Biology subjects

Gendre, M.

Publications and source records attributed to Gendre, M..

3 recordsLinked to original sources

bioRxiv2026

TREM2 drives accumulation of pro-scarring monocyte-derived macrophages in the infarcted myocardium

Myocardial infarction is a leading cause of death and disability worldwide. Ischemic injury leads to irreversible loss of cardiomyocytes, the contractile cells of the heart, and formation of a fibrotic scar. After infarction, macrophages massively infiltrate the heart and orchestrate the tissue repair process by removing dead cells and modulating fibroblast activation for scar formation. We previously demonstrated that diverse monocyte-derived macrophage populations dynamically accumulate in the heart following myocardial infarction, notably a pro-repair Trem2hi subset. In this study, we leveraged spatial transcriptomics, single-cell RNA-seq, and functional assays to elucidate the role of TREM2 in driving macrophage-mediated cardiac tissue repair post-infarction. We show that Trem2hi macrophages localize in scarring areas of the infarcted myocardium in the vicinity of collagen-producing myofibroblasts. In Trem2-/- mice, cardiac accumulation of monocyte-derived macrophages with a pro-scarring matrisome-associated macrophage signature was reduced. TREM2 deficiency was functionally associated with reduced fibroblast proliferation, accumulation of myofibroblasts, decreased collagen deposition in the infarcted heart, and increased infarct size. In vitro, we show that TREM2 mediates efferocytosis-induced pro-fibrotic gene expression and promotes macrophage ability to induce fibroblast migration. IL-4 priming of bone marrow-derived macrophages further increased the pro-fibrotic response in macrophages, suggesting that IL-4 and efferocytosis act synergistically to drive this phenotype. Altogether, our results show that TREM2 is essential for the accumulation and function of pro-scarring monocyte-derived macrophages in the infarcted myocardium.

immunology↗

bioRxiv2026

Neutrophil terminal programming in the ischemic heart drives fibrosis after myocardial infarction

Following myocardial infarction (MI), the heart undergoes massive neutrophil infiltration characterized by the emergence of distinct subsets, notably a SiglecF+ population that accumulates according to specific temporal dynamics. The mechanisms governing cardiac neutrophil heterogeneity and the subsequent functional impact of this diversity on tissue repair following myocardial infarction remain to be elucidated. Using single-cell RNA-sequencing of neutrophils in the heart and peripheral organs of infarcted mice, we here show that while acquisition of the SiglecF+ state only fully occurs in the ischemic heart tissue, MI primes neutrophils in the periphery to acquire Siglecf and to upregulate receptors for TGF{beta} and GM-CSF that drive acquisition of the SiglecF+ state. Ly6G targeting in vivo shifted cardiac neutrophils towards the SiglecF+ state at day 3 post-MI, induced the emergence of reprogrammed SiglecF+Ly6Glo and Retnlghi neutrophil states at day 5, and was associated with increased fibrosis of the infarct border zone. Mechanistically, Ly6G targeting reshaped the cardiac immune landscape with increased recruitment of pro-fibrotic {gamma}{delta} T cells and monocytes, and SiglecF+ neutrophils exerted direct pro-fibrotic effects on fibroblasts in a co-culture system. Altogether, our results indicate that peripheral neutrophil priming combined with their terminal programming towards a SiglecF+ state in the ischemic heart drives cardiac fibrosis after MI.

physiology↗

bioRxiv2023

Benchmarking imputation methods for discrete biological data

Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, to quantify species extinction risks, or to evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance due to incomplete sampling limited data and resource availabilities. Several imputation methods exist to predict missing values and have been successfully evaluated and used to fill the gaps in datasets of quantitative traits. Here we explore the performance of different imputation methods on discrete biological traits i.e. qualitative or categorical traits such as diet or habitat. We develop a bioinformatics pipeline to impute trait data combining phylogenetic, machine learning, and deep learning methods while integrating a simulation framework to evaluate their performance on synthetic datasets. Using this pipeline we run a wide range of simulations under different missing rates, mechanisms, and biases and different evolutionary models. Our results indicate that a new ensemble approach, where we combined the imputation results of a selection of imputation methods provides the most robust and accurate prediction of missing discrete traits. We apply our pipeline to an incomplete trait dataset of 1015 elasmobranch species (including sharks and rays) and found a high imputation accuracy of the predictions based on an expert-based assessment of the missing traits. Our bioinformatic pipeline, implemented in an open-source R package, facilitates the application and comparison of multiple imputation methods to make robust predictions of missing trait values in biological datasets.

bioinformatics↗