Biology subjects

Grenier, J.-C.

Publications and source records attributed to Grenier, J.-C..

At least 19 recordsLinked to original sources

bioRxiv2026

Epistatic evolution drives HLA-dependent CD8+ T Cell escape risk in diverse populations

Understanding how viral evolution shapes HLA-dependent T cell escape is crucial to identify individuals at risk of reduced cellular immunity to emerging variants. Nevertheless, we lack frameworks to model HLA diversity and the evolutionary feasibility of T cell-evading mutations. Here, we construct an HLA map capturing variation in epitope specificity across HLA-typed cohorts. Enhancing this framework with SARS-CoV-2 CD8 T cell escape reveals heterogeneous escape across HLA-defined groups, with clusters enriched for HLA-B*07:02, HLA-A*03:01 and HLA-A*02:01 showing higher epitope loss. To assess the evolutionary plausibility of escape, we model viral sequence fitness using an epistasis-aware protein language approach trained on coronaviruses to systematically score mutations across viral lineages. We find that the fitness effect of mutations dynamically changes with evolving sequence context, and that T cell-evading mutations become fitter with additional escape mutations. This study links host HLA diversity to viral fitness landscapes for surveillance and vaccine design.

genomics↗

bioRxiv2026

CETP alternative splicing variation impacts human traits

The cholesteryl ester transfer protein (CETP) is an important protein in reverse cholesterol transport and has been identified as a significant factor associated with cardiovascular disease (CVD), making it a widely studied pharmaceutical target. Three protein-coding isoforms of CETP exist, distinguished by the alternative splicing of one exon each. The isoform primarily responsible for cholesterol-related functions in the plasma is well studied, but specific functions of each isoform remain poorly understood. In this study, we demonstrate the significance of considering CETPs isoforms in analyses of human traits. Using bulk RNA-seq data from multiple tissues, we characterized the expression patterns and genetic regulation determinants of CETP transcripts. Leveraging publicly available GWAS summary statistics, we conducted multivariable Mendelian Randomisation (MVMR) to estimate the impact of variation in isoform proportions on phenotypes, highlighting the importance of CETPs isoforms in pituitary and thyroid glands. Furthermore, we uncovered tissue-specific associations between CETPs isoforms and CVD-associated phenotypes. Additionally, we observed that the epistatic interaction previously reported between CETP and ADCY9, a gene implicated in modulating a CETP modulators response, may be mediated through the regulation of alternative splicing of exon 9. Our results underscore the importance of a comprehensive understanding of CETPs isoforms, which can significantly impact both fundamental and clinical research efforts.

genetics↗

bioRxiv2025

Chromatin landscape and enhancer-gene interactiondifferences between three cardiac cell types

Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNP) associated with a specific traits and diseases, however, uncovering the true disease-relevant SNPs remains challenging. One limitation for prioritizing true disease-relevant SNPs from GWAS is that most of the identified SNPs are non-coding, making it difficult to unravel their mechanism of action. Nevertheless, mapping non-coding SNPs to enhancers is a validated approach to link SNPs to their target genes through the analysis of enhancer-gene interactions (EGI) and thus provide insight into their mechanism of action. While previous studies linking cardiac disease-relevant SNPs to enhancers and their target genes have focused on the principal cardiac cell type, cardiomyocytes (CMs), the analysis of other non-CM cell types has been largely ignored and has only gained attention recently. We hypothesize that characterizing cell-type-specific enhancer-gene interactions (EGIs) for these non-CMs, namely cardiac fibroblasts (CFs), endothelial cells (ECs), and smooth muscle cells (SMCs), followed by mapping cardiac-disease-associated non-coding SNPs to those enhancers will identify novel disease-relevant genes and provide insights for future mechanistic research. To identify the landscape of cell-type-specific EGIs in these cardiac cells, we have employed the activity-by-Contact (ABC) model. It integrates assay for transposase-accessible chromatin sequencing (ATAC-seq), H3K27ac chromatin immunoprecipitation with sequencing (ChIP-seq), and high-throughput chromosome conformation capture with H3K27ac immunoprecipitation (H3K27ac HiChIP) data to identify EGIs. We have identified the landscape of cell-type-specific EGIs in these cardiac cells. Furthermore, a higher similarity of the chromatin accessibility profile (ATAC-seq) between CF and SMC, compared to CF and EC, and SMC and EC was observed. Finally, overlapping identified EGIs with cardiac-disease-associated non-coding variants has allowed the identification of a QT-interval-associated SNP that is mapped to the enhancer region of an EC-specific EGI.

genomics↗

bioRxiv2025

A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction

1Accurately capturing genetic ancestry is critical for ensuring reproducibility and fairness in genomic studies and downstream health research. This study aims to address the prediction of ancestry from genetic data using deep learning, with a focus on generalizability across datasets with diverse populations and on explainability to improve model transparency. We adapt the Diet Network, a deep learning architecture proven effective in handling high-dimensional data, to learn population ancestry from single nucleotide polymorphisms (SNPs) data using the populational Thousand Genomes Project dataset. Our results highlight the models ability to generalize to diverse populations in the CARTaGENE and Montreal Heart Institute biobanks and that predictions remain robust to high levels of missing SNPs. We show that, despite the lack of North African populations in the training dataset, the model learns latent representations that reflect meaningful population structure for North African individuals in the biobanks. To improve model transparency, we apply Saliency Maps, DeepLift, GradientShap and Integrated Gradients attribution techniques and evaluate their performance in identifying SNPs leveraged by the model. Using DeepLift, we show that models predictions are driven by population-specific signals consistent with those identified by traditional population genetics metrics. This work presents a generalizable and interpretable deep learning framework for genetic ancestry inference in large-scale biobanks with genetic data. By enabling more widespread genomic ancestry characterization in these cohorts, this study contributes practical tools for integrating genetic data into downstream biomedical applications, supporting more inclusive and equitable healthcare solutions.

bioinformatics↗

bioRxiv2024

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-Scale Sequencing Data

Understanding the evolution of viral genomes is essential for elucidating how viruses adapt and change over time. Analyzing intra-host single nucleotide variants (iSNVs) provides key insights into the mechanisms driving the emergence of new viral lineages, which are crucial for predicting and mitigating future viral threats. Despite the potential of next-generation sequencing (NGS) to capture these iSNVs, the process is fraught with challenges, particularly the risk of capturing sequencing artifacts that may result in false iSNVs. To tackle this issue, we developed a workflow designed to enhance the reliability of iSNV detection in large heterogeneous collections of NGS libraries. We use over 130,000 publicly available SARS-CoV-2 NGS libraries to show how our comprehensive workflow effectively distinguishes emerging viral mutations from sequencing errors. This approach incorporates rigorous bioinformatics protocols, stringent quality control metrics, and innovative usage of dimensionality reduction methods to generate representations of this high-dimensional dataset. We identified and mitigated batch effects linked to specific sequencing centers around the world and introduced quality control metrics that consider strand coverage imbalance, enhancing iSNV reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how related groups of data points are within a two-dimensional space, enhancing our ability to explain clustering patterns based on their shared genetic characteristics. Our workflow sheds light on the complexities of viral genomic analysis with state-of-the-art sequencing technologies and advances the detection of accurate intra-host mutations, opening the door for an enhanced understanding of viral adaptation mechanisms.

bioinformatics↗

bioRxiv2023

Towards Computing Attributions for DimensionalityReduction Techniques

We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification - using the gradients of target outputs with respect to the inputs - on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets.

bioinformatics↗

bioRxiv2023

Reply to Barton et al: signatures of natural selection during the Black Death

Barton et al.1 raise several statistical concerns regarding our original analyses2 that highlight the challenge of inferring natural selection using ancient genomic data. We show here that these concerns have limited impact on our original conclusions. Specifically, we recover the same signature of enrichment for high FST values at the immune loci relative to putatively neutral sites after switching the allele frequency estimation method to a maximum likelihood approach, filtering to only consider known human variants, and down-sampling our data to the same mean coverage across sites. Furthermore, using permutations, we show that the rs2549794 variant near ERAP2 continues to emerge as the strongest candidate for selection (p = 1.2x10-5), falling below the Bonferroni-corrected significance threshold recommended by Barton et al. Importantly, the evidence for selection on ERAP2 is further supported by functional data demonstrating the impact of the ERAP2 genotype on the immune response to Y. pestis and by epidemiological data from an independent group showing that the putatively selected allele during the Black Death protects against severe respiratory infection in contemporary populations.

genomics↗

bioRxiv2023

Signatures of co-evolution and co-regulation in the CYP3A and CYP4F genes in humans

1Cytochromes P450 (CYP450) are hemoproteins generally involved in the detoxification of the body of xenobiotic molecules. They participate in the metabolism of many drugs and genetic polymorphisms in humans have been found to impact drugs responses and metabolic functions. In this study, we investigate the genetic diversity for CYP450 genes. We found that two clusters, CYP3A and CYP4F, are notably differentiated across human populations with evidence for selective pressures acting on both clusters: we found signals of recent positive selection in CYP3A and CYP4F genes and signals of balancing selection in CYP4F genes. Furthermore, unusual linkage disequilibrium pattern is detected in both clusters, suggesting co-evolution of genes within clusters. Several of these selective signals co-localize with expression quantitative trait loci, which suggest co-regulation and epistasis within these highly important gene families. We also found that SNPs under selection in Africans within the CYP3A cluster are associated to CYP3A5 expression levels which are causally associated with reticulocytes count, as established by mendelian randomization. Furthermore, as the CYP3A and CYP4F subfamilies are involved in the metabolism of nutrients and drugs, our findings linking natural selection and gene expression in these gene clusters are of importance in understanding population differences in human health.

genetics↗

bioRxiv2022

Intra-host viral populations of SARS-CoV-2 in immunosuppressed patients with hematologic cancers

Throughout the SARS-CoV-2 pandemic, several variants of concern (VOC) have been identified, many of which share recurrent mutations in the spike proteins receptor binding domain (RBD). This region coincides with known epitopes and can therefore have an impact on immune escape. Protracted infections in immunosuppressed patients have been hypothesized to lead to an enrichment of such mutations and therefore drive evolution towards VOCs. Here, we show that immunosuppressed patients with hematologic cancers develop distinct populations with immune escape mutations throughout the course of their infection. Notably, by investigating the co-occurrence of substitutions on individual sequencing reads in the RBD, we found quasispecies harboring mutations that confer resistance to known monoclonal antibodies (mAbs) such as S:E484K and S:E484A. Furthermore, we provide the first evidence for a viral reservoir based on intra-host phylogenetics. Our results on viral reservoirs can shed light on protracted infections interspersed with periods where the virus is undetectable as well as an alternative explanation for some long-COVID cases. Our findings also highlight that protracted infections should be treated with combination therapies rather than by a single mAbs to clear pre-existing resistant mutations.

genomics↗

bioRxiv2022

Selection for immune evasion in SARS-CoV-2 revealed by high-resolution epitope mapping combined with genome sequence analysis

A deeper understanding of the molecular determinants that drive humoral responses to coronaviruses, and in particular severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is critical for improving and developing diagnostics, therapies and vaccines. Moreover, viral mutations can change key antigens in a manner that alters the ability of the immune system to detect and clear infections. In this study, we exploit a deep serological profiling strategy coupled with an integrated, computational framework for the analysis of SARS-CoV-2 humoral immune responses of asymptomatic or recovered COVID-19-positive patients relative to COVID-19-negative patients. We made use of a novel high-density peptide array (HDPA) spanning the entire proteomes of SARS-CoV-2 and endemic human coronaviruses to rapidly identify B cell epitopes recognized by distinct antibody isotypes in patients blood sera. Using our integrated computational pipeline, we then evaluated the fine immunological properties of detected SARS-CoV-2 epitopes and relate them to their evolutionary and structural properties. While some epitopes are common across all CoVs, others are private to specific hCoVs. We also highlight the existence of hotspots of pre-existing immunity and identify a subset of cross-reactive epitopes that contributes to increasing the overall humoral immune response to SARS-CoV-2. Using a public dataset of over 38,000 viral genomes from the early phase of the pandemic, capturing both inter- and within-host genetic viral diversity, we determined the evolutionary profile of epitopes and the differences across proteins, waves and SARS-CoV-2 variants, which have important implications for genomic surveillance and vaccine design. Lastly, we show that mutations in Spike and Nucleocapsid epitopes are under stronger selection between than within patients, suggesting that most of the selective pressure for immune evasion occurs upon transmission between hosts.

genomics↗

bioRxiv2021

Synchrony and idiosyncrasy in the gut microbiome of wild primates

Human gut microbial dynamics are highly individualized, making it challenging to link microbiota to health and to design universal microbiome therapies. This individuality is typically attributed to variation in host genetics, diets, environments, and medications, but it could also emerge from fundamental ecological forces that shape microbiota more generally. Here we leverage extensive gut microbial time series from wild baboons--hosts who experience little interindividual dietary and environmental heterogeneity--to test whether gut microbial dynamics are synchronized across hosts or largely idiosyncratic. Despite their shared lifestyles, baboon microbiome dynamics were only weakly synchronized. The strongest synchrony occurred among baboons living in the same social group, likely because group members range over the same habitat and simultaneously encounter the same sources of food and water. However, this synchrony was modest compared to each hosts personalized dynamics. Indeed, host-specific factors, especially host identity, explained 10 times the deviance in longitudinal microbial dynamics, compared to factors shared across hosts. These results contribute to mounting evidence that highly idiosyncratic gut microbiomes are not an artifact of modern human environments, and that synchronizing forces in the gut microbiome (e.g., shared environments, diets, and microbial dispersal) are often not strong enough to overwhelm drivers of microbiome personalization, including host genetics, priority effects, horizontal gene transfer, and functional redundancy.

ecology↗

bioRxiv2021

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajimas D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

bioinformatics↗

bioRxiv2021

ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences

We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ImputeCoVNet leads to accurate results at minor allele frequencies as low as 0.0001. When compared with an approach based on Hamming distance, ImputeCoVNet achieved comparable results with significantly less computation time. We also present the provision of geographical metadata (e.g., exposed country) to decoder increases the imputation accuracy. Additionally, by visualizing the embedding results of SARS-CoV-2 variants, we show that the trained encoder of ImputeCoVNet, or the embedded results from it, recapitulates viral clades information, which means it could be used for predictive tasks using virus sequence analysis.

bioinformatics↗

bioRxiv2021

The mutational landscape of SARS-CoV-2 variants diversifies T cell targets in an HLA supertype-dependent manner

The rapid, global dispersion of SARS-CoV-2 since its initial identification in December 2019 has led to the emergence of a diverse range of variants. The initial concerns regarding the virus were quickly compounded with concerns relating to the impact of its mutated forms on viral infectivity, pathogenicity and immunogenicity. To address the latter, we seek to understand how the mutational landscape of SARS-CoV-2 has shaped HLA-restricted T cell immunity at the population level during the first year of the pandemic, before mass vaccination. We analyzed a total of 330,246 high quality SARS-CoV-2 genome assemblies sampled across 143 countries and all major continents. Strikingly, we found that specific mutational patterns in SARS-CoV-2 diversify T cell epitopes in an HLA supertype-dependent manner. In fact, we observed that proline residues are preferentially removed from the proteome of prevalent mutants, leading to a predicted global loss of SARS-CoV-2 T cell epitopes in individuals expressing HLA-B alleles of the B7 supertype family. In addition, we show that this predicted global loss of epitopes is largely driven by a dominant C-to-U mutation type at the RNA level. These results indicate that B7 supertype-associated epitopes, including the most immunodominant ones, were more likely to escape CD8+ T cell immunosurveillance during the first year of the pandemic. Together, our study lays the foundation to help understand how SARS-CoV-2 mutants shape the repertoire of T cell targets and T cell immunity across human populations. The proposed theoretical framework has implications in viral evolution, disease severity, vaccine resistance and herd immunity.

immunology↗

bioRxiv2021

A sex-specific evolutionary interaction between ADCY9 and CETP

Pharmacogenomic studies have revealed associations between rs1967309 in the adenylyl cyclase type 9 (ADCY9) gene and clinical responses to the cholesteryl ester transfer protein (CETP) modulator dalcetrapib, however, the mechanism behind this interaction is still unknown. Here, we characterized selective signals at the locus associated with the pharmacogenomic response in human populations and we show that rs1967309 region exhibits signatures of positive selection in several human populations. Furthermore, we identified a variant in CETP, rs158477, which is in long-range linkage disequilibrium with rs1967309 in the Peruvian population. The signal is mainly seen in males, a sex-specific result that is replicated in the LIMAA cohort of over 3,400 Peruvians. Analyses of RNA-seq data further suggest an epistatic interaction on CETP expression levels between the two SNPs in multiple tissues, which also differs between males and females. We also detected interaction effects of the two SNPs with sex on cardiovascular phenotypes in the UK Biobank, in line with the sex-specific genotype associations found in Peruvians at these loci. We propose that ADCY9 and CETP coevolved during recent human evolution due to sex-specific selection, which points towards a biological link between dalcetrapibs pharmacogene ADCY9 and its therapeutic target CETP.

genetics↗

bioRxiv2020

Single-cell RNA-sequencing reveals pervasive but highly cell type-specific genetic ancestry effects on the response to viral infection

Humans vary in their susceptibility to infectious disease, partly due to variation in the immune response following infection. Here, we used single-cell RNA-sequencing to quantify genetic contributions to this variation in peripheral blood mononuclear cells, focusing specifically on the transcriptional response to influenza infection. We find that monocytes are the most responsive to influenza infection, but that all cell types mount a conserved interferon response, which is stronger in individuals with increased European ancestry. By comparing European American and African American individuals, we show that genetic ancestry effects on expression are common, influencing 29% of genes, but highly cell type-specific. Further, we demonstrate that much of this population-associated expression variation is explained by cis expression quantitative trait loci, which are enriched for signatures of recent positive selection. Our findings establish common cis-regulatory variants--including those that are differentiated by genetic ancestry--as important determinants of the antiviral immune response.

genomics↗

bioRxiv2020

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

1The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hi CD66blo neutrophil and IFN{gamma}+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

bioinformatics↗

bioRxiv2020

Primate innate immune responses to bacterial and viral pathogens reveals an evolutionary trade-off between strength and specificity

Despite their close genetic relatedness, apes and African and Asian monkeys (AAMs), strongly differ in their susceptibility to severe bacterial and viral infections that are important causes of human disease. Such differences between humans and other primates are thought to be a result, at least in part, of inter-species differences in immune response to infection. However, due to the lack of comparative functional data across species, it remains unclear in what ways the immune systems of humans and other primates differ. Here, we report the whole genome transcriptomic responses of ape species (human, common chimpanzee) and AAMs (rhesus macaque and olive baboon) to bacterial and viral stimulation. We find stark differences in the responsiveness of these groups, with apes mounting a markedly stronger early transcriptional response to both viral and bacterial stimulation, altering the transcription of [~]40% more genes than AAMs. Additionally, we find that genes involved in the regulation of inflammatory and interferon responses show the most divergent early transcriptional responses across primates and that this divergence is attenuated over time. Finally, we find that relative to AAMs, apes engage a much less specific immune response to different classes of pathogens during the early hours of infection, upregulating genes typical of anti-viral and anti-bacterial responses regardless of the nature of the stimulus. Overall, these findings suggest apes exhibit increased sensitivity to bacterial and viral immune stimulation, activating a broader array of defense molecules that may be beneficial for early pathogen killing at the potential cost of increased energy expenditure and tissue damage.

evolutionary biology↗