CORE B: Computational Biology and Statistical Modeling

计算生物学和统计建模核心(核心B) (加州大学伯克利分校) 摘要 计算生物学和统计建模核心(核心B)将为 个别项目和整个方案项目(P01)，在#年的每个阶段提供统计支助 研究。关键的是，核心B将使项目能够通过应用统一 计算生物学、统计学和机器学习方法来研究自然和疫苗诱导的 登革热体液免疫和细胞免疫。在目标1中，核心B将进行自然感染的流行病学分析。 登革病毒(DENV)感染和登革疫苗队列，以告知由 项目1、2和3。对于尼加拉瓜儿童登革热队列研究，我们将与核心C密切合作，以 调查寨卡病毒传入前后的登革热发病率以及登革热病毒的变化 传播强度影响登革热的严重程度。对于宿务登革热®队列，我们将估计DENV 感染和登革热发病率按基线DENV血清状态和疫苗接种史分层以支持 项目2提出的免疫相关研究。我们还将把这两个儿科队列与 了解地理位置、DENV传播强度、寨卡病毒感染史和血清型流行率如何影响 登革热。将对来自这两个地区的所有DENV分离株进行系统发育和系统动力学分析 一群人。在目标2中，我们将单独支持每个项目，并进行跨项目分析，以确定 与预防登革热症状和严重登革热发病相关的免疫标记物 疾病。这一目标包括自然免疫和疫苗诱导的DENV免疫的免疫相关性。我们会 与每个项目合作设计病例对照研究，以测试DENV特异性血清抗体、B细胞和T细胞 细胞特性预示着不同的临床结果。核心B将分析多维数据集 由项目产生，使用简单的机器学习方法对临床结果进行分类，例如 通用的线性模型、灵活的方法，如随机森林，以及对异常值具有健壮性的方法 例如支持向量机，都具有正则化以降低模型的复杂性。在目标3中，我们将支持 研究经历过自然原发和继发登革热病毒感染的儿童的项目 确定预测维持抗-DENV免疫的免疫标志物。我们将使用回归模型来 确定感染后不久测量的抗体和辅助T细胞特征如何预测两者 交叉反应和类型特异性抗体反应的大小和持久性。然后我们将把 将预测免疫标记物引入线性和更灵活的混合效应回归模型以拟合抗体 初次和继发性DENV感染后的动态变化。将对基线进行平行分析 血清阴性和血清阳性疫苗接受者，能够直接比较免疫决定因素 自然感染DENV和接种疫苗后的寿命。我们还比较了系统血清学方法 对初诊后、初发前和初发后天然DENV感染样本进行相同的 以检测个体抗体抗原识别和Fc效应器特征的变化。总而言之，这些 三个目标对项目中提出的研究至关重要，并将朝着P01的总体目标努力 识别可提供长期保护的预测性和机械性抗DENV免疫特性 登革热。！

Computational Biology and Statistical Modeling Core (CORE B) (University of California, Berkeley) SUMMARY The Computational Biology and Statistical Modeling Core (Core B) will provide essential services to individual Projects and the Program Project (P01) as a whole by providing statistical support at each stage of research. Critically, Core B will enable the Projects to address key themes of the P01 by applying unifying computational biology, statistical, and machine learning approaches to study natural and vaccine-induced dengue humoral and cellular immunity. In Aim 1, Core B will conduct epidemiological analyses of the natural dengue virus (DENV) infection and dengue vaccine cohorts to inform the immunological studies proposed by Projects 1, 2, and 3. For the Nicaragua Pediatric Dengue Cohort Study, we will work closely with Core C to investigate dengue incidence before and after the introduction of Zika as well as how changing DENV transmission intensity affects dengue disease severity. For the Cebu Dengvaxia® cohort, we will estimate DENV infection and dengue disease incidence stratified by baseline DENV serostatus and vaccination history to support the immune correlates studies proposed by Project 2. We will also compare these two pediatric cohorts to understand how geography, DENV transmission intensity, ZIKV infection history, and serotype prevalence affect dengue disease. Phylogenetic and phylodynamic analyses will be conducted for all DENV isolates from both cohorts. In Aim 2, we will support each Project individually and conduct cross-Project analyses to identify immune markers that correlate with protection against symptomatic dengue and pathogenesis of severe dengue disease. This aim encompasses immune correlates of natural and vaccine-induced DENV immunity. We will work with each Project to design case-control studies to test how DENV-specific serum antibody, B cell, and T cell characteristics predict distinct clinical outcomes. Core B will analyze the multi-dimensional datasets produced by the Projects to classify clinical outcomes using straightforward machine learning methods such as generalized linear models, flexible approaches such as random forests, and methods that are robust to outliers such as support vector machines, all with regularization to reduce model complexity. In Aim 3, we will support the Projects in studying children who have experienced natural primary and secondary DENV infections to identify immune markers that predict maintenance anti-DENV immunity. We will use regression models to determine how antibody and helper T cell characteristics measured soon after infection predict both the magnitude and the durability of cross-reactive and type-specific antibody responses. We will then incorporate the predictive immune markers into linear and more flexible mixed-effects regression models to fit antibody dynamics following primary and secondary DENV infection. Parallel analyses will be conducted for baseline seronegative and seropositive vaccine recipients, enabling direct comparison of the determinants of immune longevity following natural DENV infection and vaccination. We also compare the systems serology measures performed on post-primary, pre-secondary, and post-secondary natural DENV infection samples in the same individuals to test for changes in antibody antigen recognition and Fc effector characteristics. Collectively, these three Aims are critical to the research proposed in the Projects and will work toward the overarching P01 goal to identify predictive and mechanistic anti-DENV immune characteristics that provide long-term protection against dengue disease.!