Manson, A. | Search bioRxiv

bioRxiv2023

Role of ugt genes in detoxification and glycosylation of 1-hydroxy phenazine (1-HP) in Caenorhabditis elegans

Caenorhabditis elegans is an ideal model organism to study the xenobiotic detoxification pathways of various natural and synthetic toxins. One toxin shown to cause death in C. elegans is 1-hydroxyphenazine (1-HP), a molecule produced by the bacterium Pseudomonas aeruginosa. We previously showed that the median lethal dose (LD50) for 1-HP in C elegans is 179 M in PD1074 and between 150-200 M in N2 (C. elegans lab strain). We also showed that C. elegans detoxifies 1-HP by glycosylation by adding one, two, or three glucose molecules in N2 worms. This study tested whether UDP-glycosyltransferase (ugt) genes play a role in 1-HP detoxification. We show that ugt-23 and ugt-49 knockout mutants are more sensitive to 1-HP. Our data also show that ugt-23 knockout mutants produce reduced amounts of the trisaccharide sugars, while the ugt-49 knockout mutants produce reduced amounts of all 1-HP derivatives except for the glucopyranosyl product. We have also characterized the structure of the trisaccharide sugar phenazine structures made by C. elegans and show that one of the sugar modifications contains an N-acetylglucosamine (GlcNAc) in place of glucose. This implies broad specificity regarding UGT function and the role of genes other than ogt-1 in adding GlcNAc, at least in small-molecule detoxification.

biochemistry↗

bioRxiv2023

SAP: Synteny-aware gene function prediction for bacteria using protein embeddings

MotivationToday, we know the function of only a small fraction of the protein sequences predicted from genomic data. This problem is even more salient for bacteria, which represent some of the most phylogenetically and metabolically diverse taxa on Earth. This low rate of bacterial gene annotation is compounded by the fact that most function prediction algorithms have focused on eukaryotes, and conventional annotation approaches rely on the presence of similar sequences in existing databases. However, often there are no such sequences for novel bacterial proteins. Thus, we need improved gene function prediction methods tailored for prokaryotes. Recently, transformer-based language models - adopted from the natural language processing field - have been used to obtain new representations of proteins, to replace amino acid sequences. These representations, referred to as protein embeddings, have shown promise for improving annotation of eukaryotes, but there have been only limited applications on bacterial genomes. ResultsTo predict gene functions in bacteria, we developed SAP, a novel synteny-aware gene function prediction tool based on protein embeddings from state-of-the-art protein language models. SAP also leverages the unique operon structure of bacteria through conserved synteny. SAP outperformed both conventional sequence-based annotation methods and state-of-the-art methods on multiple bacterial species, including for distant homolog detection, where the sequence similarity to the proteins in the training set was as low as 40%. Using SAP to identify gene functions across diverse enterococci, of which some species are major clinical threats, we identified 11 previously unrecognized putative novel toxins, with potential significance to human and animal health. Availabilityhttps://github.com/AbeelLab/sap Contactt.abeel@tudelft.nl Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗