Diagnosing and predicting risk in children with SARS-CoV-2- related illness

诊断和预测患有 SARS-CoV-2 相关疾病的儿童的风险

• 批准号: 10847801
• 负责人: JANE C BURNS
• 金额: $ 123.82万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10847801
• 关键词: 2019-nCoV; Acute; Address; Adult; Age Months; Antibodies; Antibody Repertoire; Antibody Response; Antibody-Dependent Enhancement; Antigens; Aptamer Technology; Bioinformatics; Biology; Burn injury; COVID-19; COVID-19 pandemic; COVID-19 patient; California; Cardiogenic Shock; Case Study; Child; Childhood; Clinical; Clinical Data; Clinical Research; Collaborations; Communicable Diseases; Coronavirus; Country; Coupled; Critical Illness; Data; Data Coordinating Center; Data Set; Deposition; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Disease Progression; Fever; Funding; Gene Expression Profiling; Immune response; Immunity; Industrialization; Infant; Infection; Inflammatory; Institutional Review Boards; Lateral; Link; Liquid Chromatography; London; Measures; Metadata; Molecular; Morbidity - disease rate; Mucocutaneous Lymph Node Syndrome; Multisystem Inflammatory Syndrome in Children; Pathogenesis; Patient Recruitments; Patients; Pattern; Peptides; Performance; Phase; Plasma; Productivity; Proteins; Proteomics; RADx Radical; RNA; Research; Resources; SARS-CoV-2 antibody; SARS-CoV-2 exposure; SARS-CoV-2 infection; Sampling; Serum; Services; Severities; Severity of illness; Shapes; Site; System; Testing; Transcript; Universities; Validation; Whole Blood; Work; aptamer; clinical predictors; cohort; college; data sharing; diagnostic signature; diagnostic tool; human coronavirus; inflammatory marker; machine learning method; pediatrician; post SARS-CoV-2 infection; predictive signature; prognostic tool; programs; progression risk; prospective; prototype; rapid diagnosis; response; risk prediction; severe COVID-19; tandem mass spectrometry; testing services; tool; transcriptome sequencing; transcriptomics; two-dimensional; vaccine development

In the wake of COVID-19 pandemic, Multisystem Inflammatory Syndrome in Children (MIS-C) has evolved as a new threat to children exposed to SARS-CoV-2. The emergence of MIS-C is so new and so rapidly evolving that there are currently no diagnostic tests to identify these patients nor are there tools to predict disease progression. Through established, funded, multi-center consortia in the U.S. (CHARMS: Characterization of MIS-C and its Relationship to Kawasaki Disease funded by PCORI) and the UK (DIAMONDS), we will collect clinical data and samples to support the proposed studies. First, we will generate transcript, protein and antibody datasets from children with COVID-19, MIS-C, and with other febrile illnesses. Next, we will use these data to devise tests to distinguish children at risk of progression to severe COVID-19 or MIS-C and diagnostic tests to distinguish these conditions from other causes of fever in children. Continuing our established collaboration with Columbia University, we will define the antibody repertoire against all known human coronaviruses and determine how pre-existing antibody to other coronaviruses may shape the immune response in acute SARS-CoV-2 infection and MIS-C. The first two years (R61) will build on the expertise of the assembled teams to discover unique proteomic and transcriptomic patterns in MIS-C and SARS-CoV-2- infected patients and relate clinical parameters to the antibody response to coronaviral antigens profiled on peptide arrays. This work will leverage already banked plasma, serum, and RNA samples from children with COVID-19, MIS-C, Kawasaki disease and other inflammatory conditions. Rigorous Go/NoGo criteria have been established and will determine progression to the R33 phase. The final two years (R33) will focus on platform development and multicenter and bi-national test validation to diagnose and predict severity in children with SARS-CoV-2 infection or MIS-C based on aptamer technology, lateral-flow protein detection, point-of-service RNA or antibody profiling with commercial partners. De-identified clinical and molecular data will be deposited in the RADx-rad hub to facilitate data sharing. Many potential hurdles in this type of research have already been overcome: a) IRB-approved patient recruitment for data and samples is on-going, b) clinical samples have been banked, c) strong preliminary data has been generated on RNAseq, aptamer proteomics, and coronaviral antibody responses, and d) the teams have a strong track record of previous collaboration and productivity. The synergistic expertise of these investigative teams in this multi-center proposal provides a unique opportunity to create diagnostic and prognostic tools for children suffering from the spectrum of SARS- CoV-2 illnesses. 1

在COVID-19大流行之后，儿童多系统炎症综合征（MIS-C）已演变为 对暴露于SARS-COV-2的儿童的新威胁。 MIS-C的出现是如此新，并且迅速发展 目前尚无诊断测试来识别这些患者，也没有预测疾病的工具 进展。通过在美国建立的，资助的多中心财团（魅力：特征 MIS-C及其与PCORI资助的川崎病的关系）和英国（钻石），我们将收集 临床数据和样本以支持拟议的研究。首先，我们将生成成绩单，蛋白质和 来自COVID-19，MIS-C和其他发热疾病的儿童的抗体数据集。接下来，我们将使用这些 数据以设计测试，以区分有进展为严重covid-19或mis-c和诊断的儿童 测试将这些疾病与儿童发烧的其他原因区分开。继续我们的建立 与哥伦比亚大学合作，我们将定义针对所有已知人类的抗体库 冠状病毒并确定对其他冠状病毒的预先存在的抗体如何形成免疫 急性SARS-COV-2感染和MIS-C的反应。最初两年（R61）将建立在专业知识的基础上 组装的团队在MIS-C和SARS-COV-2-中发现独特的蛋白质组学和转录模式 受感染的患者并将临床参数与对冠状病毒抗原的抗体反应有关 肽阵列。这项工作将利用已经存放了血浆，血清和RNA样品的儿童 COVID-19，MIS-C，川崎疾病和其他炎症状况。严格的GO/NOGO标准 已建立，并将确定到R33阶段的进展。最后两年（R33）将重点关注 平台开发以及多中心和双国测试验证，以诊断和预测严重程度 SARS-COV-2感染或MIS-C的儿童基于适体技术，侧流蛋白检测， 与商业合作伙伴进行服务点RNA或抗体分析。去识别的临床和分子数据 将存放在RADX-RAD中心中，以促进数据共享。这种研究中的许多潜在障碍 已经克服了：a）IRB批准的数据和样本的招募正在进行中，b）临床 样本已被存储，c）在适体蛋白质组学上生成了强大的初步数据， 以及冠状动脉抗体的反应，d）团队在以前的合作和 生产率。这些调查团队在此多中心提案中的协同专业知识提供了 为患有SARS范围的儿童创建诊断和预后工具的独特机会 - COV-2疾病。 1