Biomedical subjects

Michel Salzet

Publications and source records attributed to Michel Salzet.

3 recordsLinked to original sources

PubMed2026

Epithelial-Mesenchymal Transition Shapes the Lipotoxic Response of Colon Cancer Cells to Palmitic Acid.

Saturated fatty acids such as palmitic acid (PA) can induce lipotoxic stress, whereas monounsaturated fatty acids like oleic acid (OA) often promote adaptive responses through lipid droplets (LDs) formation. Here, we reveal that epithelial-mesenchymal transition (EMT) profoundly influences the lipotoxic response of colorectal cancer cells. Using the epithelial-like HCT15 and mesenchymal-like HCT116 cell lines, we combined proteomic, metabolic, and imaging analyses to elucidate how EMT status determines lipid storage capacity and resistance to PA-induced toxicity. A basal proteomic profiling highlighted a striking divergence in metabolic changes: HCT15 cells displayed enhanced glycolysis and reduced expression of LDs biogenesis proteins, while HCT116 cells exhibited oxidative metabolism and a "lipid-rich" proteomic signature enriched in PLIN2, GPAT3, and DGAT1. Functionally, PA triggered massive cytotoxicity and failed to induce LDs in HCT15 cells, correlating with DGAT1/2 downregulation and suppressed triacylglycerol synthesis. In contrast, HCT116 cells showed modest LDs accumulation, preserved mitochondrial function, and strong resistance to lipotoxic stress. OA treatment restored LDs formation and cell viability in both models, underscoring the protective role of unsaturated fatty acids. Notably, forced EMT induction in HCT15 cells by PMA markedly enhanced LDs accumulation and reduced PA-induced death, confirming that EMT confers metabolic plasticity and lipid-buffering capacity. These findings demonstrate that EMT status modulates differential lipid handling and stress adaptation in colon cancer cells, linking mesenchymal transition to enhanced LDs biogenesis and survival under lipotoxic conditions. Data are available via ProteomeXchange with identifier PXD071641.

Humans↗

PubMed2026

Profiler: an open web platform for multi-omics analysis.

MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).

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PubMed2024

Deciphering the ghost proteome in ovarian cancer cells by deep proteogenomic characterization.

Proteogenomics is becoming a powerful tool in personalized medicine by linking genomics, transcriptomics and mass spectrometry (MS)-based proteomics. Due to increasing evidence of alternative open reading frame-encoded proteins (AltProts), proteogenomics has a high potential to unravel the characteristics, variants, expression levels of the alternative proteome, in addition to already annotated proteins (RefProts). To obtain a broader view of the proteome of ovarian cancer cells compared to ovarian epithelial cells, cell-specific total RNA-sequencing profiles and customized protein databases were generated. In total, 128 RefProts and 30 AltProts were identified exclusively in SKOV-3 and PEO-4 cells. Among them, an AltProt variant of IP_715944, translated from DHX8, was found mutated (p.Leu44Pro). We show high variation in protein expression levels of RefProts and AltProts in different subcellular compartments. The presence of 117 RefProt and two AltProt variants was described, along with their possible implications in the different physiological/pathological characteristics. To identify the possible involvement of AltProts in cellular processes, cross-linking-MS (XL-MS) was performed in each cell line to identify AltProt-RefProt interactions. This approach revealed an interaction between POLD3 and the AltProt IP_183088, which after molecular docking, was placed between POLD3-POLD2 binding sites, highlighting its possibility of the involvement in DNA replication and repair.

Humans↗