A systems-based framework to computationally describe putative transcription factors and signaling pathways regulating glycan biosynthesis

Abstract

Glycosylation is a common posttranslational modification, and glycan biosynthesis is regulated by a set of glycogenes. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation pathways is largely unknown. In this work, we performed data mining of TF–glycogene relationships from the Cistrome Cancer database (DB), which integrates chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA-Seq data to constitute regulatory relationships. In total, we observed 22,654 potentially significant TF–glycogene relationships, which include interactions involving 526 unique TFs and 341 glycogenes that span 29 the Cancer Genome Atlas (TCGA) cancer types. Here, TF–glycogene interactions appeared in clusters or so-called communities, suggesting that changes in single TF expression during both health and disease may affect multiple carbohydrate structures. Upon applying the Fisher’s exact test along with glycogene pathway classification, we identified TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with Reactome DB knowledge provided an avenue to relate cell-signaling pathways to TFs and cellular glycosylation state. Whereas analysis results are presented for all 29 cancer types, specific focus is placed on human luminal and basal breast cancer disease progression. Overall, the article presents a computational approach to describe TF–glycogene relationships, the starting point for experimental system-wide validation.

GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology

“…The availability of GlycoEnzyme biochemical knowledge in OWL format using GlycoEnzOnto can allow the integration of glycoEnzyme knowledge into glycan structure databases, along with additional information, e.g. transcription factor binding sites and other gene regulatory properties ( Groth et al , 2021 ). Such curation can also facilitate the development of glycan reaction networks stored in RDF format based on the provided reaction rules.…”

Spatiotemporal diversity and regulation of glycosaminoglycans in cell homeostasis and human disease

“…Recent advances in genomic technologies and bioinformatic tools have enhanced our ability to explore the genome to identify novel regulatory factors involved in GAG assembly. A recent data mining effort utilizing publicly available chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing data uncovered the transcription factor, MEF2C, as a potential regulator of HS and CS enzyme expression ( 122 ). Bioinformatic analyses examining the promoter region of all HS enzymes for enrichment of transcription factor binding motifs led to the discovery of ZNF263 as a novel transcriptional repressor of HS3ST1 and HS3ST3A1 expression ( 123 ).…”

Glycoinformatics in the Artificial Intelligence Era

“…First steps in this direction have added partial/fragmentary glycan information to the analysis of single cells and/or a combination with their transcriptome. , Data integration will be facilitated by collecting same level information from different and complementing -omics. Nonetheless, attempts at combining views on regulation have already emerged, for example, by considering the interplay between microRNA and glycan expression or between gene transcription and glycan expression, and these are likely to expand in the very near future. In all cases, both bioinformatics and glycoinformatics resources are needed in this context.…”