Human transcription factor protein interaction networks

Abstract

Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here, we identify 6703 and 1536 protein–protein interactions for 109 different human TFs through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-MS), respectively. The BioID analysis identifies more high-confidence interactions, highlighting the transient and dynamic nature of many of the TF interactions. By performing clustering and correlation analyses, we identify subgroups of TFs associated with specific biological functions, such as RNA splicing or chromatin remodeling. We also observe 202 TF-TF interactions, of which 118 are interactions with nuclear factor 1 (NFI) family members, indicating uncharacterized cross-talk between NFI signaling and other TF signaling pathways. Moreover, TF interactions with basal transcription machinery are mainly observed through TFIID and SAGA complexes. This study provides a rich resource of human TF interactions and also act as a starting point for future studies aimed at understanding TF-mediated transcription.

Promoter sequence and architecture determine expression variability and confer robustness to genetic variants

“…This ensures proper gene expression across different cell types ( FANTOM Consortium and the RIKEN PMI and CLST (DGT) et al, 2014 ; Kawaji et al, 2006 ) and developmental stages ( Haberle et al, 2014 ). Interestingly, ETS factors, here associated with low variable promoters, can interact with transcriptional co-activators and chromatin modifying complexes ( Curina et al, 2017 ; Göös et al, 2022 ). ETS factors may therefore play a role in TSS selection in promoters with multi-modal architectures ( Lam et al, 2019 ).…”

Promoter sequence and architecture determine expression variability and confer robustness to genetic variants

“…Rather, a flexible initiation architecture enables several points of entries for RNA polymerase II to initiate in the same promoter, ensuring gene expression across different cell types (FANTOM Consortium and the RIKEN PMI and CLST (DGT) et al, 2014;Kawaji et al, 2006) and developmental stages (Haberle et al, 2014). Interestingly, ETS factors, here associated with low variable promoters, can interact with transcriptional co-activators and chromatin modifying complexes (Göös et al, 2022), and may therefore play a role in TSS selection in promoters with multi-modal architectures (Lam et al, 2019). Here we show that such flexibility also attenuates variability across individuals for the same cell type.…”

TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology

“…Coding gene-related relationships are from HURI [23] , Bioplex [26] , PhosphoSitePlus [27] , BioGRID [28] , InnateDB [29] , MINT [30] , INSIDER [31] , and DIP [32] . TF regulatory relationships are from TRRUST [33] , KnockTF [34] and IntACT [35] . log2FC > 1 and p value < 0.01 were utilized to filter KnockTF data, and TF data from BioID and AP-MS experiments in IntACT were retained.…”