Computational inference methods for selective sweeps arising in acute HIV infection

Area of Science:

• Virology
• Immunology
• Computational Biology

Background:

• Cytotoxic T-lymphocytes (CTLs) exert selective pressure on human immunodeficiency virus-1 (HIV-1) populations early in infection.
• HIV-1 develops escape mutations to evade CTL responses, complicating viral dynamics.
• Existing methods for estimating HIV escape rates are limited to single CTL responses and single escape pathways.

Purpose of the Study:

• To develop a novel model for analyzing complex HIV-1 escape pathways from multi-epitope CTL responses during early infection.
• To establish robust Bayesian and hypothesis-testing inference methods for estimating HIV-1 escape rates.

Main Methods:

• Developed a new mathematical model extending the standard model to incorporate multi-epitope responses and complex mutation pathways.
• Implemented Bayesian inference and hypothesis-testing frameworks for escape rate estimation.
• Applied the developed methods to analyze HIV-1 patient data.

Main Results:

• The new model effectively describes multi-epitope CTL responses and complex HIV-1 escape pathways.
• The developed inference methods accurately estimate HIV-1 escape rates in patient data.
• Demonstrated the practical utility of the approach in analyzing real-world HIV-1 infection dynamics.

Conclusions:

• The developed model and inference methods provide a powerful quantitative framework for studying HIV-1 escape from CTL pressure.
• This approach advances our understanding of early HIV-1 evolution and immune evasion.
• The methods offer improved tools for analyzing complex viral escape dynamics in patient populations.