• Medientyp: E-Artikel
  • Titel: A Diagnostic HIV-1 Tropism System Based on Sequence Relatedness
  • Beteiligte: Edwards, Suzanne; Stucki, Heinz; Bader, Joëlle; Vidal, Vincent; Kaiser, Rolf; Battegay, Manuel; Klimkait, Thomas
  • Erschienen: American Society for Microbiology, 2015
  • Erschienen in: Journal of Clinical Microbiology, 53 (2015) 2, Seite 597-610
  • Sprache: Englisch
  • DOI: 10.1128/jcm.02762-14
  • ISSN: 0095-1137; 1098-660X
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  • Beschreibung: ABSTRACTKey clinical studies for HIV coreceptor antagonists have used the phenotyping-based Trofile test. Meanwhile various simpler-to-do genotypic tests have become available that are compatible with standard laboratory equipment and Web-based interpretation tools. However, these systems typically analyze only the most prominent virus sequence in a specimen. We present a new diagnostic HIV tropism test not needing DNA sequencing. The system, XTrack, uses physical properties of DNA duplexes after hybridization of single-stranded HIV-1envV3 loop probes to the clinical specimen. Resulting “heteroduplexes” possess unique properties driven by sequence relatedness to the reference and resulting in a discrete electrophoretic mobility. A detailed optimization process identified diagnostic probe candidates relating best to a large number of HIV-1 sequences with known tropism. From over 500 V3 sequences representing all main HIV-1 subtypes (Los Alamos database), we obtained a small set of probes to determine the tropism in clinical samples. We found a high concordance with the commercial TrofileES test (84.9%) and the Web-based tool Geno2Pheno (83.0%). Moreover, the new system reveals mixed virus populations, and it was successful on specimens with low virus loads or on provirus from leukocytes. A replicative phenotyping system was used for validation. Our data show that the XTrack test is favorably suitable for routine diagnostics. It detects and dissects mixed virus populations and viral minorities; samples with viral loads (VL) of <200 copies/ml are successfully analyzed. We further expect that the principles of the platform can be adapted also to other sequence-divergent pathogens, such as hepatitis B and C viruses.
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