Inferring human immunodeficiency virus 1 proviral integration dates with Bayesian Inference

Human immunodeficiency virus 1 (HIV) proviruses archived in the persistent reservoir currently pose the greatest obstacle to HIV cure due to their evasion of combined antiretroviral therapy and ability to reseed HIV infection. Understanding the dynamics of the HIV persistent reservoir is imperative for discovering a durable HIV cure. Here, we explore Bayesian methods using the software BEAST2 to estimate HIV proviral integration dates. We started with within-host longitudinal HIV sequences collected prior to therapy, along with sequences collected from the persistent reservoir during suppressive therapy. We built a BEAST2 model to estimate integration dates of proviral sequences collected during suppressive therapy, implementing a tip date random walker to adjust the sequence tip dates and a latency-specific prior to inform the dates. To validate our method, we implemented it on both simulated and empirical data sets. Consistent with previous studies, we found that proviral integration ..., ,