Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection

Abstract

During the first weeks of human immunodeficiency virus-1 (HIV-1) infection, cytotoxic T-lymphocytes (CTLs) select for multiple escape mutations in the infecting HIV population. In recent years, methods that use escape mutation data to estimate rates of HIV escape have been developed, thereby providing a quantitative framework for exploring HIV escape from CTL response. Current methods for escape-rate inference focus on a specific HIV mutant selected by a single CTL response. However, recent studies have shown that during the first weeks of infection, CTL responses occur at one to three epitopes and HIV escape occurs through complex mutation pathways. Consequently, HIV escape from CTL response forms a complex, selective sweep that is difficult to analyze. In this work, we develop a model of initial infection, based on the well-known standard model, that allows for a description of multi-epitope response and the complex mutation pathways of HIV escape. Under this model, we develop Bayesian and hypothesis-test inference methods that allow us to analyze and estimate HIV escape rates. The methods are applied to two HIV patient data sets, concretely demonstrating the utility of our approach.A CUTE HIV-1 infection is marked by an initial period of 2-4 weeks in which the viral population expands from one to five infected cells to 10 9 infected cells. Following this expansion period, in the subsequent 1-2 months, the viral load drops and reaches a setpoint (Fiebig et al. 2003;Mehandru et al. 2004Mehandru et al. , 2007Keele et al. 2008). Cytotoxic T lymphocytes (CTLs) are thought to play an important role in shaping acute infection (Goulder and Watkins 2004;Cohen et al. 2011). The onset of CTL response is temporally correlated with the end of the expansion period, suggesting a role for CTLs in controlling viral load. Numerous studies have shown that during acute infection, specific HIV mutations on CTL targeted epitopes sweep to fixation, providing more direct evidence that CTL response shapes the infecting HIV population; see Goulder and Watkins (2004) for a review.In recent years, full-genome sequencing studies have provided an increasingly detailed description of CTL response during acute infection, e.g., Fernandez et al. Recent deep-sequencing data sets have provided a picture of HIV escape at the CTL targeted epitopes, e.g., Fisher et al. (2010), Henn et al. (2012), Bimber et al. (2009). HIV escape mutations at the first CTL targeted epitope rise to significant proportions 1-3 weeks after peak viral load. Escape mutations at the next series of epitopes targeted rise to significant proportion within roughly 4-6 weeks of peak viral load. Importantly, deep-sequencing studies have shown that HIV escape at a targeted epitope often occurs along multiple mutation pathways. The different mutation pathways are simply different nucleotide substitutions in the targeted epitope. During an HIV escape, these different mutations sweep to significant frequencies simultaneously, thereby replacing an HIV population typically ...

Inferring HIV Escape Rates from Multi-Locus Genotype Data

“…Combining these two approaches, intra-epitope competition as modeled in (12) and the between epitope competition studied here, would be an interesting extension. Similar ideas have been developed in the context of mutations in cancer or evolution experiments (40).…”

What do mathematical models tell us about killing rates during HIV-1 infection?

“…One major problem with such models is that the escapes are treated independently, whereas in reality the immune escapes appear more or less sequentially, i.e., subsequent escapes can only evolve after previous immune escapes have become established. This competition is known as "clonal interference" in population genetics [32,33], and several authors have recently shown that clonal interference can markedly decrease the escape rate inferred independently for each epitope [31,[34][35][36][37]. For example, Kessinger et al [35] developed a novel mathematical model combining the escape data from all epitopes, and showed that their estimation procedure markedly increased the most likely set of escape rates.…”

“…Fortunately, with the current rise of NGS approaches [39,40], and the novel models taking clonal interference into account [31,[34][35][36][37], this is expected to improve once we can afford to sample the viral quasi species very frequently. This is important because immune escape and reversions provide natural in vivo experiments providing information on the role of CTLs in HIV-1 infection.…”

Effectiveness evaluation of dendritic cell immunotherapy for osteosarcoma on survival rate and in vitro immune response

“…Accordingly, increasing interest has been displayed in the potential role of DCs in tumor biotherapy (Leviyang, 2013;Kim et al, 2014;Li et al, 2014a). Recently, tumor immunotherapy based on DCs has gained credence for clinical practice, as DCs have been demonstrated to protect animals from immune attack in addition to the induction of a specific proliferation response (Li et al, 2014b).…”