Predictive models of eukaryotic transcriptional regulation reveals changes in transcription factor roles and promoter usage between metabolic conditions.

Petter Holland,Guodong Liu,Christoph S Börlin,David Bergenholm,Jens Nielsen

doi:10.1093/nar/gkz253

Petter Holland, Guodong Liu + Show 3 more

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https://doi.org/10.1093/nar/gkz253

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Abstract

Transcription factors (TF) are central to transcriptional regulation, but they are often studied in relative isolation and without close control of the metabolic state of the cell. Here, we describe genome-wide binding (by ChIP-exo) of 15 yeast TFs in four chemostat conditions that cover a range of metabolic states. We integrate this data with transcriptomics and six additional recently mapped TFs to identify predictive models describing how TFs control gene expression in different metabolic conditions. Contributions by TFs to gene regulation are predicted to be mostly activating, additive and well approximated by assuming linear effects from TF binding signal. Notably, using TF binding peaks from peak finding algorithms gave distinctly worse predictions than simply summing the low-noise and high-resolution TF ChIP-exo reads on promoters. Finally, we discover indications of a novel functional role for three TFs; Gcn4, Ert1 and Sut1 during nitrogen limited aerobic fermentation. In only this condition, the three TFs have correlated binding to a large number of genes (enriched for glycolytic and translation processes) and a negative correlation to target gene transcript levels.

Highlights

The relationship between transcription factor (TF) binding to DNA and gene transcription in eukaryotes is complex
These four states of metabolism should involve large changes in central carbon metabolism and we focused on Transcription factors (TF) that have enriched binding to central carbon metabolism enzymes
To define a list of TFs to focus on we started from the landmark dataset collected by Harbison et al containing TF promoter enrichment genome-wide for a majority of yeast TFs mapped by chromatin immunoprecipitation (ChIP)-chip in batch cultures with rich media [4]

Summary

Introduction

The relationship between transcription factor (TF) binding to DNA and gene transcription in eukaryotes is complex. This is highlighted in several studies integrating chromatin immunoprecipitation (ChIP)-based TF binding data with transcriptomics from knockout or knockdown experiments of the TF with the goal of defining regulatory targets. Strong predictive models were created and analysis of the models suggested a highly interconnected regulatory system where TF binding has functional interactions with both nucleosome occupancy and histone modifications to regulate transcriptional outcomes [8]. A different approach to create predictive models of transcriptional regulation based only on TF binding was to build a model from TF-association scores that includes both the strength of the binding event and the distance from a given gene in data collected from

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