用于量化流感感染期间的先天性和适应性免疫的顺序感染实验
Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection
摘要：
实验室模型通常用于了解相关病原体通过宿主免疫的相互作用。例如，最近的实验表明，雪貂在短时间内暴露于两种流感病毒株，交叉免疫的效果如何随着暴露时间和使用的特定病毒株而变化。另一方面，研究免疫反应不同分支的运作及其相对重要性，通常使用涉及单一感染的实验。然而，从这种类型的数据推断不同免疫组分的相对重要性是具有挑战性的。使用模拟和数学建模，在这里，我们调查是否顺序感染实验设计不仅可以用来确定有助于交叉保护的免疫组分，而且还可以在单次感染期间深入了解免疫反应。我们表明，从连续感染实验的病毒学数据可以用来准确地提取交叉保护的时间和程度。此外，可以确定负责这种交叉保护的广泛免疫成分。这些数据也可用于推断某些免疫成分在控制原发性感染中的时间和强度，即使在缺乏血清学数据的情况下。相比之下，单个感染数据不能用于可靠地恢复此信息。因此，连续感染数据增强了我们对感染控制和解决机制的理解，并对先前的暴露如何影响后续感染的时间进程产生了新的见解。
英文摘要：
Laboratory models are often used to understand the interaction of related pathogens via host immunity. For example, recent experiments where ferrets were exposed to two influenza strains within a short period of time have shown how the effects of cross-immunity vary with the time between exposures and the specific strains used. On the other hand, studies of the workings of different arms of the immune response, and their relative importance, typically use experiments involving a single infection. However, inferring the relative importance of different immune components from this type of data is challenging. Using simulations and mathematical modelling, here we investigate whether the sequential infection experiment design can be used not only to determine immune components contributing to cross-protection, but also to gain insight into the immune response during a single infection. We show that virological data from sequential infection experiments can be used to accurately extract the timing and extent of cross-protection. Moreover, the broad immune components responsible for such cross-protection can be determined. Such data can also be used to infer the timing and strength of some immune components in controlling a primary infection, even in the absence of serological data. By contrast, single infection data cannot be used to reliably recover this information. Hence, sequential infection data enhances our understanding of the mechanisms underlying the control and resolution of infection, and generates new insight into how previous exposure influences the time course of a subsequent infection.
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