Systems Immunology of COVID-19

COVID-19 的系统免疫学

• 批准号: 10272262
• 负责人: John Tsang
• 金额: $ 91.81万
• 依托单位: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10272262
• 关键词: 2019-nCoV; ATAC-seq; Affect; Antigen Presentation; Atlases; B-Lymphocytes; CD8-Positive T-Lymphocytes; COVID-19; Cell surface; Cells; Clinical; Clinical Trials; Complement; Computer Models; Convalescence; Data; Data Set; Defect; Dendritic Cells; Disease; Disease model; Drug Targeting; Epigenetic Process; Genetic; Genetic Transcription; Goals; Immune; Immune response; Immune system; Immunology; Infection; Inflammatory; Interferons; Lymphocyte Activation; Machine Learning; Measurement; Measures; Mediating; Membrane Proteins; Methods; Middle East Respiratory Syndrome; Molecular; Patients; Peripheral Blood Mononuclear Cell; Pregnancy; Proteins; Proteome; Proteomics; Receptors, Antigen, B-Cell; Research Project Grants; Respiratory Tract Infections; Risk; Role; SARS coronavirus; Sampling; Serologic tests; Severities; Severity of illness; Stains; Symptoms; System; T-Cell Receptor; T-cell receptor repertoire; Testing; Therapeutic; Therapeutic Intervention; Vaccines; Whole Blood; cohort; epigenome; exhaustion; experimental study; flu; monocyte; multimodal data; neutrophil; protein profiling; remdesivir; response; targeted treatment; transcriptome; transcriptome sequencing

Our goal is to integrate computational approaches and several cutting-edge high-throughput methods to assess changes in the epigenome, transcriptome, and proteome throughout the course of COVID-19 disease. Using CITE-seq, we will simultaneously interrogate the immune cell surface proteome, transcriptome, and B-cell receptor (BCR)/T-cell receptor (TCR) repertoire at the single cell level of peripheral blood mononuclear cells (PBMCs) over the course of infection and convalescence. To complement this experiment, whole-blood RNA-seq will provide information on the transcriptome in neutrophils in addition to PBMCs. To understand the epigenetic and transcriptional network underpinnings of these cellular responses, single cell ATAC-seq will be used and integrated. High-parameter proteomics using a Somalogic panel measuring approximately 5,000 proteins will be used to deeply profile the circulating proteome. In addition, similar approaches will be used to assess the molecular and cellular responses of the immune system over the course of clinical trials to test experimental vaccines and therapeutics including remdesivir. We will also examine if pregnancy affects response to SAR-CoV-2 infection. We will develop and use machine learning and computational modeling approaches to integrate such multi-modal data to better understand the immune systems role in disease and protection. They hope to find predictors of infection response severity, which may help pre-identify patients with increased risk of severe symptoms. Elucidating the molecular and cellular networks that orchestrate the immune response to infection could also serve to identify targets for therapeutic intervention. Furthermore, comparing the new data on COVID-19 to existing data on other respiratory infections such as flu, SARS-CoV, or MERS as well as other immune-mediated diseases may pinpoint reusable drug targets. All results will be integrated with the data from the other Immune Response to COVID-19 research projects, such as host genetics, serology, deep immune repertoires, and clinical measurements. Progress and highlights from April to August, 2020: 1) We have refined and tested new surface protein staining panels for CITE-seq and have applied them to generate a single cell atlas data set (two panels measuring 120+ and 190+ proteins) for a longitudinal hospitalized COVID-19 cohort and matching healthy controls. In the same single cells, TCR/BCR data were also generated. Separately, scATAC-seq data was generated from the same samples. In the same set of patients but a separate set of healthy controls, we also generated circulating protein profiles covering close to 5000 proteins. 2) We have integrate the single cell data with circulating protein data to model disease severity. We found multiple inflammatory defects in innate immune cells such as monocytes and also antigen presentation defects in both dendritic cells and B cells. 3) We detected robust IFN signatures but they tend to be downregulated in more severe patients. 4) we detected robust lymphocyte activation and expansion and no clear signs of CD8+ T cell exhaustion.

我们的目标是整合计算方法和几种尖端的高通量方法，以评估整个COVID-19疾病过程中表观基因组，转录组和蛋白质组的变化。使用Cite-seq，我们将同时询问外周血单核细胞（PBMC）的单细胞水平，在感染和康复过程中，在单细胞单核细胞（PBMC）的单细胞水平上询问免疫细胞表面蛋白质组，转录组和B细胞受体（BCR）/T细胞受体（TCR）曲目。为了补充该实验，全血RNA-Seq除PBMC以外还将提供有关中性粒细胞中转录组的信息。为了了解这些细胞反应的表观遗传和转录网络的基础，将使用和集成单细胞ATAC-SEQ。使用测量约5,000种蛋白质的somalogic面板的高参数蛋白质组学将用于深层介绍循环蛋白质组。此外，在临床试验过程中，将使用类似的方法来评估免疫系统的分子和细胞反应，以测试包括Remdesivir在内的实验疫苗和治疗剂。我们还将检查怀孕是否影响对SAR-COV-2感染的反应。我们将开发和使用机器学习和计算建模方法来整合此类多模式数据，以更好地了解疾病和保护中的免疫系统作用。他们希望找到感染反应严重程度的预测指标，这可能有助于识别患有严重症状风险增加的患者。阐明协调对感染免疫反应的分子和细胞网络也可能有助于确定治疗性干预的靶标。此外，将COVID-19的新数据与其他呼吸道感染（如流感，SARS-COV或MERS以及其他免疫介导的疾病）进行比较可能会指出可重复使用的药物靶标。所有结果将与来自其他免疫反应的数据集成到COVID-19研究项目，例如宿主遗传学，血清学，深层免疫曲目和临床测量结果。 从2020年4月到8月的进度和亮点： 1）我们已经对Cite-Seq进行了完善并测试了新的表面蛋白染色面板，并将其应用于单个细胞地图集数据集（两个尺寸为120+和190+蛋白的面板），用于纵向住院的Covid-19同加群，并与健康对照匹配。在同一单个单元格中，还会生成TCR/BCR数据。另外，从相同的样品中生成了scatac-seq数据。在同一组患者中，但一组单独的健康对照组中，我们还产生了覆盖近5000种蛋白质的循环蛋白谱。 2）我们将单细胞数据与循环蛋白数据集成在一起，以模拟疾病的严重程度。我们发现了先天免疫细胞中的多种炎症缺陷，例如单核细胞以及树突状细胞和B细胞中的抗原表现缺陷。 3）我们检测到了强大的IFN特征，但在更严重的患者中往往会下调。 4）我们检测到了强大的淋巴细胞激活和膨胀，并且没有CD8+ T细胞耗尽的明显迹象。