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: 10732857
• 负责人: JANE C BURNS
• 金额: $ 127.5万
• 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/10732857
• 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）已演变为一种 对接触SARS-CoV-2的儿童的新威胁。MIS-C的出现是如此的新颖和快速的发展 目前没有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、多中心的联盟（CHARMS： MIS-C及其与川崎病的关系由PCRI）和英国（DIAMONDS）资助，我们将收集 支持拟定研究的临床数据和样本。首先，我们将生成转录本、蛋白质和 来自COVID-19、MIS-C和其他发热性疾病儿童的抗体数据集。接下来，我们将使用这些 设计测试的数据，以区分有进展为严重COVID-19或MIS-C风险的儿童和诊断 测试，以区分这些条件从其他原因引起的儿童发烧。继续我们既定的 与哥伦比亚大学的合作，我们将定义针对所有已知人类的抗体库。 冠状病毒，并确定如何预先存在的抗体，以其他冠状病毒可能塑造免疫 急性SARS-CoV-2感染和MIS-C的反应。头两年（R61）将建立在 组建团队，发现MIS-C和SARS-CoV-2中独特的蛋白质组和转录组模式， 感染的患者和相关的临床参数，以抗体反应冠状病毒抗原的轮廓 肽阵列这项工作将利用已经储存的来自儿童的血浆、血清和RNA样本， COVID-19、MIS-C、川崎和其他炎症性疾病。严格的Go/NoGo标准 已经建立，并将决定进展到R33阶段。最后两年（R33）将侧重于 平台开发以及多中心和两国测试验证，以诊断和预测 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。去识别临床和分子数据 将存放在RADx-rad中心，以促进数据共享。这类研究中的许多潜在障碍 已经克服：a）IRB批准的数据和样本患者招募正在进行中，B）临床 样本已经入库，c）在RNAseq，适体蛋白质组学， 和冠状病毒抗体反应，以及d）团队在以前的合作中有很好的记录， 生产力在这个多中心提案中，这些调查团队的协同专业知识提供了 为患有SARS谱系的儿童创造诊断和预后工具的独特机会- CoV-2疾病 1