1
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Mayer A, Li J, McLaughlin G, Gladfelter A, Roper M. Mitigating transcription noise via protein sharing in syncytial cells. Biophys J 2024; 123:968-978. [PMID: 38459697 PMCID: PMC11052695 DOI: 10.1016/j.bpj.2024.03.009] [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: 11/14/2023] [Revised: 02/19/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024] Open
Abstract
Bursty transcription allows nuclei to concentrate the work of transcribing mRNA into short, intermittent intervals, potentially reducing transcriptional interference. However, bursts of mRNA production can increase noise in protein abundances. Here, we formulate models for gene expression in syncytia, or multinucleate cells, showing that protein abundance noise may be mitigated locally via spatial averaging of diffuse proteins. Our modeling shows a universal reduction in protein noise, which increases with the average number of nuclei per cell and persists even when the number of nuclei is itself a random variable. Experimental data comparing distributions of a cyclin mRNA that is conserved between brewer's yeast and a closely related filamentous fungus Ashbya gossypii confirm that syncytism is permissive of greater levels of transcriptional noise. Our findings suggest that division of transcriptional labor between nuclei allows syncytia to sidestep tradeoffs between efficiency and precision of gene expression.
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Affiliation(s)
- Alex Mayer
- Department of Mathematics, UCLA, Los Angeles, California.
| | - Jiayu Li
- Department of Mathematics, UCLA, Los Angeles, California
| | - Grace McLaughlin
- Department of Biology, Duke University, Durham, North Carolina; Department of Biology, UNC, Chapel Hill, North Carolina
| | - Amy Gladfelter
- Department of Biology, Duke University, Durham, North Carolina
| | - Marcus Roper
- Department of Mathematics, UCLA, Los Angeles, California; Department of Computational Medicine, UCLA, Los Angeles, California
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2
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Gorin G, Yoshida S, Pachter L. Assessing Markovian and Delay Models for Single-Nucleus RNA Sequencing. Bull Math Biol 2023; 85:114. [PMID: 37828255 DOI: 10.1007/s11538-023-01213-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 09/11/2023] [Indexed: 10/14/2023]
Abstract
The serial nature of reactions involved in the RNA life-cycle motivates the incorporation of delays in models of transcriptional dynamics. The models couple a transcriptional process to a fairly general set of delayed monomolecular reactions with no feedback. We provide numerical strategies for calculating the RNA copy number distributions induced by these models, and solve several systems with splicing, degradation, and catalysis. An analysis of single-cell and single-nucleus RNA sequencing data using these models reveals that the kinetics of nuclear export do not appear to require invocation of a non-Markovian waiting time.
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Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Shawn Yoshida
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA.
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3
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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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4
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Weidemann DE, Holehouse J, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. SCIENCE ADVANCES 2023; 9:eadh5138. [PMID: 37556551 PMCID: PMC10411910 DOI: 10.1126/sciadv.adh5138] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/07/2023] [Indexed: 08/11/2023]
Abstract
Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, noise cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit is typically assumed to be Poissonian noise, wherein the variance in mRNA numbers is equal to their mean. Here, we demonstrate that several cell division genes in fission yeast exhibit mRNA variances significantly below this limit. The reduced variance can be explained by a gene expression model incorporating multiple transcription and mRNA degradation steps. Notably, in this sub-Poissonian regime, distinct from Poissonian or super-Poissonian regimes, cytoplasmic noise is effectively suppressed through a higher mRNA export rate. Our findings redefine the lower limit of eukaryotic gene expression noise and uncover molecular requirements for achieving ultralow noise, which is expected to be important for vital cellular functions.
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Affiliation(s)
- Douglas E. Weidemann
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
| | - James Holehouse
- The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87510, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK
| | - Silke Hauf
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
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5
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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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6
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Fralix B, Holmes M, Löpker A. A Markovian arrival stream approach to stochastic gene expression in cells. J Math Biol 2023; 86:79. [PMID: 37086292 DOI: 10.1007/s00285-023-01913-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 12/22/2022] [Accepted: 03/31/2023] [Indexed: 04/23/2023]
Abstract
We analyse a generalisation of the stochastic gene expression model studied recently in Fromion et al. (SIAM J Appl Math 73:195-211, 2013) and Robert (Probab Surv 16:277-332, 2019) that keeps track of the production of both mRNA and protein molecules, using techniques from the theory of point processes, as well as ideas from the theory of matrix-analytic methods. Here, both the activity of a gene and the creation of mRNA are modelled with an arbitrary Markovian Arrival Process governed by finitely many phases, and each mRNA molecule during its lifetime gives rise to protein molecules in accordance with a Poisson process. This modification is important, as Markovian Arrival Processes can be used to approximate many types of point processes on the nonnegative real line, meaning this framework allows us to further relax our assumptions on the overall process of transcription.
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Affiliation(s)
- Brian Fralix
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, USA.
| | - Mark Holmes
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Andreas Löpker
- Department of Computer Science and Mathematics, HTW Dresden, University of Applied Sciences, Dresden, Germany
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7
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Weidemann DE, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531283. [PMID: 36945401 PMCID: PMC10028819 DOI: 10.1101/2023.03.06.531283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Stochastic variation in gene products ("noise") is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.
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Affiliation(s)
- Douglas E Weidemann
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, Scotland, UK
| | - Silke Hauf
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
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8
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Li X, Chou T. Stochastic dynamics and ribosome-RNAP interactions in transcription-translation coupling. Biophys J 2023; 122:254-266. [PMID: 36199250 PMCID: PMC9822797 DOI: 10.1016/j.bpj.2022.09.041] [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: 06/23/2022] [Revised: 09/22/2022] [Accepted: 09/27/2022] [Indexed: 01/11/2023] Open
Abstract
Under certain cellular conditions, transcription and mRNA translation in prokaryotes appear to be "coupled," in which the formation of mRNA transcript and production of its associated protein are temporally correlated. Such transcription-translation coupling (TTC) has been evoked as a mechanism that speeds up the overall process, provides protection against premature termination, and/or regulates the timing of transcript and protein formation. What molecular mechanisms underlie ribosome-RNAP coupling and how they can perform these functions have not been explicitly modeled. We develop and analyze a continuous-time stochastic model that incorporates ribosome and RNAP elongation rates, initiation and termination rates, RNAP pausing, and direct ribosome and RNAP interactions (exclusion and binding). Our model predicts how distributions of delay times depend on these molecular features of transcription and translation. We also propose additional measures for TTC: a direct ribosome-RNAP binding probability and the fraction of time the translation-transcription process is "protected" from attack by transcription-terminating proteins. These metrics quantify different aspects of TTC and differentially depend on parameters of known molecular processes. We use our metrics to reveal how and when our model can exhibit either acceleration or deceleration of transcription, as well as protection from termination. Our detailed mechanistic model provides a basis for designing new experimental assays that can better elucidate the mechanisms of TTC.
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Affiliation(s)
- Xiangting Li
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, California
| | - Tom Chou
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, California; Department of Mathematics, University of California, Los Angeles, Los Angeles, California.
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9
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Deng Q, Chen A, Qiu H, Zhou T. Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:8426-8451. [PMID: 35801472 DOI: 10.3934/mbe.2022392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena. In order to solve this model, we introduce an effective transition rate for each reaction. These effective transition rates, which explicitly decode the effect of molecular memory, can transform the original non-Markov issue into an equivalent Markov one. Based on this technique, we derive analytical results, showing that molecular memory can significantly affect the nuclear and cytoplasmic mRNA mean and noise. In addition to the results providing insights into the role of molecular memory in gene expression, our modeling and analysis provide a paradigm for studying more complex stochastic transcription processes.
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Affiliation(s)
- Qiqi Deng
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Aimin Chen
- School of Mathematics and Statistics, Henan University, Kaifeng 475004, China
| | - Huahai Qiu
- School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
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10
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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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11
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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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12
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Jiao F, Lin G, Yu J. Approximating gene transcription dynamics using steady-state formulas. Phys Rev E 2021; 104:014401. [PMID: 34412315 DOI: 10.1103/physreve.104.014401] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/02/2021] [Indexed: 01/01/2023]
Abstract
Understanding how genes in a single cell respond to dynamically changing signals has been a central question in stochastic gene transcription research. Recent studies have generated massive steady-state or snapshot mRNA distribution data of individual cells, and inferred a large spectrum of kinetic transcription parameters under varying conditions. However, there have been few algorithms to convert these static data into the temporal variation of kinetic rates. Real-time imaging has been developed to monitor stochastic transcription processes at the single-cell level, but the immense technicality has prevented its application to most endogenous loci in mammalian cells. In this article, we introduced a stochastic gene transcription model with variable kinetic rates induced by unstable cellular conditions. We approximated the transcription dynamics using easily obtained steady-state formulas in the model. We tested the approximation against experimental data in both prokaryotic and eukaryotic cells and further solidified the conditions that guarantee the robustness of the method. The method can be easily implemented to provide convenient tools for quantifying dynamic kinetics and mechanisms underlying the widespread static transcription data, and may shed a light on circumventing the limitation of current bursting data on transcriptional real-time imaging.
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Affiliation(s)
- Feng Jiao
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, People's Republic of China.,College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, People's Republic of China
| | - Genghong Lin
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, People's Republic of China.,College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, People's Republic of China
| | - Jianshe Yu
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, People's Republic of China
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13
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Jiang Q, Fu X, Yan S, Li R, Du W, Cao Z, Qian F, Grima R. Neural network aided approximation and parameter inference of non-Markovian models of gene expression. Nat Commun 2021; 12:2618. [PMID: 33976195 PMCID: PMC8113478 DOI: 10.1038/s41467-021-22919-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 04/07/2021] [Indexed: 02/03/2023] Open
Abstract
Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system's history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.
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Affiliation(s)
- Qingchao Jiang
- grid.28056.390000 0001 2163 4895Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xiaoming Fu
- grid.28056.390000 0001 2163 4895Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China ,grid.4305.20000 0004 1936 7988School of Biological Sciences, The University of Edinburgh, Edinburgh, Scotland UK
| | - Shifu Yan
- grid.28056.390000 0001 2163 4895Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Runlai Li
- grid.4280.e0000 0001 2180 6431Department of Chemistry, National University of Singapore, Singapore, Singapore
| | - Wenli Du
- grid.28056.390000 0001 2163 4895Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Zhixing Cao
- grid.28056.390000 0001 2163 4895Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China ,grid.28056.390000 0001 2163 4895State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
| | - Feng Qian
- grid.28056.390000 0001 2163 4895Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Ramon Grima
- grid.4305.20000 0004 1936 7988School of Biological Sciences, The University of Edinburgh, Edinburgh, Scotland UK
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14
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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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15
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Filatova T, Popovic N, Grima R. Statistics of Nascent and Mature RNA Fluctuations in a Stochastic Model of Transcriptional Initiation, Elongation, Pausing, and Termination. Bull Math Biol 2020; 83:3. [PMID: 33351158 PMCID: PMC7755674 DOI: 10.1007/s11538-020-00827-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/26/2020] [Indexed: 12/20/2022]
Abstract
Recent advances in fluorescence microscopy have made it possible to measure the fluctuations of nascent (actively transcribed) RNA. These closely reflect transcription kinetics, as opposed to conventional measurements of mature (cellular) RNA, whose kinetics is affected by additional processes downstream of transcription. Here, we formulate a stochastic model which describes promoter switching, initiation, elongation, premature detachment, pausing, and termination while being analytically tractable. We derive exact closed-form expressions for the mean and variance of nascent RNA fluctuations on gene segments, as well as of total nascent RNA on a gene. We also obtain exact expressions for the first two moments of mature RNA fluctuations and approximate distributions for total numbers of nascent and mature RNA. Our results, which are verified by stochastic simulation, uncover the explicit dependence of the statistics of both types of RNA on transcriptional parameters and potentially provide a means to estimate parameter values from experimental data.
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Affiliation(s)
- Tatiana Filatova
- School of Biological Sciences, The University of Edinburgh, Edinburgh, UK.,School of Mathematics and Maxwell Institute for Mathematical Sciences, The University of Edinburgh, Edinburgh, UK
| | - Nikola Popovic
- School of Mathematics and Maxwell Institute for Mathematical Sciences, The University of Edinburgh, Edinburgh, UK
| | - Ramon Grima
- School of Biological Sciences, The University of Edinburgh, Edinburgh, UK.
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16
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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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17
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Gene Transcription as a Limiting Factor in Protein Production and Cell Growth. G3-GENES GENOMES GENETICS 2020; 10:3229-3242. [PMID: 32694199 PMCID: PMC7466996 DOI: 10.1534/g3.120.401303] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Cell growth is driven by the synthesis of proteins, genes, and other cellular components. Defining processes that limit biosynthesis rates is fundamental for understanding the determinants of cell physiology. Here, we analyze the consequences of engineering cells to express extremely high levels of mCherry proteins, as a tool to define limiting processes that fail to adapt upon increasing biosynthetic demands. Protein-burdened cells were transcriptionally and phenotypically similar to mutants of the Mediator, a transcription coactivator complex. However, our binding data suggest that the Mediator was not depleted from endogenous promoters. Burdened cells showed an overall increase in the abundance of the majority of endogenous transcripts, except for highly expressed genes. Our results, supported by mathematical modeling, suggest that wild-type cells transcribe highly expressed genes at the maximal possible rate, as defined by the transcription machinery’s physical properties. We discuss the possible cellular benefit of maximal transcription rates to allow a coordinated optimization of cell size and cell growth.
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18
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Cao Z, Filatova T, Oyarzún DA, Grima R. A Stochastic Model of Gene Expression with Polymerase Recruitment and Pause Release. Biophys J 2020; 119:1002-1014. [PMID: 32814062 DOI: 10.1016/j.bpj.2020.07.020] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 02/27/2020] [Accepted: 07/23/2020] [Indexed: 12/14/2022] Open
Abstract
Transcriptional bursting is a major source of noise in gene expression. The telegraph model of gene expression, whereby transcription switches between on and off states, is the dominant model for bursting. Recently, it was shown that the telegraph model cannot explain a number of experimental observations from perturbation data. Here, we study an alternative model that is consistent with the data and which explicitly describes RNA polymerase recruitment and polymerase pause release, two steps necessary for messenger RNA (mRNA) production. We derive the exact steady-state distribution of mRNA numbers and an approximate steady-state distribution of protein numbers, which are given by generalized hypergeometric functions. The theory is used to calculate the relative sensitivity of the coefficient of variation of mRNA fluctuations for thousands of genes in mouse fibroblasts. This indicates that the size of fluctuations is mostly sensitive to the rate of burst initiation and the mRNA degradation rate. Furthermore, we show that 1) the time-dependent distribution of mRNA numbers is accurately approximated by a modified telegraph model with a Michaelis-Menten like dependence of the effective transcription rate on RNA polymerase abundance, and 2) the model predicts that if the polymerase recruitment rate is comparable or less than the pause release rate, then upon gene replication, the mean number of RNA per cell remains approximately constant. This gene dosage compensation property has been experimentally observed and cannot be explained by the telegraph model with constant rates.
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Affiliation(s)
- Zhixing Cao
- The Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China; School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom; Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Tatiana Filatova
- School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom; School of Mathematics, the University of Edinburgh, Edinburgh, United Kingdom
| | - Diego A Oyarzún
- School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom; School of Informatics, the University of Edinburgh, Edinburgh, United Kingdom
| | - Ramon Grima
- School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom.
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19
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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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20
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Foreman R, Wollman R. Mammalian gene expression variability is explained by underlying cell state. Mol Syst Biol 2020; 16:e9146. [PMID: 32043799 PMCID: PMC7011657 DOI: 10.15252/msb.20199146] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 12/04/2019] [Accepted: 01/07/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.
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Affiliation(s)
- Robert Foreman
- Institute for Quantitative and Computational BiosciencesUniversity of California, Los AngelesLos AngelesCAUSA
- Program in Bioinformatics and Systems BiologyUniversity of California, San DiegoSan DiegoCAUSA
| | - Roy Wollman
- Institute for Quantitative and Computational BiosciencesUniversity of California, Los AngelesLos AngelesCAUSA
- Program in Bioinformatics and Systems BiologyUniversity of California, San DiegoSan DiegoCAUSA
- Department of Integrative Biology and PhysiologyDepartment of Chemistry and BiochemistryUniversity of California, Los AngelesLos AngelesCAUSA
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21
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Lammers NC, Galstyan V, Reimer A, Medin SA, Wiggins CH, Garcia HG. Multimodal transcriptional control of pattern formation in embryonic development. Proc Natl Acad Sci U S A 2020; 117:836-847. [PMID: 31882445 PMCID: PMC6969519 DOI: 10.1073/pnas.1912500117] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.
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Affiliation(s)
| | - Vahe Galstyan
- Biochemistry and Molecular Biophysics Option, California Institute of Technology, Pasadena, CA 91126
- Department of Physics, Columbia University, New York, NY 10027
| | - Armando Reimer
- Biophysics Graduate Group, University of California, Berkeley, CA 94720
| | - Sean A Medin
- Department of Physics, University of California, Berkeley, CA 94720
| | - Chris H Wiggins
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027;
- Data Science Institute, Columbia University, New York, NY 10027
- Department of Systems Biology, Columbia University, New York, NY 10027
- Department of Statistics, Columbia University, New York, NY 10027
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California, Berkeley, CA 94720;
- Department of Physics, University of California, Berkeley, CA 94720
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, CA 94720
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22
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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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23
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Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality. Nat Microbiol 2019; 4:2118-2127. [PMID: 31527794 PMCID: PMC6879826 DOI: 10.1038/s41564-019-0553-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/02/2019] [Indexed: 12/26/2022]
Abstract
Single-cell measurements of mRNA copy-number inform our understanding of stochastic gene expression [1-3], but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation stochastically take plasce [4, 5]. Here we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. Interpreted using a theoretical model for mRNA dynamics, the single-cell data allows us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity, and instead highlight the contribution of previously hidden variables to the observed population heterogeneity.
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24
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Falo-Sanjuan J, Lammers NC, Garcia HG, Bray SJ. Enhancer Priming Enables Fast and Sustained Transcriptional Responses to Notch Signaling. Dev Cell 2019; 50:411-425.e8. [PMID: 31378591 PMCID: PMC6706658 DOI: 10.1016/j.devcel.2019.07.002] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 05/23/2019] [Accepted: 07/01/2019] [Indexed: 11/23/2022]
Abstract
Information from developmental signaling pathways must be accurately decoded to generate transcriptional outcomes. In the case of Notch, the intracellular domain (NICD) transduces the signal directly to the nucleus. How enhancers decipher NICD in the real time of developmental decisions is not known. Using the MS2-MCP system to visualize nascent transcripts in single cells in Drosophila embryos, we reveal how two target enhancers read Notch activity to produce synchronized and sustained profiles of transcription. By manipulating the levels of NICD and altering specific motifs within the enhancers, we uncover two key principles. First, increased NICD levels alter transcription by increasing duration rather than frequency of transcriptional bursts. Second, priming of enhancers by tissue-specific transcription factors is required for NICD to confer synchronized and sustained activity; in their absence, transcription is stochastic and bursty. The dynamic response of an individual enhancer to NICD thus differs depending on the cellular context.
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Affiliation(s)
- Julia Falo-Sanjuan
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | | | - Hernan G Garcia
- Biophysics Graduate Group, UC Berkeley, Berkeley, CA 94720, USA; Department of Physics, UC Berkeley, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, UC Berkeley, Berkeley, CA 94720, USA; Institute for Quantitative Biosciences-QB3, UC Berkeley, Berkeley, CA 94720, USA
| | - Sarah J Bray
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK.
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25
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Choubey S, Kondev J, Sanchez A. Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells. Biophys J 2019; 114:2072-2082. [PMID: 29742401 DOI: 10.1016/j.bpj.2018.03.031] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 03/18/2018] [Accepted: 03/29/2018] [Indexed: 11/25/2022] Open
Abstract
Transcription is the dominant point of control of gene expression. Biochemical studies have revealed key molecular components of transcription and their interactions, but the dynamics of transcription initiation in cells is still poorly understood. This state of affairs is being remedied with experiments that observe transcriptional dynamics in single cells using fluorescent reporters. Quantitative information about transcription initiation dynamics can also be extracted from experiments that use electron micrographs of RNA polymerases caught in the act of transcribing a gene (Miller spreads). Inspired by these data, we analyze a general stochastic model of transcription initiation and elongation and compute the distribution of transcription initiation times. We show that different mechanisms of initiation leave distinct signatures in the distribution of initiation times that can be compared to experiments. We analyze published data from micrographs of RNA polymerases transcribing ribosomal RNA genes in Escherichia coli and compare the observed distributions of interpolymerase distances with the predictions from previously hypothesized mechanisms for the regulation of these genes. Our analysis demonstrates the potential of measuring the distribution of time intervals between initiation events as a probe for dissecting mechanisms of transcription initiation in live cells.
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Affiliation(s)
- Sandeep Choubey
- Department of Physics, Brandeis University, Waltham, Massachusetts
| | - Jane Kondev
- Department of Physics, Brandeis University, Waltham, Massachusetts
| | - Alvaro Sanchez
- Rowland Institute at Harvard, Harvard University, Cambridge, Massachusetts; Department of Ecology and Evolutionary Biology, Microbial Sciences Institute, Yale University, New Haven, Connecticut.
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26
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Kim S, Jacobs-Wagner C. Effects of mRNA Degradation and Site-Specific Transcriptional Pausing on Protein Expression Noise. Biophys J 2019; 114:1718-1729. [PMID: 29642040 DOI: 10.1016/j.bpj.2018.02.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 01/30/2018] [Accepted: 02/07/2018] [Indexed: 12/20/2022] Open
Abstract
Genetically identical cells exhibit diverse phenotypes even when experiencing the same environment. This phenomenon in part originates from cell-to-cell variability (noise) in protein expression. Although various kinetic schemes of stochastic transcription initiation are known to affect gene expression noise, how posttranscription initiation events contribute to noise at the protein level remains incompletely understood. To address this question, we developed a stochastic simulation-based model of bacterial gene expression that integrates well-known dependencies between transcription initiation, transcription elongation dynamics, mRNA degradation, and translation. We identified realistic conditions under which mRNA lifetime and transcriptional pauses modulate the protein expression noise initially introduced by the promoter architecture. For instance, we found that the short lifetime of bacterial mRNAs facilitates the production of protein bursts. Conversely, RNA polymerase (RNAP) pausing at specific sites during transcription elongation can attenuate protein bursts by fluidizing the RNAP traffic to the point of erasing the effect of a bursty promoter. Pause-prone sites, if located close to the promoter, can also affect noise indirectly by reducing both transcription and translation initiation due to RNAP and ribosome congestion. Our findings highlight how the interplay between transcription initiation, transcription elongation, translation, and mRNA degradation shapes the distribution in protein numbers. They also have implications for our understanding of gene evolution and suggest combinatorial strategies for modulating phenotypic variability by genetic engineering.
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Affiliation(s)
- Sangjin Kim
- Microbial Sciences Institute, West Haven, Connecticut; Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut; Howard Hughes Medical Institute, New Haven, Connecticut
| | - Christine Jacobs-Wagner
- Microbial Sciences Institute, West Haven, Connecticut; Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut; Howard Hughes Medical Institute, New Haven, Connecticut; Department of Microbial Pathogenesis, Yale School of Medicine, Yale University, New Haven, Connecticut.
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27
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Wang Y, Ni T, Wang W, Liu F. Gene transcription in bursting: a unified mode for realizing accuracy and stochasticity. Biol Rev Camb Philos Soc 2019; 94:248-258. [PMID: 30024089 PMCID: PMC7379551 DOI: 10.1111/brv.12452] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 06/13/2018] [Accepted: 06/27/2018] [Indexed: 01/24/2023]
Abstract
There is accumulating evidence that, from bacteria to mammalian cells, messenger RNAs (mRNAs) are produced in intermittent bursts - a much 'noisier' process than traditionally thought. Based on quantitative measurements at individual promoters, diverse phenomenological models have been proposed for transcriptional bursting. Nevertheless, the underlying molecular mechanisms and significance for cellular signalling remain elusive. Here, we review recent progress, address the above issues and illuminate our viewpoints with simulation results. Despite being widely used in modelling and in interpreting experimental data, the traditional two-state model is far from adequate to describe or infer the molecular basis and stochastic principles of transcription. In bacteria, DNA supercoiling contributes to the bursting of those genes that express at high levels and are topologically constrained in short loops; moreover, low-affinity cis-regulatory elements and unstable protein complexes can play a key role in transcriptional regulation. Integrating data on the architecture, kinetics, and transcriptional input-output function is a promising approach to uncovering the underlying dynamic mechanism. For eukaryotes, distinct bursting features described by the multi-scale and continuum models coincide with those predicted by four theoretically derived principles that govern how the transcription apparatus operates dynamically. This consistency suggests a unified framework for comprehending bursting dynamics at the level of the structural and kinetic basis of transcription. Moreover, the existing models can be unified by a generic model. Remarkably, transcriptional bursting enables regulatory information to be transmitted in a digital manner, with the burst frequency representing the strength of regulatory signals. Such a mode guarantees high fidelity for precise transcriptional regulation and also provides sufficient randomness for realizing cellular heterogeneity.
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Affiliation(s)
- Yaolai Wang
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
- School of ScienceJiangnan UniversityWuxi214122China
| | - Tengfei Ni
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
| | - Wei Wang
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
| | - Feng Liu
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
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28
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Thompson VF, Victor RA, Morera AA, Moinpour M, Liu MN, Kisiel CC, Pickrel K, Springhower CE, Schwartz JC. Transcription-Dependent Formation of Nuclear Granules Containing FUS and RNA Pol II. Biochemistry 2018; 57:7021-7032. [PMID: 30488693 DOI: 10.1021/acs.biochem.8b01097] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Purified recombinant FUsed in Sarcoma (FUS) assembles into an oligomeric state in an RNA-dependent manner to form large condensates. FUS condensates bind and concentrate the C-terminal domain of RNA polymerase II (RNA Pol II). We asked whether a granule in cells contained FUS and RNA Pol II as suggested by the binding of FUS condensates to the polymerase. We developed cross-linking protocols to recover protein particles containing FUS from cells and separated them by size exclusion chromatography. We found a significant fraction of RNA Pol II in large granules containing FUS with diameters of >50 nm or twice that of the RNA Pol II holoenzyme. Inhibition of transcription prevented the polymerase from associating with the granules. Altogether, we found physical evidence of granules containing FUS and RNA Pol II in cells that possess properties comparable to those of in vitro FUS condensates.
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29
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Hansen MMK, Desai RV, Simpson ML, Weinberger LS. Cytoplasmic Amplification of Transcriptional Noise Generates Substantial Cell-to-Cell Variability. Cell Syst 2018; 7:384-397.e6. [PMID: 30243562 PMCID: PMC6202163 DOI: 10.1016/j.cels.2018.08.002] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 06/14/2018] [Accepted: 08/02/2018] [Indexed: 12/15/2022]
Abstract
Transcription is an episodic process characterized by probabilistic bursts, but how the transcriptional noise from these bursts is modulated by cellular physiology remains unclear. Using simulations and single-molecule RNA counting, we examined how cellular processes influence cell-to-cell variability (noise). The results show that RNA noise is higher in the cytoplasm than the nucleus in ∼85% of genes across diverse promoters, genomic loci, and cell types (human and mouse). Measurements show further amplification of RNA noise in the cytoplasm, fitting a model of biphasic mRNA conversion between translation- and degradation-competent states. This multi-state translation-degradation of mRNA also causes substantial noise amplification in protein levels, ultimately accounting for ∼74% of intrinsic protein variability in cell populations. Overall, the results demonstrate how noise from transcriptional bursts is intrinsically amplified by mRNA processing, leading to a large super-Poissonian variability in protein levels.
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Affiliation(s)
- Maike M K Hansen
- Gladstone|UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Ravi V Desai
- Gladstone|UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Michael L Simpson
- Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Leor S Weinberger
- Gladstone|UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA.
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30
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Zoller B, Little SC, Gregor T. Diverse Spatial Expression Patterns Emerge from Unified Kinetics of Transcriptional Bursting. Cell 2018; 175:835-847.e25. [PMID: 30340044 PMCID: PMC6779125 DOI: 10.1016/j.cell.2018.09.056] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 07/02/2018] [Accepted: 09/26/2018] [Indexed: 01/14/2023]
Abstract
How transcriptional bursting relates to gene regulation is a central question that has persisted for more than a decade. Here, we measure nascent transcriptional activity in early Drosophila embryos and characterize the variability in absolute activity levels across expression boundaries. We demonstrate that boundary formation follows a common transcription principle: a single control parameter determines the distribution of transcriptional activity, regardless of gene identity, boundary position, or enhancer-promoter architecture. We infer the underlying bursting kinetics and identify the key regulatory parameter as the fraction of time a gene is in a transcriptionally active state. Unexpectedly, both the rate of polymerase initiation and the switching rates are tightly constrained across all expression levels, predicting synchronous patterning outcomes at all positions in the embryo. These results point to a shared simplicity underlying the apparently complex transcriptional processes of early embryonic patterning and indicate a path to general rules in transcriptional regulation.
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Affiliation(s)
- Benjamin Zoller
- Joseph Henry Laboratories of Physics and the Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Shawn C Little
- Department of Molecular Biology and Howard Hughes Medical Institute, Princeton University, Princeton, NJ 08544, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Thomas Gregor
- Joseph Henry Laboratories of Physics and the Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France.
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31
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Choubey S. Nascent RNA kinetics: Transient and steady state behavior of models of transcription. Phys Rev E 2018; 97:022402. [PMID: 29548128 DOI: 10.1103/physreve.97.022402] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2017] [Indexed: 11/07/2022]
Abstract
Regulation of transcription is a vital process in cells, but mechanistic details of this regulation still remain elusive. The dominant approach to unravel the dynamics of transcriptional regulation is to first develop mathematical models of transcription and then experimentally test the predictions these models make for the distribution of mRNA and protein molecules at the individual cell level. However, these measurements are affected by a multitude of downstream processes which make it difficult to interpret the measurements. Recent experimental advancements allow for counting the nascent mRNA number of a gene as a function of time at the single-cell level. These measurements closely reflect the dynamics of transcription. In this paper, we consider a general mechanism of transcription with stochastic initiation and deterministic elongation and probe its impact on the temporal behavior of nascent RNA levels. Using techniques from queueing theory, we derive exact analytical expressions for the mean and variance of the nascent RNA distribution as functions of time. We apply these analytical results to obtain the mean and variance of nascent RNA distribution for specific models of transcription. These models of initiation exhibit qualitatively distinct transient behaviors for both the mean and variance which further allows us to discriminate between them. Stochastic simulations confirm these results. Overall the analytical results presented here provide the necessary tools to connect mechanisms of transcription initiation to single-cell measurements of nascent RNA.
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Affiliation(s)
- Sandeep Choubey
- FAS Center for Systems Biology and Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA
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Brown T, Howe FS, Murray SC, Wouters M, Lorenz P, Seward E, Rata S, Angel A, Mellor J. Antisense transcription-dependent chromatin signature modulates sense transcript dynamics. Mol Syst Biol 2018; 14:e8007. [PMID: 29440389 PMCID: PMC5810148 DOI: 10.15252/msb.20178007] [Citation(s) in RCA: 27] [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: 09/26/2017] [Revised: 01/13/2018] [Accepted: 01/16/2018] [Indexed: 12/22/2022] Open
Abstract
Antisense transcription is widespread in genomes. Despite large differences in gene size and architecture, we find that yeast and human genes share a unique, antisense transcription-associated chromatin signature. We asked whether this signature is related to a biological function for antisense transcription. Using quantitative RNA-FISH, we observed changes in sense transcript distributions in nuclei and cytoplasm as antisense transcript levels were altered. To determine the mechanistic differences underlying these distributions, we developed a mathematical framework describing transcription from initiation to transcript degradation. At GAL1, high levels of antisense transcription alter sense transcription dynamics, reducing rates of transcript production and processing, while increasing transcript stability. This relationship with transcript stability is also observed as a genome-wide association. Establishing the antisense transcription-associated chromatin signature through disruption of the Set3C histone deacetylase activity is sufficient to similarly change these rates even in the absence of antisense transcription. Thus, antisense transcription alters sense transcription dynamics in a chromatin-dependent manner.
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Affiliation(s)
- Thomas Brown
- Department of Biochemistry, University of Oxford, Oxford, UK
| | | | - Struan C Murray
- Department of Biochemistry, University of Oxford, Oxford, UK
| | | | - Philipp Lorenz
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Emily Seward
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Scott Rata
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Andrew Angel
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Jane Mellor
- Department of Biochemistry, University of Oxford, Oxford, UK
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33
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Bentovim L, Harden TT, DePace AH. Transcriptional precision and accuracy in development: from measurements to models and mechanisms. Development 2017; 144:3855-3866. [PMID: 29089359 PMCID: PMC5702068 DOI: 10.1242/dev.146563] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
During development, genes are transcribed at specific times, locations and levels. In recent years, the emergence of quantitative tools has significantly advanced our ability to measure transcription with high spatiotemporal resolution in vivo. Here, we highlight recent studies that have used these tools to characterize transcription during development, and discuss the mechanisms that contribute to the precision and accuracy of the timing, location and level of transcription. We attempt to disentangle the discrepancies in how physicists and biologists use the term ‘precision' to facilitate interactions using a common language. We also highlight selected examples in which the coupling of mathematical modeling with experimental approaches has provided important mechanistic insights, and call for a more expansive use of mathematical modeling to exploit the wealth of quantitative data and advance our understanding of animal transcription. Summary: This Review highlights how high-resolution quantitative tools and theoretical models have formed our current view of the mechanisms determining precision and accuracy in the timing, location and level of transcription in the Drosophila embryo.
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Affiliation(s)
- Lital Bentovim
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Timothy T Harden
- 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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34
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Holloway DM, Spirov AV. Transcriptional bursting in Drosophila development: Stochastic dynamics of eve stripe 2 expression. PLoS One 2017; 12:e0176228. [PMID: 28437444 PMCID: PMC5402966 DOI: 10.1371/journal.pone.0176228] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 04/08/2017] [Indexed: 01/17/2023] Open
Abstract
Anterior-posterior (AP) body segmentation of the fruit fly (Drosophila) is first seen in the 7-stripe spatial expression patterns of the pair-rule genes, which regulate downstream genes determining specific segment identities. Regulation of pair-rule expression has been extensively studied for the even-skipped (eve) gene. Recent live imaging, of a reporter for the 2ndeve stripe, has demonstrated the stochastic nature of this process, with ‘bursts’ in the number of RNA transcripts being made over time. We developed a stochastic model of the spatial and temporal expression of eve stripe 2 (binding by transcriptional activators (Bicoid and Hunchback proteins) and repressors (Giant and Krüppel proteins), transcriptional initiation and termination; with all rate parameters constrained by features of the experimental data) in order to analyze the noisy experimental time series and test hypotheses for how eve transcription is regulated. These include whether eve transcription is simply OFF or ON, with a single ON rate, or whether it proceeds by a more complex mechanism, with multiple ON rates. We find that both mechanisms can produce long (multi-minute) RNA bursts, but that the short-time (minute-to-minute) statistics of the data is indicative of eve being transcribed with at least two distinct ON rates, consistent with data on the joint activation of eve by Bicoid and Hunchback. We also predict distinct statistical signatures for cases in which eve is repressed (e.g. along the edges of the stripe) vs. cases in which activation is reduced (e.g. by mutagenesis of transcription factor binding sites). Fundamental developmental processes such as gene transcription are intrinsically noisy; our approach presents a new way to quantify and analyze time series data during developmental patterning in order to understand regulatory mechanisms and how they propagate noise and impact embryonic robustness.
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Affiliation(s)
- David M. Holloway
- Mathematics Department, British Columbia Institute of Technology, Burnaby, B.C., Canada
- Biology Department, University of Victoria, Victoria, B.C., Canada
- * E-mail:
| | - Alexander V. Spirov
- Computer Science, and Center of Excellence in Wireless and Information Technology, State University of New York, Stony Brook, New York, United States of America
- Sechenov Institute of Evolutionary Physiology and Biochemistry, St. Petersburg, Russia
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35
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Das D, Dey S, Brewster RC, Choubey S. Effect of transcription factor resource sharing on gene expression noise. PLoS Comput Biol 2017; 13:e1005491. [PMID: 28414750 PMCID: PMC5411101 DOI: 10.1371/journal.pcbi.1005491] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 05/01/2017] [Accepted: 03/31/2017] [Indexed: 12/31/2022] Open
Abstract
Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it’s TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression. Genetically identical cells, even when they are exposed to the same environmental conditions, display incredible diversity. Gene expression noise is attributed to be a key source of this phenotypic diversity. Transcriptional dynamics is a dominant source of expression noise. Although scores of theoretical and experimental studies have explored how noise is regulated at the level of transcription, most of them focus on the gene specific, cis regulatory elements, such as the number of transcription factor (TF) binding sites, their binding strength, etc. However, how the global properties of transcription, such as the limited availability of TFs impact noise in gene expression remains rather elusive. Here we build a theoretical model that incorporates the effect of limiting TF pool on gene expression noise. We find that competition between genes for TFs leads to enhanced variability in mRNA copy number across an isogenic population. Moreover, for gene copies sharing TFs with other competitor sites, mRNA variance as a function of the mean shows distinct imprints for one gene copy and multiple gene copies respectively. This stands in sharp contrast to the universal behavior found in mean expression irrespective of the different scenarios of competition. An interesting feature of competition is that introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution, which could lead to phenotypic variability.
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Affiliation(s)
- Dipjyoti Das
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
| | - Supravat Dey
- Laboratoire Charles Coulomb, Université de Montpellier and CNRS, Montpellier, France
| | - Robert C. Brewster
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- * E-mail: (RCB); (SC)
| | - Sandeep Choubey
- FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, United States of America
- * E-mail: (RCB); (SC)
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36
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Scholes C, DePace AH, Sánchez Á. Combinatorial Gene Regulation through Kinetic Control of the Transcription Cycle. Cell Syst 2016; 4:97-108.e9. [PMID: 28041762 DOI: 10.1016/j.cels.2016.11.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 08/09/2016] [Accepted: 11/23/2016] [Indexed: 11/20/2022]
Abstract
Cells decide when, where, and to what level to express their genes by "computing" information from transcription factors (TFs) binding to regulatory DNA. How is the information contained in multiple TF-binding sites integrated to dictate the rate of transcription? The dominant conceptual and quantitative model is that TFs combinatorially recruit one another and RNA polymerase to the promoter by direct physical interactions. Here, we develop a quantitative framework to explore kinetic control, an alternative model in which combinatorial gene regulation can result from TFs working on different kinetic steps of the transcription cycle. Kinetic control can generate a wide range of analog and Boolean computations without requiring the input TFs to be simultaneously bound to regulatory DNA. We propose experiments that will illuminate the role of kinetic control in transcription and discuss implications for deciphering the cis-regulatory "code."
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Affiliation(s)
- Clarissa Scholes
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
| | - Álvaro Sánchez
- The Rowland Institute at Harvard, Harvard University, Cambridge, MA 02142, USA.
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37
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Xu H, Skinner SO, Sokac AM, Golding I. Stochastic Kinetics of Nascent RNA. PHYSICAL REVIEW LETTERS 2016; 117:128101. [PMID: 27667861 PMCID: PMC5033037 DOI: 10.1103/physrevlett.117.128101] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The stochastic kinetics of transcription is typically inferred from the distribution of RNA numbers in individual cells. However, cellular RNA reflects additional processes downstream of transcription, hampering this analysis. In contrast, nascent (actively transcribed) RNA closely reflects the kinetics of transcription. We present a theoretical model for the stochastic kinetics of nascent RNA, which we solve to obtain the probability distribution of nascent RNA per gene. The model allows us to evaluate the kinetic parameters of transcription from single-cell measurements of nascent RNA. The model also predicts surprising discontinuities in the distribution of nascent RNA, a feature which we verify experimentally.
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Affiliation(s)
- Heng Xu
- Center for Theoretical Biological Physics, Rice University, Houston,
Texas, USA
- Center for the Physics of Living Cells, University of Illinois at
Urbana-Champaign, Urbana, Illinois, USA
- Verna & Marrs McLean Department of Biochemistry and Molecular
Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Samuel O. Skinner
- Center for Theoretical Biological Physics, Rice University, Houston,
Texas, USA
- Center for the Physics of Living Cells, University of Illinois at
Urbana-Champaign, Urbana, Illinois, USA
- Verna & Marrs McLean Department of Biochemistry and Molecular
Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Anna Marie Sokac
- Verna & Marrs McLean Department of Biochemistry and Molecular
Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Ido Golding
- Center for Theoretical Biological Physics, Rice University, Houston,
Texas, USA
- Center for the Physics of Living Cells, University of Illinois at
Urbana-Champaign, Urbana, Illinois, USA
- Verna & Marrs McLean Department of Biochemistry and Molecular
Biology, Baylor College of Medicine, Houston, Texas, USA
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38
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Krasnov AN, Mazina MY, Nikolenko JV, Vorobyeva NE. On the way of revealing coactivator complexes cross-talk during transcriptional activation. Cell Biosci 2016; 6:15. [PMID: 26913181 PMCID: PMC4765067 DOI: 10.1186/s13578-016-0081-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 02/09/2016] [Indexed: 08/07/2023] Open
Abstract
Transcriptional activation is a complex, multistage process implemented by hundreds of proteins. Many transcriptional proteins are organized into coactivator complexes, which participate in transcription regulation at numerous genes and are a driver of this process. The molecular action mechanisms of coactivator complexes remain largely understudied. Relevant publications usually deal with the involvement of these complexes in the entire process of transcription, and only a few studies are aimed to elucidate their functions at its particular stages. This review summarizes available information on the participation of key coactivator complexes in transcriptional activation. The timing of coactivator complex binding/removal has been used for restructuring previously described information about the transcriptional process. Several major stages of transcriptional activation have been distinguished based on the presence of covalent histone modifications and general transcriptional factors, and the recruitment and/or removal phases have been determined for each coactivator included in analysis. Recruitment of Mediator, SWItch/Sucrose Non-Fermentable and NUcleosome Remodeling Factor complexes during transcription activation has been investigated thoroughly; CHD and INOsitol auxotrophy 80 families are less well studied. In most cases, the molecular mechanisms responsible for the removal of certain coactivator complexes after the termination of their functions at the promoters are still not understood. On the basis of the summarized information, we propose a scheme that illustrates the involvement of coactivator complexes in different stages of the transcription activation process. This scheme may help to gain a deeper insight into the molecular mechanism of functioning of coactivator complexes, find novel participants of the process, and reveal novel structural or functional connections between different coactivators.
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Affiliation(s)
- Aleksey N Krasnov
- Department of Transcription Regulation and Chromatin Dynamic, Institute of Gene Biology, Russian Academy of Sciences, Moscow, 119334 Russia
| | - Marina Yu Mazina
- Department of Transcription Regulation and Chromatin Dynamic, Institute of Gene Biology, Russian Academy of Sciences, Moscow, 119334 Russia
| | - Julia V Nikolenko
- Department of Transcription Regulation and Chromatin Dynamic, Institute of Gene Biology, Russian Academy of Sciences, Moscow, 119334 Russia
| | - Nadezhda E Vorobyeva
- Department of Transcription Regulation and Chromatin Dynamic, Institute of Gene Biology, Russian Academy of Sciences, Moscow, 119334 Russia
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