ArticleBMC biology2015
A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions.
Article in BMC biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
- scYeast: a biological-knowledge-guided foundation model on yeast single-cell transcriptomics.Synthetic and systems biotechnology · 2027Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.Molecular systems biology · 2024Article
- Optimal trade-off between boosted tolerance and growth fitness during adaptive evolution of yeast to ethanol shocks.Biotechnology for biofuels and bioproducts · 2024Article
- Antagonistic interactions safeguard mitotic propagation of genetic and epigenetic information in zebrafish.Communications biology · 2024Article
- Subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia.Haematologica · 2023Article
- Phenomics approaches to understand genetic networks and gene function in yeast.Biochemical Society transactions · 2022Article
- Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.Bioinformatics (Oxford, England) · 2021Article
- Article
- A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Mechanisms of tissue and cell-type specificity in heritable traits and diseases.Nature reviews. Genetics · 2020Review
- Recent insights into the genotype-phenotype relationship from massively parallel genetic assays.Evolutionary applications · 2019Review
- The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern.PLoS computational biology · 2019Article
- Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease.Frontiers in genetics · 2019Article
- Inferring modulators of genetic interactions with epistatic nested effects models.PLoS computational biology · 2017Article
- Transcriptome analysis of coding and long non-coding RNAs highlights the regulatory network of cascade initiation of permanent molars in miniature pigs.BMC genomics · 2017Article
- Growth condition dependency is the major cause of non-responsiveness upon genetic perturbation.PloS one · 2017Article
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Authors and funding
15 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease.
resultsTo investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression".
conclusionsThese mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.
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