Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection
Transposon insertion sequencing (TIS) is a powerful high-throughput genetic technique that is transforming functional genomics in prokaryotes, because it enables genome-wide mapping of the determinants of fitness. However, current approaches for analyzing TIS data assume that selective pressures are...
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American Society for Microbiology
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ftpubmed:oai:pubmedcentral.nih.gov:5626973 2023-05-15T18:41:14+02:00 Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection Yang, Guanhua Billings, Gabriel Hubbard, Troy P. Park, Joseph S. Yin Leung, Ka Liu, Qin Davis, Brigid M. Zhang, Yuanxing Wang, Qiyao Waldor, Matthew K. 2017-10-03 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5626973/ http://www.ncbi.nlm.nih.gov/pubmed/28974620 https://doi.org/10.1128/mBio.01581-17 en eng American Society for Microbiology http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5626973/ http://www.ncbi.nlm.nih.gov/pubmed/28974620 http://dx.doi.org/10.1128/mBio.01581-17 Copyright © 2017 Yang et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . CC-BY Research Article Text 2017 ftpubmed https://doi.org/10.1128/mBio.01581-17 2017-10-08T00:34:45Z Transposon insertion sequencing (TIS) is a powerful high-throughput genetic technique that is transforming functional genomics in prokaryotes, because it enables genome-wide mapping of the determinants of fitness. However, current approaches for analyzing TIS data assume that selective pressures are constant over time and thus do not yield information regarding changes in the genetic requirements for growth in dynamic environments (e.g., during infection). Here, we describe structured analysis of TIS data collected as a time series, termed pattern analysis of conditional essentiality (PACE). From a temporal series of TIS data, PACE derives a quantitative assessment of each mutant’s fitness over the course of an experiment and identifies mutants with related fitness profiles. In so doing, PACE circumvents major limitations of existing methodologies, specifically the need for artificial effect size thresholds and enumeration of bacterial population expansion. We used PACE to analyze TIS samples of Edwardsiella piscicida (a fish pathogen) collected over a 2-week infection period from a natural host (the flatfish turbot). PACE uncovered more genes that affect E. piscicida’s fitness in vivo than were detected using a cutoff at a terminal sampling point, and it identified subpopulations of mutants with distinct fitness profiles, one of which informed the design of new live vaccine candidates. Overall, PACE enables efficient mining of time series TIS data and enhances the power and sensitivity of TIS-based analyses. Text Turbot PubMed Central (PMC) mBio 8 5 |
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Research Article Yang, Guanhua Billings, Gabriel Hubbard, Troy P. Park, Joseph S. Yin Leung, Ka Liu, Qin Davis, Brigid M. Zhang, Yuanxing Wang, Qiyao Waldor, Matthew K. Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection |
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Research Article |
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Transposon insertion sequencing (TIS) is a powerful high-throughput genetic technique that is transforming functional genomics in prokaryotes, because it enables genome-wide mapping of the determinants of fitness. However, current approaches for analyzing TIS data assume that selective pressures are constant over time and thus do not yield information regarding changes in the genetic requirements for growth in dynamic environments (e.g., during infection). Here, we describe structured analysis of TIS data collected as a time series, termed pattern analysis of conditional essentiality (PACE). From a temporal series of TIS data, PACE derives a quantitative assessment of each mutant’s fitness over the course of an experiment and identifies mutants with related fitness profiles. In so doing, PACE circumvents major limitations of existing methodologies, specifically the need for artificial effect size thresholds and enumeration of bacterial population expansion. We used PACE to analyze TIS samples of Edwardsiella piscicida (a fish pathogen) collected over a 2-week infection period from a natural host (the flatfish turbot). PACE uncovered more genes that affect E. piscicida’s fitness in vivo than were detected using a cutoff at a terminal sampling point, and it identified subpopulations of mutants with distinct fitness profiles, one of which informed the design of new live vaccine candidates. Overall, PACE enables efficient mining of time series TIS data and enhances the power and sensitivity of TIS-based analyses. |
format |
Text |
author |
Yang, Guanhua Billings, Gabriel Hubbard, Troy P. Park, Joseph S. Yin Leung, Ka Liu, Qin Davis, Brigid M. Zhang, Yuanxing Wang, Qiyao Waldor, Matthew K. |
author_facet |
Yang, Guanhua Billings, Gabriel Hubbard, Troy P. Park, Joseph S. Yin Leung, Ka Liu, Qin Davis, Brigid M. Zhang, Yuanxing Wang, Qiyao Waldor, Matthew K. |
author_sort |
Yang, Guanhua |
title |
Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection |
title_short |
Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection |
title_full |
Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection |
title_fullStr |
Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection |
title_full_unstemmed |
Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection |
title_sort |
time-resolved transposon insertion sequencing reveals genome-wide fitness dynamics during infection |
publisher |
American Society for Microbiology |
publishDate |
2017 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5626973/ http://www.ncbi.nlm.nih.gov/pubmed/28974620 https://doi.org/10.1128/mBio.01581-17 |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5626973/ http://www.ncbi.nlm.nih.gov/pubmed/28974620 http://dx.doi.org/10.1128/mBio.01581-17 |
op_rights |
Copyright © 2017 Yang et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
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CC-BY |
op_doi |
https://doi.org/10.1128/mBio.01581-17 |
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