Core C - Modeling Core

01.项目总结 流感感染会导致不同的临床结果，从良性症状到住院和 有时死亡(在美国每年从12,000人到56,000人不等)。然而，生物标志物 预测流感疾病的结果(即症状严重程度、病毒复制)，这是预防的关键 药物还没有确定。此外，对支配预测的分子成分的阐明 签名将提供重要线索，指导开发新的治疗策略。这是U19 将建立在之前的FluOMICS财团已经收集的大量数据的基础上，并寻求两个融合 假设1)多条和离散的宿主免疫反应途径协同作用，以确定致病因素 流感感染的结果2)流感和这些宿主途径之间的串扰导致 在多个组织中建立相关的表观遗传、转录、翻译后、代谢特征 和细胞类型。建模核心的主要目标将是使用基于网络的建模方法来 整合项目1和项目2产生的实验数据，确定可以 与项目1和项目2合作，预测流感疾病的结果，并在功能上进行验证。 在目标1中，建模核心与技术核心和数据管理和 生物信息学核心，将进行预处理，应用适当的统计分析和解释所有大型 在项目1和项目2中生成的(OMIC)数据集。我们将提供数据集成和可视化表示 对于给出差异表达标记的数目和身份的每个基因组数据集和汇总表 (基因/蛋白质/翻译后修饰/代谢物)与疾病结局有关。在目标2中， 建模核心将通过将所有这些数据集映射到转录的网络来提供所有这些数据集的集成视图 由不同的感染条件和信号转导途径差异触发的节点和信号转导通路 模拟主机之间的差异。重要的是，建模核心将应用启发式方法和迭代 项目1和项目2的过程，以解开和改进生物网络之间的相关性 以正交样本类型(即人/鼠血液、小鼠肺和人类细胞)收集的通路 由内核产生。在目标3中，建模核心将使用机器学习技术来生成预测 基于能够预测与各种疾病相关的流感病毒株反应的优先标记物集的模型 各州。这些标记物将包括宿主因素和宿主-病原体相互作用，它们预测临床结果和 可能与症状的严重程度有关。建模核心将在这款U19中作为终极 基础设施和资源，将集成在此计划中生成的大量数据，并提供给 科学界重要的交付成果，即经过验证的疾病严重性签名、生物标志物 预防医学和机制提示临床结果的多样性。

01. PROJECT SUMMARY Influenza infection leads to different clinical outcomes that range from benign symptoms to hospitalization and sometimes death (ranging from 12,000 to 56,000 deaths per year in the United States). However, biomarkers predictive of influenza disease outcomes (i.e. symptoms severity, viral replication) which are key for preventive medicine have not yet been identified. Moreover, elucidation of the molecular components that govern predictive signatures will provide important clues that will guide the development of novel therapeutic strategies. This U19 will build on a wealth of data gathered already by the previous FluOMICS consortium and pursue two converging hypotheses 1) multiple and discrete host immune response pathways act in concert to determine the pathogenic outcome of influenza infection 2) the crosstalk between influenza and these host pathways results in the establishment of correlated epigenetic, transcriptional, post-translational, metabolic signatures in multiple tissues and cell types. The major objective of the Modeling Core will be to use network-based modeling approaches to integrate the experimental data generated by Project 1 and Project 2, identify biological processes that can predict influenza disease outcomes and validate them functionally in collaboration with Project 1 and Project 2. In Aim 1 the Modeling Core, in collaboration with the Technology Core and the Data Management and Bioinformatics Core, will preprocess, apply the appropriate statistical analysis and interpret all large-scale (OMIC) datasets generated in Project 1 and Project 2. We will provide data integration and visual representation for each OMIC dataset and summary tables giving the number and identity of differentially expressed markers (genes/proteins/post-translational modifications/metabolites) associated with disease outcomes. In Aim 2, the Modeling Core will provide an integrated view of all these datasets by mapping them to networks of transcriptional nodes and signal transduction pathways which are differentially triggered by different conditions of infection and mimic differences between hosts. Critically, the Modeling Core will apply heuristic approaches and an iterative process with the Project 1 and Project 2 to unravel and improve on correlations between networks of biological pathways collected in orthogonal sample types (i.e. human/mouse blood, mouse lungs, and human cells) generated by the cores. In Aim 3 the Modeling Core will use a machine learning technique to generate predictive models based on prioritized set of markers able to predict responses to influenza strains linked to various disease states. These markers will include host factors and host-pathogen interactions that predict clinical outcomes and may be mechanistically implicated in symptoms severity. The Modeling Core will stand in this U19 as the ultimate infrastructure and resource that will integrate the large body of data generated in this program and provide to the scientific community important deliverables namely validated signatures of disease severity, biomarkers for preventive medicine and mechanistic cues to the diversity of clinical outcomes.!