Prediction of COPD acute exacerbation in response to air pollution using exosomal circRNA profile and Machine learning.
Prediction of COPD acute exacerbation in response to air pollution using exosomal circRNA profile and Machine learning.
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使用外泌体 circRNA 谱和机器学习预测因空气污染而导致的 COPD 急性加重
DOI：
10.1016/j.envint.2022.107469
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发表时间：
2022-10
影响因子：
11.8
通讯作者：
Chen, Rui
中科院分区：
文献类型：
--
作者：
Meng, Qingtao;Wang, Jiajia;Cui, Jian;Li, Bin;Wu, Shenshen;Yun, Jun;Aschner, Michael;Wang, Chengshuo;Zhang, Luo;Li, Xiaobo;Chen, Rui
环境细颗粒物（PM2.5）与慢性阻塞性肺疾病（COPD）加重的风险增加有关，这会显著增加COPD患者的死亡风险。识别COPD患者对环境攻击敏感的亚型是必要的。使用体外和体内PM2.5暴露模型，我们证明外泌体hsa_circ_0005045被PM2.5上调并与蛋白质货物过氧化物氧还蛋白2结合，其通过募集中性粒细胞弹性蛋白酶并触发炎症细胞原位释放肿瘤坏死因子（TNF）-α而在功能上减轻COPD的特征。使用外泌体移植和条件性circRNA敲低鼠模型验证与COPD加重相关的hsa_circ_0005045的生物学功能。通过对PM2.5的主要成分进行分类，我们发现PM2.5结合的重金属，与香烟烟雾中的成分有区别，触发了外泌体hsa_circ_0005045的升高。最后，在327名COPD患者的队列中使用机器学习模型，PM2.5监测敏感性COPD患者的特征在于相对高的hsa_circ_0005045表达、不吸烟和C组（mMRC 0-1（或CAT < 10）和过去一年中≥ 2次加重（或≥ 1次导致住院的加重））。因此，我们的研究结果表明，环境中PM2.5排放的减少提供了一种有针对性的方法，以保护非吸烟COPD患者免受空气污染相关疾病的恶化。
Ambient fine particulate matter (PM2.5) is linked to an increased risk of chronic obstructive pulmonary disease (COPD) exacerbations, which significantly increase the risk of mortality in COPD patients. Identifying the subtype of COPD patients who are sensitive to environmental aggressions is necessary. Using in vitro and in vivo PM2.5 exposure models, we demonstrate that exosomal hsa_circ_0005045 is upregulated by PM2.5 and binds to the protein cargo peroxiredoxin2, which functionally aggravates hallmarks of COPD by recruiting neutrophil elastase and triggering in situ release of tumor necrosis factor (TNF)-α by inflammatory cells. The biological function of hsa_circ_0005045 associated with aggravation of COPD is validated using exosome-transplantation and conditional circRNA-knockdown murine models. By sorting the major components of PM2.5, we find that PM2.5-bound heavy metals, which are distinguishable from the components of cigarette smoke, trigger the elevation of exosomal hsa_circ_0005045. Finally, using machine learning models in a cohort with 327 COPD patients, the PM2.5 exposure-sensitive COPD patients are characterized by relatively high hsa_circ_0005045 expression, non-smoking, and group C (mMRC 0–1 (or CAT < 10) and ≥ 2 exacerbations (or ≥ 1 exacerbation leading to hospital admission) in the past year). Thus, our results suggest that environmental reduction in PM2.5 emission provides a targeted approach to protecting non-smoking COPD patients against air pollution-related disease exacerbation.
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DOI：
10.3390/ijerph19052823
发表时间：
2022-02-28
影响因子：
--
作者：
Jung YJ;Kim EJ;Heo JY;Choi YH;Kim DJ;Ha KH
通讯作者：
Ha KH
影响因子：
5.8
作者：
Boxio R;Wartelle J;Nawrocki-Raby B;Lagrange B;Malleret L;Hirche T;Taggart C;Pacheco Y;Devouassoux G;Bentaher A
通讯作者：
Bentaher A
影响因子：
10
作者：
Atkinson RW;Kang S;Anderson HR;Mills IC;Walton HA
通讯作者：
Walton HA
影响因子：
4.8
作者：
Bayer, Simone B.;Maghzal, Ghassan;Winterbourn, Christine C.
通讯作者：
Winterbourn, Christine C.
影响因子：
37.3
作者：
Fan, Liyuan;Cao, Qiang;Li, Baosheng
通讯作者：
Li, Baosheng