Augmentation of HIV-specific T cell function by immediate treatment of hyperacute HIV-1 infection

Ndhlovu, Zaza M.; Kazer, Samuel W.; Nkosi, Thandeka; Ogunshola, Funsho; Muema, Daniel M.; Anmole, Gursev; Swann, Shayda A.; Moodley, Amber; Dong, Krista; Reddy, Tarylee; Brockman, Mark A.; Shalek, Alexander K; Ndung’u, Thumbi; Walker, Bruce D.

doi:10.1126/scitranslmed.aau0528

“…Similar patterns have been found for HIV-1, where ART initiation during acute HIV infection can lead to an incomplete HIV-specific humoral immune response, as measured by diagnostic assays [38,39]. On the other hand, patients treated during Fiebig stage I or II have been shown to develop detectable HIV-specific CD8 + T-cell responses [40]. Although these responses are lower in magnitude and breadth than CD8 + T-cell responses from untreated individuals, they show enhanced differentiation into the effector-memory T-cell phenotype, leading to a more functional CD8 + memory T-cell pool compared to patients for whom treatment was initiated later.…”

Section: Discussionsupporting

confidence: 66%

Models of SIV rebound after treatment interruption that involve multiple reactivation events

Dorp

¹

,

Conway

²

,

Barouch

³

et al. 2020

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In order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevych et al. and Hill et al. of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate (95% credible interval: [18%, 62%]), and a 11% decrease of the viral growth rate (95% credible interval: [4%, 20%]). We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event.

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“…Similar patterns have been found for HIV-1, where ART initiation during acute HIV infection can lead to an incomplete HIV-specific humoral immune response, as measured by diagnostic assays [12,25]. On the other hand, patients treated during Fiebig stage I or II have been shown to develop detectable HIV-specific CD8 + T-cell responses [27]. Although these responses are lower in magnitude and breadth than CD8 + T-cell responses from untreated individuals, they show enhanced differentiation into the effector-memory Tcell phenotype, leading to a more functional CD8 + memory T-cell pool compared to patients for whom treatment was initiated later.…”

Section: Discussionsupporting

confidence: 66%

Models of SIV rebound after treatment interruption that involve multiple reactivation events

Dorp

¹

,

Conway

²

,

Whitney

³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

In order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevych et al. and Hill et al. of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate, and a 11% decrease of the viral growth rate. We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event.Author SummaryHIV-1 persists during suppressive antiretroviral therapy (ART) due to a reservoir of latently infected cells. When ART is stopped, HIV generally rebounds within a few weeks. However, there is a small fraction of patients that do not rebound over a period of months or years. A variety of treatments are being tested for their ability to reduce the size of the latent reservoir, to induce effective immune responses against the virus, or to prevent or prolong the time to viral rebound after ART interruption. These novel treatments are typically first tested in SIV infected macaques, and the efficacy of the treatment assessed by interrupting ART and measuring the time to viral rebound. Here, we develop and test a mathematical and statistical model that describes the process of viral rebound. The model can be used for statistical inference of the efficacy of newly developed treatments. Importantly, the model takes into account that multiple recrudescence events can precede rebound. We test the model using data from early treated SIV infected macaques.

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“…In fact, we note enrichment of TRBV and TRAV genes known to construct prevalent CDR3 sequences that bind common HIV epitopes 87,88 : TRBV28 (QW9/FL8/KF11/KK10/NV9, χ 2 test p=2.4*10 -26 ), TRAV4 (KK10, χ 2 test p=3.5*10 -6 ), and TRBV20-1 (KK10/KF11/GY9/NV9, χ 2 test p=0.059). Our single-cell data here expand our recently published bulk RNA-Seq data on HIV-specific CTLs in this cohort 89 , but also enable us to elucidate heterogeneity in this proliferating cytotoxic response as a function of time.…”

Section: Resultssupporting

confidence: 59%

Integrated Single-Cell Analysis of Multicellular Immune Dynamics during Hyper-Acute HIV-1 Infection

Kazer

¹

,

Aicher

²

,

Muema

³

et al. 2019

Preprint

Self Cite

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30Cellular immunity is critical for controlling intracellular pathogens, but the dynamics and 31 cooperativity of the evolving host response to infection are not well defined. Here, we apply single-32 cell RNA-sequencing to longitudinally profile pre-and immediately post-HIV infection peripheral 33 immune responses of multiple cell types in four untreated individuals. Onset of viremia induces a 34 strong transcriptional interferon response integrated across most cell types, with subsequent pro-35 inflammatory T cell differentiation, monocyte MHC-II upregulation, and cytolytic killing. With 36 longitudinal sampling, we nominate key intra-and extracellular drivers that induce these 37 programs, and assign their multi-cellular targets, temporal ordering, and duration in acute 38 infection. Two individuals studied developed spontaneous viral control, associated with initial 39 elevated frequencies of proliferating cytotoxic cells, inclusive of a previously unappreciated 40 proliferating natural killer (NK) cell subset. Our study presents a unified framework for 41 characterizing immune evolution during a persistent human viral infection at single-cell resolution, 42 and highlights programs that may drive response coordination and influence clinical trajectory. 43 44 54Recently, high-throughput single-cell RNA-sequencing (scRNA-Seq) has emerged as a 55 powerful tool to characterize, transcriptome-wide, complex human systems in health and disease 56 at single-cell resolution [3][4][5][6][7][8][9] . When applied to a collection of samples across a disease setting, this 57 approach provides a platform for investigating cell types, states, interactions, and drivers 58 associated with that disease; this information can be used to develop testable hypotheses on 59 therapeutic modulations that may ameliorate disease state 7,8 . Meanwhile, within an individual, 60longitudinal sampling provides an opportunity to decipher, at unprecedented resolution and 61 absent potentially confounding inter-individual variability 7 , shifts in these same variables, and to 62 3 associate observed changes with internal or external perturbations 10-12 . Such sampling of a host's 63 exposure to a pathogen could provide foundational insights into essential cellular response 64 features and their coordination, empowering the rational design of improved prophylactic 65 interventions. 66Illustratively, a better understanding of the interplay between innate and adaptive immune 67 responses at the very earliest stages of a viral infection, and its impact on long-term disease, 68 could reveal principles to accelerate prevention efforts. Human Immunodeficiency Virus (HIV) has 69 been the subject of thorough study, and thus is a well-considered model system for examining 70 host responses to a pathogen. Moreover, although the development of antiretroviral therapy 71 (ART) 13 , as well as implementation of pre-exposure prophylaxis (PrEP) 14 and combination 72 prevention efforts, has improved the lives of persons living with HIV, increased life expectancie...

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Augmentation of HIV-specific T cell function by immediate treatment of hyperacute HIV-1 infection

Cited by 60 publications

References 63 publications

Models of SIV rebound after treatment interruption that involve multiple reactivation events

Models of SIV rebound after treatment interruption that involve multiple reactivation events

Models of SIV rebound after treatment interruption that involve multiple reactivation events

Integrated Single-Cell Analysis of Multicellular Immune Dynamics during Hyper-Acute HIV-1 Infection

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