Inferring genome-wide functional modulatory network: a case study on NF-κB/RelA transcription factor

Abstract

How different pathways lead to the activation of a specific transcription factor (TF) with specific effects is not fully understood. We model context-specific transcriptional regulation as a modulatory network: triplets composed of a TF, target gene, and modulator. Modulators usually affect the activity of a specific TF at the posttranscriptional level in a target gene-specific action mode. This action may be classified as enhancement, attenuation, or inversion of either activation or inhibition. As a case study, we inferred, from a large collection of expression profiles, all potential modulations of NF-κB/RelA. The predicted modulators include many proteins previously not reported as physically binding to RelA but with relevant functions, such as RNA processing, cell cycle, mitochondrion, ubiquitin-dependent proteolysis, and chromatin modification. Modulators from different processes exert specific prevalent action modes on distinct pathways. Modulators from noncoding RNA, RNA-binding proteins, TFs, and kinases modulate the NF-κB/RelA activity with specific action modes consistent with their molecular functions and modulation level. The modulatory networks of NF-κB/RelA in the context epithelial-mesenchymal transition (EMT) and burn injury have different modulators, including those involved in extracellular matrix (FBN1), cytoskeletal regulation (ACTN1), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long intergenic nonprotein coding RNA, and tumor suppression (FOXP1) for EMT, and TXNIP, GAPDH, PKM2, IFIT5, LDHA, NID1, and TPP1 for burn injury.

Keywords: NF-κB; integrative probabilistic model; modulator; modulatory network; transcription factor.

FIG. 1.

Protein network of the top RelA modulators. The network was generated by Reactome FI Cytoscape Plugin with all modulators with at least five target genes as input. Edge width is proportional to the betweenness coefficient of the edge. Node size is proportional to the linker degree of each modulator.

FIG. 2.

Heatmap of the clustered enrichment (red) or depletion (blue) of the action modes of different molecular functional groups of modulators.

FIG. 3.

Enrichment (red) and depletion (blue) of activation enhancement (AE) action mode between cross-processes modulation.

FIG. 4.

Enrichment (red) and depletion (blue) of activation attenuation (AA) action mode between cross-process modulation.

FIG. 5.

Modulatory network involved in epithelial–mesenchymal transition. For visualization, the modulatory network was restricted to modulators with at least six target genes at probeset level. As a predicted modulator, VEGFA and its target genes were removed as VEGFA is a target itself. Diamonds are target genes and circles are modulators of RelA. For edge color, dark green represents inhibition attenuation (IA); light green, inhibition enhancement (IE); yellow, inhibition inversion (II); orange, activation inversion (AI); dark orange, activation enhancement (AE); and red, activation attenuation (AA). Edge widths are proportional to the −log10(p-value of γ). For node color, green indicates downregulated genes and red indicates upregulated genes in EMT.