1
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van der Sijs A, Visser T, Moerman P, Folkers G, Kegel W. Broccoli aptamer allows quantitative transcription regulation studies in vitro. PLoS One 2024; 19:e0304677. [PMID: 38870160 PMCID: PMC11175446 DOI: 10.1371/journal.pone.0304677] [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: 02/16/2024] [Accepted: 05/15/2024] [Indexed: 06/15/2024] Open
Abstract
Quantitative transcription regulation studies in vivo and in vitro often make use of reporter proteins. Here we show that using Broccoli aptamers, quantitative study of transcription in various regulatory scenarios is possible without a translational step. To explore the method we studied several regulatory scenarios that we analyzed using thermodynamic occupancy-based models, and found excellent agreement with previous studies. In the next step we show that non-coding DNA can have a dramatic effect on the level of transcription, similar to the influence of the lac repressor with a strong affinity to operator sites. Finally, we point out the limitations of the method in terms of delay times coupled to the folding of the aptamer. We conclude that the Broccoli aptamer is suitable for quantitative transcription measurements.
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Affiliation(s)
- Amanda van der Sijs
- Van ’t Hoff Laboratory for Physical and Colloidal Chemistry, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands
| | - Thomas Visser
- Van ’t Hoff Laboratory for Physical and Colloidal Chemistry, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands
| | - Pepijn Moerman
- Self-Organizing Soft Matter, Chemical Engineering and Chemistry, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Gert Folkers
- Utrecht NMR Group, Bijvoet Centre fo Biomolecular Research, Utrecht University, Utrecht, The Netherlands
| | - Willem Kegel
- Van ’t Hoff Laboratory for Physical and Colloidal Chemistry, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands
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2
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Liu Z, Gillis TG, Raman S, Cui Q. A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery. eLife 2024; 12:RP92262. [PMID: 38836839 PMCID: PMC11152574 DOI: 10.7554/elife.92262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024] Open
Abstract
New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.
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Affiliation(s)
- Zhuang Liu
- Department of Physics, Boston UniversityBostonUnited States
| | - Thomas G Gillis
- Department of Biochemistry, University of WisconsinMadisonUnited States
| | - Srivatsan Raman
- Department of Biochemistry, University of WisconsinMadisonUnited States
- Department of Chemistry, University of WisconsinMadisonUnited States
- Department of Bacteriology, University of WisconsinMadisonUnited States
| | - Qiang Cui
- Department of Physics, Boston UniversityBostonUnited States
- Department of Chemistry, Boston UniversityBostonUnited States
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3
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Jiang J, Chen S, Tsou T, McGinnis CS, Khazaei T, Zhu Q, Park JH, Strazhnik IM, Vielmetter J, Gong Y, Hanna J, Chow ED, Sivak DA, Gartner ZJ, Thomson M. D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.19.537364. [PMID: 37131803 PMCID: PMC10153191 DOI: 10.1101/2023.04.19.537364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.
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Affiliation(s)
- Jialong Jiang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
| | - Sisi Chen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
- Beckman Single-Cell Profiling and Engineering Center, California Institute of Technology, Pasadena, CA, 91125, USA
- Apertura Gene Therapy, 345 Park Ave South, New York, NY 10010
| | - Tiffany Tsou
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
- Beckman Single-Cell Profiling and Engineering Center, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Christopher S. McGinnis
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Tahmineh Khazaei
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
| | - Qin Zhu
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Jong H. Park
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
- Beckman Single-Cell Profiling and Engineering Center, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Inna-Marie Strazhnik
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
| | - Jost Vielmetter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
| | - Yingying Gong
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
| | - John Hanna
- Department of Pathology, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Eric D. Chow
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, 94143, USA
- Center for Advanced Technology, University of California San Francisco, San Francisco, CA, 94143, USA
| | - David A. Sivak
- Department of Physics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Zev J. Gartner
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94143, USA
- Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, 94115, USA
- Chan Zuckerberg BioHub, University of California San Francisco, San Francisco, CA, 94143, USA
- Center for Cellular Construction, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, 91125, USA
- Beckman Single-Cell Profiling and Engineering Center, California Institute of Technology, Pasadena, CA, 91125, USA
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4
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Lally P, Gómez-Romero L, Tierrafría VH, Aquino P, Rioualen C, Zhang X, Kim S, Baniulyte G, Plitnick J, Smith C, Babu M, Collado-Vides J, Wade JT, Galagan JE. Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.594371. [PMID: 38826350 PMCID: PMC11142182 DOI: 10.1101/2024.05.23.594371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.
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Affiliation(s)
- Patrick Lally
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215
| | - Laura Gómez-Romero
- Instituto Nacional de Medicina Genómica, Periférico Sur 4809, Arenal Tepepan, Ciudad de México 14610, México
- Escuela de Medicina y Ciencias de la Salud, Tecnológico de Monterrey, Ciudad de México, México
| | - Víctor H. Tierrafría
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Avenida Universidad s/n, Cuernavaca 62210, Morelos, México
| | - Patricia Aquino
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215
| | - Claire Rioualen
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Avenida Universidad s/n, Cuernavaca 62210, Morelos, México
| | - Xiaoman Zhang
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215
| | - Sunyoung Kim
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, SK S4S 0A2, Canada
| | | | - Jonathan Plitnick
- Wadsworth Center, New York State Department of Health, Albany, NY, USA
| | - Carol Smith
- Wadsworth Center, New York State Department of Health, Albany, NY, USA
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, SK S4S 0A2, Canada
| | - Julio Collado-Vides
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Avenida Universidad s/n, Cuernavaca 62210, Morelos, México
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Joseph T. Wade
- Wadsworth Center, New York State Department of Health, Albany, NY, USA
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY, USA
| | - James E. Galagan
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215
- Bioinformatics Program, Boston University, 24 Cummington Mall, Boston, MA 02215
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5
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He J, Huo X, Pei G, Jia Z, Yan Y, Yu J, Qu H, Xie Y, Yuan J, Zheng Y, Hu Y, Shi M, You K, Li T, Ma T, Zhang MQ, Ding S, Li P, Li Y. Dual-role transcription factors stabilize intermediate expression levels. Cell 2024; 187:2746-2766.e25. [PMID: 38631355 DOI: 10.1016/j.cell.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/08/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.
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Affiliation(s)
- Jinnan He
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Xiangru Huo
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Gaofeng Pei
- State Key Laboratory of Membrane Biology, Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Zeran Jia
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yiming Yan
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Jiawei Yu
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Haozhi Qu
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yunxin Xie
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Junsong Yuan
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yuan Zheng
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yanyan Hu
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Minglei Shi
- Bioinformatics Division, National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Kaiqiang You
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Tingting Li
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Tianhua Ma
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Michael Q Zhang
- Bioinformatics Division, National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing 100084, China; Department of Biological Sciences, Center for Systems Biology, The University of Texas, Dallas, TX 75080-3021, USA
| | - Sheng Ding
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Pilong Li
- State Key Laboratory of Membrane Biology, Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China.
| | - Yinqing Li
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China.
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6
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Jiao F, Li J, Liu T, Zhu Y, Che W, Bleris L, Jia C. What can we learn when fitting a simple telegraph model to a complex gene expression model? PLoS Comput Biol 2024; 20:e1012118. [PMID: 38743803 PMCID: PMC11125521 DOI: 10.1371/journal.pcbi.1012118] [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: 02/06/2024] [Revised: 05/24/2024] [Accepted: 04/27/2024] [Indexed: 05/16/2024] Open
Abstract
In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.
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Affiliation(s)
- Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Jing Li
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Ting Liu
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Yifeng Zhu
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Wenhao Che
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Leonidas Bleris
- Bioengineering Department, The University of Texas at Dallas, Richardson, Texas, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas, United States of America
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, United States of America
| | - Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing, China
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7
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Paczkó M, Vörös D, Szabó P, Jékely G, Szathmáry E, Szilágyi A. A neural network-based model framework for cell-fate decisions and development. Commun Biol 2024; 7:323. [PMID: 38486083 PMCID: PMC10940658 DOI: 10.1038/s42003-024-05985-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.
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Affiliation(s)
- Mátyás Paczkó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Dániel Vörös
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary
| | - Péter Szabó
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
| | - Gáspár Jékely
- Living Systems Institute, University of Exeter, Stocker Road 4QD, EX4, Exeter, UK
| | - Eörs Szathmáry
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary.
- Center for the Conceptual Foundations of Science, Parmenides Foundation, Hindenburgstr. 15, 82343, Pöcking, Germany.
- Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary.
| | - András Szilágyi
- Institute of Evolution, HUN-REN Centre for Ecological Research, Konkoly-Thege M. út 29-33, 1121, Budapest, Hungary
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8
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Sánchez Á, Arrabal A, San Román M, Díaz-Colunga J. The optimization of microbial functions through rational environmental manipulations. Mol Microbiol 2024. [PMID: 38372207 DOI: 10.1111/mmi.15236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/19/2024] [Accepted: 01/20/2024] [Indexed: 02/20/2024]
Abstract
Microorganisms play a central role in biotechnology and it is key that we develop strategies to engineer and optimize their functionality. To this end, most efforts have focused on introducing genetic manipulations in microorganisms which are then grown either in monoculture or in mixed-species consortia. An alternative strategy to optimize microbial processes is to rationally engineer the environment in which microbes grow. The microbial environment is multidimensional, including factors such as temperature, pH, salinity, nutrient composition, etc. These environmental factors all influence the growth and phenotypes of microorganisms and they generally "interact" with one another, combining their effects in complex, non-additive ways. In this piece, we overview the origins and consequences of these "interactions" between environmental factors and discuss how they have been built into statistical, bottom-up predictive models of microbial function to identify optimal environmental conditions for monocultures and microbial consortia. We also overview alternative "top-down" approaches, such as genetic algorithms, to finding optimal combinations of environmental factors. By providing a brief summary of the state of this field, we hope to stimulate further work on the rational manipulation and optimization of the microbial environment.
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Affiliation(s)
- Álvaro Sánchez
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, CNB - CSIC, Campus de Cantoblanco, Madrid, Spain
- Instituto de Biología Funcional y Genómica, IBFG-CSIC, Universidad de Salamanca, Salamanca, Spain
| | - Andrea Arrabal
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, CNB - CSIC, Campus de Cantoblanco, Madrid, Spain
- Instituto de Biología Funcional y Genómica, IBFG-CSIC, Universidad de Salamanca, Salamanca, Spain
| | - Magdalena San Román
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, CNB - CSIC, Campus de Cantoblanco, Madrid, Spain
- Instituto de Biología Funcional y Genómica, IBFG-CSIC, Universidad de Salamanca, Salamanca, Spain
| | - Juan Díaz-Colunga
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, CNB - CSIC, Campus de Cantoblanco, Madrid, Spain
- Instituto de Biología Funcional y Genómica, IBFG-CSIC, Universidad de Salamanca, Salamanca, Spain
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9
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Van Hove B, De Wannemaeker L, Missiaen I, Maertens J, De Mey M. Taming CRISPRi: Dynamic range tuning through guide RNA diversion. N Biotechnol 2023; 77:50-57. [PMID: 37422184 DOI: 10.1016/j.nbt.2023.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023]
Abstract
CRISPRi is a powerful technique to repress gene expression in a targeted and highly efficient manner. However, this potency is a double-edged sword in inducible systems, as even leaky expression of guide RNA results in a repression phenotype, complicating applications such as dynamic metabolic engineering. We evaluated three methods to enhance the controllability of CRISPRi by modulating the level of free and DNA-bound guide RNA complexes. Overall repression can be attenuated through rationally designed mismatches in the reversibility determining region of the guide RNA sequence; decoy target sites can selectively modulate repression at low levels of induction; and the implementation of feedback control not only enhances the linearity of induction, but broadens the dynamic range of the output as well. Furthermore, feedback control significantly enhances the recovery rate after induction is removed. Used in combination, these techniques enable the fine-tuning of CRISPRi to meet restrictions imposed by the target and match the input signal required for induction.
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Affiliation(s)
- Bob Van Hove
- Centre for Synthetic Biology, Ghent University, Coupure links 653, 9000 Ghent, Belgium; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lien De Wannemaeker
- Centre for Synthetic Biology, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Isolde Missiaen
- Centre for Synthetic Biology, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Jo Maertens
- Centre for Synthetic Biology, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Marjan De Mey
- Centre for Synthetic Biology, Ghent University, Coupure links 653, 9000 Ghent, Belgium.
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10
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Pezzotta A, Briscoe J. Optimal control of gene regulatory networks for morphogen-driven tissue patterning. Cell Syst 2023; 14:940-952.e11. [PMID: 37972560 DOI: 10.1016/j.cels.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 06/06/2023] [Accepted: 10/10/2023] [Indexed: 11/19/2023]
Abstract
The generation of distinct cell types in developing tissues depends on establishing spatial patterns of gene expression. Often, this is directed by spatially graded chemical signals-known as morphogens. In the "French Flag model," morphogen concentration instructs cells to acquire specific fates. How this mechanism produces timely and organized cell-fate decisions, despite the presence of changing morphogen levels, molecular noise, and individual variability, is unclear. Moreover, feedback is present at various levels in developing tissues, breaking the link between morphogen concentration, signaling activity, and position. Here, we develop an alternative framework using optimal control theory to tackle the problem of morphogen-driven patterning: intracellular signaling is derived as the control strategy that guides cells to the correct fate while minimizing a combination of signaling levels and time. This approach recovers experimentally observed properties of patterning strategies and offers insight into design principles that produce timely, precise, and reproducible morphogen patterning.
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Affiliation(s)
- Alberto Pezzotta
- Developmental Dynamics Laboratory, The Francis Crick Institute, 1 Midland Road, NW1 1AT London, UK; Gatsby Computational Neuroscience Unit, University College London, 25 Howland Street, W1T 4JG London, UK.
| | - James Briscoe
- Developmental Dynamics Laboratory, The Francis Crick Institute, 1 Midland Road, NW1 1AT London, UK.
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11
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Schardt S, Fischer SC. Adjusting the range of cell-cell communication enables fine-tuning of cell fate patterns from checkerboard to engulfing. J Math Biol 2023; 87:54. [PMID: 37679573 PMCID: PMC10485129 DOI: 10.1007/s00285-023-01959-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/09/2023]
Abstract
During development, spatio-temporal patterns ranging from checkerboard to engulfing occur with precise proportions of the respective cell fates. Key developmental regulators are intracellular transcriptional interactions and intercellular signaling. We present an analytically tractable mathematical model based on signaling that reliably generates different cell type patterns with specified proportions. Employing statistical mechanics, We derived a cell fate decision model for two cell types. A detailed steady state analysis on the resulting dynamical system yielded necessary conditions to generate spatially heterogeneous patterns. This allows the cell type proportions to be controlled by a single model parameter. Cell-cell communication is realized by local and global signaling mechanisms. These result in different cell type patterns. A nearest neighbor signal yields checkerboard patterns. Increasing the signal dispersion, cell fate clusters and an engulfing pattern can be generated. Altogether, the presented model allows us to reliably generate heterogeneous cell type patterns of different kinds as well as desired proportions.
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Affiliation(s)
- Simon Schardt
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
| | - Sabine C. Fischer
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
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12
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Collado-Vides J. Regulatory promoter architectures in the hands of thermodynamic modelling. Nat Rev Genet 2023; 24:349. [PMID: 36747003 DOI: 10.1038/s41576-023-00578-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Julio Collado-Vides
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Mexico.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Center for Genomic Regulation (CRG), Universitat Pompeu Fabra, Barcelona, Spain.
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13
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Martinez-Corral R, Park M, Biette KM, Friedrich D, Scholes C, Khalil AS, Gunawardena J, DePace AH. Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology. Cell Syst 2023; 14:324-339.e7. [PMID: 37080164 PMCID: PMC10472254 DOI: 10.1016/j.cels.2023.02.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 08/22/2022] [Accepted: 02/10/2023] [Indexed: 04/22/2023]
Abstract
Transcription factors (TFs) control gene expression, often acting synergistically. Classical thermodynamic models offer a biophysical explanation for synergy based on binding cooperativity and regulated recruitment of RNA polymerase. Because transcription requires polymerase to transition through multiple states, recent work suggests that "kinetic synergy" can arise through TFs acting on distinct steps of the transcription cycle. These types of synergy are not mutually exclusive and are difficult to disentangle conceptually and experimentally. Here, we model and build a synthetic circuit in which TFs bind to a single shared site on DNA, such that TFs cannot synergize by simultaneous binding. We model mRNA production as a function of both TF binding and regulation of the transcription cycle, revealing a complex landscape dependent on TF concentration, DNA binding affinity, and regulatory activity. We use synthetic TFs to confirm that the transcription cycle must be integrated with recruitment for a quantitative understanding of gene regulation.
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Affiliation(s)
| | - Minhee Park
- Biological Design Center, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Kelly M Biette
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Dhana Friedrich
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Clarissa Scholes
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ahmad S Khalil
- Biological Design Center, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Jeremy Gunawardena
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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14
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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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15
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Chen X, Liu Z, Lou C, Guan Y, Ouyang Q, Xiang Y. Improving cooperativity of transcription activators by oligomerization domains in mammalian cells. Synth Syst Biotechnol 2023; 8:114-120. [PMID: 36605704 PMCID: PMC9804245 DOI: 10.1016/j.synbio.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Cooperative activation is critical for the applications of synthetic biology in mammalian cells. In this study, we have developed cooperative transcription factor by fusing oligomerization domain in mammalian cells. Firstly, we demonstrated that two oligomerized domains (CI434 and CI) successfully improved transcription factor cooperativity in bacterial cells but failed to increase cooperativity in mammalian cells, possibly because the additional mammalian activation domain disrupted their oligomerization capability. Therefore, we chose a different type of oligomerized domain (CarHC), whose ability to oligomerize is not dependent on its C-terminal domains, to fuse with a transcription factor (RpaR) and activation domain (VTR3), forming a potential cooperative transcription activator RpaR-CarH-VTR3 for mammalian regulatory systems. Compared with RpaR-VTR3, the cooperativity of RpaR-CarH-VTR3 was significantly improved with higher Hill coefficient and a narrower input range in the inducible switch system in mammalian cells. Moreover, a mathematical model based on statistical mechanics model was developed and the simulation results supported the hypothesis that the tetramer of the CarH domain in mammalian cells was the reason for the cooperative capacity of RpaR-CarH-VTR3.
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Affiliation(s)
- Xinmao Chen
- School of Physics, Peking University, Beijing, 100871, China
| | - Ziming Liu
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Chunbo Lou
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ying Guan
- School of Physics, Peking University, Beijing, 100871, China
- Department of Chemical Engineering, Tsinghua University, Beijing, 100871, China
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qi Ouyang
- School of Physics, Peking University, Beijing, 100871, China
| | - Yanhui Xiang
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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16
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Engelmann N, Schwarz T, Kubaczka E, Hochberger C, Koeppl H. Context-Aware Technology Mapping in Genetic Design Automation. ACS Synth Biol 2023; 12:446-459. [PMID: 36693176 PMCID: PMC9942193 DOI: 10.1021/acssynbio.2c00361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Genetic design automation (GDA) tools hold promise to speed-up circuit design in synthetic biology. Their widespread adoption is hampered by their limited predictive power, resulting in frequent deviations between the in silico and in vivo performance of a genetic circuit. Context effects, i.e., the change in overall circuit functioning, due to the intracellular environment of the host and due to cross-talk among circuits components are believed to be a major source for the aforementioned deviations. Incorporating these effects in computational models of GDA tools is challenging but is expected to boost their predictive power and hence their deployment. Using fine-grained thermodynamic models of promoter activity, we show in this work how to account for two major components of cellular context effects: (i) crosstalk due to limited specificity of used regulators and (ii) titration of circuit regulators to off-target binding sites on the host genome. We show how we can compensate the incurred increase in computational complexity through dedicated branch-and-bound techniques during the technology mapping process. Using the synthesis of several combinational logic circuits based on Cello's device library as a case study, we analyze the effect of different intensities and distributions of crosstalk on circuit performance and on the usability of a given device library.
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Affiliation(s)
- Nicolai Engelmann
- Department
of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt64283, Germany
| | - Tobias Schwarz
- Department
of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt64283, Germany
| | - Erik Kubaczka
- Department
of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt64283, Germany
| | - Christian Hochberger
- Department
of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt64283, Germany,Centre
for Synthetic Biology, TU Darmstadt, Darmstadt64283, Germany
| | - Heinz Koeppl
- Department
of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt64283, Germany,Centre
for Synthetic Biology, TU Darmstadt, Darmstadt64283, Germany,
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17
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Abraha BW, Marchisio MA. Design of Gene Boolean Gates and Circuits with Convergent Promoters. Methods Mol Biol 2023; 2553:121-154. [PMID: 36227542 DOI: 10.1007/978-1-0716-2617-7_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Gene digital circuits are the subject of many research works due to their various potential applications, from hazard detection to medical diagnostic. Moreover, a remarkable number of techniques, developed in electronics, can be used for the construction of biological digital systems. In our previous works, we showed how to automatize the design and modeling of gene digital circuits whose gates were based on transcription and translation regulation. In this chapter, we illustrate how Boolean gates could be implemented by following a particular architecture, the convergent promoter one, rather diffuse in nature but seldom adopted in Synthetic Biology. Beside gate design, we also explain how to extend our previous modeling approach, based on composable parts and pools of molecules, to quantitatively describe and simulate this particular kind of digital biological devices.
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Affiliation(s)
- Biruck Woldai Abraha
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
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18
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Understanding the Genome-Wide Transcription Response To Various cAMP Levels in Bacteria Using Phenomenological Models. mSystems 2022; 7:e0090022. [PMID: 36409084 PMCID: PMC9765429 DOI: 10.1128/msystems.00900-22] [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] [Indexed: 11/23/2022] Open
Abstract
Attempts to understand gene regulation by global transcription factors have largely been limited to expression studies under binary conditions of presence and absence of the transcription factor. Studies addressing genome-wide transcriptional responses to changing transcription factor concentration at high resolution are lacking. Here, we create a data set containing the entire Escherichia coli transcriptome in Luria-Bertani (LB) broth as it responds to 10 different cAMP concentrations spanning the biological range. We use the Hill's model to accurately summarize individual gene responses into three intuitively understandable parameters, Emax, n, and k, reflecting the sensitivity, nonlinearity, and midpoint of the dynamic range. Our data show that most cAMP-regulated genes have an n of >2, with their k values centered around the wild-type concentration of cAMP. Additionally, cAMP receptor protein (CRP) affinity to a promoter is correlated with Emax but not k, hinting that a high-affinity CRP promoter need not ensure transcriptional activation at lower cAMP concentrations and instead affects the magnitude of the response. Finally, genes belonging to different functional classes are tuned to have different k, n, and Emax values. We demonstrate that phenomenological models are a better alternative for studying gene expression trends than classical clustering methods, with the phenomenological constants providing greater insights into how genes are tuned in a regulatory network. IMPORTANCE Different genes may follow different trends in response to various transcription factor concentrations. In this study, we ask two questions: (i) what are the trends that different genes follow in response to changing transcription factor concentrations and (ii) what methods can be used to extract information from the gene trends so obtained. We demonstrate a method to analyze transcription factor concentration-dependent genome-wide expression data using phenomenological models. Conventional clustering methods and principal-component analysis (PCA) can be used to summarize trends in data but have limited interpretability. The use of phenomenological models greatly enhances the interpretability and thus utility of conventional clustering. Transformation of dose-response data into phenomenological constants opens up avenues to ask and answer many different kinds of question. We show that the phenomenological constants obtained from the model fits can be used to generate insights about network topology and allows integration of other experimental data such as chromatin immunoprecipitation sequencing (ChIP-seq) to understand the system in greater detail.
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19
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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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20
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Balakrishnan R, Mori M, Segota I, Zhang Z, Aebersold R, Ludwig C, Hwa T. Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria. Science 2022; 378:eabk2066. [PMID: 36480614 PMCID: PMC9804519 DOI: 10.1126/science.abk2066] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Protein concentrations are set by a complex interplay between gene-specific regulatory processes and systemic factors, including cell volume and shared gene expression machineries. Elucidating this interplay is crucial for discerning and designing gene regulatory systems. We quantitatively characterized gene-specific and systemic factors that affect transcription and translation genome-wide for Escherichia coli across many conditions. The results revealed two design principles that make regulation of gene expression insulated from concentrations of shared machineries: RNA polymerase activity is fine-tuned to match translational output, and translational characteristics are similar across most messenger RNAs (mRNAs). Consequently, in bacteria, protein concentration is set primarily at the promoter level. A simple mathematical formula relates promoter activities and protein concentrations across growth conditions, enabling quantitative inference of gene regulation from omics data.
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Affiliation(s)
- Rohan Balakrishnan
- Department of Physics, University of California at San Diego, La Jolla, California 92093-0374
| | - Matteo Mori
- Department of Physics, University of California at San Diego, La Jolla, California 92093-0374
| | - Igor Segota
- Departments of Medicine and Pharmacology, University of California at San Diego, La Jolla, California 92093
| | - Zhongge Zhang
- Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, California 92093
| | - Ruedi Aebersold
- Faculty of Science, University of Zurich, Zurich, Switzerland
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Christina Ludwig
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich (TUM), Freising, Germany
| | - Terence Hwa
- Department of Physics, University of California at San Diego, La Jolla, California 92093-0374
- Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, California 92093
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21
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Frank SA. Automatic differentiation and the optimization of differential equation models in biology. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.1010278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
A computational revolution unleashed the power of artificial neural networks. At the heart of that revolution is automatic differentiation, which calculates the derivative of a performance measure relative to a large number of parameters. Differentiation enhances the discovery of improved performance in large models, an achievement that was previously difficult or impossible. Recently, a second computational advance optimizes the temporal trajectories traced by differential equations. Optimization requires differentiating a measure of performance over a trajectory, such as the closeness of tracking the environment, with respect to the parameters of the differential equations. Because model trajectories are usually calculated numerically by multistep algorithms, such as Runge-Kutta, the automatic differentiation must be passed through the numerical algorithm. This article explains how such automatic differentiation of trajectories is achieved. It also discusses why such computational breakthroughs are likely to advance theoretical and statistical studies of biological problems, in which one can consider variables as dynamic paths over time and space. Many common problems arise between improving success in computational learning models over performance landscapes, improving evolutionary fitness over adaptive landscapes, and improving statistical fits to data over information landscapes.
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22
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How does an organism extract relevant information from transcription factor concentrations? Biochem Soc Trans 2022; 50:1365-1376. [DOI: 10.1042/bst20220333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022]
Abstract
How does an organism regulate its genes? The involved regulation typically occurs in terms of a signal processing chain: an externally applied stimulus or a maternally supplied transcription factor leads to the expression of some downstream genes, which, in turn, are transcription factors for further genes. Especially during development, these transcription factors are frequently expressed in amounts where noise is still important; yet, the signals that they provide must not be lost in the noise. Thus, the organism needs to extract exactly relevant information in the signal. New experimental approaches involving single-molecule measurements at high temporal precision as well as increased precision in manipulations directly on the genome are allowing us to tackle this question anew. These new experimental advances mean that also from the theoretical side, theoretical advances should be possible. In this review, I will describe, specifically on the example of fly embryo gene regulation, how theoretical approaches, especially from inference and information theory, can help in understanding gene regulation. To do so, I will first review some more traditional theoretical models for gene regulation, followed by a brief discussion of information-theoretical approaches and when they can be applied. I will then introduce early fly development as an exemplary system where such information-theoretical approaches have traditionally been applied and can be applied; I will specifically focus on how one such method, namely the information bottleneck approach, has recently been used to infer structural features of enhancer architecture.
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23
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Frank SA. Optimization of Transcription Factor Genetic Circuits. BIOLOGY 2022; 11:1294. [PMID: 36138773 PMCID: PMC9495410 DOI: 10.3390/biology11091294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 04/13/2023]
Abstract
Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day-night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance.
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Affiliation(s)
- Steven A Frank
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA
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24
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Mellul M, Lahav S, Imashimizu M, Tokunaga Y, Lukatsky DB, Ram O. Repetitive DNA symmetry elements negatively regulate gene expression in embryonic stem cells. Biophys J 2022; 121:3126-3135. [PMID: 35810331 PMCID: PMC9463640 DOI: 10.1016/j.bpj.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 06/13/2022] [Accepted: 07/07/2022] [Indexed: 11/30/2022] Open
Abstract
Transcription factor (TF) binding to genomic DNA elements constitutes one of the key mechanisms that regulates gene expression program in cells. Both consensus and nonconsensus DNA sequence elements influence the recognition specificity of TFs. Based on the analysis of experimentally determined c-Myc binding preferences to genomic DNA, here we statistically predict that certain repetitive, nonconsensus DNA symmetry elements can relatively reduce TF-DNA binding preferences. This is in contrast to a different set of repetitive, nonconsensus symmetry elements that can increase the strength of TF-DNA binding. Using c-Myc enhancer reporter system containing consensus motif flanked by nonconsensus sequences in embryonic stem cells, we directly demonstrate that the enrichment in such negatively regulating repetitive symmetry elements is sufficient to reduce the gene expression level compared with native genomic sequences. Negatively regulating repetitive symmetry elements around consensus c-Myc motif and DNA sequences containing consensus c-Myc motif flanked by entirely randomized sequences show similar expression baseline. A possible explanation for this observation is that rather than complete repression, negatively regulating repetitive symmetry elements play a regulatory role in fine-tuning the reduction of gene expression, most probably by binding TFs other than c-Myc.
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Affiliation(s)
- Meir Mellul
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, Israel
| | - Shlomtzion Lahav
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, Israel
| | - Masahiko Imashimizu
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Yuji Tokunaga
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo, Japan
| | - David B Lukatsky
- Department of Chemistry, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Oren Ram
- Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, Israel.
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25
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Choi J, Kim R, Koh J. Quantitative Frameworks for Multivalent Macromolecular Interactions in Biological Linear Lattice Systems. Mol Cells 2022; 45:444-453. [PMID: 35754369 PMCID: PMC9260134 DOI: 10.14348/molcells.2022.0035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 11/29/2022] Open
Abstract
Multivalent macromolecular interactions underlie dynamic regulation of diverse biological processes in ever-changing cellular states. These interactions often involve binding of multiple proteins to a linear lattice including intrinsically disordered proteins and the chromosomal DNA with many repeating recognition motifs. Quantitative understanding of such multivalent interactions on a linear lattice is crucial for exploring their unique regulatory potentials in the cellular processes. In this review, the distinctive molecular features of the linear lattice system are first discussed with a particular focus on the overlapping nature of potential protein binding sites within a lattice. Then, we introduce two general quantitative frameworks, combinatorial and conditional probability models, dealing with the overlap problem and relating the binding parameters to the experimentally measurable properties of the linear lattice-protein interactions. To this end, we present two specific examples where the quantitative models have been applied and further extended to provide biological insights into specific cellular processes. In the first case, the conditional probability model was extended to highlight the significant impact of nonspecific binding of transcription factors to the chromosomal DNA on gene-specific transcriptional activities. The second case presents the recently developed combinatorial models to unravel the complex organization of target protein binding sites within an intrinsically disordered region (IDR) of a nucleoporin. In particular, these models have suggested a unique function of IDRs as a molecular switch coupling distinct cellular processes. The quantitative models reviewed here are envisioned to further advance for dissection and functional studies of more complex systems including phase-separated biomolecular condensates.
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Affiliation(s)
- Jaejun Choi
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Ryeonghyeon Kim
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Junseock Koh
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
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26
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Forquet R, Nasser W, Reverchon S, Meyer S. Quantitative contribution of the spacer length in the supercoiling-sensitivity of bacterial promoters. Nucleic Acids Res 2022; 50:7287-7297. [PMID: 35776118 PMCID: PMC9303308 DOI: 10.1093/nar/gkac579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 06/15/2022] [Accepted: 06/30/2022] [Indexed: 11/16/2022] Open
Abstract
DNA supercoiling acts as a global transcriptional regulator in bacteria, but the promoter sequence or structural determinants controlling its effect remain unclear. It was previously proposed to modulate the torsional angle between the -10 and -35 hexamers, and thereby regulate the formation of the closed-complex depending on the length of the 'spacer' between them. Here, we develop a thermodynamic model of this notion based on DNA elasticity, providing quantitative and parameter-free predictions of the relative activation of promoters containing a short versus long spacer when the DNA supercoiling level is varied. The model is tested through an analysis of in vitro and in vivo expression assays of mutant promoters with variable spacer lengths, confirming its accuracy for spacers ranging from 15 to 19 nucleotides, except those of 16 nucleotides where other regulatory mechanisms likely overcome the effect of this specific step. An analysis at the whole-genome scale in Escherichia coli then demonstrates a significant effect of the spacer length on the genomic expression after transient or inheritable superhelical variations, validating the model's predictions. Altogether, this study shows an example of mechanical constraints associated to promoter binding by RNA Polymerase underpinning a basal and global regulatory mechanism.
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Affiliation(s)
- Raphaël Forquet
- Université de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240 MAP, F-69622, France
| | - William Nasser
- Université de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240 MAP, F-69622, France
| | - Sylvie Reverchon
- Université de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240 MAP, F-69622, France
| | - Sam Meyer
- Université de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240 MAP, F-69622, France
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27
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Das AK. Stochastic gene transcription with non-competitive transcription regulatory architecture. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2022; 45:61. [PMID: 35831727 DOI: 10.1140/epje/s10189-022-00213-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
The transcription factors, such as activators and repressors, can interact with the promoter of gene either in a competitive or non-competitive way. In this paper, we construct a stochastic model with non-competitive transcriptional regulatory architecture and develop an analytical theory that re-establishes the experimental results with an improved data fitting. The analytical expressions in the theory allow us to study the nature of the system corresponding to any of its parameters and hence, enable us to find out the factors that govern the regulation of gene expression for that architecture. We notice that, along with transcriptional reinitiation and repressors, there are other parameters that can control the noisiness of this network. We also observe that, the Fano factor (at mRNA level) varies from sub-Poissonian regime to super-Poissonian regime. In addition to the aforementioned properties, we observe some anomalous characteristics of the Fano factor (at mRNA level) and that of the variance of protein at lower activator concentrations in the presence of repressor molecules. This model is useful to understand the architecture of interactions which may buffer the stochasticity inherent to gene transcription.
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28
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Femerling G, Gama-Castro S, Lara P, Ledezma-Tejeida D, Tierrafría VH, Muñiz-Rascado L, Bonavides-Martínez C, Collado-Vides J. Sensory Systems and Transcriptional Regulation in Escherichia coli. Front Bioeng Biotechnol 2022; 10:823240. [PMID: 35237580 PMCID: PMC8882922 DOI: 10.3389/fbioe.2022.823240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
In free-living bacteria, the ability to regulate gene expression is at the core of adapting and interacting with the environment. For these systems to have a logic, a signal must trigger a genetic change that helps the cell to deal with what implies its presence in the environment; briefly, the response is expected to include a feedback to the signal. Thus, it makes sense to think of genetic sensory mechanisms of gene regulation. Escherichia coli K-12 is the bacterium model for which the largest number of regulatory systems and its sensing capabilities have been studied in detail at the molecular level. In this special issue focused on biomolecular sensing systems, we offer an overview of the transcriptional regulatory corpus of knowledge for E. coli that has been gathered in our database, RegulonDB, from the perspective of sensing regulatory systems. Thus, we start with the beginning of the information flux, which is the signal's chemical or physical elements detected by the cell as changes in the environment; these signals are internally transduced to transcription factors and alter their conformation. Signals transduced to effectors bind allosterically to transcription factors, and this defines the dominant sensing mechanism in E. coli. We offer an updated list of the repertoire of known allosteric effectors, as well as a list of the currently known different mechanisms of this sensing capability. Our previous definition of elementary genetic sensory-response units, GENSOR units for short, that integrate signals, transport, gene regulation, and the biochemical response of the regulated gene products of a given transcriptional factor fit perfectly with the purpose of this overview. We summarize the functional heterogeneity of their response, based on our updated collection of GENSORs, and we use them to identify the expected feedback as part of their response. Finally, we address the question of multiple sensing in the regulatory network of E. coli. This overview introduces the architecture of sensing and regulation of native components in E.coli K-12, which might be a source of inspiration to bioengineering applications.
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Affiliation(s)
- Georgette Femerling
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Socorro Gama-Castro
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Paloma Lara
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | | | - Víctor H. Tierrafría
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Luis Muñiz-Rascado
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | | | - Julio Collado-Vides
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Universitat Pompeu Fabra (UPF), Barcelona, Spain
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29
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Zhu R, Del Rio-Salgado JM, Garcia-Ojalvo J, Elowitz MB. Synthetic multistability in mammalian cells. Science 2022; 375:eabg9765. [PMID: 35050677 DOI: 10.1126/science.abg9765] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In multicellular organisms, gene regulatory circuits generate thousands of molecularly distinct, mitotically heritable states through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and would allow engineering of multicellular programs that require interactions among distinct cell types. We created MultiFate, a naturally inspired, synthetic circuit that supports long-term, controllable, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using a model-based design, we engineered MultiFate circuits that generate as many as seven states, each stable for at least 18 days. MultiFate permits controlled state switching and modulation of state stability through external inputs and can be expanded with additional transcription factors. These results provide a foundation for engineering multicellular behaviors in mammalian cells.
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Affiliation(s)
- Ronghui Zhu
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Jesus M Del Rio-Salgado
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Jordi Garcia-Ojalvo
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.,Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA
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30
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Murugan R, Kreiman G. Multiple transcription auto regulatory loops can act as robust oscillators and decision-making motifs. Comput Struct Biotechnol J 2022; 20:5115-5135. [PMID: 36187915 PMCID: PMC9493064 DOI: 10.1016/j.csbj.2022.08.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/28/2022] [Accepted: 08/30/2022] [Indexed: 11/29/2022] Open
Abstract
We have shown that: Negative transcription auto regulation can speed up the response time at the cost of reduced steady state protein levels. Under strong binding conditions, one can increase the steady state protein level by increasing the gene copy number without a compromise on the response time. Multiple negative transcription autoregulatory motifs can be tuned for both the response time as well as steady state protein levels by varying the gene copy number. Multiple negative autoregulatory loops can act as robust genetic oscillators. Dual feedback motifs constructed with multiple negative and positive autoregulatory loop components can act as robust oscillators and bistable decision making units within the transcription factor networks.
Response time decides how fast a gene can react against an external signal at the transcription level in a signalling cascade. The steady state protein levels of the responding genes decide the coupling between two consecutive members of a signalling cascade. A negative autoregulatory loop (NARL) present in a transcription factor network can speed up the response time of the regulated gene at the cost of reduced steady state protein level. We present here a multi NARL motif which can be tuned for both the steady state protein level as well as response time in the required direction. Remarkably, there exists an optimum Hill coefficient nopt≅4 at which the response time of the NARL motif is at minimum. When the Hill coefficient is n < nopt, then under strong binding conditions, one can raise the steady state protein level by increasing the gene copy number with almost no change in the response time of the multi NARL motif. Using detailed computational analysis, we show that the coupled multi NARL and positive auto regulatory loop (PARL) motifs can act as an oscillator as well as decision making component which are robust against extrinsic fluctuations in the control parameters. We further demonstrate that the period of oscillation of the coupled multi NARL-PARL dual feedback oscillator can also be fine-tuned by the gene copy number apart from the inducer concentration. We finally demonstrate robustness of bistable dual feedback decision making motifs with multi autoregulatory loop component.
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Affiliation(s)
- Rajamanickam Murugan
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, India
| | - Gabriel Kreiman
- Children’s Hospital Boston, Harvard Medical School, Boston, USA
- Corresponding author at: Children’s Hospital Boston, Harvard Medical School, Boston, USA.
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31
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Tan H, Liu T, Zhou T. Exploring the role of eRNA in regulating gene expression. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2095-2119. [PMID: 35135243 DOI: 10.3934/mbe.2022098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
eRNAs as the products of enhancers can regulate gene expression via various possible ways, but which regulation way is more reasonable is debatable in biology, and in particular, how eRNAs impact gene expression remains unclear. Here we introduce a mechanistic model of gene expression to address these issues. This model considers three possible regulation ways of eRNA: Type-I by which eRNA regulates transcriptional activity by facilitating the formation of enhancer-promoter (E-P) loop, Type-II by which eRNA directly promotes the mRNA production rate, and mixed regulation (i.e., the combination of Type-I and Type-II). We show that with the increase of the E-P loop length, mRNA distribution can transition from unimodality to bimodality or vice versa in all the three regulation cases. However, in contrast to the other two regulations, Type-II regulation can lead to the highest mean mRNA level and the lowest mRNA noise, independent of the E-P loop length. These results would not only reveal the essential mechanism of how eRNA regulates gene expression, but also imply a new mechanism for phenotypic switching, namely the E-P loop can induce phenotypic switching.
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Affiliation(s)
- Heli Tan
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, China
| | - Tuoqi Liu
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
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32
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Chowdhury D, Wang C, Lu A, Zhu H. Cis-Regulatory Logic Produces Gene-Expression Noise Describing Phenotypic Heterogeneity in Bacteria. Front Genet 2021; 12:698910. [PMID: 34650591 PMCID: PMC8506120 DOI: 10.3389/fgene.2021.698910] [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: 04/22/2021] [Accepted: 08/31/2021] [Indexed: 12/04/2022] Open
Abstract
Gene transcriptional process is random. It occurs in bursts and follows single-molecular kinetics. Intermittent bursts are measured based on their frequency and size. They influence temporal fluctuations in the abundance of total mRNA and proteins by generating distinct transcriptional variations referred to as “noise”. Noisy expression induces uncertainty because the association between transcriptional variation and the extent of gene expression fluctuation is ambiguous. The promoter architecture and remote interference of different cis-regulatory elements are the crucial determinants of noise, which is reflected in phenotypic heterogeneity. An alternative perspective considers that cellular parameters dictating genome-wide transcriptional kinetics follow a universal pattern. Research on noise and systematic perturbations of promoter sequences reinforces that both gene-specific and genome-wide regulation occur across species ranging from bacteria and yeast to animal cells. Thus, deciphering gene-expression noise is essential across different genomics applications. Amidst the mounting conflict, it is imperative to reconsider the scope, progression, and rational construction of diversified viewpoints underlying the origin of the noise. Here, we have established an indication connecting noise, gene expression variations, and bacterial phenotypic variability. This review will enhance the understanding of gene-expression noise in various scientific contexts and applications.
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Affiliation(s)
- Debajyoti Chowdhury
- HKBU Institute for Research and Continuing Education, Shenzhen, China.,Computational Medicine Lab, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine and Translational Sciences, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Chao Wang
- HKBU Institute for Research and Continuing Education, Shenzhen, China.,Institute of Integrated Bioinformedicine and Translational Sciences, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Aiping Lu
- Computational Medicine Lab, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine and Translational Sciences, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Hailong Zhu
- HKBU Institute for Research and Continuing Education, Shenzhen, China.,Computational Medicine Lab, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine and Translational Sciences, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
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33
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Abstract
Because gene expression is important for evolutionary adaptation, its misregulation is an important cause of maladaptation. A misregulated gene can be incorrectly silent ("off") when a transcription factor (TF) that is required for its activation does not binds its regulatory region. Conversely, a misregulated gene can be incorrectly active ("on") when a TF not normally involved in its activation binds its regulatory region, a phenomenon also known as regulatory crosstalk. DNA mutations that destroy or create TF binding sites on DNA are an important source of misregulation and crosstalk. Although misregulation reduces fitness in an environment to which an organism is well-adapted, it may become adaptive in a new environment. Here, I derive simple yet general mathematical expressions that delimit the conditions under which misregulation can be adaptive. These expressions depend on the strength of selection against misregulation, on the fraction of DNA sequence space filled with TF binding sites, and on the fraction of genes that must be expressed for optimal adaptation. I then use empirical data from RNA sequencing, protein-binding microarrays, and genome evolution, together with population genetic simulations to ask when these conditions are likely to be met. I show that they can be met under realistic circumstances, but these circumstances may vary among organisms and environments. My analysis provides a framework in which improved theory and data collection can help us demonstrate the role of misregulation in adaptation. It also shows that misregulation, like DNA mutation, is one of life's many imperfections that can help propel Darwinian evolution.
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Affiliation(s)
- Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, CH-8057, Switzerland.,The Santa Fe Institute, Santa Fe, NM 87501, USA.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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34
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Forquet R, Pineau M, Nasser W, Reverchon S, Meyer S. Role of the Discriminator Sequence in the Supercoiling Sensitivity of Bacterial Promoters. mSystems 2021; 6:e0097821. [PMID: 34427530 PMCID: PMC8422995 DOI: 10.1128/msystems.00978-21] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 08/03/2021] [Indexed: 11/20/2022] Open
Abstract
DNA supercoiling acts as a global transcriptional regulator that contributes to the rapid transcriptional response of bacteria to many environmental changes. Although a large fraction of promoters from phylogenetically distant species respond to superhelical variations, the sequence or structural determinants of this behavior remain elusive. Here, we focus on the sequence of the "discriminator" element that was shown to modulate this response in several promoters. We develop a quantitative thermodynamic model of this regulatory effect, focusing on open complex formation during transcription initiation independently from promoter-specific regulatory proteins. We analyze previous and new expression data and show that the model predictions quantitatively match the in vitro and in vivo supercoiling response of selected promoters with mutated discriminator sequences. We then test the universality of this mechanism by a statistical analysis of promoter sequences from transcriptomes of phylogenetically distant bacteria under conditions of supercoiling variations (i) by gyrase inhibitors, (ii) by environmental stresses, or (iii) inherited in the longest-running evolution experiment. In all cases, we identify a robust and significant sequence signature in the discriminator region, suggesting that supercoiling-modulated promoter opening underpins a ubiquitous regulatory mechanism in the prokaryotic kingdom based on the fundamental mechanical properties of DNA and its basal interaction with RNA polymerase. IMPORTANCE In this study, we highlight the role of the discriminator as a global sensor of supercoiling variations and propose the first quantitative regulatory model of this principle, based on the specific step of promoter opening during transcription initiation. It defines the predictive rule by which DNA supercoiling quantitatively modulates the expression rate of bacterial promoters, depending on the G/C content of their discriminator and independently from promoter-specific regulatory proteins. This basal mechanism affects a wide range of species, which is tested by an extensive analysis of global high-throughput expression data. Altogether, ours results confirm and provide a quantitative framework for the long-proposed notion that the discriminator sequence is a significant determinant of promoter supercoiling sensitivity, underpinning the ubiquitous regulatory action of DNA supercoiling on the core transcriptional machinery, in particular in response to quick environmental changes.
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Affiliation(s)
- Raphaël Forquet
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240, MAP, Lyon, France
| | - Maïwenn Pineau
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240, MAP, Lyon, France
| | - William Nasser
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240, MAP, Lyon, France
| | - Sylvie Reverchon
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240, MAP, Lyon, France
| | - Sam Meyer
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, UMR5240, MAP, Lyon, France
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35
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Garbuzov FE, Gursky VV. Nonequilibrium model of short-range repression in gene transcription regulation. Phys Rev E 2021; 104:014407. [PMID: 34412298 DOI: 10.1103/physreve.104.014407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 06/24/2021] [Indexed: 11/07/2022]
Abstract
Transcription factors are proteins that regulate gene activity by activating or repressing gene transcription. A special class of transcriptional repressors operates via a short-range mechanism, making local DNA regions inaccessible to binding by activators, and thus providing an indirect repressive action on the target gene. This mechanism is commonly modeled assuming that repressors interact with DNA under thermodynamic equilibrium and neglecting some configurations of the gene regulatory region. We elaborate on a more general nonequilibrium model of short-range repression using the graph formalism for transitions between gene states, and we apply analytical calculations to compare it with the equilibrium model in terms of the repression strength and expression noise. In contrast to the equilibrium approach, the new model allows us to separate two basic mechanisms of short-range repression. The first mechanism is associated with the recruiting of factors that mediate chromatin condensation, and the second one concerns the blocking of factors that mediate chromatin loosening. The nonequilibrium model demonstrates better performance on previously published gene expression data obtained for transcription factors controlling Drosophila development, and furthermore it predicts that the first repression mechanism is the most favorable in this system. The presented approach can be scaled to larger gene networks and can be used to infer specific modes and parameters of transcriptional regulation from gene expression data.
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Affiliation(s)
- F E Garbuzov
- Ioffe Institute, 26 Polytekhnicheskaya, St. Petersburg 194021, Russia
| | - V V Gursky
- Ioffe Institute, 26 Polytekhnicheskaya, St. Petersburg 194021, Russia
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36
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Xiao JY, Hafner A, Boettiger AN. How subtle changes in 3D structure can create large changes in transcription. eLife 2021; 10:e64320. [PMID: 34240703 PMCID: PMC8352591 DOI: 10.7554/elife.64320] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 06/25/2021] [Indexed: 12/17/2022] Open
Abstract
Animal genomes are organized into topologically associated domains (TADs). TADs are thought to contribute to gene regulation by facilitating enhancer-promoter (E-P) contacts within a TAD and preventing these contacts across TAD borders. However, the absolute difference in contact frequency across TAD boundaries is usually less than 2-fold, even though disruptions of TAD borders can change gene expression by 10-fold. Existing models fail to explain this hypersensitive response. Here, we propose a futile cycle model of enhancer-mediated regulation that can exhibit hypersensitivity through bistability and hysteresis. Consistent with recent experiments, this regulation does not exhibit strong correlation between E-P contact and promoter activity, even though regulation occurs through contact. Through mathematical analysis and stochastic simulation, we show that this system can create an illusion of E-P biochemical specificity and explain the importance of weak TAD boundaries. It also offers a mechanism to reconcile apparently contradictory results from recent global TAD disruption with local TAD boundary deletion experiments. Together, these analyses advance our understanding of cis-regulatory contacts in controlling gene expression and suggest new experimental directions.
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Affiliation(s)
| | - Antonina Hafner
- Department of Developmental Biology, Stanford UniversityStanfordUnited States
| | - Alistair N Boettiger
- Program in Biophysics, Stanford UniversityStanfordUnited States
- Department of Developmental Biology, Stanford UniversityStanfordUnited States
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37
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Wang N, Lefaudeux D, Mazumder A, Li JJ, Hoffmann A. Identifying the combinatorial control of signal-dependent transcription factors. PLoS Comput Biol 2021; 17:e1009095. [PMID: 34166361 PMCID: PMC8263068 DOI: 10.1371/journal.pcbi.1009095] [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: 11/06/2020] [Revised: 07/07/2021] [Accepted: 05/18/2021] [Indexed: 12/13/2022] Open
Abstract
The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strategy (GRS) associated with each response gene. Here, we examined whether the GRSs of target genes may be inferred from stimulus-response (input-output) datasets, which remains an unresolved model-identifiability challenge. We developed a mechanistic modeling framework and computational workflow to determine the identifiability of all possible combinations of synergistic (AND) or non-synergistic (OR) GRSs involving three transcription factors. Considering different sets of perturbations for stimulus-response studies, we found that two thirds of GRSs are easily distinguishable but that substantially more quantitative data is required to distinguish the remaining third. To enhance the accuracy of the inference with timecourse experimental data, we developed an advanced error model that avoids error overestimates by distinguishing between value and temporal error. Incorporating this error model into a Bayesian framework, we show that GRS models can be identified for individual genes by considering multiple datasets. Our analysis rationalizes the allocation of experimental resources by identifying most informative TF stimulation conditions. Applying this computational workflow to experimental data of immune response genes in macrophages, we found that a much greater fraction of genes are combinatorially controlled than previously reported by considering compensation among transcription factors. Specifically, we revealed that a group of known NFκB target genes may also be regulated by IRF3, which is supported by chromatin immuno-precipitation analysis. Our study provides a computational workflow for designing and interpreting stimulus-response gene expression studies to identify underlying gene regulatory strategies and further a mechanistic understanding.
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Affiliation(s)
- Ning Wang
- Institute for Quantitative and Computational Biosciences (QCBio), University of California, Los Angeles, California, United States of America
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, California, United States of America
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, United States of America
| | - Diane Lefaudeux
- Institute for Quantitative and Computational Biosciences (QCBio), University of California, Los Angeles, California, United States of America
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, California, United States of America
| | - Anup Mazumder
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, California, United States of America
| | - Jingyi Jessica Li
- Institute for Quantitative and Computational Biosciences (QCBio), University of California, Los Angeles, California, United States of America
- Department of Statistics, University of California, Los Angeles, California, United States of America
| | - Alexander Hoffmann
- Institute for Quantitative and Computational Biosciences (QCBio), University of California, Los Angeles, California, United States of America
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, California, United States of America
- * E-mail:
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38
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Gautam P, Kumar Sinha S. Anticipating response function in gene regulatory networks. J R Soc Interface 2021; 18:20210206. [PMID: 34062105 DOI: 10.1098/rsif.2021.0206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The origin of an ordered genetic response of a complex and noisy biological cell is intimately related to the detailed mechanism of protein-DNA interactions present in a wide variety of gene regulatory (GR) systems. However, the quantitative prediction of genetic response and the correlation between the mechanism and the response curve is poorly understood. Here, we report in silico binding studies of GR systems to show that the transcription factor (TF) binds to multiple DNA sites with high cooperativity spreads from specific binding sites into adjacent non-specific DNA and bends the DNA. Our analysis is not limited only to the isolated model system but also can be applied to a system containing multiple interacting genes. The controlling role of TF oligomerization, TF-ligand interactions, and DNA looping for gene expression has been also characterized. The predictions are validated against detailed grand canonical Monte Carlo simulations and published data for the lac operon system. Overall, our study reveals that the expression of target genes can be quantitatively controlled by modulating TF-ligand interactions and the bending energy of DNA.
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Affiliation(s)
- Pankaj Gautam
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology, Ropar 140001, India
| | - Sudipta Kumar Sinha
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology, Ropar 140001, India
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39
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Carzaniga T, Zanchetta G, Frezza E, Casiraghi L, Vanjur L, Nava G, Tagliabue G, Dieci G, Buscaglia M, Bellini T. A Bit Stickier, a Bit Slower, a Lot Stiffer: Specific vs. Nonspecific Binding of Gal4 to DNA. Int J Mol Sci 2021; 22:ijms22083813. [PMID: 33916983 PMCID: PMC8067546 DOI: 10.3390/ijms22083813] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 03/26/2021] [Accepted: 04/02/2021] [Indexed: 12/15/2022] Open
Abstract
Transcription factors regulate gene activity by binding specific regions of genomic DNA thanks to a subtle interplay of specific and nonspecific interactions that is challenging to quantify. Here, we exploit Reflective Phantom Interface (RPI), a label-free biosensor based on optical reflectivity, to investigate the binding of the N-terminal domain of Gal4, a well-known gene regulator, to double-stranded DNA fragments containing or not its consensus sequence. The analysis of RPI-binding curves provides interaction strength and kinetics and their dependence on temperature and ionic strength. We found that the binding of Gal4 to its cognate site is stronger, as expected, but also markedly slower. We performed a combined analysis of specific and nonspecific binding—equilibrium and kinetics—by means of a simple model based on nested potential wells and found that the free energy gap between specific and nonspecific binding is of the order of one kcal/mol only. We investigated the origin of such a small value by performing all-atom molecular dynamics simulations of Gal4–DNA interactions. We found a strong enthalpy–entropy compensation, by which the binding of Gal4 to its cognate sequence entails a DNA bending and a striking conformational freezing, which could be instrumental in the biological function of Gal4.
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Affiliation(s)
- Thomas Carzaniga
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università di Milano, 20054 Segrate (MI), Italy; (T.C.); (L.C.); (L.V.); (G.N.)
| | - Giuliano Zanchetta
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università di Milano, 20054 Segrate (MI), Italy; (T.C.); (L.C.); (L.V.); (G.N.)
- Correspondence: (G.Z.); (M.B.); (T.B.)
| | - Elisa Frezza
- CiTCoM, CNRS, Université de Paris, F-75006 Paris, France;
| | - Luca Casiraghi
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università di Milano, 20054 Segrate (MI), Italy; (T.C.); (L.C.); (L.V.); (G.N.)
| | - Luka Vanjur
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università di Milano, 20054 Segrate (MI), Italy; (T.C.); (L.C.); (L.V.); (G.N.)
| | - Giovanni Nava
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università di Milano, 20054 Segrate (MI), Italy; (T.C.); (L.C.); (L.V.); (G.N.)
| | | | - Giorgio Dieci
- Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, 43124 Parma, Italy;
| | - Marco Buscaglia
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università di Milano, 20054 Segrate (MI), Italy; (T.C.); (L.C.); (L.V.); (G.N.)
- Correspondence: (G.Z.); (M.B.); (T.B.)
| | - Tommaso Bellini
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università di Milano, 20054 Segrate (MI), Italy; (T.C.); (L.C.); (L.V.); (G.N.)
- Correspondence: (G.Z.); (M.B.); (T.B.)
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40
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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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41
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Abstract
Simple biophysical models successfully describe bacterial regulatory code, by predicting gene expression from DNA sequences that bind specialized regulatory proteins. Analogous simple models fail in multicellular organisms, where regulatory proteins bind DNA very transiently, yet, nevertheless, effect precise control over gene expression. To date, the more general, “nonequilibrium” models have proven difficult to analyze and connect to data. Here, we reduce this complexity theoretically, by constructing simple nonequilibrium models which perform optimal gene regulation within known experimental constraints. In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future.
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42
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Abbadi M, Spurgeon S, Warren M, Khan N, Kräutler B. Using sliding mode observers to estimate BtuB concentration from measured vitamin B 12 concentration. IET Syst Biol 2020; 14:334-342. [PMID: 33399097 PMCID: PMC8687388 DOI: 10.1049/iet-syb.2020.0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/11/2020] [Accepted: 08/03/2020] [Indexed: 11/20/2022] Open
Abstract
A simple model for the B12-riboswitch regulatory network in Escherichia coli is first described and the same analysis is applied when changing the strain to Salmonella enterica. Model validation is undertaken by linking the dynamics of the riboswitch model to bacterial growth and comparing the results obtained with in vivo experimental measurements. Measurements of bacterial growth are relatively straightforward to obtain experimentally, but experimental measurements relating to the operation of the riboswitch are more difficult. Using the validated model, sliding mode observer design methods are used to estimate BtuB given measurements of the concentration of vitamin B12. The sliding mode approach is selected because of its inherent robustness properties as well as for the ease of implementation. Validation of the estimates of BtuB produced by the observer is undertaken by comparing the BtuB and vitamin B12 concentrations estimated from the observer with green fluorescent protein production and the concentration of vitamin B12 obtained experimentally. These experimental results also provide further validation of the underpinning mathematical model. The results establish that using a sliding mode observer as a soft sensor is a useful approach to explore the operation of a vitamin B12 riboswitch given measurements of the concentration of vitamin B12.
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Affiliation(s)
- Mohammad Abbadi
- Department of Electronic & Electrical Engineering, University College London, Gower St, Bloomsbury, London WC1E 6BT, UK
| | - Sarah Spurgeon
- Department of Electronic & Electrical Engineering, University College London, Gower St, Bloomsbury, London WC1E 6BT, UK.
| | | | - Naziyat Khan
- School of Biosciences, University of Kent, Canterbury CT2 7NZ, UK
| | - Bernhard Kräutler
- Institute of Organic Chemistry & Center of Molecular Biosciences (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria
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43
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Martin PC, Zabet NR. Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework. Comput Struct Biotechnol J 2020; 18:3590-3605. [PMID: 33304457 PMCID: PMC7708957 DOI: 10.1016/j.csbj.2020.11.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 01/22/2023] Open
Abstract
Transcription Factors (TFs) bind to DNA and control activity of target genes. Here, we present ChIPanalyser, a user-friendly, versatile and powerful R/Bioconductor package predicting and modelling the binding of TFs to DNA. ChIPanalyser performs similarly to state-of-the-art tools, but is an explainable model and provides biological insights into binding mechanisms of TFs. We focused on investigating the binding mechanisms of three TFs that are known architectural proteins CTCF, BEAF-32 and su(Hw) in three Drosophila cell lines (BG3, Kc167 and S2). While CTCF preferentially binds only to a subset of high affinity sites located mainly in open chromatin, BEAF-32 binds to most of its high affinity binding sites available in open chromatin. In contrast, su(Hw) binds to both open chromatin and also partially closed chromatin. Most importantly, differences in TF binding profiles between cell lines for these TFs are mainly driven by differences in DNA accessibility and not by differences in TF concentrations between cell lines. Finally, we investigated binding of Hox TFs in Drosophila and found that Ubx binds only in open chromatin, while Abd-B and Dfd are capable to bind in both open and partially closed chromatin. Overall, our results show that TFs display different binding mechanisms and that our model is able to recapitulate their specific binding behaviour.
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Affiliation(s)
- Patrick C.N. Martin
- School of Life Sciences, University of Essex, Colchester CO4 3SQ, UK
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Nicolae Radu Zabet
- School of Life Sciences, University of Essex, Colchester CO4 3SQ, UK
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
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44
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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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45
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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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46
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Enhancement of gene expression noise from transcription factor binding to genomic decoy sites. Sci Rep 2020; 10:9126. [PMID: 32499583 PMCID: PMC7272470 DOI: 10.1038/s41598-020-65750-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/08/2020] [Indexed: 12/29/2022] Open
Abstract
The genome contains several high-affinity non-functional binding sites for transcription factors (TFs) creating a hidden and unexplored layer of gene regulation. We investigate the role of such “decoy sites” in controlling noise (random fluctuations) in the level of a TF that is synthesized in stochastic bursts. Prior studies have assumed that decoy-bound TFs are protected from degradation, and in this case decoys function to buffer noise. Relaxing this assumption to consider arbitrary degradation rates for both bound/unbound TF states, we find rich noise behaviors. For low-affinity decoys, noise in the level of unbound TF always monotonically decreases to the Poisson limit with increasing decoy numbers. In contrast, for high-affinity decoys, noise levels first increase with increasing decoy numbers, before decreasing back to the Poisson limit. Interestingly, while protection of bound TFs from degradation slows the time-scale of fluctuations in the unbound TF levels, the decay of bound TFs leads to faster fluctuations and smaller noise propagation to downstream target proteins. In summary, our analysis reveals stochastic dynamics emerging from nonspecific binding of TFs and highlights the dual role of decoys as attenuators or amplifiers of gene expression noise depending on their binding affinity and stability of the bound TF.
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47
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Becskei A. Tuning up Transcription Factors for Therapy. Molecules 2020; 25:E1902. [PMID: 32326099 PMCID: PMC7221782 DOI: 10.3390/molecules25081902] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/16/2020] [Accepted: 04/17/2020] [Indexed: 12/19/2022] Open
Abstract
The recent developments in the delivery and design of transcription factors put their therapeutic applications within reach, exemplified by cell replacement, cancer differentiation and T-cell based cancer therapies. The success of such applications depends on the efficacy and precision in the action of transcription factors. The biophysical and genetic characterization of the paradigmatic prokaryotic repressors, LacI and TetR and the designer transcription factors, transcription activator-like effector (TALE) and CRISPR-dCas9 revealed common principles behind their efficacy, which can aid the optimization of transcriptional activators and repressors. Further studies will be required to analyze the linkage between dissociation constants and enzymatic activity, the role of phase separation and squelching in activation and repression and the long-range interaction of transcription factors with epigenetic regulators in the context of the chromosomes. Understanding these mechanisms will help to tailor natural and synthetic transcription factors to the needs of specific applications.
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Affiliation(s)
- Attila Becskei
- Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland
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48
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Phillips R, Belliveau NM, Chure G, Garcia HG, Razo-Mejia M, Scholes C. Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression. Annu Rev Biophys 2020; 48:121-163. [PMID: 31084583 DOI: 10.1146/annurev-biophys-052118-115525] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
It is tempting to believe that we now own the genome. The ability to read and rewrite it at will has ushered in a stunning period in the history of science. Nonetheless, there is an Achilles' heel exposed by all of the genomic data that has accrued: We still do not know how to interpret them. Many genes are subject to sophisticated programs of transcriptional regulation, mediated by DNA sequences that harbor binding sites for transcription factors, which can up- or down-regulate gene expression depending upon environmental conditions. This gives rise to an input-output function describing how the level of expression depends upon the parameters of the regulated gene-for instance, on the number and type of binding sites in its regulatory sequence. In recent years, the ability to make precision measurements of expression, coupled with the ability to make increasingly sophisticated theoretical predictions, has enabled an explicit dialogue between theory and experiment that holds the promise of covering this genomic Achilles' heel. The goal is to reach a predictive understanding of transcriptional regulation that makes it possible to calculate gene expression levels from DNA regulatory sequence. This review focuses on the canonical simple repression motif to ask how well the models that have been used to characterize it actually work. We consider a hierarchy of increasingly sophisticated experiments in which the minimal parameter set learned at one level is applied to make quantitative predictions at the next. We show that these careful quantitative dissections provide a template for a predictive understanding of the many more complex regulatory arrangements found across all domains of life.
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Affiliation(s)
- 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
| | - Nathan M Belliveau
- Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA.,Department of Biology, University of Washington, Seattle, Washington 98195, USA
| | - Griffin Chure
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Hernan G Garcia
- Department of Molecular & Cell Biology, Department of Physics, Biophysics Graduate Group, and Institute for Quantitative Biosciences-QB3, University of California, Berkeley, California, USA
| | - Manuel Razo-Mejia
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Clarissa Scholes
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
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49
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Abstract
In order to respond to environmental signals, cells often use small molecular circuits to transmit information about their surroundings. Recently, motivated by specific examples in signaling and gene regulation, a body of work has focused on the properties of circuits that function out of equilibrium and dissipate energy. We briefly review the probabilistic measures of information and dissipation and use simple models to discuss and illustrate trade-offs between information and dissipation in biological circuits. We find that circuits with non-steady state initial conditions can transmit more information at small readout delays than steady state circuits. The dissipative cost of this additional information proves marginal compared to the steady state dissipation. Feedback does not significantly increase the transmitted information for out of steady state circuits but does decrease dissipative costs. Lastly, we discuss the case of bursty gene regulatory circuits that, even in the fast switching limit, function out of equilibrium.
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50
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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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