Biology subjects

Tellier, A.

Publications and source records attributed to Tellier, A..

At least 19 recordsLinked to original sources

bioRxiv2026

HIV-1 Tat protects macrophages from killing by the Exoenzyme U from Pseudomonas aeruginosa

People living with HIV (PLWH) have a higher risk of developing other diseases, such as bacterial pneumonia. While Pseudomonas aeruginosa (PAE) is a common cause of pneumonia in humans, PLWHs are infrequently infected by PAE. The reasons for this relative resistance of PLWH to PAE are not known. The most virulent PAE strains produce ExoU which is a key effector of PAE cytotoxicity. Upon injection into the target cell cytosol by the type III secretion system, ExoU binds to PI(4,5)P2 on the inner leaflet of the plasma membrane. Its phospholipase activity then induces a loss of plasma membrane integrity, rapidly leading to cell death. HIV-Tat is secreted by HIV-infected cells, leading to nanomolar concentrations of Tat in the sera of PLWH, even under antiretroviral therapy. Circulating Tat can be endocytosed by uninfected cells, translocate to the cytosol and bind to PI(4,5)P2 at the plasma membrane. In uninfected cells only, Tat is palmitoylated, enabling Tat to become resident on PI(4,5)P2. We found that Tat can interfere with the recruitment of ExoU by PI(4,5)P2, thereby protecting macrophages from PAE toxicity. HIV Tat could therefore be involved in the relative protection of PLWH against the most aggressive PAE isolates and their ExoU type III effector. Tat nevertheless enhances the toxicity of ExoU-deficient PAE strains toward macrophages. ImportancePeople living with HIV (PLWH) are at risk of developing bacterial pneumonia. A widespread cause of pneumonia is Pseudomonas aeruginosa (PAE) but, for unknown reasons, PLWHs are infrequently infected by PAE. A key effector of PAE cytotoxicity is ExoU that is injected by PAE into the target cell cytosol, then binds to PI(4,5)P2 on the inner leaflet of the plasma membrane. The potent phospholipase activity of ExoU then induces a loss of plasma membrane integrity, rapidly leading to cell death. HIV-Tat is present in the serum of PLWH. Circulating Tat is endocytosed by cells, translocates to the cytosol and bind to PI(4,5)P2 at the plasma membrane with a very high affinity. This study indicates that Tat interferes with the recruitment of ExoU by PI(4,5)P2, thereby protecting macrophages from ExoU+ PAE. HIV Tat could therefore be involved in the relative protection of PLWH against the most aggressive PAE isolates.

microbiology↗

bioRxiv2026

Evolutionary transitions to self-fertilization influence the inference of introgression history

The availability of polymorphism data and statistical inference methods allows documenting the widespread occurrence of introgression and hybridization across the tree of life. However, these methods are primarily optimized for outcrossing species without generation overlap or seed banking, thereby ignoring the consequences of life-history traits (and their evolution) on genome-wide polymorphism patterns. We investigate how a transition from outcrossing to selfing, a common feature of plant species, may affect the inference of introgression history. We simulate six demographic models with different histories of gene flow under two mating-system scenarios: a constant high selfing rate and a transition-to-selfing scenario. Using an Approximate Bayesian Computation framework with random forests, we compare model choice based on genotypic summary statistics alone and in combination with coalescent statistics derived from coalescent tree sequences. Including coalescent information substantially improves model classification, especially for distinguishing secondary contact and continuous gene flow. Cross-classification of pseudo-observed datasets shows that ignoring a transition to selfing can lead to false demographic inferences, with transition-to-selfing data often misclassified as ancient gene flow or secondary contact when analyzed under a constant selfing model. We then apply this inference framework to genomic data from Arabis nemorensis and Arabis sagittata, two predominantly selfing species with evidence for post-split hybridization. Our analyses reveal a likely transition to selfing roughly 470,000-890,000 years ago, and a likely continuous level of gene flow after the species split. The latter results lead us to revisit our previous scenario of gene flow due to secondary contact between species inferred under constant selfing. Changes in mating systems and, by extension, life-history traits can therefore bias inference about introgression if they are not explicitly modeled. Tree-sequence-based coalescent statistics provide useful information for inferring complex demographic histories that involve both gene flow and transitions to selfing.

evolutionary biology↗

bioRxiv2026

Proteogenomics of Blumeria hordei supports RNA and protein coding innovative potential derived from transposable elements

Some filamentous plant-pathogenic fungi have comparably large genome sizes within the fungal kingdom due to the proliferation of transposable elements (TEs). Blumeria hordei (Bh), the causal agent of the powdery mildew disease on barley, is a filamentous obligate biotrophic fungus. Compared to other ascomycetes, it contains a low number of genes but a high genomic TE content of approximately 75%. Yet, a comprehensive understanding of the contribution of TEs to the RNA and protein landscape of Bh is lacking. Here, we use Bh as a model to study transcripts and proteins derived from genes and individual TEs. Therefore, we created two high-quality genome assemblies of the German Bh isolate TUM1 and the Australian Bh isolate AUS1. We applied deep proteomics with mass spectrometry, long-read and short-read sequencing on both DNA and RNA. Based on these multi-omic resources, we completed nearly gapless genome assemblies, new gene and TE annotations, and effector predictions. Using long-read RNA sequencing, we detected extensive co-transcription of TEs and genes as TE-gene chimeric transcripts. We identified previously unpredicted splice variants or genes, partially supported by proteomics. The intergenic and TE genomic space of Bh TUM1 gives rise to thousands of transcripts and several novel TE-derived proteins that lack from previous TE protein predictions. Together, this supports an existing potential for expression of novel transcripts and proteins from highly abundant TEs in the Bh genome.

plant biology↗

bioRxiv2026

The crop pathogen Blumeria hordei exhibits genome-wide pervasive selective and neutral sweepstakes reproduction signatures.

The fungal crop pathogen Blumeria hordei, causal agent of powdery mildew on barley, presents life-history and epidemiological characteristics, as well as and selective pressures due to modern agriculture leading to expected sweepstakes reproduction, that is highly skewed offspring distributions. Using genome-wide polymorphism data and population genomics inferences, we aim to 1) infer the past demographic history and the strength of sweepstakes reproduction in B. hordei, and 2) quantify the contributions of these selective and neutral processes in the genome. An new inference method based on Neural Posterior Estimation and diversity and linkage disequilibrium statistics was developed and tested on simulated and B. hordei genomic data. We confirm that B. hordei exhibits a moderate sweepstakes reproduction (-parameter of 1.6). We highlight that the Site Frequency Spectrum (SFS) appears sensitive to the joint occurrence of sweepstakes and recent demographic changes, which may caution on the reliability of the SFS to infer sweepstakes reproduction. We then scan the genome for selective sweeps, adjusting the significance thresholds of the methods for demographic history and sweepstakes reproduction, thereby yielding a counterintuitive result. When conditioning the significance threshold for sweep detection on simulations under sweepstakes and demography, a very large number of putatively selected regions is found (11.6% of the genome). We suggest that sweepstakes reproduction in B. hordei is due to 1) neutrality (clonal/sexual phases and Boom-and-Bust cycles) generating a genome-wide level of background noise in the coalescent genealogies, and 2) selective sweepstakes due to pervasive positive selection. Our findings have important implications for both population genomic methodology and our understanding of pathogen evolution.

evolutionary biology↗

bioRxiv2025

Contemporary hybridization among Arabis floodplain species creates opportunities for adaptation

Hybridization between closely related species is increasingly recognized as a major source of biodiversity. Yet, whether it can create advantageous trait combinations while purging harmful alleles remains unknown. We studied Arabis nemorensis and A. sagittata, two endangered species that currently hybridize in a single hotspot. We measured 22 phenotypic traits and mapped their genetic basis in an F2 population, after generating high quality genome assemblies for both species. In total, 58 QTLs were identified for 20 traits, with additive and dominance effects best fitting Gaussian and logistic distributions, respectively. Six large-effect QTLs were linked to significant hybrid fitness loss. Two genomic regions showed strong transmission bias favoring A. sagittata alleles, potentially accelerating their introgression. However, 48% of QTLs were unlinked to reduced fitness or segregation distortion and may generate genotypes exceeding parental performance. Notably, a major QTL affecting flowering time explained 23% of phenotypic variation and implicated TFL1 as a candidate gene for life history adaptation. While most QTLs lacked overlap with past selective sweeps, indicating limited recent positive selection, 5 of 7 QTLs for rosette size overlapped with sweep signatures in the parental lineages. Overall, our findings offer unique insights into incipient stages of hybridization.

evolutionary biology↗

bioRxiv2025

Coalescence and Translation: A Language Model for Population Genetics

Probabilistic models such as the sequentially Markovian coalescent (SMC) have long provided a powerful framework for population genetic inference, enabling reconstruction of demographic history and ancestral relationships from genomic data. However, these methods are inherently specialized, relying on predefined assumptions and/or limited scalability. Recent advances in simulation and deep learning provide an alternative approach: learning directly to generalize from synthetic genetic data to infer specific hidden evolutionary processes. Here we reframe the inference of coalescence times as a problem of translation between two biological languages: the sparse, observable patterns of mutation along the genome and the unobservable ancestral recombination graph (ARG) that gave rise to them. Inspired by large language models, we develop cxt, a decoder-only transformer that autoregressively predicts coalescent events conditioned on local mutational context. We show that cxt performs on par with state-of-the-art MCMC-based likelihood models across a broad range of demographic scenarios, including both in-distribution and out-of-distribution settings. Trained on simulations spanning the stdpopsim catalog, the model generalizes robustly and enables efficient inference at scale, producing over a million coalescence predictions in minutes. In addition cxt produces a well calibrated approximate posterior distribution of its predictions, enabling principled uncertainty quantification. We apply cxt to population genomic data from both humans and mosquitoes, highlighting the models ability to deal with the complexities of empirical data. Significance statementcxt is a language model for population genetics which introduces next-coalescence prediction as translation from observed mutations to coalescence times by modeling the coalescent with recombination as a conditional stochastic process. It learns implicit priors from stdpopsim and generalizes across both known and novel demographies. cxt generates millions of TMRCA estimates in minutes and samples well-calibrated posteriors for uncertainty quantification. A simple post-hoc correction aligns predicted diversity with the species mutation rate, ensuring robustness to novel evolutionary scenarios.

evolutionary biology↗

bioRxiv2024

Methylomes reveal recent evolutionary changes in populations of two plant species

Plant DNA methylation changes occur hundreds to thousands of times faster than DNA mutations and can be transmitted transgenerationally, making them useful for studying population-scale patterns in clonal or selfing species. However, a state-of-the-art approach to use them for inferring population genetic processes and demographic histories is lacking. To address this, we compare evolutionary signatures extracted from CG methylomes and genomes in Arabidopsis thaliana and Brachypodium distachyon. While methylation variants (SMPs) are less effective than single nucleotide polymorphisms (SNPs) for identifying population differentiation in A. thaliana, they can classify phenotypically divergent B. distachyon subgroups that are otherwise genetically indistinguishable. The site frequency spectra generated using methylation sites from varied genomic locations and evolutionary conservation exhibit an excess of rare alleles. Nucleotide diversity estimates were three orders of magnitude higher for methylation variants than for SNPs in both species, driven by the higher epimutation rate. Correlations between SNPs and SMPs in nucleotide diversity and allele frequencies at gene exons are weak or absent in A. thaliana, possibly because the two sources of variation reflect evolutionary forces acting at different timescales. Linkage disequilibrium quickly decays within 100 bp for methylation variants in both plant species. Finally, we developed a deep learning-based demographic inference approach. We identified recent population expansions in A. thaliana and B. distachyon using methylation variants that were not identified when using SNPs. Our study demonstrates the unique evolutionary insights methylomes provide that SNPs alone cannot reveal.

evolutionary biology↗

bioRxiv2023

Contrasting patterns of presence-absence variation of NLRS within S. chilense are mainly shaped by past demographic history

Understanding the evolution of pathogen resistance genes (nucleotide-binding site-leucine-rich repeats, also known as NLRs) within a species requires a comprehensive examination of factors that affect gene loss and gain. We present a new reference genome of Solanum chilense, that leads to an increased number and more accurate annotation of NLRs. Next, using a target-capture approach, we quantify the presence-absence variation (PAV) of NLR loci across 20 populations from different habitats. We build a rigorous pipeline to validate the identification of PAV of NLRs, then show that PAV is larger within populations than between populations, suggesting that maintenance of NLR diversity is linked to population dynamics. Furthermore, the amount of PAV is not correlated with the NLR presence in gene clusters in the genome, but rather with the past demographic history of the species, with loss of NLRs in diverging populations at the distribution edges and smaller population sizes. Finally, using a redundancy analysis, we find limited evidence of PAV being linked to environmental gradients. Our results contradict the classic assumptions of the important selective role of PAV for NLRs, and suggest that NLRs PAV is driven by random processes (and weak selection) in an outcrossing plant with high nucleotide diversity.

evolutionary biology↗

bioRxiv2023

Copy number variations shape genomic structural diversity underpinning ecological adaptation in the wild tomato Solanum chilense

Copy Number Variation (CNV) is a prevalent type of variation affecting large genomic regions which contributes to both genetic diversity and ecological adaptation in plants. The target genes involved in adaptation through CNV in tomato and its wild relatives remain unexplored at the population level. Therefore, we characterized the CNV landscape of Solanum chilense, a wild tomato species adapted to dry habitats, using whole-genome short-read data of 35 individuals from seven populations. We identified 212,207 CNVs, including 160,926 deletions and 51,281 duplications. We found a higher number of CNVs in diverging populations occupying stressful habitats. CNVs and single nucleotide polymorphisms analyses concordantly revealed the known species population structure, underscoring the impact of historical demographic and recent colonization events shaping genome-wide CNVs. Furthermore, we identified 3,539 candidate genes with highly divergent CNV profiles across populations. Interestingly, these genes are functionally associated with response to abiotic stress and linked to multiple pathways of flowering time regulation. Gene CNVs in S. chilense exhibit two evolutionary trends: gene loss in ancestral lineages distributed in central and southern coast populations and gene gain in the most recent diverged lineage from the southern highland region. Environmental association of the CNVs ultimately linked the dynamics of gene copy number to six climatic variables. It suggests that natural selection has likely shaped CNV patterns in stress-response genes promoting the colonization of contrasting habitats. Our findings provide insights into the role of CNV underlying adaptation during recent range expansion.

evolutionary biology↗

bioRxiv2023

Inference of host-pathogen interaction matrices from genome-wide polymorphism data

Coevolution is defined as the evolutionary change in one antagonist (host) in response to changes in the other antagonist (pathogen). At the genetic level, these changes are determined by genotype x genotype (GxG) interactions. We build on a general theoretical model of a host-pathogen interaction to derive four indices to retrieve key features of GxG interactions. The four developed indices extract relevant information from polymorphism data of randomly sampled uninfected hosts as well as infected hosts and their respective pathogen strains. Using these indices as summary statistics in an Approximate Bayesian Computation method, we can show their power to discriminate between GxG interaction matrices. Second, we apply our ABC method to a SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains supplemented by polymorphism data of 503 individuals from the 1,000 genomes project. As our indices encompass and extend previous natural co-GWAs we recover many of the associations previously reported for this dataset and infer their underlying interaction matrix. We reveal a new candidate gene for resistance to HCV in the human genome, and two groups of significant GxG associations exhibiting gene-for-gene interactions. We suggest that the inferred types of GxG interactions result from the recent expansion, adaptation and low prevalence of the HCV virus population in Europe. Significance statementWhy are some host individuals susceptible/resistant to infection by certain pathogen genotypes and others not? Understanding the genetic characteristics of genes driving host-pathogen interactions is crucial to predict epidemics. We develop four indices based on a mathematical model and build a Bayesian statistical method computing these indices on full genome data of infected hosts and their infecting pathogen strains and data of non-infected hosts. We can pinpoint the genes underlying host-pathogen interactions and infer their characteristics. Applying our framework to data from European humans and the Hepatitis C virus, we discover a new potential resistance gene in humans and reveal how the virus has adapted in the last 150 years to match the genetic diversity of the European human population.

evolutionary biology↗

bioRxiv2023

Next-generation inference of past population history by integrating diverse types of genomic markers

With the availability of high quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, e.g. bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to 1) improve the accuracy of demographic inference in recent times, 2) uncover past demographic events hidden to SNP-based inference methods, and 3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demographic inference in A. thaliana using DNA methylation diversity data from 10 European natural accessions. We demonstrate that segregating Single Methylated Polymorphisms (SMPs) satisfy the modelling assumptions of the SMC framework, while Differentially Methylated Regions (DMRs) are not suitable as their length exceeds that of the genomic distance between two recombination events. Combining SNPs and SMPs while accounting for site- and region-level epimutation processes, we provide new estimates of the glacial age bottleneck and post glacial population expansion of the European A. thaliana population. Our SMC framework readily accounts for a wide range of heritable genomic markers, thus paving the way for next generation inference of evolutionary history by combining information from several genetic and epigenetic markers.

evolutionary biology↗

bioRxiv2023

Evolution of two gene networks underlying adaptation to drought stress in the wild tomato Solanum chilense

Drought stress is a key factor limiting plant growth and the colonization of arid habitats by plants. Here, we study the evolution of gene expression response to drought stress in a wild tomato, Solanum chilense naturally occurring around the Atacama Desert in South America. We conduct a transcriptome analysis of plants under standard and drought experimental conditions to understand the evolution of drought-response gene networks. We identify two main regulatory networks corresponding to two typical drought-responsive strategies: cell cycle and fundamental metabolic processes. We estimate the age of the genes in these networks and the age of the gene expression network, revealing that the metabolic network has a younger origin and more variable transcriptome than the cell-cycle network. Combining with analyses of population genetics, we found that a higher proportion of the metabolic network genes show signatures of recent positive selection underlying recent adaptation within S. chilense, while the cell-cycle network appears of ancient origin and is more conserved. For both networks, however, we find that genes showing older age of selective sweeps are the more connected in the network. Adaptation to southern arid habitats over the last 50,000 years occurred in S. chilense by adaptive changes core genes with substantial network rewiring and subsequently by smaller changes at peripheral genes.

evolutionary biology↗

bioRxiv2022

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering the full trajectory compared to few time samples. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetics changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

evolutionary biology↗

bioRxiv2022

Interplay between demography and selection: a forward model illuminates the temporal genetic signatures of rapid adaptation in plant pathogens

Pathogen species are experiencing strong joint demographic and selective events, especially when they adapt to a new host, for example through overcoming plant resistance. Stochasticity in the founding event and the associated demographic variations hinder our understanding of the expected evolutionary trajectories and the genetic structure emerging at both neutral and selected loci. What would be the typical genetic signatures of such a rapid adaptation event is not elucidated. Here, we build a demogenetic model to monitor pathogen population dynamics and genetic evolution on two host compartments (susceptible and resistant). We design our model to fit two plant pathogen life cycles, with and without host alternation. Our aim is to draw a typology of eco-evolutionary dynamics. Using time-series clustering, we identify three main scenarios: 1) small variations in the pathogen population size and small changes in genetic structure, 2) a strong founder event on the resistant host that in turn leads to the emergence of genetic structure on the susceptible host, and 3) evolutionary rescue that results in a strong founder event on the resistant host, preceded by a bottleneck on the susceptible host. We pinpoint differences between life cycles with notably more evolutionary rescue with host alternation. Beyond the selective event itself, the demographic trajectory imposes specific changes in the genetic structure of the pathogen population. Most of these genetic changes are transient, with a signature of resistance overcoming that vanishes within a few years only. Considering time-series is therefore of utmost importance to accurately decipher pathogen evolution.

evolutionary biology↗

bioRxiv2022

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent

The reproductive mechanism of a species is a key driver of genome evolution. The standard Wright-Fisher model for the reproduction of individuals in a population assumes that each individual produces a number of offspring negligible compared to the total population size. Yet many species of plants, invertebrates, prokaryotes or fish exhibit neutrally skewed offspring distribution or strong selection events yielding few individuals to produce a number of offspring of up to the same magnitude as the population size. As a result, the genealogy of a sample is characterized by multiple individuals (more than two) coalescing simultaneously to the same common ancestor. The current methods developed to detect such multiple merger events do not account for complex demographic scenarios or recombination, and require large sample sizes. We tackle these limitations by developing two novel and different approaches to infer multiple merger events from sequence data or the ancestral recombination graph (ARG): a sequentially Markovian coalescent (SM{beta}C) and a graph neural network (GNNcoal). We first give proof of the accuracy of our methods to estimate the multiple merger parameter and past demographic history using simulated data under the {beta}-coalescent model. Secondly, we show that our approaches can also recover the effect of positive selective sweeps along the genome. Finally, we are able to distinguish skewed offspring distribution from selection while simultaneously inferring the past variation of population size. Our findings stress the aptitude of neural networks to leverage information from the ARG for inference but also the urgent need for more accurate ARG inference approaches.

genetics↗

bioRxiv2022

Inference of evolutionary transitions to self-fertilization using whole-genome sequences

The evolution from outcrossing to selfing is a transition that occurred recurrently throughout the eukaryote tree of life, in plants, animals, fungi and algae. Despite some short-term advantages, selfing is supposed to be an evolutionary dead-end reproductive strategy on the long-term and its tippy distribution on phylogenies suggests that most selfing species are of recent origin. However, dating such transitions is challenging while it is central for this hypothesis. We develop two methods making use of full genome polymorphism data to 1) test if a transition from outcrossing to selfing occurred, and 2) infer its age. The sequentially Markov coalescent based (teSMC) and the Approximate Bayesian Computation (tsABC) methods use a common framework based on a transition matrix summarizing the distribution of times to the most recent common ancestor along the genome, allowing to estimate changes in the ratio of population recombination and mutation rates in time. We first demonstrate that our methods can disentangle between past change in selfing rate from past changes in demographic history. Second, we assess the accuracy of our methods and show that transitions to selfing as old as approximatively 2.5Ne generations can be identified from polymorphism data. Third, our estimates are robust to the presence of linked negative selection on coding sequences. Finally, as a proof of principle, we apply both methods to three populations from Arabidopsis thaliana, recovering a transition to selfing which occurred approximately 600,000 years ago. Our methods pave the way to study recent transitions to predominant self-fertilization in selfing organisms. Significance statementSelf-fertilization evolved recurrently from outcrossing in many groups of organisms. When, why, and at what pace such transitions occurred are central to understand the evolution of reproductive systems but dating them remains highly challenging. While experimental work can be conducted in ecological set-ups, it is difficult to reconstruct and empirically test the past ecological conditions which could have driven transitions from outcrossing to self-fertilizing reproduction. We suggest here to use full genome data of several individuals per population to estimate if and when a transition in reproductive strategy occurred. We develop two methods which can be applied to estimate the age of such transitions jointly with the species demographic history.

evolutionary biology↗

bioRxiv2022

Weak seed banks influence the signature and detectability of selective sweeps

Seed banking (or dormancy) is a widespread bet-hedging strategy, generating a form of population overlap, which decreases the magnitude of genetic drift. The methodological complexity of integrating this trait implies it is ignored when developing tools to detect selective sweeps. But, as dormancy lengthens the ancestral recombination graph (ARG), increasing times to fixation, it can change the genomic signatures of selection. To detect genes under positive selection in seed banking species it is important to 1) determine whether the efficacy of selection is affected, and 2) predict the patterns of nucleotide diversity at and around positively selected alleles. We present the first tree sequence-based simulation program integrating a weak seed bank to examine the dynamics and genomic footprints of beneficial alleles in a finite population. We find that seed banking does not affect the probability of fixation and confirm expectations of increased times to fixation. We also confirm earlier findings that, for strong selection, the times to fixation are not scaled by the inbreeding effective population size in the presence of seed banks, but are shorter than would be expected. As seed banking increases the effective recombination rate, footprints of sweeps appear narrower around the selected sites and due to the scaling of the ARG are detectable for longer periods of time. The developed simulation tool can be used to predict the footprints of selection and draw statistical inference of past evolutionary events in plants, invertebrates, or fungi with seed banks.

evolutionary biology↗

bioRxiv2021

Local and temporal adaptation to climatic change in a wild tomato species via selective sweeps.

O_LIPositive selection is the driving force underpinning local adaptation, and leaves footprints of selective sweeps at the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plants species occurring in steep and varying environmental gradients is crucial to predict a species ability colonize new niches. C_LIO_LIWe use whole genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events, and distribution shifts. C_LIO_LIWe find evidence for several selective sweeps targeting regulatory networks involved in root hair development in low altitude, and response to photoperiod and vernalization in high altitude populations. These sweeps occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. C_LIO_LIWe decipher the genetic bases and the timing of local adaptation during plant colonization of semi-arid habitats using a unique combination of genome scans for selection and modelling of past climatic data. C_LI

evolutionary biology↗