1
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Mellis IA, Melzer ME, Bodkin N, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. Genome Biol 2024; 25:217. [PMID: 39135102 PMCID: PMC11320884 DOI: 10.1186/s13059-024-03351-2] [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: 12/13/2023] [Accepted: 07/25/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. RESULTS We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation. CONCLUSIONS Our integrative approach identifies several putative hits-genes demonstrating possible transcriptional adaptation-to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
| | - Madeline E Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- CZ Biohub Chicago, LLC, Chicago, IL, USA.
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2
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Pan RW, Röschinger T, Faizi K, Garcia H, Phillips R. Deciphering regulatory architectures from synthetic single-cell expression patterns. ARXIV 2024:arXiv:2401.15880v2. [PMID: 38351929 PMCID: PMC10862939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.
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Affiliation(s)
- Rosalind Wenshan Pan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Tom Röschinger
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Kian Faizi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Hernan Garcia
- Biophysics Graduate Group, University of California, Berkeley, CA
- Department of Physics, University of California, Berkeley, CA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, CA
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
- Department of Physics, California Institute of Technology, Pasadena, CA
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3
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Pan RW, Röschinger T, Faizi K, Garcia H, Phillips R. Deciphering regulatory architectures from synthetic single-cell expression patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.28.577658. [PMID: 38352569 PMCID: PMC10862715 DOI: 10.1101/2024.01.28.577658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.
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Affiliation(s)
- Rosalind Wenshan Pan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Tom Röschinger
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Kian Faizi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Hernan Garcia
- Biophysics Graduate Group, University of California, Berkeley, CA
- Department of Physics, University of California, Berkeley, CA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA
- Institute for Quantitative Biosciences-QB3, University of California, Berkeley, CA
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
- Department of Physics, California Institute of Technology, Pasadena, CA
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4
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Zamanian MY, Alsaab HO, Golmohammadi M, Yumashev A, Jabba AM, Abid MK, Joshi A, Alawadi AH, Jafer NS, Kianifar F, Obakiro SB. NF-κB pathway as a molecular target for curcumin in diabetes mellitus treatment: Focusing on oxidative stress and inflammation. Cell Biochem Funct 2024; 42:e4030. [PMID: 38720663 DOI: 10.1002/cbf.4030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/05/2024] [Accepted: 04/25/2024] [Indexed: 08/03/2024]
Abstract
Diabetes mellitus (DM) is a collection of metabolic disorder that is characterized by chronic hyperglycemia. Recent studies have demonstrated the crucial involvement of oxidative stress (OS) and inflammatory reactions in the development of DM. Curcumin (CUR), a natural compound derived from turmeric, exerts beneficial effects on diabetes mellitus through its interaction with the nuclear factor kappa B (NF-κB) pathway. Research indicates that CUR targets inflammatory mediators in diabetes, including tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6), by modulating the NF-κB signaling pathway. By reducing the expression of these inflammatory factors, CUR demonstrates protective effects in DM by improving pancreatic β-cells function, normalizing inflammatory cytokines, reducing OS and enhancing insulin sensitivity. The findings reveal that CUR administration effectively lowered blood glucose elevation, reinstated diminished serum insulin levels, and enhanced body weight in Streptozotocin -induced diabetic rats. CUR exerts its beneficial effects in management of diabetic complications through regulation of signaling pathways, such as calcium-calmodulin (CaM)-dependent protein kinase II (CaMKII), peroxisome proliferator-activated receptor gamma (PPAR-γ), NF-κB, and transforming growth factor β1 (TGFB1). Moreover, CUR reversed the heightened expression of inflammatory cytokines (TNF-α, Interleukin-1 beta (IL-1β), IL-6) and chemokines like MCP-1 in diabetic specimens, vindicating its anti-inflammatory potency in counteracting hyperglycemia-induced alterations. CUR diminishes OS, avert structural kidney damage linked to diabetic nephropathy, and suppress NF-κB activity. Furthermore, CUR exhibited a protective effect against diabetic cardiomyopathy, lung injury, and diabetic gastroparesis. Conclusively, the study posits that CUR could potentially offer therapeutic benefits in relieving diabetic complications through its influence on the NF-κB pathway.
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Affiliation(s)
- Mohammad Yasin Zamanian
- Department of Physiology, Hamadan University of Medical Sciences, Hamadan, Iran
- Department of Pharmacology and Toxicology, School of Pharmacy, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Hashem O Alsaab
- Department of Pharmaceutics and Pharmaceutical Technology, Taif University, Taif, Saudi Arabia
| | - Maryam Golmohammadi
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alexey Yumashev
- Department of Prosthetic Dentistry, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Abeer Mhussan Jabba
- Colleges of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq
| | - Mohammed Kadhem Abid
- Department of Anesthesia, College of Health & Medical Technology, Al-Ayen University, Nasiriyah, Iraq
| | - Abhishek Joshi
- Department of Liberal Arts School of Liberal Arts, Uttaranchal University, Dehradun, India
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, The Islamic University, Najaf, Iraq
- College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
- College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq
| | - Noor S Jafer
- Department of Medical Laboratory Technologies, Al Rafidain University College, Bagdad, Iraq
| | - Farzaneh Kianifar
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Samuel Baker Obakiro
- Department of Pharmacology and Therapeutics, Faculty of Health Sciences, Busitema University, Mbale, Uganda
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5
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Mellis IA, Bodkin N, Melzer ME, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.14.553318. [PMID: 37645989 PMCID: PMC10462021 DOI: 10.1101/2023.08.14.553318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A. Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Madeline E. Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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6
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Srivastava V, Hu JL, Garbe JC, Veytsman B, Shalabi SF, Yllanes D, Thomson M, LaBarge MA, Huber G, Gartner ZJ. Configurational entropy is an intrinsic driver of tissue structural heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.01.546933. [PMID: 37425903 PMCID: PMC10327153 DOI: 10.1101/2023.07.01.546933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters - the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.
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Affiliation(s)
- Vasudha Srivastava
- Dept. of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jennifer L. Hu
- UC Berkeley-UC San Francisco Graduate Program in Bioengineering, Berkeley, CA 94720, USA
| | - James C. Garbe
- Dept. of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Boris Veytsman
- Chan Zuckerberg Initiative, Redwood City, CA 94963, USA
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
| | | | - David Yllanes
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Instituto de Biocomputaciòn y Fìsica de Sistemas Complejos (BIFI), 50018 Zaragoza, Spain
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Mark A. LaBarge
- Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Greg Huber
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Zev J. Gartner
- Dept. of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Center for Cellular Construction, University of California, San Francisco, CA 94158, USA
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7
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Grewal S. An interview with Hernan Garcia. Development 2023; 150:dev201945. [PMID: 37309909 DOI: 10.1242/dev.201945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Hernan Garcia is a Principal Investigator and Associate Professor of Genetics, Genomics and Development and Physics at the University of California, Berkley (USA). His research aims to understand, predict and control developmental programs. In 2022, Hernan was awarded the Elizabeth D. Hay New Investigator award by the Society for Developmental Biology (SDB) recognizing his outstanding research in developmental biology. We spoke to Hernan to learn more about his education, career path and approach to running a lab.
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8
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Alamos S, Reimer A, Westrum C, Turner MA, Talledo P, Zhao J, Luu E, Garcia HG. Minimal synthetic enhancers reveal control of the probability of transcriptional engagement and its timing by a morphogen gradient. Cell Syst 2023; 14:220-236.e3. [PMID: 36696901 PMCID: PMC10125799 DOI: 10.1016/j.cels.2022.12.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/03/2022] [Accepted: 12/21/2022] [Indexed: 01/26/2023]
Abstract
How enhancers interpret morphogen gradients to generate gene expression patterns is a central question in developmental biology. Recent studies have proposed that enhancers can dictate whether, when, and at what rate promoters engage in transcription, but the complexity of endogenous enhancers calls for theoretical models with too many free parameters to quantitatively dissect these regulatory strategies. To overcome this limitation, we established a minimal promoter-proximal synthetic enhancer in embryos of Drosophila melanogaster. Here, a gradient of the Dorsal activator is read by a single Dorsal DNA binding site. Using live imaging to quantify transcriptional activity, we found that a single binding site can regulate whether promoters engage in transcription in a concentration-dependent manner. By modulating the binding-site affinity, we determined that a gene's decision to transcribe and its transcriptional onset time can be explained by a simple model where the promoter traverses multiple kinetic barriers before transcription can ensue.
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Affiliation(s)
- Simon Alamos
- Department of Plant and Microbial Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Armando Reimer
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA
| | - Clay Westrum
- Department of Physics, University of California at Berkeley, Berkeley, CA, USA
| | - Meghan A Turner
- Department of Plant and Microbial Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Paul Talledo
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Jiaxi Zhao
- Department of Physics, University of California at Berkeley, Berkeley, CA, USA
| | - Emma Luu
- Department of Physics, University of California at Berkeley, Berkeley, CA, USA
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA; Department of Physics, University of California at Berkeley, Berkeley, CA, USA; Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA; Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA.
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9
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Kim YJ, Rhee K, Liu J, Jeammet S, Turner MA, Small SJ, Garcia HG. Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer. eLife 2022; 11:73395. [PMID: 36503705 PMCID: PMC9836395 DOI: 10.7554/elife.73395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity captures the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature.
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Affiliation(s)
- Yang Joon Kim
- Chan Zuckerberg Biohub, San Francisco, United States
| | - Kaitlin Rhee
- Department of Chemical Biology, University of California, Berkeley, Berkeley, United States
| | - Jonathan Liu
- Department of Physics, University of California, Berkeley, Berkeley, United States
| | - Selene Jeammet
- Department of Biology, Ecole Polytechnique, Paris, France
| | - Meghan A Turner
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States
| | - Stephen J Small
- Department of Biology, New York University, New York, United States
| | - Hernan G Garcia
- Chan Zuckerberg Biohub, San Francisco, United States.,Department of Physics, University of California, Berkeley, Berkeley, United States.,Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, United States
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10
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Chou CT. Using transcription-based detectors to emulate the behavior of sequential probability ratio-based concentration detectors. Phys Rev E 2022; 106:054403. [PMID: 36559424 DOI: 10.1103/physreve.106.054403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 10/04/2022] [Indexed: 12/24/2022]
Abstract
The sequential probability ratio test (SPRT) from statistics is known to have the least mean decision time compared to other sequential or fixed-time tests for given error rates. In some circumstances, cells need to make decisions accurately and quickly, therefore it has been suggested that the SPRT may be used to understand the speed-accuracy tradeoff in cellular decision-making. It is generally thought that in order for cells to make use of the SPRT, it is necessary to find biochemical circuits that can compute the log-likelihood ratio needed for the SPRT. However, this paper takes a different approach. We recognize that the high-level behavior of the SPRT is defined by its positive detection or hit rate, and the computation of the log-likelihood ratio is just one way to realize this behavior. In this paper, we will present a method in which a transcription-based detector is used to emulate the hit rate of the SPRT without computing the exact log-likelihood ratio. We consider the problem of using a promoter with multiple binding sites to accurately and quickly detect whether the concentration of a transcription factor is above a target level. We show that it is possible to find binding and unbinding rates of the transcription factor to the promoter's binding sites so that the probability that the amount of mRNA produced will be higher than a threshold is approximately equal to the hit rate of the SPRT detector. Moreover, we show that the average time that this transcription-based detector needs to make a positive detection is less than or equal to that of the SPRT for a wide range of concentrations. We remark that the last statement does not contradict Wald's optimality result because our transcription-based detector uses an open-ended test.
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Affiliation(s)
- Chun Tung Chou
- School of Computer Science and Engineering, University of New South Wales, Sydney NSW 2052, Australia
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11
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Fleig P, Nemenman I. Statistical properties of large data sets with linear latent features. Phys Rev E 2022; 106:014102. [PMID: 35974629 DOI: 10.1103/physreve.106.014102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
Analytical understanding of how low-dimensional latent features reveal themselves in large-dimensional data is still lacking. We study this by defining a probabilistic linear latent features model with additive noise and by analytically and numerically computing the statistical distributions of pairwise correlations and eigenvalues of the data correlation matrix. This allows us to resolve the latent feature structure across a wide range of data regimes set by the number of recorded variables, observations, latent features, and the signal-to-noise ratio. We find a characteristic imprint of latent features in the distribution of correlations and eigenvalues and provide an analytic estimate for the boundary between signal and noise, even in the absence of a spectral gap.
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Affiliation(s)
- Philipp Fleig
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Ilya Nemenman
- Department of Physics, Emory University, Atlanta, Georgia 30322, USA; Department of Biology, Emory University, Atlanta, Georgia 30322, USA; and Initiative in Theory and Modeling of Living Systems, Atlanta, Georgia 30322, USA
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12
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Perkins ML, Gandara L, Crocker J. A synthetic synthesis to explore animal evolution and development. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200517. [PMID: 35634925 PMCID: PMC9149795 DOI: 10.1098/rstb.2020.0517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution. This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.
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Affiliation(s)
- Mindy Liu Perkins
- Developmental Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Lautaro Gandara
- Developmental Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Justin Crocker
- Developmental Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
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13
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Alamos S. Noise-cancelling translation syncs cellular clocks. NATURE PLANTS 2022; 8:455-456. [PMID: 35501453 DOI: 10.1038/s41477-022-01143-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Simon Alamos
- University of California, Berkeley, Berkeley, CA, USA.
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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14
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Guharajan S, Chhabra S, Parisutham V, Brewster RC. Quantifying the regulatory role of individual transcription factors in Escherichia coli. Cell Rep 2021; 37:109952. [PMID: 34758318 PMCID: PMC8667592 DOI: 10.1016/j.celrep.2021.109952] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 08/02/2021] [Accepted: 10/13/2021] [Indexed: 11/30/2022] Open
Abstract
Gene regulation often results from the action of multiple transcription factors (TFs) acting at a promoter, obscuring the individual regulatory effect of each TF on RNA polymerase (RNAP). Here we measure the fundamental regulatory interactions of TFs in E. coli by designing synthetic target genes that isolate individual TFs' regulatory effects. Using a thermodynamic model, each TF's regulatory interactions are decoupled from TF occupancy and interpreted as acting through (de)stabilization of RNAP and (de)acceleration of transcription initiation. We find that the contribution of each mechanism depends on TF identity and binding location; regulation immediately downstream of the promoter is insensitive to TF identity, but the same TFs regulate by distinct mechanisms upstream of the promoter. These two mechanisms are uncoupled and can act coherently, to reinforce the observed regulatory role (activation/repression), or incoherently, wherein the TF regulates two distinct steps with opposing effects.
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Affiliation(s)
- Sunil Guharajan
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Shivani Chhabra
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Vinuselvi Parisutham
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Robert C Brewster
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.
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15
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Waymack R, Gad M, Wunderlich Z. Molecular competition can shape enhancer activity in the Drosophila embryo. iScience 2021; 24:103034. [PMID: 34568782 PMCID: PMC8449247 DOI: 10.1016/j.isci.2021.103034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/27/2021] [Accepted: 08/20/2021] [Indexed: 01/12/2023] Open
Abstract
Transgenic reporters allow the measurement of regulatory DNA activity in vivo and consequently have long been useful tools for studying enhancers. Despite their utility, few studies have investigated the effects these reporters may have on the expression of other genes. Understanding these effects is required to accurately interpret reporter data and characterize gene regulatory mechanisms. By measuring the expression of Kruppel (Kr) enhancer reporters in live Drosophila embryos, we find reporters inhibit one another's expression and that of a nearby endogenous gene. Using synthetic transcription factor (TF) binding site arrays, we present evidence that competition for TFs is partially responsible for the observed transcriptional inhibition. We develop a simple thermodynamic model that predicts competition of the measured magnitude specifically when TF binding is restricted to distinct nuclear subregions. Our findings underline an unexpected role of the non-homogenous nature of the nucleus in regulating gene expression.
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Affiliation(s)
- Rachel Waymack
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA
| | - Mario Gad
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA
| | - Zeba Wunderlich
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA
- Department of Biology, Boston University, 610 Commonwealth Ave., Boston, MA 02215, USA
- Biological Design Center, Boston University, 610 Commonwealth Avenue, Boston, MA 02215, USA
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16
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Park J, Wang HH. Systematic dissection of σ 70 sequence diversity and function in bacteria. Cell Rep 2021; 36:109590. [PMID: 34433066 PMCID: PMC8716302 DOI: 10.1016/j.celrep.2021.109590] [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: 05/18/2020] [Revised: 04/19/2021] [Accepted: 08/02/2021] [Indexed: 10/29/2022] Open
Abstract
Primary σ70 factors are key conserved bacterial regulatory proteins that interact with regulatory DNA to control gene expression. It is, however, poorly understood whether σ70 sequence diversity in different bacteria reflects functional differences. Here, we employ comparative and functional genomics to explore the sequence and function relationship of primary σ70. Using multiplex automated genome engineering and deep sequencing (MAGE-seq), we generate a saturation mutagenesis library and high-resolution fitness map of E. coli σ70 in domains 2-4. Mapping natural σ70 sequence diversity to the E. coli σ70 fitness landscape reveals significant predicted fitness deficits across σ70 orthologs. Interestingly, these predicted deficits are larger than observed fitness changes for 15 σ70 orthologs introduced into E. coli. Finally, we use a multiplexed transcriptional reporter assay and RNA sequencing (RNA-seq) to explore functional differences of several σ70 orthologs. This work provides an in-depth analysis of σ70 sequence and function to improve efforts to understand the evolution and engineering potential of this global regulator.
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Affiliation(s)
- Jimin Park
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Irving Medical Center, New York, NY, USA.
| | - Harris H Wang
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
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17
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Alamos S, Reimer A, Niyogi KK, Garcia HG. Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics. NATURE PLANTS 2021; 7:1037-1049. [PMID: 34373604 PMCID: PMC8616715 DOI: 10.1038/s41477-021-00976-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 06/22/2021] [Indexed: 05/18/2023]
Abstract
The responses of plants to their environment are often dependent on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, the lack of implementation of these technologies in plants has limited concomitant quantitative studies in this kingdom. Here, we applied the PP7 and MS2 RNA-labelling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we counted nascent RNA transcripts in real time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana. Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the proportion of cells that switch from an undetectable basal state to a high-transcription state, instead of modulating the rate of transcription across all cells in a graded fashion. This switch-like behaviour, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from stochasticity intrinsic to individual alleles instead of variability in cellular composition. Together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.
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Affiliation(s)
- Simon Alamos
- Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA, USA
| | - Armando Reimer
- Biophysics Graduate Group, University of California Berkeley, Berkeley, CA, USA
| | - Krishna K Niyogi
- Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA, USA.
- Howard Hughes Medical Institute, University of California Berkeley, Berkeley, CA, USA.
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California Berkeley, Berkeley, CA, USA.
- Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA, USA.
- Department of Physics, University of California Berkeley, Berkeley, CA, USA.
- Institute for Quantitative Biosciences-QB3, University of California Berkeley, Berkeley, CA, USA.
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18
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Razo-Mejia M, Marzen S, Chure G, Taubman R, Morrison M, Phillips R. First-principles prediction of the information processing capacity of a simple genetic circuit. Phys Rev E 2021; 102:022404. [PMID: 32942428 DOI: 10.1103/physreve.102.022404] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 07/02/2020] [Indexed: 11/07/2022]
Abstract
Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.
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Affiliation(s)
- Manuel Razo-Mejia
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Sarah Marzen
- Department of Physics, W. M. Keck Science Department, Claremont McKenna College, Claremont, California 91711, USA
| | - Griffin Chure
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Rachel Taubman
- Department of Physics, W. M. Keck Science Department, Claremont McKenna College, Claremont, California 91711, USA
| | - Muir Morrison
- Department of Physics, California Institute of Technology, Pasadena, California 91125, USA
| | - Rob Phillips
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA.,Department of Physics, California Institute of Technology, Pasadena, California 91125, USA
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19
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Gritti N, Oriola D, Trivedi V. Rethinking embryology in vitro: A synergy between engineering, data science and theory. Dev Biol 2021; 474:48-61. [DOI: 10.1016/j.ydbio.2020.10.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/21/2020] [Accepted: 10/26/2020] [Indexed: 02/06/2023]
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20
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Morrison M, Razo-Mejia M, Phillips R. Reconciling kinetic and thermodynamic models of bacterial transcription. PLoS Comput Biol 2021; 17:e1008572. [PMID: 33465069 PMCID: PMC7845990 DOI: 10.1371/journal.pcbi.1008572] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 01/29/2021] [Accepted: 11/28/2020] [Indexed: 11/18/2022] Open
Abstract
The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.
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Affiliation(s)
- Muir Morrison
- Department of Physics, California Institute of Technology, Pasadena, California, USA
| | - Manuel Razo-Mejia
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Rob Phillips
- Department of Physics, California Institute of Technology, Pasadena, California, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
- * E-mail:
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21
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Abstract
Determining whether and how a gene is transcribed are two of the central processes of life. The conceptual basis for understanding such gene regulation arose from pioneering biophysical studies in eubacteria. However, eukaryotic genomes exhibit vastly greater complexity, which raises questions not addressed by this bacterial paradigm. First, how is information integrated from many widely separated binding sites to determine how a gene is transcribed? Second, does the presence of multiple energy-expending mechanisms, which are absent from eubacterial genomes, indicate that eukaryotes are capable of improved forms of genetic information processing? An updated biophysical foundation is needed to answer such questions. We describe the linear framework, a graph-based approach to Markov processes, and show that it can accommodate many previous studies in the field. Under the assumption of thermodynamic equilibrium, we introduce a language of higher-order cooperativities and show how it can rigorously quantify gene regulatory properties suggested by experiment. We point out that fundamental limits to information processing arise at thermodynamic equilibrium and can only be bypassed through energy expenditure. Finally, we outline some of the mathematical challenges that must be overcome to construct an improved biophysical understanding of gene regulation.
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Affiliation(s)
- Felix Wong
- Institute for Medical Engineering & Science, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Jeremy Gunawardena
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA;
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22
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Sanford EM, Emert BL, Coté A, Raj A. Gene regulation gravitates toward either addition or multiplication when combining the effects of two signals. eLife 2020; 9:e59388. [PMID: 33284110 PMCID: PMC7771960 DOI: 10.7554/elife.59388] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 12/04/2020] [Indexed: 01/07/2023] Open
Abstract
Two different cell signals often affect transcription of the same gene. In such cases, it is natural to ask how the combined transcriptional response compares to the individual responses. The most commonly used mechanistic models predict additive or multiplicative combined responses, but a systematic genome-wide evaluation of these predictions is not available. Here, we analyzed the transcriptional response of human MCF-7 cells to retinoic acid and TGF-β, applied individually and in combination. The combined transcriptional responses of induced genes exhibited a range of behaviors, but clearly favored both additive and multiplicative outcomes. We performed paired chromatin accessibility measurements and found that increases in accessibility were largely additive. There was some association between super-additivity of accessibility and multiplicative or super-multiplicative combined transcriptional responses, while sub-additivity of accessibility associated with additive transcriptional responses. Our findings suggest that mechanistic models of combined transcriptional regulation must be able to reproduce a range of behaviors.
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Affiliation(s)
- Eric M Sanford
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Benjamin L Emert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Allison Coté
- Department of Bioengineering, School of Engineering and Applied Sciences, University of PennsylvaniaPhiladelphiaUnited States
- Department of Genetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of PennsylvaniaPhiladelphiaUnited States
- Department of Genetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
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23
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Frezzato D. Sensitivity analysis of the reaction occurrence and recurrence times in steady-state biochemical networks. Math Biosci 2020; 332:108518. [PMID: 33278402 DOI: 10.1016/j.mbs.2020.108518] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 11/26/2020] [Accepted: 11/26/2020] [Indexed: 11/17/2022]
Abstract
Continuous-time stationary Markov jump processes among discrete sites are encountered in disparate biochemical ambits. Sites and connecting dynamical events form a 'network' in which the sites are the available system's states, and the events are site-to-site transitions, or even neutral processes in which the system does not change site but the event is however detectable. Examples include conformational transitions in single biomolecules, stochastic chemical kinetics in the space of the molecules copy numbers, and even macroscopic steady-state reactive mixtures if one adopts the viewpoint of a tagged molecule (or even of a molecular moiety) whose state may change when it is involved in a chemical reaction. Among the variety of dynamical descriptors, here we focus on the first occurrence times (starting from a given site) and on the recurrence times of an event of interest. We develop the sensitivity analysis for the lowest moments of the statistical distribution of such times with respect to the rate constants of the network. In particular, simple expressions and inequalities allow us to establish a direct relationship between selective variation of rate constants and effect on average times and variances. As illustrative cases we treat the substrate inhibition in enzymatic catalysis in which a tagged enzyme molecule jumps between three states, and the basic two-site model of stochastic gene expression in which the single gene switches between active and inactive forms.
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Affiliation(s)
- Diego Frezzato
- Department of Chemical Sciences, University of Padova, via Marzolo 1, I-35131, Padova, Italy.
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24
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Lammers NC, Kim YJ, Zhao J, Garcia HG. A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting. Curr Opin Cell Biol 2020; 67:147-157. [PMID: 33242838 PMCID: PMC8498946 DOI: 10.1016/j.ceb.2020.08.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 08/03/2020] [Indexed: 12/18/2022]
Abstract
Eukaryotic transcription generally occurs in bursts of activity lasting minutes to hours; however, state-of-the-art measurements have revealed that many of the molecular processes that underlie bursting, such as transcription factor binding to DNA, unfold on timescales of seconds. This temporal disconnect lies at the heart of a broader challenge in physical biology of predicting transcriptional outcomes and cellular decision-making from the dynamics of underlying molecular processes. Here, we review how new dynamical information about the processes underlying transcriptional control can be combined with theoretical models that predict not only averaged transcriptional dynamics, but also their variability, to formulate testable hypotheses about the molecular mechanisms underlying transcriptional bursting and control.
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Affiliation(s)
- Nicholas C Lammers
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA
| | - Yang Joon Kim
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA
| | - Jiaxi Zhao
- Department of Physics, University of California at Berkeley, Berkeley, CA, USA
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA; Department of Physics, University of California at Berkeley, Berkeley, CA, USA; Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA; Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, CA, USA.
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25
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Gómez-Schiavon M, Dods G, El-Samad H, Ng AH. Multidimensional Characterization of Parts Enhances Modeling Accuracy in Genetic Circuits. ACS Synth Biol 2020; 9:2917-2926. [PMID: 33166452 DOI: 10.1021/acssynbio.0c00288] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Mathematical models can aid the design of genetic circuits, but may yield inaccurate results if individual parts are not modeled at the appropriate resolution. To illustrate the importance of this concept, we study transcriptional cascades consisting of two inducible synthetic transcription factors connected in series. Despite the simplicity of this design, we find that accurate prediction of circuit behavior requires mapping the dose responses of each circuit component along the dimensions of both its expression level and its inducer concentration. Using this multidimensional characterization, we were able to computationally explore the behavior of 16 different circuit designs. We experimentally verified a subset of these predictions and found substantial agreement. This method of biological part characterization enables the use of models to identify (un)desired circuit behaviors prior to experimental implementation, thus shortening the design-build-test cycle for more complex circuits.
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Affiliation(s)
- Mariana Gómez-Schiavon
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California 94158, United States
| | - Galen Dods
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California 94158, United States
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California 94158, United States
- Chan−Zuckerberg Biohub, San Francisco, California 94158, United States
- Cell Design Institute, University of California, San Francisco, San Francisco, California 94158, United States
| | - Andrew H. Ng
- Cell Design Institute, University of California, San Francisco, San Francisco, California 94158, United States
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California 94158, United States
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26
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Mejía-Almonte C, Busby SJW, Wade JT, van Helden J, Arkin AP, Stormo GD, Eilbeck K, Palsson BO, Galagan JE, Collado-Vides J. Redefining fundamental concepts of transcription initiation in bacteria. Nat Rev Genet 2020; 21:699-714. [PMID: 32665585 PMCID: PMC7990032 DOI: 10.1038/s41576-020-0254-8] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2020] [Indexed: 12/15/2022]
Abstract
Despite enormous progress in understanding the fundamentals of bacterial gene regulation, our knowledge remains limited when compared with the number of bacterial genomes and regulatory systems to be discovered. Derived from a small number of initial studies, classic definitions for concepts of gene regulation have evolved as the number of characterized promoters has increased. Together with discoveries made using new technologies, this knowledge has led to revised generalizations and principles. In this Expert Recommendation, we suggest precise, updated definitions that support a logical, consistent conceptual framework of bacterial gene regulation, focusing on transcription initiation. The resulting concepts can be formalized by ontologies for computational modelling, laying the foundation for improved bioinformatics tools, knowledge-based resources and scientific communication. Thus, this work will help researchers construct better predictive models, with different formalisms, that will be useful in engineering, synthetic biology, microbiology and genetics.
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Affiliation(s)
- Citlalli Mejía-Almonte
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, México
| | | | - Joseph T Wade
- Division of Genetics, Wadsworth Center, New York State Department of Health, Albany, NY, USA
| | - Jacques van Helden
- Aix-Marseille University, INSERM UMR S 1090, Theory and Approaches of Genome Complexity (TAGC), Marseille, France
- CNRS, Institut Français de Bioinformatique, IFB-core, UMS 3601, Evry, France
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Gary D Stormo
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - James E Galagan
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, México.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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27
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Eck E, Liu J, Kazemzadeh-Atoufi M, Ghoreishi S, Blythe SA, Garcia HG. Quantitative dissection of transcription in development yields evidence for transcription-factor-driven chromatin accessibility. eLife 2020; 9:e56429. [PMID: 33074101 PMCID: PMC7738189 DOI: 10.7554/elife.56429] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 10/16/2020] [Indexed: 12/28/2022] Open
Abstract
Thermodynamic models of gene regulation can predict transcriptional regulation in bacteria, but in eukaryotes, chromatin accessibility and energy expenditure may call for a different framework. Here, we systematically tested the predictive power of models of DNA accessibility based on the Monod-Wyman-Changeux (MWC) model of allostery, which posits that chromatin fluctuates between accessible and inaccessible states. We dissected the regulatory dynamics of hunchback by the activator Bicoid and the pioneer-like transcription factor Zelda in living Drosophila embryos and showed that no thermodynamic or non-equilibrium MWC model can recapitulate hunchback transcription. Therefore, we explored a model where DNA accessibility is not the result of thermal fluctuations but is catalyzed by Bicoid and Zelda, possibly through histone acetylation, and found that this model can predict hunchback dynamics. Thus, our theory-experiment dialogue uncovered potential molecular mechanisms of transcriptional regulatory dynamics, a key step toward reaching a predictive understanding of developmental decision-making.
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Affiliation(s)
- Elizabeth Eck
- Biophysics Graduate Group, University of California at BerkeleyBerkeleyUnited States
| | - Jonathan Liu
- Department of Physics, University of California at BerkeleyBerkeleyUnited States
| | | | - Sydney Ghoreishi
- Department of Molecular and Cell Biology, University of California at BerkeleyBerkeleyUnited States
| | - Shelby A Blythe
- Department of Molecular Biosciences, Northwestern UniversityEvanstonUnited States
| | - Hernan G Garcia
- Biophysics Graduate Group, University of California at BerkeleyBerkeleyUnited States
- Department of Physics, University of California at BerkeleyBerkeleyUnited States
- Department of Molecular and Cell Biology, University of California at BerkeleyBerkeleyUnited States
- Institute for Quantitative Biosciences-QB3, University of California at BerkeleyBerkeleyUnited States
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28
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Ireland WT, Beeler SM, Flores-Bautista E, McCarty NS, Röschinger T, Belliveau NM, Sweredoski MJ, Moradian A, Kinney JB, Phillips R. Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time. eLife 2020; 9:e55308. [PMID: 32955440 PMCID: PMC7567609 DOI: 10.7554/elife.55308] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 09/18/2020] [Indexed: 01/28/2023] Open
Abstract
Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than a E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.
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Affiliation(s)
- William T Ireland
- Department of Physics, California Institute of TechnologyPasadenaUnited States
| | - Suzannah M Beeler
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Emanuel Flores-Bautista
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Nicholas S McCarty
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Tom Röschinger
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadenaUnited States
| | - Nathan M Belliveau
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Michael J Sweredoski
- Proteome Exploration Laboratory, Division of Biology and Biological Engineering, Beckman Institute, California Institute of TechnologyPasadenaUnited States
| | - Annie Moradian
- Proteome Exploration Laboratory, Division of Biology and Biological Engineering, Beckman Institute, California Institute of TechnologyPasadenaUnited States
| | - Justin B Kinney
- Simons Center for Quantitative Biology, Cold Spring Harbor LaboratoryCold Spring HarborUnited States
| | - Rob Phillips
- Department of Physics, California Institute of TechnologyPasadenaUnited States
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
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29
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Chaires JB, Gray RD, Dean WL, Monsen R, DeLeeuw LW, Stribinskis V, Trent JO. Human POT1 unfolds G-quadruplexes by conformational selection. Nucleic Acids Res 2020; 48:4976-4991. [PMID: 32232414 PMCID: PMC7229828 DOI: 10.1093/nar/gkaa202] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 03/13/2020] [Accepted: 03/18/2020] [Indexed: 12/14/2022] Open
Abstract
The reaction mechanism by which the shelterin protein POT1 (Protection of Telomeres 1) unfolds human telomeric G-quadruplex structures is not fully understood. We report here kinetic, thermodynamic, hydrodynamic and computational studies that show that a conformational selection mechanism, in which POT1 binding is coupled to an obligatory unfolding reaction, is the most plausible mechanism. Stopped-flow kinetic and spectroscopic titration studies, along with isothermal calorimetry, were used to show that binding of the single-strand oligonucleotide d[TTAGGGTTAG] to POT1 is both fast (80 ms) and strong (-10.1 ± 0.3 kcal mol-1). In sharp contrast, kinetic studies showed the binding of POT1 to an initially folded 24 nt G-quadruplex structure is four orders of magnitude slower. Fluorescence, circular dichroism and analytical ultracentrifugation studies showed that POT1 binding is coupled to quadruplex unfolding, with a final complex with a stoichiometry of 2 POT1 per 24 nt DNA. The binding isotherm for the POT1-quadruplex interaction was sigmoidal, indicative of a complex reaction. A conformational selection model that includes equilibrium constants for both G-quadruplex unfolding and POT1 binding to the resultant single-strand provided an excellent quantitative fit to the experimental binding data. POT1 unfolded and bound to any conformational form of human telomeric G-quadruplex (antiparallel, hybrid, parallel monomers or a 48 nt sequence with two contiguous quadruplexes), but did not avidly interact with duplex DNA or with other G-quadruplex structures. Finally, molecular dynamics simulations provided a detailed structural model of a 2:1 POT1:DNA complex that is fully consistent with experimental biophysical results.
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Affiliation(s)
- Jonathan B Chaires
- James Graham Brown Cancer Center, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA
| | - Robert D Gray
- James Graham Brown Cancer Center, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA
| | - William L Dean
- James Graham Brown Cancer Center, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA
| | - Robert Monsen
- James Graham Brown Cancer Center, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA
| | - Lynn W DeLeeuw
- James Graham Brown Cancer Center, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA
| | - Vilius Stribinskis
- James Graham Brown Cancer Center, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA
| | - John O Trent
- James Graham Brown Cancer Center, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA
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30
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Cao Z, Filatova T, Oyarzún DA, Grima R. A Stochastic Model of Gene Expression with Polymerase Recruitment and Pause Release. Biophys J 2020; 119:1002-1014. [PMID: 32814062 DOI: 10.1016/j.bpj.2020.07.020] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 02/27/2020] [Accepted: 07/23/2020] [Indexed: 12/14/2022] Open
Abstract
Transcriptional bursting is a major source of noise in gene expression. The telegraph model of gene expression, whereby transcription switches between on and off states, is the dominant model for bursting. Recently, it was shown that the telegraph model cannot explain a number of experimental observations from perturbation data. Here, we study an alternative model that is consistent with the data and which explicitly describes RNA polymerase recruitment and polymerase pause release, two steps necessary for messenger RNA (mRNA) production. We derive the exact steady-state distribution of mRNA numbers and an approximate steady-state distribution of protein numbers, which are given by generalized hypergeometric functions. The theory is used to calculate the relative sensitivity of the coefficient of variation of mRNA fluctuations for thousands of genes in mouse fibroblasts. This indicates that the size of fluctuations is mostly sensitive to the rate of burst initiation and the mRNA degradation rate. Furthermore, we show that 1) the time-dependent distribution of mRNA numbers is accurately approximated by a modified telegraph model with a Michaelis-Menten like dependence of the effective transcription rate on RNA polymerase abundance, and 2) the model predicts that if the polymerase recruitment rate is comparable or less than the pause release rate, then upon gene replication, the mean number of RNA per cell remains approximately constant. This gene dosage compensation property has been experimentally observed and cannot be explained by the telegraph model with constant rates.
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Affiliation(s)
- Zhixing Cao
- The Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China; School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom; Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Tatiana Filatova
- School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom; School of Mathematics, the University of Edinburgh, Edinburgh, United Kingdom
| | - Diego A Oyarzún
- School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom; School of Informatics, the University of Edinburgh, Edinburgh, United Kingdom
| | - Ramon Grima
- School of Biological Sciences, the University of Edinburgh, Edinburgh, United Kingdom.
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31
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Ali MZ, Parisutham V, Choubey S, Brewster RC. Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif. eLife 2020; 9:56517. [PMID: 32808926 PMCID: PMC7505660 DOI: 10.7554/elife.56517] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 08/07/2020] [Indexed: 12/20/2022] Open
Abstract
Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF-binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.
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Affiliation(s)
- Md Zulfikar Ali
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, United States
| | - Vinuselvi Parisutham
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, United States
| | - Sandeep Choubey
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Robert C Brewster
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, United States
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32
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Schuh L, Saint-Antoine M, Sanford EM, Emert BL, Singh A, Marr C, Raj A, Goyal Y. Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer. Cell Syst 2020; 10:363-378.e12. [PMID: 32325034 PMCID: PMC7293108 DOI: 10.1016/j.cels.2020.03.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 02/03/2020] [Accepted: 03/24/2020] [Indexed: 10/24/2022]
Abstract
Non-genetic transcriptional variability is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes and survive therapy. How might these rare states arise and disappear within the population? It is unclear whether the canonical models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified mechanisms. We show that a minimal model of transcriptional bursting and gene interactions can give rise to rare coordinated high expression states. These states occur more frequently in networks with low connectivity and depend on three parameters. While entry into these states is initiated by a long transcriptional burst that also triggers entry of other genes, the exit occurs through independent inactivation of individual genes. Together, we demonstrate that established principles of gene regulation are sufficient to describe this behavior and argue for its more general existence. A record of this paper's transparent peer review process is included in the Supplemental Information.
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Affiliation(s)
- Lea Schuh
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany; Department of Mathematics, Technical University of Munich, Garching 85748, Germany
| | - Michael Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19716, USA
| | - Eric M Sanford
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Benjamin L Emert
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Abhyudai Singh
- Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
| | - Carsten Marr
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany
| | - Arjun Raj
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yogesh Goyal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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33
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Laohakunakorn N, Grasemann L, Lavickova B, Michielin G, Shahein A, Swank Z, Maerkl SJ. Bottom-Up Construction of Complex Biomolecular Systems With Cell-Free Synthetic Biology. Front Bioeng Biotechnol 2020; 8:213. [PMID: 32266240 PMCID: PMC7105575 DOI: 10.3389/fbioe.2020.00213] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 03/03/2020] [Indexed: 12/16/2022] Open
Abstract
Cell-free systems offer a promising approach to engineer biology since their open nature allows for well-controlled and characterized reaction conditions. In this review, we discuss the history and recent developments in engineering recombinant and crude extract systems, as well as breakthroughs in enabling technologies, that have facilitated increased throughput, compartmentalization, and spatial control of cell-free protein synthesis reactions. Combined with a deeper understanding of the cell-free systems themselves, these advances improve our ability to address a range of scientific questions. By mastering control of the cell-free platform, we will be in a position to construct increasingly complex biomolecular systems, and approach natural biological complexity in a bottom-up manner.
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Affiliation(s)
- Nadanai Laohakunakorn
- School of Biological Sciences, Institute of Quantitative Biology, Biochemistry, and Biotechnology, University of Edinburgh, Edinburgh, United Kingdom
| | - Laura Grasemann
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Barbora Lavickova
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Grégoire Michielin
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Amir Shahein
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Zoe Swank
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Sebastian J. Maerkl
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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34
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Garcia HG, Berrocal A, Kim YJ, Martini G, Zhao J. Lighting up the central dogma for predictive developmental biology. Curr Top Dev Biol 2019; 137:1-35. [PMID: 32143740 DOI: 10.1016/bs.ctdb.2019.10.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Although the last 30years have witnessed the mapping of the wiring diagrams of the gene regulatory networks that dictate cell fate and animal body plans, specific understanding building on such network diagrams that shows how DNA regulatory regions control gene expression lags far behind. These networks have yet to yield the predictive power necessary to, for example, calculate how the concentration dynamics of input transcription factors and DNA regulatory sequence prescribes output patterns of gene expression that, in turn, determine body plans themselves. Here, we argue that reaching a predictive understanding of developmental decision-making calls for an interplay between theory and experiment aimed at revealing how the regulation of the processes of the central dogma dictate network connections and how network topology guides cells toward their ultimate developmental fate. To make this possible, it is crucial to break free from the snapshot-based understanding of embryonic development facilitated by fixed-tissue approaches and embrace new technologies that capture the dynamics of developmental decision-making at the single cell level, in living embryos.
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Affiliation(s)
- Hernan G Garcia
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, United States; Department of Physics, University of California at Berkeley, Berkeley, CA, United States; Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, United States; Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, CA, United States.
| | - Augusto Berrocal
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, United States
| | - Yang Joon Kim
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, United States
| | - Gabriella Martini
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, United States
| | - Jiaxi Zhao
- Department of Physics, University of California at Berkeley, Berkeley, CA, United States
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35
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Tungtur S, Schwingen KM, Riepe JJ, Weeramange CJ, Swint-Kruse L. Homolog comparisons further reconcile in vitro and in vivo correlations of protein activities by revealing over-looked physiological factors. Protein Sci 2019; 28:1806-1818. [PMID: 31351028 DOI: 10.1002/pro.3695] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 07/10/2019] [Accepted: 07/22/2019] [Indexed: 12/22/2022]
Abstract
To bridge biological and biochemical disciplines, the relationship between in vitro protein biochemical function and in vivo activity must be established. Such studies can (a) help determine whether properties measured in simple, dilute solutions extrapolate to the complex in vivo conditions and (b) illuminate cryptic biological factors that are new avenues for study. We have explored the in vivo-in vitro relationship for chimeras built from LacI/GalR transcription regulators. In prior studies of individual chimeras, amino acid changes that altered in vitro DNA binding affinity exhibited correlated changes in in vivo transcription repression. However, discrepancies arose when the two datasets were compared to each other: Although their DNA binding domains were identical and their in vitro binding affinities spanned the same range, their in vivo repression ranges differed by >50-fold. Here, we determined that the presence of endogenous ligand for one chimera further exacerbated the offset, but that different abilities to simultaneously bind and "loop" two DNA operators resolves the discrepancy. Indeed, results suggest that the lac operon can be looped by even weakly interacting repressor dimers. For looping-competent repressors, we measured in vitro binding to the secondary operator. Surprisingly, this was largely insensitive to amino acid changes in the repressor protein, which reflects altered specificity; this supports the emerging view that the locations of specificity determining positions can be unique to each protein homolog. In aggregate, this work illustrates how a comparative approach can enrich understanding of the in vivo-in vitro relationship and suggest unexpected avenues for future study.
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Affiliation(s)
- Sudheer Tungtur
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas
| | - Kristen M Schwingen
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas
| | - Joshua J Riepe
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas
| | - Chamitha J Weeramange
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas
| | - Liskin Swint-Kruse
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas
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