Modeling and characterization of inter-individual variability in CD8 T cell responses in mice.

Q2 Medicine In Silico Biology Pub Date : 2021-01-01 DOI:10.3233/ISB-200205
Chloe Audebert, Daphné Laubreton, Christophe Arpin, Olivier Gandrillon, Jacqueline Marvel, Fabien Crauste
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引用次数: 1

Abstract

To develop vaccines it is mandatory yet challenging to account for inter-individual variability during immune responses. Even in laboratory mice, T cell responses of single individuals exhibit a high heterogeneity that may come from genetic backgrounds, intra-specific processes (e.g. antigen-processing and presentation) and immunization protocols.To account for inter-individual variability in CD8 T cell responses in mice, we propose a dynamical model coupled to a statistical, nonlinear mixed effects model. Average and individual dynamics during a CD8 T cell response are characterized in different immunization contexts (vaccinia virus and tumor). On one hand, we identify biological processes that generate inter-individual variability (activation rate of naive cells, the mortality rate of effector cells, and dynamics of the immunogen). On the other hand, introducing categorical covariates to analyze two different immunization regimens, we highlight the steps of the response impacted by immunogens (priming, differentiation of naive cells, expansion of effector cells and generation of memory cells). The robustness of the model is assessed by confrontation to new experimental data.Our approach allows to investigate immune responses in various immunization contexts, when measurements are scarce or missing, and contributes to a better understanding of inter-individual variability in CD8 T cell immune responses.

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小鼠CD8 T细胞反应的个体间变异的建模和表征。
为了开发疫苗,必须考虑免疫反应期间的个体间差异,但这具有挑战性。即使在实验室小鼠中,单个个体的T细胞反应也表现出高度的异质性,这可能来自遗传背景、特异性过程(例如抗原加工和呈递)和免疫方案。为了解释小鼠CD8 T细胞反应的个体间差异,我们提出了一个与统计非线性混合效应模型耦合的动态模型。CD8 T细胞反应期间的平均和个体动态在不同的免疫环境(牛痘病毒和肿瘤)中被表征。一方面,我们确定了产生个体间变异的生物过程(初始细胞的激活率、效应细胞的死亡率和免疫原的动力学)。另一方面,通过引入分类协变量分析两种不同的免疫方案,我们强调了免疫原影响应答的步骤(启动,幼稚细胞的分化,效应细胞的扩增和记忆细胞的产生)。通过与新实验数据的对比来评估模型的鲁棒性。我们的方法允许在缺乏或缺少测量的情况下研究各种免疫背景下的免疫反应,并有助于更好地理解CD8 T细胞免疫反应的个体间变异性。
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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