HPMI: Host Pathogen Mapping Initiative

宿主病原体分布图倡议2.0 总体汇总 宿主-病原体地图倡议（HPMI）2.0是一个跨学科的计划，旨在提高我们的 了解宿主细胞系统和呼吸道病原体之间的相互作用，并建立在 上一次迭代的成功。最终目标是确定治疗靶点，治疗方式， 预测疾病的严重程度。HPMI 2.0将把重点放在细菌病原体结核分枝杆菌上 (Mtb)和病毒性呼吸道病原体，如SARS-CoV-2和相关变体，如omicron和delta， 以及流感病毒、副流感病毒和呼吸道合胞病毒以及其它RNA病毒。我们将使用 蛋白质组学、遗传学和结构生物学方法来研究与这些感染性疾病相关的宿主因素， 疾病相关细胞模型和患者样本中的疾病，并联合收割机将我们的数据与现有的 数据集。网络和结构建模方法将用于整合这些数据集，使其可测试 预测宿主中调节感染的蛋白质、复合物和途径以及疾病预后。 我们的建议将集中在人类样本的分析，以破译潜在的传染性网络 呼吸道疾病。为了生成病毒性呼吸道疾病的临床相关数据集，我们将使用人类 用于系统生物学分析的肺原代细胞和三维人类气道类器官（HAO）， 确定SARS-CoV-2感染患者血浆样本的总体蛋白质组，并整合我们的数据 临床数据集。为了揭示结核病易感性异质性的决定因素，我们将利用 结合全基因组序列的结核易感基因全基因组关联研究（GWAS） 在牛津大学临床研究中心的越南结核病患者队列中， （OUCRU）。我们将进一步分析来自健康人的肺泡巨噬细胞和外周血来源的巨噬细胞， 捐助者了解Mtb感染和细胞类型的具体特征的先天免疫反应的机制。 我们假设不同的结核病临床结果是由感染者的特定分子网络调节的， 巨噬细胞我们用于识别组合生物标志物以预测疾病结果的模型将是 在越南的结核病感染队列和UCSF的COVID-19患者中进行评估。最后，我们将探索潜力。 结核分枝杆菌和SARS-CoV-2之间的共同点，因为严重形式的结核病和COVID-19伴随着 加剧炎症反应和“细胞因子风暴”。 对宿主-病原体复合体的功能和机制的更好的分子理解可能揭示新的 干预的治疗策略，包括规避免疫缺陷的针对宿主的治疗策略， 目前使用抗生素或抗病毒药的药物治疗方案的局限性， 降低药效。我们的宿主-病原体细胞图谱将有助于构建可解释的深度学习系统， 预测传染病风险、疾病严重程度和 M.的危险因素结核病和SARS-CoV-2感染。

THE HOST PATHOGEN MAP INITIATIVE 2.0 OVERALL SUMMARY The Host-Pathogen Map Initiative (HPMI) 2.0 is an interdisciplinary program that aims to improve our understanding of the interactions between host cellular systems and respiratory pathogens and builds upon the success of its previous iteration. The ultimate goals are identifying therapeutic targets, treatment modalities, and predicting disease severity. HPMI 2.0 will focus its efforts on the bacterial pathogen Mycobacterium tuberculosis (Mtb) and viral respiratory pathogens such as SARS-CoV-2 and variants of concern, e.g. omicron and delta, as well as influenza, parainfluenza virus, and respiratory syncytial virus, among other RNA viruses. We will use proteomics, genetics and structural biology approaches to study the host factors relevant to these infectious diseases in disease-relevant cell models and patient samples, and combine our data with existing -omic datasets. Network and structure modeling approaches will be used to integrate these datasets to make testable predictions about proteins, complexes and pathways in the host regulating infection as well as disease prognosis. Our proposal will be centered on the profiling of human samples to decipher networks underlying infectious respiratory diseases. To generate clinically relevant datasets on viral respiratory disease, we will use human lung primary cells and three-dimensional human airway organoids (HAO) for systems biology analyses, determine the global proteome of plasma samples from SARS-CoV-2-infected patients, and integrate our data with clinical datasets. To uncover the determinants of the heterogeneity of susceptibility to TB, we will utilize genome-wide association studies (GWAS) for TB susceptibility genes combined with whole genome sequences of the infecting Mtb strain in a cohort of TB patients in Vietnam at the Oxford University Clinical Research Unit (OUCRU). We will further profile alveolar macrophages and peripheral blood derived macrophages from healthy donors to understand mechanisms of innate immune responses to Mtb infection and cell-type specific features. We hypothesize that different TB clinical outcomes are regulated by specific molecular networks in infected macrophages. Our models for the identification of combinatorial biomarkers to predict disease outcomes will be assessed on the Mtb-infected cohort in Vietnam and COVID-19 patients at UCSF. Lastly, we will explore potential commonalities between Mtb and SARS-CoV-2, as severe forms of TB and COVID-19 are accompanied by exacerbated inflammatory responses and “cytokine storms”. A better molecular understanding of the functions and mechanisms of host-pathogen complexes may reveal new therapeutic strategies for intervention, including strategies of host-directed therapies that circumvent the limitations of current drug regimens using antibiotics or antivirals where mutations in the pathogen proteins can diminish drug efficacy. Our host-pathogen cell maps will help build interpretable deep learning systems using state-of-the-art modeling approaches for prediction of infectious disease risk, disease severity and combinatorial risk factors for M. tuberculosis and SARS-CoV-2 infection in clinical settings.