Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection
Yin Leung, Ka
Davis, Brigid M.
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CitationYang, Guanhua, Gabriel Billings, Troy P. Hubbard, Joseph S. Park, Ka Yin Leung, Qin Liu, Brigid M. Davis, Yuanxing Zhang, Qiyao Wang, and Matthew K. Waldor. 2017. “Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection.” mBio 8 (5): e01581-17. doi:10.1128/mBio.01581-17. http://dx.doi.org/10.1128/mBio.01581-17.
AbstractABSTRACT 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.
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