Bharadwaj Vemparala, Vincent Madelain, Caroline Passaes, Antoine Millet, Véronique Avettand-Fenoel, Ramsès Djidjou-Demasse, Nathalie Dereuddre-Bosquet, Roger Le Grand, Christine Rouzioux, Bruno Vaslin, Asier Sáez-Cirión, Jérémie Guedj, Narendra M. Dixit
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For each macaque, we considered, in addition to the canonical <jats:italic>in vivo</jats:italic> plasma viral load and SIV DNA data, longitudinal <jats:italic>ex vivo</jats:italic> measurements of the virus suppressive capacity of CD8 T-cells. Available mathematical models do not allow analysis of such combined <jats:italic>in vivo</jats:italic>-<jats:italic>ex vivo</jats:italic> datasets. We explicitly modeled the <jats:italic>ex vivo</jats:italic> assay, derived analytical approximations that link the <jats:italic>ex vivo</jats:italic> measurements with the <jats:italic>in vivo</jats:italic> effector function of CD8-T cells, and integrated them with an <jats:italic>in vivo</jats:italic> 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). 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引用次数: 0
摘要
虽然大多数人在感染艾滋病病毒后会出现进行性疾病,但也有一小部分人会自发控制感染。虽然 CD8 T 细胞与这种自然控制有关,但它们的作用机制尚未确定。在这里,我们结合数学建模和对之前发表的 16 只 SIV 感染猕猴(其中 12 只为自然控制者)的数据分析,来阐明 CD8 T 细胞在自然控制中的作用。对于每只猕猴,除了常规的体内血浆病毒载量和 SIV DNA 数据外,我们还考虑了 CD8 T 细胞抑制病毒能力的纵向体外测量数据。现有的数学模型无法对这种体内-体外联合数据集进行分析。我们对体内外检测进行了明确建模,得出了将体内外测量结果与体内 CD8-T 细胞效应功能联系起来的分析近似值,并将其与体内病毒动态模型进行了整合,从而建立了一个新的学习框架,使分析成为可能。我们的模型与数据拟合良好,估计控制者的 CD8 T 细胞招募率和/或最大杀伤率是非控制者的 2 倍(p = 0.013)。重要的是,在感染的前 4-6 周,CD8 T 细胞的累积抑制能力与病毒控制有关(Spearman's ρ = -0.51; p = 0.05)。因此,我们的分析确定了 CD8 T 细胞的早期累积抑制能力是自然控制的预测因素。此外,我们的模型模拟了一个庞大的虚拟群体,量化了长期控制所需的这种早期 CD8 T 细胞反应的最低能力。我们的研究对 CD8 T 细胞在艾滋病病毒感染的自然控制中的作用提出了新的定量见解,并对缓解策略产生了影响。
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
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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