1
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Mondal A, Kolomeisky AB. How Transcription Factors Binding Stimulates Transcriptional Bursting. J Phys Chem Lett 2024; 15:8781-8789. [PMID: 39163638 DOI: 10.1021/acs.jpclett.4c02050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Transcription is a fundamental biological process of transferring genetic information which often occurs in stochastic bursts when periods of intense activity alternate with quiescent phases. Recent experiments identified strong correlations between the association of transcription factors (TFs) to gene promoters on DNA and transcriptional activity. However, the underlying molecular mechanisms of this phenomenon remain not well understood. Here, we present a theoretical framework that allowed us to investigate how binding dynamics of TF influences transcriptional bursting. Our minimal theoretical model incorporates the most relevant physical-chemical features, including TF exchange among multiple binding sites at gene promoters and TF association/dissociation dynamics. Using analytical calculations supported by Monte Carlo computer simulations, it is demonstrated that transcriptional bursting dynamics depends on the strength of TF binding and the number of binding sites. Stronger TF binding affinity prolongs burst duration but reduces variability, while an optimal number of binding sites maximizes transcriptional noise, facilitating cellular adaptation. Our theoretical method explains available experimental observations quantitatively, confirming the model's predictive accuracy. This study provides important insights into molecular mechanisms of gene expression and regulation, offering a new theoretical tool for understanding complex biological processes.
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Affiliation(s)
- Anupam Mondal
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Anatoly B Kolomeisky
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
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2
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Trzaskoma P, Jung S, Pękowska A, Bohrer CH, Wang X, Naz F, Dell’Orso S, Dubois WD, Olivera A, Vartak SV, Zhao Y, Nayak S, Overmiller A, Morasso MI, Sartorelli V, Larson DR, Chow CC, Casellas R, O’Shea JJ. 3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network. SCIENCE ADVANCES 2024; 10:eadl4893. [PMID: 39121214 PMCID: PMC11313860 DOI: 10.1126/sciadv.adl4893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 07/08/2024] [Indexed: 08/11/2024]
Abstract
Discontinuous transcription is evolutionarily conserved and a fundamental feature of gene regulation; yet, the exact mechanisms underlying transcriptional bursting are unresolved. Analyses of bursting transcriptome-wide have focused on the role of cis-regulatory elements, but other factors that regulate this process remain elusive. We applied mathematical modeling to single-cell RNA sequencing data to infer bursting dynamics transcriptome-wide under multiple conditions to identify possible molecular mechanisms. We found that Mediator complex subunit 26 (MED26) primarily regulates frequency, MYC regulates burst size, while cohesin and Bromodomain-containing protein 4 (BRD4) can modulate both. Despite comparable effects on RNA levels among these perturbations, acute depletion of MED26 had the most profound impact on the entire gene regulatory network, acting downstream of chromatin spatial architecture and without affecting TATA box-binding protein (TBP) recruitment. These results indicate that later steps in the initiation of transcriptional bursts are primary nodes for integrating gene networks in single cells.
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Affiliation(s)
- Pawel Trzaskoma
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - SeolKyoung Jung
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Aleksandra Pękowska
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
- Dioscuri Centre for Chromatin Biology and Epigenomics, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warsaw, Poland
| | | | - Xiang Wang
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Faiza Naz
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Stefania Dell’Orso
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Wendy D. Dubois
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ana Olivera
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Supriya V. Vartak
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Yongbing Zhao
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Subhashree Nayak
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Andrew Overmiller
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Maria I. Morasso
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Vittorio Sartorelli
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Daniel R. Larson
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carson C. Chow
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Rafael Casellas
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - John J. O’Shea
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
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3
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Chen PT, Levo M, Zoller B, Gregor T. Gene activity fully predicts transcriptional bursting dynamics. ARXIV 2024:arXiv:2304.08770v3. [PMID: 37131882 PMCID: PMC10153294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict individual alleles' average ON and OFF periods. Lowly transcribing alleles predominantly modulate OFF periods (burst frequency), while highly transcribing alleles primarily tune ON periods (burst size). These relationships persist even under perturbations of cis-regulatory elements or trans-factors and account for bursting dynamics measured in other species. Our results suggest a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes.
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Affiliation(s)
- Po-Ta Chen
- Joseph Henry Laboratories of Physics & Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Michal Levo
- Joseph Henry Laboratories of Physics & Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Benjamin Zoller
- Joseph Henry Laboratories of Physics & Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
- Department of Stem Cell and Developmental Biology, CNRS UMR3738 Paris Cité, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France
| | - Thomas Gregor
- Joseph Henry Laboratories of Physics & Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
- Department of Stem Cell and Developmental Biology, CNRS UMR3738 Paris Cité, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France
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4
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D'Orso I. The HIV-1 Transcriptional Program: From Initiation to Elongation Control. J Mol Biol 2024:168690. [PMID: 38936695 DOI: 10.1016/j.jmb.2024.168690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 06/29/2024]
Abstract
A large body of work in the last four decades has revealed the key pillars of HIV-1 transcription control at the initiation and elongation steps. Here, I provide a recount of this collective knowledge starting with the genomic elements (DNA and nascent TAR RNA stem-loop) and transcription factors (cellular and the viral transactivator Tat), and later transitioning to the assembly and regulation of transcription initiation and elongation complexes, and the role of chromatin structure. Compelling evidence support a core HIV-1 transcriptional program regulated by the sequential and concerted action of cellular transcription factors and Tat to promote initiation and sustain elongation, highlighting the efficiency of a small virus to take over its host to produce the high levels of transcription required for viral replication. I summarize new advances including the use of CRISPR-Cas9, genetic tools for acute factor depletion, and imaging to study transcriptional dynamics, bursting and the progression through the multiple phases of the transcriptional cycle. Finally, I describe current challenges to future major advances and discuss areas that deserve more attention to both bolster our basic knowledge of the core HIV-1 transcriptional program and open up new therapeutic opportunities.
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Affiliation(s)
- Iván D'Orso
- Department of Microbiology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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5
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Berrocal A, Lammers NC, Garcia HG, Eisen MB. Unified bursting strategies in ectopic and endogenous even-skipped expression patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.09.527927. [PMID: 36798351 PMCID: PMC9934701 DOI: 10.1101/2023.02.09.527927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Transcription often occurs in bursts as gene promoters switch stochastically between active and inactive states. Enhancers can dictate transcriptional activity in animal development through the modulation of burst frequency, duration, or amplitude. Previous studies observed that different enhancers can achieve a wide range of transcriptional outputs through the same strategies of bursting control. For example, despite responding to different transcription factors, all even-skipped enhancers increase transcription by upregulating burst frequency and amplitude while burst duration remains largely constant. These shared bursting strategies suggest that a unified molecular mechanism constraints how enhancers modulate transcriptional output. Alternatively, different enhancers could have converged on the same bursting control strategy because of natural selection favoring one of these particular strategies. To distinguish between these two scenarios, we compared transcriptional bursting between endogenous and ectopic gene expression patterns. Because enhancers act under different regulatory inputs in ectopic patterns, dissimilar bursting control strategies between endogenous and ectopic patterns would suggest that enhancers adapted their bursting strategies to their trans-regulatory environment. Here, we generated ectopic even-skipped transcription patterns in fruit fly embryos and discovered that bursting strategies remain consistent in endogenous and ectopic even-skipped expression. These results provide evidence for a unified molecular mechanism shaping even-skipped bursting strategies and serve as a starting point to uncover the realm of strategies employed by other enhancers.
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Affiliation(s)
- Augusto Berrocal
- Department of Molecular & Cell Biology, University of California at Berkeley, Berkeley, CA, United States
- Current Address: Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, CA, United States
| | - Nicholas C Lammers
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, United States
- Current Address: Department of Genome Sciences, University of Washington, Seattle, WA, United States
| | - Hernan G Garcia
- Department of Molecular & Cell Biology, University of California at Berkeley, Berkeley, CA, United States
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, United States
- Department of Physics, University of California at Berkeley, Berkeley, CA, United States
- California Institute for Quantitative Biosciences (QB3), University of California at Berkeley, Berkeley, CA, United States
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, CA, United States
| | - Michael B Eisen
- Department of Molecular & Cell Biology, University of California at Berkeley, Berkeley, CA, United States
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, United States
- California Institute for Quantitative Biosciences (QB3), University of California at Berkeley, Berkeley, CA, United States
- Howard Hughes Medical Institute, University of California at Berkeley, Berkeley, CA, United States
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6
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Sood V, Holewinski R, Andresson T, Larson DR, Misteli T. Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.08.597999. [PMID: 38903099 PMCID: PMC11188098 DOI: 10.1101/2024.06.08.597999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Stochastic transcriptional bursting is a universal property of active genes. While different genes exhibit distinct bursting patterns, the molecular mechanisms for gene-specific stochastic bursting are largely unknown. We have developed and applied a high-throughput-imaging based screening strategy to identify cellular factors and molecular mechanisms that determine the bursting behavior of human genes. Focusing on epigenetic regulators, we find that protein acetylation is a strong acute modulator of burst frequency, burst size and heterogeneity of bursting. Acetylation globally affects the Off-time of genes but has gene-specific effects on the On-time. Yet, these effects are not strongly linked to promoter acetylation, which do not correlate with bursting properties, and forced promoter acetylation has variable effects on bursting. Instead, we demonstrate acetylation of the Integrator complex as a key determinant of gene bursting. Specifically, we find that elevated Integrator acetylation decreases bursting frequency. Taken together our results suggest a prominent role of non-histone proteins in determining gene bursting properties, and they identify histone-independent acetylation of a transcription cofactor as an allosteric modulator of bursting via a far-downstream bursting checkpoint.
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Affiliation(s)
- Varun Sood
- National Cancer Institute, Bethesda, MD, USA
| | - Ronald Holewinski
- Protein Characterization Laboratory, National Cancer Institute, Frederick, MD, USA
| | - Thorkell Andresson
- Protein Characterization Laboratory, National Cancer Institute, Frederick, MD, USA
| | | | - Tom Misteli
- National Cancer Institute, Bethesda, MD, USA
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7
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Hong CKY, Ramu A, Zhao S, Cohen BA. Effect of genomic and cellular environments on gene expression noise. Genome Biol 2024; 25:137. [PMID: 38790076 PMCID: PMC11127367 DOI: 10.1186/s13059-024-03277-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This "noise" in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome. RESULTS To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign the extrinsic component to differences between the CD24+ "stem-like" substate and the more "differentiated" substate. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for "safe-harbor" loci. CONCLUSIONS Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome and that the data generatd by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.
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Affiliation(s)
- Clarice K Y Hong
- The Edison Family Center for Genome Sciences and Systems Biology, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA
- Department of Genetics, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA
| | - Avinash Ramu
- The Edison Family Center for Genome Sciences and Systems Biology, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA
- Department of Genetics, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA
| | - Siqi Zhao
- The Edison Family Center for Genome Sciences and Systems Biology, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA
- Department of Genetics, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA
| | - Barak A Cohen
- The Edison Family Center for Genome Sciences and Systems Biology, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA.
- Department of Genetics, School of Medicine, Washington University in St. Louis, Saint Louis, MO, 63110, USA.
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8
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He J, Huo X, Pei G, Jia Z, Yan Y, Yu J, Qu H, Xie Y, Yuan J, Zheng Y, Hu Y, Shi M, You K, Li T, Ma T, Zhang MQ, Ding S, Li P, Li Y. Dual-role transcription factors stabilize intermediate expression levels. Cell 2024; 187:2746-2766.e25. [PMID: 38631355 DOI: 10.1016/j.cell.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/08/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.
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Affiliation(s)
- Jinnan He
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Xiangru Huo
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Gaofeng Pei
- State Key Laboratory of Membrane Biology, Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Zeran Jia
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yiming Yan
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Jiawei Yu
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Haozhi Qu
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yunxin Xie
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Junsong Yuan
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yuan Zheng
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yanyan Hu
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Minglei Shi
- Bioinformatics Division, National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Kaiqiang You
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Tingting Li
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Tianhua Ma
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Michael Q Zhang
- Bioinformatics Division, National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing 100084, China; Department of Biological Sciences, Center for Systems Biology, The University of Texas, Dallas, TX 75080-3021, USA
| | - Sheng Ding
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Pilong Li
- State Key Laboratory of Membrane Biology, Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China.
| | - Yinqing Li
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China.
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9
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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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10
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Friedman MJ, Wagner T, Lee H, Rosenfeld MG, Oh S. Enhancer-promoter specificity in gene transcription: molecular mechanisms and disease associations. Exp Mol Med 2024; 56:772-787. [PMID: 38658702 PMCID: PMC11058250 DOI: 10.1038/s12276-024-01233-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 02/28/2024] [Accepted: 03/05/2024] [Indexed: 04/26/2024] Open
Abstract
Although often located at a distance from their target gene promoters, enhancers are the primary genomic determinants of temporal and spatial transcriptional specificity in metazoans. Since the discovery of the first enhancer element in simian virus 40, there has been substantial interest in unraveling the mechanism(s) by which enhancers communicate with their partner promoters to ensure proper gene expression. These research efforts have benefited considerably from the application of increasingly sophisticated sequencing- and imaging-based approaches in conjunction with innovative (epi)genome-editing technologies; however, despite various proposed models, the principles of enhancer-promoter interaction have still not been fully elucidated. In this review, we provide an overview of recent progress in the eukaryotic gene transcription field pertaining to enhancer-promoter specificity. A better understanding of the mechanistic basis of lineage- and context-dependent enhancer-promoter engagement, along with the continued identification of functional enhancers, will provide key insights into the spatiotemporal control of gene expression that can reveal therapeutic opportunities for a range of enhancer-related diseases.
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Affiliation(s)
- Meyer J Friedman
- Department and School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Tobias Wagner
- Department and School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Haram Lee
- College of Pharmacy Korea University, 2511 Sejong-ro, Sejong, 30019, Republic of Korea
| | - Michael G Rosenfeld
- Department and School of Medicine, University of California, San Diego, La Jolla, CA, USA.
| | - Soohwan Oh
- College of Pharmacy Korea University, 2511 Sejong-ro, Sejong, 30019, Republic of Korea.
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11
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Kagan Ben Tikva S, Gurwitz N, Sivan E, Hirsch D, Hezroni-Barvyi H, Biram A, Moss L, Wigoda N, Egozi A, Monziani A, Golani O, Gross M, Tenenbaum A, Shulman Z. T cell help induces Myc transcriptional bursts in germinal center B cells during positive selection. Sci Immunol 2024; 9:eadj7124. [PMID: 38552029 DOI: 10.1126/sciimmunol.adj7124] [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: 07/12/2023] [Accepted: 02/09/2024] [Indexed: 04/02/2024]
Abstract
Antibody affinity maturation occurs in secondary lymphoid organs within germinal centers (GCs). At these sites, B cells mutate their antibody-encoding genes in the dark zone, followed by preferential selection of the high-affinity variants in the light zone by T cells. The strength of the T cell-derived selection signals is proportional to the B cell receptor affinity and to the magnitude of subsequent Myc expression. However, because the lifetime of Myc mRNA and its corresponding protein is very short, it remains unclear how T cells induce sustained Myc levels in positively selected B cells. Here, by direct visualization of mRNA and active transcription sites in situ, we found that an increase in transcriptional bursts promotes Myc expression during B cell positive selection in GCs. Elevated T cell help signals predominantly enhance the percentage of cells expressing Myc in GCs as opposed to augmenting the quantity of Myc transcripts per individual cell. Visualization of transcription start sites in situ revealed that T cell help promotes an increase in the frequency of transcriptional bursts at the Myc locus in GC B cells located primarily in the LZ apical rim. Thus, the rise in Myc, which governs positive selection of B cells in GCs, reflects an integration of transcriptional activity over time rather than an accumulation of transcripts at a specific time point.
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Affiliation(s)
- Sharon Kagan Ben Tikva
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Neta Gurwitz
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ehud Sivan
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Dana Hirsch
- Department of Veterinary Resources, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Hadas Hezroni-Barvyi
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Adi Biram
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Lihee Moss
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Noa Wigoda
- Bioinformatics unit, Life Science Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Adi Egozi
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Alan Monziani
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ofra Golani
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Menachem Gross
- Department of Otolaryngology-Head and Neck Surgery, Hadassah Medical Center, Jerusalem 9112102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Ariel Tenenbaum
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Pediatrics, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Ziv Shulman
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
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12
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Pomp W, Meeussen JVW, Lenstra TL. Transcription factor exchange enables prolonged transcriptional bursts. Mol Cell 2024; 84:1036-1048.e9. [PMID: 38377994 PMCID: PMC10962226 DOI: 10.1016/j.molcel.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/27/2023] [Accepted: 01/24/2024] [Indexed: 02/22/2024]
Abstract
Single-molecule imaging inside living cells has revealed that transcription factors (TFs) bind to DNA transiently, but a long-standing question is how this transient binding is related to transcription activation. Here, we devised a microscopy method to simultaneously measure transient TF binding at a single locus and the effect of these binding events on transcription. We show that DNA binding of the yeast TF Gal4 activates transcription of a target gene within a few seconds, with at least ∼20% efficiency and with a high initiation rate of ∼1 RNA/s. Gal4 DNA dissociation decreases transcription rapidly. Moreover, at a gene with multiple binding sites, individual Gal4 molecules only rarely stay bound throughout the entire burst but instead frequently exchange during a burst to increase the transcriptional burst duration. Our results suggest a mechanism for enhancer regulation in more complex eukaryotes, where TF cooperativity and exchange enable robust and responsive transcription regulation.
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Affiliation(s)
- Wim Pomp
- Division of Gene Regulation, the Netherlands Cancer Institute, Oncode Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Joseph V W Meeussen
- Division of Gene Regulation, the Netherlands Cancer Institute, Oncode Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Tineke L Lenstra
- Division of Gene Regulation, the Netherlands Cancer Institute, Oncode Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands.
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13
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Wiggan O, Stasevich TJ. Single molecule imaging of the central dogma reveals myosin-2A gene expression is regulated by contextual translational buffering. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.11.579797. [PMID: 38370738 PMCID: PMC10871341 DOI: 10.1101/2024.02.11.579797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
While protein homeostasis is a hallmark of gene regulation, unraveling the hidden regulatory mechanisms that maintain homeostasis is difficult using traditional methods. To confront this problem, we CRISPR engineered a human cell line with multiple tags in the endogenous MYH9 gene, which encodes the essential and ubiquitous myosin-2A cytoskeletal motor. Using these cells, we imaged MYH9 transcription, translation, and mature mRNA and protein in distinct colors, enabling a full dissection of the central dogma. Our data show that MYH9 transcription is upregulated in an SRF-dependent manner in response to cytoskeletal cues and that MYH9 translation can either buffer or match the transcriptional response depending on context. Upon knockdown of actin-depolymerizing proteins like cofilin, translation efficiency drops by a factor of two to buffer strong transcriptional upregulation, likely to help prevent excessive myosin activity. In contrast, following serum stimulation, translation matches the transcriptional response to readily reestablish steady state. Our results identify contextual translational buffering as an important regulatory mechanism driving stable MYH9 expression. They also demonstrate the power and broad applicability of our cell line, which can now be used to accurately quantify central dogma dynamics in response to diverse forms of cellular perturbations.
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Affiliation(s)
- O'Neil Wiggan
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, 80525
| | - Timothy J Stasevich
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, 80525
- Cell Biology Center and World Research Hub Initiative, Tokyo Institute of Technology, Yokohama, Japan
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14
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Wang Z, Zhang Z, Luo S, Zhou T, Zhang J. Power-law behavior of transcriptional bursting regulated by enhancer-promoter communication. Genome Res 2024; 34:106-118. [PMID: 38171575 PMCID: PMC10903953 DOI: 10.1101/gr.278631.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024]
Abstract
Revealing how transcriptional bursting kinetics are genomically encoded is challenging because genome structures are stochastic at the organization level and are suggestively linked to gene transcription. To address this challenge, we develop a generic theoretical framework that integrates chromatin dynamics, enhancer-promoter (E-P) communication, and gene-state switching to study transcriptional bursting. The theory predicts that power law can be a general rule to quantitatively describe bursting modulations by E-P spatial communication. Specifically, burst frequency and burst size are up-regulated by E-P communication strength, following power laws with positive exponents. Analysis of the scaling exponents further reveals that burst frequency is preferentially regulated. Bursting kinetics are down-regulated by E-P genomic distance with negative power-law exponents, and this negative modulation desensitizes at large distances. The mutual information between burst frequency (or burst size) and E-P spatial distance further reveals essential characteristics of the information transfer from E-P communication to transcriptional bursting kinetics. These findings, which are in agreement with experimental observations, not only reveal fundamental principles of E-P communication in transcriptional bursting but also are essential for understanding cellular decision-making.
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Affiliation(s)
- Zihao Wang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, P.R. China
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, P.R. China
| | - 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 510275, P.R. China
| | - Songhao Luo
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, P.R. China
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, P.R. China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, P.R. China;
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, P.R. China
| | - Jiajun 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 510275, P.R. China
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15
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Zhang C, Jiao F. Using steady-state formula to estimate time-dependent parameters of stochastic gene transcription models. Biosystems 2024; 236:105128. [PMID: 38280446 DOI: 10.1016/j.biosystems.2024.105128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/20/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
When studying stochastic gene transcription, it is important to understand how system parameters are temporally modulated in response to varying environments. Experimentally, the dynamic distribution data of RNA copy numbers measured at multiple time points are often fitted to stochastic transcription models to estimate time-dependent parameters. However, current methods require determining which parameters are time-dependent, as well as their analytical formulas, before the optimal fit. In this study, we developed a method to estimate time-dependent parameters in a classical two-state model without prior assumptions regarding the system parameters. At each measured time point, the method fitted the dynamic distribution data using a steady-state distribution formula, in which the estimated constant parameters were approximated as time-dependent parameter values at the measured time point. The accuracy of this method can be guaranteed for RNA molecules with relatively high degradation rates and genes with relatively slow responses to induction. We quantify the accuracy of the method and implemented this method on two sets of dynamic distribution data from prokaryotic and eukaryotic cells, and revealed the temporal modulation of transcription burst size in response to environmental changes.
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Affiliation(s)
- Congrun Zhang
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, PR China; College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, China
| | - Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, PR China; College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, China.
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16
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Stossi F, Rivera Tostado A, Johnson HL, Mistry RM, Mancini MG, Mancini MA. Gene transcription regulation by ER at the single cell and allele level. Steroids 2023; 200:109313. [PMID: 37758052 PMCID: PMC10842394 DOI: 10.1016/j.steroids.2023.109313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/12/2023] [Accepted: 09/23/2023] [Indexed: 10/03/2023]
Abstract
In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States.
| | | | - Hannah L Johnson
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States
| | - Ragini M Mistry
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States
| | - Maureen G Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, United States; Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, United States.
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17
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Wang Z, Luo S, Zhang Z, Zhou T, Zhang J. 4D nucleome equation predicts gene expression controlled by long-range enhancer-promoter interaction. PLoS Comput Biol 2023; 19:e1011722. [PMID: 38109463 PMCID: PMC10760824 DOI: 10.1371/journal.pcbi.1011722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 01/02/2024] [Accepted: 11/28/2023] [Indexed: 12/20/2023] Open
Abstract
Recent experimental evidence strongly supports that three-dimensional (3D) long-range enhancer-promoter (E-P) interactions have important influences on gene-expression dynamics, but it is unclear how the interaction information is translated into gene expression over time (4D). To address this question, we developed a general theoretical framework (named as a 4D nucleome equation), which integrates E-P interactions on chromatin and biochemical reactions of gene transcription. With this equation, we first present the distribution of mRNA counts as a function of the E-P genomic distance and then reveal a power-law scaling of the expression level in this distance. Interestingly, we find that long-range E-P interactions can induce bimodal and trimodal mRNA distributions. The 4D nucleome equation also allows for model selection and parameter inference. When this equation is applied to the mouse embryonic stem cell smRNA-FISH data and the E-P genomic-distance data, the predicted E-P contact probability and mRNA distribution are in good agreement with experimental results. Further statistical inference indicates that the E-P interactions prefer to modulate the mRNA level by controlling promoter activation and transcription initiation rates. Our model and results provide quantitative insights into both spatiotemporal gene-expression determinants (i.e., long-range E-P interactions) and cellular fates during development.
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Affiliation(s)
- Zihao Wang
- Guangdong Province Key Laboratory of Computational, Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Songhao Luo
- Guangdong Province Key Laboratory of Computational, Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Zhenquan Zhang
- Guangdong Province Key Laboratory of Computational, Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational, Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational, Sun Yat-sen University, Guangzhou, People’s Republic of China
- School of Mathematics, Sun Yat-Sen University, Guangzhou, People’s Republic of China
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18
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Syed S, Duan Y, Lim B. Modulation of protein-DNA binding reveals mechanisms of spatiotemporal gene control in early Drosophila embryos. eLife 2023; 12:e85997. [PMID: 37934571 PMCID: PMC10629816 DOI: 10.7554/elife.85997] [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: 01/06/2023] [Accepted: 10/19/2023] [Indexed: 11/08/2023] Open
Abstract
It is well known that enhancers regulate the spatiotemporal expression of their target genes by recruiting transcription factors (TFs) to the cognate binding sites in the region. However, the role of multiple binding sites for the same TFs and their specific spatial arrangement in determining the overall competency of the enhancer has yet to be fully understood. In this study, we utilized the MS2-MCP live imaging technique to quantitatively analyze the regulatory logic of the snail distal enhancer in early Drosophila embryos. Through systematic modulation of Dorsal and Twist binding motifs in this enhancer, we found that a mutation in any one of these binding sites causes a drastic reduction in transcriptional amplitude, resulting in a reduction in mRNA production of the target gene. We provide evidence of synergy, such that multiple binding sites with moderate affinities cooperatively recruit more TFs to drive stronger transcriptional activity than a single site. Moreover, a Hidden Markov-based stochastic model of transcription reveals that embryos with mutated binding sites have a higher probability of returning to the inactive promoter state. We propose that TF-DNA binding regulates spatial and temporal gene expression and drives robust pattern formation by modulating transcriptional kinetics and tuning bursting rates.
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Affiliation(s)
- Sahla Syed
- Department of Chemical and Biomolecular Engineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Yifei Duan
- Department of Chemical and Biomolecular Engineering, University of PennsylvaniaPhiladelphiaUnited States
- Master of Biotechnology Program, University of PennsylvaniaPhiladelphiaUnited States
| | - Bomyi Lim
- Department of Chemical and Biomolecular Engineering, University of PennsylvaniaPhiladelphiaUnited States
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19
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Wang X, Li Y, Jia C. Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks. J R Soc Interface 2023; 20:20230467. [PMID: 38016635 PMCID: PMC10684348 DOI: 10.1098/rsif.2023.0467] [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: 08/11/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023] Open
Abstract
Stochastic gene expression dynamics can be modelled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiner's Poisson representation. Here, we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. This generalizes the results obtained in Dattani & Barahona (Dattani & Barahona 2017 J. R. Soc. Interface 14, 20160833 (doi:10.1098/rsif.2016.0833)). In addition, using a simple counter-example, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.
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Affiliation(s)
- Xinyu Wang
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, People’s Republic of China
| | - Youming Li
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
| | - Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing 100193, People’s Republic of China
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20
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D’Orso I, Forst CV. Mathematical Models of HIV-1 Dynamics, Transcription, and Latency. Viruses 2023; 15:2119. [PMID: 37896896 PMCID: PMC10612035 DOI: 10.3390/v15102119] [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: 08/31/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
HIV-1 latency is a major barrier to curing infections with antiretroviral therapy and, consequently, to eliminating the disease globally. The establishment, maintenance, and potential clearance of latent infection are complex dynamic processes and can be best described with the help of mathematical models followed by experimental validation. Here, we review the use of viral dynamics models for HIV-1, with a focus on applications to the latent reservoir. Such models have been used to explain the multi-phasic decay of viral load during antiretroviral therapy, the early seeding of the latent reservoir during acute infection and the limited inflow during treatment, the dynamics of viral blips, and the phenomenon of post-treatment control. Finally, we discuss that mathematical models have been used to predict the efficacy of potential HIV-1 cure strategies, such as latency-reversing agents, early treatment initiation, or gene therapies, and to provide guidance for designing trials of these novel interventions.
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Affiliation(s)
- Iván D’Orso
- Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Christian V. Forst
- Department of Genetics and Genomic Sciences, Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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21
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Abstract
Cells must tightly regulate their gene expression programs and yet rapidly respond to acute biochemical and biophysical cues within their environment. This information is transmitted to the nucleus through various signaling cascades, culminating in the activation or repression of target genes. Transcription factors (TFs) are key mediators of these signals, binding to specific regulatory elements within chromatin. While live-cell imaging has conclusively proven that TF-chromatin interactions are highly dynamic, how such transient interactions can have long-term impacts on developmental trajectories and disease progression is still largely unclear. In this review, we summarize our current understanding of the dynamic nature of TF functions, starting with a historical overview of early live-cell experiments. We highlight key factors that govern TF dynamics and how TF dynamics, in turn, affect downstream transcriptional bursting. Finally, we conclude with open challenges and emerging technologies that will further our understanding of transcriptional regulation.
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Affiliation(s)
- Kaustubh Wagh
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA; , ,
- Department of Physics, University of Maryland, College Park, Maryland, USA;
| | - Diana A Stavreva
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA; , ,
| | - Arpita Upadhyaya
- Department of Physics, University of Maryland, College Park, Maryland, USA;
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA
| | - Gordon L Hager
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA; , ,
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22
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Mazzocca M, Loffreda A, Colombo E, Fillot T, Gnani D, Falletta P, Monteleone E, Capozi S, Bertrand E, Legube G, Lavagnino Z, Tacchetti C, Mazza D. Chromatin organization drives the search mechanism of nuclear factors. Nat Commun 2023; 14:6433. [PMID: 37833263 PMCID: PMC10575952 DOI: 10.1038/s41467-023-42133-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
Nuclear factors rapidly scan the genome for their targets, but the role of nuclear organization in such search is uncharted. Here we analyzed how multiple factors explore chromatin, combining live-cell single-molecule tracking with multifocal structured illumination of DNA density. We find that factors displaying higher bound fractions sample DNA-dense regions more exhaustively. Focusing on the tumor-suppressor p53, we demonstrate that it searches for targets by alternating between rapid diffusion in the interchromatin compartment and compact sampling of chromatin dense regions. Efficient targeting requires balanced interactions with chromatin: fusing p53 with an exogenous intrinsically disordered region potentiates p53-mediated target gene activation at low concentrations, but leads to condensates at higher levels, derailing its search and downregulating transcription. Our findings highlight the role of disordered regions on factors search and showcase a powerful method to generate traffic maps of the eukaryotic nucleus to dissect how its organization guides nuclear factors action.
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Affiliation(s)
- Matteo Mazzocca
- Università Vita-Salute San Raffaele, Via Olgettina 58, 20132, Milan, Italy
| | - Alessia Loffreda
- IRCCS Ospedale San Raffaele, Experimental Imaging Center, Via Olgettina 58, 20132, Milan, Italy
| | - Emanuele Colombo
- Università Vita-Salute San Raffaele, Via Olgettina 58, 20132, Milan, Italy
| | - Tom Fillot
- Università Vita-Salute San Raffaele, Via Olgettina 58, 20132, Milan, Italy
- IRCCS Ospedale San Raffaele, Experimental Imaging Center, Via Olgettina 58, 20132, Milan, Italy
| | - Daniela Gnani
- Università Vita-Salute San Raffaele, Via Olgettina 58, 20132, Milan, Italy
| | - Paola Falletta
- Università Vita-Salute San Raffaele, Via Olgettina 58, 20132, Milan, Italy
| | | | - Serena Capozi
- Institut de Génétique Moléculaire de Montpellier, CNRS, Montpellier, 34293, France
| | - Edouard Bertrand
- Institut de Génétique Moléculaire de Montpellier, CNRS, Montpellier, 34293, France
| | - Gaelle Legube
- MCD, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse, UT3, Toulouse, France
| | - Zeno Lavagnino
- IRCCS Ospedale San Raffaele, Experimental Imaging Center, Via Olgettina 58, 20132, Milan, Italy
- IFOM ETS- The AIRC Institute of Molecular Oncology-Via Adamello 16, 20139, Milan, Italy
| | - Carlo Tacchetti
- Università Vita-Salute San Raffaele, Via Olgettina 58, 20132, Milan, Italy
- IRCCS Ospedale San Raffaele, Experimental Imaging Center, Via Olgettina 58, 20132, Milan, Italy
| | - Davide Mazza
- Università Vita-Salute San Raffaele, Via Olgettina 58, 20132, Milan, Italy.
- IRCCS Ospedale San Raffaele, Experimental Imaging Center, Via Olgettina 58, 20132, Milan, Italy.
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23
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Szavits-Nossan J, Grima R. Uncovering the effect of RNA polymerase steric interactions on gene expression noise: Analytical distributions of nascent and mature RNA numbers. Phys Rev E 2023; 108:034405. [PMID: 37849194 DOI: 10.1103/physreve.108.034405] [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: 04/12/2023] [Accepted: 08/24/2023] [Indexed: 10/19/2023]
Abstract
The telegraph model is the standard model of stochastic gene expression, which can be solved exactly to obtain the distribution of mature RNA numbers per cell. A modification of this model also leads to an analytical distribution of nascent RNA numbers. These solutions are routinely used for the analysis of single-cell data, including the inference of transcriptional parameters. However, these models neglect important mechanistic features of transcription elongation, such as the stochastic movement of RNA polymerases and their steric (excluded-volume) interactions. Here we construct a model of gene expression describing promoter switching between inactive and active states, binding of RNA polymerases in the active state, their stochastic movement including steric interactions along the gene, and their unbinding leading to a mature transcript that subsequently decays. We derive the steady-state distributions of the nascent and mature RNA numbers in two important limiting cases: constitutive expression and slow promoter switching. We show that RNA fluctuations are suppressed by steric interactions between RNA polymerases, and that this suppression can in some instances even lead to sub-Poissonian fluctuations; these effects are most pronounced for nascent RNA and less prominent for mature RNA, since the latter is not a direct sensor of transcription. We find a relationship between the parameters of our microscopic mechanistic model and those of the standard models that ensures excellent consistency in their prediction of the first and second RNA number moments over vast regions of parameter space, encompassing slow, intermediate, and rapid promoter switching, provided the RNA number distributions are Poissonian or super-Poissonian. Furthermore, we identify the limitations of inference from mature RNA data, specifically showing that it cannot differentiate between highly distinct RNA polymerase traffic patterns on a gene.
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Affiliation(s)
- Juraj Szavits-Nossan
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, United Kingdom
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, United Kingdom
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24
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Das S, Singh A, Shah P. Evaluating single-cell variability in proteasomal decay. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.22.554358. [PMID: 37662347 PMCID: PMC10473619 DOI: 10.1101/2023.08.22.554358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. Here we estimate the single-cell protein decay rates of two degron GFPs in Saccharomyces cerevisiae using time-lapse microscopy. We find substantial cell-to-cell variability in the decay rates of the degron GFPs. We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs. Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.
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Affiliation(s)
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, Biomedical Engineering, University of Delaware
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25
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Ramu A, Cohen BA. Transcription factor fluctuations underlie cell-to-cell variability in a signaling pathway response. Genetics 2023; 224:iyad094. [PMID: 37226217 PMCID: PMC10691749 DOI: 10.1093/genetics/iyad094] [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: 03/08/2023] [Revised: 04/25/2023] [Accepted: 05/05/2023] [Indexed: 05/26/2023] Open
Abstract
Stochastic differences among clonal cells can initiate cell fate decisions in development or cause cell-to-cell differences in the responses to drugs or extracellular ligands. One hypothesis is that some of this phenotypic variability is caused by stochastic fluctuations in the activities of transcription factors (TFs). We tested this hypothesis in NIH3T3-CG cells using the response to Hedgehog signaling as a model cellular response. Here, we present evidence for the existence of distinct fast- and slow-responding substates in NIH3T3-CG cells. These two substates have distinct expression profiles, and fluctuations in the Prrx1 TF underlie some of the differences in expression and responsiveness between fast and slow cells. Our results show that fluctuations in TFs can contribute to cell-to-cell differences in Hedgehog signaling.
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Affiliation(s)
- Avinash Ramu
- The Edison Family Center for Genome Sciences and Systems Biology, School of Medicine, Washington University in St. Louis, Saint Louis, MO 63110, USA
- Department of Genetics, School of Medicine, Washington University in St. Louis, Saint Louis, MO 63110, USA
| | - Barak A Cohen
- The Edison Family Center for Genome Sciences and Systems Biology, School of Medicine, Washington University in St. Louis, Saint Louis, MO 63110, USA
- Department of Genetics, School of Medicine, Washington University in St. Louis, Saint Louis, MO 63110, USA
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26
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Aristodemou AEN, Rueda DS, Taylor GP, Bangham CRM. The transcriptome of HTLV-1-infected primary cells following reactivation reveals changes to host gene expression central to the proviral life cycle. PLoS Pathog 2023; 19:e1011494. [PMID: 37523412 PMCID: PMC10431621 DOI: 10.1371/journal.ppat.1011494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 08/16/2023] [Accepted: 06/19/2023] [Indexed: 08/02/2023] Open
Abstract
Infections by Human T cell Leukaemia Virus type 1 (HTLV-1) persist for the lifetime of the host by integrating into the genome of CD4+ T cells. Proviral gene expression is essential for proviral survival and the maintenance of the proviral load, through the pro-proliferative changes it induces in infected cells. Despite their role in HTLV-1 infection and a persistent cytotoxic T lymphocyte response raised against the virus, proviral transcripts from the sense-strand are rarely detected in fresh cells extracted from the peripheral blood, and have recently been found to be expressed intermittently by a small subset of cells at a given time. Ex vivo culture of infected cells prompts synchronised proviral expression in infected cells from peripheral blood, allowing the study of factors involved in reactivation in primary cells. Here, we used bulk RNA-seq to examine the host transcriptome over six days in vitro, following proviral reactivation in primary peripheral CD4+ T cells isolated from subjects with non-malignant HTLV-1 infection. Infected cells displayed a conserved response to reactivation, characterised by discrete stages of gene expression, cell division and subsequently horizontal transmission of the virus. We observed widespread changes in Polycomb gene expression following reactivation, including an increase in PRC2 transcript levels and diverse changes in the expression of PRC1 components. We hypothesize that these transcriptional changes constitute a negative feedback loop that maintains proviral latency by re-deposition of H2AK119ub1 following the end of proviral expression. Using RNAi, we found that certain deubiquitinases, BAP1, USP14 and OTUD5 each promote proviral transcription. These data demonstrate the detailed trajectory of HTLV-1 proviral reactivation in primary HTLV-1-carrier lymphocytes and the impact on the host cell.
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Affiliation(s)
- Aris E. N. Aristodemou
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - David S. Rueda
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
- Single Molecule Imaging Group, MRC-London Institute of Medical Sciences, London, United Kingdom
| | - Graham P. Taylor
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Charles R. M. Bangham
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
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27
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Oehler M, Geisser L, Diernfellner ACR, Brunner M. Transcription activator WCC recruits deacetylase HDA3 to control transcription dynamics and bursting in Neurospora. SCIENCE ADVANCES 2023; 9:eadh0721. [PMID: 37390199 PMCID: PMC10313174 DOI: 10.1126/sciadv.adh0721] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/25/2023] [Indexed: 07/02/2023]
Abstract
RNA polymerase II initiates transcription either randomly or in bursts. We examined the light-dependent transcriptional activator White Collar Complex (WCC) of Neurospora to characterize the transcriptional dynamics of the strong vivid (vvd) promoter and the weaker frequency (frq) promoter. We show that WCC is not only an activator but also represses transcription by recruiting histone deacetylase 3 (HDA3). Our data suggest that bursts of frq transcription are governed by a long-lived refractory state established and maintained by WCC and HDA3 at the core promoter, whereas transcription of vvd is determined by WCC binding dynamics at an upstream activating sequence. Thus, in addition to stochastic binding of transcription factors, transcription factor-mediated repression may also influence transcriptional bursting.
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Affiliation(s)
- Michael Oehler
- Heidelberg University Biochemistry Center, Im Neuenheimer Feld 328, D-60120 Heidelberg, Germany
| | - Leonie Geisser
- Heidelberg University Biochemistry Center, Im Neuenheimer Feld 328, D-60120 Heidelberg, Germany
| | - Axel C. R. Diernfellner
- Heidelberg University Biochemistry Center, Im Neuenheimer Feld 328, D-60120 Heidelberg, Germany
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28
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Wildner C, Mehta GD, Ball DA, Karpova TS, Koeppl H. Bayesian analysis dissects kinetic modulation during non-stationary gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.20.545522. [PMID: 37503023 PMCID: PMC10370195 DOI: 10.1101/2023.06.20.545522] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Labelling of nascent stem loops with fluorescent proteins has fostered the visualization of transcription in living cells. Quantitative analysis of recorded fluorescence traces can shed light on kinetic transcription parameters and regulatory mechanisms. However, existing methods typically focus on steady state dynamics. Here, we combine a stochastic process transcription model with a hierarchical Bayesian method to infer global as well locally shared parameters for groups of cells and recover unobserved quantities such as initiation times and polymerase loading of the gene. We apply our approach to the cyclic response of the yeast CUP1 locus to heavy metal stress. Within the previously described slow cycle of transcriptional activity on the scale of minutes, we discover fast time-modulated bursting on the scale of seconds. Model comparison suggests that slow oscillations of transcriptional output are regulated by the amplitude of the bursts. Several polymerases may initiate during a burst.
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Affiliation(s)
- Christian Wildner
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, 64283, Germany
| | - Gunjan D. Mehta
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana-502285, India
| | - David A. Ball
- National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Tatiana S. Karpova
- National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Heinz Koeppl
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, 64283, Germany
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29
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Leyes Porello EA, Trudeau RT, Lim B. Transcriptional bursting: stochasticity in deterministic development. Development 2023; 150:dev201546. [PMID: 37337971 PMCID: PMC10323239 DOI: 10.1242/dev.201546] [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] [Indexed: 06/21/2023]
Abstract
The transcription of DNA by RNA polymerase occurs as a discontinuous process described as transcriptional bursting. This bursting behavior is observed across species and has been quantified using various stochastic modeling approaches. There is a large body of evidence that suggests the bursts are actively modulated by transcriptional machinery and play a role in regulating developmental processes. Under a commonly used two-state model of transcription, various enhancer-, promoter- and chromatin microenvironment-associated features are found to differentially influence the size and frequency of bursting events - key parameters of the two-state model. Advancement of modeling and analysis tools has revealed that the simple two-state model and associated parameters may not sufficiently characterize the complex relationship between these features. The majority of experimental and modeling findings support the view of bursting as an evolutionarily conserved transcriptional control feature rather than an unintended byproduct of the transcription process. Stochastic transcriptional patterns contribute to enhanced cellular fitness and execution of proper development programs, which posit this mode of transcription as an important feature in developmental gene regulation. In this Review, we present compelling examples of the role of transcriptional bursting in development and explore the question of how stochastic transcription leads to deterministic organism development.
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Affiliation(s)
- Emilia A. Leyes Porello
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert T. Trudeau
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bomyi Lim
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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30
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Vo HD, Forero-Quintero LS, Aguilera LU, Munsky B. Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise. Front Cell Dev Biol 2023; 11:1133994. [PMID: 37305680 PMCID: PMC10250612 DOI: 10.3389/fcell.2023.1133994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.
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Affiliation(s)
- Huy D. Vo
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
| | - Linda S. Forero-Quintero
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
| | - Luis U. Aguilera
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
| | - Brian Munsky
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
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31
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Carilli M, Gorin G, Choi Y, Chari T, Pachter L. Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.13.523995. [PMID: 36712140 PMCID: PMC9882246 DOI: 10.1101/2023.01.13.523995] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strategy for treating multimodal datasets generated by high-throughput, single-cell genomic assays.
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Affiliation(s)
- Maria Carilli
- Division of Biology and Biological Engineering, California Institute of Technology
| | - Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology
| | - Yongin Choi
- Biomedical Engineering Graduate Group, University of California, Davis
- Genome Center, University of California, Davis
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology
- Department of Computing and Mathematical Sciences, California Institute of Technology
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32
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Forbes Beadle L, Zhou H, Rattray M, Ashe HL. Modulation of transcription burst amplitude underpins dosage compensation in the Drosophila embryo. Cell Rep 2023; 42:112382. [PMID: 37060568 PMCID: PMC10283159 DOI: 10.1016/j.celrep.2023.112382] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 02/03/2023] [Accepted: 03/27/2023] [Indexed: 04/16/2023] Open
Abstract
Dosage compensation, the balancing of X-linked gene expression between sexes and to the autosomes, is critical to an organism's fitness and survival. In Drosophila, dosage compensation involves hypertranscription of the male X chromosome. Here, we use quantitative live imaging and modeling at single-cell resolution to study X chromosome dosage compensation in Drosophila. We show that the four X chromosome genes studied undergo transcriptional bursting in male and female embryos. Mechanistically, our data reveal that transcriptional upregulation of male X chromosome genes is primarily mediated by a higher RNA polymerase II initiation rate and burst amplitude across the expression domain. In contrast, burst frequency is spatially modulated in nuclei within the expression domain in response to different transcription factor concentrations to tune the transcriptional response. Together, these data show how the local and global regulation of distinct burst parameters can establish the complex transcriptional outputs underpinning developmental patterning.
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Affiliation(s)
- Lauren Forbes Beadle
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK
| | - Hongpeng Zhou
- 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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33
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Calia GP, Chen X, Zuckerman B, Weinberger LS. Comparative analysis between single-cell RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532632. [PMID: 36993609 PMCID: PMC10055090 DOI: 10.1101/2023.03.14.532632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability, but the physiological roles of noise have remained difficult to determine in the absence of generalized noise-modulation approaches. Previous single-cell RNA-sequencing (scRNA-seq) suggested that the pyrimidine-base analog (5'-iodo-2'-deoxyuridine, IdU) could generally amplify noise without substantially altering mean-expression levels but scRNA-seq technical drawbacks potentially obscured the penetrance of IdU-induced transcriptional noise amplification. Here we quantify global-vs.-partial penetrance of IdU-induced noise amplification by assessing scRNA-seq data using numerous normalization algorithms and directly quantifying noise using single-molecule RNA FISH (smFISH) for a panel of genes from across the transcriptome. Alternate scRNA-seq analyses indicate IdU-induced noise amplification for ~90% of genes, and smFISH data verified noise amplification for ~90% of tested genes. Collectively, this analysis indicates which scRNA-seq algorithms are appropriate for quantifying noise and argues that IdU is a globally penetrant noise-enhancer molecule that could enable investigations of the physiological impacts of transcriptional noise.
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Affiliation(s)
- Giuliana P. Calia
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
| | - Xinyue Chen
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
| | - Binyamin Zuckerman
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
| | - Leor S. Weinberger
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
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34
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Syed S, Duan Y, Lim B. Modulation of protein-DNA binding reveals mechanisms of spatiotemporal gene control in early Drosophila embryos. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522923. [PMID: 36711729 PMCID: PMC9881968 DOI: 10.1101/2023.01.05.522923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
It is well known that enhancers regulate the spatiotemporal expression of their target genes by recruiting transcription factors (TFs) to the cognate binding sites in the region. However, the role of multiple binding sites for the same TFs and their specific spatial arrangement in determining the overall competency of the enhancer has yet to be fully understood. In this study, we utilized the MS2-MCP live imaging technique to quantitatively analyze the regulatory logic of the snail distal enhancer in early Drosophila embryos. Through systematic modulation of Dorsal and Twist binding motifs in this enhancer, we found that a mutation in any one of these binding sites causes a drastic reduction in transcriptional amplitude, resulting in a reduction in total mRNA production of the target gene. We provide evidence of synergy, such that multiple binding sites with moderate affinities cooperatively recruit more TFs to drive stronger transcriptional activity than a single site. Moreover, a Hidden Markov-based stochastic model of transcription reveals that embryos with mutated binding sites have a higher probability of returning to the inactive promoter state. We propose that TF-DNA binding regulates spatial and temporal gene expression and drives robust pattern formation by modulating transcriptional kinetics and tuning bursting rates.
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Affiliation(s)
- Sahla Syed
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Yifei Duan
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Master of Biotechnology Program, University of Pennsylvania, Philadelphia, PA 19104
| | - Bomyi Lim
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104
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35
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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: 8] [Impact Index Per Article: 4.0] [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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36
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Ohishi H, Shimada S, Uchino S, Li J, Sato Y, Shintani M, Owada H, Ohkawa Y, Pertsinidis A, Yamamoto T, Kimura H, Ochiai H. STREAMING-tag system reveals spatiotemporal relationships between transcriptional regulatory factors and transcriptional activity. Nat Commun 2022; 13:7672. [PMID: 36539402 PMCID: PMC9768169 DOI: 10.1038/s41467-022-35286-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
Transcription is a dynamic process. To detect the dynamic relationship among protein clusters of RNA polymerase II and coactivators, gene loci, and transcriptional activity, we insert an MS2 repeat, a TetO repeat, and inteins with a selection marker just downstream of the transcription start site. By optimizing the individual elements, we develop the Spliced TetO REpeAt, MS2 repeat, and INtein sandwiched reporter Gene tag (STREAMING-tag) system. Clusters of RNA polymerase II and BRD4 are observed proximal to the transcription start site of Nanog when the gene is transcribed in mouse embryonic stem cells. In contrast, clusters of MED19 and MED22 tend to be located near the transcription start site, even without transcription activity. Thus, the STREAMING-tag system reveals the spatiotemporal relationships between transcriptional activity and protein clusters near the gene. This powerful tool is useful for quantitatively understanding transcriptional regulation in living cells.
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Affiliation(s)
- Hiroaki Ohishi
- grid.257022.00000 0000 8711 3200Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, 739-0046 Japan
| | - Seiru Shimada
- grid.257022.00000 0000 8711 3200Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, 739-0046 Japan
| | - Satoshi Uchino
- grid.32197.3e0000 0001 2179 2105School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501 Japan
| | - Jieru Li
- grid.51462.340000 0001 2171 9952Structural Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Yuko Sato
- grid.32197.3e0000 0001 2179 2105School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501 Japan ,grid.32197.3e0000 0001 2179 2105Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, 226-8503 Japan
| | - Manabu Shintani
- grid.257022.00000 0000 8711 3200Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, 739-0046 Japan
| | - Hitoshi Owada
- grid.257022.00000 0000 8711 3200Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, 739-0046 Japan
| | - Yasuyuki Ohkawa
- grid.177174.30000 0001 2242 4849Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, 812-8582 Japan
| | - Alexandros Pertsinidis
- grid.51462.340000 0001 2171 9952Structural Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Takashi Yamamoto
- grid.257022.00000 0000 8711 3200Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, 739-0046 Japan
| | - Hiroshi Kimura
- grid.32197.3e0000 0001 2179 2105School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501 Japan ,grid.32197.3e0000 0001 2179 2105Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, 226-8503 Japan
| | - Hiroshi Ochiai
- grid.257022.00000 0000 8711 3200Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, 739-0046 Japan
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37
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Ball DA, Jalloh B, Karpova TS. Impact of Saccharomyces cerevisiae on the Field of Single-Molecule Biophysics. Int J Mol Sci 2022; 23:ijms232415895. [PMID: 36555532 PMCID: PMC9781480 DOI: 10.3390/ijms232415895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/10/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022] Open
Abstract
Cellular functions depend on the dynamic assembly of protein regulator complexes at specific cellular locations. Single Molecule Tracking (SMT) is a method of choice for the biochemical characterization of protein dynamics in vitro and in vivo. SMT follows individual molecules in live cells and provides direct information about their behavior. SMT was successfully applied to mammalian models. However, mammalian cells provide a complex environment where protein mobility depends on numerous factors that are difficult to control experimentally. Therefore, yeast cells, which are unicellular and well-studied with a small and completely sequenced genome, provide an attractive alternative for SMT. The simplicity of organization, ease of genetic manipulation, and tolerance to gene fusions all make yeast a great model for quantifying the kinetics of major enzymes, membrane proteins, and nuclear and cellular bodies. However, very few researchers apply SMT techniques to yeast. Our goal is to promote SMT in yeast to a wider research community. Our review serves a dual purpose. We explain how SMT is conducted in yeast cells, and we discuss the latest insights from yeast SMT while putting them in perspective with SMT of higher eukaryotes.
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38
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Liang Y, Xu H, Cheng T, Fu Y, Huang H, Qian W, Wang J, Zhou Y, Qian P, Yin Y, Xu P, Zou W, Chen B. Gene activation guided by nascent RNA-bound transcription factors. Nat Commun 2022; 13:7329. [PMID: 36443367 PMCID: PMC9705438 DOI: 10.1038/s41467-022-35041-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/16/2022] [Indexed: 11/29/2022] Open
Abstract
Technologies for gene activation are valuable tools for the study of gene functions and have a wide range of potential applications in bioengineering and medicine. In contrast to existing methods based on recruiting transcriptional modulators via DNA-binding proteins, we developed a strategy termed Narta (nascent RNA-guided transcriptional activation) to achieve gene activation by recruiting artificial transcription factors (aTFs) to transcription sites through nascent RNAs of the target gene. Using Narta, we demonstrate robust activation of a broad range of exogenous and endogenous genes in various cell types, including zebrafish embryos, mouse and human cells. Importantly, the activation is reversible, tunable and specific. Moreover, Narta provides better activation potency of some expressed genes than CRISPRa and, when used in combination with CRISPRa, has an enhancing effect on gene activation. Quantitative imaging illustrated that nascent RNA-directed aTFs could induce the high-density assembly of coactivators at transcription sites, which may explain the larger transcriptional burst size induced by Narta. Overall, our work expands the gene activation toolbox for biomedical research.
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Affiliation(s)
- Ying Liang
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China ,grid.13402.340000 0004 1759 700XLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
| | - Haiyue Xu
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China ,grid.13402.340000 0004 1759 700XLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
| | - Tao Cheng
- grid.13402.340000 0004 1759 700XWomen’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yujuan Fu
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hanwei Huang
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchang Qian
- grid.13402.340000 0004 1759 700XCenter of Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Junyan Wang
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuenan Zhou
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Pengxu Qian
- grid.13402.340000 0004 1759 700XCenter of Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Yafei Yin
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Pengfei Xu
- grid.13402.340000 0004 1759 700XWomen’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Zou
- grid.13402.340000 0004 1759 700XThe Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China ,grid.13402.340000 0004 1759 700XInsititute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Baohui Chen
- grid.13402.340000 0004 1759 700XDepartment of Cell Biology and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China ,grid.13402.340000 0004 1759 700XLiangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China ,grid.13402.340000 0004 1759 700XInstitute of Hematology, Zhejiang University & Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China ,grid.13402.340000 0004 1759 700XZhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Hangzhou, China
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39
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A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription. PLoS Comput Biol 2022; 18:e1010152. [PMID: 36084132 PMCID: PMC9491597 DOI: 10.1371/journal.pcbi.1010152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/21/2022] [Accepted: 08/12/2022] [Indexed: 11/23/2022] Open
Abstract
Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway. Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways.
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40
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Gupta A, Martin-Rufino JD, Jones TR, Subramanian V, Qiu X, Grody EI, Bloemendal A, Weng C, Niu SY, Min KH, Mehta A, Zhang K, Siraj L, Al' Khafaji A, Sankaran VG, Raychaudhuri S, Cleary B, Grossman S, Lander ES. Inferring gene regulation from stochastic transcriptional variation across single cells at steady state. Proc Natl Acad Sci U S A 2022; 119:e2207392119. [PMID: 35969771 PMCID: PMC9407670 DOI: 10.1073/pnas.2207392119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
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Affiliation(s)
- Anika Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | - Jorge D. Martin-Rufino
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | | | | | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- HHMI, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Chen Weng
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
| | | | - Kyung Hoi Min
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Arnav Mehta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Kaite Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Layla Siraj
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Vijay G. Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | - Soumya Raychaudhuri
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA 02115
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Eric S. Lander
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115
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41
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Robles-Rebollo I, Cuartero S, Canellas-Socias A, Wells S, Karimi MM, Mereu E, Chivu AG, Heyn H, Whilding C, Dormann D, Marguerat S, Rioja I, Prinjha RK, Stumpf MPH, Fisher AG, Merkenschlager M. Cohesin couples transcriptional bursting probabilities of inducible enhancers and promoters. Nat Commun 2022; 13:4342. [PMID: 35896525 PMCID: PMC9329429 DOI: 10.1038/s41467-022-31192-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/06/2022] [Indexed: 01/25/2023] Open
Abstract
Innate immune responses rely on inducible gene expression programmes which, in contrast to steady-state transcription, are highly dependent on cohesin. Here we address transcriptional parameters underlying this cohesin-dependence by single-molecule RNA-FISH and single-cell RNA-sequencing. We show that inducible innate immune genes are regulated predominantly by an increase in the probability of active transcription, and that probabilities of enhancer and promoter transcription are coordinated. Cohesin has no major impact on the fraction of transcribed inducible enhancers, or the number of mature mRNAs produced per transcribing cell. Cohesin is, however, required for coupling the probabilities of enhancer and promoter transcription. Enhancer-promoter coupling may not be explained by spatial proximity alone, and at the model locus Il12b can be disrupted by selective inhibition of the cohesinopathy-associated BET bromodomain BD2. Our data identify discrete steps in enhancer-mediated inducible gene expression that differ in cohesin-dependence, and suggest that cohesin and BD2 may act on shared pathways.
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Affiliation(s)
- Irene Robles-Rebollo
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Theoretical Systems Biology Group, Imperial College London, London, UK
| | - Sergi Cuartero
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Josep Carreras Leukaemia Research Institute, Badalona, Spain
| | - Adria Canellas-Socias
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- IRB, Institute for Research in Biomedicine, Barcelona, Spain
| | - Sarah Wells
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Mohammad M Karimi
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Elisabetta Mereu
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Alexandra G Chivu
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Department of Molecular Biology and Genetics, Cornell University, New York, USA
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Chad Whilding
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Dirk Dormann
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Samuel Marguerat
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Inmaculada Rioja
- Epigenetics RU, GlaxoSmithKline Medicines Research Centre, Stevenage, UK
| | - Rab K Prinjha
- Epigenetics RU, GlaxoSmithKline Medicines Research Centre, Stevenage, UK
| | - Michael P H Stumpf
- Theoretical Systems Biology Group, Imperial College London, London, UK
- School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Amanda G Fisher
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Matthias Merkenschlager
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK.
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK.
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42
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Loell K, Wu Y, Staller MV, Cohen B. Activation domains can decouple the mean and noise of gene expression. Cell Rep 2022; 40:111118. [PMID: 35858548 PMCID: PMC9912357 DOI: 10.1016/j.celrep.2022.111118] [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: 09/08/2021] [Revised: 01/18/2022] [Accepted: 06/28/2022] [Indexed: 11/03/2022] Open
Abstract
Regulatory mechanisms set a gene's average level of expression, but a gene's expression constantly fluctuates around that average. These stochastic fluctuations, or expression noise, play a role in cell-fate transitions, bet hedging in microbes, and the development of chemotherapeutic resistance in cancer. An outstanding question is what regulatory mechanisms contribute to noise. Here, we demonstrate that, for a fixed mean level of expression, strong activation domains (ADs) at low abundance produce high expression noise, while weak ADs at high abundance generate lower expression noise. We conclude that differences in noise can be explained by the interplay between a TF's nuclear concentration and the strength of its AD's effect on mean expression, without invoking differences between classes of ADs. These results raise the possibility of engineering gene expression noise independently of mean levels in synthetic biology contexts and provide a potential mechanism for natural selection to tune the noisiness of gene expression.
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Affiliation(s)
- Kaiser Loell
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA,The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Yawei Wu
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA,The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Max V. Staller
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Barak Cohen
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA; The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA.
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43
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Stochastic Transcription with Alterable Synthesis Rates. MATHEMATICS 2022. [DOI: 10.3390/math10132189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Gene transcription is a random bursting process that leads to large variability in mRNA numbers in single cells. The main cause is largely attributed to random switching between periods of active and inactive gene transcription. In some experiments, it has been observed that variation in the number of active transcription sites causes the initiation rate to vary during elongation. Results: We established a mathematical model based on the molecular reaction mechanism in single cells and studied a stochastic transcription system consisting of two active states and one inactive state, in which mRNA molecules are produced with two different synthesis rates. Conclusions: By calculation, we obtained the average mRNA expression level, the noise strength, and the skewness of transcripts. We gave a necessary and sufficient condition that causes the average mRNA level to peak at a limited time. The model could help us to distinguish an appropriate mechanism that may be employed by cells to transcribe mRNA molecules. Our simulations were in agreement with some experimental data and showed that the skewness can measure the deviation of the distribution of transcripts from the mean value. Especially for mature mRNAs, their distributions were almost able to be determined by the mean, the noise (or the noise strength), and the skewness.
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44
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Forouhan M, Lim WF, Zanetti-Domingues LC, Tynan CJ, Roberts TC, Malik B, Manzano R, Speciale AA, Ellerington R, Garcia-Guerra A, Fratta P, Sorarú G, Greensmith L, Pennuto M, Wood MJA, Rinaldi C. AR cooperates with SMAD4 to maintain skeletal muscle homeostasis. Acta Neuropathol 2022; 143:713-731. [PMID: 35522298 PMCID: PMC9107400 DOI: 10.1007/s00401-022-02428-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 04/10/2022] [Accepted: 04/27/2022] [Indexed: 12/27/2022]
Abstract
Androgens and androgen-related molecules exert a plethora of functions across different tissues, mainly through binding to the transcription factor androgen receptor (AR). Despite widespread therapeutic use and misuse of androgens as potent anabolic agents, the molecular mechanisms of this effect on skeletal muscle are currently unknown. Muscle mass in adulthood is mainly regulated by the bone morphogenetic protein (BMP) axis of the transforming growth factor (TGF)-β pathway via recruitment of mothers against decapentaplegic homolog 4 (SMAD4) protein. Here we show that, upon activation, AR forms a transcriptional complex with SMAD4 to orchestrate a muscle hypertrophy programme by modulating SMAD4 chromatin binding dynamics and enhancing its transactivation activity. We challenged this mechanism of action using spinal and bulbar muscular atrophy (SBMA) as a model of study. This adult-onset neuromuscular disease is caused by a polyglutamine expansion (polyQ) in AR and is characterized by progressive muscle weakness and atrophy secondary to a combination of lower motor neuron degeneration and primary muscle atrophy. Here we found that the presence of an elongated polyQ tract impairs AR cooperativity with SMAD4, leading to an inability to mount an effective anti-atrophy gene expression programme in skeletal muscle in response to denervation. Furthermore, adeno-associated virus, serotype 9 (AAV9)-mediated muscle-restricted delivery of BMP7 is able to rescue the muscle atrophy in SBMA mice, supporting the development of treatments able to fine-tune AR-SMAD4 transcriptional cooperativity as a promising target for SBMA and other conditions associated with muscle loss.
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Affiliation(s)
- Mitra Forouhan
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK
| | - Wooi Fang Lim
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK
| | - Laura C Zanetti-Domingues
- Central Laser Facility, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Didcot, Oxfordshire, UK
| | - Christopher J Tynan
- Central Laser Facility, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Didcot, Oxfordshire, UK
| | - Thomas C Roberts
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK
| | - Bilal Malik
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Raquel Manzano
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Alfina A Speciale
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK
| | - Ruth Ellerington
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK
| | - Antonio Garcia-Guerra
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK
| | - Pietro Fratta
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Gianni Sorarú
- Department of Neurosciences, Neurology Unit, University of Padova, Padova, Italy
- Venetian Institute of Molecular Medicine (VIMM), Padova, Italy
| | - Linda Greensmith
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Maria Pennuto
- Venetian Institute of Molecular Medicine (VIMM), Padova, Italy
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Matthew J A Wood
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK
- MDUK Oxford Neuromuscular Centre, University of Oxford, Oxford, UK
| | - Carlo Rinaldi
- Department of Paediatrics, University of Oxford, South Parks Road, Oxford, OX1 3QX, UK.
- MDUK Oxford Neuromuscular Centre, University of Oxford, Oxford, UK.
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45
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Nollmann M, Bennabi I, Götz M, Gregor T. The Impact of Space and Time on the Functional Output of the Genome. Cold Spring Harb Perspect Biol 2022; 14:a040378. [PMID: 34230036 PMCID: PMC8733053 DOI: 10.1101/cshperspect.a040378] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Over the past two decades, it has become clear that the multiscale spatial and temporal organization of the genome has important implications for nuclear function. This review centers on insights gained from recent advances in light microscopy on our understanding of transcription. We discuss spatial and temporal aspects that shape nuclear order and their consequences on regulatory components, focusing on genomic scales most relevant to function. The emerging picture is that spatiotemporal constraints increase the complexity in transcriptional regulation, highlighting new challenges, such as uncertainty about how information travels from molecular factors through the genome and space to generate a functional output.
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Affiliation(s)
- Marcelo Nollmann
- Centre de Biologie Structurale, CNRS UMR5048, INSERM U1054, Univ Montpellier, 34090 Montpellier, France
| | - Isma Bennabi
- Department of Stem Cell and Developmental Biology, CNRS UMR3738, Institut Pasteur, 75015 Paris, France
| | - Markus Götz
- Centre de Biologie Structurale, CNRS UMR5048, INSERM U1054, Univ Montpellier, 34090 Montpellier, France
| | - Thomas Gregor
- Department of Stem Cell and Developmental Biology, CNRS UMR3738, Institut Pasteur, 75015 Paris, France
- Joseph Henry Laboratory of Physics & Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
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46
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A Novel Dynamical Regulation of mRNA Distribution by Cross-Talking Pathways. MATHEMATICS 2022. [DOI: 10.3390/math10091515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In this paper, we use a similar approach to the one proposed by Chen and Jiao to calculate the mathematical formulas of the generating function V(z,t) and the mass function Pm(t) of a cross-talking pathways model in large parameter regions. Together with kinetic rates from yeast and mouse genes, our numerical examples reveal novel bimodal mRNA distributions for intermediate times, whereby the mode of distribution Pm(t) displays unimodality with the peak at m=0 for initial and long times, which has not been obtained in previous works. Such regulation of mRNA distribution exactly matches the transcriptional dynamics for the osmosensitive genes in Saccharomyces cerevisiae, which has not been generated by those models with one single pathway or feedback loops. This paper may provide us with a novel observation on transcriptional distribution dynamics regulated by multiple signaling pathways in response to environmental changes and genetic perturbations.
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Filatova T, Popović N, Grima R. Modulation of nuclear and cytoplasmic mRNA fluctuations by time-dependent stimuli: Analytical distributions. Math Biosci 2022; 347:108828. [DOI: 10.1016/j.mbs.2022.108828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/15/2022] [Accepted: 04/15/2022] [Indexed: 10/18/2022]
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Chen L, Lin G, Jiao F. Using average transcription level to understand the regulation of stochastic gene activation. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211757. [PMID: 35223065 PMCID: PMC8847896 DOI: 10.1098/rsos.211757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/24/2022] [Indexed: 05/03/2023]
Abstract
Gene activation is a random process, modelled as a framework of multiple rate-limiting steps listed sequentially, in parallel or in combination. Together with suitably assumed processes of gene inactivation, transcript birth and death, the step numbers and parameters in activation frameworks can be estimated by fitting single-cell transcription data. However, current algorithms require computing master equations that are tightly correlated with prior hypothetical frameworks of gene activation. We found that prior estimation of the framework can be facilitated by the traditional dynamical data of mRNA average level M(t), presenting discriminated dynamical features. Rigorous theory regarding M(t) profiles allows to confidently rule out the frameworks that fail to capture M(t) features and to test potential competent frameworks by fitting M(t) data. We implemented this procedure for a large number of mouse fibroblast genes under tumour necrosis factor induction and determined exactly the 'cross-talking n-state' framework; the cross-talk between the signalling and basal pathways is crucial to trigger the first peak of M(t), while the following damped gentle M(t) oscillation is regulated by the multi-step basal pathway. This framework can be used to fit sophisticated single-cell data and may facilitate a more accurate understanding of stochastic activation of mouse fibroblast genes.
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Affiliation(s)
- Liang Chen
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, People’s Republic of China
- School of Mathematics and Information Sciences, Guangzhou University, Guangzhou, People’s Republic of China
| | - Genghong Lin
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, People’s Republic of China
- School of Mathematics and Information Sciences, Guangzhou University, Guangzhou, People’s Republic of China
| | - Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, People’s Republic of China
- School of Mathematics and Information Sciences, Guangzhou University, Guangzhou, People’s Republic of China
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Racca JD, Chatterjee D, Chen YS, Rai RK, Yang Y, Georgiadis MM, Haas E, Weiss MA. Tenuous transcriptional threshold of human sex determination. II. SRY exploits water-mediated clamp at the edge of ambiguity. Front Endocrinol (Lausanne) 2022; 13:1029177. [PMID: 36568077 PMCID: PMC9771472 DOI: 10.3389/fendo.2022.1029177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022] Open
Abstract
Y-encoded transcription factor SRY initiates male differentiation in therian mammals. This factor contains a high-mobility-group (HMG) box, which mediates sequence-specific DNA binding with sharp DNA bending. A companion article in this issue described sex-reversal mutations at box position 72 (residue 127 in human SRY), invariant as Tyr among mammalian orthologs. Although not contacting DNA, the aromatic ring seals the domain's minor wing at a solvent-exposed junction with a basic tail. A seeming paradox was posed by the native-like biochemical properties of inherited Swyer variant Y72F: its near-native gene-regulatory activity is consistent with the father's male development, but at odds with the daughter's XY female somatic phenotype. Surprisingly, aromatic rings (Y72, F72 or W72) confer higher transcriptional activity than do basic or polar side chains generally observed at solvated DNA interfaces (Arg, Lys, His or Gln). Whereas biophysical studies (time-resolved fluorescence resonance energy transfer and heteronuclear NMR spectroscopy) uncovered only subtle perturbations, dissociation of the Y72F complex was markedly accelerated relative to wild-type. Studies of protein-DNA solvation by molecular-dynamics (MD) simulations of an homologous high-resolution crystal structure (SOX18) suggest that Y72 para-OH anchors a network of water molecules at the tail-DNA interface, perturbed in the variant in association with nonlocal conformational fluctuations. Loss of the Y72 anchor among SRY variants presumably "unclamps" its basic tail, leading to (a) rapid DNA dissociation despite native affinity and (b) attenuated transcriptional activity at the edge of sexual ambiguity. Conservation of Y72 suggests that this water-mediated clamp operates generally among SRY and metazoan SOX domains.
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Affiliation(s)
- Joseph D. Racca
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
- *Correspondence: Joseph D. Racca, ; Michael A. Weiss,
| | - Deepak Chatterjee
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Yen-Shan Chen
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Ratan K. Rai
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Yanwu Yang
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Millie M. Georgiadis
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Elisha Haas
- Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
| | - Michael A. Weiss
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
- *Correspondence: Joseph D. Racca, ; Michael A. Weiss,
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A Novel Approach for Calculating Exact Forms of mRNA Distribution in Single-Cell Measurements. MATHEMATICS 2021. [DOI: 10.3390/math10010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Gene transcription is a stochastic process manifested by fluctuations in mRNA copy numbers in individual isogenic cells. Together with mathematical models of stochastic transcription, the massive mRNA distribution data that can be used to quantify fluctuations in mRNA levels can be fitted by Pm(t), which is the probability of producing m mRNA molecules at time t in a single cell. Tremendous efforts have been made to derive analytical forms of Pm(t), which rely on solving infinite arrays of the master equations of models. However, current approaches focus on the steady-state (t→∞) or require several parameters to be zero or infinity. Here, we present an approach for calculating Pm(t) with time, where all parameters are positive and finite. Our approach was successfully implemented for the classical two-state model and the widely used three-state model and may be further developed for different models with constant kinetic rates of transcription. Furthermore, the direct computations of Pm(t) for the two-state model and three-state model showed that the different regulations of gene activation can generate discriminated dynamical bimodal features of mRNA distribution under the same kinetic rates and similar steady-state mRNA distribution.
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