Evidence map›Paper›PMID 22830600›Full record

ArticleRetrovirology2012

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

Katarzyna Bozek, Manon Eckhardt, Saleta Sierra, Maria Anders, Rolf Kaiser, Hans-Georg Kräusslich, Barbara Müller, Thomas Lengauer

Abstract read
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Article in Retrovirology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Katarzyna BozekDepartment of Computational Biology and Applied Algorithmics, Max Planck for Computer Sciences, Campus E1 4 66123, Saarbrücken, Germany.
Manon Eckhardt
Saleta Sierra
Maria Anders
Rolf Kaiser
Hans-Georg Kräusslich
Barbara Müller
Thomas Lengauer

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEntry 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.

resultsHere, 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.

conclusionsOur 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.

Indexed as

Virus InternalizationAmino Acid SequenceBenzylaminesCCR5 Receptor AntagonistsComputational BiologyCyclamsCyclohexanesGenetic VectorsHEK293 CellsHeterocyclic CompoundsHIV-1HIV Fusion InhibitorsHIV InfectionsHumansMaravirocModels, BiologicalBenzylaminesCCR5 Receptor AntagonistsCXCR4 protein, humanCyclamsCyclohexanesHeterocyclic CompoundsHIV Fusion InhibitorsMaravirocplerixaforReceptors, CCR5Receptors, CXCR4Triazoles

Identifiers

PMID22830600
PMCPMC3464718

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.