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Tenenbaum D, Inlow K, Friedman LJ, Cai A, Gelles J, Kondev J. RNA polymerase sliding on DNA can couple the transcription of nearby bacterial operons. Proc Natl Acad Sci U S A 2023; 120:e2301402120. [PMID: 37459525 PMCID: PMC10372574 DOI: 10.1073/pnas.2301402120] [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: 02/01/2023] [Accepted: 05/19/2023] [Indexed: 07/20/2023] Open
Abstract
DNA transcription initiates after an RNA polymerase (RNAP) molecule binds to the promoter of a gene. In bacteria, the canonical picture is that RNAP comes from the cytoplasmic pool of freely diffusing RNAP molecules. Recent experiments suggest the possible existence of a separate pool of polymerases, competent for initiation, which freely slide on the DNA after having terminated one round of transcription. Promoter-dependent transcription reinitiation from this pool of posttermination RNAP may lead to coupled initiation at nearby operons, but it is unclear whether this can occur over the distance and timescales needed for it to function widely on a bacterial genome in vivo. Here, we mathematically model the hypothesized reinitiation mechanism as a diffusion-to-capture process and compute the distances over which significant interoperon coupling can occur and the time required. These quantities depend on molecular association and dissociation rate constants between DNA, RNAP, and the transcription initiation factor σ70; we measure these rate constants using single-molecule experiments in vitro. Our combined theory/experimental results demonstrate that efficient coupling can occur at physiologically relevant σ70 concentrations and on timescales appropriate for transcript synthesis. Coupling is efficient over terminator-promoter distances up to ∼1,000 bp, which includes the majority of terminator-promoter nearest neighbor pairs in the Escherichia coli genome. The results suggest a generalized mechanism that couples the transcription of nearby operons and breaks the paradigm that each binding of RNAP to DNA can produce at most one messenger RNA.
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Affiliation(s)
- Debora Tenenbaum
- Department of Biochemistry, Brandeis University, Waltham, MA02453
- Department of Physics, Brandeis University, Waltham, MA02453
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY11724
| | - Koe Inlow
- Department of Biochemistry, Brandeis University, Waltham, MA02453
| | | | - Anthony Cai
- Department of Biochemistry, Brandeis University, Waltham, MA02453
| | - Jeff Gelles
- Department of Biochemistry, Brandeis University, Waltham, MA02453
| | - Jane Kondev
- Department of Physics, Brandeis University, Waltham, MA02453
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2
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Tenenbaum D, Inlow K, Friedman L, Cai A, Gelles J, Kondev J. RNA polymerase sliding on DNA can couple the transcription of nearby bacterial operons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.528045. [PMID: 36798213 PMCID: PMC9934669 DOI: 10.1101/2023.02.10.528045] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
Abstract
DNA transcription initiates after an RNA polymerase (RNAP) molecule binds to the promoter of a gene. In bacteria, the canonical picture is that RNAP comes from the cytoplasmic pool of freely diffusing RNAP molecules. Recent experiments suggest the possible existence of a separate pool of polymerases, competent for initiation, which freely slide on the DNA after having terminated one round of transcription. Promoter-dependent transcription reinitiation from this pool of post-termination RNAP may lead to coupled initiation at nearby operons, but it is unclear whether this can occur over the distance- and time-scales needed for it to function widely on a bacterial genome in vivo. Here, we mathematically model the hypothesized reinitiation mechanism as a diffusion-to-capture process and compute the distances over which significant inter-operon coupling can occur and the time required. These quantities depend on previously uncharacterized molecular association and dissociation rate constants between DNA, RNAP and the transcription initiation factor σ 70 ; we measure these rate constants using single-molecule experiments in vitro. Our combined theory/experimental results demonstrate that efficient coupling can occur at physiologically relevant σ 70 concentrations and on timescales appropriate for transcript synthesis. Coupling is efficient over terminator-promoter distances up to ∼ 1, 000 bp, which includes the majority of terminator-promoter nearest neighbor pairs in the E. coli genome. The results suggest a generalized mechanism that couples the transcription of nearby operons and breaks the paradigm that each binding of RNAP to DNA can produce at most one messenger RNA. SIGNIFICANCE STATEMENT After transcribing an operon, a bacterial RNA polymerase can stay bound to DNA, slide along it, and reini-tiate transcription of the same or a different operon. Quantitative single-molecule biophysics experiments combined with mathematical theory demonstrate that this reinitiation process can be quick and efficient over gene spacings typical of a bacterial genome. Reinitiation may provide a mechanism to orchestrate the transcriptional activities of groups of nearby operons.
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Affiliation(s)
- Debora Tenenbaum
- Department of Biochemistry, Brandeis University, Waltham, MA, United States
- Department of Physics, Brandeis University, Waltham, MA, United States
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Koe Inlow
- Department of Biochemistry, Brandeis University, Waltham, MA, United States
| | - Larry Friedman
- Department of Biochemistry, Brandeis University, Waltham, MA, United States
| | - Anthony Cai
- Department of Biochemistry, Brandeis University, Waltham, MA, United States
| | - Jeff Gelles
- Department of Biochemistry, Brandeis University, Waltham, MA, United States
| | - Jane Kondev
- Department of Physics, Brandeis University, Waltham, MA, United States
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Yang X, Wang Z, Wu Y, Zhou T, Zhang J. Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3313-3336. [PMID: 35341253 DOI: 10.3934/mbe.2022153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
While transcription often occurs in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important question: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that burst size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, China
| | - Zihao Wang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Yahao Wu
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
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Razo-Mejia M, Marzen S, Chure G, Taubman R, Morrison M, Phillips R. First-principles prediction of the information processing capacity of a simple genetic circuit. Phys Rev E 2021; 102:022404. [PMID: 32942428 DOI: 10.1103/physreve.102.022404] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 07/02/2020] [Indexed: 11/07/2022]
Abstract
Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.
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Affiliation(s)
- Manuel Razo-Mejia
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Sarah Marzen
- Department of Physics, W. M. Keck Science Department, Claremont McKenna College, Claremont, California 91711, USA
| | - Griffin Chure
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Rachel Taubman
- Department of Physics, W. M. Keck Science Department, Claremont McKenna College, Claremont, California 91711, USA
| | - Muir Morrison
- Department of Physics, California Institute of Technology, Pasadena, California 91125, USA
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA.,Department of Physics, California Institute of Technology, Pasadena, California 91125, USA
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5
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Liu J, Hansen D, Eck E, Kim YJ, Turner M, Alamos S, Garcia HG. Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage. PLoS Comput Biol 2021; 17:e1008999. [PMID: 34003867 PMCID: PMC8162642 DOI: 10.1371/journal.pcbi.1008999] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 05/28/2021] [Accepted: 04/23/2021] [Indexed: 12/23/2022] Open
Abstract
The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.
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Affiliation(s)
- Jonathan Liu
- Department of Physics, University of California at Berkeley, Berkeley, California, United States of America
| | - Donald Hansen
- Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany
| | - Elizabeth Eck
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
| | - Yang Joon Kim
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
| | - Meghan Turner
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
| | - Simon Alamos
- Department of Plant and Microbial Biology, University of California at Berkeley, Berkeley, California, United States of America
| | - Hernan G. Garcia
- Department of Physics, University of California at Berkeley, Berkeley, California, United States of America
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, California, United States of America
- Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, California, United States of America
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6
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Berrocal A, Lammers NC, Garcia HG, Eisen MB. Kinetic sculpting of the seven stripes of the Drosophila even-skipped gene. eLife 2020; 9:61635. [PMID: 33300492 PMCID: PMC7864633 DOI: 10.7554/elife.61635] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/09/2020] [Indexed: 12/14/2022] Open
Abstract
We used live imaging to visualize the transcriptional dynamics of the Drosophila melanogaster even-skipped gene at single-cell and high-temporal resolution as its seven stripe expression pattern forms, and developed tools to characterize and visualize how transcriptional bursting varies over time and space. We find that despite being created by the independent activity of five enhancers, even-skipped stripes are sculpted by the same kinetic phenomena: a coupled increase of burst frequency and amplitude. By tracking the position and activity of individual nuclei, we show that stripe movement is driven by the exchange of bursting nuclei from the posterior to anterior stripe flanks. Our work provides a conceptual, theoretical and computational framework for dissecting pattern formation in space and time, and reveals how the coordinated transcriptional activity of individual nuclei shapes complex developmental patterns.
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Affiliation(s)
- Augusto Berrocal
- Department of Molecular & Cell Biology, University of California at Berkeley, Berkeley, United States
| | - Nicholas C Lammers
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, United States
| | - Hernan G Garcia
- Department of Molecular & Cell Biology, University of California at Berkeley, Berkeley, United States.,Biophysics Graduate Group, University of California at Berkeley, Berkeley, United States.,Department of Physics, University of California at Berkeley, Berkeley, United States.,Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, United States
| | - Michael B Eisen
- Department of Molecular & Cell Biology, University of California at Berkeley, Berkeley, United States.,Biophysics Graduate Group, University of California at Berkeley, Berkeley, United States.,Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, United States.,Department of Integrative Biology, University of California at Berkeley, Berkeley, United States.,Howard Hughes Medical Institute, University of California at Berkeley, Berkeley, United States
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7
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Azpeitia E, Wagner A. Short Residence Times of DNA-Bound Transcription Factors Can Reduce Gene Expression Noise and Increase the Transmission of Information in a Gene Regulation System. Front Mol Biosci 2020; 7:67. [PMID: 32411721 PMCID: PMC7198700 DOI: 10.3389/fmolb.2020.00067] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/25/2020] [Indexed: 12/14/2022] Open
Abstract
Gene expression noise is not just ubiquitous but also variable, and we still do not understand some of the most elementary factors that affect it. Among them is the residence time of a transcription factor (TF) on DNA, the mean time that a DNA-bound TF remains bound. Here, we use a stochastic model of transcriptional regulation to study how residence time affects the gene expression noise that arises when a TF induces gene expression. We find that the effect of residence time on gene expression noise depends on the TF’s concentration and its affinity to DNA, which determine the level of induction of the gene. At high levels of induction, residence time has no effect on gene expression noise. However, as the level of induction decreases, short residence times reduce gene expression noise. The reason is that fast on-off TF binding dynamics prevent long periods where proteins are predominantly synthesized or degraded, which can cause excessive fluctuations in gene expression. As a consequence, short residence times can help a gene regulation system acquire information about the cellular environment it operates in. Our predictions are consistent with the observation that experimentally measured residence times are usually modest and lie between seconds to minutes.
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Affiliation(s)
- Eugenio Azpeitia
- Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Centro de Ciencias Matemáticas, UNAM, Morelia, Mexico
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Santa Fe Institute, Santa Fe, NM, United States
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Ali MZ, Choubey S, Das D, Brewster RC. Probing Mechanisms of Transcription Elongation Through Cell-to-Cell Variability of RNA Polymerase. Biophys J 2020; 118:1769-1781. [PMID: 32101716 PMCID: PMC7136280 DOI: 10.1016/j.bpj.2020.02.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 01/31/2020] [Accepted: 02/03/2020] [Indexed: 11/17/2022] Open
Abstract
The process of transcription initiation and elongation are primary points of control in the regulation of gene expression. Although biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifest in vivo at the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNA polymerase (RNAP) molecules engaged in the process of transcribing a gene of interest. In this article, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and interpolymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures, which can further be used to discern between them. Most intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. To demonstrate the value of this framework, we analyze RNAP number distribution data for ribosomal genes in Saccharomyces cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes.
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Affiliation(s)
- Md Zulfikar Ali
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts; Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Sandeep Choubey
- Max Planck institute for the Physics of Complex Systems, Dresden, Germany.
| | - Dipjyoti Das
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Nadia, West Bengal, India
| | - Robert C Brewster
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts; Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts.
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Kumar N, Kulkarni RV. Constraining the complexity of promoter dynamics using fluctuations in gene expression. Phys Biol 2019; 17:015001. [PMID: 31618721 DOI: 10.1088/1478-3975/ab4e57] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Gene expression is an inherently stochastic process with transcription of mRNAs often occurring in bursts: short periods of activity followed by typically longer periods of inactivity. While a simple model involving switching between two promoter states has been widely used to analyze transcription dynamics, recent experimental observations have provided evidence for more complex kinetic schemes underlying bursting. Specifically, experiments provide evidence for complexity in promoter dynamics during the switch from the transcriptionally inactive to the transcriptionally active state. An open question in the field is: what is the minimal complexity needed to model promoter dynamics and how can we determine this? Here, we show that measurements of mRNA fluctuations can be used to set fundamental bounds on the complexity of promoter dynamics. We study models wherein the switching time distribution from transcriptionally inactive to active states is described by a general waiting-time distribution. Using approaches from renewal theory and queueing theory, we derive analytical expressions which connect the Fano factor of mRNA distributions to the waiting-time distribution for promoter switching between inactive and active states. The results derived lead to bounds on the minimal number of promoter states and thus allow us to derive bounds on the minimal complexity of promoter dynamics based on single-cell measurements of mRNA levels.
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Affiliation(s)
- Niraj Kumar
- Department of Physics, University of Massachusetts Boston, Boston, MA 02125, United States of America
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Ali MZ, Choubey S. Decoding the grammar of transcriptional regulation from RNA polymerase measurements: models and their applications. Phys Biol 2019; 16:061001. [PMID: 31603077 DOI: 10.1088/1478-3975/ab45bf] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The genomic revolution has indubitably brought about a paradigm shift in the field of molecular biology, wherein we can sequence, write and re-write genomes. In spite of achieving such feats, we still lack a quantitative understanding of how cells integrate environmental and intra-cellular signals at the promoter and accordingly regulate the production of messenger RNAs. This current state of affairs is being redressed by recent experimental breakthroughs which enable the counting of RNA polymerase molecules (or the corresponding nascent RNAs) engaged in the process of transcribing a gene at the single-cell level. Theorists, in conjunction, have sought to unravel the grammar of transcriptional regulation by harnessing the various statistical properties of these measurements. In this review, we focus on the recent progress in developing falsifiable models of transcription that aim to connect the molecular mechanisms of transcription to single-cell polymerase measurements. We discuss studies where the application of such models to the experimental data have led to novel mechanistic insights into the process of transcriptional regulation. Such interplay between theory and experiments will likely contribute towards the exciting journey of unfurling the governing principles of transcriptional regulation ranging from bacteria to higher organisms.
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Affiliation(s)
- Md Zulfikar Ali
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA, United States of America. Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, United States of America
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