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Benjamin P. Barre

As seen in: Nature, ACS Publications, BioRxiv
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Articles

Proteomic Profiling of Endothelial Cells Under Laminar Shear Stress Confirms the Importance of KLF4 in the Regulation of Membrane Protein Expression Compared to Oscillatory Flow Click to copy article link Article link copied!

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By Léa Réthoré, Benjamin P. Barre, Anne-Laure Guihot
| ACS Publications
Cite Jump to ExpandCollapse ArticleJuly 6, 2026 Proteomic Profiling of Endothelial Cells Under Laminar Shear Stress Confirms the Importance of KLF4 in the Regulation of Membrane Protein Expression Compared to Oscillatory Flow Click to copy article linkArticle link copied! Léa Réthoré Léa Réthoré Univ Angers, INSERM, CNRS, MITOVASC, Equipe CarME, SFR ICAT, F-49000 Angers, France Benjamin Barré Benjamin Barré Univ Angers, INSERM, CNRS, MITOVASC, Equipe CarME, SFR ICAT, F-49000 Angers, France...
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The adaptive molecular landscape of reprogrammed telomeric sequences

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By Benjamin P. Barre, Sakshi Khaiwal, Julia Muenzner, Johan Hallin, Matteo De Chiara, Nicolò Tellini, Jonas Warringer, Markus Ralser, Eric Gilson, Gianni Liti, Melania D’Angiolo
| Nature Verified
Abstract Telomeric sequences vary across the tree of life and intimately co-evolve with telomere-binding protein complexes. However, the molecular mechanisms allowing organisms to adapt to new telomeric sequences are difficult to gauge from extant species. Here, we engineer multiple yeast lines to human-like telomeric repeats to unveil their molecular and fitness response to reprogrammed telomeres.
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Predicting the natural yeast phenotypic landscape with machine learning

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By Matteo De Chiara, Inigo Barrio-Hernandez, Sakshi Khaiwal, Benjamin P. Barre
| BioRxiv
Abstract Most organisms' traits result from the complex interplay of many genetic and environmental factors, making their prediction from genotypes difficult. Here, we used machine learning models to explore genotype-phenotype connections for 223 life history traits measured across 1011 genome-sequenced Saccharomyces cerevisiae strains. Firstly, we used genome-wide association studies to connect genetic variants with the phenotypes.
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