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Benet, M.

Publications and source records attributed to Benet, M..

2 recordsLinked to original sources

Comparative analyses of the N-glycoproteomes in HCT116 cancer cells and their non-tumorigenic DNMT1/3b double knockout (DKO1) cells and insight into the Mannose-6-phosphate pathway

N-glycoproteomic analyses provide valuable resources for investigation of cancer mechanisms, biomarkers, and therapeutic targets. Here, we mapped and compared the site-specific N-glycoproteomes of colon cancer HCT116 cells and isogenic non-tumorigenic DNMT1/3b double knockout (DKO1) cells using Fbs1-GYR N-glycopeptide enrichment technology and trapped ion mobility spectrometry. Many significant changes in site-specific N-glycosylation were revealed, providing a molecular basis for further elucidation of the role of N-glycosylation in protein function. HCT116 cells display hypersialylation especially in cell surface membrane proteins. Both HCT116 and DKO1 show an abundance of paucimannose and 80% of paucimannose-rich proteins are annotated to reside in exosomes. The most striking N-glycosylation alteration was the degree of mannose-6-phosphate (M6P) modification. N-glycoproteomic analyses revealed that HCT116 display hyper-M6P modification, which was orthogonally validated by M6P immunodetection. Significant observed differences in N-glycosylation patterns of the major M6P receptor, CI-MPR in HCT116 and DKO1 may contribute to the hyper-M6P phenotype of HCT116 cells.

biochemistry↗

FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism

The use of stable isotope tracers and mass spectrometry (MS) is the gold standard method for the analysis of fatty acids (FAs) metabolism. Yet current state-of-the-art tools provide limited and difficult to interpret information about FA biosynthetic routes. Here we present FAMetA, an R-package and a web-based application (www.fameta.es) that use 13C mass-isotopologue profiles to estimate FA import, de novo lipogenesis, elongation, and desaturation in a user-friendly platform. The FAMetA workflow covers all the functionalities needed for MS data analyses. To illustrate its utility, different in vitro and in vivo experimental settings are used in which FA metabolism is modified. Thanks to the comprehensive characterisation of FA biosynthesis and the easy-to-interpret graphical representations compared to previous tools, FAMetA discloses unnoticed insights into how cells reprogramme their FA metabolism and, when combined with FASN, SCD1 and FADS2 inhibitors, it enables the straightforward identification of new FAs by the metabolic reconstruction of their synthesis route.

systems biology↗