ArticleNucleic acids research2019
Predictive models of eukaryotic transcriptional regulation reveals changes in transcription factor roles and promoter usage between metabolic conditions.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 26 citations in OpenAlex.
- Cis- and trans-regulatory factors contributing to divergent activity of the TDH3 promoter in Saccharomyces yeast.Molecular biology and evolution · 2026Article
- Post-transcriptional regulatory networks: The dynamic interplay of RNA-binding proteins.The FEBS journal · 2026Review
- Article
- A high copy suppressor screen identifies factors enhancing the allotopic production of subunit II of cytochrome c oxidase.G3 (Bethesda, Md.) · 2025Article
- Engineering artificial cross-species promoters with different transcriptional strengths.Synthetic and systems biotechnology · 2025Article
- NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration.Bioinformatics (Oxford, England) · 2023Article
- Predicting which genes will respond to transcription factor perturbations.G3 (Bethesda, Md.) · 2022Article
- Cancer progression as a learning process.iScience · 2022Review
- Genome-scale modeling of yeast metabolism: retrospectives and perspectives.FEMS yeast research · 2022Review
- Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.Frontiers in artificial intelligence · 2022Article
- Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.Bioinformatics (Oxford, England) · 2021Article
- Differential activation mechanisms of two isoforms of Gcr1 transcription factor generated from spliced and un-spliced transcripts in Saccharomyces cerevisiae.Nucleic acids research · 2021Article
- Rational gRNA design based on transcription factor binding data.Synthetic biology (Oxford, England) · 2021Article
- Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.Nature communications · 2020Article
- Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program.PLoS genetics · 2020Article
- The transcription factor Leu3 shows differential binding behavior in response to changing leucine availability.FEMS microbiology letters · 2020Article
- Obtaining a series of native gradient promoter-5'-UTR sequences in Corynebacterium glutamicum ATCC 13032.Microbial cell factories · 2020Article
- Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses.Genome research · 2020Article
- ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast.Nucleic acids research · 2019Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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