Applied Systems Biology, Leibniz Institute for Natural Product Research Infection Biology, Hans Knöll Institute (HKI), Jena, Germany.
Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany.
PLoS One. 2021 Apr 1;16(4):e0249372. doi: 10.1371/journal.pone.0249372. eCollection 2021.
Computer simulations of mathematical models open up the possibility of assessing hypotheses generated by experiments on pathogen immune evasion in human whole-blood infection assays. We apply an interdisciplinary systems biology approach in which virtual infection models implemented for the dissection of specific immune mechanisms are combined with experimental studies to validate or falsify the respective hypotheses. Focusing on the assessment of mechanisms that enable pathogens to evade the immune response in the early time course of a whole-blood infection, the least-square error (LSE) as a measure for the quantitative agreement between the theoretical and experimental kinetics is combined with the Akaike information criterion (AIC) as a measure for the model quality depending on its complexity. In particular, we compare mathematical models with three different types of pathogen immune evasion as well as all their combinations: (i) spontaneous immune evasion, (ii) evasion mediated by immune cells, and (iii) pre-existence of an immune-evasive pathogen subpopulation. For example, by testing theoretical predictions in subsequent imaging experiments, we demonstrate that the simple hypothesis of having a subpopulation of pre-existing immune-evasive pathogens can be ruled out. Furthermore, in this study we extend our previous whole-blood infection assays for the two fungal pathogens Candida albicans and C. glabrata by the bacterial pathogen Staphylococcus aureus and calibrated the model predictions to the time-resolved experimental data for each pathogen. Our quantitative assessment generally reveals that models with a lower number of parameters are not only scored with better AIC values, but also exhibit lower values for the LSE. Furthermore, we describe in detail model-specific and pathogen-specific patterns in the kinetics of cell populations that may be measured in future experiments to distinguish and pinpoint the underlying immune mechanisms.
计算机模拟数学模型为评估在人类全血感染实验中病原体免疫逃逸产生的假设提供了可能性。我们应用了一种跨学科的系统生物学方法，其中为剖析特定免疫机制而实施的虚拟感染模型与实验研究相结合，以验证或证伪各自的假设。我们专注于评估使病原体能够在全血感染的早期时间过程中逃避免疫反应的机制，将最小二乘法误差 (LSE) 作为理论和实验动力学之间定量一致性的度量，与 Akaike 信息准则 (AIC) 相结合，作为依赖于模型复杂性的模型质量的度量。特别是，我们比较了三种不同类型的病原体免疫逃逸的数学模型及其所有组合：(i) 自发免疫逃逸，(ii) 免疫细胞介导的逃逸，和 (iii) 免疫逃避病原体亚群的预先存在。例如，通过在后续的成像实验中测试理论预测，我们证明了预先存在免疫逃避病原体亚群的简单假设可以被排除。此外，在这项研究中，我们通过细菌病原体金黄色葡萄球菌扩展了我们之前用于两种真菌病原体白色念珠菌和近平滑念珠菌的全血感染实验，并将模型预测校准到每个病原体的时间分辨实验数据。我们的定量评估普遍表明，参数较少的模型不仅具有更好的 AIC 值，而且 LSE 值也较低。此外，我们详细描述了细胞群体动力学中的模型特异性和病原体特异性模式，这些模式可能在未来的实验中进行测量，以区分和确定潜在的免疫机制。
