The evolution of homologous sequences affected by recombination or gene conversion cannot be adequately explained by a single phylogenetic tree. Many tree-based methods for sequence analysis, for example, those used for detecting sites evolving nonneutrally, have been shown to fail if such phylogenetic incongruity is ignored. However, it may be possible to propose several phylogenies that can correctly model the evolution of nonrecombinant fragments. We propose a model-based framework that uses a genetic algorithm to search a multiple-sequence alignment for putative recombination break points, quantifies the level of support for their locations, and identifies sequences or clades involved in putative recombination events. The software implementation can be run quickly and efficiently in a distributed computing environment, and various components of the methods can be chosen for computational expediency or statistical rigor. We evaluate the performance of the new method on simulated alignments and on an array of published benchmark data sets. Finally, we demonstrate that prescreening alignments with our method allows one to analyze recombinant sequences for positive selection.

The possibility of HIV-1 eradication has been limited by the existence of latently infected cellular reservoirs. Studies to examine control of HIV latency and potential reactivation have been hindered by the small numbers of latently infected cells found in vivo. Major conceptual leaps have been facilitated by the use of latently infected T cell lines and primary cells. However, notable differences exist among cell model systems. Furthermore, screening efforts in specific cell models have identified drug candidates for “anti-latency” therapy, which often fail to reactivate HIV uniformly across different models. Therefore, the activity of a given drug candidate, demonstrated in a particular cellular model, cannot reliably predict its activity in other cell model systems or in infected patient cells, tested ex vivo. This situation represents a critical knowledge gap that adversely affects our ability to identify promising treatment compounds and hinders the advancement of drug testing into relevant animal models and clinical trials. To begin to understand the biological characteristics that are inherent to each HIV-1 latency model, we compared the response properties of five primary T cell models, four J-Lat cell models and those obtained with a viral outgrowth assay using patient-derived infected cells. A panel of thirteen stimuli that are known to reactivate HIV by defined mechanisms of action was selected and tested in parallel in all models. Our results indicate that no single in vitro cell model alone is able to capture accurately the ex vivo response characteristics of latently infected T cells from patients. Most cell models demonstrated that sensitivity to HIV reactivation was skewed toward or against specific drug classes. Protein kinase C agonists and PHA reactivated latent HIV uniformly across models, although drugs in most other classes did not.

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Deoxyribonucleic acid (DNA) of the human immunodeficiency virus (HIV) provides the most sensitive measurement of residual infection in patients on effective combination antiretroviral therapy (cART). Droplet digital PCR (ddPCR) has recently been shown to provide highly accurate quantification of DNA copy number, but its application to quantification of HIV DNA, or other equally rare targets, has not been reported. This paper demonstrates and analyzes the application of ddPCR to measure the frequency of total HIV DNA (pol copies per million cells), and episomal 2-LTR (long terminal repeat) circles in cells isolated from infected patients. Analysis of over 300 clinical samples, including over 150 clinical samples assayed in triplicate by ddPCR and by real-time PCR (qPCR), demonstrates a significant increase in precision, with an average 5-fold decrease in the coefficient of variation of pol copy numbers and a >20-fold accuracy improvement for 2-LTR circles. Additional benefits of the ddPCR assay over qPCR include absolute quantification without reliance on an external standard and relative insensitivity to mismatches in primer and probe sequences. These features make digital PCR an attractive alternative for measurement of HIV DNA in clinical specimens. The improved sensitivity and precision of measurement of these rare events should facilitate measurements to characterize the latent HIV reservoir and interventions to eradicate it.