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.

项目总结/文摘