大气细颗粒物暴露导致慢阻肺的暴露组学与系统生物学研究

慢性阻塞性肺疾病（COPD）发病机制与吸入细颗粒物(PM2.5)相关。目前，亟需阐明PM2.5中导致COPD的主要组分及致病的毒理机制。本项目有密切相关的四部分。 (一). PM2.5组分复杂、本申请利用大气环境化学方法，开展效应导向的细颗粒物组分鉴定、甄别。在精细组分的水平上，研究PM2.5呼吸系统毒理机制和生物分子组的响应。 (二). 部署本团队开发的个体水平PM2.5暴露检测系统，开展基于个体水平暴露的环境流行病学研究。 (三). 在PM2.5精细组分和个体水平暴露的基础上，进行动物模型暴露试验和流行病定群研究，观测暴露后的肺功能变化，收集呼吸系统组织样本。 (四). 采集海量数据。运用系统生物学方法，分析整合高维度数据，揭示大气细颗粒物毒性组分诱发呼吸系统炎症，器官功能紊乱和COPD的细胞和分子机制；建立PM2.5暴露引发慢阻肺的细胞、组织、和分子水平的评价方法。

Fine particles (e.g., PM2.5) are important components in air pollution among major Chinese cities, and attracted increasing concerns regarding to their adverse impact on human health, especially the respiratory systems. Chronic obstructive pulmonary disease (COPD) is one of the four major chronic diseases defined by the World Health Organization (WHO), associated with high morbidity and mortality rate. In China, COPD affects 10% of adult population and could be closely related to air pollution exposure. However, the etiological mechanism that inhaled air fine particles damage respiratory organs and induces COPD remains unclear. The proposed project consists four inter-connect aims. (1) Ambient fine particles are a complex mixture of various components, and the toxicity is primarily governed by the composition and concentration of the composites. Previous epidemiology studies mainly focus on overall pollutant concentration or a rather coarse component dissection. In this aim, we collect ambient air sample during exposure experiments (ie, human subjects longitudinal cohort and animal models), and apply state-of-art methods to quantify the fine particle components in a great detail. By these means, we derive the granular dose-response relationship between PM2.5 exposure and adverse respiratory consequences. (2) Due to lack of adequate tools, previous environmental epidemiology studies were done at the community average level, or crudely assessed individual exposure level. Herein, we introduce an integrated solution combined portable device and cloud computing to real time monitor and record the amount of inhaled PM2.5 at individual level. In the end, we obtain accurate cumulative expose dosage and exposure pattern and relate to lung function and molecular profiles. (3) We will carry out exposure experiments on human subjects and animal models in parallel. For human, we organize an observational longitudinal study in Shanghai consists of COPD patients and healthy control exposed to ambient air. Animal models will be exposed to Shanghai ambient air, simulated air pollutant, and filtered clean air. Lung function measurement and specimen collection will be conducted at multiple time points during the experiment. (4) We will generate –omics data for airway microbiome, interleukin profile and transcriptome levels. In mouse study, airway and lung tissue will be collected after exposure experiments for –omics profiling; in human cohort study, sputum specimen will be collected at each follow-up visit for microbiome and interleukin profiling. We will also leverage existing transcriptome data cumulated by our group on 600 COPD and 800 non-COPD subjects, where whole genome transcriptome and genotype data are available. On the existing human lung study cohort, we defined the quantitative trait loci (eQTL), co-expression modules and directed networks. The genes, pathways and modules which shifted from non-COPD states to COPD states were identified, termed as COPD molecular signatures. We will apply systems biology approach to compare and overlay omics data generated in the current study to our existing COPD signature and pinpoint the genes and pathways that response to PM2.5 exposure and in turn lead to airway damage and COPD. The key driver analysis (KDA) will discover key regulatory gene/interleukin that play central role in the pathways or co-expression modules. We will also explore for sputum microbiome or interleukin biomarker that indicate immune response, airway damage and COPD progression.