Broad CTL Response in Early HIV Infection Drives Multiple Concurrent CTL Escapes

Abstract

Recent studies have highlighted the ability of HIV to escape from cytotoxic T lymphocyte (CTL) responses that concurrently target multiple viral epitopes. Yet, the viral dynamics involved in such escape are incompletely understood. Previous analyses have made several strong assumptions regarding HIV escape from CTL responses such as independent or non-concurrent escape from individual CTL responses. Using experimental data from evolution of HIV half genomes in four patients we observe concurrent viral escape from multiple CTL responses during early infection (first 100 days of infection), providing confirmation of a recent result found in a study of one HIV-infected patient. We show that current methods of estimating CTL escape rates, based on the assumption of independent escapes, are biased and perform poorly when CTL escape proceeds concurrently at multiple epitopes. We propose a new method for analyzing longitudinal sequence data to estimate the rate of CTL escape across multiple epitopes; this method involves few parameters and performs well in simulation studies. By applying our novel method to experimental data, we find that concurrent multiple escapes occur at rates between 0.03 and 0.4 day−1, a relatively broad range that reflects uncertainty due to sparse sampling and wide ranges of parameter values. However, we show that concurrent escape at rates 0.1–0.2 day−1 across multiple epitopes is consistent with our patient datasets.

Insight into treatment ofHIVinfection from viral dynamics models

“…Infected cells and free virus are not generally cleared at a constant rate throughout infection because they are targeted and cleared by adaptive immune responses that expand in response to infection. Many models of antiviral immunity have been developed to explain different features of infection . Inclusion of immune system effects is needed to reproduce the large drops from peak viremia to setpoint and explain patterns of viral evolution (eg, References ).…”

“…9 During this time, viral populations diversify and diverge from the strains that founded infection, 10 often displaying population genetic signs of strong selection. 11,12 CD4+ T cells slowly decrease over the course of chronic infection and eventually become so low (<200 cells/uL blood) that opportunistic infections occur and the individual classified as having AIDS. Early in the epidemic, these characteristic trends inspired the use of mathematical models to understand these dynamics and help generate ideas about how to treat the infection.…”

“…Many models of antiviral immunity have been developed to explain different features of infection. 12,19,40,41 Inclusion of immune system effects is needed to reproduce the large drops from peak viremia to setpoint 42,43 and explain patterns of viral evolution (eg, References 40,44,45). When treatment reduces R 0 < 1 in this model, the simplest forms of the model predict that infection will eventually be completely cleared.…”

Time intervals in sequence sampling, not data modifications, have a major impact on estimates of HIV escape rates

“…However, this conclusion was challenged on multiple levels including the need for modification of the data to provide minimal estimates of the escape rate [29]. Indeed, it was possible that for many late escapes previous studies provided minimal estimates of the escape rate and thus the observed pattern of the escape rate decline with time since infection was an artifact of data modification [26,29,30]. To address this specific point we re-analyzed data on HIV escape published previously [16,25].…”

“…It remains unknown whether such data modifications were important for making general observations regarding HIV escape rates; previous studies utilizing such data modifications suggested that the rate of HIV escape declines with time since infection suggesting potential weakening of the immune responses or a simple consequence of the virus escaping in least costly positions [25,28]. More recent studies highlighted that the proposed data modification may have had a strong impact on estimated escape rates, thus questioning the validity of previous conclusions [29,30]. Here we extend previous analyses by applying a novel method of estimating rates of viral escape from poorly sampled data which does not involve modification of the data.…”