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Park S, Lee G, Yoon KJ, Yoo K. Elucidating airborne bacterial communities and their potential pathogenic risks in urban metro environments. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2025; 292:117936. [PMID: 39987686 DOI: 10.1016/j.ecoenv.2025.117936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2024] [Revised: 02/10/2025] [Accepted: 02/17/2025] [Indexed: 02/25/2025]
Abstract
Metros are the predominant mode of transportation for urban residents. Because of high passenger volume and pollutant concentrations, concern is growing regarding the potential health hazards of exposure to potential pathogenic airborne bacteria in metros. However, the risks of airborne bacterial communities in metros have not been assessed. Therefore, this study was conducted to explore the airborne bacterial communities and potential pathogenic risk of bacteria in the inner metro train (IM) and metro stations (MS) in Busan, South Korea. The concentrations of culturable total airborne bacteria (CABs) and culturable total airborne Staphylococcus (CAS) were higher in the MS samples than in the IM samples. Bacterial community analysis revealed that although the overall metro environment was dominated by human-associated bacteria, such as Corynebacterium and Staphylococcus genera, the IM and MS samples exhibited significantly distinct core bacterial taxa despite their similar bacterial communities; this is a result of human activity rather than the presence of passengers. Through multilocus sequence typing (MLST), the isolated S. epidermidis from both the IM and MS samples was identified as a human pathogen with four sequence types (ST190, ST54, ST992, and ST817). Furthermore, the MLST results were significantly positively correlated with the CABs and CASs in both the IM and MS samples. The S. aureus infection pathway was predicted in all samples using PICRUSt2 and was significantly higher in the IM samples than in the MS samples. The findings of this study can serve as a reference for developing microbial public health provisions for metro systems.
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Affiliation(s)
- Sena Park
- Department of Environmental Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea; Interdisciplinary Major of Ocean Renewable Energy Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea
| | - Gihan Lee
- Department of Environmental Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea; Interdisciplinary Major of Ocean Renewable Energy Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea
| | - Keum Ju Yoon
- Department of Environmental Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea
| | - Keunje Yoo
- Department of Environmental Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea; Interdisciplinary Major of Ocean Renewable Energy Engineering, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea.
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Liu Y, Fachrul M, Inouye M, Méric G. Harnessing human microbiomes for disease prediction. Trends Microbiol 2024; 32:707-719. [PMID: 38246848 DOI: 10.1016/j.tim.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024]
Abstract
The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.
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Affiliation(s)
- Yang Liu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Muhamad Fachrul
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Human Genomics and Evolution Unit, St Vincent's Institute of Medical Research, Victoria, Australia; Melbourne Integrative Genomics, University of Melbourne, Parkville, Victoria, Australia; School of BioSciences, University of Melbourne, Parkville, Victoria, Australia
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia; Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Medical Science, Molecular Epidemiology, Uppsala University, Uppsala, Sweden; Department of Cardiovascular Research, Translation, and Implementation, La Trobe University, Melbourne, Victoria, Australia.
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