Signatures of Immunity on the Antigenic Diversity of Pathogens

描述（由申请人提供）：许多人类病原体显示出广泛的抗原多样性。对一种抗原变体感染的免疫力通常对另一种抗原变体的感染具有部分保护作用，导致抗原相似菌株之间竞争易感宿主。了解这种竞争背后的免疫学机制是预测菌株动态的重要一步，包括新的抗原变体是否会导致居民灭绝，以及针对某些菌株的疫苗接种如何影响非靶向的竞争菌株。菌株竞争的经典模型预测了由持久的菌株特异性免疫驱动的负频率依赖性选择。虽然有时是一个有用的近似，这些简单的模型揭示了其他已知的免疫机制如何解释相关病原体抗原多样性模式的细微差异。该项目的目标是检验不同种类的免疫力在病原体多样性中塑造群体水平模式的一般假设。第一个目的是评估先天性和基于抗体的免疫在两种常见细菌病原体肺炎链球菌（肺炎球菌）和脑膜炎奈瑟菌（脑膜炎球菌）中产生抗原多样性模式的作用。通过比较观察到的主要抗原类型的流行率与基于个体的模型的结果，评价每种免疫的各自贡献，其中异源免疫的强度、广度和持续时间根据每种免疫类型而变化。第二个目标使用相同的三个因素来推断先天免疫、细胞免疫和体液免疫对流感病毒和副流感病毒动态的贡献。将通过将常微分方程模型拟合到每种主要抗原类型（副流感病毒血清型1-3和流感病毒A型（亚型H3 N2）、A型（H1 N1）和B）病例的时间序列，直接估计这三个因子。第三个目标是探讨个体宿主之间的差异在多大程度上可能影响菌株竞争的结果。这些差异可能具有遗传起源，例如宿主的MHC I型和II型等位基因，或者来自宿主的特定感染史;初步工作表明，对特定表位的高度偏倚反应促进了抗原类型的共存。为了衡量异质性响应的影响，将使用基于个体的模型在异质性和同质性假设下比较平衡和非平衡多样性水平。这个为期三年的博士后研究培训计划将提高研究人员对传染病免疫学的理解，不同免疫机制影响宿主-病原体动力学的方式，以及不同情况下适用的建模方法。通过一系列多样化的专业培训机会，这种经验将为独立的研究职位做好准备。这些知识最终将有助于研究人员和其他科学家寻求开发竞争病原体菌株动态的预测模型。

DESCRIPTION (provided by applicant): Many human pathogens show extensive antigenic diversity. Immunity from infection with one antigenic variant is often partially protective against infection with another antigenic variant, resulting in competition between antigenically similar strains for susceptible hosts. Understanding the immunological mechanisms underlying this competition is an important step in predicting strain dynamics, including such questions as whether a new antigenic variant will drive residents extinct, and how vaccination against some strains will affect competing strains that aren't targeted. Classic models of strain competition predict negative frequency-dependent selection driven by long-lasting, strain-specific immunity. Though sometimes a useful approximation, these simple models shed little light on how other known mechanisms of immunity might explain subtle differences in patterns of antigenic diversity of related pathogens. The goal of this project is to test the general hypothesis that different kinds of immunity shape population-level patterns in pathogen diversity. The first aim is to assess the roles of innate and antibody-based immunity in generating the patterns of antigenic diversity in two common bacterial pathogens, Streptococcus pneumoniae (pneumococcus) and Neisseria meningitidis (meningococcus). The respective contributions of each kind of immunity will be evaluated by comparing the observed prevalence of major antigenic types with results from an individual-based model in which the strength, breadth, and duration of heterologous immunity are varied in accordance with each type of immunity. The second aim uses the same three factors to infer the contributions of innate, cellular, and humoral immunity to the dynamics of two viruses, influenza and parainfluenza. The three factors will be estimated directly by fitting models of ordinary differential equations to time series of cases of each major antigenic type (serotypes 1-3 of parainfluenza and type A (subtype H3N2), A (H1N1), and B of influenza). The third aim explores to what extent differences between individual hosts might influence the outcomes of strain competition. These differences might have a genetic origin, such as hosts' MHC type I and II alleles, or derive from hosts' specific infection histories; preliminary work suggests highly biased responses to particular epitopes promote coexistence of antigenic types. To measure the effects of heterogeneous responses, individual-based models will be used to compare equilibrium and nonequilibrium diversity levels under assumptions of heterogeneity and homogeneity. This three-year postdoctoral research training plan will improve the investigator's understanding of the immunology of infectious diseases, the ways in which different immune mechanisms can affect host-pathogen dynamics, and which modeling approaches are appropriate in different situations. Augmented by a diverse set of professional training opportunities, this experience will prepare the investigator for an independent research position. This knowledge will ultimately be useful to the investigator and other scientists seeking to develop predictive models of the dynamics of competing pathogen strains.