{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T22:59:27Z","timestamp":1779231567989,"version":"3.51.4"},"reference-count":40,"publisher":"American Society for Microbiology","issue":"9","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/100004897","name":"Pennsylvania Department of Health","doi-asserted-by":"crossref","award":["SAP # 4100068710"],"award-info":[{"award-number":["SAP # 4100068710"]}],"id":[{"id":"10.13039\/100004897","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100006458","name":"Children's Hospital of Philadelphia","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006458","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["mSphere"],"published-print":{"date-parts":[[2024,9,25]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>Although antibiotics induce sizable perturbations in the human microbiome, we lack a systematic and quantitative method to measure and predict the microbiome's response to specific antibiotics. Here, we introduce such a method, which takes the form of a microbiome response index (MiRIx) for each antibiotic. Antibiotic-specific MiRIx values quantify the overall susceptibility of the microbiota to an antibiotic, based on databases of bacterial phenotypes and published data on intrinsic antibiotic susceptibility. We applied our approach to five published microbiome studies that carried out antibiotic interventions with vancomycin, metronidazole, ciprofloxacin, amoxicillin, and doxycycline. We show how MiRIx can be used in conjunction with existing microbiome analytical approaches to gain a deeper understanding of the microbiome response to antibiotics. Finally, we generate antibiotic response predictions for the oral, skin, and gut microbiome in healthy humans. Our approach is implemented as open-source software and is readily applied to microbiome data sets generated by 16S rRNA marker gene sequencing or shotgun metagenomics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>Antibiotics are potent influencers of the human microbiome and can be a source for enduring dysbiosis and antibiotic resistance in healthcare. Existing microbiome data analysis methods can quantify perturbations of bacterial communities but cannot evaluate whether the differences are aligned with the expected activity of a specific antibiotic. Here, we present a novel method to quantify and predict antibiotic-specific microbiome changes, implemented in a ready-to-use software package. This has the potential to be a critical tool to broaden our understanding of the relationship between the microbiome and antibiotics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/msphere.00488-24","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T09:02:26Z","timestamp":1725440546000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":5,"title":["A quantitative approach to measure and predict microbiome response to antibiotics"],"prefix":"10.1128","volume":"9","author":[{"given":"Vincent","family":"Tu","sequence":"first","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Children\u2019s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Ren","sequence":"additional","affiliation":[{"name":"Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ceylan","family":"Tanes","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Children\u2019s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sagori","family":"Mukhopadhyay","sequence":"additional","affiliation":[{"name":"Division of Neonatology, Children\u2019s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA"},{"name":"Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA"},{"name":"Center for Pediatric Clinical Effectiveness, Children\u2019s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott G.","family":"Daniel","sequence":"additional","affiliation":[{"name":"Division of 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