Abstract
Identifying targets of antibacterial compounds remains a challenging step in the development of antibiotics. We have developed a two-pronged functional genomics approach to predict mechanism of action that uses mutant fitness data from antibiotic-treated transposon libraries containing both upregulation and inactivation mutants. We treated a Staphylococcus aureus transposon library containing 690,000 unique insertions with 32 antibiotics. Upregulation signatures identified from directional biases in insertions revealed known molecular targets and resistance mechanisms for the majority of these. Because single-gene upregulation does not always confer resistance, we used a complementary machine-learning approach to predict the mechanism from inactivation mutant fitness profiles. This approach suggested the cell wall precursor Lipid II as the molecular target of the lysocins, a mechanism we have confirmed. We conclude that docking to membrane-anchored Lipid II precedes the selective bacteriolysis that distinguishes these lytic natural products, showing the utility of our approach for nominating the antibiotic mechanism of action.
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Change history
23 April 2018
In the version of this article originally published, the link for the Supplementary Note in the Supplementary Information section incorrectly led to the file for the Supplementary Text and Figures. The error has been corrected in the HTML version of this article.
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Acknowledgements
We gratefully acknowledge fellowship support from the NIH for M.S. (F31AI114131) and from the NSF for T.D. (DGE1144152). The work was supported by NIH grants (P01 AI083214, U19 AI109764, and R01 GM076710). We thank C. Bader and H. Steinmetz at Helmholtz Center for Infection Research (HZI) for help with compound isolation and structure analysis, N. Zaburanyi at HZI for genome analysis of the producer strain, V. Schmitt at HZI for cultivation, fermentation, and DNA isolation, M. Bischoff Saarland University Hospital for S. aureus isolates, and E. Skaar at Vanderbilt University Medical Center for generously sharing the ΔmenB and ΔmenB Newman strains.
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T.C.M. and S.W. designed and supervised the research. M.S., W.L., M.R., T.D., and T.C.M. prepared samples for transposon sequencing. M.S. and K.A.C. designed and implemented computational methods for identifying upregulated genes and for predicting antibiotic mechanism of action. M.S. validated upregulated genes that confer daptomycin resistance; W.L. performed all other upregulation validation experiments. R.M. and A.A.F. isolated and determined the structure of the lysocin compounds. W.L. and F.H. obtained lysocin MICs. W.L. performed all validation experiments on lysocin compounds, with assistance from V.S. for Lipid II preparation. All authors contributed to manuscript preparation.
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Supplementary Tables 1–7, Supplementary Figures 1–13
Supplementary Data
The data set contains comma-separated file with the fitness values for each gene under each antibiotic treatment, along with files tabulating the gene-by-gene sequencing read counts for the antibiotic-treated and untreated samples
Supplementary Note
Experimental procedures for NMR analysis
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Santiago, M., Lee, W., Fayad, A.A. et al. Genome-wide mutant profiling predicts the mechanism of a Lipid II binding antibiotic. Nat Chem Biol 14, 601–608 (2018). https://doi.org/10.1038/s41589-018-0041-4
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DOI: https://doi.org/10.1038/s41589-018-0041-4
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