Biology subjects

Menager, H.

Publications and source records attributed to Menager, H..

6 recordsLinked to original sources

bioRxiv2026

HARIBOSS++: An Integrated Platform for RNA-Targeted Drug Design

Targeting RNA with small molecules is increasingly viewed as a pivotal strategy for future therapeutics. A major limitation in studying RNA-small molecule (RNA-SM) interactions is the lack of available experimental structures. HARIBOSS established a milestone as a curated database of RNA-SM complexes derived from the Protein Data Bank. Building on its community engagement, we present HARIBOSS++, an updated and next-generation platform designed to facilitate the study of future RNA-targeted drugs. Major updates include an extension of the pocketome to encompass binding sites where small molecules interact not only with RNA but also with other biomolecules, enhanced analysis of RNA molecules by family, detailed 3D structural characterization, improved analysis of ligands chemical space, addition of experimental binding affinity as well as molecular dynamics data, when available. HARIBOSS++ opens new avenues for biologists, bioinformaticians, and machine learning researchers to investigate RNA-SM interactions on a new scale. HARIBOSS++ can be explored using a web interface freely available at https://hariboss.pasteur.cloud.

bioinformatics↗

bioRxiv2023

Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with anincreased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearsons {rho} equal to 0.43 between the observed and predicted gain, P < 1.6 x 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.

genetics↗

bioRxiv2022

HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design

RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases, and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 8undruggable9 protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that has hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule recognition. Here, we present HARIBOSS, a curated collection of RNA-small molecule structures determined by X-ray crystallography, Nuclear Magnetic Resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties, and ultimately the development of in silico strategies to identify RNA-targeting small molecules. HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud.

bioinformatics↗

bioRxiv2020

Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

BackgroundGenome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy. ResultsWe conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters. ConclusionsMultitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

genetics↗

bioRxiv2020

The Flemmingsome reveals an ESCRT-to-membrane coupling required for completion of cytokinesis

Cytokinesis requires the constriction of ESCRT-III filaments on the side of the midbody, where abscission occurs. After ESCRT recruitment at the midbody, it is not known how the ESCRT-III machinery localizes to the abscission site. To reveal novel actors involved in abscission, we obtained the proteome of intact, post-abscission midbodies (Flemmingsome) and identified 489 proteins enriched in this organelle. Among those proteins, we further characterized a plasma membrane-to-ESCRT module composed of the transmembrane proteoglycan syndecan-4, ALIX and syntenin, a protein that bridges ESCRT-III/ALIX to syndecans. The three proteins were highly recruited first at the midbody then at the abscission site, and their depletion delayed abscission. Mechanistically, direct interactions between ALIX, syntenin and syndecan-4 were essential for proper enrichment of the ESCRT-III machinery at the abscission site, but not at the midbody. We propose that the ESCRT-III machinery must be physically coupled to a membrane protein at the cytokinetic abscission site for efficient scission, revealing novel common requirements in cytokinesis, exosome formation and HIV budding.

cell biology↗

bioRxiv2019

JASS: Command Line and Web interface for the joint analysis of GWAS results

Genome Wide Association Study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available, and studies have demonstrated their utility for a range of secondary analyses. This includes in particular the joint analysis of multiple GWAS to identify new genetic variants missed by univariate screenings. However, although several methods have been proposed, there are very few large scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package solves all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of various joint statistics, an optimized data management process, and a web interface for exploration purposes. Benchmark analyses confirmed the strong performances of JASS. We also performed multiple real data analyses demonstrating the strong potential of JASS for the detection of new associated genetic variants across various scenarios. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

bioinformatics↗