Giant MagnetoResistive (GMR) Sensors for Measuring Influenza Vaccine

用于测量流感疫苗的巨磁阻 (GMR) 传感器

基本信息

批准号：
9975682
负责人：
PAUL JOSEPH UTZ
金额：
$ 84.2万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-10 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9975682
关键词：
ATAC-seq Aging B cell repertoire Big Data Bioinformatics Biological Biological Assay Biological Markers Biological Process Biology Blood specimen CEB1 Gene Cell surface Clinic Clinical Communicable Diseases Complex DNA Data Set Emerging Communicable Diseases Enrollment Evaluation Event Flow Cytometry Foundations Funding Genes Goals Hepatitis B Antibodies Human IFI6 gene Immune Immunity Immunologic Monitoring Immunology Infection Infectious Agent Influenza Influenza vaccination LY6E gene Measurement Measures Messenger RNA Microfluidics National Institute of Allergy and Infectious Disease Outcome Pathway interactions Patient-Focused Outcomes Patients Peptides Plasma Proteins RNA Recovery Research Sampling Serum Signal Transduction System Systems Biology T-Lymphocyte Technology Testing Time Transcript Translating Translations Vaccination Vaccines Validation Viral Vaccines Virus Diseases assay development base biomarker panel cohort cytokine flexibility genetic signature human subject improved outcome influenza virus vaccine influenzavirus multidisciplinary new therapeutic target pathogen predicting response predictive marker predictive signature priority pathogen response rural setting seasonal influenza sensor transcriptome sequencing vaccine response vaccine trial

项目摘要

7. Project Summary/Abstract The overarching goal of this New Aim 4 is to test the hypothesis that SARS-CoV-2 (CoV-2) causes autoimmune disease (AI) in a subset of infected patients. Our preliminary studies on 336 COVID-19 samples from 282 COVID19 patients (four COVID-19 cohorts in three geographically distinct regions) have identified autoantibodies and clinical evidence of AI. To test the hypothesis that autoantibodies develop following CoV-2 infection, we will use autoantigen arrays to identify proteins targeted by autoantibodies, some of which may cause pathogenic inflammatory responses that could mediate lung, skin, and other tissue injury, dysregulated coagulation, endothelial dysfunction, and vasculopathy. We will then test the hypothesis that autoantibodies develop through different mechanisms including molecular mimicry and generation of receptor-blocking anti-cytokine antibodies (ACA) in response to “cytokine storm”. We hypothesize that infection with CoV-2 induces 2 different outcomes: (i) the desired outcome - protective responses that neutralize CoV-2; or (ii) pathogenic responses that lead to symptomatic autoimmunity or autoinflammatory disease. Aim 4.1 will test the hypothesis that CoV-2 causes development of autoantibodies and classifiable autoimmune diseases by leveraging our custom “COVID-19 Autoantigen Array” comprising common autoantigens from diseases that affect the lung, endothelium and skin. Aim 4.2 will characterize serum antibodies specific for proteins from CoV-2 and other coronaviruses, and correlate with autoantibodies in Aim 1, by using our “COVID-19 Viral Array” capable of simultaneously quantitating antibodies against many different wild-type and mutant viral proteins and peptides. Viral responses will be correlated with clinical outcomes including development of autoantibodies, and progression to clinical autoimmunity. Aim 4.3 will test the hypothesis that CoV-2 causes autoimmunity through mechanisms including cross-reactivity (molecular mimicry) and cytokine storm which generates receptor-blocking ACA. We will use a variety of lab-based techniques to explore these mechanisms, including purification of antigen-specific IgG from serum, co-immunoprecipitation, and cross-binding assays. Together, the proposed experiments will begin to quantify the impact of CoV-2 on AI, identify which antigens and specific AI are associated with CoV-2, and contribute to our mechanistic understanding of COVID-19 pathogenesis, setting the stage for large-scale epidemiology studies to determine the extent of autoimmunity that results from CoV-2 infection, as well as longterm impacts on the health care system and economy.

7. 项目总结/摘要新目标 4 的首要目标是检验 SARS-CoV-2 (CoV-2) 导致自身免疫性疾病的假设我们对来自 282 名 COVID-19 患者（位于三个不同地理区域的四个 COVID-19 队列）的 336 个 COVID-19 样本进行了初步研究，发现了自身抗体和为了检验 CoV-2 感染后产生自身抗体的假设，我们将使用 AI 的临床证据。自身抗原阵列可识别自身抗体靶向的蛋白质，其中一些可能导致致病性炎症反应可能介导肺、皮肤和其他组织损伤、凝血失调、然后我们将检验自身抗体通过以下途径产生的假设。不同的机制，包括分子模拟和受体阻断性抗细胞因子抗体的产生（ACA）针对“细胞因子风暴”的反应，我们发现感染 CoV-2 会导致 2 种不同的结果： (i) 期望的结果——中和 CoV-2 的保护性反应，或 (ii) 导致的致病性反应；目标 4.1 将检验 CoV-2 引起的假设。利用我们定制的“COVID-19”开发自身抗体和可分类的自身免疫性疾病 “自身抗原阵列”包含来自影响肺、内皮和皮肤的疾病的常见自身抗原。目标 4.2 将表征 CoV-2 和其他冠状病毒蛋白的特异性血清抗体，以及通过使用我们能够同时进行的“COVID-19 病毒阵列”，与目标 1 中的自身抗体相关联定量针对许多不同野生型和突变型病毒蛋白和肽的抗体。将与临床结果相关，包括自身抗体的发展和临床进展目标 4.3 将检验 CoV-2 通过以下机制引起自身免疫的假设。交叉反应（分子拟态）和细胞因子风暴会产生受体阻断 ACA。各种基于实验室的技术来探索这些机制，包括从其中纯化抗原特异性 IgG 血清、免疫共沉淀和交叉结合测定将一起开始进行。量化 CoV-2 对 AI 的影响，确定哪些抗原和特定 AI 与 CoV-2 相关，以及有助于我们对 COVID-19 发病机制的机械理解，为大规模研究奠定基础流行病学研究，以确定 CoV-2 感染引起的自身免疫的程度，以及对医疗保健系统和经济的长期影响。