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.

Keywords: ChIP-Seq; TCGA transcription factor; glycoinformatics; glycosylation.

Figure 1

A systems glycobiology framework to link multi-OMICs data. a) Cell signaling proceeds to trigger TF activity. The binding of TFs to sites proximal to the TSS triggers glycogene expression. A complex set of reaction pathways then results in the synthesis of various carbohydrate types, many of which are either secreted or expressed on the cell surface. b) Data available at various resources can establish the link between cell signaling and glycan biosynthesis. The Reactome DB contains cell signaling knowledge. Chip-Seq and RNA-Seq data available at the Cistrome Cancer DB describe the link between the TFs and glycogenes. Pathway curation at GlycoEnzDB establishes the link between glycogenes and glycan structures. Cell illustration created using BioRender (https://biorender.com/).

Figure 2

Analysis workflow: ChiP-Seq provides evidence of TF binding to promoter regions with 0 ≤ RP ≤ 1, quantifying the likelihood that this is functionally important. RNA-Seq quantifies Spearman’s correlation (ρ) between TF and gene expression. Filtering these data establishes potential TF–glycogene interactions in specific cancer types. TFs disproportionately regulating specific glycosylation pathways were identified using the above TF–glycogene relationships as well as biochemical knowledge available at GlycoEnzDB (green region). Reactome DB analysis helped to establish cell signaling-TF–glycosylation pathway connectivity that are visualized using alluvial plots. Independently, Cytoscape maps enabled visualization of TF–glycogene relationships in different cancer types (orange region). Clusters in the resulting interactomes were related to pathway maps and signaling processes, and thus developing TF–community signaling pathway relationships.

Figure 3

Summary of TFs enriched to glycosylation pathways for luminal and basal breast cancer: The TFs found to be enriched to glycosylation pathways and the glycogenes they regulate are shown in pink for luminal and orange for basal breast cancer. Note that some of the TFs shown above do not appear in the alluvial plots in the subsequent figures because they were not enriched to a signaling pathway in Reactome. The glycans synthesized by the enriched glycogenes are shown in SNFG format [18]. All figures were generated using DrawGlycan-SNFG [19].

Figure 4

Luminal breast cancer signaling pathway enrichment and glycogene connections. a) TF-to-glycogene communities in luminal breast cancer: Three large TF-to-glycogene communities were discovered in the luminal breast subnetwork. Community 1 was enriched for pathways involving RUNX3, RUNX1, IL-21, and PTEN. Communities 2 and 3 consist primarily of chromatin-modifying enzymes. b) Signaling pathway enrichment analysis for luminal breast cancer: Connections between signaling pathways and TFs found to be statistically significant for luminal breast cancer. Some pathways enriched to TFs were condensed to conserve space. More TF-to-glycogene relationships exist in luminal breast cancer and these can be viewed in the Cytoscape figures (Supporting Information File 1).

Figure 5

Basal breast cancer signaling pathway enrichments and glycogene connections. a) TF-to-glycogene communities in basal breast cancer: Three large TF-to-glycogene communities were discovered in the basal breast subnetwork. Community 1 has TFs enriched to chromatin-modifying enzymes, and community 2 has TFs enriched to interferon α/β/γ signaling. Community 3 did not have any signaling pathways enriched. b) Signaling pathway enrichment analysis for basal breast cancer: Connections between signaling pathways and TFs found to be statistically significant for basal breast cancer. TFs displayed have been enriched to the displayed glycosylation pathways using Fisher's exact test.

Figure 6

Summary of TF–glycopathway enrichments across all cancer types: TF enrichments to glycopathways across all cancer types are depicted as dots (Fisher’s exact test adjusted P < 0.05 for overrepresentation). The dot size corresponds to the number of TFs that were found to regulate the pathway. The degree of regulation is defined as the sum of all −log₁₀ (adjusted enrichment p-values) across all TFs for a given cancer–glycopathway pair.