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

Area of Science:

• Virology
• Immunology
• Computational Biology

Background:

• Human immunodeficiency virus type 1 (HIV-1) entry relies on viral envelope glycoproteins interacting with CD4 and coreceptors (CCR5/CXCR4).
• Viral tropism, or coreceptor preference, is primarily determined by the V3 loop of the gp120 envelope glycoprotein.
• Accurate tropism prediction is crucial for effective antiretroviral therapy using coreceptor antagonists.

Purpose of the Study:

• To develop an extended description of the HIV entry phenotype.
• To create a more accurate prediction of HIV-1 entry phenotype from genotypic data.
• To reflect the co-dependence of entry on multiple key determinants.

Main Methods:

• Established a new protocol for quantitating HIV entry efficiency.
• Developed regression models integrating viral V3 loop sequence, receptor/coreceptor levels, and antagonist concentrations.
• Utilized single-cell data to construct a multivariate phenotype descriptor (phenotype vector).

Main Results:

• Developed a multivariate phenotype descriptor for detailed HIV entry characterization, surpassing binary tropism classifications.
• Identified substantial divergences between the multivariate phenotype vector and existing tropism predictions for some variants.
• Created computational methods for predicting entry phenotypes based on V3 sequence.

Conclusions:

• The novel multivariate representation of the HIV cell entry phenotype enhances understanding.
• This approach has potential applications in optimizing the administration of entry inhibitors for antiretroviral therapies.