Back to overview

A diagnostic HIV-1 tropism system based on sequence relatedness.

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Edwards Suzanne, Stucki Heinz, Bader Joëlle, Vidal Vincent, Kaiser Rolf, Battegay Manuel, Klimkait Thomas,
Project Swiss HIV Cohort Study (SHCS)
Show all

Original article (peer-reviewed)

Journal Journal of clinical microbiology
Volume (Issue) 53(2)
Page(s) 597 - 610
Title of proceedings Journal of clinical microbiology
DOI 10.1128/jcm.02762-14

Open Access

Type of Open Access Publisher (Gold Open Access)


Key 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-1 env V3 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.