Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Abstract

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1,183 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by investigating hallmarks of TF activity profiles inferred from the transcriptomes of three different cancer types. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

“…For the bulk RNA-seq datasets, we first checked whether any CRISPR targets with significant paralog upregulation were transcription factors. Next, we searched the DoRothEA regulon database for common downstream targets of CRISPR targets and their paralogs (i.e., common regulons) 63,64 . We found common regulons with high-quality annotations for 4 target-paralog pairs for targets demonstrating possible transcriptional adaptation: RUNX3-RUNX1, SP1-SP4, ZEB2-ZEB1, and Myc-Mycn .…”

“…Six pairs of top-100 CRISPR targets-paralog genes spanning 4 distinct CRISPR targets were transcription factors. We then searched the DoRothEA regulon database for common downstream targets of CRISPR targets and their paralogs (i.e., common regulons) 63,64 . We found overlapping regulons with high-quality annotations for 3 of the 6 top-100 target-paralog pairs: IRF4-IRF1 , SMAD4-SMAD1 , and TFAP2A-TFAP2C (Figure 5C-E).…”

Cell‐type‐specific gene expression and regulation in the cerebral cortex and kidney of atypical Setbp1S858R Schinzel Giedion Syndrome mice

“…We inferred transcription factor (TF) activity using decoupleR 25 (v.2.6.0) by combining our scRNA‐seq data with prior knowledge from CollecTRI, a comprehensive network of TFs and their direction of regulation on transcriptional targets (accessed May 2023) 26 . We calculated activity scores for all 732 TFs by using a Multivariate Linear Model, only including TFs with a minimum of five targets.…”

The landscape ofSETBP1gene expression and transcription factor activity across human tissues

“…We acquired prior knowledge on the direction of TF regulation from CollecTRI (accessed May 2023) (Müller-Dott et al, 2023) and combined it with GTEx expression TPM to infer TF activity using decoupleR (Badia-I-Mompel et al, 2022) (v.2.6.0). We used a multivariate linear model (run_mlm) with a minimum threshold of 5 targets per TF to calculate activity scores (represented as t-values) for all 758 TFs.…”