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Membrane transporters are responsible for moving a wide variety of molecules across biological membranes, making them integral to key biological pathways in all organisms. Identifying all membrane transporters within a (meta-)proteome, along with their specific substrates, provides important information for various research fields, including biotechnology, pharmacology, and metabolomics. Protein datasets are frequently annotated with thousands of molecular functions that form complex networks, often with partial or full redundancy and hierarchical relationships. This complexity, along with the low sample count for more specific functions, makes them unsuitable as classes for supervised learning methods, meaning that the creation of an optimal subset of annotations is required. However, selection of this subset requires extensive manual effort, along with knowledge about the biology behind the respective functions. Here, we present an automated pipeline to address this problem. Unlike previous approaches for reducing redundancy in GO datasets, we employ machine learning to identify a subset of functional annotations in a training dataset. Classes in the resulting predictive model meet four essential criteria: sufficient sample size for training predictive models, minimal redundancy, strong class separability, and relevance to substrate transport. Furthermore, we implemented a pipeline for creating training datasets of transmembrane transporters that cover a wide range of organisms, including plants, bacteria, mammals, and single-cell eukaryotes. For a dataset containing 98.1% of transporters from S. cerevisiae, the pipeline automatically reduced the number of functional annotations from 287 to 11 GO terms that could be classified with a median pairwise F1 score of 0.87±0.16. For a meta-organism dataset containing 96% of all transport proteins from S. cerevisiae, A. thaliana, E. coli and human, the number of classes was reduced from 695 to 49, with a median F1 score of 0.92±0.10 between pairs of GO terms. When lowering the percentage of covered proteins down to 67%, the pipeline found a subset of 30 GO terms with a median F1 score of 0.95±0.06. [ABSTRACT FROM AUTHOR]

TNF-related apoptosis inducing ligand (TRAIL) is expressed on cytotoxic T lymphocytes (CTLs) and TRAIL is linked to progression of diabetes. However, the impact of high glucose on TRAIL expression and its related killing function in CTLs still remains largely elusive. Here, we report that TRAIL is substantially up-regulated in CTLs in environments with high glucose (HG) both in vitro and in vivo. Non-mitochondrial reactive oxygen species, NFκB and PI3K/Akt are essential in HG-induced TRAIL upregulation in CTLs. TRAILhigh CTLs induce apoptosis of pancreatic beta cell line 1.4E7. Treatment with metformin and vitamin D reduces HG-enhanced expression of TRAIL in CTLs and coherently protects 1.4E7 cells from TRAIL-mediated apoptosis. Our work suggests that HG-induced TRAILhigh CTLs might contribute to the destruction of pancreatic beta cells in a hyperglycemia condition. [ABSTRACT FROM AUTHOR]