Biology subjects

Mahe, M.

Publications and source records attributed to Mahe, M..

4 recordsLinked to original sources

bioRxiv2025

Unraveling Enteroendocrine Cell lineage dynamics and associated gene regulatory networks during intestinal development

Enteroendocrine cells (EECs) are rare intestinal epithelial cells producing multiple hormones that regulate essential aspects of digestion and energy. EEC subtypes, their hormone repertoire and differentiation mechanisms from intestinal stem cells have been characterized in the adult intestine. However, although EECs need to be functional from birth, as their absence causes severe intestinal malabsorption in newborns, the mechanisms underlying EEC subtype specification during development are largely unknown. We used mouse embryos, human pluripotent stem cell derived intestinal organoid models and single-cell transcriptomics to characterize EEC lineages and dynamics during development. Our findings demonstrate that in both mice and humans, the majority of EECs are specified during development through similar differentiation trajectories as observed in the adult intestine. This suggests that EEC subtypes specification occurs independently of fully organized crypt-villus structures and stimulation by diet or microbiota. However, the emergence of certain EEC subtypes depends on tissue maturation. Finally, our integrative approach infers lineage-specific regulators dynamically, identifying new candidates controlling EEC differentiation in the developing human gut. HIGHLIGHTSO_LIMost enteroendocrine cells (EECs) are specified during development. C_LIO_LICrypt-villus organization, diet or microbiota are not essential for EEC formation. C_LIO_LIIn pluripotent stem cell derived human intestinal organoids (HIOs), EECs differentiate along 3 main trajectories, one driven by maturation processes. C_LIO_LINovel Gene Regulatory Networks are identified governing human EEC differentiation in HIOs. C_LI

developmental biology↗

bioRxiv2025

HUMESS: Integrating Quantitative Transcriptomic Analysis and Metabolic Modeling to Unveil Condition-Specific Gene Signatures

Transcriptomic analysis is a key tool for exploring gene expression, but the complexity of biological systems often limits its insights. In particular, the lack of intermodal or multi-layered analysis hinders the ability to fully capture key cellular functions such as metabolism from transcriptomic data alone. Here, we introduce a novel approach that integrates transcriptomic data with metabolic network modeling to address this. Unlike traditional methods, HUMESS prioritizes genes based on their metabolic significance, offering a deeper understanding of condition-specific gene expression. Our computational pipeline, supported by a user-friendly Rshiny application, enhances gene expression analysis by uncovering metabolic phenotypic signatures.

systems biology↗

bioRxiv2023

Community metabolic modeling of host-microbiota interactions through multi-objective optimization

The human gut microbiota comprises various microorganisms engaged in intricate interactions among themselves and with the host, affecting its health. While advancements in omics technologies have led to the inference of clear associations between microbiome composition and health conditions, we usually lack a causal and mechanistic understanding of these associations. For modeling mechanisms driving the interactions, we simulated the organisms metabolism using in silico Genome-Scale Metabolic Models (GEMs). We used multi-objective optimization to predict and explain metabolic interactions among gut microbes and an intestinal epithelial cell. We developed a score integrating model simulation results to predict the type (competition, neutralism, mutualism) and quantify the interaction between several organisms. This framework uncovered a potential cross-feeding for choline, explaining the predicted mutualism between Lactobacillus rhamnosus GG and the epithelial cell. Finally, we analyzed a five-organism ecosystem, revealing that a minimal microbiota can favor the epithelial cells maintenance.

systems biology↗

bioRxiv2023

Multiple Myeloma associated DIS3 mutations drive AID-dependent IgH Translocations

Role of dominant DIS3 mutations in multiple myeloma (MM) remains elusive. These mutations decrease the exoribonucleolytic activity of DIS3, a key nuclear RNA-degrading enzyme. Utilizing knock-in mice with clinical Dis3 G766R variant, we demonstrate an increased frequency of aberrant chromosomal translocations in B-cells, leading to plasmacytoma, an early-stage MM model. DIS3-dependent translocations display characteristics of aberrant activation-induced deaminase (AID) activity. In clinical MM samples with DIS3 mutations, driver genes also show AID-dependent lesions. Mechanistically, mutated DIS3 accumulates on chromatin-associated RNA substrates, including aberrant AID action sites, fostering oncogenic chromosomal rearrangements. Translocations occur during immunoglobulin class switch recombination, a process otherwise unaffected in MM patients or mice with mutated DIS3. Dis3 G766R mutation does not alter chromatin architecture in activated B-cells but hijacks it to bring together enhancers with proto-oncogenes permanently. In conclusion, gain-of-function DIS3 mutations increase nuclear exosome and AID affinity to chromatin, facilitating IGH translocations and driving MM transformation.

molecular biology↗