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Hacker WC, Elcock AH. spotter: a single-nucleotide resolution stochastic simulation model of supercoiling-mediated transcription and translation in prokaryotes. Nucleic Acids Res 2023; 51:e92. [PMID: 37602419 PMCID: PMC10516669 DOI: 10.1093/nar/gkad682] [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: 05/01/2023] [Revised: 07/25/2023] [Accepted: 08/09/2023] [Indexed: 08/22/2023] Open
Abstract
Stochastic simulation models have played an important role in efforts to understand the mechanistic basis of prokaryotic transcription and translation. Despite the fundamental linkage of these processes in bacterial cells, however, most simulation models have been limited to representations of either transcription or translation. In addition, the available simulation models typically either attempt to recapitulate data from single-molecule experiments without considering cellular-scale high-throughput sequencing data or, conversely, seek to reproduce cellular-scale data without paying close attention to many of the mechanistic details. To address these limitations, we here present spotter (Simulation of Prokaryotic Operon Transcription & Translation Elongation Reactions), a flexible, user-friendly simulation model that offers highly-detailed combined representations of prokaryotic transcription, translation, and DNA supercoiling. In incorporating nascent transcript and ribosomal profiling sequencing data, spotter provides a critical bridge between data collected in single-molecule experiments and data collected at the cellular scale. Importantly, in addition to rapidly generating output that can be aggregated for comparison with next-generation sequencing and proteomics data, spotter produces residue-level positional information that can be used to visualize individual simulation trajectories in detail. We anticipate that spotter will be a useful tool in exploring the interplay of processes that are crucially linked in prokaryotes.
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Affiliation(s)
- William C Hacker
- Department of Biochemistry & Molecular Biology, University of Iowa, Iowa City, IA, USA
| | - Adrian H Elcock
- Department of Biochemistry & Molecular Biology, University of Iowa, Iowa City, IA, USA
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Hacker WC, Elcock AH. spotter : A single-nucleotide resolution stochastic simulation model of supercoiling-mediated transcription and translation in prokaryotes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537861. [PMID: 37131791 PMCID: PMC10153252 DOI: 10.1101/2023.04.21.537861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Stochastic simulation models have played an important role in efforts to understand the mechanistic basis of prokaryotic transcription and translation. Despite the fundamental linkage of these processes in bacterial cells, however, most simulation models have been limited to representations of either transcription or translation. In addition, the available simulation models typically either attempt to recapitulate data from single-molecule experiments without considering cellular-scale high-throughput sequencing data or, conversely, seek to reproduce cellular-scale data without paying close attention to many of the mechanistic details. To address these limitations, we here present spotter (Simulation of Prokaryotic Operon Transcription & Translation Elongation Reactions), a flexible, user-friendly simulation model that offers highly-detailed combined representations of prokaryotic transcription, translation, and DNA supercoiling. In incorporating nascent transcript and ribosomal profiling sequencing data, spotter provides a critical bridge between data collected in single-molecule experiments and data collected at the cellular scale. Importantly, in addition to rapidly generating output that can be aggregated for comparison with next-generation sequencing and proteomics data, spotter produces residue-level positional information that can be used to visualize individual simulation trajectories in detail. We anticipate that spotter will be a useful tool in exploring the interplay of processes that are crucially linked in prokaryotes.
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Shah SB, Hill AM, Wilke CO, Hockenberry AJ. Generating dynamic gene expression patterns without the need for regulatory circuits. PLoS One 2022; 17:e0268883. [PMID: 35617346 PMCID: PMC9135205 DOI: 10.1371/journal.pone.0268883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 05/10/2022] [Indexed: 11/18/2022] Open
Abstract
Synthetic biology has successfully advanced our ability to design and implement complex, time-varying genetic circuits to control the expression of recombinant proteins. However, these circuits typically require the production of regulatory genes whose only purpose is to coordinate expression of other genes. When designing very small genetic constructs, such as viral genomes, we may want to avoid introducing such auxiliary gene products while nevertheless encoding complex expression dynamics. To this end, here we demonstrate that varying only the placement and strengths of promoters, terminators, and RNase cleavage sites in a computational model of a bacteriophage genome is sufficient to achieve solutions to a variety of basic gene expression patterns. We discover these genetic solutions by computationally evolving genomes to reproduce desired gene expression time-course data. Our approach shows that non-trivial patterns can be evolved, including patterns where the relative ordering of genes by abundance changes over time. We find that some patterns are easier to evolve than others, and comparable expression patterns can be achieved via different genetic architectures. Our work opens up a novel avenue to genome engineering via fine-tuning the balance of gene expression and gene degradation rates.
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Affiliation(s)
- Sahil B. Shah
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, United States of America
| | - Alexis M. Hill
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, United States of America
| | - Claus O. Wilke
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, United States of America
- * E-mail: (COW); (AJH)
| | - Adam J. Hockenberry
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, United States of America
- * E-mail: (COW); (AJH)
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Jack BR, Boutz DR, Paff ML, Smith BL, Wilke CO. Transcript degradation and codon usage regulate gene expression in a lytic phage. Virus Evol 2019; 5:vez055. [PMID: 31908847 PMCID: PMC6938266 DOI: 10.1093/ve/vez055] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Many viral genomes are small, containing only single- or double-digit numbers of genes and relatively few regulatory elements. Yet viruses successfully execute complex regulatory programs as they take over their host cells. Here, we propose that some viruses regulate gene expression via a carefully balanced interplay between transcription, translation, and transcript degradation. As our model system, we employ bacteriophage T7, whose genome of approximately sixty genes is well annotated and for which there is a long history of computational models of gene regulation. We expand upon prior modeling work by implementing a stochastic gene expression simulator that tracks individual transcripts, polymerases, ribosomes, and ribonucleases participating in the transcription, translation, and transcript-degradation processes occurring during a T7 infection. By combining this detailed mechanistic modeling of a phage infection with high-throughput gene expression measurements of several strains of bacteriophage T7, evolved and engineered, we can show that both the dynamic interplay between transcription and transcript degradation, and between these two processes and translation, appear to be critical components of T7 gene regulation. Our results point to targeted degradation as a generic gene regulation strategy that may have evolved in many other viruses. Further, our results suggest that detailed mechanistic modeling may uncover the biological mechanisms at work in both evolved and engineered virus variants.
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Affiliation(s)
- Benjamin R Jack
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA
| | - Daniel R Boutz
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Matthew L Paff
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA
| | - Bartram L Smith
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA
| | - Claus O Wilke
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA
- Corresponding author: E-mail:
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