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      The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

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          Abstract

          Stochastic variation in gene products (“noise”) is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.

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          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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            Stochasticity in gene expression: from theories to phenotypes.

            Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
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              Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0)

              PANTHER Classification System ( www.pantherdb.org ) is a comprehensive system that combines genomes, gene function classifications, pathways and statistical analysis tools to enable biologists to analyze large-scale genome-wide experimental data. The current system (PANTHER v.14.0) covers 131 complete genomes organized into gene families and subfamilies; evolutionary relationships between genes are represented in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models, or HMMs). The families and subfamilies are annotated with Gene Ontology terms and sequences are assigned to PANTHER pathways. A suite of tools has been built to allow users to browse and query gene functions, and analyze large-scale experimental data with a number of statistical tests. PANTHER is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. Since the protocol to use this tool (v8.0) was originally published in 2013, there have been significant improvements and updates in the areas of data quality, data coverage, statistical algorithms and user experience. This Protocol Update will provide a detailed description of how to analyze genome-wide experimental data in the PANTHER Classification System.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                08 March 2023
                : 2023.03.06.531283
                Affiliations
                [1 ]Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
                [2 ]Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
                [3 ]Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
                [4 ]Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
                [5 ]School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, Scotland, UK
                Author notes

                Author contributions

                Conceptualization, DEW and SH; Software, DEW, RG and AS; Formal analysis, DEW and RG; Investigation, DEW; Writing–Original Draft, SH; Writing–Review & Editing, DEW, RG and AS; Visualization, DEW, RG and SH; Supervision, SH; Funding acquisition: SH, RG and AS.

                [* ]Correspondence: silke@ 123456vt.edu
                Article
                10.1101/2023.03.06.531283
                10028819
                36945401
                1ee908ca-d5d7-4632-a627-2cdd35a72e6e

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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