POMGNT2

Last update 08 May 2025

Basic Info

Synonyms

AGO61, C3orf39, EOGTL

+ [7]

Introduction

O-linked mannose beta-1,4-N-acetylglucosaminyltransferase that transfers UDP-N-acetyl-D-glucosamine to the 4-position of the mannose to generate N-acetyl-D-glucosamine-beta-1,4-O-D-mannosylprotein. Involved in the biosynthesis of the phosphorylated O-mannosyl trisaccharide (N-acetylgalactosamine-beta-3-N-acetylglucosamine-beta-4-(phosphate-6-)mannose), a carbohydrate structure present in alpha-dystroglycan (DAG1), which is required for binding laminin G-like domain-containing extracellular proteins with high affinity.

100 Clinical Results associated with POMGNT2

100 Translational Medicine associated with POMGNT2

0 Patents (Medical) associated with POMGNT2

Literatures (Medical) associated with POMGNT2

01 Mar 2025·Animal Nutrition

Transcriptome analysis reveals modulations in glycosylation profiles of the mucosal barrier and their potential interaction with gut microbiota in weaned piglets

Article

Author: Wang, Jing ; Ma, Xiaokang ; Zhao, Yujie ; Yang, Kai ; Cheng, Hao ; Tan, Bie ; Li, Hao

The current study aims to investigate the potential interaction between glycosylation profiles of the Ningxiang breed (NX) and Western Duroc × Landrace × Yorkshire breed (DLY) weaned piglets, and their characteristic microbes, employing integrated analyses of transcriptomics and metagenomics. Twenty-four (12 NX and 12 DLY) at 28 days of age were transported into an experimental house and fed the same weaned piglet diet. The trail period was 7 days. Results revealed that the NX piglets had a higher growth-to-feed ratio, body weight gain scale, and lower pathological score of intestinal injury compared with the DLY piglets (P < 0.01). DLY piglets displayed elevated mRNA expression levels of MUC2 and MUC5AC in colonic mucosal tissue than NX piglets (P < 0.05). Within the O-linked glycosylated differentially expressed genes (DEGs), FNTA, GALNT18, POMGNT1, POMGNT2, and POMT1 were significantly upregulated in DLY piglets relative to NX piglets (P < 0.05). Conversely, C1GALT2, GALNT1, KMT2C, and OGT were significantly downregulated in DLY piglets compared to NX piglets (P < 0.05). The KMT2C gene was hardly expressed in the transcriptome of DLY piglets. At the phylum taxonomic level, NX piglets had a higher abundance of Firmicutes, while DLY piglets had a higher abundance of Proteobacteria. At the genus taxonomic level, NX piglets had a higher abundance of Lactobacillus, whereas DLY piglets had a higher abundance of Collinsella, Enterococcus and Escherichia. The results of the correlation between intestinal differential bacteria and O-chain glycosylated DEG showed that C1GALT2, GALNT1 and KMT2 were associated with Lactobacillus_pontis showed a positive correlation (R = 0.67). Through comparative analysis of differentially glycosylated genes and their associated functions, this study highlights the potential role of reduced expression of GALNT1 and KMT2C genes, involved in O-linked protein and glycan reactions, in impairing the intestinal barrier function of DLY piglets. Furthermore, members of the Lactobacillus and Prevotella genera may actively contribute to the regulation of piglet colon glycosylation profiles.

01 Nov 2024·Heliyon

Construction and experimental verification of a novel nine-glycosylation-related gene prognostic risk model for clear cell renal carcinoma

Article

Author: Guo, Long ; Ye, Jianqing ; Liu, Fei ; Sun, Wanlei ; Liu, Hongliang ; Ying, Xin ; Zhou, Min ; Shu, Cong

ObjectivePatients with clear cell renal carcinoma (ccRCC) typically have poor prognosis. Glycosylation modification plays an important role in ccRCC. This study aimed to develop a novel signature for predicting ccRCC prognosis based on glycosylation-related genes (GRGs).MethodsDifferentially expressed GRGs (DE_GRGs) were identified using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus and used for constructing the risk model. The function of key genes was validated.ResultsTwenty-two DE_GRGs were intersected between GSE53757 and TCGA. Patients with ccRCC were divided into two clusters based on the expression profile of these DE_GRGs. Significant differences in the infiltration of 10 immune cell types were observed between two subclusters. Subsequently, the prognostic signatures of nine DE_GRGs (CHST9, COLGALT1, FUT3, FUT6, HS3ST2, POMGNT2, ST8SIA4, UGT3A1, and UGT8) were established. Patients in the high-risk group showed a poorer prognosis relative to the low-risk group. According to univariate and multivariate Cox regression analyses, the risk score, stage, and grade could be independent prognostic factors. A nomogram incorporating information on gender, age, risk group, TNM stage, and clinical stage showed accurate prediction in the survival probability. Except for CHST9, HS3ST2, and ST8SIA4, expression patterns of the remaining 6 DE_GRGs in Caki-1 and 786-O cells were confirmed by quantitative real-time PCR. FUT6 overexpression resulted in the inhibition of proliferation, migration, and invasion of ccRCC cells.ConclusionThis study established a nine-DE_GRG-based prognostic signature, which independently predicted ccRCC prognosis. This finding emphasizes that GRGs are stratification factors for the precise prognosis of ccRCC.

01 Apr 2024·Glycoconjugate Journal

Identification of drug responsible glycogene signature in liver carcinoma from meta-analysis using RNA-seq data

Article

Author: Koreeda, Tatsuya ; Honda, Hiroshi

Glycans have attracted much attention in cancer therapeutic strategies, and cell surface proteins and lipids with glycans are known to be altered during the carcinogenic process. However, our understanding of how the glycogenes profile responds to drug stimulation remains incomplete. In this study, we search public databases for Sequence Read Archive data on drug-treated liver cancer cells, with the aim to comprehensively analyze the drug responses of glycogenes via bioinformatic meta-analysis. The study comprised 86 datasets, encompassing eight distinct liver cancer cell lines and 13 different drugs. Differentially expressed genes were quantified, and 399 glycogenes were identified. The glycogenes signature was then analyzed using bioinformatics methodologies. In the Protein-protein interaction network analysis, we identified drug-responsive glycogenes such as Beta-1,4-Galactosyltransferase 1, GDP-Mannose 4,6-Dehydratase, UDP-Glucose Ceramide Glucosyltransferase, and Solute Carrier Family 2 Member 4 as key glycan biomarkers. In the enrichment analysis using the pathway list of glycogenes, the results also demonstrated that drug stimulation resulted in alterations to glycopathway-related genes involved in several processes, namely O-Mannosylation, POMGNT2 Type, Capping, Heparan Sulfate Sulfation, and Glucuronidation pathways. These genes and pathways commonly exhibit variable expression across multiple liver cancer cells in response to the same drug, making them potential targets for new cancer therapies. In addition to their primary roles, drugs may also participate in the regulation of glycans. The insights from this study could pave the way for the development of liver cancer therapies that target the regulation of gene profiles involved in the biosynthesis of glycans.

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POMGNT2

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