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.

项目概要/摘要 哮喘是全球最常见的慢性呼吸道疾病之一。肠道微生物失调。 肺部与哮喘的发病率和严重程度越来越相关，这表明潜在的 然而，微生物组是哮喘发病机制的决定因素。 在肠道和肺部之间，血液微生物组在“肠肺轴”中对哮喘的作用仍不清楚 由于缺乏具有成本效益和高通量的测序方法而导致的发病机制确实如此。 传统测序方法处理微生物 DNA 是不可能的，或者成本过高 微量、严重降解或以宿主 DNA 为主的样本，例如人类血液中的样本。 血液中微生物的存在与哮喘发生的风险密切相关， 这些微生物血液生物标志物可以通过简化的宏基因组测序方法来捕获 借助深度学习框架诊断甚至早期检测哮喘。 将开发用于简化宏基因组测序的应变解析计算管道来分析 “维生素 D 产前哮喘减少试验”中的血液微生物组——一项正在进行的随机、双重试验 对 881 名孕妇进行的盲法、安慰剂对照临床试验，包括问卷调查和母体、脐带、 同时，将开发一个深度学习框架来优化。 最后，借助血液微生物组数据来提高哮喘诊断和预测模型的准确性。 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—— 将研究孙博士在分子生物学方面的培训， 生物信息学和宏基因组学使他为这项拟议的研究做好了充分的准备。 通过分析血液微生物组数据了解与哮喘相关的分子基础是一个 孙博士将利用其优秀的智力来完成需要在特定领域进一步培训的挑战性任务。 哈佛医学院（HMS）及其教学医院布莱根妇女医院的环境 (BWH)。他将通过正式方式获得 BWH 和 HMS 的大量计算资源。 课程和研讨会，并在强大的指导团队和世界一流的顾问的帮助下 孙博士将沉浸在一个具有互补专业知识的委员会中，专注于以下方面的培训计划： 高级编程、统计建模、深度学习、呼吸病理生理学和临床 孙博士将定期或根据需要与他的两位导师和顾问委员会成员会面。 以此为基础展示孙博士的训练和研究进展并获得及时的反馈和建议。 计划将使他能够扩展他当前的技能组合，包括解决低收入挑战的能力 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以及确定微生物的作用 血液微生物在哮喘发病机制中的作用，最终有助于肺部疾病的精准医学。