Understanding the Failure of CD8<sup>+</sup>T-Cell Vaccination against Simian/Human Immunodeficiency Virus

Boer, Rob J. de

doi:10.1128/jvi.01914-06

Cited by 65 publications

(81 citation statements)

References 107 publications

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“…These parameter choices fall within the typical range used for HIV, e.g. [11,8,2,1], with the caveat that λ, k must be converted to per body units and remembering that we assume virions are in steady state. (For example, the k = 2 * 10 −3 in our model corresponds to k = 1.2 * 10 −5 in the model of [1], approximately the value cited in that paper.)…”

Section: Discussionmentioning

confidence: 99%

“…To compute 10 7 MCMC steps takes 1 day on an Intel I7-2600 using our C++ implementation. In all our results, we use a uniform prior on [21,2] for each escape rate.…”

Section: Computing Posteriorsmentioning

confidence: 99%

“…• Given the simulated dataset constructed in step 2, we used the standard model (1) with the I v equation replaced by (2) to compute a least-squares estimate of escape rates. Stochasticity corresponding to sampling was preserved.…”

Section: S5 Numerical Experiments -Parameter Inferencementioning

confidence: 99%

“…The numerical experiment used to examine parameter inference error consisted of the following steps: [11,2,8]. Each δ v,1 (the death rate of variant v prior to CTL response) was chosen uniformly from [.4, .5] [9].…”

Section: S5 Numerical Experiments -Parameter Inferencementioning

confidence: 99%

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Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection

Leviyang

2013

Genetics

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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 ...

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Section: Discussionmentioning

confidence: 99%

“…To compute 10 7 MCMC steps takes 1 day on an Intel I7-2600 using our C++ implementation. In all our results, we use a uniform prior on [21,2] for each escape rate.…”

Section: Computing Posteriorsmentioning

confidence: 99%

Section: S5 Numerical Experiments -Parameter Inferencementioning

confidence: 99%

Section: S5 Numerical Experiments -Parameter Inferencementioning

confidence: 99%

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Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection

Leviyang

2013

Genetics

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“…Additionally, we assume that during the invasion of the escape variant, the ratio of the infected cells over the virus-producing cells, I e =P e , is approaching quasi-steady-state, i.e. we assume dðI e =P e Þ= dt ¼ 0 [34]. These assumptions allows us to express the 'infection rate' b as a function of the other parameters…”

Section: Model (A) Multiple Infections Of Cellsmentioning

confidence: 99%

Impaired immune evasion in HIV through intracellular delays and multiple infection of cells

Althaus

Boer

2012

Proc. R. Soc. B.

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With its high mutation rate, HIV is capable of escape from recognition, suppression and/or killing by CD8 + cytotoxic T lymphocytes (CTLs). The rate at which escape variants replace each other can give insights into the selective pressure imposed by single CTL clones. We investigate the effects of specific characteristics of the HIV life cycle on the dynamics of immune escape. First, it has been found that cells in HIV-infected patients can carry multiple copies of proviruses. To investigate how this process affects the emergence of immune escape, we develop a mathematical model of HIV dynamics with multiple infections of cells. Increasing the frequency of multiple-infected cells delays the appearance of immune escape variants, slows down the rate at which they replace the wild-type variant and can even prevent escape variants from taking over the quasi-species. Second, we study the effect of the intracellular eclipse phase on the rate of escape and show that escape rates are expected to be slower than previously anticipated. In summary, slow escape rates do not necessarily imply inefficient CTL-mediated killing of HIV-infected cells, but are at least partly a result of the specific characteristics of the viral life cycle.

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Bifurcation analysis of a multidelayed HIV model in presence of immune response and understanding of in‐host viral dynamics

Adak

Bairagi

2019

Math Methods in App Sciences

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In this paper, we proposed a multidelayed in‐host HIV model to represent the interaction between human immunodeficiency virus and immune response. One delay was considered to incorporate the time required by the virus for various intracellular events to make a host cell productively infective, and the second delay was introduced to take into account the time required for adaptive immune system to respond against infection. We extensively analyzed this multidelayed model analytically and numerically. We show that delay may have both destabilizing and stabilizing effects even when the system contains a single immune response delay. It happens when there exists two sequences of critical values of this delay. If the system starts with stable state in absence of delay, then the smallest value of these critical delays causes instability and the next higher value causes stability. The system may also show stability switching for different values of the virus replication factor. These results demonstrate the possible reasons of intrapatients and interpatients variability of CD4+ T cells and virus counts in HIV‐infected patients.

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Understanding the Failure of CD8⁺T-Cell Vaccination against Simian/Human Immunodeficiency Virus

Cited by 65 publications

References 107 publications

Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection

Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection

Impaired immune evasion in HIV through intracellular delays and multiple infection of cells

Bifurcation analysis of a multidelayed HIV model in presence of immune response and understanding of in‐host viral dynamics

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Understanding the Failure of CD8+T-Cell Vaccination against Simian/Human Immunodeficiency Virus

Cited by 65 publications

References 107 publications

Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection

Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection

Impaired immune evasion in HIV through intracellular delays and multiple infection of cells

Bifurcation analysis of a multidelayed HIV model in presence of immune response and understanding of in‐host viral dynamics

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Product

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Understanding the Failure of CD8⁺T-Cell Vaccination against Simian/Human Immunodeficiency Virus