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

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.

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

“…We also ran DTO on ChIP-exo data from yeast (Rhee and Pugh 2011;Bergenholm et al 2018;Rossi et al 2018a,b;Holland et al 2019). Twenty TFs had data in ChIP-exo, Harbison ChIP-chip, TFKO, and ZEV15, enabling all-way comparisons.…”

“…The "25&37C merged MockIP controls" file was obtained from the authors. ChIP-exo data for 26 TFs were compiled from several studies (Rhee and Pugh 2011;Bergenholm et al 2018;Rossi et al 2018a,b;Holland et al 2019). We obtained data directly from the authors of Holland et al (2019) and Bergenholm et al (2018) and focused on the glucose-limited chemostat data as that gave the best agreement with both TFKO and ZEV15 response data (Supplemental Fig.…”

Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

“…We anticipate improvements coming from better network maps. One likely source of better maps is new, more accurate methods for measuring TF binding locations (53,(75)(76)(77)(78). The input network could also be improved by obtaining TF perturbation data from cells grown in new conditions.…”

The transcription factor Leu3 shows differential binding behavior in response to changing leucine availability

“…In a previous study from our lab, in which we used high resolution chromatin immunoprecipitation followed by exonuclease treatment and sequencing (ChIP-exo), we observed that Leu3 showed differential binding behavior at four different cultivation conditions, covering respiratory and fermentative growth (Holland et al . 2019 ). The four conditions were, however, so different that it was difficult to assess if this change in binding behavior was connected to changes in leucine availability or caused by other effects.…”