TY - JOUR
T1 - Genome-wide mutant profiling predicts the mechanism of a Lipid II binding antibiotic article
AU - Santiago, Marina
AU - Lee, Wonsik
AU - Fayad, Antoine Abou
AU - Coe, Kathryn A.
AU - Rajagopal, Mithila
AU - Do, Truc
AU - Hennessen, Fabienne
AU - Srisuknimit, Veerasak
AU - Müller, Rolf
AU - Meredith, Timothy C.
AU - Walker, Suzanne
N1 - Publisher Copyright:
© 2018 The Author(s).
PY - 2018/6/1
Y1 - 2018/6/1
N2 - 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.
AB - 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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U2 - 10.1038/s41589-018-0041-4
DO - 10.1038/s41589-018-0041-4
M3 - Article
C2 - 29662210
AN - SCOPUS:85045437346
SN - 1552-4450
VL - 14
SP - 601
EP - 608
JO - Nature Chemical Biology
JF - Nature Chemical Biology
IS - 6
ER -