ArticleGenome research2015
Noise in gene expression is coupled to growth rate.
Article in Genome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The cellular growth rate controls overall mRNA turnover, and modulates either transcription or degradation rates of particular gene regulons.Nucleic acids research · 2016Pooled it
- Environmental impacts on gene expression noise and its relationship with fitness.bioRxiv : the preprint server for biology · 2026Article
- Growth control as a central regulator for tuning the cellular context.Journal of biological engineering · 2026Article
- Article
- Article
- Gradient matching accelerates mixed-effects inference for biochemical networks.Bioinformatics (Oxford, England) · 2025Article
- Single-cell data reveal heterogeneity of investment in ribosomes across a bacterial population.Nature communications · 2025Article
- Histone deacetylase Hos2 regulates protein expression noise by potentially modulating the protein translation machinery.Nucleic acids research · 2024Article
- Modification of Seurat v4 for the Development of a Phase Assignment Tool Able to Distinguish between G2 and Mitotic Cells.International journal of molecular sciences · 2024Article
- Microfluidic single-cell measurements of oxidative stress as a function of cell cycle position.Analytical and bioanalytical chemistry · 2023Article
- Shift in vacuolar to cytosolic regime of infecting Salmonella from a dual proteome perspective.PLoS pathogens · 2023Article
- Effective bet-hedging through growth rate dependent stability.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Application of anEngineering in life sciences · 2023Article
- Transcription factor binding process is the primary driver of noise in gene expression.PLoS genetics · 2022Article
- Article
- Gene regulation by a protein translation factor at the single-cell level.PLoS computational biology · 2022Article
- Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network.PLoS biology · 2021Article
- Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli.Nature communications · 2021Article
- Identifying chromatin features that regulate gene expression distribution.Scientific reports · 2020Article
- Topology-dependent interference of synthetic gene circuit function by growth feedback.Nature chemical biology · 2020Article
Corrections and comments
- Commented on by
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle-regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.