ArticlePLoS computational biology2024
Antiviral capacity of the early CD8 T-cell response is predictive of natural control of SIV infection: Learning in vivo dynamics using ex vivo data.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AZD5582 robustly reactivates latently infected cells and clears the majority of those reactivated from the SIV reservoir.bioRxiv : the preprint server for biology · 2026Article
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Bistability and bifurcations in HIV-1 infection model with non-monotone responses.Scientific reports · 2025Article
- Trade-off between the antiviral and vaccinal effects of antibody therapy in the humoral response to HIV.Journal of the Royal Society, Interface · 2024Article
- Modelling HIV-1 control and remission.NPJ systems biology and applications · 2024Review
- Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion.PLoS pathogens · 2024Article
- Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
13 authors.
Funding
Abstract
While most individuals suffer progressive disease following HIV infection, a small fraction spontaneously controls the infection. Although CD8 T-cells have been implicated in this natural control, their mechanistic roles are yet to be established. Here, we combined mathematical modeling and analysis of previously published data from 16 SIV-infected macaques, of which 12 were natural controllers, to elucidate the role of CD8 T-cells in natural control. For each macaque, we considered, in addition to the canonical in vivo plasma viral load and SIV DNA data, longitudinal ex vivo measurements of the virus suppressive capacity of CD8 T-cells. Available mathematical models do not allow analysis of such combined in vivo-ex vivo datasets. We explicitly modeled the ex vivo assay, derived analytical approximations that link the ex vivo measurements with the in vivo effector function of CD8-T cells, and integrated them with an in vivo model of virus dynamics, thus developing a new learning framework that enabled the analysis. Our model fit the data well and estimated the recruitment rate and/or maximal killing rate of CD8 T-cells to be up to 2-fold higher in controllers than non-controllers (p = 0.013). Importantly, the cumulative suppressive capacity of CD8 T-cells over the first 4-6 weeks of infection was associated with virus control (Spearman's ρ = -0.51; p = 0.05). Thus, our analysis identified the early cumulative suppressive capacity of CD8 T-cells as a predictor of natural control. Furthermore, simulating a large virtual population, our model quantified the minimum capacity of this early CD8 T-cell response necessary for long-term control. Our study presents new, quantitative insights into the role of CD8 T-cells in the natural control of HIV infection and has implications for remission strategies.
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