Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections
Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen’s susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequen...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
American Association for the Advancement of Science
2022
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| _version_ | 1797081112321720320 |
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| author | Stracy, M Snitser, O Yelin, I Amer, Y Parizade, M Katz, R Rimler, G Wolf, T Herzel, E Koren, G Kuint, J Foxman, B Chodick, G Shalev, V Kishony, R |
| author_facet | Stracy, M Snitser, O Yelin, I Amer, Y Parizade, M Katz, R Rimler, G Wolf, T Herzel, E Koren, G Kuint, J Foxman, B Chodick, G Shalev, V Kishony, R |
| author_sort | Stracy, M |
| collection | OXFORD |
| description | Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen’s susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient’s own microbiota, these resistance-gaining recurrences can be predicted using the patient’s past infection history and minimized by machine learning–personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens. |
| first_indexed | 2024-03-07T01:09:53Z |
| format | Journal article |
| id | oxford-uuid:8ca27a6a-6b3a-4db4-9092-64fb66d3d947 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:09:53Z |
| publishDate | 2022 |
| publisher | American Association for the Advancement of Science |
| record_format | dspace |
| spelling | oxford-uuid:8ca27a6a-6b3a-4db4-9092-64fb66d3d9472022-03-26T22:45:54ZMinimizing treatment-induced emergence of antibiotic resistance in bacterial infectionsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8ca27a6a-6b3a-4db4-9092-64fb66d3d947EnglishSymplectic ElementsAmerican Association for the Advancement of Science2022Stracy, MSnitser, OYelin, IAmer, YParizade, MKatz, RRimler, GWolf, THerzel, EKoren, GKuint, JFoxman, BChodick, GShalev, VKishony, RTreatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen’s susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient’s own microbiota, these resistance-gaining recurrences can be predicted using the patient’s past infection history and minimized by machine learning–personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens. |
| spellingShingle | Stracy, M Snitser, O Yelin, I Amer, Y Parizade, M Katz, R Rimler, G Wolf, T Herzel, E Koren, G Kuint, J Foxman, B Chodick, G Shalev, V Kishony, R Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections |
| title | Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections |
| title_full | Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections |
| title_fullStr | Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections |
| title_full_unstemmed | Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections |
| title_short | Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections |
| title_sort | minimizing treatment induced emergence of antibiotic resistance in bacterial infections |
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