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Le Clerc, S.

Publications and source records attributed to Le Clerc, S..

5 recordsLinked to original sources

HaploExplore, first software specifically designed for the detection of minor allele (MiA-) Haploblocks

MotivationHaplotype blocks in the genome are informative of evolutionary processes and they play a pivotal role in describing the genomic variability across human populations and susceptibility/resistance to diseases. Several software have been developed for haplotype blocks detection, but they do not distinguish between the impacts of major and minor SNP alleles. In this study, we present a powerful haploblock detection software, specifically designed for identifying haploblocks associated with SNP minor allele haploblocks (MiA-haploblocks). These haploblocks are particularly important as they can significantly influence phenotypic traits, offering a novel approach for studying genetic associations and complex traits. ResultsHaploExplore operates on VCF files containing phased data, exhibiting rapid processing times and generating user-friendly outputs. Its results are convergent for populations starting from 100 individuals. A comparative analysis of HaploExplore against other haploblock detection software revealed its superiority in terms of either simplicity, or flexibility, or speed, with the unique capability to target minor alleles. HaploExplore will be very useful for evolutionary genomics and for GWAS analysis in human diseases, given that the effects of genetic associations may accumulate within a specific haploblock.

genomics↗

Transcriptomic analysis of chronic chikungunya in the Reunionese CHIKGene cohort uncovers a shift in gene expression more than 10 years after infection

AimIn 2005-2006, a chikungunya epidemic of unprecedented magnitude hit Reunion Island, which raised a public health concern through the substantial proportions of long-lasting manifestations. To understand the pathophysiology underlying chronic chikungunya (CC), we designed the CHIKGene cohort study and collected blood samples from 133 subjects diagnosed with CC and from 86 control individuals that had recovered within 3 months, 12-to-15 years after exposure. MethodsWe conducted bulk RNAseq analysis on peripheral blood mononuclear cells to find differentially expressed genes (DEGs), gene set enrichment analysis (GSEA) and gene ontologies to uncover top-level enriched terms associated with DEGs, and weighted gene correlation network analysis (WGCNA) to elucidate underlying cellular processes. ResultsAmong 1549 DEGs, gene expression analysis identified 10 top genes including NR4A2 and TRIM58 (upregulated in CC), IGHG3 and IGHV3-49 (downregulated in CC) linked to immune regulation, OSBP2 (upregulated in CC) and SEMA6B (downregulated in CC) linked to neuronal homeostasis and axon guidance, respectively. GSEA and WGCNA unveiled cellular processes such as "Metabolism of RNA" and "Cell Cycle". ConclusionsThis study uncovers a shift in gene expression of CC subjects. IGHG3 and IGHV3-49 gene shut-offs spotlight the importance of neutralizing antibodies against chikungunya virus in the progression to chronic disease. Human diseases associations highlight connections to rheumatoid arthritis, nervous and cardiac systems. GSEA and WGCNA bounce the hypotheses of a persistent viral reservoir or an increased susceptibility to RNA viral pathogens with new onset infections. Together, our findings might offer potential targets for therapeutic options aimed at alleviating chronic chikungunya.

bioinformatics↗

Deep analysis of the Major Histocompatibility Complex associations using covariate analysis and haploblocks unravels new mechanisms for the molecular etiology of Elite Control in AIDS

IntroductionWe have reanalyzed the genomic data from the International Collaboration for the Genomics of HIV (ICGH), focusing on HIV-1 Elite Controllers (EC). MethodsA genome-wide association study (GWAS) was performed, comparing 543 HIV-1 EC individuals with 3,272 uninfected controls (CTR) of European ancestry. 8 million single nucleotide polymorphisms (SNPs) and HLA class I and class II gene alleles were imputed to compare EC and CTR. Results2,626 SNPs were associated with EC (p<5.10-8), all located within the Major Histocompatibility Complex (MHC) region. Stepwise regression analysis narrowed this list to 17 SNPs. In parallel, 22 HLA class I and II alleles were associated with EC. Through meticulous mapping of the LD between all identified signals and employing reciprocal covariate analyses, we delineated a final set of 6 independent SNPs and 3 HLA class I gene alleles that accounted for most of the associations observed with EC. Our study revealed the presence of cumulative haploblock effects (SNP rs9264942 contributing to the HLA-B*57:01 effect) and that several HLA allele associations were in fact caused by SNPs in linkage disequilibrium (LD). Upon investigating SNPs in LD with the selected 6 SNPs and 3 HLA class I alleles for their impact on protein function (either damaging or differential expression), we identified several compelling mechanisms potentially explaining EC among which: a multi-action mechanism of HLA-B*57:01 involving MICA mutations and MICB differential expression overcoming the HIV-1 blockade of NK cell response, and overexpression of ZBTB12 with a possible anti-HIV-1 effect through HERV-K interference; a deleterious mutation in PPP1R18 favoring viral budding associated with rs1233396. ConclusionOur results show that MHC influence on EC likely extends beyond traditional HLA class I or class II allele associations, encompassing other MHC SNPs with various biological impacts. They point to the key role of NK cells in preventing HIV-1 infection. Our analysis shows that HLA-B*57:01 is indeed associated with a partially functional NK cell response which could also explain this markers involvement in other diseases such as psoriasis. More broadly, our findings suggest that within any HLA class I and II association in diseases, there may exist distinct causal SNPs within this crucial, gene-rich, and LD-rich MHC region.

immunology↗

High tissue-specificity of lncRNAs maximises the prediction of tissue of origin of circulating DNA

Several studies have made it possible to envision a translational application of plasma DNA sequencing in cancer diagnosis and monitoring. However, the extremely low concentration of circulating tumour DNA (ctDNA) fragments among the total cell-free DNA (cfDNA) remains a formidable challenge to overcome and statistical models have yet to be improved enough to become of practical use. In this study, we set about appraising the predictive value of a variety of binary classification models based on cfDNA sequencing using fragmentation features extracted around transcription start sites (TSSs). We investigated (1) features summarising mapped fragment density around each TSS, (2) long non-coding RNA (lncRNA) genes versus coding genes and (3) selection criteria to generate gene classes to be assigned by the model. Given that, in healthy samples, most of the cfDNA comes from lymphomyeloid lineages, we could identify the model parametrisation with the best accuracy in those lineages using publicly available datasets of healthy patients cfDNA. Our results show that (1) the way tissue-specific gene classes are defined matters more than what fragmentation features are included, and (2) in particular, lncRNAs are more tissue specific than coding genes and stand out in terms of both sensitivity and specificity in our results. Author summaryDying cells, even in healthy individuals, release a fraction of the digested fragments of their genetic material into the bloodstream. Interestingly, these circulating cell-free DNA (cfDNA) fragments bear the footprint left by nucleosomes ; the position of which depends on the transcriptional state specific to each tissue. This footprint, given away by the sizes and genomic positions of cfDNA fragments, can be revealed by deep sequencing and statistical models can be trained to recognise the tissue from where those fragments originate. This information, if made sensitive enough, could be a useful medical technique to carry out so-called "liquid biopsies", allowing clinicians to diagnose at an early stage or to precisely monitor a number of diseases, including cancer. In this work, we comprehensively evaluated the features of circulating DNA fragments in the vicinity of transcriptional start sites to increase the ability of statistical models to recognise the tissue of origin of cfDNA of healthy individuals. Broadly speaking, nucleosome patterns allow a statistical model distinguish active versus inactive genes or tissue-specific versus housekeeping genes. The purpose of this study was to find the classes of genes with the strongest ability to recognise the true tissue of origin. From this work, we conclude that long non-coding RNA genes allow for a more sensitive and specific detection of the tissue of origin.

bioinformatics↗

Wasl is crucial to maintain microglial core activities during glioblastoma initiation stages.

Microglia actively promote the growth of high-grade gliomas. Within the glioma microenvironment an activated (amoeboid) microglial morphology has been observed, however the underlying causes and the related impact on microglia functions and their tumour promoting activities is unclear. Using the advantages of the larval zebrafish model, we demonstrate that pre-neoplastic glioma cells have an immediate impact on microglial morphology and functions. Overexpression of human HRasV12 in proliferating domains of the larval brain induces an amoeboid morphology of microglia, increases microglial numbers and decreases their motility and phagocytic activity. RNA sequencing analysis revealed lower expression levels of the actin nucleation promoting factor wasla in microglia. Importantly, a microglia specific rescue of wasla expression restores microglial morphology and functions. This results in increased phagocytosis of pre-neoplastic cells and slows down tumour progression. In conclusion, we identified a mechanism that de-activates core microglial functions within the emerging glioma microenvironment.

cancer biology↗