Lijun Zhang , Xiaojing Li , Yongping Liu , Yi Zheng , Lisha Shi , Yichen Ding , Jian Chen , Ping Xiao
{"title":"中国上海地铁空气中的细菌结构及其相关 ARGs","authors":"Lijun Zhang , Xiaojing Li , Yongping Liu , Yi Zheng , Lisha Shi , Yichen Ding , Jian Chen , Ping Xiao","doi":"10.1016/j.jaerosci.2024.106383","DOIUrl":null,"url":null,"abstract":"<div><p>The air in the metro or subway system, which is a major form of public transportation in many metropolises, contributes to the transmission of pathogenic microorganisms. In this study, 18 aerosol samples were collected from two typical Shanghai subway stations (A and B) in the summer, transition, and winter seasons. Bacterial communities and their associated antibiotic resistance genes (ARGs) were analyzed using shotgun metagenomic sequencing. Metagenomic analysis approaches and random forest classification were used to compare and screen the distribution of key target species and ARGs. Bacteria were the predominant microbial kingdom with a relative abundance of 88.28%. In total, 5303 bacterial species were identified in subway stations A and B. The top three abundant bacterial species were <em>unclassified_Pseudomonas</em>, <em>Ewingella_americana</em>, and <em>Halalkalicoccus_subterraneus.</em> Microbial diversity analysis revealed that the microbial communities significantly varied between the three seasons (<em>P</em> < 0.05). Additionally, factors, such as temperature, relative humidity, and fine particulate matter (PM<sub>2.5</sub>) significantly influenced bacterial community structure (<em>P</em> < 0.05). The random forest algorithm was used to screen indicators in bacterial communities. Some of these bacterial communities, which were primarily derived from environmental sources, may pose health risks. In total, 312 ARGs subtypes related to 20 ARGs classes were identified in subway stations A and B. Random forest classification results revealed 20 indicative types of ARGs, including those involved in <em>metabolizing aminoglycoside, beta-lactam, multidrug,</em> and <em>rifamycin-type antibiotics</em>. This study provides novel insights into microbial communities and ARGs in typical subway micro-environments and their dissemination in subway environments.</p></div>","PeriodicalId":14880,"journal":{"name":"Journal of Aerosol Science","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0021850224000508/pdfft?md5=ee887872a65a52962e6861e5e13407ce&pid=1-s2.0-S0021850224000508-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Bacterial structures and their associated ARGs in Shanghai subway air, China\",\"authors\":\"Lijun Zhang , Xiaojing Li , Yongping Liu , Yi Zheng , Lisha Shi , Yichen Ding , Jian Chen , Ping Xiao\",\"doi\":\"10.1016/j.jaerosci.2024.106383\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The air in the metro or subway system, which is a major form of public transportation in many metropolises, contributes to the transmission of pathogenic microorganisms. In this study, 18 aerosol samples were collected from two typical Shanghai subway stations (A and B) in the summer, transition, and winter seasons. Bacterial communities and their associated antibiotic resistance genes (ARGs) were analyzed using shotgun metagenomic sequencing. Metagenomic analysis approaches and random forest classification were used to compare and screen the distribution of key target species and ARGs. Bacteria were the predominant microbial kingdom with a relative abundance of 88.28%. In total, 5303 bacterial species were identified in subway stations A and B. The top three abundant bacterial species were <em>unclassified_Pseudomonas</em>, <em>Ewingella_americana</em>, and <em>Halalkalicoccus_subterraneus.</em> Microbial diversity analysis revealed that the microbial communities significantly varied between the three seasons (<em>P</em> < 0.05). Additionally, factors, such as temperature, relative humidity, and fine particulate matter (PM<sub>2.5</sub>) significantly influenced bacterial community structure (<em>P</em> < 0.05). The random forest algorithm was used to screen indicators in bacterial communities. Some of these bacterial communities, which were primarily derived from environmental sources, may pose health risks. In total, 312 ARGs subtypes related to 20 ARGs classes were identified in subway stations A and B. Random forest classification results revealed 20 indicative types of ARGs, including those involved in <em>metabolizing aminoglycoside, beta-lactam, multidrug,</em> and <em>rifamycin-type antibiotics</em>. This study provides novel insights into microbial communities and ARGs in typical subway micro-environments and their dissemination in subway environments.</p></div>\",\"PeriodicalId\":14880,\"journal\":{\"name\":\"Journal of Aerosol Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0021850224000508/pdfft?md5=ee887872a65a52962e6861e5e13407ce&pid=1-s2.0-S0021850224000508-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Aerosol Science\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0021850224000508\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Aerosol Science","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0021850224000508","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
摘要
地铁或地铁系统是许多大都市的主要公共交通方式,地铁或地铁系统中的空气会造成病原微生物的传播。本研究从上海两个典型的地铁站(A 站和 B 站)采集了 18 份气溶胶样本,分别取自夏季、过渡季和冬季。采用枪式元基因组测序分析了细菌群落及其相关抗生素耐药基因(ARGs)。元基因组分析方法和随机森林分类法用于比较和筛选关键目标物种和 ARGs 的分布。细菌是最主要的微生物王国,相对丰度为 88.28%。在地铁站 A 和 B 中,共鉴定出 5303 个细菌物种,其中数量最多的前三个细菌物种是未分类的假单胞菌、美洲鞘氨醇杆菌和次鞘氨醇杆菌。微生物多样性分析表明,微生物群落在三个季节之间存在显著差异(P < 0.05)。此外,温度、相对湿度和细颗粒物(PM2.5)等因素也对细菌群落结构有明显影响(P < 0.05)。随机森林算法用于筛选细菌群落中的指标。其中一些细菌群落主要来源于环境,可能会对健康造成危害。随机森林分类结果显示了20种具有指示性的ARGs类型,包括参与代谢氨基糖苷类、β-内酰胺类、多药类和利福平类抗生素的ARGs。这项研究为了解典型地铁微环境中的微生物群落和 ARGs 及其在地铁环境中的传播提供了新的视角。
Bacterial structures and their associated ARGs in Shanghai subway air, China
The air in the metro or subway system, which is a major form of public transportation in many metropolises, contributes to the transmission of pathogenic microorganisms. In this study, 18 aerosol samples were collected from two typical Shanghai subway stations (A and B) in the summer, transition, and winter seasons. Bacterial communities and their associated antibiotic resistance genes (ARGs) were analyzed using shotgun metagenomic sequencing. Metagenomic analysis approaches and random forest classification were used to compare and screen the distribution of key target species and ARGs. Bacteria were the predominant microbial kingdom with a relative abundance of 88.28%. In total, 5303 bacterial species were identified in subway stations A and B. The top three abundant bacterial species were unclassified_Pseudomonas, Ewingella_americana, and Halalkalicoccus_subterraneus. Microbial diversity analysis revealed that the microbial communities significantly varied between the three seasons (P < 0.05). Additionally, factors, such as temperature, relative humidity, and fine particulate matter (PM2.5) significantly influenced bacterial community structure (P < 0.05). The random forest algorithm was used to screen indicators in bacterial communities. Some of these bacterial communities, which were primarily derived from environmental sources, may pose health risks. In total, 312 ARGs subtypes related to 20 ARGs classes were identified in subway stations A and B. Random forest classification results revealed 20 indicative types of ARGs, including those involved in metabolizing aminoglycoside, beta-lactam, multidrug, and rifamycin-type antibiotics. This study provides novel insights into microbial communities and ARGs in typical subway micro-environments and their dissemination in subway environments.
期刊介绍:
Founded in 1970, the Journal of Aerosol Science considers itself the prime vehicle for the publication of original work as well as reviews related to fundamental and applied aerosol research, as well as aerosol instrumentation. Its content is directed at scientists working in engineering disciplines, as well as physics, chemistry, and environmental sciences.
The editors welcome submissions of papers describing recent experimental, numerical, and theoretical research related to the following topics:
1. Fundamental Aerosol Science.
2. Applied Aerosol Science.
3. Instrumentation & Measurement Methods.