51
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Morgan MD, Patin E, Jagla B, Hasan M, Quintana-Murci L, Marioni JC. Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels. PLoS Genet 2020; 16:e1008686. [PMID: 32168362 PMCID: PMC7094872 DOI: 10.1371/journal.pgen.1008686] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/25/2020] [Accepted: 02/19/2020] [Indexed: 11/19/2022] Open
Abstract
Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.
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Affiliation(s)
- Michael D. Morgan
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Cancer Research UK–Cambridge Institute, Robinson Way, Cambridge, United Kingdom
| | - Etienne Patin
- Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, Paris, France
| | - Bernd Jagla
- Cytometry and Biomarkers UTechS, Institut Pasteur, Paris, France
- Hub Bioinformatique et Biostatisque, Départment de Biologie Computationalle—USR 3756 CNRS, Institut Pasteur, Paris, France
| | - Milena Hasan
- Cytometry and Biomarkers UTechS, Institut Pasteur, Paris, France
| | - Lluís Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, Paris, France
- Human Genomics and Evolution, Collège de France, Paris, France
| | - John C. Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- Cancer Research UK–Cambridge Institute, Robinson Way, Cambridge, United Kingdom
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
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52
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Rodrigo G. Ab initio scaling laws between noise and mean of gene expression. Phys Rev E 2020; 100:032415. [PMID: 31640034 DOI: 10.1103/physreve.100.032415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Indexed: 12/17/2022]
Abstract
Gene expression is inherently noisy due to fluctuations occurring at the molecular level. From a top-down perspective, noise has been traditionally decomposed into an intrinsic component that scales inversely with the mean expression level and an extrinsic component that is constant in absence of regulatory changes. Here, we adopt a bottom-up approach to reveal that extrinsic noise, by itself, can follow the aforementioned decomposition, which entails that one component of it can be confounded with intrinsic noise. Analytical expressions of the noise-mean relationship were derived for different scenarios, which were in part supported by numerical simulations.
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Affiliation(s)
- Guillermo Rodrigo
- Institute for Integrative Systems Biology (I2SysBio), CSIC-University Valencia, 46980 Paterna, Spain
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53
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Domingues AF, Kulkarni R, Giotopoulos G, Gupta S, Vinnenberg L, Arede L, Foerner E, Khalili M, Adao RR, Johns A, Tan S, Zeka K, Huntly BJ, Prabakaran S, Pina C. Loss of Kat2a enhances transcriptional noise and depletes acute myeloid leukemia stem-like cells. eLife 2020; 9:e51754. [PMID: 31985402 PMCID: PMC7039681 DOI: 10.7554/elife.51754] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 01/24/2020] [Indexed: 12/21/2022] Open
Abstract
Acute Myeloid Leukemia (AML) is an aggressive hematological malignancy with abnormal progenitor self-renewal and defective white blood cell differentiation. Its pathogenesis comprises subversion of transcriptional regulation, through mutation and by hijacking normal chromatin regulation. Kat2a is a histone acetyltransferase central to promoter activity, that we recently associated with stability of pluripotency networks, and identified as a genetic vulnerability in AML. Through combined chromatin profiling and single-cell transcriptomics of a conditional knockout mouse, we demonstrate that Kat2a contributes to leukemia propagation through preservation of leukemia stem-like cells. Kat2a loss impacts transcription factor binding and reduces transcriptional burst frequency in a subset of gene promoters, generating enhanced variability of transcript levels. Destabilization of target programs shifts leukemia cell fate out of self-renewal into differentiation. We propose that control of transcriptional variability is central to leukemia stem-like cell propagation, and establish a paradigm exploitable in different tumors and distinct stages of cancer evolution.
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Affiliation(s)
- Ana Filipa Domingues
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
| | - Rashmi Kulkarni
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
| | - George Giotopoulos
- Department of HaematologyUniversity of Cambridge, Cambridge Institute for Medical ResearchCambridgeUnited Kingdom
- Wellcome Trust-Medical Research Council Cambridge Stem Cell InstituteCambridgeUnited Kingdom
| | - Shikha Gupta
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
- Department of GeneticsUniversity of CambridgeCambridgeUnited Kingdom
| | - Laura Vinnenberg
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
| | - Liliana Arede
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
- Department of GeneticsUniversity of CambridgeCambridgeUnited Kingdom
| | - Elena Foerner
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
| | - Mitra Khalili
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
- Department of Medical Genetics and Molecular Medicine, School of MedicineZanjan University of Medical Sciences (ZUMS)ZanjanIslamic Republic of Iran
| | - Rita Romano Adao
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
| | - Ayona Johns
- Division of Biosciences, College of Health and Life SciencesBrunel University LondonUxbridgeUnited Kingdom
| | - Shengjiang Tan
- Department of HaematologyUniversity of Cambridge, Cambridge Institute for Medical ResearchCambridgeUnited Kingdom
| | - Keti Zeka
- Department of HaematologyUniversity of Cambridge, NHS-BT Blood Donor CentreCambridgeUnited Kingdom
- Department of GeneticsUniversity of CambridgeCambridgeUnited Kingdom
| | - Brian J Huntly
- Department of HaematologyUniversity of Cambridge, Cambridge Institute for Medical ResearchCambridgeUnited Kingdom
- Wellcome Trust-Medical Research Council Cambridge Stem Cell InstituteCambridgeUnited Kingdom
| | - Sudhakaran Prabakaran
- Department of GeneticsUniversity of CambridgeCambridgeUnited Kingdom
- Department of BiologyIISERPuneIndia
| | - Cristina Pina
- Department of GeneticsUniversity of CambridgeCambridgeUnited Kingdom
- Division of Biosciences, College of Health and Life SciencesBrunel University LondonUxbridgeUnited Kingdom
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54
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Markovian approaches to modeling intracellular reaction processes with molecular memory. Proc Natl Acad Sci U S A 2019; 116:23542-23550. [PMID: 31685609 PMCID: PMC6876203 DOI: 10.1073/pnas.1913926116] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Many cellular processes are governed by stochastic reaction events. These events do not necessarily occur in single steps of individual molecules, and, conversely, each birth or death of a macromolecule (e.g., protein) could involve several small reaction steps, creating a memory between individual events and thus leading to nonmarkovian reaction kinetics. Characterizing this kinetics is challenging. Here, we develop a systematic approach for a general reaction network with arbitrary intrinsic waiting-time distributions, which includes the stationary generalized chemical-master equation (sgCME), the stationary generalized Fokker-Planck equation, and the generalized linear-noise approximation. The first formulation converts a nonmarkovian issue into a markovian one by introducing effective transition rates (that explicitly decode the effect of molecular memory) for the reactions in an equivalent reaction network with the same substrates but without molecular memory. Nonmarkovian features of the reaction kinetics can be revealed by solving the sgCME. The latter 2 formulations can be used in the fast evaluation of fluctuations. These formulations can have broad applications, and, in particular, they may help us discover new biological knowledge underlying memory effects. When they are applied to generalized stochastic models of gene-expression regulation, we find that molecular memory is in effect equivalent to a feedback and can induce bimodality, fine-tune the expression noise, and induce switch.
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55
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Chapal M, Mintzer S, Brodsky S, Carmi M, Barkai N. Resolving noise-control conflict by gene duplication. PLoS Biol 2019; 17:e3000289. [PMID: 31756183 PMCID: PMC6874299 DOI: 10.1371/journal.pbio.3000289] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 10/21/2019] [Indexed: 12/15/2022] Open
Abstract
Gene duplication promotes adaptive evolution in two main ways: allowing one duplicate to evolve a new function and splitting ancestral functions between the duplicates. The second scenario may resolve adaptive conflicts that can rise when one gene performs different functions. In an apparent departure from both scenarios, low-expressing transcription factor (TF) duplicates commonly bind to the same DNA motifs and act in overlapping conditions. To examine for possible benefits of this apparent redundancy, we examined the Msn2 and Msn4 duplicates in budding yeast. We show that Msn2,4 function as one unit by inducing the same set of target genes in overlapping conditions. Yet, the two-factor composition allows this unit's expression to be both environmentally responsive and with low noise, resolving an adaptive conflict that limits expression of single genes. We propose that duplication can provide adaptive benefit through cooperation rather than functional divergence, allowing two-factor dynamics with beneficial properties that cannot be achieved by a single gene.
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Affiliation(s)
- Michal Chapal
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Sefi Mintzer
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Sagie Brodsky
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Miri Carmi
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Naama Barkai
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
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56
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Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 2019; 116:21085-21093. [PMID: 31570626 DOI: 10.1073/pnas.1902823116] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the 10 genes surveyed and are inconsistent with normality. For example, all 10 distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene's expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.
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57
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Abstract
Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.
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Affiliation(s)
- Nils Eling
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, UK.
| | | | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
- Wellcome Sanger Institute, Welcome Genome Campus, Hinxton, UK.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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58
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Schmiedel JM, Carey LB, Lehner B. Empirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise. Nat Commun 2019; 10:3180. [PMID: 31320634 PMCID: PMC6639414 DOI: 10.1038/s41467-019-11116-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 06/21/2019] [Indexed: 12/23/2022] Open
Abstract
The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify because of the mechanistic coupling of noise to mean expression. To independently quantify the effects of changes in mean expression and noise we determine the fitness landscapes in mean-noise expression space for 33 genes in yeast. For most genes, short-lived (noise) deviations away from the expression optimum are nearly as detrimental as sustained (mean) deviations. Fitness landscapes can be classified by a combination of each gene’s sensitivity to protein shortage or surplus. We use this classification to explore evolutionary scenarios for gene expression and find that certain landscape topologies can break the mechanistic coupling of mean and noise, thus promoting independent optimization of both properties. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems. Quantifying the effects of noise in gene expression is difficult since noise and mean expression are coupled. Here the authors determine fitness landscapes in mean-noise expression space to uncouple these two parameters and show that changes in noise and mean expression are similarly detrimental to fitness.
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Affiliation(s)
- Jörn M Schmiedel
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Doctor Aiguader 88, 08003, Barcelona, Spain.
| | - Lucas B Carey
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Doctor Aiguader 88, 08003, Barcelona, Spain.,Center for Quantitative Biology and Peking-Tsinghua Center for the Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Doctor Aiguader 88, 08003, Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain. .,ICREA, Passeig Lluís Companys 23, 08010, Barcelona, Spain.
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59
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Qiu H, Zhang B, Zhou T. Analytical results for a generalized model of bursty gene expression with molecular memory. Phys Rev E 2019; 100:012128. [PMID: 31499786 DOI: 10.1103/physreve.100.012128] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Indexed: 06/10/2023]
Abstract
The activation of a gene is a complex biochemical process and could involve small steps, creating a memory between individual events. However, the effect of this molecular memory was often neglected in previous work. How the molecular memory affects gene expression remains not fully explored. We analyze a stochastic model of bursty gene expression, where the waiting time from inactivation to activation is assumed to follow a nonexponential (in fact, Erlang) distribution. We derive the analytical expression for the gene-product distribution, which explicitly traces the effect of molecule memory. Interestingly, we find that the effect of molecular memory is equivalent to the introduction of feedback. In addition, we analytically show that the stationary distribution is always super-Poissonian, independent of the detail of the waiting-time distribution, and there is the optimal step size that minimizes the Fano factor for any given mean burst size and is a decreasing function of the mean burst size. These analytical results indicate that molecular memory is an unneglectable factor affecting gene expression.
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Affiliation(s)
- Huahai Qiu
- School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, People's Republic of China
| | - Bengong Zhang
- School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, People's Republic of China
| | - Tianshou Zhou
- School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, People's Republic of China
- Key Laboratory of Computational Mathematics, Guangdong Province, and School of Mathematics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China
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60
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de Jong TV, Moshkin YM, Guryev V. Gene expression variability: the other dimension in transcriptome analysis. Physiol Genomics 2019; 51:145-158. [DOI: 10.1152/physiolgenomics.00128.2018] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method.
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Affiliation(s)
- Tristan V. de Jong
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Yuri M. Moshkin
- Institute of Cytology and Genetics, Siberian Branch of RAS, Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of RAS, Novosibirsk, Russia
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
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61
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Abstract
Mammalian gene expression is inherently stochastic1,2resulting in discrete bursts of RNA molecules synthesised from each allele3–7. Although known to be regulated by promoters and enhancers, it is unclear how cis-regulatory sequences encode transcriptional burst kinetics. Characterization of transcriptional bursting, including the burst size and frequency, have mainly relied on live-cell4,6,8 or single-molecule RNA-FISH3,5,8,9 recordings of selected loci. Here, we inferred transcriptome-wide burst frequencies and sizes for endogenous genes using allele-sensitive single-cell RNA-sequencing (scRNA-seq). We show that core promoter elements affect burst size and uncover synergistic effects between TATA and Initiator elements, which were masked at mean expression levels. Importantly, we provide transcriptome-wide support for enhancers controlling burst frequencies and we additionally demonstrate that cell-type-specific gene expression is primarily shaped by changes in burst frequencies. Altogether, our data show that burst frequency is primarily encoded in enhancers and burst size in core promoters, and that allelic scRNA-seq is a powerful paradigm for investigating transcriptional kinetics.
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62
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Urban EA, Johnston RJ. Buffering and Amplifying Transcriptional Noise During Cell Fate Specification. Front Genet 2018; 9:591. [PMID: 30555516 PMCID: PMC6282114 DOI: 10.3389/fgene.2018.00591] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/15/2018] [Indexed: 11/29/2022] Open
Abstract
The molecular processes that drive gene transcription are inherently noisy. This noise often manifests in the form of transcriptional bursts, producing fluctuations in gene activity over time. During cell fate specification, this noise is often buffered to ensure reproducible developmental outcomes. However, sometimes noise is utilized as a “bet-hedging” mechanism to diversify functional roles across a population of cells. Studies of bacteria, yeast, and cultured cells have provided insights into the nature and roles of noise in transcription, yet we are only beginning to understand the mechanisms by which noise influences the development of multicellular organisms. Here we discuss the sources of transcriptional noise and the mechanisms that either buffer noise to drive reproducible fate choices or amplify noise to randomly specify fates.
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Affiliation(s)
- Elizabeth A Urban
- Department of Biology, Johns Hopkins University, Baltimore, MD, United States
| | - Robert J Johnston
- Department of Biology, Johns Hopkins University, Baltimore, MD, United States
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63
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Schöne S, Bothe M, Einfeldt E, Borschiwer M, Benner P, Vingron M, Thomas-Chollier M, Meijsing SH. Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise. PLoS Genet 2018; 14:e1007793. [PMID: 30427832 PMCID: PMC6261644 DOI: 10.1371/journal.pgen.1007793] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 11/28/2018] [Accepted: 10/26/2018] [Indexed: 12/29/2022] Open
Abstract
The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of tens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GR’s core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed specific DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise. The expression level of genes is controlled by transcription factors, which are proteins that bind to genomic response elements that contain their recognition DNA sequence. Importantly, genes are not simply turned on but need to be expressed at the right level. This is, at least in part, assured by the sequence composition of genomic response elements. Here, we studied how the recognition DNA sequence influences gene regulation by a transcription factor called the glucocorticoid receptor. Specifically, we developed a method to test the activity of variants in a highly parallelized setting where everything is kept identical except for the sequence of the binding site. The systematic analysis of tens of thousands of sequence variants facilitated the identification of a previously unknown sequence variant with high activity. Moreover, we report how sequence variation of the response element influences cell-to-cell variability in expression levels. Finally, we observe similar activity profiles for distinct sequence variants that share similar three-dimensional DNA shape characteristics arguing that the three-dimensional perception of DNA by the glucocorticoid receptor, modulates its activity towards individual target genes.
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Affiliation(s)
- Stefanie Schöne
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Melissa Bothe
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Edda Einfeldt
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | | | - Philipp Benner
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Martin Vingron
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Morgane Thomas-Chollier
- Institut de biologie de l’Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Université Paris, Paris, France
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64
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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: 95] [Impact Index Per Article: 13.6] [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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65
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Haberle V, Stark A. Eukaryotic core promoters and the functional basis of transcription initiation. Nat Rev Mol Cell Biol 2018; 19:621-637. [PMID: 29946135 PMCID: PMC6205604 DOI: 10.1038/s41580-018-0028-8] [Citation(s) in RCA: 447] [Impact Index Per Article: 63.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
RNA polymerase II (Pol II) core promoters are specialized DNA sequences at transcription start sites of protein-coding and non-coding genes that support the assembly of the transcription machinery and transcription initiation. They enable the highly regulated transcription of genes by selectively integrating regulatory cues from distal enhancers and their associated regulatory proteins. In this Review, we discuss the defining properties of gene core promoters, including their sequence features, chromatin architecture and transcription initiation patterns. We provide an overview of molecular mechanisms underlying the function and regulation of core promoters and their emerging functional diversity, which defines distinct transcription programmes. On the basis of the established properties of gene core promoters, we discuss transcription start sites within enhancers and integrate recent results obtained from dedicated functional assays to propose a functional model of transcription initiation. This model can explain the nature and function of transcription initiation at gene starts and at enhancers and can explain the different roles of core promoters, of Pol II and its associated factors and of the activating cues provided by enhancers and the transcription factors and cofactors they recruit.
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Affiliation(s)
- Vanja Haberle
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
| | - Alexander Stark
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria.
- Medical University of Vienna, Vienna Biocenter (VBC), Vienna, Austria.
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66
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Eling N, Richard AC, Richardson S, Marioni JC, Vallejos CA. Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data. Cell Syst 2018; 7:284-294.e12. [PMID: 30172840 PMCID: PMC6167088 DOI: 10.1016/j.cels.2018.06.011] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 05/02/2018] [Accepted: 06/25/2018] [Indexed: 01/10/2023]
Abstract
Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays an important role in tissue function and development. Single-cell RNA sequencing can characterize this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinder meaningful comparison of expression variability between cell populations. To address this problem, we introduce an analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. This includes a robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronization of biosynthetic machinery components in immune cells upon activation. In contrast to the uniform up-regulation of the biosynthetic machinery, CD4+ T cells show heterogeneous up-regulation of immune-related and lineage-defining genes during activation and differentiation.
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Affiliation(s)
- Nils Eling
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, UK
| | - Arianne C Richard
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, UK; Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0XY, UK
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, UK.
| | - Catalina A Vallejos
- The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK; Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK; MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XY, UK.
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67
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Liu J, Lestrade D, Arabaciyan S, Cescut J, François JM, Capp JP. A GRX1 Promoter Variant Confers Constitutive Noisy Bimodal Expression That Increases Oxidative Stress Resistance in Yeast. Front Microbiol 2018; 9:2158. [PMID: 30283413 PMCID: PMC6156533 DOI: 10.3389/fmicb.2018.02158] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 08/23/2018] [Indexed: 11/13/2022] Open
Abstract
Higher noise in the expression of stress-related genes was previously shown to confer better resistance in selective conditions. Thus, evolving the promoter of such genes toward higher transcriptional noise appears to be an attractive strategy to engineer microbial strains with enhanced stress resistance. Here we generated hundreds of promoter variants of the GRX1 gene involved in oxidative stress resistance in Saccharomyces cerevisiae and created a yeast library by replacing the native GRX1 promoter by these variants at the native locus. An outlier clone with very strong increase in noise (6-times) at the same mean expression level as the native strain was identified whereas the other noisiest clones were only 3-times increased. This variant provides constitutive bimodal expression and consists in 3 repeated but differently mutated copies of the GRX1 promoter. In spite of the multi-factorial oxidative stress-response in yeast, replacement of the native promoter by this variant is sufficient alone to confer strongly enhanced resistance to H2O2 and cumene hydroperoxide. New replacement of this variant by the native promoter in the resistant strain suppresses the resistance. This work shows that increasing noise of target genes in a relevant strategy to engineer microbial strains toward better stress resistance. Multiple promoter replacement could synergize the effect observed here with the sole GRX1 promoter replacement. Finally this work suggests that combining several mutated copies of the target promoter could allow enhancing transcriptional-mediated noise at higher levels than mutating a single copy by providing constitutive bimodal and highly heterogeneous expression distribution.
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Affiliation(s)
- Jian Liu
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, UMR CNRS 5504, UMR INRA 792, Institut National des Sciences Appliquées de Toulouse, Université de Toulouse, Toulouse, France
| | - Delphine Lestrade
- Toulouse White Biotechnology, UMS INRA 1337, UMS CNRS 3582, Institut National des Sciences Appliquées de Toulouse, Toulouse, France
| | - Sevan Arabaciyan
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, UMR CNRS 5504, UMR INRA 792, Institut National des Sciences Appliquées de Toulouse, Université de Toulouse, Toulouse, France
| | - Julien Cescut
- Toulouse White Biotechnology, UMS INRA 1337, UMS CNRS 3582, Institut National des Sciences Appliquées de Toulouse, Toulouse, France
| | - Jean-Marie François
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, UMR CNRS 5504, UMR INRA 792, Institut National des Sciences Appliquées de Toulouse, Université de Toulouse, Toulouse, France.,Toulouse White Biotechnology, UMS INRA 1337, UMS CNRS 3582, Institut National des Sciences Appliquées de Toulouse, Toulouse, France
| | - Jean-Pascal Capp
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, UMR CNRS 5504, UMR INRA 792, Institut National des Sciences Appliquées de Toulouse, Université de Toulouse, Toulouse, France
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68
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Duveau F, Hodgins-Davis A, Metzger BP, Yang B, Tryban S, Walker EA, Lybrook T, Wittkopp PJ. Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. eLife 2018; 7:37272. [PMID: 30124429 PMCID: PMC6133559 DOI: 10.7554/elife.37272] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 08/17/2018] [Indexed: 01/22/2023] Open
Abstract
Gene expression noise is an evolvable property of biological systems that describes differences in expression among genetically identical cells in the same environment. Prior work has shown that expression noise is heritable and can be shaped by selection, but the impact of variation in expression noise on organismal fitness has proven difficult to measure. Here, we quantify the fitness effects of altering expression noise for the TDH3 gene in Saccharomyces cerevisiae. We show that increases in expression noise can be deleterious or beneficial depending on the difference between the average expression level of a genotype and the expression level maximizing fitness. We also show that a simple model relating single-cell expression levels to population growth produces patterns consistent with our empirical data. We use this model to explore a broad range of average expression levels and expression noise, providing additional insight into the fitness effects of variation in expression noise.
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Affiliation(s)
- Fabien Duveau
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Laboratoire Matière et Systèmes Complexes, CNRS UMR 7057, Université Paris Diderot, Paris, France
| | - Andrea Hodgins-Davis
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Brian Ph Metzger
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Department of Ecology and Evolution, University of Chicago, Chicago, United States
| | - Bing Yang
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Stephen Tryban
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Elizabeth A Walker
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Tricia Lybrook
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Patricia J Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, United States
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69
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Nicolas D, Phillips NE, Naef F. What shapes eukaryotic transcriptional bursting? MOLECULAR BIOSYSTEMS 2018; 13:1280-1290. [PMID: 28573295 DOI: 10.1039/c7mb00154a] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Isogenic cells in a common environment present a large degree of heterogeneity in gene expression. Part of this variability is attributed to transcriptional bursting: the stochastic activation and inactivation of promoters that leads to the discontinuous production of mRNA. The diversity in bursting patterns displayed by different genes suggests the existence of a connection between bursting and gene regulation. Experimental strategies such as single-molecule RNA FISH, MS2-GFP or short-lived protein reporters allow the quantification of transcriptional bursting and the comparison of bursting kinetics between conditions, allowing therefore the identification of molecular mechanisms modulating transcriptional bursting. In this review we recapitulate the impact on transcriptional bursting of different molecular aspects of transcription such as the chromatin environment, nucleosome occupancy, histone modifications, the number and affinity of regulatory elements, DNA looping and transcription factor availability. More specifically, we examine their role in tuning the burst size or the burst frequency. While some molecular mechanisms involved in transcription such as histone marks can affect every aspect of bursting, others predominantly influence the burst size (e.g. the number and affinity of cis-regulatory elements) or frequency (e.g. transcription factor availability).
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Affiliation(s)
- Damien Nicolas
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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70
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Li C, Cesbron F, Oehler M, Brunner M, Höfer T. Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation. Cell Syst 2018; 6:409-423.e11. [PMID: 29454937 DOI: 10.1016/j.cels.2018.01.012] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 11/16/2017] [Accepted: 01/11/2018] [Indexed: 01/17/2023]
Abstract
Gene regulation is a complex non-equilibrium process. Here, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive, active, and refractory) provides a parsimonious framework for analyzing gene regulation. Our theory makes two non-intuitive predictions. First, for transcription factors (TFs) that regulate transcription burst frequency, as opposed to amplitude or duration, weak TF binding is sufficient to elicit strong transcriptional responses. Second, refractoriness of a gene after a transcription burst enables rapid responses to stimuli. We validate both predictions experimentally by exploiting the natural, optogenetic-like responsiveness of the Neurospora GATA-type TF White Collar Complex (WCC) to blue light. Further, we demonstrate that differential regulation of WCC target genes is caused by different gene activation rates, not different TF occupancy, and that these rates are tuned by both the core promoter and the distance between TF-binding site and core promoter. In total, our work demonstrates the relevance of a kinetic, non-equilibrium framework for understanding transcriptional regulation.
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Affiliation(s)
- Congxin Li
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Bioquant Center, Heidelberg University, 69120 Heidelberg, Germany
| | - François Cesbron
- Biochemistry Center, Heidelberg University, 69120 Heidelberg, Germany
| | - Michael Oehler
- Biochemistry Center, Heidelberg University, 69120 Heidelberg, Germany
| | - Michael Brunner
- Biochemistry Center, Heidelberg University, 69120 Heidelberg, Germany.
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Bioquant Center, Heidelberg University, 69120 Heidelberg, Germany.
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71
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Catarino RR, Stark A. Assessing sufficiency and necessity of enhancer activities for gene expression and the mechanisms of transcription activation. Genes Dev 2018; 32:202-223. [PMID: 29491135 PMCID: PMC5859963 DOI: 10.1101/gad.310367.117] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Enhancers are important genomic regulatory elements directing cell type-specific transcription. They assume a key role during development and disease, and their identification and functional characterization have long been the focus of scientific interest. The advent of next-generation sequencing and clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9-based genome editing has revolutionized the means by which we study enhancer biology. In this review, we cover recent developments in the prediction of enhancers based on chromatin characteristics and their identification by functional reporter assays and endogenous DNA perturbations. We discuss that the two latter approaches provide different and complementary insights, especially in assessing enhancer sufficiency and necessity for transcription activation. Furthermore, we discuss recent insights into mechanistic aspects of enhancer function, including findings about cofactor requirements and the role of post-translational histone modifications such as monomethylation of histone H3 Lys4 (H3K4me1). Finally, we survey how these approaches advance our understanding of transcription regulation with respect to promoter specificity and transcriptional bursting and provide an outlook covering open questions and promising developments.
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Affiliation(s)
- Rui R Catarino
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), 1030 Vienna, Austria
| | - Alexander Stark
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), 1030 Vienna, Austria
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72
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Effects of mutation and selection on plasticity of a promoter activity in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 2017; 114:E11218-E11227. [PMID: 29259117 DOI: 10.1073/pnas.1713960115] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Phenotypic plasticity is an evolvable property of biological systems that can arise from environment-specific regulation of gene expression. To better understand the evolutionary and molecular mechanisms that give rise to plasticity in gene expression, we quantified the effects of 235 single-nucleotide mutations in the Saccharomyces cerevisiae TDH3 promoter (PTDH3 ) on the activity of this promoter in media containing glucose, galactose, or glycerol as a carbon source. We found that the distributions of mutational effects differed among environments because many mutations altered the plastic response exhibited by the wild-type allele. Comparing the effects of these mutations with the effects of 30 PTDH3 polymorphisms on expression plasticity in the same environments provided evidence of natural selection acting to prevent the plastic response in PTDH3 activity between glucose and galactose from becoming larger. The largest changes in expression plasticity were observed between fermentable (glucose or galactose) and nonfermentable (glycerol) carbon sources and were caused by mutations located in the RAP1 and GCR1 transcription factor binding sites. Mutations altered expression plasticity most frequently between the two fermentable environments, with mutations causing significant changes in plasticity between glucose and galactose distributed throughout the promoter, suggesting they might affect chromatin structure. Taken together, these results provide insight into the molecular mechanisms underlying gene-by-environment interactions affecting gene expression as well as the evolutionary dynamics affecting natural variation in plasticity of gene expression.
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73
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Faure AJ, Schmiedel JM, Lehner B. Systematic Analysis of the Determinants of Gene Expression Noise in Embryonic Stem Cells. Cell Syst 2017; 5:471-484.e4. [DOI: 10.1016/j.cels.2017.10.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 06/06/2017] [Accepted: 10/02/2017] [Indexed: 01/23/2023]
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74
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Hendy O, Campbell J, Weissman JD, Larson DR, Singer DS. Differential context-specific impact of individual core promoter elements on transcriptional dynamics. Mol Biol Cell 2017; 28:3360-3370. [PMID: 28931597 PMCID: PMC5687036 DOI: 10.1091/mbc.e17-06-0408] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 08/23/2017] [Accepted: 09/11/2017] [Indexed: 11/11/2022] Open
Abstract
The roles of individual core promoter elements in transcriptional dynamics of MHC class I gene expression were determined by smFISH in primary B-cells. The elements individually modulated transcriptional bursting, differentially contributing to burst size or burst frequency, to enable combinatorial fine-tuning of the level of transcription. Eukaryotic transcription occurs in bursts that vary in size and frequency, but the contribution of individual core promoter elements to transcriptional bursting is not known. Here we analyze the relative contributions to bursting of the individual core promoter elements—CCAAT, TATAA-like, Sp1BS, and Inr—of an MHC class I gene in primary B-cells during both basal and activated transcription. The TATAA-like, Sp1BS, and Inr elements all function as negative regulators of transcription, and each was found to contribute differentially to the overall bursting pattern of the promoter during basal transcription. Whereas the Sp1BS element regulates burst size, the Inr element regulates burst frequency. The TATAA-like element contributes to both. Surprisingly, each element has a distinct role in bursting during transcriptional activation by γ-interferon. The CCAAT element does not contribute significantly to the constitutive transcriptional dynamics of primary B-cells, but modulates both burst size and frequency in response to γ-interferon activation. The ability of core promoter elements to modulate transcriptional bursting individually allows combinatorial fine-tuning of the level of MHC class I gene expression in response to intrinsic and extrinsic signals.
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Affiliation(s)
- Oliver Hendy
- Experimental Immunology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - John Campbell
- Experimental Immunology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Jocelyn D Weissman
- Experimental Immunology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Daniel R Larson
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Dinah S Singer
- Experimental Immunology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
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75
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van Boxtel C, van Heerden JH, Nordholt N, Schmidt P, Bruggeman FJ. Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments. J R Soc Interface 2017; 14:20170141. [PMID: 28701503 PMCID: PMC5550968 DOI: 10.1098/rsif.2017.0141] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 06/16/2017] [Indexed: 01/08/2023] Open
Abstract
Natural selection has shaped the strategies for survival and growth of microorganisms. The success of microorganisms depends not only on slow evolutionary tuning but also on the ability to adapt to unpredictable changes in their environment. In principle, adaptive strategies range from purely deterministic mechanisms to those that exploit the randomness intrinsic to many cellular and molecular processes. Depending on the environment and selective pressures, particular strategies can lie somewhere along this continuum. In recent years, non-genetic cell-to-cell differences have received a lot of attention, not least because of their potential impact on the ability of microbial populations to survive in dynamic environments. Using several examples, we describe the origins of spontaneous and induced mechanisms of phenotypic adaptation. We identify some of the commonalities of these examples and consider the potential role of chance and constraints in microbial phenotypic adaptation.
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Affiliation(s)
- Coco van Boxtel
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
| | - Johan H van Heerden
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
| | - Niclas Nordholt
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
| | - Phillipp Schmidt
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
| | - Frank J Bruggeman
- Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
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76
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Wu S, Li K, Li Y, Zhao T, Li T, Yang YF, Qian W. Independent regulation of gene expression level and noise by histone modifications. PLoS Comput Biol 2017; 13:e1005585. [PMID: 28665997 PMCID: PMC5513504 DOI: 10.1371/journal.pcbi.1005585] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 07/17/2017] [Accepted: 05/22/2017] [Indexed: 11/21/2022] Open
Abstract
The inherent stochasticity generates substantial gene expression variation among isogenic cells under identical conditions, which is frequently referred to as gene expression noise or cell-to-cell expression variability. Similar to (average) expression level, expression noise is also subject to natural selection. Yet it has been observed that noise is negatively correlated with expression level, which manifests as a potential constraint for simultaneous optimization of both. Here, we studied expression noise in human embryonic cells with computational analysis on single-cell RNA-seq data and in yeast with flow cytometry experiments. We showed that this coupling is overcome, to a certain degree, by a histone modification strategy in multiple embryonic developmental stages in human, as well as in yeast. Importantly, this epigenetic strategy could fit into a burst-like gene expression model: promoter-localized histone modifications (such as H3K4 methylation) are associated with both burst size and burst frequency, which together influence expression level, while gene-body-localized ones (such as H3K79 methylation) are more associated with burst frequency, which influences both expression level and noise. We further knocked out the only “writer” of H3K79 methylation in yeast, and observed that expression noise is indeed increased. Consistently, dosage sensitive genes, such as genes in the Wnt signaling pathway, tend to be marked with gene-body-localized histone modifications, while stress responding genes, such as genes regulating autophagy, tend to be marked with promoter-localized ones. Our findings elucidate that the “division of labor” among histone modifications facilitates the independent regulation of expression level and noise, extend the “histone code” hypothesis to include expression noise, and shed light on the optimization of transcriptome in evolution. Gene expression noise, or cell-to-cell expression variability, has been a topic of intense interest for more than a decade. The prevailing model of “burst-like transcription” mediated by the promoter transitions between on and off states explains the formation of noise in eukaryotes. Albeit widely accepted, the cis- elements that determine the burst frequency and burst size remain largely unknown. Here we systematically examined the relationship between transcriptional burst frequency/size and all major histone modifications in various cell types, including human embryonic cells, mouse embryonic stem cells, and yeast, and found that histone markers can be divided into two groups based on their associations with burst frequency/ size. Coincidently, promoter-localized histone markers are associated with both burst size and burst frequency whereas gene-body-localized ones are more associated with burst frequency. We further knocked out a gene that is responsible for “writing” a gene-body histone mark in yeast, and found that burst frequency is indeed reduced. Our findings reveal a new mechanism of transcriptional burst regulation and shed light on the simultaneous optimization of gene expression level and noise in evolution.
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Affiliation(s)
- Shaohuan Wu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- * E-mail: (WQ); (SW)
| | - Ke Li
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Yingshu Li
- Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Tong Zhao
- Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Ting Li
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Yu-Fei Yang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wenfeng Qian
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- * E-mail: (WQ); (SW)
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77
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Delvigne F, Baert J, Sassi H, Fickers P, Grünberger A, Dusny C. Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses. Biotechnol J 2017; 12. [PMID: 28544731 DOI: 10.1002/biot.201600549] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/10/2017] [Accepted: 04/12/2017] [Indexed: 01/19/2023]
Abstract
Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.
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Affiliation(s)
- Frank Delvigne
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Jonathan Baert
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Hosni Sassi
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Patrick Fickers
- University of Liège, TERRA research center, Gembloux Agro-Bio Tech, Microbial Processes and Interactions (MiPI lab), Gembloux, Belgium
| | - Alexander Grünberger
- Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, Jülich, Germany.,Multiscale Bioengineering, Bielefeld University, Bielefeld, Germany
| | - Christian Dusny
- Department Solar Materials, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
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78
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Evolution of Brain Active Gene Promoters in Human Lineage Towards the Increased Plasticity of Gene Regulation. Mol Neurobiol 2017; 55:1871-1904. [PMID: 28233272 DOI: 10.1007/s12035-017-0427-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 01/26/2017] [Indexed: 01/31/2023]
Abstract
Adaptability to a variety of environmental conditions is a prominent feature of Homo sapiens. We hypothesize that this feature can be explained by evolutionary changes in gene promoters active in the brain prefrontal cortex leading to a more flexible gene regulation network. The genotype-dependent range of gene expression can be broader in humans than in other higher primates. Thus, we searched for specific signatures of evolutionary changes in promoter architectures of multiple hominid genes, including the genes active in human cortical neurons that may indicate an increase of variability of gene expression rather than just changes in the level of expression, such as downregulation or upregulation of the genes. We performed a whole-genome search for genetic-based alterations that may impact gene regulation "flexibility" in a process of hominids evolution, such as (i) CpG dinucleotide content, (ii) predicted nucleosome-DNA dissociation constant, and (iii) predicted affinities for TATA-binding protein (TBP) in gene promoters. We tested all putative promoter regions across the human genome and especially gene promoters in active chromatin state in neurons of prefrontal cortex, the brain region critical for abstract thinking and social and behavioral adaptation. Our data imply that the origin of modern man has been associated with an increase of flexibility of promoter-driven gene regulation in brain. In contrast, after splitting from the ancestral lineages of H. sapiens, the evolution of ape species is characterized by reduced flexibility of gene promoter functioning, underlying reduced variability of the gene expression.
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80
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Yao J. Imaging Transcriptional Regulation of Eukaryotic mRNA Genes: Advances and Outlook. J Mol Biol 2017; 429:14-31. [DOI: 10.1016/j.jmb.2016.11.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 11/03/2016] [Accepted: 11/10/2016] [Indexed: 01/07/2023]
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81
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de Jonge WJ, O'Duibhir E, Lijnzaad P, van Leenen D, Groot Koerkamp MJ, Kemmeren P, Holstege FC. Molecular mechanisms that distinguish TFIID housekeeping from regulatable SAGA promoters. EMBO J 2016; 36:274-290. [PMID: 27979920 PMCID: PMC5286361 DOI: 10.15252/embj.201695621] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/18/2016] [Accepted: 11/01/2016] [Indexed: 11/28/2022] Open
Abstract
An important distinction is frequently made between constitutively expressed housekeeping genes versus regulated genes. Although generally characterized by different DNA elements, chromatin architecture and cofactors, it is not known to what degree promoter classes strictly follow regulatability rules and which molecular mechanisms dictate such differences. We show that SAGA‐dominated/TATA‐box promoters are more responsive to changes in the amount of activator, even compared to TFIID/TATA‐like promoters that depend on the same activator Hsf1. Regulatability is therefore an inherent property of promoter class. Further analyses show that SAGA/TATA‐box promoters are more dynamic because TATA‐binding protein recruitment through SAGA is susceptible to removal by Mot1. In addition, the nucleosome configuration upon activator depletion shifts on SAGA/TATA‐box promoters and seems less amenable to preinitiation complex formation. The results explain the fundamental difference between housekeeping and regulatable genes, revealing an additional facet of combinatorial control: an activator can elicit a different response dependent on core promoter class.
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Affiliation(s)
- Wim J de Jonge
- Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Eoghan O'Duibhir
- Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Philip Lijnzaad
- Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Dik van Leenen
- Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marian Ja Groot Koerkamp
- Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Patrick Kemmeren
- Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Frank Cp Holstege
- Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands .,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
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82
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Soltani M, Singh A. Effects of cell-cycle-dependent expression on random fluctuations in protein levels. ROYAL SOCIETY OPEN SCIENCE 2016; 3:160578. [PMID: 28083102 PMCID: PMC5210684 DOI: 10.1098/rsos.160578] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 11/10/2016] [Indexed: 06/06/2023]
Abstract
Expression of many genes varies as a cell transitions through different cell-cycle stages. How coupling between stochastic expression and cell cycle impacts cell-to-cell variability (noise) in the level of protein is not well understood. We analyse a model where a stable protein is synthesized in random bursts, and the frequency with which bursts occur varies within the cell cycle. Formulae quantifying the extent of fluctuations in the protein copy number are derived and decomposed into components arising from the cell cycle and stochastic processes. The latter stochastic component represents contributions from bursty expression and errors incurred during partitioning of molecules between daughter cells. These formulae reveal an interesting trade-off: cell-cycle dependencies that amplify the noise contribution from bursty expression also attenuate the contribution from partitioning errors. We investigate the existence of optimum strategies for coupling expression to the cell cycle that minimize the stochastic component. Intriguingly, results show that a zero production rate throughout the cell cycle, with expression only occurring just before cell division, minimizes noise from bursty expression for a fixed mean protein level. By contrast, the optimal strategy in the case of partitioning errors is to make the protein just after cell division. We provide examples of regulatory proteins that are expressed only towards the end of the cell cycle, and argue that such strategies enhance robustness of cell-cycle decisions to the intrinsic stochasticity of gene expression.
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Affiliation(s)
- Mohammad Soltani
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
- Department of Mathematical Sciences, University of Delaware, Newark, DE, USA
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA
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83
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Yang E, Wang G, Yang J, Zhou B, Tian Y, Cai JJ. Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action. Hum Mol Genet 2016; 25:4911-4919. [PMID: 28171656 PMCID: PMC6078589 DOI: 10.1093/hmg/ddw314] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 08/19/2016] [Accepted: 09/12/2016] [Indexed: 11/14/2022] Open
Abstract
Increasing evidence shows that phenotypic variance is genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e. genomic loci associated with gene expression variance, in humans. We discovered that common genetic variants may contribute to increasing gene expression variance via two distinct modes of action—epistasis and destabilization. Specifically, epistasis explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of ‘third-party’ eQTLs that influence the gene expression mean in a fraction, rather than the entire set, of sampled individuals. On the other hand, the destabilization model explains the other three-quarters of evQTLs, caused by mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and estimated the stability of gene expression in single samples using repetitive qRT-PCR assays. The mutations that cause destabilizing evQTLs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common genetic variants work either interactively or independently to shape the variability of gene expression in humans. Our findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variance-centred analytic methods for quantitative trait mapping.
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Affiliation(s)
- Ence Yang
- Institute for Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
- Department of Veterinary Integrative Biosciences
| | - Gang Wang
- Department of Veterinary Integrative Biosciences
| | - Jizhou Yang
- Department of Veterinary Integrative Biosciences
| | - Beiyan Zhou
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
- Department of Immunology, University of Connecticut Health Center, Farmington, CT, USA and
| | - Yanan Tian
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| | - James J. Cai
- Department of Veterinary Integrative Biosciences
- Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX, USA
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84
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Jędrak J, Ochab-Marcinek A. Time-dependent solutions for a stochastic model of gene expression with molecule production in the form of a compound Poisson process. Phys Rev E 2016; 94:032401. [PMID: 27739798 DOI: 10.1103/physreve.94.032401] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Indexed: 06/06/2023]
Abstract
We study a stochastic model of gene expression, in which protein production has a form of random bursts whose size distribution is arbitrary, whereas protein decay is a first-order reaction. We find exact analytical expressions for the time evolution of the cumulant-generating function for the most general case when both the burst size probability distribution and the model parameters depend on time in an arbitrary (e.g., oscillatory) manner, and for arbitrary initial conditions. We show that in the case of periodic external activation and constant protein degradation rate, the response of the gene is analogous to the resistor-capacitor low-pass filter, where slow oscillations of the external driving have a greater effect on gene expression than the fast ones. We also demonstrate that the nth cumulant of the protein number distribution depends on the nth moment of the burst size distribution. We use these results to show that different measures of noise (coefficient of variation, Fano factor, fractional change of variance) may vary in time in a different manner. Therefore, any biological hypothesis of evolutionary optimization based on the nonmonotonic dependence of a chosen measure of noise on time must justify why it assumes that biological evolution quantifies noise in that particular way. Finally, we show that not only for exponentially distributed burst sizes but also for a wider class of burst size distributions (e.g., Dirac delta and gamma) the control of gene expression level by burst frequency modulation gives rise to proportional scaling of variance of the protein number distribution to its mean, whereas the control by amplitude modulation implies proportionality of protein number variance to the mean squared.
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Affiliation(s)
- Jakub Jędrak
- Institute of Physical Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Anna Ochab-Marcinek
- Institute of Physical Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224 Warsaw, Poland
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85
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Soltani M, Vargas-Garcia CA, Antunes D, Singh A. Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes. PLoS Comput Biol 2016; 12:e1004972. [PMID: 27536771 PMCID: PMC4990281 DOI: 10.1371/journal.pcbi.1004972] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 07/29/2016] [Indexed: 12/22/2022] Open
Abstract
Inside individual cells, expression of genes is inherently stochastic and manifests as cell-to-cell variability or noise in protein copy numbers. Since proteins half-lives can be comparable to the cell-cycle length, randomness in cell-division times generates additional intercellular variability in protein levels. Moreover, as many mRNA/protein species are expressed at low-copy numbers, errors incurred in partitioning of molecules between two daughter cells are significant. We derive analytical formulas for the total noise in protein levels when the cell-cycle duration follows a general class of probability distributions. Using a novel hybrid approach the total noise is decomposed into components arising from i) stochastic expression; ii) partitioning errors at the time of cell division and iii) random cell-division events. These formulas reveal that random cell-division times not only generate additional extrinsic noise, but also critically affect the mean protein copy numbers and intrinsic noise components. Counter intuitively, in some parameter regimes, noise in protein levels can decrease as cell-division times become more stochastic. Computations are extended to consider genome duplication, where transcription rate is increased at a random point in the cell cycle. We systematically investigate how the timing of genome duplication influences different protein noise components. Intriguingly, results show that noise contribution from stochastic expression is minimized at an optimal genome-duplication time. Our theoretical results motivate new experimental methods for decomposing protein noise levels from synchronized and asynchronized single-cell expression data. Characterizing the contributions of individual noise mechanisms will lead to precise estimates of gene expression parameters and techniques for altering stochasticity to change phenotype of individual cells. Inside individual cells, gene products often occur at low molecular counts and are subject to considerable stochastic fluctuations (noise) in copy numbers over time. An important consequence of noisy expression is that the level of a protein can vary considerably even among genetically identical cells exposed to the same environment. Such non-genetic phenotypic heterogeneity is physiologically relevant and critically influences diverse cellular processes. In addition to noise sources inherent in gene product synthesis, recent experimental studies have uncovered additional noise mechanisms that critically effect expression. For example, the time within the cell cycle when a gene duplicates, and the time taken to complete cell cycle are governed by random processes. The key contribution of this work is development of novel mathematical results quantifying how cell cycle-related noise sources combine with stochastic expression to drive intercellular variability in protein molecular counts. Derived formulas lead to many counterintuitive results, such as increasing randomness in the timing of cell division can lower noise in the level of a protein. Finally, these results inform experimental strategies to systematically dissect the contributions of different noise sources in the expression of a gene of interest.
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Affiliation(s)
- Mohammad Soltani
- Electrical and Computer Engineering Department, University of Delaware, Newark, Delaware, United States of America
| | - Cesar A. Vargas-Garcia
- Electrical and Computer Engineering Department, University of Delaware, Newark, Delaware, United States of America
| | - Duarte Antunes
- Mechanical Engineering Department, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Abhyudai Singh
- Electrical and Computer Engineering Department, University of Delaware, Newark, Delaware, United States of America
- Biomedical Engineering Department, University of Delaware, Newark, Delaware, United States of America
- Mathematical Sciences Department, University of Delaware, Newark, Delaware, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
- * E-mail:
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86
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Abstract
The production of a single mRNA is the result of many sequential steps, from docking of transcription factors to polymerase initiation, elongation, splicing, and, finally, termination. Much of our knowledge about the fundamentals of RNA synthesis and processing come from ensemble in vitro biochemical measurements. Single-molecule approaches are very much in this same reductionist tradition but offer exquisite sensitivity in space and time along with the ability to observe heterogeneous behavior and actually manipulate macromolecules. These techniques can also be applied in vivo, allowing one to address questions in living cells that were previously restricted to reconstituted systems. In this review, we examine the unique insights that single-molecule techniques have yielded on the mechanisms of gene expression.
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Affiliation(s)
- Huimin Chen
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Daniel R Larson
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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87
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Abstract
The transcription cycle can be roughly divided into three stages: initiation, elongation, and termination. Understanding the molecular events that regulate all these stages requires a dynamic view of the underlying processes. The development of techniques to visualize and quantify transcription in single living cells has been essential in revealing the transcription kinetics. They have revealed that (a) transcription is heterogeneous between cells and (b) transcription can be discontinuous within a cell. In this review, we discuss the progress in our quantitative understanding of transcription dynamics in living cells, focusing on all parts of the transcription cycle. We present the techniques allowing for single-cell transcription measurements, review evidence from different organisms, and discuss how these experiments have broadened our mechanistic understanding of transcription regulation.
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Affiliation(s)
- Tineke L Lenstra
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892;
| | - Joseph Rodriguez
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892;
| | - Huimin Chen
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892;
| | - Daniel R Larson
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892;
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88
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Liu J, François JM, Capp JP. Use of noise in gene expression as an experimental parameter to test phenotypic effects. Yeast 2016; 33:209-16. [DOI: 10.1002/yea.3152] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 01/12/2016] [Accepted: 01/14/2016] [Indexed: 01/12/2023] Open
Affiliation(s)
- Jian Liu
- INSA/Université Fédérale Toulouse Midi-Pyrénées; Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés; UMR CNRS 5504 UMR INRA 792 Toulouse France
| | - Jean-Marie François
- INSA/Université Fédérale Toulouse Midi-Pyrénées; Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés; UMR CNRS 5504 UMR INRA 792 Toulouse France
| | - Jean-Pascal Capp
- INSA/Université Fédérale Toulouse Midi-Pyrénées; Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés; UMR CNRS 5504 UMR INRA 792 Toulouse France
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89
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Turnaev II, Rasskazov DA, Arkova OV, Ponomarenko MP, Ponomarenko PM, Savinkova LK, Kolchanov NA. Hypothetical SNP markers that significantly affect the affinity of the TATA-binding protein to VEGFA, ERBB2, IGF1R, FLT1, KDR, and MET oncogene promoters as chemotherapy targets. Mol Biol 2016. [DOI: 10.1134/s0026893316010209] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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90
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Ravarani CNJ, Chalancon G, Breker M, de Groot NS, Babu MM. Affinity and competition for TBP are molecular determinants of gene expression noise. Nat Commun 2016; 7:10417. [PMID: 26832815 PMCID: PMC4740812 DOI: 10.1038/ncomms10417] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 12/09/2015] [Indexed: 12/14/2022] Open
Abstract
Cell-to-cell variation in gene expression levels (noise) generates phenotypic diversity and is an important phenomenon in evolution, development and disease. TATA-box binding protein (TBP) is an essential factor that is required at virtually every eukaryotic promoter to initiate transcription. While the presence of a TATA-box motif in the promoter has been strongly linked with noise, the molecular mechanism driving this relationship is less well understood. Through an integrated analysis of multiple large-scale data sets, computer simulation and experimental validation in yeast, we provide molecular insights into how noise arises as an emergent property of variable binding affinity of TBP for different promoter sequences, competition between interaction partners to bind the same surface on TBP (to either promote or disrupt transcription initiation) and variable residence times of TBP complexes at a promoter. These determinants may be fine-tuned under different conditions and during evolution to modulate eukaryotic gene expression noise.
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Affiliation(s)
- Charles N J Ravarani
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Guilhem Chalancon
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Michal Breker
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | | | - M Madan Babu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
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91
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Metzger BPH, Duveau F, Yuan DC, Tryban S, Yang B, Wittkopp PJ. Contrasting Frequencies and Effects of cis- and trans-Regulatory Mutations Affecting Gene Expression. Mol Biol Evol 2016; 33:1131-46. [PMID: 26782996 DOI: 10.1093/molbev/msw011] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Heritable differences in gene expression are caused by mutations in DNA sequences encoding cis-regulatory elements and trans-regulatory factors. These two classes of regulatory change differ in their relative contributions to expression differences in natural populations because of the combined effects of mutation and natural selection. Here, we investigate how new mutations create the regulatory variation upon which natural selection acts by quantifying the frequencies and effects of hundreds of new cis- and trans-acting mutations altering activity of the TDH3 promoter in the yeast Saccharomyces cerevisiae in the absence of natural selection. We find that cis-regulatory mutations have larger effects on expression than trans-regulatory mutations and that while trans-regulatory mutations are more common overall, cis- and trans-regulatory changes in expression are equally abundant when only the largest changes in expression are considered. In addition, we find that cis-regulatory mutations are skewed toward decreased expression while trans-regulatory mutations are skewed toward increased expression. We also measure the effects of cis- and trans-regulatory mutations on the variability in gene expression among genetically identical cells, a property of gene expression known as expression noise, finding that trans-regulatory mutations are much more likely to decrease expression noise than cis-regulatory mutations. Because new mutations are the raw material upon which natural selection acts, these differences in the frequencies and effects of cis- and trans-regulatory mutations should be considered in models of regulatory evolution.
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Affiliation(s)
- Brian P H Metzger
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor
| | - Fabien Duveau
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor
| | - David C Yuan
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor Department of Biology, Stanford University
| | - Stephen Tryban
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor
| | - Bing Yang
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor
| | - Patricia J Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor
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92
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93
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Guantes R, Díaz-Colunga J, Iborra FJ. Mitochondria and the non-genetic origins of cell-to-cell variability: More is different. Bioessays 2015; 38:64-76. [PMID: 26660201 DOI: 10.1002/bies.201500082] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Gene expression activity is heterogeneous in a population of isogenic cells. Identifying the molecular basis of this variability will improve our understanding of phenomena like tumor resistance to drugs, virus infection, or cell fate choice. The complexity of the molecular steps and machines involved in transcription and translation could introduce sources of randomness at many levels, but a common constraint to most of these processes is its energy dependence. In eukaryotic cells, most of this energy is provided by mitochondria. A clonal population of cells may show a large variability in the number and functionality of mitochondria. Here, we discuss how differences in the mitochondrial content of each cell contribute to heterogeneity in gene products. Changes in the amount of mitochondria can also entail drastic alterations of a cell's gene expression program, which ultimately leads to phenotypic diversity. Also watch the Video Abstract.
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Affiliation(s)
- Raúl Guantes
- Department of Condensed Matter Physics, Materials Science Institute 'Nicolás Cabrera' and Institute of Condensed Matter Physics (IFIMAC), Universidad Autónoma de Madrid, Campus de Cantoblanco, Madrid, Spain
| | - Juan Díaz-Colunga
- Centro Nacional de Biotecnología, CSIC, Campus de Cantoblanco, Madrid, Spain
| | - Francisco J Iborra
- Centro Nacional de Biotecnología, CSIC, Campus de Cantoblanco, Madrid, Spain
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94
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Roberts E, Be'er S, Bohrer C, Sharma R, Assaf M. Dynamics of simple gene-network motifs subject to extrinsic fluctuations. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:062717. [PMID: 26764737 DOI: 10.1103/physreve.92.062717] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Indexed: 06/05/2023]
Abstract
Cellular processes do not follow deterministic rules; even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise (IN) of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise (EN) has been experimentally shown to dominate the variation. Here, we develop an analytical formalism that allows for calculation of the effect of EN on gene-expression motifs. We introduce a method for modeling bounded EN as an auxiliary species in the master equation. The method is fully generic and is not limited to systems with small EN magnitudes. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a nonregulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the EN properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of EN on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene-expression experiments.
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Affiliation(s)
- Elijah Roberts
- Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Shay Be'er
- Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Chris Bohrer
- Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Rati Sharma
- Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Michael Assaf
- Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904, Israel
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95
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Ponomarenko PM, Ponomarenko MP. Sequence-based prediction of transcription upregulation by auxin in plants. J Bioinform Comput Biol 2015; 13:1540009. [PMID: 25666655 DOI: 10.1142/s0219720015400090] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Auxin is one of the main regulators of growth and development in plants. Prediction of auxin response based on gene sequence is of high importance. We found the TGTCNC consensus of 111 known natural and artificially mutated auxin response elements (AuxREs) with measured auxin-caused relative increase in genes' transcription levels, so-called either "a response to auxin" or "an auxin response." This consensus was identical to the most cited AuxRE motif. Also, we found several DNA sequence features that correlate with auxin-caused increase in genes' transcription levels, namely: number of matches with TGTCNC, homology score based on nucleotide frequencies at the consensus positions, abundances of five trinucleotides and five B-helical DNA features around these known AuxREs. We combined these correlations using a four-step empirical model of auxin response based on a gene's sequence with four steps, namely: (1) search for AuxREs with no auxin; (2) stop at the found AuxRE; (3) repression of the basal transcription of the gene having this AuxRE; and (4) manifold increase of this gene's transcription in response to auxin. Independently measured increases in transcription levels in response to auxin for 70 Arabidopsis genes were found to significantly correlate with predictions of this equation (r = 0.44, p < 0.001) as well as with TATA-binding protein (TBP)'s affinity to promoters of these genes and with nucleosome packing of these promoters (both, p < 0.025). Finally, we improved our equation for prediction of a gene's transcription increase in response to auxin by taking into account TBP-binding and nucleosome packing (r = 0.53, p < 10(-6)). Fisher's F-test validated the significant impact of both TBP/promoter-affinity and promoter nucleosome on auxin response in addition to those of AuxRE, F = 4.07, p < 0.025. It means that both TATA-box and nucleosome should be taken into account to recognize transcription factor binding sites upon DNA sequences: in the case of the TATA-less nucleosome-rich promoters, recognition scores must be higher than in the case of the TATA-containing nucleosome-free promoters at the same transcription activity.
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Affiliation(s)
- Petr M Ponomarenko
- Children's Hospital Los Angeles, 4640 Hollywood Blvd, Los Angeles, CA 90027, USA
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96
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Brenner N, Newman CM, Osmanović D, Rabin Y, Salman H, Stein DL. Universal protein distributions in a model of cell growth and division. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:042713. [PMID: 26565278 DOI: 10.1103/physreve.92.042713] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Indexed: 06/05/2023]
Abstract
Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means. For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division across generations. Here we present a stochastic growth-and-division model with feedback which captures these observed properties. The limiting copy number distribution is calculated exactly, and a single parameter is found to determine the distribution shape and the variance-to-mean relation. Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy and leads to predictions for future experiments.
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Affiliation(s)
- Naama Brenner
- Department of Chemical Engineering and Laboratory of Network Biology, Technion, Haifa 32000, Israel
| | - C M Newman
- Courant Institute of Mathematical Sciences, New York, New York 10012 USA and NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai, 3663 Zhongshan Road North, Shanghai 200062, China
| | - Dino Osmanović
- Department of Physics and Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Yitzhak Rabin
- Department of Physics and Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Hanna Salman
- Department of Physics and Astronomy, Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - D L Stein
- Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, New York 10012 USA and NYU-ECNU Institutes of Physics and Mathematical Sciences at NYU Shanghai, 3663 Zhongshan Road North, Shanghai, 200062, China
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97
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Keren L, van Dijk D, Weingarten-Gabbay S, Davidi D, Jona G, Weinberger A, Milo R, Segal E. Noise in gene expression is coupled to growth rate. Genome Res 2015; 25:1893-902. [PMID: 26355006 PMCID: PMC4665010 DOI: 10.1101/gr.191635.115] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 09/09/2015] [Indexed: 11/24/2022]
Abstract
Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle–regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications.
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Affiliation(s)
- Leeat Keren
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - David van Dijk
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, New York 10027, USA
| | - Shira Weingarten-Gabbay
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Dan Davidi
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ghil Jona
- Biological Services Unit, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ron Milo
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
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98
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Vance KW, Woodcock DJ, Reid JE, Bretschneider T, Ott S, Koentges G. Conserved Cis-Regulatory Modules Control Robustness in Msx1 Expression at Single-Cell Resolution. Genome Biol Evol 2015; 7:2762-78. [PMID: 26342140 PMCID: PMC4607535 DOI: 10.1093/gbe/evv179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The process of transcription is highly stochastic leading to cell-to-cell variations and noise in gene expression levels. However, key essential genes have to be precisely expressed at the correct amount and time to ensure proper cellular development and function. Studies in yeast and bacterial systems have shown that gene expression noise decreases as mean expression levels increase, a relationship that is controlled by promoter DNA sequence. However, the function of distal cis-regulatory modules (CRMs), an evolutionary novelty of metazoans, in controlling transcriptional robustness and variability is poorly understood. In this study, we used live cell imaging of transfected reporters combined with a mathematical modelling and statistical inference scheme to quantify the function of conserved Msx1 CRMs and promoters in modulating single-cell real-time transcription rates in C2C12 mouse myoblasts. The results show that the mean expression–noise relationship is solely promoter controlled for this key pluripotency regulator. In addition, we demonstrate that CRMs modulate single-cell basal promoter rate distributions in a graded manner across a population of cells. This extends the rheostatic model of CRM action to provide a more detailed understanding of CRM function at single-cell resolution. We also identify a novel CRM transcriptional filter function that acts to reduce intracellular variability in transcription rates and show that this can be phylogenetically separable from rate modulating CRM activities. These results are important for understanding how the expression of key vertebrate developmental transcription factors is precisely controlled both within and between individual cells.
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Affiliation(s)
- Keith W Vance
- Department of Biology and Biochemistry, University of Bath, United Kingdom Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Dan J Woodcock
- Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom
| | - John E Reid
- MRC Biostatistics Unit, Robinson Way, Cambridge, United Kingdom
| | - Till Bretschneider
- Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom
| | - Sascha Ott
- Warwick Systems Biology Centre, University of Warwick, Coventry, United Kingdom
| | - Georgy Koentges
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
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99
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Hansen AS, O'Shea EK. cis Determinants of Promoter Threshold and Activation Timescale. Cell Rep 2015; 12:1226-33. [PMID: 26279577 DOI: 10.1016/j.celrep.2015.07.035] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Revised: 07/08/2015] [Accepted: 07/15/2015] [Indexed: 11/16/2022] Open
Abstract
Although the relationship between DNA cis-regulatory sequences and gene expression has been extensively studied at steady state, how cis-regulatory sequences affect the dynamics of gene induction is not known. The dynamics of gene induction can be described by the promoter activation timescale (AcTime) and amplitude threshold (AmpThr). Combining high-throughput microfluidics with quantitative time-lapse microscopy, we control the activation dynamics of the budding yeast transcription factor, Msn2, and reveal how cis-regulatory motifs in 20 promoter variants of the Msn2-target-gene SIP18 affect AcTime and AmpThr. By modulating Msn2 binding sites, we can decouple AmpThr from AcTime and switch the SIP18 promoter class from high AmpThr and slow AcTime to low AmpThr and either fast or slow AcTime. We present a model that quantitatively explains gene-induction dynamics on the basis of the Msn2-binding-site number, TATA box location, and promoter nucleosome organization. Overall, we elucidate the cis-regulatory logic underlying promoter decoding of TF dynamics.
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Affiliation(s)
- Anders S Hansen
- Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Northwest Laboratory, 52 Oxford Street, Cambridge, MA 02138, USA; Faculty of Arts and Sciences Center for Systems Biology, Harvard University, Northwest Laboratory, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Erin K O'Shea
- Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Northwest Laboratory, 52 Oxford Street, Cambridge, MA 02138, USA; Faculty of Arts and Sciences Center for Systems Biology, Harvard University, Northwest Laboratory, 52 Oxford Street, Cambridge, MA 02138, USA; Department of Molecular and Cellular Biology, Harvard University, Northwest Laboratory, 52 Oxford Street, Cambridge, MA 02138, USA.
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100
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Zoller B, Nicolas D, Molina N, Naef F. Structure of silent transcription intervals and noise characteristics of mammalian genes. Mol Syst Biol 2015. [PMID: 26215071 PMCID: PMC4547851 DOI: 10.15252/msb.20156257] [Citation(s) in RCA: 81] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Mammalian transcription occurs stochastically in short bursts interspersed by silent intervals showing a refractory period. However, the underlying processes and consequences on fluctuations in gene products are poorly understood. Here, we use single allele time-lapse recordings in mouse cells to identify minimal models of promoter cycles, which inform on the number and durations of rate-limiting steps responsible for refractory periods. The structure of promoter cycles is gene specific and independent of genomic location. Typically, five rate-limiting steps underlie the silent periods of endogenous promoters, while minimal synthetic promoters exhibit only one. Strikingly, endogenous or synthetic promoters with TATA boxes show simplified two-state promoter cycles. Since transcriptional bursting constrains intrinsic noise depending on the number of promoter steps, this explains why TATA box genes display increased intrinsic noise genome-wide in mammals, as revealed by single-cell RNA-seq. These findings have implications for basic transcription biology and shed light on interpreting single-cell RNA-counting experiments.
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Affiliation(s)
- Benjamin Zoller
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Damien Nicolas
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Nacho Molina
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Felix Naef
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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