1
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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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2
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Hosseini SH, Roussel MR. Analytic delay distributions for a family of gene transcription models. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:6225-6262. [PMID: 39176425 DOI: 10.3934/mbe.2024273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Models intended to describe the time evolution of a gene network must somehow include transcription, the DNA-templated synthesis of RNA, and translation, the RNA-templated synthesis of proteins. In eukaryotes, the DNA template for transcription can be very long, often consisting of tens of thousands of nucleotides, and lengthy pauses may punctuate this process. Accordingly, transcription can last for many minutes, in some cases hours. There is a long history of introducing delays in gene expression models to take the transcription and translation times into account. Here we study a family of detailed transcription models that includes initiation, elongation, and termination reactions. We establish a framework for computing the distribution of transcription times, and work out these distributions for some typical cases. For elongation, a fixed delay is a good model provided elongation is fast compared to initiation and termination, and there are no sites where long pauses occur. The initiation and termination phases of the model then generate a nontrivial delay distribution, and elongation shifts this distribution by an amount corresponding to the elongation delay. When initiation and termination are relatively fast, the distribution of elongation times can be approximated by a Gaussian. A convolution of this Gaussian with the initiation and termination time distributions gives another analytic approximation to the transcription time distribution. If there are long pauses during elongation, because of the modularity of the family of models considered, the elongation phase can be partitioned into reactions generating a simple delay (elongation through regions where there are no long pauses), and reactions whose distribution of waiting times must be considered explicitly (initiation, termination, and motion through regions where long pauses are likely). In these cases, the distribution of transcription times again involves a nontrivial part and a shift due to fast elongation processes.
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Affiliation(s)
- S Hossein Hosseini
- Alberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Marc R Roussel
- Alberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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3
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Geisberg JV, Moqtaderi Z, Struhl K. Chromatin regulates alternative polyadenylation via the RNA polymerase II elongation rate. Proc Natl Acad Sci U S A 2024; 121:e2405827121. [PMID: 38748572 PMCID: PMC11127049 DOI: 10.1073/pnas.2405827121] [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: 03/22/2024] [Accepted: 04/15/2024] [Indexed: 05/22/2024] Open
Abstract
The RNA polymerase II (Pol II) elongation rate influences poly(A) site selection, with slow and fast Pol II derivatives causing upstream and downstream shifts, respectively, in poly(A) site utilization. In yeast, depletion of either of the histone chaperones FACT or Spt6 causes an upstream shift of poly(A) site use that strongly resembles the poly(A) profiles of slow Pol II mutant strains. Like slow Pol II mutant strains, FACT- and Spt6-depleted cells exhibit Pol II processivity defects, indicating that both Spt6 and FACT stimulate the Pol II elongation rate. Poly(A) profiles of some genes show atypical downstream shifts; this subset of genes overlaps well for FACT- or Spt6-depleted strains but is different from the atypical genes in Pol II speed mutant strains. In contrast, depletion of histone H3 or H4 causes a downstream shift of poly(A) sites for most genes, indicating that nucleosomes inhibit the Pol II elongation rate in vivo. Thus, chromatin-based control of the Pol II elongation rate is a potential mechanism, distinct from direct effects on the cleavage/polyadenylation machinery, to regulate alternative polyadenylation in response to genetic or environmental changes.
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Affiliation(s)
- Joseph V. Geisberg
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA02115
| | - Zarmik Moqtaderi
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA02115
| | - Kevin Struhl
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA02115
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4
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Kindongo O, Lieb G, Skaggs B, Dusserre Y, Vincenzetti V, Pelet S. Implication of polymerase recycling for nascent transcript quantification by live cell imaging. Yeast 2024; 41:279-294. [PMID: 38389243 DOI: 10.1002/yea.3929] [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: 09/25/2023] [Revised: 12/29/2023] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
Transcription enables the production of RNA from a DNA template. Due to the highly dynamic nature of transcription, live-cell imaging methods play a crucial role in measuring the kinetics of this process. For instance, transcriptional bursts have been visualized using fluorescent phage-coat proteins that associate tightly with messenger RNA (mRNA) stem loops formed on nascent transcripts. To convert the signal emanating from a transcription site into meaningful estimates of transcription dynamics, the influence of various parameters on the measured signal must be evaluated. Here, the effect of gene length on the intensity of the transcription site focus was analyzed. Intuitively, a longer gene can support a larger number of transcribing polymerases, thus leading to an increase in the measured signal. However, measurements of transcription induced by hyper-osmotic stress responsive promoters display independence from gene length. A mathematical model of the stress-induced transcription process suggests that the formation of gene loops that favor the recycling of polymerase from the terminator to the promoter can explain the observed behavior. One experimentally validated prediction from this model is that the amount of mRNA produced from a short gene should be higher than for a long one as the density of active polymerase on the short gene will be increased by polymerase recycling. Our data suggest that this recycling contributes significantly to the expression output from a gene and that polymerase recycling is modulated by the promoter identity and the cellular state.
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Affiliation(s)
- Olivia Kindongo
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Guillaume Lieb
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Benjamin Skaggs
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Yves Dusserre
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Vincent Vincenzetti
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Serge Pelet
- Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
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5
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Hwang DW, Maekiniemi A, Singer RH, Sato H. Real-time single-molecule imaging of transcriptional regulatory networks in living cells. Nat Rev Genet 2024; 25:272-285. [PMID: 38195868 DOI: 10.1038/s41576-023-00684-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 01/11/2024]
Abstract
Gene regulatory networks drive the specific transcriptional programmes responsible for the diversification of cell types during the development of multicellular organisms. Although our knowledge of the genes involved in these dynamic networks has expanded rapidly, our understanding of how transcription is spatiotemporally regulated at the molecular level over a wide range of timescales in the small volume of the nucleus remains limited. Over the past few decades, advances in the field of single-molecule fluorescence imaging have enabled real-time behaviours of individual transcriptional components to be measured in living cells and organisms. These efforts are now shedding light on the dynamic mechanisms of transcription, revealing not only the temporal rules but also the spatial coordination of underlying molecular interactions during various biological events.
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Affiliation(s)
- Dong-Woo Hwang
- Department of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA
| | - Anna Maekiniemi
- Department of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA
| | - Robert H Singer
- Department of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA
| | - Hanae Sato
- Department of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA.
- Nano Life Science Institute (WPI-Nano LSI), Kanazawa University, Kakuma-machi, Kanazawa, Japan.
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6
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Alfano C, Fichou Y, Huber K, Weiss M, Spruijt E, Ebbinghaus S, De Luca G, Morando MA, Vetri V, Temussi PA, Pastore A. Molecular Crowding: The History and Development of a Scientific Paradigm. Chem Rev 2024; 124:3186-3219. [PMID: 38466779 PMCID: PMC10979406 DOI: 10.1021/acs.chemrev.3c00615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/13/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024]
Abstract
It is now generally accepted that macromolecules do not act in isolation but "live" in a crowded environment, that is, an environment populated by numerous different molecules. The field of molecular crowding has its origins in the far 80s but became accepted only by the end of the 90s. In the present issue, we discuss various aspects that are influenced by crowding and need to consider its effects. This Review is meant as an introduction to the theme and an analysis of the evolution of the crowding concept through time from colloidal and polymer physics to a more biological perspective. We introduce themes that will be more thoroughly treated in other Reviews of the present issue. In our intentions, each Review may stand by itself, but the complete collection has the aspiration to provide different but complementary perspectives to propose a more holistic view of molecular crowding.
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Affiliation(s)
- Caterina Alfano
- Structural
Biology and Biophysics Unit, Fondazione
Ri.MED, 90100 Palermo, Italy
| | - Yann Fichou
- CNRS,
Bordeaux INP, CBMN UMR 5248, IECB, University
of Bordeaux, F-33600 Pessac, France
| | - Klaus Huber
- Department
of Chemistry, University of Paderborn, 33098 Paderborn, Germany
| | - Matthias Weiss
- Experimental
Physics I, Physics of Living Matter, University
of Bayreuth, 95440 Bayreuth, Germany
| | - Evan Spruijt
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Simon Ebbinghaus
- Lehrstuhl
für Biophysikalische Chemie and Research Center Chemical Sciences
and Sustainability, Research Alliance Ruhr, Ruhr-Universität Bochum, 44780 Bochum, Germany
| | - Giuseppe De Luca
- Dipartimento
di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche, Università degli Studi di Palermo, Viale delle Scienze, 90128 Palermo, Italy
| | | | - Valeria Vetri
- Dipartimento
di Fisica e Chimica − Emilio Segrè, Università degli Studi di Palermo, Viale delle Scienze, 90128 Palermo, Italy
| | | | - Annalisa Pastore
- King’s
College London, Denmark
Hill Campus, SE5 9RT London, United Kingdom
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7
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Zhang J, Han X, Ma L, Xu S, Lin Y. Deciphering a global source of non-genetic heterogeneity in cancer cells. Nucleic Acids Res 2023; 51:9019-9038. [PMID: 37587722 PMCID: PMC10516630 DOI: 10.1093/nar/gkad666] [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: 09/13/2022] [Revised: 07/09/2023] [Accepted: 08/09/2023] [Indexed: 08/18/2023] Open
Abstract
Cell-to-cell variability within a clonal population, also known as non-genetic heterogeneity, has created significant challenges for intervening with diseases such as cancer. While non-genetic heterogeneity can arise from the variability in the expression of specific genes, it remains largely unclear whether and how clonal cells could be heterogeneous in the expression of the entire transcriptome. Here, we showed that gene transcriptional activity is globally modulated in individual cancer cells, leading to non-genetic heterogeneity in the global transcription rate. Such heterogeneity contributes to cell-to-cell variability in transcriptome size and displays both dynamic and static characteristics, with the global transcription rate temporally modulated in a cell-cycle-coupled manner and the time-averaged rate being distinct between cells and heritable across generations. Additional evidence indicated the role of ATP metabolism in this heterogeneity, and suggested its implication in intrinsic cancer drug tolerance. Collectively, our work shed light on the mode, mechanism, and implication of a global but often hidden source of non-genetic heterogeneity.
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Affiliation(s)
- Jianhan Zhang
- Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xu Han
- Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
| | - Liang Ma
- Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
| | - Shuhui Xu
- Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
| | - Yihan Lin
- Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
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8
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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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9
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Martinez-Corral R, Park M, Biette KM, Friedrich D, Scholes C, Khalil AS, Gunawardena J, DePace AH. Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology. Cell Syst 2023; 14:324-339.e7. [PMID: 37080164 PMCID: PMC10472254 DOI: 10.1016/j.cels.2023.02.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 08/22/2022] [Accepted: 02/10/2023] [Indexed: 04/22/2023]
Abstract
Transcription factors (TFs) control gene expression, often acting synergistically. Classical thermodynamic models offer a biophysical explanation for synergy based on binding cooperativity and regulated recruitment of RNA polymerase. Because transcription requires polymerase to transition through multiple states, recent work suggests that "kinetic synergy" can arise through TFs acting on distinct steps of the transcription cycle. These types of synergy are not mutually exclusive and are difficult to disentangle conceptually and experimentally. Here, we model and build a synthetic circuit in which TFs bind to a single shared site on DNA, such that TFs cannot synergize by simultaneous binding. We model mRNA production as a function of both TF binding and regulation of the transcription cycle, revealing a complex landscape dependent on TF concentration, DNA binding affinity, and regulatory activity. We use synthetic TFs to confirm that the transcription cycle must be integrated with recruitment for a quantitative understanding of gene regulation.
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Affiliation(s)
| | - Minhee Park
- Biological Design Center, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Kelly M Biette
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Dhana Friedrich
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Clarissa Scholes
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ahmad S Khalil
- Biological Design Center, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Jeremy Gunawardena
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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10
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Alamos S, Reimer A, Westrum C, Turner MA, Talledo P, Zhao J, Luu E, Garcia HG. Minimal synthetic enhancers reveal control of the probability of transcriptional engagement and its timing by a morphogen gradient. Cell Syst 2023; 14:220-236.e3. [PMID: 36696901 PMCID: PMC10125799 DOI: 10.1016/j.cels.2022.12.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/03/2022] [Accepted: 12/21/2022] [Indexed: 01/26/2023]
Abstract
How enhancers interpret morphogen gradients to generate gene expression patterns is a central question in developmental biology. Recent studies have proposed that enhancers can dictate whether, when, and at what rate promoters engage in transcription, but the complexity of endogenous enhancers calls for theoretical models with too many free parameters to quantitatively dissect these regulatory strategies. To overcome this limitation, we established a minimal promoter-proximal synthetic enhancer in embryos of Drosophila melanogaster. Here, a gradient of the Dorsal activator is read by a single Dorsal DNA binding site. Using live imaging to quantify transcriptional activity, we found that a single binding site can regulate whether promoters engage in transcription in a concentration-dependent manner. By modulating the binding-site affinity, we determined that a gene's decision to transcribe and its transcriptional onset time can be explained by a simple model where the promoter traverses multiple kinetic barriers before transcription can ensue.
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Affiliation(s)
- Simon Alamos
- Department of Plant and Microbial Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Armando Reimer
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA
| | - Clay Westrum
- Department of Physics, University of California at Berkeley, Berkeley, CA, USA
| | - Meghan A Turner
- Department of Plant and Microbial Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Paul Talledo
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Jiaxi Zhao
- Department of Physics, University of California at Berkeley, Berkeley, CA, USA
| | - Emma Luu
- Department of Physics, University of California at Berkeley, Berkeley, CA, USA
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA; Department of Physics, University of California at Berkeley, Berkeley, CA, USA; Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA; Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA.
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11
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Kilic Z, Schweiger M, Moyer C, Shepherd D, Pressé S. Gene expression model inference from snapshot RNA data using Bayesian non-parametrics. NATURE COMPUTATIONAL SCIENCE 2023; 3:174-183. [PMID: 38125199 PMCID: PMC10732567 DOI: 10.1038/s43588-022-00392-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2023]
Abstract
Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational frameworks do not perform simultaneous Bayesian inference of gene expression models and parameters from such data. Rather, gene expression models-composed of gene states, their connectivities and associated parameters-are currently deduced by pre-specifying gene state numbers and connectivity before learning associated rate parameters. Here we propose a method to learn full distributions over gene states, state connectivities and associated rate parameters, simultaneously and self-consistently from single-molecule RNA counts. We propagate noise from fluctuating RNA counts over models by treating models themselves as random variables. We achieve this within a Bayesian non-parametric paradigm. We demonstrate our method on the Escherichia coli lacZ pathway and the Saccharomyces cerevisiae STL1 pathway, and verify its robustness on synthetic data.
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Affiliation(s)
- Zeliha Kilic
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA
- These authors contributed equally: Zeliha Kilic, Max Schweiger
| | - Max Schweiger
- Center for Biological Physics, ASU, Tempe, AZ, USA
- Department of Physics, ASU, Tempe, AZ, USA
- These authors contributed equally: Zeliha Kilic, Max Schweiger
| | - Camille Moyer
- Center for Biological Physics, ASU, Tempe, AZ, USA
- School of Mathematics and Statistical Sciences, ASU, Tempe, AZ, USA
| | - Douglas Shepherd
- Center for Biological Physics, ASU, Tempe, AZ, USA
- Department of Physics, ASU, Tempe, AZ, USA
| | - Steve Pressé
- Center for Biological Physics, ASU, Tempe, AZ, USA
- Department of Physics, ASU, Tempe, AZ, USA
- School of Molecular Sciences, ASU, Tempe, AZ, USA
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12
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Choubey S. Gene regulation meets Bayesian non-parametrics. NATURE COMPUTATIONAL SCIENCE 2023; 3:126-127. [PMID: 38177629 DOI: 10.1038/s43588-023-00405-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Affiliation(s)
- Sandeep Choubey
- Institute of Mathematical Sciences, Chennai, India.
- Homi Bhabha National Institute, Mumbai, India.
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13
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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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14
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Sevier SA, Hormoz S. Collective polymerase dynamics emerge from DNA supercoiling during transcription. Biophys J 2022; 121:4153-4165. [PMID: 36171726 PMCID: PMC9675029 DOI: 10.1016/j.bpj.2022.09.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/19/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022] Open
Abstract
All biological processes ultimately come from physical interactions. The mechanical properties of DNA play a critical role in transcription. RNA polymerase can over or under twist DNA (referred to as DNA supercoiling) when it moves along a gene, resulting in mechanical stresses in DNA that impact its own motion and that of other polymerases. For example, when enough supercoiling accumulates, an isolated polymerase halts, and transcription stops. DNA supercoiling can also mediate nonlocal interactions between polymerases that shape gene expression fluctuations. Here, we construct a comprehensive model of transcription that captures how RNA polymerase motion changes the degree of DNA supercoiling, which in turn feeds back into the rate at which polymerases are recruited and move along the DNA. Surprisingly, our model predicts that a group of three or more polymerases move together at a constant velocity and sustain their motion (forming what we call a polymeton), whereas one or two polymerases would have halted. We further show that accounting for the impact of DNA supercoiling on both RNA polymerase recruitment and velocity recapitulates empirical observations of gene expression fluctuations. Finally, we propose a mechanical toggle switch whereby interactions between genes are mediated by DNA twisting as opposed to proteins. Understanding the mechanical regulation of gene expression provides new insights into how endogenous genes can interact and informs the design of new forms of engineered interactions.
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Affiliation(s)
- Stuart A Sevier
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts; Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts; Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
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15
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Falo-Sanjuan J, Bray S. Notch-dependent and -independent transcription are modulated by tissue movements at gastrulation. eLife 2022; 11:e73656. [PMID: 35583918 PMCID: PMC9183233 DOI: 10.7554/elife.73656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 04/27/2022] [Indexed: 12/30/2022] Open
Abstract
Cells sense and integrate external information from diverse sources that include mechanical cues. Shaping of tissues during development may thus require coordination between mechanical forces from morphogenesis and cell-cell signalling to confer appropriate changes in gene expression. By live-imaging Notch-induced transcription in real time, we have discovered that morphogenetic movements during Drosophila gastrulation bring about an increase in activity-levels of a Notch-responsive enhancer. Mutations that disrupt the timing of gastrulation resulted in concomitant delays in transcription up-regulation that correlated with the start of mesoderm invagination. As a similar gastrulation-induced effect was detected when transcription was elicited by the intracellular domain NICD, it cannot be attributed to forces exerted on Notch receptor activation. A Notch-independent vnd enhancer also exhibited a modest gastrulation-induced activity increase in the same stripe of cells. Together, these observations argue that gastrulation-associated forces act on the nucleus to modulate transcription levels. This regulation was uncoupled when the complex linking the nucleoskeleton and cytoskeleton (LINC) was disrupted, indicating a likely conduit. We propose that the coupling between tissue-level mechanics, arising from gastrulation, and enhancer activity represents a general mechanism for ensuring correct tissue specification during development and that Notch-dependent enhancers are highly sensitive to this regulation.
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Affiliation(s)
- Julia Falo-Sanjuan
- Department of Physiology, Development and Neuroscience, University of CambridgeCambridgeUnited Kingdom
| | - Sarah Bray
- Department of Physiology, Development and Neuroscience, University of CambridgeCambridgeUnited Kingdom
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16
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Kim DW, Hong H, Kim JK. Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number. SCIENCE ADVANCES 2022; 8:eabl4598. [PMID: 35302852 PMCID: PMC8932658 DOI: 10.1126/sciadv.abl4598] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Identifying the sources of cell-to-cell variability in signaling dynamics is essential to understand drug response variability and develop effective therapeutics. However, it is challenging because not all signaling intermediate reactions can be experimentally measured simultaneously. This can be overcome by replacing them with a single random time delay, but the resulting process is non-Markovian, making it difficult to infer cell-to-cell heterogeneity in reaction rates and time delays. To address this, we developed an efficient and scalable moment-based Bayesian inference method (MBI) with a user-friendly computational package that infers cell-to-cell heterogeneity in the non-Markovian signaling process. We applied MBI to single-cell expression profiles from promoters responding to antibiotics and discovered a major source of cell-to-cell variability in antibiotic stress response: the number of rate-limiting steps in signaling cascades. This knowledge can help identify effective therapies that destroy all pathogenic or cancer cells, and the approach can be applied to precision medicine.
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Affiliation(s)
- Dae Wook Kim
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Hyukpyo Hong
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Republic of Korea
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17
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Szavits-Nossan J, Grima R. Mean-field theory accurately captures the variation of copy number distributions across the mRNA life cycle. Phys Rev E 2022; 105:014410. [PMID: 35193216 DOI: 10.1103/physreve.105.014410] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
We consider a stochastic model where a gene switches between two states, an mRNA transcript is released in the active state, and subsequently it undergoes an arbitrary number of sequential unimolecular steps before being degraded. The reactions effectively describe various stages of the mRNA life cycle such as initiation, elongation, termination, splicing, export, and degradation. We construct a mean-field approach that leads to closed-form steady-state distributions for the number of transcript molecules at each stage of the mRNA life cycle. By comparison with stochastic simulations, we show that the approximation is highly accurate over all the parameter space, independent of the type of expression (constitutive or bursty) and of the shape of the distribution (unimodal, bimodal, and nearly bimodal). The theory predicts that in a population of identical cells, any bimodality is gradually washed away as the mRNA progresses through its life cycle.
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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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18
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Duk MA, Gursky VV, Samsonova MG, Surkova SY. Application of Domain- and Genotype-Specific Models to Infer Post-Transcriptional Regulation of Segmentation Gene Expression in Drosophila. Life (Basel) 2021; 11:life11111232. [PMID: 34833107 PMCID: PMC8618293 DOI: 10.3390/life11111232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/05/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022] Open
Abstract
Unlike transcriptional regulation, the post-transcriptional mechanisms underlying zygotic segmentation gene expression in early Drosophila embryo have been insufficiently investigated. Condition-specific post-transcriptional regulation plays an important role in the development of many organisms. Our recent study revealed the domain- and genotype-specific differences between mRNA and the protein expression of Drosophila hb, gt, and eve genes in cleavage cycle 14A. Here, we use this dataset and the dynamic mathematical model to recapitulate protein expression from the corresponding mRNA patterns. The condition-specific nonuniformity in parameter values is further interpreted in terms of possible post-transcriptional modifications. For hb expression in wild-type embryos, our results predict the position-specific differences in protein production. The protein synthesis rate parameter is significantly higher in hb anterior domain compared to the posterior domain. The parameter sets describing Gt protein dynamics in wild-type embryos and Kr mutants are genotype-specific. The spatial discrepancy between gt mRNA and protein posterior expression in Kr mutants is well reproduced by the whole axis model, thus rejecting the involvement of post-transcriptional mechanisms. Our models fail to describe the full dynamics of eve expression, presumably due to its complex shape and the variable time delays between mRNA and protein patterns, which likely require a more complex model. Overall, our modeling approach enables the prediction of regulatory scenarios underlying the condition-specific differences between mRNA and protein expression in early embryo.
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Affiliation(s)
- Maria A. Duk
- Mathematical Biology and Bioinformatics Laboratory, Peter the Great Saint Petersburg Polytechnic University, 195251 St. Petersburg, Russia; (M.A.D.); (M.G.S.)
- Theoretical Department, Ioffe Institute, 194021 St. Petersburg, Russia;
| | - Vitaly V. Gursky
- Theoretical Department, Ioffe Institute, 194021 St. Petersburg, Russia;
| | - Maria G. Samsonova
- Mathematical Biology and Bioinformatics Laboratory, Peter the Great Saint Petersburg Polytechnic University, 195251 St. Petersburg, Russia; (M.A.D.); (M.G.S.)
| | - Svetlana Yu. Surkova
- Mathematical Biology and Bioinformatics Laboratory, Peter the Great Saint Petersburg Polytechnic University, 195251 St. Petersburg, Russia; (M.A.D.); (M.G.S.)
- Correspondence:
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