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Ananthakrishnan, A. N.

Publications and source records attributed to Ananthakrishnan, A. N..

3 recordsLinked to original sources
bioRxiv2024

The Ulcerative Colitis-Associated Gene NXPE1 Catalyzes Glycan Modifications on Colonic Mucin

Colonic mucus forms a first line of defense against bacterial invasion while providing nutrition to support coinhabiting microbes in the gut. Mucus is composed of polymeric networks of mucin proteins, which are heavily modified post-translationally. The full compendium of enzymes responsible for these modifications and their roles in health and disease remain incompletely understood. Herein we determine the biochemical function of NXPE1, a gene implicated in ulcerative colitis (UC), and demonstrate that it encodes an acetyltransferase that modifies mucin glycans. Specifically, NXPE1 utilizes acetyl-CoA to regioselectively modify the mucus sialic acid, 5-N-acetylneuraminic acid (Neu5Ac), at the 9-OH group to generate 9-O-acetylated Neu5Ac (Neu5,9Ac2). We further demonstrate that colonic organoids derived from donors harboring the missense variant NXPE1 G353R, which is protective against UC, exhibit severely impaired acetylation of Neu5Ac on mucins. Together, our findings support a model in which NXPE1 masks the alcohols of mucus sialoglycans via acetylation, which is important for modulating mucus barrier properties that limit interactions with commensal microbes.

immunology↗
bioRxiv2023

Contextualizing protein representations using deep learning on protein networks and single-cell data

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce PO_SCPLOWINNACLEC_SCPLOW, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, PO_SCPLOWINNACLEC_SCPLOW learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PO_SCPLOWINNACLEC_SCPLOWs embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs effects across cell types. PO_SCPLOWINNACLEC_SCPLOW outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. PO_SCPLOWINNACLEC_SCPLOWs ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

bioinformatics↗