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Mellis IA, Melzer ME, Bodkin N, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. Genome Biol 2024; 25:217. [PMID: 39135102 DOI: 10.1186/s13059-024-03351-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 07/25/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. RESULTS We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation. CONCLUSIONS Our integrative approach identifies several putative hits-genes demonstrating possible transcriptional adaptation-to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
| | - Madeline E Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- CZ Biohub Chicago, LLC, Chicago, IL, USA.
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Pal S, Dhar R. Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making. FEBS Lett 2024; 598:1673-1691. [PMID: 38724715 DOI: 10.1002/1873-3468.14898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 07/23/2024]
Abstract
The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.
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Affiliation(s)
- Sampriti Pal
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
| | - Riddhiman Dhar
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
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Mellis IA, Bodkin N, Melzer ME, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.14.553318. [PMID: 37645989 PMCID: PMC10462021 DOI: 10.1101/2023.08.14.553318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A. Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Madeline E. Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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Pashkov EA, Momot VY, Pak AV, Samoilikov RV, Pashkov GA, Usatova GN, Kravtsova EO, Poddubikov AV, Nagieva FG, Sidorov AV, Pashkov EP, Svitich OA, Zverev VV. [Influence of siRNA complexes on the reproduction of influenza A virus (Orthomyxoviridae: Alphainfluenzavirus) in vivo]. Vopr Virusol 2023; 68:95-104. [PMID: 37264844 DOI: 10.36233/0507-4088-159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Indexed: 06/03/2023]
Abstract
INTRODUCTION Influenza is one of the most pressing global health problems. Despite the wide range of available anti-influenza drugs, the viral drug resistance is an increasing concern and requires the search for new approaches to overcome it. A promising solution is the development of drugs with action that is based on the inhibition of the activity of cellular genes through RNA interference. AIM Evaluation in vivo of the preventive potential of miRNAs directed to the cellular genes FLT4, Nup98 and Nup205 against influenza infection. MATERIALS AND METHODS The A/California/7/09 strain of influenza virus (H1N1) and BALB/c mice were used in the study. The administration of siRNA and experimental infection of animals were performed intranasally. The results of the experiment were analyzed using molecular genetic and virological methods. RESULTS The use of siRNA complexes Nup98.1 and Nup205.1 led to a significant decrease in viral reproduction and concentration of viral RNA on the 3rd day after infection. When two siRNA complexes (Nup98.1 and Nup205.1) were administered simultaneously, a significant decrease in viral titer and concentration of viral RNA was also noted compared with the control groups. CONCLUSIONS The use of siRNAs in vivo can lead to an antiviral effect when the activity of single or several cellular genes is suppressed. The results indicate that the use of siRNAs targeting the cellular genes whose expression products are involved in viral reproduction is one of the promising methods for the prevention and treatment of not only influenza, but also other respiratory infections.
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Affiliation(s)
- E A Pashkov
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
- I.I. Mechnikov Scientific and Research Institute of Vaccines and Sera
| | - V Y Momot
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
| | - A V Pak
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
| | - R V Samoilikov
- I.I. Mechnikov Scientific and Research Institute of Vaccines and Sera
| | - G A Pashkov
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
| | - G N Usatova
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
| | - E O Kravtsova
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
| | - A V Poddubikov
- I.I. Mechnikov Scientific and Research Institute of Vaccines and Sera
| | - F G Nagieva
- I.I. Mechnikov Scientific and Research Institute of Vaccines and Sera
| | - A V Sidorov
- I.I. Mechnikov Scientific and Research Institute of Vaccines and Sera
| | - E P Pashkov
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
| | - O A Svitich
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
- I.I. Mechnikov Scientific and Research Institute of Vaccines and Sera
| | - V V Zverev
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)
- I.I. Mechnikov Scientific and Research Institute of Vaccines and Sera
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Wang Y, He S. Inference on autoregulation in gene expression with variance-to-mean ratio. J Math Biol 2023; 86:87. [PMID: 37131095 PMCID: PMC10154285 DOI: 10.1007/s00285-023-01924-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/04/2023]
Abstract
Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
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Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, CA, 90095, USA.
- Institut des Hautes Études Scientifiques (IHÉS), Bures-sur-Yvette, 91440, Essonne, France.
| | - Siqi He
- Simons Center for Geometry and Physics, Stony Brook University, Stony Brook, NY, 11794, USA
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Wang Y, He S. Using Fano factors to determine certain types of gene autoregulation. ARXIV 2023:arXiv:2301.06692v2. [PMID: 36713249 PMCID: PMC9882590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The expression of one gene might be regulated by its corresponding protein, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation in certain scenarios from gene expression data. This method only depends on the Fano factor, namely the ratio of variance and mean of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
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Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
- Institut des Hautes Études Scientifiques, Bures-sur-Yvette, Essonne, France
| | - Siqi He
- Simons Center for Geometry and Physics, Stony Brook University, Stony Brook, New York, United States of America
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Weidemann DE, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531283. [PMID: 36945401 PMCID: PMC10028819 DOI: 10.1101/2023.03.06.531283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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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Affiliation(s)
- Douglas E Weidemann
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, Scotland, UK
| | - Silke Hauf
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
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Dean J, Ganesh A. Noise dissipation in gene regulatory networks via second order statistics of networks of infinite server queues. J Math Biol 2022; 85:14. [DOI: 10.1007/s00285-022-01781-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/10/2022] [Accepted: 07/05/2022] [Indexed: 10/16/2022]
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Righetti E, Uluşeker C, Kahramanoğulları O. Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design. BIOLOGY 2021; 10:biology10080724. [PMID: 34439956 PMCID: PMC8389217 DOI: 10.3390/biology10080724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/05/2021] [Accepted: 07/22/2021] [Indexed: 11/18/2022]
Abstract
Simple Summary Synthetic biology is an emerging discipline, offering new perspectives in many industrial fields, from pharma and row-material production to renewable energy. Developing synthetic biology applications is often a lengthy and expensive process with extensive and tedious trial-and-error runs. Computational models can direct the engineering of biological circuits in a computer-aided design setting. By providing a virtual lab environment, in silico models of synthetic circuits can contribute to a quantitative understanding of the underlying molecular pathways before a wet-lab implementation. Here, we illustrate this notion from the point of view of signal fidelity and noise relationship. Noise in gene expression can undermine signal fidelity with implications on the well-functioning of the engineered organisms. For our analysis, we use a specific biological circuit that regulates the gene expression in bacterial inorganic phosphate economy. Applications that use this circuit include those in pollutant detection and wastewater treatment. We provide computational models with different levels of molecular detail as virtual labs. We show that inherent fluctuations in the gene expression machinery can be predicted via stochastic simulations to introduce control in the synthetic promoter design process. Our analysis suggests that noise in the system can be alleviated by strong synthetic promoters with slow unbinding rates. Overall, we provide a recipe for the computer-aided design of synthetic promoter libraries with specific signal to noise characteristics. Abstract The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics.
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Affiliation(s)
- Elena Righetti
- Department of Mathematics, University of Trento, 38123 Trento, Italy;
| | - Cansu Uluşeker
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, 4036 Stavanger, Norway;
| | - Ozan Kahramanoğulları
- Department of Mathematics, University of Trento, 38123 Trento, Italy;
- Correspondence:
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Western Diet Decreases the Liver Mitochondrial Oxidative Flux of Succinate: Insight from a Murine NAFLD Model. Int J Mol Sci 2021; 22:ijms22136908. [PMID: 34199098 PMCID: PMC8268937 DOI: 10.3390/ijms22136908] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/20/2021] [Accepted: 06/23/2021] [Indexed: 12/12/2022] Open
Abstract
Mitochondria play an essential role in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). Previously, we found that succinate-activated respiration was the most affected mitochondrial parameter in mice with mild NAFLD. In this study, we focused on the role of succinate dehydrogenase (SDH) in NAFLD pathogenesis. To induce the progression of NAFLD to nonalcoholic steatohepatitis (NASH), C57BL/6J mice were fed a Western-style diet (WD) or control diet for 30 weeks. NAFLD severity was evaluated histologically and the expression of selected proteins and genes was assessed. Mitochondrial respiration was measured by high-resolution respirometry. Liver redox status was assessed using glutathione, malondialdehyde, and mitochondrial production of reactive oxygen species (ROS). Metabolomic analysis was performed by GC/MS. WD consumption for 30 weeks led to reduced succinate-activated respiration. We also observed decreased SDH activity, decreased expression of the SDH activator sirtuin 3, decreased gene expression of SDH subunits, and increased levels of hepatic succinate, an important signaling molecule. Succinate receptor 1 (SUCNR1) gene and protein expression were reduced in the livers of WD-fed mice. We did not observe signs of oxidative damage compared to the control group. The changes observed in WD-fed mice appear to be adaptive to prevent mitochondrial respiratory chain overload and massive ROS production.
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Enhancement of gene expression noise from transcription factor binding to genomic decoy sites. Sci Rep 2020; 10:9126. [PMID: 32499583 PMCID: PMC7272470 DOI: 10.1038/s41598-020-65750-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/08/2020] [Indexed: 12/29/2022] Open
Abstract
The genome contains several high-affinity non-functional binding sites for transcription factors (TFs) creating a hidden and unexplored layer of gene regulation. We investigate the role of such “decoy sites” in controlling noise (random fluctuations) in the level of a TF that is synthesized in stochastic bursts. Prior studies have assumed that decoy-bound TFs are protected from degradation, and in this case decoys function to buffer noise. Relaxing this assumption to consider arbitrary degradation rates for both bound/unbound TF states, we find rich noise behaviors. For low-affinity decoys, noise in the level of unbound TF always monotonically decreases to the Poisson limit with increasing decoy numbers. In contrast, for high-affinity decoys, noise levels first increase with increasing decoy numbers, before decreasing back to the Poisson limit. Interestingly, while protection of bound TFs from degradation slows the time-scale of fluctuations in the unbound TF levels, the decay of bound TFs leads to faster fluctuations and smaller noise propagation to downstream target proteins. In summary, our analysis reveals stochastic dynamics emerging from nonspecific binding of TFs and highlights the dual role of decoys as attenuators or amplifiers of gene expression noise depending on their binding affinity and stability of the bound TF.
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