Systematic Target Function Annotation of Human Transcription Factors

TL;DR

This study systematically annotates human transcription factors by integrating genome-wide binding data and functional annotations, revealing their diverse roles and connections to diseases and phenotypes, thus aiding future research.

Contribution

It provides a comprehensive computational framework for annotating TF functions using large-scale data, expanding understanding of TF roles in human biology and disease.

Findings

Over 9,000 functional annotations for 279 TFs.

Extensive links between TFs and diseases, phenotypes, pathways.

TFs connect unrelated functions and exhibit pleiotropy.

Abstract

Transcription factors (TFs), the key players in transcriptional regulation, have attracted great experimental attention, yet the functions of most human TFs remain poorly understood. Recent capabilities in genome-wide protein binding profiling have stimulated systematic studies of the hierarchical organization of human gene regulatory network and DNA-binding specificity of TFs, shedding light on combinatorial gene regulation. We show here that these data also enable a systematic annotation of the biological functions and functional diversity of TFs. We compiled a human gene regulatory network for 384 TFs covering the 146,096 TF-target gene relationships, extracted from over 850 ChIP-seq experiments as well as the literature. By integrating this network of TF-TF and TF-target gene relationships with 3,715 functional concepts from six sources of gene function annotations, we obtained over 9,000 confident functional annotations for 279 TFs. We observe extensive connectivity between transcription factors and Mendelian diseases, GWAS phenotypes, and pharmacogenetic pathways. Further, we show that transcription factors link apparently unrelated functions, even when the two functions do not share common genes. Finally, we analyze the pleiotropic functions of TFs and suggest that increased number of upstream regulators contributes to the functional pleiotropy of TFs. Our computational approach is complementary to focused experimental studies on TF functions, and the resulting knowledge can guide experimental design for discovering the unknown roles of TFs in human disease and drug response.