{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:31:42Z","timestamp":1785411102258,"version":"3.56.0"},"posted":{"date-parts":[[2026,1,21]]},"group-title":"Microbiology","reference-count":52,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["AI168159"],"award-info":[{"award-number":["AI168159"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["GM143053"],"award-info":[{"award-number":["GM143053"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"id":[{"id":"https:\/\/ror.org\/049emcs32","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  <jats:italic>Acinetobacter baumannii<\/jats:italic>\n                  is a major cause of multidrug-resistant nosocomial infections, particularly ventilator-associated pneumonia, for which therapeutic options are increasingly limited. Colistin, a polymyxin antibiotic, is a drug of last resort for\n                  <jats:italic>A. baumannii,<\/jats:italic>\n                  boasting high susceptibility rates. Yet, despite relatively low rates of breakpoint-defined colistin resistance, clinical outcomes are highly variable, and the bacterial strategies that enable survival during colistin therapy remain poorly understood. Here, we integrate supervised machine-learning\u2013guided genomic prioritization with functional, physiological, and in vivo analyses to interrogate the genetic basis of colistin response in\n                  <jats:italic>A. baumannii<\/jats:italic>\n                  . Machine-learning analysis of clinical isolates identified candidate loci associated with colistin survival, many of which did not alter minimum inhibitory concentration (MIC) when disrupted. Instead, growth-dynamic assays uncovered a subset of mutants capable of maintaining fitness upon inhibitory colistin exposure despite classification as susceptible via standardized antibiotic susceptibility testing. We define this phenotype as clinically silent resistance (CSR), a genetically encoded, MIC-independent survival state. Using a murine pneumonia model, we further demonstrate that CSR mutants thrive during colistin therapy in vivo. Together, these findings reveal a hidden layer of colistin survival that is not captured by standard susceptibility testing and highlight fundamental limitations of breakpoint-centric paradigms for predicting treatment outcomes in\n                  <jats:italic>A. baumannii<\/jats:italic>\n                  .\n                <\/jats:p>","DOI":"10.64898\/2026.01.20.700680","type":"posted-content","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T22:36:27Z","timestamp":1769034987000},"source":"Crossref","is-referenced-by-count":0,"title":["A Clinically Silent Resistance Phenotype That Promotes\n                  <i>Acinetobacter baumannii<\/i>\n                  Survival During Colistin Therapy"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3038-5826","authenticated-orcid":false,"given":"Muneer","family":"Yaqub","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, The University of Texas at Dallas, Richardson, TX, 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