Lijun Zhang , Xiaojing Li , Yongping Liu , Yi Zheng , Lisha Shi , Yichen Ding , Jian Chen , Ping Xiao
{"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}
引用次数: 0
Abstract
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.