Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria
Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megate...
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Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 115; no. 19; pp. E4340 - E4349 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
08.05.2018
|
Series | PNAS Plus |
Subjects | |
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Abstract | Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. |
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AbstractList | Native cell-free transcription-translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host,
, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to
to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription-translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription-translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. Significance Nonmodel bacteria have essential roles to play in the future development of biotechnology by providing new sources of biocatalysts, antibiotics, hosts for bioproduction, and engineered “living therapies.” The characterization of such hosts can be challenging, as many are not tractable to standard molecular biology techniques. This paper presents a rapid and automated methodology for characterizing new DNA parts from a nonmodel bacterium using cell-free transcription–translation. Data analysis was performed with Bayesian parameter inference to provide an understanding of gene-expression dynamics and resource sharing. We suggest that our integrated approach is expandable to a whole range of nonmodel bacteria for the characterization of new DNA parts within a native cell-free background for new biotechnology applications. Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium , is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. Nonmodel bacteria have essential roles to play in the future development of biotechnology by providing new sources of biocatalysts, antibiotics, hosts for bioproduction, and engineered “living therapies.” The characterization of such hosts can be challenging, as many are not tractable to standard molecular biology techniques. This paper presents a rapid and automated methodology for characterizing new DNA parts from a nonmodel bacterium using cell-free transcription–translation. Data analysis was performed with Bayesian parameter inference to provide an understanding of gene-expression dynamics and resource sharing. We suggest that our integrated approach is expandable to a whole range of nonmodel bacteria for the characterization of new DNA parts within a native cell-free background for new biotechnology applications. Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium , is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. |
Author | McClymont, David W. Kylilis, Nicolas Aw, Rochelle Wienecke, Sarah Tsipa, Argyro Polizzi, Karen M. Bell, David J. Freemont, Paul S. Jensen, Kirsten Biedendieck, Rebekka MacDonald, James T. Moore, Simon J. Ishwarbhai, Alka |
Author_xml | – sequence: 1 givenname: Simon J. surname: Moore fullname: Moore, Simon J. organization: Section for Structural Biology, Department of Medicine, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 2 givenname: James T. surname: MacDonald fullname: MacDonald, James T. organization: Section for Structural Biology, Department of Medicine, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 3 givenname: Sarah surname: Wienecke fullname: Wienecke, Sarah organization: Braunschweig Integrated Centre of Systems Biology, Institute of Microbiology, Technische Universität Braunschweig, 38106 Braunschweig, Germany – sequence: 4 givenname: Alka surname: Ishwarbhai fullname: Ishwarbhai, Alka organization: London DNA Foundry, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 5 givenname: Argyro surname: Tsipa fullname: Tsipa, Argyro organization: London DNA Foundry, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 6 givenname: Rochelle surname: Aw fullname: Aw, Rochelle organization: Department of Life Sciences, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 7 givenname: Nicolas surname: Kylilis fullname: Kylilis, Nicolas organization: Section for Structural Biology, Department of Medicine, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 8 givenname: David J. surname: Bell fullname: Bell, David J. organization: Section for Structural Biology, Department of Medicine, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 9 givenname: David W. surname: McClymont fullname: McClymont, David W. organization: Section for Structural Biology, Department of Medicine, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 10 givenname: Kirsten surname: Jensen fullname: Jensen, Kirsten organization: Section for Structural Biology, Department of Medicine, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 11 givenname: Karen M. surname: Polizzi fullname: Polizzi, Karen M. organization: Department of Life Sciences, Imperial College London, SW7 2AZ London, United Kingdom – sequence: 12 givenname: Rebekka surname: Biedendieck fullname: Biedendieck, Rebekka organization: Braunschweig Integrated Centre of Systems Biology, Institute of Microbiology, Technische Universität Braunschweig, 38106 Braunschweig, Germany – sequence: 13 givenname: Paul S. surname: Freemont fullname: Freemont, Paul S. organization: Section for Structural Biology, Department of Medicine, Imperial College London, SW7 2AZ London, United Kingdom |
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Copyright | Volumes 1–89 and 106–114, copyright as a collective work only; author(s) retains copyright to individual articles Copyright © 2018 the Author(s). Published by PNAS. Copyright National Academy of Sciences May 8, 2018 Copyright © 2018 the Author(s). Published by PNAS. 2018 |
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Keywords | automation modeling in vitro transcription–translation cell-free synthetic biology Bacillus |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 1S.J.M. and J.T.M. contributed equally to this work. Author contributions: S.J.M., J.T.M., D.W.M., K.J., K.M.P., R.B., and P.S.F. designed research; S.J.M., J.T.M., S.W., A.I., A.T., R.A., N.K., D.J.B., D.W.M., and K.J. performed research; J.T.M. contributed new reagents/analytic tools; S.J.M., J.T.M., S.W., A.T., D.J.B., D.W.M., and R.B. analyzed data; and S.J.M., J.T.M., D.W.M., K.J., K.M.P., R.B., and P.S.F. wrote the paper. Edited by James J. Collins, Massachusetts Institute of Technology, Boston, MA, and approved March 26, 2018 (received for review September 7, 2017) |
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Snippet | Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene... Native cell-free transcription-translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene... Significance Nonmodel bacteria have essential roles to play in the future development of biotechnology by providing new sources of biocatalysts, antibiotics,... Nonmodel bacteria have essential roles to play in the future development of biotechnology by providing new sources of biocatalysts, antibiotics, hosts for... |
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SubjectTerms | Amino acids Bacillus megaterium - chemistry Bacillus megaterium - genetics Bacillus megaterium - metabolism Bacteria Bayesian analysis Binding sites Biological Sciences Biotechnology Cell culture Cell-Free System - chemistry Cell-Free System - metabolism Competition Differential equations Gene expression Mathematical models Microorganisms Modelling Models, Biological Nucleotides PNAS Plus Promoters Protein Biosynthesis Regulatory sequences Ribonucleic acid Ribosomes RNA Transcription factors Transcription, Genetic Translation |
Title | Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria |
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