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 哮喘是世界范围内最常见的慢性呼吸道疾病之一。肠道中的微生物失调 肺越来越多地与哮喘的发病率和严重程度联系在一起，这表明 微生物组在哮喘发病机制中是一个决定性因素。然而，作为最有可能的联系 在肠道和肺部之间，血液微生物群在“肠道-肺轴”中的作用在哮喘中的作用仍不清楚。 由于缺乏高性价比和高通量的测序方法。事实上，它要么是 对于传统的处理微生物DNA的测序方法来说，这是不可能的，或者昂贵得令人望而却步 微量、严重降解或以宿主DNA为主的样本，例如，在人类血液中。我们 假设血液中微生物的存在与哮喘发生的风险有关， 这些微生物血液生物标记物可以通过简化的元基因组测序方法来捕获 在深度学习框架的帮助下，对哮喘进行诊断甚至早期发现。在此应用程序中，一个 简化的元基因组测序的应变解析计算流水线将被开发为轮廓 “维生素D产前哮喘缓解试验”中的血液微生物组-一项正在进行的随机双盲试验 对881名孕妇进行了盲法、安慰剂对照的临床试验，包括问卷调查和孕妇、脐带、 还有儿童血液样本。同时，将开发一个深度学习框架，以优化 应用血液微生物组数据诊断和预测哮喘模型的准确性。最后，在 新的计算管道和深度学习框架，血液微生物群在肠道中的作用- 将对肺轴和哮喘的发病机制进行研究。孙博士在分子生物学方面的训练， 生物信息学和元基因组学为这项拟议的研究做好了充分的准备。然而， 通过分析血液微生物组数据来理解哮喘的分子基础是一种 具有挑战性的任务，需要在特定领域进行进一步培训。孙博士将利用优秀的知识分子 哈佛医学院(HMS)及其教学医院布里格姆妇女医院的环境 (BWH)。他将获得BWH和HMS的大量计算资源。通过正式 课程和研讨会，并在强大的指导团队和世界级咨询的帮助下 与专业知识互补的委员会，孙先生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。孙先生将定期或必要地与他的两位导师和顾问委员会成员会面 以此为基础介绍他的进展，并得到及时的反馈和建议。总之，孙中山先生的培训和研究 计划将使他能够扩展他目前的技能集，包括应对Low挑战的能力 血液样本中微生物生物量测序，深入学习在微生物组研究中的作用 血液微生物在哮喘发病机制中的作用，最终促成精准医学对肺部疾病的研究。