Biology subjects

Chatelain, C.

Publications and source records attributed to Chatelain, C..

9 recordsLinked to original sources

bioRxiv2026

RNA Editing of DEGS1 Links Genetic Risk to Ceramide Dysregulation and Astrocyte Toxicity in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with ~90% of cases being sporadic. Although genome-wide association studies have identified numerous genetic risk loci, these variants account for only a portion of ALS heritability, suggesting that additional genetic and regulatory mechanisms contribute to disease risk. Furthermore, the mechanisms linking common variants to disease pathogenesis remain poorly understood. Adenosine-to-inosine (A-to-I) RNA editing is dysregulated in ALS, yet systematic identification of disease-relevant editing events and their functional consequences has been limited. We performed comprehensive RNA editing analysis across eight central nervous system regions from a Target ALS cohort, identifying 752 differentially edited sites in 304 genes. Global RNA editing was significantly reduced in ALS tissues, particularly in spinal cord and choroid plexus, with altered editing in genes involved in immune signaling, stress response and RNA processing. Through genetic colocalization analysis integrating editing quantitative trait loci (edQTLs) with ALS GWAS data, we identified common variants at the DEGS1 locus associated with both increased 3' UTR editing and elevated ALS risk (posterior probability >0.88). DEGS1 encodes dihydroceramide desaturase 1, a key enzyme converting dihydroceramide to ceramide, the precursor to all sphingolipid species. Using CRISPR-generated DEGS1 knockout iPSCs and lipidomic profiling, we demonstrated that DEGS1 is essential for ceramide synthesis and downstream sphingolipid production in astrocytes. Functional validation revealed that ALS-associated editing of the DEGS1 3' UTR significantly increased DEGS1 protein expression. Critically, astrocytes expressing the fully edited DEGS1 3' UTR increased ceramide levels and reduced cell viability compared to those expressing the unedited construct. These findings establish a novel regulatory link between RNA editing, sphingolipid metabolism and ALS pathogenesis. Our results demonstrate that common genetic variants can influence disease risk through post-transcriptional mechanisms that alter lipid homeostasis in astrocytes, a cell type increasingly recognized as central to ALS progression.

neuroscience↗

bioRxiv2026

A novel tumor-targeted interferon-α/-β receptor 1 antagonist increases replication of oncolytic vesicular stomatitis virus in a mouse mesothelioma model

Type I Interferons (IFN-I) are cytokines with pleiotropic activities involved in antiviral and antitumor immune responses. They can reduce oncolytic virus replication in tumor cells by inducing expression of interferon stimulated genes (ISG) with antiviral functions. To specifically neutralize the IFN-/-{beta} receptor (IFNAR) on specific cell types, we created novel IFNAR1-targeted antagonists constituted of a high-affinity nanobody targeting a specific cell surface marker conjugated to a low-affinity blocking nanobody targeting IFNAR1. We first show in vitro and in vivo that such an antagonist targeting the mouse CD20 molecule (mCD20) inhibits IFNAR signaling only in B cells among splenocytes. We then showed in vitro that a human CD20 (hCD20)-targeted antagonist blocks IFNAR signaling and induces vesicular stomatitis virus (VSV) oncolytic activity against IFN-11-treated AK7 mesothelioma or B16 melanoma cells only if these cells express hCD20. In vivo, we show that the antagonist binds to hCD20 and enhances VSV replication by inhibiting ISG expression specifically in hCD20+ AK7 mesothelioma tumors. Altogether our results demonstrate the efficient and cell-type specific inhibition of IFNAR signaling through the use of these novel IFNAR1 antagonists, both in vitro and in vivo. These antagonists could have many therapeutic applications given the importance of IFN-I in numerous diseases. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/729496v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@4e76b6org.highwire.dtl.DTLVardef@153c39borg.highwire.dtl.DTLVardef@4f0948org.highwire.dtl.DTLVardef@ea8d85_HPS_FORMAT_FIGEXP M_FIG C_FIG eTOC synopsisIn this study, we created cell-specific IFNAR antagonists that allow to inhibit selectively IFNAR signaling in particular types of cell. We show that this antagonist can be used to target IFNAR at the surface of tumor cells that lead to the inhibition of IFNAR signaling and ISG expression in these cells rendering them more permissive to VSV replication. Beside antitumor virotherapy, these novel antagonist could be useful to study role of IFN-I in normal or pathological context.

immunology↗

bioRxiv2026

The Human Pleiotropic Map of GWAS Associations and Therapeutic Implications

Genetic support for drug targets substantially increases clinical success rates, establishing genome-wide association studies (GWAS) as central to therapeutic hypothesis generation. However, the same genetic evidence that reveals causal gene-disease relationships simultaneously exposes organism-level safety liabilities--a dimension requiring principled, genome-wide quantification. Here we systematically analyse 100,526 GWAS to yield 789,453 credible sets and gene prioritisations for 15,641 genes, with discovery showing no saturation as GWAS expand and increase diversity. We find that 64% of GWAS-implicated genes are pleiotropic, associated with traits across multiple diseases and showing a non-linear relationship between the degree of pleiotropy and clinical success. Highly pleiotropic genes--concentrated in immune, inflammatory, and oncogenic signalling programmes--are enriched in safety-terminated clinical programmes, mouse lethal knockouts, and cancer driver genes, establishing gene-level pleiotropy as a potential measure of genetically-informed organism-level safety liability. Protein-altering variant (PAV) support amplifies therapeutic signal (OR = 6.0), yet PAV targets show higher average pleiotropy, introducing a competing safety liability. Combining PAV support with intermediate pleiotropy (2-5 therapeutic areas) resolves this tension, yielding OR = 10.3 and relative success = 4.8--a profile already satisfied by 52 approved therapies. As GWAS continue to expand in scale and resolution, these findings lay the groundwork for increasingly sophisticated target discovery strategies that yield safer and more effective therapeutic hypotheses.

genomics↗

bioRxiv2025

Evidence of distal regulations orchestrated by RNAs initiating at short tandem repeats

Short Tandem Repeats (STRs), also called microsatellites, correspond to tandemly repeated short DNA motifs (1 to 6 bp) and are one of the most polymorphic and abundant repetitive elements in the human genome. Variations of their length (i.e. number of consecutive repeats) have been implicated in gene expression regulation (termed expression(e)STRs). Using captrapping followed by long read sequencing, we discovered that STRs can host transcription start sites, the presence of which depends mainly on STR flanking sequences. Here, we investigate the effect of SNPs located in these sequences and ask whether STR-initiating RNAs have regulatory potential. First, we develop fully interpretable deep learning-based models, called Modular Neural Networks, able to predict, for each STR class, the level of RNAs using 101bp-long sequences encompassing STRs. Analysis of MNN filters allows us to identify multiple regulatory elements and candidate transcription factors. Second, leveraging genome sequencing and gene expression data from the Genotype-Tissue Expression project, we use the output of our models to link the levels of STR-initiating RNAs to the expression of nearby genes. We identify 14,340 significant associations (coined RNA(r)STRs) and illustrate how this novel resource can help interpret non-coding variants associated with complex traits and diseases. Third, we unveil an intricate transcriptional interplay between STR-initiating RNAs and Alu repeats that may couple their regulatory actions, extending both the nature and the functional importance of non-coding transcription and shedding new light on the complexity of distal regulations orchestrated by repeated sequences.

genomics↗

bioRxiv2024

In vitro models to mimic tumor endothelial cell-mediated immune cell reprogramming in lung adenocarcinoma

Tumor endothelial (TECs) cells play a critical role in regulating immune responses within the tumor microenvironment (TME). However, the mechanisms by which TECs modulate immune cell population remain unclear, particularly in non-small cell lung cancer (NSCLC). Here, we investigated how NSCLC cells tweak normal endothelial cells (NECs) into TECs and the subsequent effects on immune regulation. NECs were cocultured with various NSCLC cell lines, using 2D and 3D coculture models to evaluate TEC-mediated effects on immune cells. We show that direct coculture led to significant transcriptomic, proteomic and kinomic alterations in TECs, especially in pro-inflammatory pathways. We identified a downregulation of the co-stimulatory molecule OX40L in TECs compared to NECs, suggesting impaired T-cell proliferation support. While TECs showed a limited effect on CD8+ T-cell activation, TECs supported CD4 T-cells polarization into Treg and Th22 subsets. Moreover, TECs also promoted M2-like macrophages polarization, thereby potentially contributing to the TME immunosuppression. State-of-the-art single-cell RNA sequencing of 3D multicellular tumor spheroids (MCTS) revealed distinct TEC subpopulations, including an inflammatory subset with UPR signature. The latter was absent in 2D-cultured NECs but present in freshly isolated and 2D-cultured TECs from NSCLC patients. Importantly, we also identified within MCTS a perivascular M2-like macrophage subset, predicted to interact with TECs with MIF and Midkine signaling. In conclusion, TECs in NSCLC tumors play a pivotal role in remodeling the TME immune landscape by promoting immune suppression. This study highlights the complex immunoregulatory functions of TECs within our different in vitro models that mimic aspects of the TME. Our data may provide new insights into potential therapeutic strategies targeting TECs or regulatory signaling to improve the efficacy of immunotherapy in NSCLC.

cancer biology↗

bioRxiv2020

Nonlinear post-selection inference for genome-wide association studies

To address the lack of statistical power and interpretability of genome-wide association studies (GWAS), gene-level analyses combine the p-values of individual single nucleotide polymorphisms (SNPs) into gene statistics. However, using all SNPs mapped to a gene, including those with low association scores, can mask the association signal of a gene. We therefore propose a new two-step strategy, consisting in first selecting the SNPs most associated with the phenotype within a given gene, before testing their joint effect on the phenotype. The recently proposed kernelPSI framework for kernel-based post-selection inference makes it possible to model non-linear relationships between features, as well as to obtain valid p-values that account for the selection step. In this paper, we show how we adapted kernelPSI to the setting of quantitative GWAS, using kernels to model epistatic interactions between neighboring SNPs, and post-selection inference to determine the joint effect of selected blocks of SNPs on a phenotype. We illustrate this tool on the study of two continuous phenotypes from the UKBiobank. We show that kernelPSI can be successfully used to study GWAS data and detect genes associated with a phenotype through the signal carried by the most strongly associated regions of these genes. In particular, we show that kernelPSI enjoys more statistical power than other gene-based GWAS tools, such as SKAT or MAGMA. kernelPSI is an effective tool to combine SNP-based and gene-based analyses of GWAS data, and can be used successfully to improve both statistical performance and interpretability of GWAS.

bioinformatics↗

bioRxiv2020

A systematic analysis of gene-gene interaction in multiple sclerosis

Multiple sclerosis is a complex autoimmune disease which genetic basis has been extensively investigated through genome wide association studies. So far, the conducted studies have detected a number of loci independently associated with the disease but few have investigated the interaction between distant loci, or epistasis. In the present work, we perform a gene level epistasis analysis of multiple sclerosis GWAS from the Wellcome Trust Case Control Consortium 2. We systematically study the epistatic interactions between all pairs of genes within 19 multiple sclerosis disease maps from the MetaCore pathway database. We report 4 gene pairs with epistasis involving missense variants, and 117 gene pairs with epistasis mediated by eQTLs. Our epistasis analysis is able to retrieve known interactions linked to multiple sclerosis: direct binding interaction between GLI-I and SUFU, involved in oligodendrocyte precursor cells differentiation, and regulation of IP10 transcription by NF-{kappa}B, thus validating the potential of epistasis analysis to reveal biological interaction with relevance in a disease specific context.

bioinformatics↗

bioRxiv2019

Disharmony of the world’s island floras

Disharmony is a key concept in island biology that describes the biased representation of higher taxa on islands compared to their mainland source regions. Although differential colonization success of taxa is predicted by selective dispersal, environmental and biotic filtering, the empirical evidence for disharmony remains largely anecdotal. Here, we develop a novel method for delineating island source regions and present the first global quantitative assessment of island disharmony. We analyzed the overall compositional bias of 320 island floras and examined the over- or under-representation on islands for 450 plant families. We found that the compositional bias of island floras is strongly predicted by geographical and climatic island characteristics (isolation, area, geologic origin, temperature, precipitation), whereas the representation of individual families is only weakly related to family-specific characteristics (family size, family age, taxonomic group, functional traits), indicating that the taxonomic scope of the disharmony concept has historically limited its wider applicability. Our results provide a strong foundation for integrating disharmony with quantitative functional and phylogenetic approaches in order to gain a deeper understanding of assembly processes on islands.

ecology↗

bioRxiv2018

Novel Methods for Epistasis Detection in Genome-Wide Association Studies

More and more genome-wide association studies are being designed to uncover the full genetic basis of common diseases. Nonetheless, the resulting loci are often insufficient to fully recover the observed heritability. Epistasis, or gene-gene interaction, is one of many hypotheses put forward to explain this missing heritability. In the present work, we propose epiGWAS, a new approach for epistasis detection that identifies interactions between a target SNP and the rest of the genome. This contrasts with the classical strategy of epistasis detection through exhaustive pairwise SNP testing. We draw inspiration from causal inference in randomized clinical trials, which allows us to take into account linkage disequilibrium. EpiGWAS encompasses several methods, which we compare to state-of-the-art techniques for epistasis detection on simulated and real data. The promising results demonstrate empirically the benefits of EpiGWAS to identify pairwise interactions. Author summaryGenome-wide association studies are now a major tool for the discovery of biomarkers for complex diseases. However, the complexity of genetic architecture, in particular linkage disequilibrium, complicates that mission. Moreover, intergenic interactions, or epistasis, are often not correctly captured by the classical statistical methodologies. In our work, we propose a new framework to model linkage disequilibrium, which is based on propensity scores. Our goal is to detect epistatic interactions between a predetermined target locus and the rest of the genotype. The target may be identified from the literature, experiments, or top hits in previous genome-wide association studies. Recovering interactions with validated causal loci helps improve both interpretability and statistical power. Multi-targeting drug discovery can also benefit from our work through the combination of existing drugs with new ones for greater drug response.

bioinformatics↗