Impact of Suboptimal APOBEC3G Neutralization on the Emergence of HIV Drug Resistance in Humanized Mice.

HIV diversification facilitates immune escape and complicates antiretroviral therapy. In this study, we take advantage of a humanized-mouse model to probe the contribution of APOBEC3 mutagenesis to viral evolution. Humanized mice were infected with isogenic HIV molecular clones (HIV-WT, HIV-45G, and HIV-ΔSLQ) that differ in their abilities to counteract APOBEC3G (A3G). Infected mice remained naive or were treated with the reverse transcriptase (RT) inhibitor lamivudine (3TC). Viremia, emergence of drug-resistant variants, and quasispecies diversification in the plasma compartment were determined throughout infection. While both HIV-WT and HIV-45G achieved robust infection, over time, HIV-45G replication was significantly reduced compared to that of HIV-WT in the absence of 3TC treatment. In contrast, treatment responses differed significantly between HIV-45G- and HIV-WT-infected mice. Antiretroviral treatment failed in 91% of HIV-45G-infected mice, while only 36% of HIV-WT-infected mice displayed a similar negative outcome. Emergence of 3TC-resistant variants and nucleotide diversity were determined by analyzing 155,462 single HIV reverse transcriptase gene (RT) and 6,985 vif sequences from 33 mice. Prior to treatment, variants with genotypic 3TC resistance (RT-M184I/V) were detected at low levels in over a third of all the animals. Upon treatment, the composition of the plasma quasispecies rapidly changed, leading to a majority of circulating viral variants encoding RT-184I. Interestingly, increased viral diversity prior to treatment initiation correlated with higher plasma viremia in HIV-45G-infected animals, but not in HIV-WT-infected animals. Taken together, HIV variants with suboptimal anti-A3G activity were attenuated in the absence of selection but displayed a fitness advantage in the presence of antiretroviral treatment.IMPORTANCE Both viral (e.g., RT) and host (e.g., A3G) factors can contribute to HIV sequence diversity. This study shows that suboptimal anti-A3G activity shapes viral fitness and drives viral evolution in the plasma compartment in humanized mice.

Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling.

Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered by the complexity of their interactions with the human host. This study provides an innovative approach for the identification and analysis of epidemiological associations between HIV Viral Infectivity Factor (Vif) protein mutations and four clinical endpoints (Viral load and CD4 T cell numbers at time of both clinical debut and on historical follow-up of patients. Furthermore, this study highlights an alternative approach to the analysis of imbalanced datasets, where patients without specific mutations outnumber those with mutations. Imbalanced datasets are still a challenge hindering the development of classification algorithms through machine learning. This research deals with Decision Trees, Naïve Bayes (NB), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). This paper proposes a new methodology considering an undersampling approach to deal with imbalanced datasets and introduces two novel and differing approaches (MAREV-1 and MAREV-2). As theses approaches do not involve human pre-determined and hypothesis-driven combinations of motifs having functional or clinical relevance, they provide a unique opportunity to discover novel complex motif combinations of interest. Moreover, the motif combinations found can be analyzed through traditional statistical approaches avoiding statistical corrections for multiple tests.