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Shanmugam, N. R. S.

Publications and source records attributed to Shanmugam, N. R. S..

2 recordsLinked to original sources

Mucinolysome in gut microbiomes of farm animals and humans

Mucins are glycoproteins that create a protective barrier protecting host tissues from microbial pathogens and are instrumental for host health. Here, we provide evidence that mucin glycan degradation in the gut can be mediated by mucinolysomes, defined as extracellular multi-enzyme complexes specializing in mucin glycan degradation. We computationally predicted the presence of mucinolysomes across 63 metagenome-assembled genomes (MAGs) and two isolated genomes of anaerobic Limousia bacteria, including seven MAGs from human samples of six countries. All 65 genomes were found to display core mucinolysome components, consisting of 3[~]6 scaffoldins (containing up to 12 cohesin modules) and up to 22 dockerin-containing mucin glycan-degrading CAZymes (carbohydrate active enzymes). The organization of mucinolysomes allows the assembly of up to 24 CAZymes in the same complex. We validated that a cultivated Limousia strain ET540 from chicken cecum can support growth on mucins as its sole carbon source, triggering the expression of most mucinolysome-related genes, including both scaffoldins and CAZymes. We also modeled the assembly of proteins into a multi-enzyme complex by predicting the cohesin-dockerin interactions among most of the mucinolysome proteins using AlphaFold3. While mucinolysosome-encoding Limousia have low abundance in different animal hosts, their abundance and prevalence are higher in farm animals than in humans, highlighting a potentially important role in livestock gut ecosystems. Our findings reveal a novel mechanism of mucin glycan degradation and provide a framework to explore microbial contributions to gut health and host-microbe interactions across species.

bioinformatics↗

CAZyme3D: a database of 3D structures for carbohydrate-active enzymes

CAZymes (Carbohydrate Active EnZymes) degrade, synthesize, and modify all complex carbohydrates on Earth. CAZymes are extremely important to research in human health, nutrition, gut microbiome, bioenergy, plant disease, and global carbon recycling. Current CAZyme annotation tools are all based on sequence similarity. A more powerful approach is to detect protein structural similarity between query proteins and known CAZymes indicative of distant homology. Here, we developed CAZyme3D (https://pro.unl.edu/CAZyme3D/) to fill the research gap that no dedicated 3D structure databases are currently available for CAZymes. CAZyme3D contains a total of 870,740 AlphaFold predicted 3D structures (named Whole dataset). A subset of CAZymes 3D structures from 188,574 nonredundant sequences (named ID50 dataset) were subject to structural similarity-based clustering analyses. Such clustering allowed us to organize all CAZyme structures using a hierarchical classification, which includes existing levels defined by the CAZy database (class, clan, family, subfamily) and newly defined levels (subclasses, structural cluster [SC] groups, and SCs). The inter-family structural clustering successfully grouped CAZy families and clans with the same structural folds in the same subclasses. The intra-family structural clustering classified structurally similar CAZymes into SCs, which were further classified into SC groups. SCs and SC groups differed from sequence similarity-based CAZy subfamilies. With CAZyme structures as the search database, we created job submission pages, where users can submit query protein sequences or PDB structures for a structural similarity search. CAZyme3D will be a useful new tool to assist the discovery of novel CAZymes by providing a comprehensive database of CAZyme 3D structures.

bioinformatics↗