Rapid expansion and extinction of antibiotic resistance mutations during treatment of acute bacterial respiratory infections

• Type of Material: Article
• Authors: Hattie Chung, Harvard Medical School; Christina Merakou, Boston Children’s Hospital; Matthew M Schaefers, Boston Children’s Hospital; Kelly B Flett, Boston Children’s Hospital; Sarah Martini, Boston Children’s Hospital; Roger Lu, Boston Children’s Hospital; Jennifer A Blumenthal, Boston Children’s Hospital; Shanice S Webster, Geisel School of Medicine at Dartmouth; Ashley R Cross, Emory University; Roy Al Ahmar, Marshall University; Erin Halpin, Boston Children’s Hospital; Michelle Anderson, Boston Children’s Hospital; Nicholas S Moore, Harvard Medical School; Eric C Snesrud, Walter Reed Army Institute of Research; Hongwei D Yu, Marshall University; Joanna Goldberg, Emory University; George A O'Toole, Geisel Sch Med Dartmouth; Patrick McGann, Walter Reed Army Institute of Research; Jason A Stam, Walter Reed Army Institute of Research; Mary Hinkle, Walter Reed Army Institute of Research; Alexander J McAdam, Boston Children’s Hospital; Roy Kishony, Harvard Medical School; Gregory P Priebe, Boston Children’s Hospital
• Language: English
• Date: 2022-03-09
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2022
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41467-022-28188-w
• Title of Journal or Parent Work: NATURE COMMUNICATIONS
• Volume: 13
• Issue: 1
• Start Page: 1231
• End Page: 1231
• Grant/Funding Information: This work was funded in part by the Richard A. and Susan F. Smith President’s Innovation Award (to G.P.P.) and by funds for the Translational Research for Infection Prevention in Pediatric Anesthesia and Critical Care (TRIPPACC) Program of the Department of Anesthesiology, Critical Care and Pain Medicine at Boston Children’s Hospital (to G.P.P.), U.S. National Institutes of Health grant R01 GM081617 (to R.K.) and R37 AI83256 (to G.A.O.), The Ernest and Bonnie Beutler Research Program of Excellence in Genomic Medicine (to R.K.), and European Research Council FP7 grant 281891 (to R.K.).
• Keywords: ADAPTATION; THERAPY; POPULATION; Multidisciplinary Sciences; PSEUDOMONAS-AERUGINOSA; EVOLUTIONARY PATHS; Science & Technology; VENTILATOR-ASSOCIATED PNEUMONIA; RISK-FACTORS; ESCHERICHIA-COLI; EMERGENCE; PATHOGEN; Science & Technology - Other Topics
• Research Categories: Health Sciences, Medicine and Surgery; Biology, Microbiology; Health Sciences, Immunology

Abstract

Acute bacterial infections are often treated empirically, with the choice of antibiotic therapy updated during treatment. The effects of such rapid antibiotic switching on the evolution of antibiotic resistance in individual patients are poorly understood. Here we find that low-frequency antibiotic resistance mutations emerge, contract, and even go to extinction within days of changes in therapy. We analyzed Pseudomonas aeruginosa populations in sputum samples collected serially from 7 mechanically ventilated patients at the onset of respiratory infection. Combining short- and long-read sequencing and resistance phenotyping of 420 isolates revealed that while new infections are near-clonal, reflecting a recent colonization bottleneck, resistance mutations could emerge at low frequencies within days of therapy. We then measured the in vivo frequencies of select resistance mutations in intact sputum samples with resistance-targeted deep amplicon sequencing (RETRA-Seq), which revealed that rare resistance mutations not detected by clinically used culture-based methods can increase by nearly 40-fold over 5–12 days in response to antibiotic changes. Conversely, mutations conferring resistance to antibiotics not administered diminish and even go to extinction. Our results underscore how therapy choice shapes the dynamics of low-frequency resistance mutations at short time scales, and the findings provide a possibility for driving resistance mutations to extinction during early stages of infection by designing patient-specific antibiotic cycling strategies informed by deep genomic surveillance.