Human transcription factor protein interaction networks

Abstract

In participation of transcriptional regulation, transcription factors (TFs) interact with several other proteins. Here, we identified 7233 and 2176 protein-protein interactions for 110 different human TFs through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-MS), respectively. The BioID analysis resulted more high-confident interactions, highlighting the transient and dynamic nature of many of the TF interactions. Using clustering and correlation analyses, we identified subgroups of TFs associated with specific biological functions, such as RNA-splicing, actin signalling or chromatin remodeling. We also observed 203 TF-TF interactions, of which 175 were interactions with Nuclear Factor 1 (NFI) -family members, indicating uncharacterized cross-talk between NFI signalling and numerous other TF signalling. Moreover, TF interactions with basal transcription machinery were mainly observed through TFIID and SAGA complexes. This study, not only, provides a rich resource of human TF interactions, but also act as starting point directing future studies aimed at understanding TF mediated transcription.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

“…To evaluate TF-COF interactions in a single cell type, we first examined data from cell-specific PPI predictions 45 and proximity-labeling approaches. 46 We find comparable numbers of TF interactions per COF between CoRec (average of 6.3 clusters per COF) and these published approaches (average of 3 and 9 clusters per COF for cell-specific PPI predictions 45 and BioID 46 respectively). To understand how our approach compared to larger datasets, we examined the number of TF-COF interactions reported for each COF in public PPI databases (STRING (v12.0, physical subnetwork) 47 , HIPPIE (v2.3) 48 , BioGrid (release 4.4.221) 49 , APID(version: March 2021) 50 ) (Figure 2D).…”

Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network

“…To realistically simulate the regulatory relationships of the human molecular network, we collected 11 high-quality molecular interaction databases, including HuRI [9] , BioPlex [25] , BioGRID [26] , DIP [27] , MINT [28] , InnateDB [29] , PhosphoSitePlus [30] , INSIDER [31] , KnockTF [7] , IntAct [32] , and TRRUST [33] . The molecular interactions in these databases were derived from experiments or expert-validated relationships.…”

Cracking chromatin with proteomics: From chromatome to histone modifications

“…This point has been addressed in a recent study, where Göös and colleagues performed both PBL and AP‐MS for 109 human TFs. Their results showed a minor overlap of interactors identified by both strategies, thereby suggesting the orthogonality between proximity biotinylation and enrichment approaches, where the former identifies more transient PPIs [70].…”