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An expanded model of HIV cell entry phenotype based on multi-parameter single-cell data

Katarzyna Bozek14, Manon Eckhardt25, Saleta Sierra3, Maria Anders2, Rolf Kaiser3, Hans-Georg Kräusslich2, Barbara Müller2* and Thomas Lengauer1*

Author Affiliations

1 Department of Computational Biology and Applied Algorithmics, Max Planck for Computer Sciences, Campus E1 4 66123, Saarbrücken, Germany

2 Department of Infectious Diseases Virology, University of Heidelberg, Im Neuenheimer Feld 324, 69120, Heidelberg, Germany

3 Institute of Virology, University of Cologne, Fürst-Pückler-Strasse 56 50935, Cologne, Germany

4 Current address: CAS-MPG Partner Institute for Computational Biology, Shanghai, P.R. China

5 Current address:Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA

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Retrovirology 2012, 9:60  doi:10.1186/1742-4690-9-60

Published: 25 July 2012



Entry of human immunodeficiency virus type 1 (HIV-1) into the host cell involves interactions between the viral envelope glycoproteins (Env) and the cellular receptor CD4 as well as a coreceptor molecule (most importantly CCR5 or CXCR4). Viral preference for a specific coreceptor (tropism) is in particular determined by the third variable loop (V3) of the Env glycoprotein gp120. The approval and use of a coreceptor antagonist for antiretroviral therapy make detailed understanding of tropism and its accurate prediction from patient derived virus isolates essential. The aim of the present study is the development of an extended description of the HIV entry phenotype reflecting its co-dependence on several key determinants as the basis for a more accurate prediction of HIV-1 entry phenotype from genotypic data.


Here, we established a new protocol of quantitation and computational analysis of the dependence of HIV entry efficiency on receptor and coreceptor cell surface levels as well as viral V3 loop sequence and the presence of two prototypic coreceptor antagonists in varying concentrations. Based on data collected at the single-cell level, we constructed regression models of the HIV-1 entry phenotype integrating the measured determinants. We developed a multivariate phenotype descriptor, termed phenotype vector, which facilitates a more detailed characterization of HIV entry phenotypes than currently used binary tropism classifications. For some of the tested virus variants, the multivariant phenotype vector revealed substantial divergences from existing tropism predictions. We also developed methods for computational prediction of the entry phenotypes based on the V3 sequence and performed an extrapolating calculation of the effectiveness of this computational procedure.


Our study of the HIV cell entry phenotype and the novel multivariate representation developed here contributes to a more detailed understanding of this phenotype and offers potential for future application in the effective administration of entry inhibitors in antiretroviral therapies.