Predictive models of eukaryotic transcriptional regulation reveals changes in transcription factor roles and promoter usage between metabolic conditions
Journal article, 2019

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.

Author

Petter Holland

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

David Bergenholm

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Christoph Sebastian Börlin

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Guodong Liu

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

Novo Nordisk Fonden

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Technical University of Denmark (DTU)

Nucleic Acids Research

0305-1048 (ISSN) 1362-4962 (eISSN)

Vol. 47 10 4986-5000

Subject Categories

Microbiology

Bioinformatics and Systems Biology

Genetics

DOI

10.1093/nar/gkz253

PubMed

30976803

More information

Latest update

7/16/2019