1
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McQueen E, Yang B, Wittkopp PJ. Epistatic impacts of cis- and trans-regulatory mutations on the distribution of mutational effects for gene expression in Saccharomyces cerevisiae. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.26.645465. [PMID: 40196697 PMCID: PMC11974906 DOI: 10.1101/2025.03.26.645465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
Epistasis can influence evolution by causing the distribution of phenotypic effects for new mutations to vary among genotypes. Here, we investigate how epistatic interactions between new mutations and an existing regulatory mutation might impact the evolution of gene expression using Saccharomyces cerevisiae. We do so by estimating the distribution of mutational effects for expression of a fluorescent reporter protein driven by the S. cerevisiae TDH3 promoter in a reference strain as well as in eight mutant strains. Each of the mutant strains differed from the reference strain by a single mutation affecting expression of the focal gene. We found that half of these regulatory mutations changed the variance and/or skewness of the distribution of mutational effects. A change in variance indicates a change in mutational robustness, and we found that one initial regulatory mutation increased mutational robustness while another decreased it. A change in skewness indicates a change in the relative frequency and/or effect size of mutations increasing or decreasing expression, and we found that the initial regulatory mutation in four strains had such an effect. Strikingly, in all four of these cases, the change in skewness increased the likelihood that new mutations would at least partially compensate for the effects of the initial regulatory mutation. If this form of epistatic impact on the distribution of mutational effects is common, it could provide a neutral mechanism reducing the divergence of gene expression and help explain the prevalence of alleles with compensatory effects in natural populations of S. cerevisiae.
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Affiliation(s)
- Eden McQueen
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Bing Yang
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Patricia J. Wittkopp
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, United States
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
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2
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Kozubowski L, Berman J. The impact of phenotypic heterogeneity on fungal pathogenicity and drug resistance. FEMS Microbiol Rev 2025; 49:fuaf001. [PMID: 39809571 PMCID: PMC11756289 DOI: 10.1093/femsre/fuaf001] [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: 11/16/2023] [Revised: 11/26/2024] [Accepted: 01/13/2025] [Indexed: 01/16/2025] Open
Abstract
Phenotypic heterogeneity in genetically clonal populations facilitates cellular adaptation to adverse environmental conditions while enabling a return to the basal physiological state. It also plays a crucial role in pathogenicity and the acquisition of drug resistance in unicellular organisms and cancer cells, yet the exact contributing factors remain elusive. In this review, we outline the current state of understanding concerning the contribution of phenotypic heterogeneity to fungal pathogenesis and antifungal drug resistance.
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Affiliation(s)
- Lukasz Kozubowski
- Eukaryotic Pathogens Innovation Center, Department of Genetics and Biochemistry, Clemson University, Clemson, SC, 29634, USA
| | - Judith Berman
- Shmunis School of Biomedical and Cancer Research, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
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3
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Zhou L, Chen H, Zhang J, Zhang J, Qiu H, Zhou T. Exact burst-size distributions for gene-expression models with complex promoter structure. Biosystems 2024; 246:105337. [PMID: 39299486 DOI: 10.1016/j.biosystems.2024.105337] [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: 05/08/2024] [Revised: 09/14/2024] [Accepted: 09/14/2024] [Indexed: 09/22/2024]
Abstract
In prokaryotic and eukaryotic cells, most genes are transcribed in a bursty fashion on one hand and complex gene regulations may lead to complex promoter structure on the other hand. This raises an unsolved issue: how does promoter structure shape transcriptional bursting kinetics characterized by burst size and frequency? Here we analyze stochastic models of gene transcription, which consider complex regulatory mechanisms. Notably, we develop an efficient method to derive exact burst-size distributions. The analytical results show that if the promoter of a gene contains only one active state, the burst size indeed follows a geometric distribution, in agreement with the previous result derived under certain limiting conditions. However, if it contains a multitude of active states, the burst size in general obeys a non-geometric distribution, which is a linearly weighted sum of geometric distributions. This superposition principle reveals the essential feature of bursting kinetics in complex cases of transcriptional regulation although it seems that there has been no direct experimental confirmation. The derived burst-size distributions not only highlight the importance of promoter structure in regulating bursting kinetics, but can be also used in the exact inference of this kinetics based on experimental data.
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Affiliation(s)
- Liying Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, PR China
| | - Haowen Chen
- School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, PR China
| | - Jinqiang Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, PR China
| | - Jiajun Zhang
- Key Laboratory of Computational Mathematics, Guangdong Province, PR China; School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, PR China
| | - Huahai Qiu
- School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, PR China.
| | - Tianshou Zhou
- Key Laboratory of Computational Mathematics, Guangdong Province, PR China; School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, PR China.
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4
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Shi C, Yang X, Zhou T, Zhang J. Nascent RNA kinetics with complex promoter architecture: Analytic results and parameter inference. Phys Rev E 2024; 110:034413. [PMID: 39425372 DOI: 10.1103/physreve.110.034413] [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: 01/11/2024] [Accepted: 09/11/2024] [Indexed: 10/21/2024]
Abstract
Transcription is a stochastic process that involves several downstream operations which make it difficult to model and infer transcription kinetics from mature RNA numbers in individual cell. However, recent advances in single-cell technologies have enabled a more precise measurement of the fluctuations of nascent RNA that closely reflect transcription kinetics. In this paper we introduce a general stochastic model to mimic nascent RNA kinetics with complex promoter architecture. We derive the exact distribution and moments of nascent RNA using queuing theory techniques, which provide valuable insights into the effect of the molecular memory created by the multistep activation and deactivation on the stochastic kinetics of nascent RNA. Moreover, based on the analytical results, we develop a statistical method to infer the promoter memory from stationary nascent RNA distributions. Data analysis of synthetic data and a realistic example, the HIV-1 gene, verifies the validity of this inference method.
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5
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Schofield JA, Hahn S. Transcriptional noise, gene activation, and roles of SAGA and Mediator Tail measured using nucleotide recoding single-cell RNA-seq. Cell Rep 2024; 43:114593. [PMID: 39102335 PMCID: PMC11405135 DOI: 10.1016/j.celrep.2024.114593] [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: 02/26/2024] [Revised: 06/29/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024] Open
Abstract
We describe a time-resolved nascent single-cell RNA sequencing (RNA-seq) approach that measures gene-specific transcriptional noise and the fraction of active genes in S. cerevisiae. Most genes are expressed with near-constitutive behavior, while a subset of genes show high mRNA variance suggestive of transcription bursting. Transcriptional noise is highest in the cofactor/coactivator-redundant (CR) gene class (dependent on both SAGA and TFIID) and strongest in TATA-containing CR genes. Using this approach, we also find that histone gene transcription switches from a low-level, low-noise constitutive mode during M and M/G1 to an activated state in S phase that shows both an increase in the fraction of active promoters and a switch to a noisy and bursty transcription mode. Rapid depletion of cofactors SAGA and MED Tail indicates that both factors play an important role in stimulating the fraction of active promoters at CR genes, with a more modest role in transcriptional noise.
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Affiliation(s)
| | - Steven Hahn
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
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6
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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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7
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Breimann L, Bahry E, Zouinkhi M, Kolyvanov K, Street LA, Preibisch S, Ercan S. Analysis of developmental gene expression using smFISH and in silico staging of C. elegans embryos. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594414. [PMID: 38798598 PMCID: PMC11118362 DOI: 10.1101/2024.05.15.594414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Regulation of transcription during embryogenesis is key to development and differentiation. To study transcript expression throughout Caenorhabditis elegans embryogenesis at single-molecule resolution, we developed a high-throughput single-molecule fluorescence in situ hybridization (smFISH) method that relies on computational methods to developmentally stage embryos and quantify individual mRNA molecules in single embryos. We applied our system to sdc-2, a zygotically transcribed gene essential for hermaphrodite development and dosage compensation. We found that sdc-2 is rapidly activated during early embryogenesis by increasing both the number of mRNAs produced per transcription site and the frequency of sites engaged in transcription. Knockdown of sdc-2 and dpy-27, a subunit of the dosage compensation complex (DCC), increased the number of active transcription sites for the X chromosomal gene dpy-23 but not the autosomal gene mdh-1, suggesting that the DCC reduces the frequency of dpy-23 transcription. The temporal resolution from in silico staging of embryos showed that the deletion of a single DCC recruitment element near the dpy-23 gene causes higher dpy-23 mRNA expression after the start of dosage compensation, which could not be resolved using mRNAseq from mixed-stage embryos. In summary, we have established a computational approach to quantify temporal regulation of transcription throughout C. elegans embryogenesis and demonstrated its potential to provide new insights into developmental gene regulation.
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Affiliation(s)
- Laura Breimann
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Ella Bahry
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
- Helmholtz Imaging, Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany
| | - Marwan Zouinkhi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Klim Kolyvanov
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Lena Annika Street
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Stephan Preibisch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sevinç Ercan
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA
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8
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Jiao Y, Nigam D, Barry K, Daum C, Yoshinaga Y, Lipzen A, Khan A, Parasa SP, Wei S, Lu Z, Tello-Ruiz MK, Dhiman P, Burow G, Hayes C, Chen J, Brandizzi F, Mortimer J, Ware D, Xin Z. A large sequenced mutant library - valuable reverse genetic resource that covers 98% of sorghum genes. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1543-1557. [PMID: 38100514 DOI: 10.1111/tpj.16582] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 09/08/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023]
Abstract
Mutant populations are crucial for functional genomics and discovering novel traits for crop breeding. Sorghum, a drought and heat-tolerant C4 species, requires a vast, large-scale, annotated, and sequenced mutant resource to enhance crop improvement through functional genomics research. Here, we report a sorghum large-scale sequenced mutant population with 9.5 million ethyl methane sulfonate (EMS)-induced mutations that covered 98% of sorghum's annotated genes using inbred line BTx623. Remarkably, a total of 610 320 mutations within the promoter and enhancer regions of 18 000 and 11 790 genes, respectively, can be leveraged for novel research of cis-regulatory elements. A comparison of the distribution of mutations in the large-scale mutant library and sorghum association panel (SAP) provides insights into the influence of selection. EMS-induced mutations appeared to be random across different regions of the genome without significant enrichment in different sections of a gene, including the 5' UTR, gene body, and 3'-UTR. In contrast, there were low variation density in the coding and UTR regions in the SAP. Based on the Ka /Ks value, the mutant library (~1) experienced little selection, unlike the SAP (0.40), which has been strongly selected through breeding. All mutation data are publicly searchable through SorbMutDB (https://www.depts.ttu.edu/igcast/sorbmutdb.php) and SorghumBase (https://sorghumbase.org/). This current large-scale sequence-indexed sorghum mutant population is a crucial resource that enriched the sorghum gene pool with novel diversity and a highly valuable tool for the Poaceae family, that will advance plant biology research and crop breeding.
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Affiliation(s)
- Yinping Jiao
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance, Texas Tech University, Lubbock, Texas, 79409, USA
| | - Deepti Nigam
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance, Texas Tech University, Lubbock, Texas, 79409, USA
| | - Kerrie Barry
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Chris Daum
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Yuko Yoshinaga
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Anna Lipzen
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Adil Khan
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance, Texas Tech University, Lubbock, Texas, 79409, USA
| | - Sai-Praneeth Parasa
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance, Texas Tech University, Lubbock, Texas, 79409, USA
| | - Sharon Wei
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
| | - Zhenyuan Lu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
| | | | - Pallavi Dhiman
- Department of Plant and Soil Science, Institute of Genomics for Crop Abiotic Stress Tolerance, Texas Tech University, Lubbock, Texas, 79409, USA
| | - Gloria Burow
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, Texas, 79424, USA
| | - Chad Hayes
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, Texas, 79424, USA
| | - Junping Chen
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, Texas, 79424, USA
| | - Federica Brandizzi
- MSU-DOE Plant Research Lab, Michigan State University, East Lansing, Michigan, USA
- Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, Michigan, USA
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, USA
| | - Jenny Mortimer
- Joint BioEnergy Institute, Emeryville, California, 94608, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California, 94720, USA
- School of Agriculture, Food and Wine, Waite Research Institute, Waite Research Precinct, University of Adelaide, Glen Osmond, South Australia, 5064, Australia
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
- USDA-ARS NAA Robert W. Holley Center for Agriculture and Health, Agricultural Research Service, Ithaca, New York, 14853, USA
| | - Zhanguo Xin
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, Texas, 79424, USA
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9
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Meeussen JVW, Lenstra TL. Time will tell: comparing timescales to gain insight into transcriptional bursting. Trends Genet 2024; 40:160-174. [PMID: 38216391 PMCID: PMC10860890 DOI: 10.1016/j.tig.2023.11.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/27/2023] [Accepted: 11/27/2023] [Indexed: 01/14/2024]
Abstract
Recent imaging studies have captured the dynamics of regulatory events of transcription inside living cells. These events include transcription factor (TF) DNA binding, chromatin remodeling and modification, enhancer-promoter (E-P) proximity, cluster formation, and preinitiation complex (PIC) assembly. Together, these molecular events culminate in stochastic bursts of RNA synthesis, but their kinetic relationship remains largely unclear. In this review, we compare the timescales of upstream regulatory steps (input) with the kinetics of transcriptional bursting (output) to generate mechanistic models of transcription dynamics in single cells. We highlight open questions and potential technical advances to guide future endeavors toward a quantitative and kinetic understanding of transcription regulation.
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Affiliation(s)
- Joseph V W Meeussen
- Division of Gene Regulation, The Netherlands Cancer Institute, Oncode Institute, Amsterdam 1066CX, The Netherlands
| | - Tineke L Lenstra
- Division of Gene Regulation, The Netherlands Cancer Institute, Oncode Institute, Amsterdam 1066CX, The Netherlands.
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10
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Kleinschmidt H, Xu C, Bai L. Using Synthetic DNA Libraries to Investigate Chromatin and Gene Regulation. Chromosoma 2023; 132:167-189. [PMID: 37184694 PMCID: PMC10542970 DOI: 10.1007/s00412-023-00796-5] [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: 02/05/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/16/2023]
Abstract
Despite the recent explosion in genome-wide studies in chromatin and gene regulation, we are still far from extracting a set of genetic rules that can predict the function of the regulatory genome. One major reason for this deficiency is that gene regulation is a multi-layered process that involves an enormous variable space, which cannot be fully explored using native genomes. This problem can be partially solved by introducing synthetic DNA libraries into cells, a method that can test the regulatory roles of thousands to millions of sequences with limited variables. Here, we review recent applications of this method to study transcription factor (TF) binding, nucleosome positioning, and transcriptional activity. We discuss the design principles, experimental procedures, and major findings from these studies and compare the pros and cons of different approaches.
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Affiliation(s)
- Holly Kleinschmidt
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Cheng Xu
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Lu Bai
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA.
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA.
- Department of Physics, The Pennsylvania State University, University Park, PA, 16802, USA.
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11
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Weidemann DE, Holehouse J, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. SCIENCE ADVANCES 2023; 9:eadh5138. [PMID: 37556551 PMCID: PMC10411910 DOI: 10.1126/sciadv.adh5138] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/07/2023] [Indexed: 08/11/2023]
Abstract
Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, noise cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit is typically assumed to be Poissonian noise, wherein the variance in mRNA numbers is equal to their mean. Here, we demonstrate that several cell division genes in fission yeast exhibit mRNA variances significantly below this limit. The reduced variance can be explained by a gene expression model incorporating multiple transcription and mRNA degradation steps. Notably, in this sub-Poissonian regime, distinct from Poissonian or super-Poissonian regimes, cytoplasmic noise is effectively suppressed through a higher mRNA export rate. Our findings redefine the lower limit of eukaryotic gene expression noise and uncover molecular requirements for achieving ultralow noise, which is expected to be important for vital cellular 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
| | - James Holehouse
- The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87510, 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, 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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12
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Wittkopp PJ. Contributions of mutation and selection to regulatory variation: lessons from the Saccharomyces cerevisiae TDH3 gene. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220057. [PMID: 37004723 PMCID: PMC10067266 DOI: 10.1098/rstb.2022.0057] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/16/2023] [Indexed: 04/04/2023] Open
Abstract
Heritable variation in gene expression is common within and among species and contributes to phenotypic diversity. Mutations affecting either cis- or trans-regulatory sequences controlling gene expression give rise to variation in gene expression, and natural selection acting on this variation causes some regulatory variants to persist in a population for longer than others. To understand how mutation and selection interact to produce the patterns of regulatory variation we see within and among species, my colleagues and I have been systematically determining the effects of new mutations on expression of the TDH3 gene in Saccharomyces cerevisiae and comparing them to the effects of polymorphisms segregating within this species. We have also investigated the molecular mechanisms by which regulatory variants act. Over the past decade, this work has revealed properties of cis- and trans-regulatory mutations including their relative frequency, effects, dominance, pleiotropy and fitness consequences. Comparing these mutational effects to the effects of polymorphisms in natural populations, we have inferred selection acting on expression level, expression noise and phenotypic plasticity. Here, I summarize this body of work and synthesize its findings to make inferences not readily discernible from the individual studies alone. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Patricia J. Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
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13
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Schofield JA, Hahn S. Broad compatibility between yeast UAS elements and core promoters and identification of promoter elements that determine cofactor specificity. Cell Rep 2023; 42:112387. [PMID: 37058407 PMCID: PMC10567116 DOI: 10.1016/j.celrep.2023.112387] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/30/2023] [Accepted: 03/28/2023] [Indexed: 04/15/2023] Open
Abstract
Three classes of yeast protein-coding genes are distinguished by their dependence on the transcription cofactors TFIID, SAGA, and Mediator (MED) Tail, but whether this dependence is determined by the core promoter, upstream activating sequences (UASs), or other gene features is unclear. Also unclear is whether UASs can broadly activate transcription from the different promoter classes. Here, we measure transcription and cofactor specificity for thousands of UAS-core promoter combinations and find that most UASs broadly activate promoters regardless of regulatory class, while few display strong promoter specificity. However, matching UASs and promoters from the same gene class is generally important for optimal expression. We find that sensitivity to rapid depletion of MED Tail or SAGA is dependent on the identity of both UAS and core promoter, while dependence on TFIID localizes to only the promoter. Finally, our results suggest the role of TATA and TATA-like promoter sequences in MED Tail function.
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Affiliation(s)
- Jeremy A Schofield
- Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98105, USA
| | - Steven Hahn
- Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98105, USA.
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14
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Kumar DK, Jonas F, Jana T, Brodsky S, Carmi M, Barkai N. Complementary strategies for directing in vivo transcription factor binding through DNA binding domains and intrinsically disordered regions. Mol Cell 2023; 83:1462-1473.e5. [PMID: 37116493 DOI: 10.1016/j.molcel.2023.04.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 01/17/2023] [Accepted: 01/30/2023] [Indexed: 04/30/2023]
Abstract
DNA binding domains (DBDs) of transcription factors (TFs) recognize DNA sequence motifs that are highly abundant in genomes. Within cells, TFs bind a subset of motif-containing sites as directed by either their DBDs or DBD-external (nonDBD) sequences. To define the relative roles of DBDs and nonDBDs in directing binding preferences, we compared the genome-wide binding of 48 (∼30%) budding yeast TFs with their DBD-only, nonDBD-truncated, and nonDBD-only mutants. With a few exceptions, binding locations differed between DBDs and TFs, resulting from the cumulative action of multiple determinants mapped mostly to disordered nonDBD regions. Furthermore, TFs' preferences for promoters of the fuzzy nucleosome architecture were lost in DBD-only mutants, whose binding spread across promoters, implicating nonDBDs' preferences in this hallmark of budding yeast regulatory design. We conclude that DBDs and nonDBDs employ complementary DNA-targeting strategies, whose balance defines TF binding specificity along genomes.
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Affiliation(s)
- Divya Krishna Kumar
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Felix Jonas
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Tamar Jana
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Sagie Brodsky
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Miri Carmi
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Naama Barkai
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel.
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15
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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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16
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Lambert É, Puwakdandawa K, Tao YF, Robert F. From structure to molecular condensates: emerging mechanisms for Mediator function. FEBS J 2023; 290:286-309. [PMID: 34698446 DOI: 10.1111/febs.16250] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/15/2021] [Accepted: 10/25/2021] [Indexed: 02/05/2023]
Abstract
Mediator is a large modular protein assembly whose function as a coactivator of transcription is conserved in all eukaryotes. The Mediator complex can integrate and relay signals from gene-specific activators bound at enhancers to activate the general transcription machinery located at promoters. It has thus been described as a bridge between these elements during initiation of transcription. Here, we review recent studies on Mediator relating to its structure, gene specificity and general requirement, roles in chromatin architecture as well as novel concepts involving phase separation and transcriptional bursting. We revisit the mechanism of action of Mediator and ultimately put forward models for its mode of action in gene activation.
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Affiliation(s)
- Élie Lambert
- Institut de recherches cliniques de Montréal, Canada
| | | | - Yi Fei Tao
- Institut de recherches cliniques de Montréal, Canada
| | - François Robert
- Institut de recherches cliniques de Montréal, Canada.,Département de Médecine, Faculté de Médecine, Université de Montréal, Canada
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17
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Luo S, Wang Z, Zhang Z, Zhou T, Zhang J. Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics. Nucleic Acids Res 2022; 51:68-83. [PMID: 36583343 PMCID: PMC9874261 DOI: 10.1093/nar/gkac1204] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/06/2022] [Accepted: 12/06/2022] [Indexed: 12/31/2022] Open
Abstract
Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.
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Affiliation(s)
| | | | - Zhenquan Zhang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510275, P. R. China,School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, 510275, P. R. China
| | - Tianshou Zhou
- Correspondence may also be addressed to Tianshou Zhou. Tel: +86 20 84134958;
| | - Jiajun Zhang
- To whom correspondence should be addressed. Tel: +86 20 84111829;
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18
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Parab L, Pal S, Dhar R. Transcription factor binding process is the primary driver of noise in gene expression. PLoS Genet 2022; 18:e1010535. [PMID: 36508455 PMCID: PMC9779669 DOI: 10.1371/journal.pgen.1010535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/22/2022] [Accepted: 11/16/2022] [Indexed: 12/14/2022] Open
Abstract
Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.
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Affiliation(s)
- Lavisha Parab
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
- Max-Planck-Institute for Evolutionary Biology, Plön, Germany
| | - Sampriti Pal
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
| | - Riddhiman Dhar
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
- * E-mail:
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19
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Jayaraman V, Toledo‐Patiño S, Noda‐García L, Laurino P. Mechanisms of protein evolution. Protein Sci 2022; 31:e4362. [PMID: 35762715 PMCID: PMC9214755 DOI: 10.1002/pro.4362] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/11/2022] [Accepted: 05/14/2022] [Indexed: 11/06/2022]
Abstract
How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.
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Affiliation(s)
- Vijay Jayaraman
- Department of Molecular Cell BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Saacnicteh Toledo‐Patiño
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Lianet Noda‐García
- Department of Plant Pathology and Microbiology, Institute of Environmental Sciences, Robert H. Smith Faculty of Agriculture, Food and EnvironmentHebrew University of JerusalemRehovotIsrael
| | - Paola Laurino
- Protein Engineering and Evolution UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
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20
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Gene regulation in Escherichia coli is commonly selected for both high plasticity and low noise. Nat Ecol Evol 2022; 6:1165-1179. [PMID: 35726087 DOI: 10.1038/s41559-022-01783-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 05/03/2022] [Indexed: 11/08/2022]
Abstract
Bacteria often respond to dynamically changing environments by regulating gene expression. Despite this regulation being critically important for growth and survival, little is known about how selection shapes gene regulation in natural populations. To better understand the role natural selection plays in shaping bacterial gene regulation, here we compare differences in the regulatory behaviour of naturally segregating promoter variants from Escherichia coli (which have been subject to natural selection) to randomly mutated promoter variants (which have never been exposed to natural selection). We quantify gene expression phenotypes (expression level, plasticity and noise) for hundreds of promoter variants across multiple environments and show that segregating promoter variants are enriched for mutations with minimal effects on expression level. In many promoters, we infer that there is strong selection to maintain high levels of plasticity, and direct selection to decrease or increase cell-to-cell variability in expression. Taken together, these results expand our knowledge of how gene regulation is affected by natural selection and highlight the power of comparing naturally segregating polymorphisms to de novo random mutations to quantify the action of selection.
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21
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Cancer: More than a geneticist’s Pandora’s box. J Biosci 2022. [DOI: 10.1007/s12038-022-00254-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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22
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Yang X, Wang Z, Wu Y, Zhou T, Zhang J. Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3313-3336. [PMID: 35341253 DOI: 10.3934/mbe.2022153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
While transcription often occurs in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important question: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that burst size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, China
| | - Zihao Wang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Yahao Wu
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
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23
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Yang X, Luo S, Zhang Z, Wang Z, Zhou T, Zhang J. Silent transcription intervals and translational bursting lead to diverse phenotypic switching. Phys Chem Chem Phys 2022; 24:26600-26608. [DOI: 10.1039/d2cp03703c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, P. R. China
| | - Songhao Luo
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zhenquan Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zihao Wang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
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24
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Chen M, Luo S, Cao M, Guo C, Zhou T, Zhang J. Exact distributions for stochastic gene expression models with arbitrary promoter architecture and translational bursting. Phys Rev E 2022; 105:014405. [PMID: 35193181 DOI: 10.1103/physreve.105.014405] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/14/2021] [Indexed: 11/07/2022]
Abstract
Gene expression in individual cells is inherently variable and sporadic, leading to cell-to-cell variability in mRNA and protein levels. Recent single-cell and single-molecule experiments indicate that promoter architecture and translational bursting play significant roles in controlling gene expression noise and generating the phenotypic diversity that life exhibits. To quantitatively understand the impact of these factors, it is essential to construct an accurate mathematical description of stochastic gene expression and find the exact analytical results, which is a formidable task. Here, we develop a stochastic model of bursty gene expression, which considers the complex promoter architecture governing the variability in mRNA expression and a general distribution characterizing translational burst. We derive the analytical expression for the corresponding protein steady-state distribution and all moment statistics of protein counts. We show that the total protein noise can be decomposed into three parts: the low-copy noise of protein due to probabilistic individual birth and death events, the noise due to stochastic switching between promoter states, and the noise resulting from translational busting. The theoretical results derived provide quantitative insights into the biochemical mechanisms of stochastic gene expression.
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Affiliation(s)
- Meiling Chen
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Songhao Luo
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Mengfang Cao
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Chengjun Guo
- School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510275, People's Republic of China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China.,School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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25
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Yokoshi M, Kawasaki K, Cambón M, Fukaya T. Dynamic modulation of enhancer responsiveness by core promoter elements in living Drosophila embryos. Nucleic Acids Res 2021; 50:92-107. [PMID: 34897508 PMCID: PMC8754644 DOI: 10.1093/nar/gkab1177] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/08/2021] [Accepted: 11/12/2021] [Indexed: 11/12/2022] Open
Abstract
Regulatory interactions between enhancers and core promoters are fundamental for the temporal and spatial specificity of gene expression in development. The central role of core promoters is to initiate productive transcription in response to enhancer's activation cues. However, it has not been systematically assessed how individual core promoter elements affect the induction of transcriptional bursting by enhancers. Here, we provide evidence that each core promoter element differentially modulates functional parameters of transcriptional bursting in developing Drosophila embryos. Quantitative live imaging analysis revealed that the timing and the continuity of burst induction are common regulatory steps on which core promoter elements impact. We further show that the upstream TATA also affects the burst amplitude. On the other hand, Inr, MTE and DPE mainly contribute to the regulation of the burst frequency. Genome editing analysis of the pair-rule gene fushi tarazu revealed that the endogenous TATA and DPE are both essential for its correct expression and function during the establishment of body segments in early embryos. We suggest that core promoter elements serve as a key regulatory module in converting enhancer activity into transcription dynamics during animal development.
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Affiliation(s)
- Moe Yokoshi
- Laboratory of Transcription Dynamics, Research Center for Biological Visualization, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Koji Kawasaki
- Laboratory of Transcription Dynamics, Research Center for Biological Visualization, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Manuel Cambón
- Applied Mathematics Department, University of Granada, Granada, Spain
| | - Takashi Fukaya
- Laboratory of Transcription Dynamics, Research Center for Biological Visualization, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.,Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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26
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Gerhardt KP, Rao SD, Olson EJ, Igoshin OA, Tabor JJ. Independent control of mean and noise by convolution of gene expression distributions. Nat Commun 2021; 12:6957. [PMID: 34845228 PMCID: PMC8630168 DOI: 10.1038/s41467-021-27070-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 11/03/2021] [Indexed: 11/28/2022] Open
Abstract
Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.
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Affiliation(s)
- Karl P Gerhardt
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Satyajit D Rao
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Evan J Olson
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Oleg A Igoshin
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Biosciences, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Center for Theoretical Biophysics, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Jeffrey J Tabor
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
- Department of Biosciences, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
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27
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Abstract
Mammalian genomes have distinct levels of spatial organization and structure that have been hypothesized to play important roles in transcription regulation. Although much has been learned about these architectural features with ensemble techniques, single-cell studies are showing a new universal trend: Genomes are stochastic and dynamic at every level of organization. Stochastic gene expression, on the other hand, has been studied for years. In this review, we probe whether there is a causative link between the two phenomena. We specifically discuss the functionality of chromatin state, topologically associating domains (TADs), and enhancer biology in light of their stochastic nature and their specific roles in stochastic gene expression. We highlight persistent fundamental questions in this area of research.
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Affiliation(s)
- Christopher H Bohrer
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Daniel R Larson
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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28
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Chen M, Zhou T, Zhang J. Correlation between external regulators governs the mean-noise relationship in stochastic gene expression. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4713-4730. [PMID: 34198461 DOI: 10.3934/mbe.2021239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gene transcription in single cells is inherently a probabilistic process. The relationship between variance ($ \sigma^{2} $) and mean expression ($ \mu $) is of paramount importance for investigations into the evolutionary origins and consequences of noise in gene expression. It is often formulated as $ \log \left({{{\sigma}^{2}}}/{{{\mu}^{2}}}\; \right) = \beta\log\mu+\log\alpha $, where $ \beta $ is a key parameter since its sign determines the qualitative dependence of noise on mean. We reveal that the sign of $ \beta $ is controlled completely by external regulation, but independent of promoter structure. Specifically, it is negative if regulators as stochastic variables are independent but positive if they are correlated. The essential mechanism revealed here can well interpret diverse experimental phenomena underlying expression noise. Our results imply that external regulation rather than promoter sequence governs the mean-noise relationship.
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Affiliation(s)
- Meiling Chen
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
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29
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Yang X, Chen Y, Zhou T, Zhang J. Exploring dissipative sources of non-Markovian biochemical reaction systems. Phys Rev E 2021; 103:052411. [PMID: 34134237 DOI: 10.1103/physreve.103.052411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 04/29/2021] [Indexed: 11/07/2022]
Abstract
Many biological processes including important intracellular processes are governed by biochemical reaction networks. Usually, these reaction systems operate far from thermodynamic equilibrium, implying free-energy dissipation. On the other hand, single reaction events happen often in a memory manner, leading to non-Markovian kinetics. A question then arises: how do we calculate free-energy dissipation (defined as the entropy production rate) in this physically real case? We derive an analytical formula for calculating the energy consumption of a general reaction system with molecular memory characterized by nonexponential waiting-time distributions. It shows that this dissipation is composed of two parts: one from broken detailed balance of an equivalent Markovian system with the same topology and substrates, and the other from the direction-time dependence of waiting-time distributions. But, if the system is in a detailed balance and the waiting-time distribution is direction-time independent, there is no energy dissipation even in the non-Markovian case. These general results provide insights into the physical mechanisms underlying nonequilibrium processes. A continuous-time random-walk model and a generalized model of stochastic gene expression are chosen to clearly show dissipative sources and the relationship between energy dissipation and molecular memory.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, People's Republic of China
| | - Yiren Chen
- College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, People's Republic of China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.,Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.,Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, People's Republic of China
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30
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Das S, Barik D. Scaling of intrinsic noise in an autocratic reaction network. Phys Rev E 2021; 103:042403. [PMID: 34006004 DOI: 10.1103/physreve.103.042403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/16/2021] [Indexed: 11/07/2022]
Abstract
Biochemical reactions in living cells often produce stochastic trajectories due to the fluctuations of the finite number of the macromolecular species present inside the cell. A significant number of computational and theoretical studies have previously investigated stochasticity in small regulatory networks to understand its origin and regulation. At the systems level regulatory networks have been determined to be hierarchical resembling social networks. In order to determine the stochasticity in networks with hierarchical architecture, here we computationally investigated intrinsic noise in an autocratic reaction network in which only the upstream regulators regulate the downstream regulators. We studied the effects of the qualitative and quantitative nature of regulatory interactions on the stochasticity in the network. We established an unconventional scaling of noise with average abundance in which the noise passes through a minimum indicating that the network can be noisy both in the low and high abundance regimes. We determined that the bursty kinetics of the trajectories are responsible for such scaling. The scaling of noise remains intact for a mixed network that includes democratic subnetworks within the autocratic network.
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Affiliation(s)
- Soutrick Das
- School of Chemistry, University of Hyderabad, Gachibowli, 500046, Hyderabad, India
| | - Debashis Barik
- School of Chemistry, University of Hyderabad, Gachibowli, 500046, Hyderabad, India
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31
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Capp J. Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability. Evol Appl 2021; 14:893-901. [PMID: 33897810 PMCID: PMC8061278 DOI: 10.1111/eva.13204] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 12/11/2022] Open
Abstract
Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.
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Affiliation(s)
- Jean‐Pascal Capp
- Toulouse Biotechnology InstituteINSACNRSINRAEUniversity of ToulouseToulouseFrance
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32
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Ramalingam V, Natarajan M, Johnston J, Zeitlinger J. TATA and paused promoters active in differentiated tissues have distinct expression characteristics. Mol Syst Biol 2021; 17:e9866. [PMID: 33543829 PMCID: PMC7863008 DOI: 10.15252/msb.20209866] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 12/22/2020] [Accepted: 01/07/2021] [Indexed: 12/18/2022] Open
Abstract
Core promoter types differ in the extent to which RNA polymerase II (Pol II) pauses after initiation, but how this affects their tissue-specific gene expression characteristics is not well understood. While promoters with Pol II pausing elements are active throughout development, TATA promoters are highly active in differentiated tissues. We therefore used a genomics approach on late-stage Drosophila embryos to analyze the properties of promoter types. Using tissue-specific Pol II ChIP-seq, we found that paused promoters have high levels of paused Pol II throughout the embryo, even in tissues where the gene is not expressed, while TATA promoters only show Pol II occupancy when the gene is active. The promoter types are associated with different chromatin accessibility in ATAC-seq data and have different expression characteristics in single-cell RNA-seq data. The two promoter types may therefore be optimized for different properties: paused promoters show more consistent expression when active, while TATA promoters have lower background expression when inactive. We propose that tissue-specific genes have evolved to use two different strategies for their differential expression across tissues.
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Affiliation(s)
- Vivekanandan Ramalingam
- Stowers Institute for Medical ResearchKansas CityMOUSA
- Department of Pathology and Laboratory MedicineUniversity of Kansas Medical CenterKansas CityKSUSA
- Present address:
Department of GeneticsStanford UniversityStanfordCAUSA
| | - Malini Natarajan
- Stowers Institute for Medical ResearchKansas CityMOUSA
- Present address:
Department of Molecular Biology, Cell Biology and BiochemistryBrown UniversityProvidenceRIUSA
| | - Jeff Johnston
- Stowers Institute for Medical ResearchKansas CityMOUSA
- Present address:
Center for Pediatric Genomic MedicineChildren's MercyKansas CityMOUSA
| | - Julia Zeitlinger
- Stowers Institute for Medical ResearchKansas CityMOUSA
- Department of Pathology and Laboratory MedicineUniversity of Kansas Medical CenterKansas CityKSUSA
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33
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Qiu H, Zhang B, Zhou T. Explicit effect of stochastic reaction delay on gene expression. Phys Rev E 2020; 101:012405. [PMID: 32069597 DOI: 10.1103/physreve.101.012405] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Indexed: 11/07/2022]
Abstract
Apart from intrinsic stochastic variability, gene expression also involves stochastic reaction delay arising from heterogeneity and fluctuation processes, which can affect the efficiency of reactants (e.g., mRNA or protein) in exploring their environments. In contrast to the former that has been extensively investigated, the impact of the latter on gene expression remains not fully understood. Here, we analyze a non-Markovian model of bursty gene expression with general delay distribution. We analytically find that the effect of stochastic reaction delay is equivalent to the introduction of negative feedback, and stationary protein distribution only depends on the mean of the delay and is independent of its distribution. We numerically show that the stochastic reaction delay always slightly amplifies the mean protein level but remarkably reduces the protein noise (quantified by the ratio of the variance over the squared average). Our analysis indicates that stochastic reaction delay is an important factor affecting gene expression.
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Affiliation(s)
- Huahai Qiu
- School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, People's Republic of China
| | - Bengong Zhang
- School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, People's Republic of China
| | - Tianshou Zhou
- School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, People's Republic of China.,Key Laboratory of Computational Mathematics, School of Mathematics, Sun Yat-sen University, Guangdong Province, Guangzhou 510275, People's Republic of China
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34
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Zhang T, Foreman R, Wollman R. Identifying chromatin features that regulate gene expression distribution. Sci Rep 2020; 10:20566. [PMID: 33239733 PMCID: PMC7688950 DOI: 10.1038/s41598-020-77638-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 11/10/2020] [Indexed: 12/17/2022] Open
Abstract
Gene expression variability, differences in the number of mRNA per cell across a population of cells, is ubiquitous across diverse organisms with broad impacts on cellular phenotypes. The role of chromatin in regulating average gene expression has been extensively studied. However, what aspects of the chromatin contribute to gene expression variability is still underexplored. Here we addressed this problem by leveraging chromatin diversity and using a systematic investigation of randomly integrated expression reporters to identify what aspects of chromatin microenvironment contribute to gene expression variability. Using DNA barcoding and split-pool decoding, we created a large library of isogenic reporter clones and identified reporter integration sites in a massive and parallel manner. By mapping our measurements of reporter expression at different genomic loci with multiple epigenetic profiles including the enrichment of transcription factors and the distance to different chromatin states, we identified new factors that impact the regulation of gene expression distributions.
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Affiliation(s)
- Thanutra Zhang
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA, USA
| | - Robert Foreman
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA, USA
| | - Roy Wollman
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA, USA.
- Departments of Integrative Biology and Physiology and Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.
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35
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Abstract
More than 50 years after the identification of RNA polymerase II, the enzyme responsible for the transcription of most eukaryotic genes, studies have continued to reveal fresh aspects of its structure and regulation. New technologies, coupled with years of development of a vast catalog of RNA polymerase II accessory proteins and activities, have led to new revelations about the transcription process. The maturation of cryo-electron microscopy as a tool for unraveling the detailed structure of large molecular machines has provided numerous structures of the enzyme and its accessory factors. Advances in biophysical methods have enabled the observation of a single polymerase’s behavior, distinct from work on aggregate population averages. Other recent work has revealed new properties and activities of the general initiation factors that RNA polymerase II employs to accurately initiate transcription, as well as chromatin proteins that control RNA polymerase II’s firing frequency, and elongation factors that facilitate the enzyme’s departure from the promoter and which control sequential steps and obstacles that must be navigated by elongating RNA polymerase II. There has also been a growing appreciation of the physical properties conferred upon many of these proteins by regions of each polypeptide that are of low primary sequence complexity and that are often intrinsically disordered. This peculiar feature of a surprisingly large number of proteins enables a disordered region of the protein to morph into a stable structure and creates an opportunity for pathway participants to dynamically partition into subcompartments of the nucleus. These subcompartments host designated portions of the chemical reactions that lead to mRNA synthesis. This article highlights a selection of recent findings that reveal some of the resolved workings of RNA polymerase II and its ensemble of supporting factors.
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Affiliation(s)
- Daniel Reines
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
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36
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Capturing and Understanding the Dynamics and Heterogeneity of Gene Expression in the Living Cell. Int J Mol Sci 2020; 21:ijms21218278. [PMID: 33167354 PMCID: PMC7663833 DOI: 10.3390/ijms21218278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/29/2020] [Accepted: 11/03/2020] [Indexed: 11/21/2022] Open
Abstract
The regulation of gene expression is a fundamental process enabling cells to respond to internal and external stimuli or to execute developmental programs. Changes in gene expression are highly dynamic and depend on many intrinsic and extrinsic factors. In this review, we highlight the dynamic nature of transient gene expression changes to better understand cell physiology and development in general. We will start by comparing recent in vivo procedures to capture gene expression in real time. Intrinsic factors modulating gene expression dynamics will then be discussed, focusing on chromatin modifications. Furthermore, we will dissect how cell physiology or age impacts on dynamic gene regulation and especially discuss molecular insights into acquired transcriptional memory. Finally, this review will give an update on the mechanisms of heterogeneous gene expression among genetically identical individual cells. We will mainly focus on state-of-the-art developments in the yeast model but also cover higher eukaryotic systems.
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37
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Liu Y, Li L, Qi H, Que H, Wang W, Zhang G. Regulation Between HSF1 Isoforms and HSPs Contributes to the Variation in Thermal Tolerance Between Two Oyster Congeners. Front Genet 2020; 11:581725. [PMID: 33193707 PMCID: PMC7652795 DOI: 10.3389/fgene.2020.581725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 10/06/2020] [Indexed: 01/09/2023] Open
Abstract
Heat shock transcription factor 1 (HSF1) plays an important role in regulating heat shock, which can activate heat shock proteins (HSPs). HSPs can protect organisms from thermal stress. Oysters in the intertidal zone can tolerate thermal stress. The Pacific oyster (Crassostrea gigas gigas) and Fujian oyster (C. gigas angulata)—allopatric subspecies with distinct thermal tolerances—make good study specimens for analyzing and comparing thermal stress regulation. We cloned and compared HSF1 isoforms, which is highly expressed under heat shock conditions in the two subspecies. The results revealed that two isoforms (HSF1a and HSF1d) respond to heat shock in both Pacific and Fujian oysters, and different heat shock conditions led to various combinations of isoforms. Subcellular localization showed that isoforms gathered in the nucleus when exposed to heat shock. The co-immunoprecipitation revealed that HSF1d can be a dimer. In addition, we selected HSPs that are expressed under the heat shock response, according to the RNA-seq and proteomic analyses. For the HSPs, we analyzed the coding part and the promoter sequences. The result showed that the domains of HSPs are conserved in two subspecies, but the promoters are significantly different. The Dual-Luciferase assay showed that the induced expression isoform HSF1d had the highest activity in C. gigas gigas, while the constitutively-expressed HSF1a was most active in C. gigas angulata. In addition, variation in the level of HSP promoters appeared to be correlated with gene expression. We argue that this gene is regulated based on the different expression levels between the two subspecies’ responses to heat shock. In summary, various stress conditions can yield different HSF1 isoforms and respond to heat shock in both oyster subspecies. Differences in how the isoforms and promoter are activated may contribute to their differential expressions. Overall, the results comparing C. gigas gigas and C. gigas angulata suggest that these isoforms have a regulatory relationship under heat shock, providing valuable information on the thermal tolerance mechanism in these commercially important oyster species.
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Affiliation(s)
- Youli Liu
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
| | - Li Li
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- National and Local Joint Engineering Laboratory of Ecological Mariculture, Qingdao, China
- *Correspondence: Li Li,
| | - Haigang Qi
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- National and Local Joint Engineering Laboratory of Ecological Mariculture, Qingdao, China
| | - Huayong Que
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- National and Local Joint Engineering Laboratory of Ecological Mariculture, Qingdao, China
| | - Wei Wang
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- National and Local Joint Engineering Laboratory of Ecological Mariculture, Qingdao, China
| | - Guofan Zhang
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- National and Local Joint Engineering Laboratory of Ecological Mariculture, Qingdao, China
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38
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Arede L, Pina C. Buffering noise: KAT2A modular contributions to stabilization of transcription and cell identity in cancer and development. Exp Hematol 2020; 93:25-37. [PMID: 33223444 DOI: 10.1016/j.exphem.2020.10.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/18/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
KAT2A is a histone acetyltransferase recently identified as a vulnerability in at least some forms of Acute Myeloid Leukemia (AML). Its loss or inhibition prompts leukemia stem cells out of self-renewal and into differentiation with ultimate exhaustion of the leukemia pool. We have recently linked the Kat2a requirement in AML to control of transcriptional noise, reflecting an evolutionary-conserved role of Kat2a in promoting burst-like promoter activity and stabilizing gene expression. We suggest that through this role, Kat2a contributes to preservation of cell identity. KAT2A exerts its acetyltransferase activity in the context of two macromolecular complexes, Spt-Ada-Gcn5-Acetyltransferase (SAGA) and Ada-Two-A-Containing (ATAC), but the specific contribution of each complex to stabilization of gene expression is currently unknown. By reviewing specific gene targets and requirements of the two complexes in cancer and development, we suggest that SAGA regulates lineage-specific programs, and ATAC maintains biosynthetic activity through control of ribosomal protein and translation-associated genes, on which cells may be differentially dependent. While our data suggest that KAT2A-mediated regulation of transcriptional noise in AML may be exerted through ATAC, we discuss potential caveats and probe general vs. complex-specific contributions of KAT2A to transcriptional stability, with implications for control and perturbation of cell identity.
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Affiliation(s)
- Liliana Arede
- Departments of Haematology; Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Cristina Pina
- College of Health, Medicine and Life Sciences - Life Sciences, Division of Biosciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom.
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39
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Cortijo S, Locke JCW. Does Gene Expression Noise Play a Functional Role in Plants? TRENDS IN PLANT SCIENCE 2020; 25:1041-1051. [PMID: 32467064 DOI: 10.1016/j.tplants.2020.04.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/22/2020] [Accepted: 04/28/2020] [Indexed: 05/20/2023]
Abstract
Gene expression in individual cells can be surprisingly noisy. In unicellular organisms this noise can be functional; for example, by allowing a subfraction of the population to prepare for environmental stress. The role of gene expression noise in multicellular organisms has, however, remained unclear. In this review, we discuss how new techniques are revealing an unexpected level of variability in gene expression between and within genetically identical plants. We describe recent progress as well as speculate on the function of transcriptional noise as a mechanism for generating functional phenotypic diversity in plants.
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Affiliation(s)
- Sandra Cortijo
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, UK
| | - James C W Locke
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, UK.
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40
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Hoppe C, Bowles JR, Minchington TG, Sutcliffe C, Upadhyai P, Rattray M, Ashe HL. Modulation of the Promoter Activation Rate Dictates the Transcriptional Response to Graded BMP Signaling Levels in the Drosophila Embryo. Dev Cell 2020; 54:727-741.e7. [PMID: 32758422 PMCID: PMC7527239 DOI: 10.1016/j.devcel.2020.07.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 05/13/2020] [Accepted: 07/11/2020] [Indexed: 01/08/2023]
Abstract
Morphogen gradients specify cell fates during development, with a classic example being the bone morphogenetic protein (BMP) gradient's conserved role in embryonic dorsal-ventral axis patterning. Here, we elucidate how the BMP gradient is interpreted in the Drosophila embryo by combining live imaging with computational modeling to infer transcriptional burst parameters at single-cell resolution. By comparing burst kinetics in cells receiving different levels of BMP signaling, we show that BMP signaling controls burst frequency by regulating the promoter activation rate. We provide evidence that the promoter activation rate is influenced by both enhancer and promoter sequences, whereas Pol II loading rate is primarily modulated by the enhancer. Consistent with BMP-dependent regulation of burst frequency, the numbers of BMP target gene transcripts per cell are graded across their expression domains. We suggest that graded mRNA output is a general feature of morphogen gradient interpretation and discuss how this can impact on cell-fate decisions.
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Affiliation(s)
- Caroline Hoppe
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Jonathan R Bowles
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Thomas G Minchington
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Catherine Sutcliffe
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Priyanka Upadhyai
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Magnus Rattray
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK.
| | - Hilary L Ashe
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK.
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41
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SIR2 Expression Noise Can Generate Heterogeneity in Viability but Does Not Affect Cell-to-Cell Epigenetic Silencing of Subtelomeric URA3 in Yeast. G3-GENES GENOMES GENETICS 2020; 10:3435-3443. [PMID: 32727919 PMCID: PMC7466964 DOI: 10.1534/g3.120.401589] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Chromatin structure clearly modulates gene expression noise, but the reverse influence has never been investigated, namely how the cell-to-cell expression heterogeneity of chromatin modifiers may generate variable rates of epigenetic modification. Sir2 is a well-characterized histone deacetylase of the Sirtuin family. It strongly influences chromatin silencing, especially at telomeres, subtelomeres and rDNA. This ability to influence epigenetic landscapes makes it a good model to study the largely unexplored interplay between gene expression noise and other epigenetic processes leading to phenotypic diversification. Here, we addressed this question by investigating whether noise in the expression of SIR2 was associated with cell-to-cell heterogeneity in the frequency of epigenetic silencing at subtelomeres in Saccharomyces cerevisiae Using cell sorting to isolate subpopulations with various expression levels, we found that heterogeneity in the cellular concentration of Sir2 does not lead to heterogeneity in the epigenetic silencing of subtelomeric URA3 between these subpopulations. We also noticed that SIR2 expression noise can generate cell-to-cell variability in viability, with lower levels being associated with better viability. This work shows that SIR2 expression fluctuations are not sufficient to generate cell-to-cell heterogeneity in the epigenetic silencing of URA3 at subtelomeres in Saccharomyces cerevisiae but can strongly affect cellular viability.
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42
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Stochastic allelic expression as trigger for contractile imbalance in hypertrophic cardiomyopathy. Biophys Rev 2020; 12:1055-1064. [PMID: 32661905 PMCID: PMC7429642 DOI: 10.1007/s12551-020-00719-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 07/02/2020] [Indexed: 12/17/2022] Open
Abstract
Hypertrophic cardiomyopathy (HCM), the most common inherited cardiac disease, is caused by several mostly heterozygous mutations in sarcomeric genes. Hallmarks of HCM are cardiomyocyte and myofibrillar disarray and hypertrophy and fibrosis of the septum and the left ventricle. To date, a pathomechanism common to all mutations remains elusive. We have proposed that contractile imbalance, an unequal force generation of neighboring cardiomyocytes, may contribute to development of HCM hallmarks. At the same calcium concentration, we found substantial differences in force generation between individual cardiomyocytes from HCM patients with mutations in β-MyHC (β-myosin heavy chain). Variability among cardiomyocytes was significantly larger in HCM patients as compared with donor controls. We assume that this heterogeneity in force generation among cardiomyocytes may lead to myocardial disarray and trigger hypertrophy and fibrosis. We provided evidence that burst-like transcription of the MYH7-gene, encoding for β-MyHC, is associated with unequal fractions of mutant per wild-type mRNA from cell to cell (cell-to-cell allelic imbalance). This will presumably lead to unequal fractions of mutant per wild-type protein from cell to cell which may underlie contractile imbalance. In this review, we discuss molecular mechanisms of burst-like transcription with regard to contractile imbalance and disease development in HCM.
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43
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Triqueneaux G, Burny C, Symmons O, Janczarski S, Gruffat H, Yvert G. Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans. Commun Biol 2020; 3:346. [PMID: 32620900 PMCID: PMC7335051 DOI: 10.1038/s42003-020-1075-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Variability in gene expression across a population of homogeneous cells is known to influence various biological processes. In model organisms, natural genetic variants were found that modify expression dispersion (variability at a fixed mean) but very few studies have detected such effects in humans. Here, we analyzed single-cell expression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derived from individuals of the Yoruba population. Using data from over 30 million cells, we found substantial inter-individual variation of dispersion. We demonstrate, via de novo cell line generation and subcloning experiments, that this variation exceeds the variation associated with cellular immortalization. We detected a genetic association between the expression dispersion of CD63 and the rs971 SNP. Our results show that human DNA variants can have inherently-probabilistic effects on gene expression. Such subtle genetic effects may participate to phenotypic variation and disease outcome. Triqueneaux, Burny, Symmons et al. show association between gene expression noise and genotypes, using single-cell expression of four proteins across human-derived lymphoblastoid cell lines. This study suggests that very subtle regulatory effects of human DNA variants may contribute to phenotypic variation and disease outcome.
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Affiliation(s)
- Gérard Triqueneaux
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France
| | - Claire Burny
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France.,Institut für Populationsgenetik, Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria
| | - Orsolya Symmons
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France.,Max Planck Institute for Biology of Ageing, Cologne, 50931, Germany
| | - Stéphane Janczarski
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France
| | - Henri Gruffat
- CIRI-Centre International de Recherche en Infectiologie, Universite Claude Bernard Lyon 1, Univ Lyon, Inserm U1111, CNRS UMR5308, Ecole Normale Superieure de Lyon, 69007, Lyon, France
| | - Gaël Yvert
- Laboratory of Biology and Modeling of the Cell, Univ Lyon, Ecole Normale Superieure de Lyon, CNRS UMR5239, Universite Claude Bernard Lyon 1, 69007, Lyon, France.
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44
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Abstract
Transcription in several organisms from certain bacteria to humans has been observed to be stochastic in nature: toggling between active and inactive states. Periods of active nascent RNA synthesis known as bursts represent individual gene activation events in which multiple polymerases are initiated. Therefore, bursting is the single locus illustration of both gene activation and repression. Although transcriptional bursting was originally observed decades ago, only recently have technological advances enabled the field to begin elucidating gene regulation at the single-locus level. In this review, we focus on how biochemical, genomic, and single-cell data describe the regulatory steps of transcriptional bursts.
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Affiliation(s)
- Joseph Rodriguez
- National Institute of Environmental Health Sciences, Durham, North Carolina 27709, USA
| | - Daniel R. Larson
- Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892, USA
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45
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Dhillon N, Shelansky R, Townshend B, Jain M, Boeger H, Endy D, Kamakaka R. Permutational analysis of Saccharomyces cerevisiae regulatory elements. Synth Biol (Oxf) 2020; 5:ysaa007. [PMID: 32775697 PMCID: PMC7402160 DOI: 10.1093/synbio/ysaa007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 05/12/2020] [Accepted: 05/29/2020] [Indexed: 01/24/2023] Open
Abstract
Gene expression in Saccharomyces cerevisiae is regulated at multiple levels. Genomic and epigenomic mapping of transcription factors and chromatin factors has led to the delineation of various modular regulatory elements—enhancers (upstream activating sequences), core promoters, 5′ untranslated regions (5′ UTRs) and transcription terminators/3′ untranslated regions (3′ UTRs). However, only a few of these elements have been tested in combinations with other elements and the functional interactions between the different modular regulatory elements remain under explored. We describe a simple and rapid approach to build a combinatorial library of regulatory elements and have used this library to study 26 different enhancers, core promoters, 5′ UTRs and transcription terminators/3′ UTRs to estimate the contribution of individual regulatory parts in gene expression. Our combinatorial analysis shows that while enhancers initiate gene expression, core promoters modulate the levels of enhancer-mediated expression and can positively or negatively affect expression from even the strongest enhancers. Principal component analysis (PCA) indicates that enhancer and promoter function can be explained by a single principal component while UTR function involves multiple functional components. The PCA also highlights outliers and suggest differences in mechanisms of regulation by individual elements. Our data also identify numerous regulatory cassettes composed of different individual regulatory elements that exhibit equivalent gene expression levels. These data thus provide a catalog of elements that could in future be used in the design of synthetic regulatory circuits.
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Affiliation(s)
- Namrita Dhillon
- Department of MCD Biology, University of California, Santa Cruz, CA, USA
| | - Robert Shelansky
- Department of MCD Biology, University of California, Santa Cruz, CA, USA
| | - Brent Townshend
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Miten Jain
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
| | - Hinrich Boeger
- Department of MCD Biology, University of California, Santa Cruz, CA, USA
| | - Drew Endy
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Rohinton Kamakaka
- Department of MCD Biology, University of California, Santa Cruz, CA, USA
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46
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Ochiai H, Hayashi T, Umeda M, Yoshimura M, Harada A, Shimizu Y, Nakano K, Saitoh N, Liu Z, Yamamoto T, Okamura T, Ohkawa Y, Kimura H, Nikaido I. Genome-wide kinetic properties of transcriptional bursting in mouse embryonic stem cells. SCIENCE ADVANCES 2020; 6:eaaz6699. [PMID: 32596448 PMCID: PMC7299619 DOI: 10.1126/sciadv.aaz6699] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 03/25/2020] [Indexed: 05/03/2023]
Abstract
Transcriptional bursting is the stochastic activation and inactivation of promoters, contributing to cell-to-cell heterogeneity in gene expression. However, the mechanism underlying the regulation of transcriptional bursting kinetics (burst size and frequency) in mammalian cells remains elusive. In this study, we performed single-cell RNA sequencing to analyze the intrinsic noise and mRNA levels for elucidating the transcriptional bursting kinetics in mouse embryonic stem cells. Informatics analyses and functional assays revealed that transcriptional bursting kinetics was regulated by a combination of promoter- and gene body-binding proteins, including the polycomb repressive complex 2 and transcription elongation factors. Furthermore, large-scale CRISPR-Cas9-based screening identified that the Akt/MAPK signaling pathway regulated bursting kinetics by modulating transcription elongation efficiency. These results uncovered the key molecular mechanisms underlying transcriptional bursting and cell-to-cell gene expression noise in mammalian cells.
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Affiliation(s)
- Hiroshi Ochiai
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-0046, Japan
- Genome Editing Innovation Center, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-0046, Japan
| | - Tetsutaro Hayashi
- Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama 351-0198, Japan
| | - Mana Umeda
- Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama 351-0198, Japan
| | - Mika Yoshimura
- Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama 351-0198, Japan
| | - Akihito Harada
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka 812-0054, Japan
| | - Yukiko Shimizu
- Department of Animal Medicine, National Center for Global Health and Medicine (NCGM), Tokyo 812-0054, Japan
| | - Kenta Nakano
- Department of Animal Medicine, National Center for Global Health and Medicine (NCGM), Tokyo 812-0054, Japan
| | - Noriko Saitoh
- Division of Cancer Biology, The Cancer Institute of JFCR, Tokyo 135-8550, Japan
| | - Zhe Liu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Takashi Yamamoto
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-0046, Japan
- Genome Editing Innovation Center, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-0046, Japan
| | - Tadashi Okamura
- Department of Animal Medicine, National Center for Global Health and Medicine (NCGM), Tokyo 812-0054, Japan
- Section of Animal Models, Department of Infectious Diseases, National Center for Global Health and Medicine (NCGM), Tokyo 812-0054, Japan
| | - Yasuyuki Ohkawa
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka 812-0054, Japan
| | - Hiroshi Kimura
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa 226-8503, Japan
| | - Itoshi Nikaido
- Laboratory for Bioinformatics Research, RIKEN BDR, Wako, Saitama 351-0198, Japan
- Bioinformatics Course, Master’s/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, Wako 351-0198, Japan
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47
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Dhar R. Role of Mitochondria in Generation of Phenotypic Heterogeneity in Yeast. J Indian Inst Sci 2020. [DOI: 10.1007/s41745-020-00176-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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48
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Engl C, Jovanovic G, Brackston RD, Kotta-Loizou I, Buck M. The route to transcription initiation determines the mode of transcriptional bursting in E. coli. Nat Commun 2020; 11:2422. [PMID: 32415118 PMCID: PMC7229158 DOI: 10.1038/s41467-020-16367-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 04/24/2020] [Indexed: 11/20/2022] Open
Abstract
Transcription is fundamentally noisy, leading to significant heterogeneity across bacterial populations. Noise is often attributed to burstiness, but the underlying mechanisms and their dependence on the mode of promotor regulation remain unclear. Here, we measure E. coli single cell mRNA levels for two stress responses that depend on bacterial sigma factors with different mode of transcription initiation (σ70 and σ54). By fitting a stochastic model to the observed mRNA distributions, we show that the transition from low to high expression of the σ70-controlled stress response is regulated via the burst size, while that of the σ54-controlled stress response is regulated via the burst frequency. Therefore, transcription initiation involving σ54 differs from other bacterial systems, and yields bursting kinetics characteristic of eukaryotic systems. Transcription noise in bacteria is often attributed to burstiness, but the mechanisms are unclear. Here, the authors show that the transition from low to high expression can be regulated via burst size or burst frequency, depending on the mode of transcription initiation determined by different sigma factors.
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Affiliation(s)
- Christoph Engl
- School of Biological & Chemical Sciences, Queen Mary University of London, London, E1 4NS, UK.
| | - Goran Jovanovic
- Faculty of Medicine, Department of Medicine, Imperial College London, London, SW7 2AZ, UK.,Faculty of Natural Sciences, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.,Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, 11042, Belgrade, Serbia
| | - Rowan D Brackston
- Faculty of Natural Sciences, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Ioly Kotta-Loizou
- Faculty of Natural Sciences, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Martin Buck
- Faculty of Natural Sciences, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
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49
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Sun M, Zhang J. Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises. Nucleic Acids Res 2020; 48:533-547. [PMID: 31799601 PMCID: PMC6954418 DOI: 10.1093/nar/gkz1134] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 10/19/2019] [Accepted: 11/20/2019] [Indexed: 01/13/2023] Open
Abstract
Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic or extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.
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Affiliation(s)
- Mengyi Sun
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
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50
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Ham L, Brackston RD, Stumpf MPH. Extrinsic Noise and Heavy-Tailed Laws in Gene Expression. PHYSICAL REVIEW LETTERS 2020; 124:108101. [PMID: 32216388 DOI: 10.1103/physrevlett.124.108101] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 02/12/2020] [Indexed: 06/10/2023]
Abstract
Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our ansatz allows us to incorporate the effects of extrinsic noise-encompassing factors external to the transcription of the individual gene-and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.
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Affiliation(s)
- Lucy Ham
- School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Parkville VIC 3010, Australia
| | - Rowan D Brackston
- Department Life Sciences, Imperial College London, SW7 2AZ, United Kingdom
| | - Michael P H Stumpf
- School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Parkville VIC 3010, Australia
- Department Life Sciences, Imperial College London, SW7 2AZ, United Kingdom
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