{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:58:40Z","timestamp":1791266320596,"version":"4.1.0"},"reference-count":40,"publisher":"American Society for Microbiology","issue":"5","license":[{"start":{"date-parts":[[2012,5,1]],"date-time":"2012-05-01T00:00:00Z","timestamp":1335830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["Antimicrob Agents Chemother"],"published-print":{"date-parts":[[2012,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Determining the phenotypic impacts of reverse transcriptase (RT) mutations on individual nucleoside RT inhibitors (NRTIs) has remained a statistical challenge because clinical NRTI-resistant HIV-1 isolates usually contain multiple mutations, often in complex patterns, complicating the task of determining the relative contribution of each mutation to HIV drug resistance. Furthermore, the NRTIs have highly variable dynamic susceptibility ranges, making it difficult to determine the relative effect of an RT mutation on susceptibility to different NRTIs. In this study, we analyzed 1,273 genotyped HIV-1 isolates for which phenotypic results were obtained using the PhenoSense assay (Monogram, South San Francisco, CA). We used a parsimonious feature selection algorithm, LASSO, to assess the possible contributions of 177 mutations that occurred in 10 or more isolates in our data set. We then used least-squares regression to quantify the impact of each LASSO-selected mutation on each NRTI. Our study provides a comprehensive view of the most common NRTI resistance mutations. Because our results were standardized, the study provides the first analysis that quantifies the relative phenotypic effects of NRTI resistance mutations on each of the NRTIs. In addition, the study contains new findings on the relative impacts of thymidine analog mutations (TAMs) on susceptibility to abacavir and tenofovir; the impacts of several known but incompletely characterized mutations, including E40F, V75T, Y115F, and K219R; and a tentative role in reduced NRTI susceptibility for K64H, a novel NRTI resistance mutation.<\/jats:p>","DOI":"10.1128\/aac.05487-11","type":"journal-article","created":{"date-parts":[[2012,2,13]],"date-time":"2012-02-13T23:11:22Z","timestamp":1329174682000},"page":"2305-2313","update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":60,"title":["Standardized Comparison of the Relative Impacts of HIV-1 Reverse Transcriptase (RT) Mutations on Nucleoside RT Inhibitor Susceptibility"],"prefix":"10.1128","volume":"56","author":[{"given":"George L.","family":"Melikian","sequence":"first","affiliation":[{"name":"Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo-Yon","family":"Rhee","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Taylor","sequence":"additional","affiliation":[{"name":"Department of Statistics, Stanford University, Stanford, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W. 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