Revealing the role of blood microbiome in childhood asthma

揭示血液微生物组在儿童哮喘中的作用

• 批准号: 10590805
• 负责人: Zheng Sun
• 金额: $ 17.28万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10590805
• 关键词: 16S ribosomal RNA sequencing; Address; Advisory Committees; Affect; Algorithms; Archaea; Area; Asthma; Bacteria; Behavior; Benchmarking; Bioinformatics; Biological Markers; Biology; Biomass; Blood; Blood specimen; Cessation of life; Child; Childhood; Childhood Asthma; Chronic Obstructive Pulmonary Disease; Classification; Clinical; Committee Members; Controlled Clinical Trials; DNA; Data; Data Analyses; Development; Diagnosis; Double-Blind Method; Early Diagnosis; Educational workshop; Environment; Equation; Etiology; Feedback; Functional disorder; Genetic Variation; Goals; Graph; Health; High-Throughput Nucleotide Sequencing; Hospitals; Human; Incidence; Knowledge; Link; Longitudinal Studies; Longitudinal cohort; Lung; Lung diseases; Mediation; Mendelian randomization; Mentors; Metagenomics; Methods; Microbe; Microbial Taxonomy; Mission; Modeling; Molecular; Molecular Biology; Nature; Organism; Outcome; Pathogenesis; Patients; Persons; Phase; Placebo Control; Play; Pregnant Women; Prospective Studies; Public Health; Questionnaires; Randomized; Reporting; Research; Research Personnel; Resolution; Risk; Role; Sampling; Severities; Site; Statistical Models; Study models; Taxonomy; Teaching Hospitals; Techniques; Testing; Time; Training; Training Programs; United States National Institutes of Health; Validation; Vitamin D; Woman; Work; antenatal; asthma model; candidate identification; chronic respiratory disease; clinical translation; cohort; computational pipelines; computing resources; cost effective; cost efficient; deep learning; diagnostic accuracy; dysbiosis; fungus; genetic information; graph neural network; gut-lung axis; improved; interest; medical schools; metagenomic sequencing; microbial; microbial composition; microbiome; microbiome research; microorganism; novel; precision medicine; predictive modeling; prevent; respiratory; skills

Project Summary/Abstract Asthma is one of the most common chronic respiratory diseases worldwide. Microbial dysbiosis in the gut and lungs has increasingly been associated with the incidence and severity of asthma, indicating the potential of the microbiome to be a determinant factor in asthma pathogenesis. However, as the most likely connection between the gut and lungs, the role of the blood microbiome in the “gut–lung axis” is still unclear for asthma pathogenesis due to the lack of cost-effective and high-throughput sequencing methods. Indeed, it is either impossible, or prohibitively expensive, for conventional sequencing methods to handle microbial DNA samples that are in trace amounts, heavily degraded, or dominated by host DNA, e.g., in human blood. We hypothesize that the presence of microorganisms in the blood is related to the risk of asthma occurrence, and these microbial blood biomarkers can be captured by a reduced metagenomic sequencing method for diagnosis or even early detection of asthma with the help of a deep learning framework. In this application, a strain-resolved computational pipeline for the reduced metagenomic sequencing will be developed to profile the blood microbiome in the “Vitamin D Antenatal Asthma Reduction Trial” --- an ongoing randomized, double- blind, placebo-controlled clinical trial of 881 pregnant women with both questionnaires and maternal, cord, and child blood samples available. Meanwhile, a deep-learning framework will be developed to optimize the accuracy of diagnosis and prediction models for asthma using blood microbiome data. Finally, with the aid of the new computational pipeline and deep-learning framework, the role of the blood microbiome in the gut– lung axis and asthma pathogenesis will be investigated. Dr. Sun’s trainings in molecular biology, bioinformatics and metagenomics have prepared him well for this proposed research. However, understanding the molecular basis connecting asthma through the analysis of blood microbiome data is a challenging task that requires further training in specific areas. Dr. Sun will leverage the excellent intellectual environment of Harvard Medical School (HMS) and its teaching hospital Brigham and Women’s Hospital (BWH). He will have access to extensive computational resources at BWH and HMS. Through formal coursework and workshops, and with the help of a strong mentoring team and a world-class advisory committee with complementary expertise, Dr. Sun will immerse himself in a training program focusing on advanced programming, statistical modeling, deep learning, respiratory pathophysiology, and clinical translation. Dr. Sun will meet with his two mentors and advisory committee members on a regular or needed basis to present his progress and get prompt feedback and advice. Altogether, Dr. Sun’s training and research plan will enable him to expand his current skill set to include the ability to address the challenges of low microbial biomass sequencing in blood sample, deep learning in microbiome study and identifying the role of blood microbes in asthma pathogenesis, and ultimately contribute to the precision medicine of lung diseases.

项目摘要/摘要 哮喘是全球最常见的慢性呼吸系统疾病之一。肠道中的微生物营养不良 肺部与哮喘的事件和严重程度有关，表明潜力 微生物组是哮喘发病机理中的决定因素。但是，作为最有可能的连接 在肠道和肺之间，血液微生物组在“肠肺轴”中的作用仍然不清楚哮喘 由于缺乏成本效益和高通量测序方法，发病机理。确实，这是 对于处理微生物DNA的常规测序方法，不可能或昂贵 痕量中的样品，大量降解或由宿主DNA（例如人类血液中）主导。我们 假设血液中微生物的存在与发生哮喘的风险有关， 这些微生物血液生物标志物可以通过降低的元基因组测序方法来捕获 借助深度学习框架的帮助，诊断甚至早期发现哮喘。在此应用程序中 减少宏基因组测序的应变分辨计算管道将开发为曲线 “维生素D产前哮喘降低试验”中的血液微生物组---持续的随机，双重的 盲人，安慰剂对照的临床试验对881名孕妇的问卷和母体，Cord， 和儿童血液样本可用。同时，将开发一个深度学习框架以优化 使用血液微生物组数据的诊断和预测模型的准确性。最后，借助 新的计算管道和深度学习框架，血液微生物组在肠道中的作用 将研究肺轴和哮喘发病机理。 Sun博士的分子生物学培训， 生物信息学和宏基因组学为这项拟议的研究做好了很好的准备。然而， 了解通过分析血液微生物组数据连接哮喘的分子基础是一个 具有挑战性的任务，需要在特定领域进行进一步培训。 Sun博士将利用优秀的知识分子 哈佛医学院（HMS）的环境及其杨百翰教学医院和妇女医院 （BWH）。他将可以在BWH和HMS获得广泛的计算资源。通过公式 课程和讲习班，并在强大的心理团队和世界一流的咨询的帮助下 具有完善专业知识的委员会，Sun博士将沉浸在一个培训计划中，重点是 高级编程，统计建模，深度学习，呼吸病理生理学和临床 翻译。 Sun博士将与他的两位导师和咨询委员会成员会面。 依靠他的进度并获得及时的反馈和建议。总共，Sun博士的培训和研究 计划将使他能够扩大目前的技能，以包括应对低挑战的能力 血液样本中的微生物生物量测序，微生物组研究中的深度学习并确定 哮喘发病机理中的血液微生物，并最终有助于肺部疾病的精确药物。