Inferring Human Immunodeficiency Virus 1 Proviral Integration Dates With Bayesian Inference

Area of Science:

• Virology
• Computational Biology
• Epidemiology

Background:

• The persistent human immunodeficiency virus 1 (HIV) reservoir, established by proviruses archived during infection, is a major barrier to curing HIV.
• These archived proviruses evade current combined antiretroviral therapy and can restart viral replication, hindering efforts towards an HIV cure.
• Understanding the temporal dynamics and integration dates of these proviruses within the persistent reservoir is crucial for developing effective cure strategies.

Purpose of the Study:

• To develop and validate novel Bayesian methods for accurately estimating the integration dates of HIV proviruses within the persistent reservoir.
• To compare the performance of the developed Bayesian approach against existing date estimation techniques using both simulated and empirical HIV sequence data.

Main Methods:

• Utilized Bayesian inference with the BEAST2 software package to model HIV proviral integration dates.
• Incorporated longitudinal within-host HIV sequences from pre-therapy and persistent reservoir samples during suppressive therapy.
• Implemented a tip-date random walker and a latency-specific prior within the BEAST2 model to refine integration date estimations.

Main Results:

• The developed Bayesian method accurately estimated HIV proviral integration dates, with a root mean squared error of 0.89 years on simulated data.
• This accuracy surpasses previously established methods, which showed root mean squared errors ranging from 1.23 to 1.89 years.
• Analysis of empirical data indicated that integration dates were distributed throughout the period of active infection, consistent with prior research.

Conclusions:

• Bayesian methods provide an adaptable and powerful framework for inferring HIV proviral integration dates, significantly improving upon existing techniques.
• Accurate estimation of integration dates is vital for understanding the dynamics of the HIV persistent reservoir and advancing HIV cure research.
• The developed Bayesian approach offers a more precise tool for researchers investigating viral reservoirs and designing novel therapeutic interventions.