Integration of RNAseq transcriptomics and <i>N</i>-glycomics reveal biosynthetic pathways and predict structure-specific <i>N</i>-glycan expression.

ABSTRACT: The processes involved in protein N-glycosylation represent new therapeutic targets for diseases but their stepwise and overlapping biosynthetic processes make it challenging to identify the specific glycogenes involved. In this work, we aimed to elucidate the interactions between glycogene expression and N-glycan abundance by constructing supervised machine-learning models for each N-glycan composition. Regression models were trained to predict N-glycan abundance (response variable) from glycogene expression (predictors) using paired LC-MS/MS N-glycomic and 3'-TagSeq transcriptomic datasets from cells derived from multiple tissue origins and treatment conditions. The datasets include cells from several tissue origins - B cell, brain, colon, lung, muscle,

SUBMITTER: Alvarez MRS 

PROVIDER: S-EPMC11970275 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature