Immunologic and Predictive Features of MIS-C

MIS-C 的免疫学和预测特征

• 批准号: 10667530
• 负责人: Dusan Bogunovic
• 金额: $ 57.43万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-18 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10667530
• 关键词: 2019-nCoV; Acute; Adult; Age; Algorithms; Antibody Repertoire; Area; Autoantibodies; Biological; Biological Assay; Biological Markers; Blood; COVID-19; COVID-19 mortality; COVID-19 pandemic; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Characteristics; Child; Child Development; Childhood; Clinical; Computer Models; Critical Care; Data; Data Collection; Data Set; Development; Diagnosis; Disease; Disease Outbreaks; Functional disorder; Genetic; Genomics; Immune; Immunologics; Immunophenotyping; Individual; Infection; Inflammation; Inflammatory; Influenza A virus; Influenza B Virus; Institutional Review Boards; Life; London; Machine Learning; Maps; Measurement; Modeling; Mononuclear; Morbidity - disease rate; Mucocutaneous Lymph Node Syndrome; Multisystem Inflammatory Syndrome in Children; New York City; Outcome; Pathogenesis; Patients; Phase; Pneumonia; Prognosis; Publishing; Relative Risks; Reporting; Respiratory Disease; Risk; Risk Assessment; SARS coronavirus; SARS-CoV-2 antibody; SARS-CoV-2 infection; SARS-CoV-2 positive; Sampling; Serology; Shock; Site; Spain; Surface; Symptoms; Syndrome; Technology; Time; Virus; World Health Organization; biomarker identification; clinical center; cohort; comparison control; coronavirus disease; disease diagnosis; disease prognosis; disorder risk; early childhood; exome sequencing; follow-up; improved; infection rate; long-term sequelae; machine learning algorithm; machine learning method; novel; pathogen; pediatric patients; post SARS-CoV-2 infection; recruit; risk prediction; risk stratification; risk variant; single-cell RNA sequencing

The novel SARS coronavirus (SARS-CoV-2) causes the severe pneumonia-like coronavirus disease (COVID-19). SARS-CoV-2 infected over 170 million individuals and has claimed over 3.5 million lives worldwide to date. If otherwise healthy, children were thought to be largely spared from SARS-CoV-2 disease. However, in areas of high SARS-CoV-2 infection rates, some children started presenting to pediatric critical care units 4-6 weeks following SARS-CoV- 2 infection with Kawasaki-like disease. Clinically, we now know that this is a distinct disease, which was recently termed - multisystem inflammatory syndrome in children (MIS-C). While the characteristic clinical features of MIS-C are becoming clear, the pathophysiology remains unknown. Here we propose to evaluate three independent cohorts of MIS-C during acute and convalescent phases of disease at clinical, genetic and immunologic levels using the latest technology. We will not only perform systemic immunological mapping of MIS-C as compared to controls, but also utilize machine learning algorithms to delineate how best to predict, diagnose and outcome stratify MIS-C. We anticipate discovering immunologic and genetic features which can aid us in assessing risks of MIS-C development, diagnosis and prognosis. In summary, our systematic analysis and computational modeling of the clinical and immune features of MIS-C will not only help illuminate the pathogenesis of this syndrome, but will also provide us with actionable biomarkers for disease risk, diagnosis and progression.

新型 SARS 冠状病毒（SARS-CoV-2）引起严重的肺炎样冠状病毒 疾病（COVID-19）。 SARS-CoV-2 感染了超过 1.7 亿人，并已导致超过 迄今为止，全世界有 350 万人生活在其中。如果其他方面健康的话，儿童被认为在很大程度上是 免受 SARS-CoV-2 疾病的侵害。然而，在 SARS-CoV-2 感染率高的地区， 一些儿童在 SARS-CoV 感染后 4-6 周开始到儿科重症监护室就诊 2.感染川崎病样病。在临床上，我们现在知道这是一种独特的疾病， 最近被称为儿童多系统炎症综合征（MIS-C）。虽然 MIS-C 的典型临床特征正在变得清晰，但病理生理学仍然存在 未知。在这里，我们建议评估急性和急性期 MIS-C 的三个独立队列。 使用最新的临床、遗传和免疫水平的疾病恢复期 技术。我们不仅会进行 MIS-C 的系统免疫图谱分析， 控制，还利用机器学习算法来描述如何最好地预测、诊断 和结果分层 MIS-C。我们期望发现免疫学和遗传特征 可以帮助我们评估 MIS-C 发生、诊断和预后的风险。综上所述，我们的 MIS-C 临床和免疫特征的系统分析和计算建模 不仅有助于阐明该综合征的发病机制，而且还将为我们提供 用于疾病风险、诊断和进展的可行生物标志物。