Phyloanatomic characterization of the distinct T cell and monocyte contributions to the peripheral blood HIV population within the host

Machine Learning Prediction and Phyloanatomic Modeling of Viral Neuroadaptive Signatures in the Macaque Model of HIV-Mediated Neuropathology

In human immunodeficiency virus (HIV) infection, virus replication in and adaptation to the central nervous system (CNS) can result in neurocognitive deficits in approximately 25% of patients with unsuppressed viremia. While no single viral mutation can be agreed upon as distinguishing the neuroadapted population, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the virus envelope glycoprotein (Gp120) predictive of disease. The S[imian]IV-infected macaque is a widely used animal model of HIV neuropathology, allowing in-depth tissue sampling infeasible for human patients. Yet, translational impact of the ML approach within the context of the macaque model has not been tested, much less the capacity for early prediction in other, noninvasive tissues. We applied the previously described ML approach to prediction of SIV-mediated encephalitis (SIVE) using gp120 sequences obtained from the CNS of animals with and without SIVE with 97% accuracy. The presence of SIVE signatures at earlier time points of infection in non-CNS tissues indicated these signatures cannot be used in a clinical setting; however, combined with protein structural mapping and statistical phylogenetic inference, results revealed common denominators associated with these signatures, including 2-acetamido-2-deoxy-beta-d-glucopyranose structural interactions and high rate of alveolar macrophage (AM) infection. AMs were also determined to be the phyloanatomic source of cranial virus in SIVE animals, but not in animals that did not develop SIVE, implicating a role for these cells in the evolution of the signatures identified as predictive of both HIV and SIV neuropathology. IMPORTANCE HIV-associated neurocognitive disorders remain prevalent among persons living with HIV (PLWH) owing to our limited understanding of the contributing viral mechanisms and ability to predict disease onset. We have expanded on a machine learning method previously used on HIV genetic sequence data to predict neurocognitive impairment in PLWH to the more extensively sampled SIV-infected macaque model in order to (i) determine the translatability of the animal model and (ii) more accurately characterize the predictive capacity of the method. We identified eight amino acid and/or biochemical signatures in the SIV envelope glycoprotein, the most predominant of which demonstrated the potential for aminoglycan interaction characteristic of previously identified HIV signatures. These signatures were not isolated to specific points in time or to the central nervous system, limiting their use as an accurate clinical predictor of neuropathogenesis; however, statistical phylogenetic and signature pattern analyses implicate the lungs as a key player in the emergence of neuroadapted viruses.

Discordance between HIV-1 Population in Plasma at Rebound after Structured Treatment Interruption and Archived Provirus Population in Peripheral Blood Mononuclear Cells

Antiretroviral therapy (ART) can sustain the suppression of plasma viremia to below detection levels. Infected individuals undergoing a treatment interruption exhibit rapid viral rebound in plasma viremia which is fueled by cellular reservoirs such as CD4+ T cells, myeloid cells, and potentially uncharacterized cellular sources. Interrogating the populations of viruses found during analytical treatment interruption (ATI) can give insights into the biologically competent reservoirs that persist under effective ART as well as the nature of the cellular reservoirs that enable viral persistence under ART. We interrogated plasma viremia from four rare cases of individuals undergoing sequential ATIs. We performed next-generation sequencing (NGS) on cell-associated viral DNA and cell-free virus to understand the interrelationship between sequential ATIs as well as the relationship between viral genomes in circulating peripheral blood mononuclear cells (PBMCs) and RNA from rebound plasma. We observed population differences between viral populations recrudescing at sequential ATIs as well as divergence between viral sequences in plasma and those in PBMCs. This indicated that viruses in PBMCs were not a major source of post-ATI viremia and highlights the role of anatomic reservoirs in post-ATI viremia and viral persistence. IMPORTANCE Even with effective ART, HIV-1 persists at undetectable levels and rebounds in individuals who stop treatment. Cellular and anatomical reservoirs ignite viral rebound upon treatment interruption, remaining one of the key obstacles for HIV-1 cure. To further examine HIV-1 persistence, a better understanding of the distinct populations that fuel viral rebound is necessary to identify and target reservoirs and the eradication of HIV-1. This study investigates the populations of viruses found from proviral genomes from PBMCs and plasma at rebound from a unique cohort of individuals who underwent multiple rounds of treatment interruption. Using NGS, we characterized the subtypes of viral sequences and found divergence in viral populations between plasma and PBMCs at each rebound, suggesting that distinct viral populations appear at each treatment interruption.

Contrast-FEL-A Test for Differences in Selective Pressures at Individual Sites among Clades and Sets of Branches

A number of evolutionary hypotheses can be tested by comparing selective pressures among sets of branches in a phylogenetic tree. When the question of interest is to identify specific sites within genes that may be evolving differently, a common approach is to perform separate analyses on subsets of sequences and compare parameter estimates in a post hoc fashion. This approach is statistically suboptimal and not always applicable. Here, we develop a simple extension of a popular fixed effects likelihood method in the context of codon-based evolutionary phylogenetic maximum likelihood testing, Contrast-FEL. It is suitable for identifying individual alignment sites where any among the K≥2 sets of branches in a phylogenetic tree have detectably different ω ratios, indicative of different selective regimes. Using extensive simulations, we show that Contrast-FEL delivers good power, exceeding 90% for sufficiently large differences, while maintaining tight control over false positive rates, when the model is correctly specified. We conclude by applying Contrast-FEL to data from five previously published studies spanning a diverse range of organisms and focusing on different evolutionary questions.