1
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Pal S, Dhar R. Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making. FEBS Lett 2024; 598:1673-1691. [PMID: 38724715 DOI: 10.1002/1873-3468.14898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 07/23/2024]
Abstract
The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.
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Affiliation(s)
- Sampriti Pal
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
| | - Riddhiman Dhar
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
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2
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Ali MZ, Guharajan S, Parisutham V, Brewster RC. Regulatory properties of transcription factors with diverse mechanistic function. PLoS Comput Biol 2024; 20:e1012194. [PMID: 38857275 PMCID: PMC11192337 DOI: 10.1371/journal.pcbi.1012194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/21/2024] [Accepted: 05/24/2024] [Indexed: 06/12/2024] Open
Abstract
Transcription factors (TFs) regulate the process of transcription through the modulation of different kinetic steps. Although models can often describe the observed transcriptional output of a measured gene, predicting a TFs role on a given promoter requires an understanding of how the TF alters each step of the transcription process. In this work, we use a simple model of transcription to assess the role of promoter identity, and the degree to which TFs alter binding of RNAP (stabilization) and initiation of transcription (acceleration) on three primary characteristics: the range of steady-state regulation, cell-to-cell variability in expression, and the dynamic response time of a regulated gene. We find that steady state regulation and the response time of a gene behave uniquely for TFs that regulate incoherently, i.e that speed up one step but slow the other. We also find that incoherent TFs have dynamic implications, with one type of incoherent mode configuring the promoter to respond more slowly at intermediate TF concentrations. We also demonstrate that the noise of gene expression for these TFs is sensitive to promoter strength, with a distinct non-monotonic profile that is apparent under stronger promoters. Taken together, our work uncovers the coupling between promoters and TF regulatory modes with implications for understanding natural promoters and engineering synthetic gene circuits with desired expression properties.
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Affiliation(s)
- Md Zulfikar Ali
- Department of Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Geology, Physics and Environmental Science, University of Southern Indiana, Evansville, Indiana, United States of America
| | - Sunil Guharajan
- Department of Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Vinuselvi Parisutham
- Department of Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Robert C. Brewster
- Department of Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
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3
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Feng H, Li F, Wang T, Xing XH, Zeng AP, Zhang C. Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision. SCIENCE ADVANCES 2023; 9:eadg5296. [PMID: 37939173 PMCID: PMC10631719 DOI: 10.1126/sciadv.adg5296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023]
Abstract
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.
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Affiliation(s)
- Huibao Feng
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Fan Li
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Tianmin Wang
- Tsinghua-Peking Center for Life Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xin-hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - An-ping Zeng
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg 21073, Germany
- Center of Synthetic Biology and Integrated Bioengineering, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
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4
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Capponi S, Daniels KG. Harnessing the power of artificial intelligence to advance cell therapy. Immunol Rev 2023; 320:147-165. [PMID: 37415280 DOI: 10.1111/imr.13236] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/17/2023] [Indexed: 07/08/2023]
Abstract
Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.
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Affiliation(s)
- Sara Capponi
- Department of Functional Genomics and Cellular Engineering, IBM Almaden Research Center, San Jose, California, USA
- Center for Cellular Construction, San Francisco, California, USA
| | - Kyle G Daniels
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
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5
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Ramalingam V, Yu X, Slaughter BD, Unruh JR, Brennan KJ, Onyshchenko A, Lange JJ, Natarajan M, Buck M, Zeitlinger J. Lola-I is a promoter pioneer factor that establishes de novo Pol II pausing during development. Nat Commun 2023; 14:5862. [PMID: 37735176 PMCID: PMC10514308 DOI: 10.1038/s41467-023-41408-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/30/2023] [Indexed: 09/23/2023] Open
Abstract
While the accessibility of enhancers is dynamically regulated during development, promoters tend to be constitutively accessible and poised for activation by paused Pol II. By studying Lola-I, a Drosophila zinc finger transcription factor, we show here that the promoter state can also be subject to developmental regulation independently of gene activation. Lola-I is ubiquitously expressed at the end of embryogenesis and causes its target promoters to become accessible and acquire paused Pol II throughout the embryo. This promoter transition is required but not sufficient for tissue-specific target gene activation. Lola-I mediates this function by depleting promoter nucleosomes, similar to the action of pioneer factors at enhancers. These results uncover a level of regulation for promoters that is normally found at enhancers and reveal a mechanism for the de novo establishment of paused Pol II at promoters.
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Affiliation(s)
- Vivekanandan Ramalingam
- Stowers Institute for Medical Research, Kansas City, MO, USA
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center----, Kansas City, KS, USA
- Department of Genetics, Stanford University, Palo Alto, CA, USA
| | - Xinyang Yu
- Department of Biochemistry, State University of New York at Buffalo, Buffalo, NY, USA
| | | | - Jay R Unruh
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | | | | | - Jeffrey J Lange
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | | | - Michael Buck
- Department of Biochemistry, State University of New York at Buffalo, Buffalo, NY, USA
- Department of Biomedical Informatics, Jacobs School of Medicine & Biomedical Sciences, Buffalo, NY, USA
| | - Julia Zeitlinger
- Stowers Institute for Medical Research, Kansas City, MO, USA.
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center----, Kansas City, KS, USA.
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6
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Das S, Singh A, Shah P. Evaluating single-cell variability in proteasomal decay. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.22.554358. [PMID: 37662347 PMCID: PMC10473619 DOI: 10.1101/2023.08.22.554358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. Here we estimate the single-cell protein decay rates of two degron GFPs in Saccharomyces cerevisiae using time-lapse microscopy. We find substantial cell-to-cell variability in the decay rates of the degron GFPs. We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs. Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.
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Affiliation(s)
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, Biomedical Engineering, University of Delaware
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7
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Gligorovski V, Sadeghi A, Rahi SJ. Multidimensional characterization of inducible promoters and a highly light-sensitive LOV-transcription factor. Nat Commun 2023; 14:3810. [PMID: 37369667 DOI: 10.1038/s41467-023-38959-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
The ability to independently control the expression of different genes is important for quantitative biology. Using budding yeast, we characterize GAL1pr, GALL, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr, Z3EV, blue-light inducible optogenetic systems El222-LIP, El222-GLIP, and red-light inducible PhyB-PIF3. We report kinetic parameters, noise scaling, impact on growth, and the fundamental leakiness of each system using an intuitive unit, maxGAL1. We uncover disadvantages of widely used tools, e.g., nonmonotonic activity of MET3pr and GALL, slow off kinetics of the doxycycline- and estradiol-inducible systems tetOpr and Z3EV, and high variability of PHO5pr and red-light activated PhyB-PIF3 system. We introduce two previously uncharacterized systems: strongLOV, a more light-sensitive El222 mutant, and ARG3pr, which is induced in the absence of arginine or presence of methionine. To demonstrate fine control over gene circuits, we experimentally tune the time between cell cycle Start and mitosis, artificially simulating near-wild-type timing. All strains, constructs, code, and data ( https://promoter-benchmark.epfl.ch/ ) are made available.
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Affiliation(s)
- Vojislav Gligorovski
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ahmad Sadeghi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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8
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Wittkopp PJ. Contributions of mutation and selection to regulatory variation: lessons from the Saccharomyces cerevisiae TDH3 gene. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220057. [PMID: 37004723 PMCID: PMC10067266 DOI: 10.1098/rstb.2022.0057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/16/2023] [Indexed: 04/04/2023] Open
Abstract
Heritable variation in gene expression is common within and among species and contributes to phenotypic diversity. Mutations affecting either cis- or trans-regulatory sequences controlling gene expression give rise to variation in gene expression, and natural selection acting on this variation causes some regulatory variants to persist in a population for longer than others. To understand how mutation and selection interact to produce the patterns of regulatory variation we see within and among species, my colleagues and I have been systematically determining the effects of new mutations on expression of the TDH3 gene in Saccharomyces cerevisiae and comparing them to the effects of polymorphisms segregating within this species. We have also investigated the molecular mechanisms by which regulatory variants act. Over the past decade, this work has revealed properties of cis- and trans-regulatory mutations including their relative frequency, effects, dominance, pleiotropy and fitness consequences. Comparing these mutational effects to the effects of polymorphisms in natural populations, we have inferred selection acting on expression level, expression noise and phenotypic plasticity. Here, I summarize this body of work and synthesize its findings to make inferences not readily discernible from the individual studies alone. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Patricia J. Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
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9
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Chen Y, Hu R, Li K, Zhang Y, Fu L, Zhang J, Si T. Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects. ACS Synth Biol 2023; 12:1461-1473. [PMID: 37066862 PMCID: PMC10204710 DOI: 10.1021/acssynbio.2c00662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Indexed: 04/18/2023]
Abstract
Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.
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Affiliation(s)
- Yongcan Chen
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruyun Hu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Keyi Li
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yating Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianzhi Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- BGI-Shenzhen, Shenzhen 518083, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
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10
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Weidemann DE, Singh A, Grima R, Hauf S. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531283. [PMID: 36945401 PMCID: PMC10028819 DOI: 10.1101/2023.03.06.531283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Stochastic variation in gene products ("noise") is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.
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Affiliation(s)
- Douglas E Weidemann
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, Scotland, UK
| | - Silke Hauf
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA 24061, USA
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11
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Enhanced Transcriptional Strength of HIV-1 Subtype C Minimizes Gene Expression Noise and Confers Stability to the Viral Latent State. J Virol 2023; 97:e0137622. [PMID: 36533949 PMCID: PMC9888270 DOI: 10.1128/jvi.01376-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Stochastic fluctuations in gene expression emanating from the HIV-1 long terminal repeat (LTR), amplified by the Tat positive feedback circuit, determine the choice between viral infection fates: active transcription (ON) or transcriptional silence (OFF). The emergence of several transcription factor binding site (TFBS) variant strains in HIV-1 subtype C (HIV-1C), especially those containing the duplication of the NF-κB motif, mandates the evaluation of the effect of enhanced transcriptional strength on gene expression noise and its influence on viral fate selection switch. Using a panel of subgenomic LTR-variant strains containing different copy numbers of the NF-κB motif (ranging from 0 to 4), we used flow cytometry, mRNA quantification, and pharmacological perturbations to demonstrate an inverse correlation between promoter strength and gene expression noise in Jurkat T cells and primary CD4+ T cells. The inverse correlation is consistent in clonal cell populations at constant intracellular concentrations of Tat and when NF-κB levels were regulated pharmacologically. Further, we show that strong LTRs containing at least two copies of the NF-κB motif in the enhancer establish a more stable latent state and demonstrate more rapid latency reversal than weak LTRs containing fewer motifs. We also demonstrate a cooperative binding of NF-κB to the motif cluster in HIV-1C LTRs containing two, three, or four NF-κB motifs (Hill coefficient [H] = 2.61, 3.56, and 3.75, respectively). The present work alludes to a possible evolution of the HIV-1C LTR toward gaining transcriptional strength associated with attenuated gene expression noise with implications for viral latency. IMPORTANCE Over the past two consecutive decades, HIV-1 subtype C (HIV-1C) has been undergoing directional evolution toward augmenting the transcriptional strength of the long terminal repeat (LTR) by adding more copies of the existing transcription factor binding site (TFBS) by sequence duplication. Additionally, the duplicated elements are genetically diverse, suggesting broader-range signal receptivity by variant LTRs. The HIV-1 promoter is inherently noisy, and the stochastic fluctuations in gene expression of variant LTRs may influence the active transcription (ON)/transcriptional silence (OFF) latency decisions. The evolving NF-κB motif variations of HIV-1C offer a powerful opportunity to examine how the transcriptional strength of the LTR might influence gene expression noise. Our work here shows that the augmented transcriptional strength of the HIV-1C LTR leads to concomitantly reduced gene expression noise, consequently leading to stabler latency maintenance and rapid latency reversal. The present work offers a novel lead toward appreciating the molecular mechanisms governing HIV-1 latency.
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Abstract
The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.
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13
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Parab L, Pal S, Dhar R. Transcription factor binding process is the primary driver of noise in gene expression. PLoS Genet 2022; 18:e1010535. [PMID: 36508455 PMCID: PMC9779669 DOI: 10.1371/journal.pgen.1010535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/22/2022] [Accepted: 11/16/2022] [Indexed: 12/14/2022] Open
Abstract
Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.
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Affiliation(s)
- Lavisha Parab
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
- Max-Planck-Institute for Evolutionary Biology, Plön, Germany
| | - Sampriti Pal
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
| | - Riddhiman Dhar
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
- * E-mail:
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14
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Controlling gene expression with deep generative design of regulatory DNA. Nat Commun 2022; 13:5099. [PMID: 36042233 PMCID: PMC9427793 DOI: 10.1038/s41467-022-32818-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022] Open
Abstract
Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue. Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.
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15
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Vaknin I, Amit R. Molecular and experimental tools to design synthetic enhancers. Curr Opin Biotechnol 2022; 76:102728. [PMID: 35525178 DOI: 10.1016/j.copbio.2022.102728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 03/16/2022] [Accepted: 04/03/2022] [Indexed: 11/03/2022]
Abstract
Understanding the grammar of enhancers and how they regulate gene expression is key for both basic research and for the pharma and biotech industries. The design and characterization of synthetic enhancers can expand the known regulatory space. This is achieved by the utilization of DNA Oligo Libraries (OLs), which facilitates screening of as many as millions of synthetic enhancer variants simultaneously. This review includes the latest commercial DNA OL synthesis technology and its capabilities, and a general 'know-how' guide for the design, construction, and analysis of OL-based synthetic enhancer characterization experiments. Specifically, we focus on synthetic-enhancer-based massively parallel reporter assay, Sort-seq methodologies (e.g. flow cytometry, deep sequencing), and a brief description of machine learning-based attempts for OL-analysis and follow-up validation experiments.
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Affiliation(s)
- Inbal Vaknin
- Department of Biotechnology and Food Engineering, Technion - Israel Institute of Technology, Haifa 3200000, Israel
| | - Roee Amit
- Department of Biotechnology and Food Engineering, Technion - Israel Institute of Technology, Haifa 3200000, Israel; The Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa 3200000, Israel.
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16
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Kim NM, Sinnott RW, Rothschild LN, Sandoval NR. Elucidation of Sequence-Function Relationships for an Improved Biobutanol In Vivo Biosensor in E. coli. Front Bioeng Biotechnol 2022; 10:821152. [PMID: 35265600 PMCID: PMC8899819 DOI: 10.3389/fbioe.2022.821152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 01/17/2022] [Indexed: 11/30/2022] Open
Abstract
Transcription factor (TF)–promoter pairs have been repurposed from native hosts to provide tools to measure intracellular biochemical production titer and dynamically control gene expression. Most often, native TF–promoter systems require rigorous screening to obtain desirable characteristics optimized for biotechnological applications. High-throughput techniques may provide a rational and less labor-intensive strategy to engineer user-defined TF–promoter pairs using fluorescence-activated cell sorting and deep sequencing methods (sort-seq). Based on the designed promoter library’s distribution characteristics, we elucidate sequence–function interactions between the TF and DNA. In this work, we use the sort-seq method to study the sequence–function relationship of a σ54-dependent, butanol-responsive TF–promoter pair, BmoR-PBMO derived from Thauera butanivorans, at the nucleotide level to improve biosensor characteristics, specifically an improved dynamic range. Activities of promoters from a mutagenized PBMO library were sorted based on gfp expression and subsequently deep sequenced to correlate site-specific sequences with changes in dynamic range. We identified site-specific mutations that increase the sensor output. Double mutant and a single mutant, CA(129,130)TC and G(205)A, in PBMO promoter increased dynamic ranges of 4-fold and 1.65-fold compared with the native system, respectively. In addition, sort-seq identified essential sites required for the proper function of the σ54-dependent promoter biosensor in the context of the host. This work can enable high-throughput screening methods for strain development.
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Affiliation(s)
- Nancy M Kim
- Interdisciplinary Bioinnovation PhD Program, Tulane University, New Orleans, LA, United States
| | - Riley W Sinnott
- Department of Chemical & Biomolecular Engineering, Tulane University, New Orleans, LA, United States
| | - Lily N Rothschild
- Department of Chemical & Biomolecular Engineering, Tulane University, New Orleans, LA, United States
| | - Nicholas R Sandoval
- Department of Chemical & Biomolecular Engineering, Tulane University, New Orleans, LA, United States
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17
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18
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Urchueguía A, Galbusera L, Chauvin D, Bellement G, Julou T, van Nimwegen E. Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network. PLoS Biol 2021; 19:e3001491. [PMID: 34919538 PMCID: PMC8719677 DOI: 10.1371/journal.pbio.3001491] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 12/31/2021] [Accepted: 11/23/2021] [Indexed: 11/21/2022] Open
Abstract
Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network. Genome-wide flow cytometry measurements reveal that gene expression noise in bacteria is highly condition-dependent; while absolute noise levels of all genes decrease with growth-rate, theoretical modeling shows that the relative noise levels of different genes are determined by the propagation of noise through the gene regulatory network (GRN). Thus GRN structure controls not only mean expression but also noise levels.
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Affiliation(s)
- Arantxa Urchueguía
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Luca Galbusera
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Dany Chauvin
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gwendoline Bellement
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Thomas Julou
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail: (TJ); (EvN)
| | - Erik van Nimwegen
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail: (TJ); (EvN)
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19
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Armetta J, Schantz-Klausen M, Shepelin D, Vazquez-Uribe R, Bahl MI, Laursen MF, Licht TR, Sommer MO. Escherichia coli Promoters with Consistent Expression throughout the Murine Gut. ACS Synth Biol 2021; 10:3359-3368. [PMID: 34842418 DOI: 10.1021/acssynbio.1c00325] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Advanced microbial therapeutics have great potential as a novel modality to diagnose and treat a wide range of diseases. Yet, to realize this potential, robust parts for regulating gene expression and consequent therapeutic activity in situ are needed. In this study, we characterized the expression level of more than 8000 variants of the Escherichia coli sigma factor 70 (σ70) promoter in a range of different environmental conditions and growth states using fluorescence-activated cell sorting and deep sequencing. Sampled conditions include aerobic and anaerobic culture in the laboratory as well as growth in several locations of the murine gastrointestinal tract. We found that σ70 promoters in E. coli generally maintain consistent expression levels across the murine gut (R2: 0.55-0.85, p value < 1 × 10-5), suggesting a limited environmental influence but a higher variability between in vitro and in vivo expression levels, highlighting the challenges of translating in vitro promoter activity to in vivo applications. Based on these data, we design the Schantzetta library, composed of eight promoters spanning a wide expression range and displaying a high degree of robustness in both laboratory and in vivo conditions (R2 = 0.98, p = 0.000827). This study provides a systematic assessment of the σ70 promoter activity in E. coli as it transits the murine gut leading to the definition of robust expression cassettes that could be a valuable tool for reliable engineering and development of advanced microbial therapeutics.
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Affiliation(s)
- Jeremy Armetta
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Michael Schantz-Klausen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Denis Shepelin
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Ruben Vazquez-Uribe
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Martin Iain Bahl
- National Food Institute, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | | | - Tine Rask Licht
- National Food Institute, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Morten O.A. Sommer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
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20
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A broad analysis of splicing regulation in yeast using a large library of synthetic introns. PLoS Genet 2021; 17:e1009805. [PMID: 34570750 PMCID: PMC8496845 DOI: 10.1371/journal.pgen.1009805] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/07/2021] [Accepted: 09/03/2021] [Indexed: 11/19/2022] Open
Abstract
RNA splicing is a key process in eukaryotic gene expression, in which an intron is spliced out of a pre-mRNA molecule to eventually produce a mature mRNA. Most intron-containing genes are constitutively spliced, hence efficient splicing of an intron is crucial for efficient regulation of gene expression. Here we use a large synthetic oligo library of ~20,000 variants to explore how different intronic sequence features affect splicing efficiency and mRNA expression levels in S. cerevisiae. Introns are defined by three functional sites, the 5’ donor site, the branch site, and the 3’ acceptor site. Using a combinatorial design of synthetic introns, we demonstrate how non-consensus splice site sequences in each of these sites affect splicing efficiency. We then show that S. cerevisiae splicing machinery tends to select alternative 3’ splice sites downstream of the original site, and we suggest that this tendency created a selective pressure, leading to the avoidance of cryptic splice site motifs near introns’ 3’ ends. We further use natural intronic sequences from other yeast species, whose splicing machineries have diverged to various extents, to show how intron architectures in the various species have been adapted to the organism’s splicing machinery. We suggest that the observed tendency for cryptic splicing is a result of a loss of a specific splicing factor, U2AF1. Lastly, we show that synthetic sequences containing two introns give rise to alternative RNA isoforms in S. cerevisiae, demonstrating that merely a synthetic fusion of two introns might be suffice to facilitate alternative splicing in yeast. Our study reveals novel mechanisms by which introns are shaped in evolution to allow cells to regulate their transcriptome. In addition, it provides a valuable resource to study the regulation of constitutive and alternative splicing in a model organism. RNA splicing is a process in which parts of a new pre-mRNA are spliced out of the mRNA molecule to produce eventually a mature mRNA. Those RNA segments that are spliced out are termed introns, and they are found in most genes in eukaryotic organisms. Hence regulation of this process has a major role in the control of gene expression. The budding yeast S. cerevisiae is a popular model organism for eukaryotic cell biology, but in terms of splicing it differs, as it has only few intron-containing genes. Nevertheless, this species has been used to study basic principles of splicing regulation based on its ~300 introns. Here we used the technology of a large synthetic genetic library to introduce many new intron-containing genes to the yeast genome, to explore splicing regulation at a wider scope than was possible so far. Reassuringly, our results confirm known regulatory mechanisms, and further expand our understanding of splicing regulation, specifically how the yeast splicing machinery interacts with the end of introns, and how through evolution introns have evolved to avoid unwanted misidentifications of this end. We further demonstrate the potential of the yeast splicing machinery to alternatively splice a two-intron gene, which is common in other eukaryotes but rare in yeast. Our work presents a first-of-its-kind resource for the systematic study of splicing in live cells.
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21
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Koberstein JN, Stewart ML, Mighell TL, Smith CB, Cohen MS. A Sort-Seq Approach to the Development of Single Fluorescent Protein Biosensors. ACS Chem Biol 2021; 16:1709-1720. [PMID: 34431656 PMCID: PMC9807264 DOI: 10.1021/acschembio.1c00423] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Motivated by the growing importance of single fluorescent protein biosensors (SFPBs) in biological research and the difficulty in rationally engineering these tools, we sought to increase the rate at which SFPB designs can be optimized. SFPBs generally consist of three components: a circularly permuted fluorescent protein, a ligand-binding domain, and linkers connecting the two domains. In the absence of predictive methods for biosensor engineering, most designs combining these three components will fail to produce allosteric coupling between ligand binding and fluorescence emission. While methods to construct diverse libraries with variation in the site of GFP insertion and linker sequences have been developed, the remaining bottleneck is the ability to test these libraries for functional biosensors. We address this challenge by applying a massively parallel assay termed "sort-seq," which combines binned fluorescence-activated cell sorting, next-generation sequencing, and maximum likelihood estimation to quantify the brightness and dynamic range for many biosensor variants in parallel. We applied this method to two common biosensor optimization tasks: the choice of insertion site and optimization of linker sequences. The sort-seq assay applied to a maltose-binding protein domain-insertion library not only identified previously described high-dynamic-range variants but also discovered new functional insertion sites with diverse properties. A sort-seq assay performed on a pyruvate biosensor linker library expressed in mammalian cell culture identified linker variants with substantially improved dynamic range. Machine learning models trained on the resulting data can predict dynamic range from linker sequences. This high-throughput approach will accelerate the design and optimization of SFPBs, expanding the biosensor toolbox.
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Affiliation(s)
- John N. Koberstein
- Vollum Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Melissa L. Stewart
- Vollum Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Taylor L. Mighell
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Chadwick B. Smith
- Vollum Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Michael S. Cohen
- Department of Physiology and Pharmacology, Oregon Health & Science University, Portland, OR 97239, USA
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22
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Shih CH, Fay J. Cis-regulatory variants affect gene expression dynamics in yeast. eLife 2021; 10:e68469. [PMID: 34369376 PMCID: PMC8367379 DOI: 10.7554/elife.68469] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/06/2021] [Indexed: 12/14/2022] Open
Abstract
Evolution of cis-regulatory sequences depends on how they affect gene expression and motivates both the identification and prediction of cis-regulatory variants responsible for expression differences within and between species. While much progress has been made in relating cis-regulatory variants to expression levels, the timing of gene activation and repression may also be important to the evolution of cis-regulatory sequences. We investigated allele-specific expression (ASE) dynamics within and between Saccharomyces species during the diauxic shift and found appreciable cis-acting variation in gene expression dynamics. Within-species ASE is associated with intergenic variants, and ASE dynamics are more strongly associated with insertions and deletions than ASE levels. To refine these associations, we used a high-throughput reporter assay to test promoter regions and individual variants. Within the subset of regions that recapitulated endogenous expression, we identified and characterized cis-regulatory variants that affect expression dynamics. Between species, chimeric promoter regions generate novel patterns and indicate constraints on the evolution of gene expression dynamics. We conclude that changes in cis-regulatory sequences can tune gene expression dynamics and that the interplay between expression dynamics and other aspects of expression is relevant to the evolution of cis-regulatory sequences.
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Affiliation(s)
- Ching-Hua Shih
- Department of Biology, University of RochesterRochesterUnited States
| | - Justin Fay
- Department of Biology, University of RochesterRochesterUnited States
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23
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Righetti E, Uluşeker C, Kahramanoğulları O. Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design. BIOLOGY 2021; 10:biology10080724. [PMID: 34439956 PMCID: PMC8389217 DOI: 10.3390/biology10080724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/05/2021] [Accepted: 07/22/2021] [Indexed: 11/18/2022]
Abstract
Simple Summary Synthetic biology is an emerging discipline, offering new perspectives in many industrial fields, from pharma and row-material production to renewable energy. Developing synthetic biology applications is often a lengthy and expensive process with extensive and tedious trial-and-error runs. Computational models can direct the engineering of biological circuits in a computer-aided design setting. By providing a virtual lab environment, in silico models of synthetic circuits can contribute to a quantitative understanding of the underlying molecular pathways before a wet-lab implementation. Here, we illustrate this notion from the point of view of signal fidelity and noise relationship. Noise in gene expression can undermine signal fidelity with implications on the well-functioning of the engineered organisms. For our analysis, we use a specific biological circuit that regulates the gene expression in bacterial inorganic phosphate economy. Applications that use this circuit include those in pollutant detection and wastewater treatment. We provide computational models with different levels of molecular detail as virtual labs. We show that inherent fluctuations in the gene expression machinery can be predicted via stochastic simulations to introduce control in the synthetic promoter design process. Our analysis suggests that noise in the system can be alleviated by strong synthetic promoters with slow unbinding rates. Overall, we provide a recipe for the computer-aided design of synthetic promoter libraries with specific signal to noise characteristics. Abstract The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics.
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Affiliation(s)
- Elena Righetti
- Department of Mathematics, University of Trento, 38123 Trento, Italy;
| | - Cansu Uluşeker
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, 4036 Stavanger, Norway;
| | - Ozan Kahramanoğulları
- Department of Mathematics, University of Trento, 38123 Trento, Italy;
- Correspondence:
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24
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Capp J. Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability. Evol Appl 2021; 14:893-901. [PMID: 33897810 PMCID: PMC8061278 DOI: 10.1111/eva.13204] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 12/11/2022] Open
Abstract
Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.
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Affiliation(s)
- Jean‐Pascal Capp
- Toulouse Biotechnology InstituteINSACNRSINRAEUniversity of ToulouseToulouseFrance
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25
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Shaban K, Sauty SM, Yankulov K. Variation, Variegation and Heritable Gene Repression in S. cerevisiae. Front Genet 2021; 12:630506. [PMID: 33747046 PMCID: PMC7970126 DOI: 10.3389/fgene.2021.630506] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 02/08/2021] [Indexed: 11/13/2022] Open
Abstract
Phenotypic heterogeneity provides growth advantages for a population upon changes of the environment. In S. cerevisiae, such heterogeneity has been observed as "on/off" states in the expression of individual genes in individual cells. These variations can persist for a limited or extended number of mitotic divisions. Such traits are known to be mediated by heritable chromatin structures, by the mitotic transmission of transcription factors involved in gene regulatory circuits or by the cytoplasmic partition of prions or other unstructured proteins. The significance of such epigenetic diversity is obvious, however, we have limited insight into the mechanisms that generate it. In this review, we summarize the current knowledge of epigenetically maintained heterogeneity of gene expression and point out similarities and converging points between different mechanisms. We discuss how the sharing of limiting repression or activation factors can contribute to cell-to-cell variations in gene expression and to the coordination between short- and long- term epigenetic strategies. Finally, we discuss the implications of such variations and strategies in adaptation and aging.
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Affiliation(s)
- Kholoud Shaban
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada
| | - Safia Mahabub Sauty
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada
| | - Krassimir Yankulov
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada
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26
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Vasdekis AE, Singh A. Microbial metabolic noise. WIREs Mech Dis 2020; 13:e1512. [PMID: 33225608 DOI: 10.1002/wsbm.1512] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 09/23/2020] [Accepted: 10/26/2020] [Indexed: 11/06/2022]
Abstract
From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.
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Affiliation(s)
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA
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27
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Cortijo S, Locke JCW. Does Gene Expression Noise Play a Functional Role in Plants? TRENDS IN PLANT SCIENCE 2020; 25:1041-1051. [PMID: 32467064 DOI: 10.1016/j.tplants.2020.04.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/22/2020] [Accepted: 04/28/2020] [Indexed: 05/20/2023]
Abstract
Gene expression in individual cells can be surprisingly noisy. In unicellular organisms this noise can be functional; for example, by allowing a subfraction of the population to prepare for environmental stress. The role of gene expression noise in multicellular organisms has, however, remained unclear. In this review, we discuss how new techniques are revealing an unexpected level of variability in gene expression between and within genetically identical plants. We describe recent progress as well as speculate on the function of transcriptional noise as a mechanism for generating functional phenotypic diversity in plants.
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Affiliation(s)
- Sandra Cortijo
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, UK
| | - James C W Locke
- Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, UK.
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28
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SIR2 Expression Noise Can Generate Heterogeneity in Viability but Does Not Affect Cell-to-Cell Epigenetic Silencing of Subtelomeric URA3 in Yeast. G3-GENES GENOMES GENETICS 2020; 10:3435-3443. [PMID: 32727919 PMCID: PMC7466964 DOI: 10.1534/g3.120.401589] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Chromatin structure clearly modulates gene expression noise, but the reverse influence has never been investigated, namely how the cell-to-cell expression heterogeneity of chromatin modifiers may generate variable rates of epigenetic modification. Sir2 is a well-characterized histone deacetylase of the Sirtuin family. It strongly influences chromatin silencing, especially at telomeres, subtelomeres and rDNA. This ability to influence epigenetic landscapes makes it a good model to study the largely unexplored interplay between gene expression noise and other epigenetic processes leading to phenotypic diversification. Here, we addressed this question by investigating whether noise in the expression of SIR2 was associated with cell-to-cell heterogeneity in the frequency of epigenetic silencing at subtelomeres in Saccharomyces cerevisiae Using cell sorting to isolate subpopulations with various expression levels, we found that heterogeneity in the cellular concentration of Sir2 does not lead to heterogeneity in the epigenetic silencing of subtelomeric URA3 between these subpopulations. We also noticed that SIR2 expression noise can generate cell-to-cell variability in viability, with lower levels being associated with better viability. This work shows that SIR2 expression fluctuations are not sufficient to generate cell-to-cell heterogeneity in the epigenetic silencing of URA3 at subtelomeres in Saccharomyces cerevisiae but can strongly affect cellular viability.
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29
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Genetic circuit design automation for yeast. Nat Microbiol 2020; 5:1349-1360. [DOI: 10.1038/s41564-020-0757-2] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/17/2020] [Indexed: 11/08/2022]
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30
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Sun M, Zhang J. Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises. Nucleic Acids Res 2020; 48:533-547. [PMID: 31799601 PMCID: PMC6954418 DOI: 10.1093/nar/gkz1134] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 10/19/2019] [Accepted: 11/20/2019] [Indexed: 01/13/2023] Open
Abstract
Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic or extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.
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Affiliation(s)
- Mengyi Sun
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
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31
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Ochola S, Huang J, Ali H, Shu H, Shen D, Qiu M, Wang L, Li X, Chen H, Kange A, Qutob D, Dong S. Editing of an effector gene promoter sequence impacts plant-Phytophthora interaction. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2020; 62:378-392. [PMID: 31691466 DOI: 10.1111/jipb.12883] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 10/31/2019] [Indexed: 06/10/2023]
Abstract
Pathogen avirulence (Avr) effectors interplay with corresponding plant resistance (R) proteins and activate robust plant immune responses. Although the expression pattern of Avr genes has been tied to their functions for a long time, it is still not clear how Avr gene expression patterns impact plant-microbe interactions. Here, we selected PsAvr3b, which shows a typical effector gene expression pattern from a soybean root pathogen Phytophthora sojae. To modulate gene expression, we engineered PsAvr3b promoter sequences by in situ substitution with promoter sequences from Actin (constitutive expression), PsXEG1 (early expression), and PsNLP1 (later expression) using the CRISPR/Cas9. PsAvr3b driven by different promoters resulted in distinct expression levels across all the tested infection time points. Importantly, those mutants with low PsAvr3b expression successfully colonized soybean plants carrying the cognate R gene Rps3b. To dissect the difference in plant responses to the PsAvr3b expression level, we conducted RNA-sequencing of different infection samples at 24 h postinfection and found soybean immune genes, including a few previously unknown genes that are associated with resistance. Our study highlights that fine-tuning in Avr gene expression impacts the compatibility of plant disease and provides clues to improve crop resistance in disease control management.
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Affiliation(s)
- Sylvans Ochola
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Jie Huang
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
- Key Laboratory of Integrated Management of Crop Diseases and Pests (Ministry of Education), Nanjing, 210095, China
| | - Haider Ali
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Haidong Shu
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Danyu Shen
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
- Key Laboratory of Integrated Management of Crop Diseases and Pests (Ministry of Education), Nanjing, 210095, China
| | - Min Qiu
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Liyuan Wang
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xi Li
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Han Chen
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Alex Kange
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Dinah Qutob
- Department of Math and Science, Walsh University, North Canton, OH, 44720, USA
| | - Suomeng Dong
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, 210095, China
- Key Laboratory of Integrated Management of Crop Diseases and Pests (Ministry of Education), Nanjing, 210095, China
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32
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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: 7] [Impact Index Per Article: 1.8] [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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33
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Controlling cell-to-cell variability with synthetic gene circuits. Biochem Soc Trans 2019; 47:1795-1804. [DOI: 10.1042/bst20190295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 11/05/2019] [Accepted: 11/06/2019] [Indexed: 02/05/2023]
Abstract
Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.
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34
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Esposito D, Weile J, Shendure J, Starita LM, Papenfuss AT, Roth FP, Fowler DM, Rubin AF. MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect. Genome Biol 2019; 20:223. [PMID: 31679514 PMCID: PMC6827219 DOI: 10.1186/s13059-019-1845-6] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 10/01/2019] [Indexed: 11/10/2022] Open
Abstract
Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.
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Affiliation(s)
- Daniel Esposito
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Jochen Weile
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Lea M Starita
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Anthony T Papenfuss
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia
- Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
- Department of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Frederick P Roth
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
- Canadian Institute for Advanced Research, Toronto, ON, Canada.
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Canadian Institute for Advanced Research, Toronto, ON, Canada.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
- Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia.
- Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
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35
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Vainberg Slutskin I, Weinberger A, Segal E. Sequence determinants of polyadenylation-mediated regulation. Genome Res 2019; 29:1635-1647. [PMID: 31530582 PMCID: PMC6771402 DOI: 10.1101/gr.247312.118] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 08/13/2019] [Indexed: 12/31/2022]
Abstract
The cleavage and polyadenylation reaction is a crucial step in transcription termination and pre-mRNA maturation in human cells. Despite extensive research, the encoding of polyadenylation-mediated regulation of gene expression within the DNA sequence is not well understood. Here, we utilized a massively parallel reporter assay to inspect the effect of over 12,000 rationally designed polyadenylation sequences (PASs) on reporter gene expression and cleavage efficiency. We find that the PAS sequence can modulate gene expression by over five orders of magnitude. By using a uniquely designed scanning mutagenesis data set, we gain mechanistic insight into various modes of action by which the cleavage efficiency affects the sensitivity or robustness of the PAS to mutation. Furthermore, we employ motif discovery to identify both known and novel sequence motifs associated with PAS-mediated regulation. By leveraging the large scale of our data, we train a deep learning model for the highly accurate prediction of RNA levels from DNA sequence alone (R = 0.83). Moreover, we devise unique approaches for predicting exact cleavage sites for our reporter constructs and for endogenous transcripts. Taken together, our results expand our understanding of PAS-mediated regulation, and provide an unprecedented resource for analyzing and predicting PAS for regulatory genomics applications.
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Affiliation(s)
- Ilya Vainberg Slutskin
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel.,Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
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36
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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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37
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Keinhörster D, Salzer A, Duque-Jaramillo A, George SE, Marincola G, Lee JC, Weidenmaier C, Wolz C. Revisiting the regulation of the capsular polysaccharide biosynthesis gene cluster in Staphylococcus aureus. Mol Microbiol 2019; 112:1083-1099. [PMID: 31283061 DOI: 10.1111/mmi.14347] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2019] [Indexed: 12/23/2022]
Abstract
Capsular polysaccharide (CP) biosynthesis in Staphylococcus aureus is tightly controlled resulting in a heterogeneous phenotype within a population and CP being mainly detectable in nongrowing cells. Expression of the corresponding biosynthesis gene cluster is driven by one promoter element (Pcap ). Here, we demonstrate that Pcap contains a main SigB-dependent promoter. The SigB consensus motif overlaps with a previously described inverted repeat (IR) that is crucial for cap expression. The essentiality of the IR is derived from this region acting as a SigB binding site rather than as an operator site for the proposed cap activators RbsR and MsaB. Furthermore, Pcap contains an extensive upstream region harboring a weak SigA-dependent promoter and binding sites for cap repressors such as SaeR, CodY and Rot. Heterogeneous CP synthesis is determined by SigB activity and repressor binding to the upstream region. SigB dependency and regulation by the upstream repressors are also sufficient to explain the temporal gene expression pattern at the transcriptional level. However, CP synthesis remains growth phase-dependent even when transcription is rendered constitutive, suggesting additional posttranscriptional regulatory circuits. Thus, the interference of multiple repressors with SigB-dependent promoter activity as well as post-transcriptional mechanisms ensure the appropriate regulation of CP synthesis.
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Affiliation(s)
- Daniela Keinhörster
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Andrea Salzer
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Alejandra Duque-Jaramillo
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Shilpa E George
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Gabriella Marincola
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Jean C Lee
- Department of Medicine (Infectious Diseases), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christopher Weidenmaier
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Christiane Wolz
- Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
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38
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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: 3.8] [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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39
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Chen J, Li E, Lai J. The coupled effect of nucleosome organization on gene transcription level and transcriptional plasticity. Nucleus 2019; 8:605-612. [PMID: 29202635 DOI: 10.1080/19491034.2017.1402152] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
Nucleosomes are the fundamental units of eukaryotic chromatin and can modulate the DNA accessibility for transcriptional regulatory elements. Many studies have demonstrated the effect of nucleosome organization on gene transcription level and transcriptional plasticity upon different conditions. Our recent study showed that nucleosome organization also plays an important role in modulating the plasticity of gene transcriptional status in maize. Here, we integrated our findings with previous studies on the role of nucleosome organization in regulation of gene transcription. We highlighted our recent finding that nucleosome organization plays an important role in determining the plasticity of gene transcription, beyond its role in regulating gene transcription level, particularly for intrinsically DNA-encoded nucleosome organization. We also discussed the features of sequence and structure of genes involved in affecting nucleosome organization around genes, as well as the potential mechanisms for overcoming the coupled effect of nucleosome organization on gene transcription level and transcriptional plasticity.
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Affiliation(s)
- Jian Chen
- a State Key Laboratory of Agrobiotechnology and National Maize Improvement Center , Department of Plant Genetics and Breeding , China Agricultural University , Beijing , P. R. China
| | - En Li
- a State Key Laboratory of Agrobiotechnology and National Maize Improvement Center , Department of Plant Genetics and Breeding , China Agricultural University , Beijing , P. R. China
| | - Jinsheng Lai
- a State Key Laboratory of Agrobiotechnology and National Maize Improvement Center , Department of Plant Genetics and Breeding , China Agricultural University , Beijing , P. R. China
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40
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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: 7.4] [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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41
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Qiu C, Kaplan CD. Functional assays for transcription mechanisms in high-throughput. Methods 2019; 159-160:115-123. [PMID: 30797033 PMCID: PMC6589137 DOI: 10.1016/j.ymeth.2019.02.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 02/18/2019] [Indexed: 01/12/2023] Open
Abstract
Dramatic increases in the scale of programmed synthesis of nucleic acid libraries coupled with deep sequencing have powered advances in understanding nucleic acid and protein biology. Biological systems centering on nucleic acids or encoded proteins greatly benefit from such high-throughput studies, given that large DNA variant pools can be synthesized and DNA, or RNA products of transcription, can be easily analyzed by deep sequencing. Here we review the scope of various high-throughput functional assays for studies of nucleic acids and proteins in general, followed by discussion of how these types of study have yielded insights into the RNA Polymerase II (Pol II) active site as an example. We discuss methodological considerations in the design and execution of these experiments that should be valuable to studies in any system.
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Affiliation(s)
- Chenxi Qiu
- Department of Medicine, Division of Translational Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Craig D Kaplan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA.
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42
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Lam KC, Chung HR, Semplicio G, Iyer SS, Gaub A, Bhardwaj V, Holz H, Georgiev P, Akhtar A. The NSL complex-mediated nucleosome landscape is required to maintain transcription fidelity and suppression of transcription noise. Genes Dev 2019; 33:452-465. [PMID: 30819819 PMCID: PMC6446542 DOI: 10.1101/gad.321489.118] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Accepted: 01/11/2019] [Indexed: 11/24/2022]
Abstract
Nucleosomal organization at gene promoters is critical for transcription, with a nucleosome-depleted region (NDR) at transcription start sites (TSSs) being required for transcription initiation. How NDRs and the precise positioning of the +1 nucleosomes are maintained on active genes remains unclear. Here, we report that the Drosophila nonspecific lethal (NSL) complex is necessary to maintain this stereotypical nucleosomal organization at promoters. Upon NSL1 depletion, nucleosomes invade the NDRs at TSSs of NSL-bound genes. NSL complex member NSL3 binds to TATA-less promoters in a sequence-dependent manner. The NSL complex interacts with the NURF chromatin remodeling complex and is necessary and sufficient to recruit NURF to target promoters. Not only is the NSL complex essential for transcription, but it is required for accurate TSS selection for genes with multiple TSSs. Furthermore, loss of the NSL complex leads to an increase in transcriptional noise. Thus, the NSL complex establishes a canonical nucleosomal organization that enables transcription and determines TSS fidelity.
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Affiliation(s)
- Kin Chung Lam
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
| | - Ho-Ryun Chung
- Otto-Warburg-Laboratory, Epigenomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.,Institute for Medical Bioinformatics and Biostatistics, Philipps-Universität Marburg, 35037 Marburg, Germany
| | - Giuseppe Semplicio
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
| | - Shantanu S Iyer
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, 79108 Freiburg, Germany.,Faculty of Biology, University of Freiburg, 79108 Freiburg, Germany
| | - Aline Gaub
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.,Faculty of Biology, University of Freiburg, 79108 Freiburg, Germany
| | - Vivek Bhardwaj
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
| | - Herbert Holz
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
| | - Plamen Georgiev
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
| | - Asifa Akhtar
- Department of Chromatin Regulation, Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
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43
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Hausser J, Mayo A, Keren L, Alon U. Central dogma rates and the trade-off between precision and economy in gene expression. Nat Commun 2019; 10:68. [PMID: 30622246 PMCID: PMC6325141 DOI: 10.1038/s41467-018-07391-8] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 10/18/2018] [Indexed: 12/31/2022] Open
Abstract
Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.
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Affiliation(s)
- Jean Hausser
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 76100, Israel.
| | - Avi Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 76100, Israel.
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44
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Weingarten-Gabbay S, Nir R, Lubliner S, Sharon E, Kalma Y, Weinberger A, Segal E. Systematic interrogation of human promoters. Genome Res 2019; 29:171-183. [PMID: 30622120 PMCID: PMC6360817 DOI: 10.1101/gr.236075.118] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 12/05/2018] [Indexed: 12/19/2022]
Abstract
Despite much research, our understanding of the architecture and cis-regulatory elements of human promoters is still lacking. Here, we devised a high-throughput assay to quantify the activity of approximately 15,000 fully designed sequences that we integrated and expressed from a fixed location within the human genome. We used this method to investigate thousands of native promoters and preinitiation complex (PIC) binding regions followed by in-depth characterization of the sequence motifs underlying promoter activity, including core promoter elements and TF binding sites. We find that core promoters drive transcription mostly unidirectionally and that sequences originating from promoters exhibit stronger activity than those originating from enhancers. By testing multiple synthetic configurations of core promoter elements, we dissect the motifs that positively and negatively regulate transcription as well as the effect of their combinations and distances, including a 10-bp periodicity in the optimal distance between the TATA and the initiator. By comprehensively screening 133 TF binding sites, we find that in contrast to core promoters, TF binding sites maintain similar activity levels in both orientations, supporting a model by which divergent transcription is driven by two distinct unidirectional core promoters sharing bidirectional TF binding sites. Finally, we find a striking agreement between the effect of binding site multiplicity of individual TFs in our assay and their tendency to appear in homotypic clusters throughout the genome. Overall, our study systematically assays the elements that drive expression in core and proximal promoter regions and sheds light on organization principles of regulatory regions in the human genome.
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Affiliation(s)
- 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
| | - Ronit Nir
- 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
| | - Shai Lubliner
- 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
| | - Eilon Sharon
- 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
| | - Yael Kalma
- 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
| | - 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
| | - 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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45
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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: 19] [Impact Index Per Article: 3.2] [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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46
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Vitkin E, Solomon O, Sultan S, Yakhini Z. Genome-wide analysis of fitness data and its application to improve metabolic models. BMC Bioinformatics 2018; 19:368. [PMID: 30305012 PMCID: PMC6180484 DOI: 10.1186/s12859-018-2341-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 08/28/2018] [Indexed: 11/17/2022] Open
Abstract
Background Synthetic biology and related techniques enable genome scale high-throughput investigation of the effect on organism fitness of different gene knock-downs/outs and of other modifications of genomic sequence. Results We develop statistical and computational pipelines and frameworks for analyzing high throughput fitness data over a genome scale set of sequence variants. Analyzing data from a high-throughput knock-down/knock-out bacterial study, we investigate differences and determinants of the effect on fitness in different conditions. Comparing fitness vectors of genes, across tens of conditions, we observe that fitness consequences strongly depend on genomic location and more weakly depend on gene sequence similarity and on functional relationships. In analyzing promoter sequences, we identified motifs associated with conditions studied in bacterial media such as Casaminos, D-glucose, Sucrose, and other sugars and amino-acid sources. We also use fitness data to infer genes associated with orphan metabolic reactions in the iJO1366 E. coli metabolic model. To do this, we developed a new computational method that integrates gene fitness and gene expression profiles within a given reaction network neighborhood to associate this reaction with a set of genes that potentially encode the catalyzing proteins. We then apply this approach to predict candidate genes for 107 orphan reactions in iJO1366. Furthermore - we validate our methodology with known reactions using a leave-one-out approach. Specifically, using top-20 candidates selected based on combined fitness and expression datasets, we correctly reconstruct 39.7% of the reactions, as compared to 33% based on fitness and to 26% based on expression separately, and to 4.02% as a random baseline. Our model improvement results include a novel association of a gene to an orphan cytosine nucleosidation reaction. Conclusion Our pipeline for metabolic modeling shows a clear benefit of using fitness data for predicting genes of orphan reactions. Along with the analysis pipelines we developed, it can be used to analyze similar high-throughput data. Electronic supplementary material The online version of this article (10.1186/s12859-018-2341-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Edward Vitkin
- Department of Computer Science, Technion, Haifa, Israel
| | - Oz Solomon
- Faculty of Biotechnology and Food Engineering, Technion, Haifa, Israel. .,School of Computer Science, The Interdisciplinary Center, Herzliya, Israel.
| | - Sharon Sultan
- School of Computer Science, The Interdisciplinary Center, Herzliya, Israel
| | - Zohar Yakhini
- Department of Computer Science, Technion, Haifa, Israel. .,School of Computer Science, The Interdisciplinary Center, Herzliya, Israel.
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47
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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: 0.5] [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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48
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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: 35] [Impact Index Per Article: 5.8] [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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49
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Elison GL, Acar M. Scarless genome editing: progress towards understanding genotype-phenotype relationships. Curr Genet 2018; 64:1229-1238. [PMID: 29872908 DOI: 10.1007/s00294-018-0850-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 05/26/2018] [Accepted: 05/31/2018] [Indexed: 01/31/2023]
Abstract
The ability to predict phenotype from genotype has been an elusive goal for the biological sciences for several decades. Progress decoding genotype-phenotype relationships has been hampered by the challenge of introducing precise genetic changes to specific genomic locations. Here we provide a comparative review of the major techniques that have been historically used to make genetic changes in cells as well as the development of the CRISPR technology which enabled the ability to make marker-free disruptions in endogenous genomic locations. We also discuss how the achievement of truly scarless genome editing has required further adjustments of the original CRISPR method. We conclude by examining recently developed genome editing methods which are not reliant on the induction of a DNA double strand break and discuss the future of both genome engineering and the study of genotype-phenotype relationships.
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Affiliation(s)
- Gregory L Elison
- Department of Molecular Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT, 06511, USA.,Systems Biology Institute, Yale University, 850 West Campus Drive, West Haven, CT, 06516, USA
| | - Murat Acar
- Department of Molecular Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT, 06511, USA. .,Systems Biology Institute, Yale University, 850 West Campus Drive, West Haven, CT, 06516, USA. .,Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 501, New Haven, CT, 06511, USA. .,Department of Physics, Yale University, Prospect Street, New Haven, CT, 06511, USA.
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50
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Unraveling the determinants of microRNA mediated regulation using a massively parallel reporter assay. Nat Commun 2018; 9:529. [PMID: 29410437 PMCID: PMC5802814 DOI: 10.1038/s41467-018-02980-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 01/11/2018] [Indexed: 12/16/2022] Open
Abstract
Despite extensive research, the sequence features affecting microRNA-mediated regulation are not well understood, limiting our ability to predict gene expression levels in both native and synthetic sequences. Here we employed a massively parallel reporter assay to investigate the effect of over 14,000 rationally designed 3′ UTR sequences on reporter construct repression. We found that multiple factors, including microRNA identity, hybridization energy, target accessibility, and target multiplicity, can be manipulated to achieve a predictable, up to 57-fold, change in protein repression. Moreover, we predict protein repression and RNA levels with high accuracy (R = 0.84 and R = 0.80, respectively) using only 3′ UTR sequence, as well as the effect of mutation in native 3′ UTRs on protein repression (R = 0.63). Taken together, our results elucidate the effect of different sequence features on miRNA-mediated regulation and demonstrate the predictability of their effect on gene expression with applications in regulatory genomics and synthetic biology. MiRNAs are known regulators of gene expression. Here the authors perform a large-scale massively parallel reporter assay to investigate the effect of a large number of designed 3′ UTR sequences on reporter expression and asses how miRNA regulatory elements features affect miRNA mediated repression.
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