventionally used to quantify genetic diversity, such as microsatellite markers, genomic approaches provide a vast amount of data that does not require previous knowledge of the studied organism, making them suitable for the study of non-model species. Secondly, herbaria worldwide serve as excellent sources of plant material for comparative studies across time with their precise chronologically recorded collection data. The accuracy of genetic diversity estimates increases with sample size, therefore a large number of vouchers is ideally required. However, the availability of specimens from the same species and populations in public herbaria is limited. Different strategies to quantify genetic diversity are proposed depending on the number of specimens available and their geographic distribution. Finally, we illustrate the potential of this approach in the most restrictive scenario, where only a few individuals are available, and there is no conspecific reference genome. Even in this restrictive scenario, there are signs of genetic depauperation in an alpine species with a narrow distribution, but not in a widely distributed congeneric.

plant biology↗

bioRxiv2024

Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal new mechanisms of genome regulation

Gene expression levels can vary substantially across cells, even in a seemingly homogeneous cell population. Identifying the relationships between genetic variation and gene expression is critical for understanding the mechanisms of genome regulation. However, the genetic control of gene expression variability among the cells within individuals has yet to be extensively examined. This is primarily due to the statistical challenges, such as the need for sufficiently powered cohorts and adjusting mean-variance dependence. Here, we introduce MEOTIVE (Mapping genetic Effects On inTra-Individual Variability of gene Expression), a novel statistical framework to identify genetic effects on the gene expression variability (sc-veQTL) accounting for the mean-variance dependence. Using single-cell RNA-seq data of 1.2 million peripheral blood mononuclear cells from 980 human donors, we identified 14 - 3,488 genes with significant sc-veQTLs (study-wide q-value < 0.05) across different blood cell types, 2,103 of which were shared across more than one cell type. We further detected 55 SNP-gene pairs (in 34 unique genes) by directly linking genetic variations with gene expression dispersion (sc-deQTL) regardless of mean-variance dependence, and these genes were enriched in biological processes relevant to immune response and viral infection. An example is rs1131017 (p<9.08x10-52), a sc-veQTL in the 5UTR of RPS26, which shows a ubiquitous dispersion effect across cell types, with higher dispersion levels associated with lower auto-immune disease risk, including rheumatoid arthritis and type 1 diabetes. Another example is LYZ, which is associated with antibacterial activity against bacterial species and was only detected with a monocyte-specific deQTL (rs1384) located at the 3 UTR region (p=1.48x10-11) and replicated in an independent cohort. Our results demonstrate an efficient and robust statistical method to identify genetic effects on gene expression variability and how these associations and their involved pathways confer auto-immune disease risk. This analytical framework provides a new approach to unravelling the genetic regulation of gene expression at the single-cell resolution, advancing our understanding of complex biological processes.

genomics↗

bioRxiv2024

Indirect genetic effects increase the heritable variation available to selection and are largest for behaviours: a meta-analysis

The evolutionary potential of traits is governed by the amount of heritable variation available to selection. While this is typically quantified based on genetic variation in a focal individual for its own traits (direct genetic effects, DGEs), when social interactions occur, genetic variation in interacting partners can influence a focal individuals traits (indirect genetic effects, IGEs). Theory and studies on domesticated species have suggested IGEs can greatly impact evolutionary trajectories, but whether this is true more broadly remains unclear. Here we perform a systematic review and meta-analysis to quantify the amount of trait variance explained by IGEs and the contribution of IGEs to predictions of adaptive potential. We identified 180 effect sizes from 47 studies across 21 species and found that, on average, IGEs of a single social partner account for a small but statistically significant amount of phenotypic variation (0.03). As IGEs affect the trait values of each interacting group member and due to a typically positive - although statistically nonsignificant - correlation with DGEs (rDGE-IGE = 0.26), IGEs ultimately increase trait heritability substantially from 0.27 (narrow-sense heritability) to 0.45 (total heritable variance). This 66% average increase in heritability suggests IGEs can increase the amount of genetic variation available to selection. Furthermore, whilst showing considerable variation across studies, IGEs were most prominent for behaviours, and to a lesser extent for reproduction and survival, in contrast to morphological, metabolic, physiological, and development traits. Our meta-analysis therefore shows that IGEs tend to enhance the evolutionary potential of traits, especially for those tightly related to interactions with other individuals such as behaviour and reproduction. Lay SummaryPredicting evolutionary change is important for breeding better livestock and crops, for understanding how biodiversity arises and how populations respond to environmental change. Normally, these predictions are based on how the genetic variants in an organism influence its own traits (characteristics). However, when organisms socially interact, for instance by fighting or cooperating, then the genes in one individual can influence the traits of others, therefore affecting the potential for evolutionary change. We compared 47 studies across 21 animal species and found that the effect of the genes of a single social partner is small but statistically significant, while the total contribution of social genetic effects to evolutionary potential is large. These effects are particularly important for the evolution of animals behaviours and reproductive traits, but less so for other traits such as body size and physiology. We also found that, because an individual can interact with many others and influence them all, social interactions can substantially increase the potential for a population to evolve from generation to generation. Our results show how social interactions can potentially alter the evolution of those traits known to respond to social interactions in comparison to standard expectations.

evolutionary biology↗

bioRxiv2024

Structure-specific variation in per- and polyfluoroalkyl substances toxicity among genetically diverse Caenorhabditis elegans strains

BackgroundThere are >14,500 structurally diverse per- and polyfluoroalkyl substances (PFAS). Despite knowledge that these "forever chemicals" are in 99% of humans, mechanisms of toxicity and adverse health effects are incompletely known. Furthermore, the contribution of genetic variation to PFAS susceptibility and health consequences is unknown. ObjectivesWe determined the toxicity of a structurally distinct set of PFAS in twelve genetically diverse strains of the genetic model system Caenorhabditis elegans. MethodsDose-response curves for four perfluoroalkyl carboxylic acids (PFNA, PFOA, PFPeA, and PFBA), two perfluoroalkyl sulfonic acids (PFOS and PFBS), two perfluoroalkyl sulfonamides (PFOSA and PFBSA), two fluoroether carboxylic acids (GenX and PFMOAA), one fluoroether sulfonic acid (PFEESA), and two fluorotelomers (6:2 FCA and 6:2 FTS) were determined in the C. elegans laboratory reference strain, N2, and eleven genetically diverse wild strains. Body length was quantified by image analysis at each dose after 48 hr of developmental exposure of L1 arrest-synchronized larvae to estimate effective concentration values (EC50). ResultsThere was a significant range in toxicity among PFAS: PFOSA > PFBSA {approx} PFOS {approx} PFNA > PFOA > GenX {approx} PFEESA > PFBS {approx} PFPeA {approx} PFBA. Long-chain PFAS had greater toxicity than short-chain, and fluorosulfonamides were more toxic than carboxylic and sulfonic acids. Genetic variation explained variation in susceptibility to PFBSA, PFOS, PFBA, PFOA, GenX, PFEESA, PFPeA, and PFBA. There was significant variation in toxicity among C. elegans strains due to chain length, functional group, and between legacy and emerging PFAS. ConclusionC. elegans respond to legacy and emerging PFAS of diverse structures, and this depends on specific structures and genetic variation. Harnessing the natural genetic diversity of C. elegans and the structural complexity of PFAS is a powerful New Approach Methodology (NAM) to investigate structure-activity relationships and mechanisms of toxicity which may inform regulation of other PFAS to improve human and environmental health.

pharmacology and toxicology↗

bioRxiv2024

Age-Associated Genetic and Environmental Contributions to Epigenetic Aging Across Adolescence and Emerging Adulthood

BackgroundEpigenetic aging estimators commonly track chronological and biological aging, quantifying its accumulation (i.e., epigenetic age acceleration) or speed (i.e., epigenetic aging pace). Their scores reflect a combination of inherent biological programming and the impact of environmental factors, which are suggested to vary at different life stages. The transition from adolescence to adulthood is an important period in this regard, marked by an increasing and, then, stabilizing epigenetic aging variance. Whether this pattern arises from environmental influences or genetic factors is still uncertain. This study delves into understanding the genetic and environmental contributions to variance in epigenetic aging across these developmental stages. Using twin modeling, we analyzed four estimators of epigenetic aging, namely Horvath Acceleration, PedBE Acceleration, GrimAge Acceleration, and DunedinPACE, based on saliva samples collected at two timepoints approximately 2.5 years apart from 976 twins of four birth cohorts (aged about 9.5, 15.5, 21.5, and 27.5 years at first and 12, 18, 24, and 30 years at second measurement occasion). ResultsHalf to two-thirds (50-68%) of the differences in epigenetic aging were due to unique environmental factors, indicating the role of life experiences and epigenetic drift, besides measurement error. The remaining variance was explained by genetic (Horvath Acceleration: 24%; GrimAge Acceleration: 32%; DunedinPACE: 47%) and shared environmental factors (Horvath Acceleration: 26%; PedBE Acceleration: 47%). The genetic and shared environmental factors represented the primary sources of stable differences in corresponding epigenetic aging estimators over 2.5 years. Age moderation analyses revealed that the variance due to individually-unique environmental sources was smaller in younger than in older cohorts in epigenetic aging estimators trained on chronological age (Horvath Acceleration: 47% to 49%; PedBE Acceleration: 33% to 68%). The variance due to genetic contributions, in turn, potentially increased across age groups for epigenetic aging estimators trained in adult samples (Horvath Acceleration: 18% to 39%; GrimAge Acceleration: 24% to 43%; DunedinPACE: 42% to 57%). ConclusionsTransition to adulthood is a period of the increasing variance in epigenetic aging. Both environmental and genetic factors contribute to this trend. The degree of environmental and genetic contributions can be partially explained by the design of epigenetic aging estimators.

genomics↗