Position-dependent function of human sequence-specific transcription factors.
Sascha H C DuttkeCarlos GuzmanMax W ChangNathaniel P Delos SantosBayley R McDonaldJialei XieAaron F CarlinSven HeinzChristopher W BennerPublished in: Nature (2024)
Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites 1-3 . Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease 4,5 . Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.
Keyphrases
- transcription factor
- dna binding
- gene expression
- amino acid
- genome wide
- endothelial cells
- cell free
- healthcare
- circulating tumor
- public health
- dna methylation
- oxidative stress
- single molecule
- electronic health record
- binding protein
- big data
- single cell
- type diabetes
- nucleic acid
- skeletal muscle
- metabolic syndrome
- artificial intelligence
- deep learning