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Biology subjects

Durot, S.

Publications and source records attributed to Durot, S..

2 recordsLinked to original sources

Multi-omics analysis of endothelial cells reveals the metabolic diversity that underlies endothelial cell functions

Endothelial cells (ECs) line the vascular system and are key players in vascular homeostasis, yet their metabolic diversity across tissues, vascular beds, and growth states remains poorly understood. This study examines metabolic differences between proliferating and quiescent ECs and compares blood and lymphatic endothelium using proteomics and metabolomics. Our findings indicate that metabolism in quiescent ECs is not dormant but reorganized in a cell-specific manner, with decreased heme intermediates in human umbilical vein ECs and increased branched-chain amino acid catabolism across all quiescent ECs. Consistent with the differences identified in the omics data, perturbation studies revealed that inhibiting enzymes involved in heme, glutamate, fatty acid, and nucleotide biosynthesis led to distinct phenotypic responses in blood and lymphatic ECs. These findings highlight the importance of metabolic pathways in sustaining both proliferating and quiescent ECs and reveal how ECs from different vascular beds rely on distinct metabolic processes to maintain their functional states.

systems biology↗

Epithelial-mesenchymal transition is the main driver of intrinsic metabolism in cancer cell lines

A fundamental feature of cancer cells is genomic heterogeneity. It is a main driver of phenotypic differences, including the response to drugs, and therefore a key factor in therapy selection. Motivated by the increasing role attributed to metabolic reprogramming in tumor development, we wondered how genomic heterogeneity affects metabolic phenotype. To this end, we profiled the intracellular metabolome of 180 cancer cell lines grown in similar conditions to exclude environmental factors. For each cell line, we estimate activity for 49 pathways across the whole metabolic network. Upon clustering of activity data, we found a convergence into only two major metabolic types. These were further characterized by 13C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. These experiments revealed differences in lipid, mitochondrial, and carbohydrate metabolism between the two major types. Finally, a thorough integration of our metabolic data with multiple omics data revealed a strong association with markers of epithelial-mesenchymal transition (EMT). Our analysis indicates that in absence of variations imposed by the microenvironment, the metabolism of cancer cell lines falls into only two major classes despite genetic heterogeneity.

systems biology↗