Biology subjects

Courcelles, M.

Publications and source records attributed to Courcelles, M..

4 recordsLinked to original sources

bioRxiv2026

Molecular epidemiology of the globally spreading genetic lineage IV of peste des petits ruminants virus

Peste des petits ruminants (PPR) is a highly contagious viral disease of small ruminants caused by the peste des petits ruminants virus (PPRV), which is classified into four distinct genetic lineages (I-IV). A critical concern in the recent epidemiological history of PPRV is the rapid and widespread expansion of lineage IV (LIV) across West Africa over the past decade. This dominance suggests a potential adaptive advantage of circulating LIV strains in the regions current epidemiological context. In this study, we obtain the genome sequence of 26 new PPRV samples, including historical (pre-2000) and many recent African LIV isolates, offering the first opportunity to investigate the evolutionary history of LIV in Africa and identify genetic events potentially associated with its recent spread. Phylogenomic analyses implemented on a dataset of 167 curated PPRV genome sequences reveal that the most ancestral LIV group comprises strains circulating in Sub-Saharan Africa (designated clade LIVssa), providing robust evidence for an African origin of lineage IV. Our results further indicate that PPRV strains linked to the recent West African expansion of LIV belong to a specific LIVssa subgroup, termed NigB. We identified multiple signatures of selection pressure within the LIVssa sublineage, particularly in the NigB cluster. Several amino acid substitutions unique to LIVssa or NigB were detected, some of which may impact protein function and warrant prioritised investigation. Additional genomic data are required to confirm the association between the NigB group and the ongoing spread of LIV in West Africa. The evolutionary adaptations observed in LIVssa - potentially enhancing transmission efficiency, host range or pathogenicity - could undermine current disease control strategies in regions where PPR poses significant threats to food security and local economies. Author SummaryPeste des petits ruminants virus (PPRV) infects sheep and goats across Africa, Middle East, Asia and Europe, causing disease with major impact on global economy and food security. One genetic lineage of PPRV, called lineage IV (LIV), is at the origin of most recent expansion of the distribution of the disease, including replacement of other lineages in areas of African where PPRV is historically present. Here, we generated genome sequences from PPRV LIV isolates from different dates and places to study the evolution of this genetic lineage and explore whether its recent spread can be associated with the appearance of new mutations in the virus genome. Our results provide evidence that the PPRV LIV originated in Sub-Saharan Africa and identify mutations present only virus isolates currently spready in new regions of Africa. Further research should investigate the impact of these mutations on protein functions and capacity of transmission of PPRV.

evolutionary biology↗

bioRxiv2026

Redundant roles of YES and SRC tyrosine kinases in driving malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumors (MPNST) are highly aggressive soft tissue sarcomas that are largely incurable with no clinically effective systemic therapies or immunotherapies for advanced disease. Here, we identify the SRC-family kinases (SFKs) YES and SRC as redundant, essential drivers of MPNST growth. Dual inhibition of YES/SRC activity by genetic silencing or pharmacological SFK inhibitors markedly suppressed the proliferation of multiple NF1-mutant MPNST cell lines. In vivo, conditional genetic depletion of YES/SRC in MPNST cells abrogated tumor growth in subcutaneous and orthotopic models, and dasatinib treatment delayed tumor progression and improved overall survival. Integrated transcriptomic and phosphotyrosine proteomic analyses revealed that YES/SRC inactivation extensively rewires MPNST signaling, coordinately repressing multiple oncogenic signaling pathways and downstream cell cycle transcriptional programs. Unexpectedly, YES/SRC inhibition also upregulated interferon and antigen processing and presentation pathways and increased cell-surface MHC class I expression, consistent with tumor-intrinsic immune reactivation. Clinically, analysis of a large sarcoma cohort demonstrated that YES1 is significantly overexpressed in MPNST compared to benign soft tissue tumors. Collectively, our findings establish YES/SRC as non-oncogene vulnerabilities in MPNST.

cancer biology↗

bioRxiv2020

Immunopeptidomics for Dummies: Detailed Experimental Protocols and Rapid, User-Friendly Visualization of MHC I and II Ligand Datasets with MhcVizPipe

Immunopeptidomics refers to the science of investigating the composition and dynamics of peptides presented by major histocompatibility complex (MHC) class I and class II molecules using mass spectrometry (MS). Here, we aim to provide a technical report to any non-expert in the field wishing to establish and/or optimize an immunopeptidomic workflow with relatively limited computational knowledge and resources. To this end, we thoroughly describe step-by-step instructions to isolate MHC class I and II-associated peptides from various biological sources, including mouse and human biospecimens. Most notably, we created MhcVizPipe (MVP) (https://github.com/CaronLab/MhcVizPipe), a new and easy-to-use open-source software tool to rapidly assess the quality and the specific enrichment of immunopeptidomic datasets upon the establishment of new workflows. In fact, MVP enables intuitive visualization of multiple immunopeptidomic datasets upon testing sample preparation protocols and new antibodies for the isolation of MHC class I and II peptides. In addition, MVP enables the identification of unexpected binding motifs and facilitates the analysis of non-canonical MHC peptides. We anticipate that the experimental and bioinformatic resources provided herein will represent a great starting point for any non-expert and will therefore foster the accessibility and expansion of the field to ultimately boost its maturity and impact.

bioinformatics↗

bioRxiv2020

Codon arrangement modulates MHC-I peptides presentation

MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se. CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome. Author summaryMHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune system to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAPs novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using an in-vitro assay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.

bioinformatics↗