Bacterial structures and their associated ARGs in Shanghai subway air, China

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 (PM_2.5) 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.