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Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics
Microbiota residing on the urban transit systems (UTSs) can be shared by travelers and have niche-specific assemblage. However, it remains unclear how the assemblages are influenced by city characteristics, rendering city-specific and microbial-aware urban planning challenging. Here, we analyzed 3,3...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101094/ https://www.ncbi.nlm.nih.gov/pubmed/36916942 http://dx.doi.org/10.1128/spectrum.00167-23 |
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author | Xiong, Guangzhou Ji, Lei Cheng, Mingyue Ning, Kang |
author_facet | Xiong, Guangzhou Ji, Lei Cheng, Mingyue Ning, Kang |
author_sort | Xiong, Guangzhou |
collection | PubMed |
description | Microbiota residing on the urban transit systems (UTSs) can be shared by travelers and have niche-specific assemblage. However, it remains unclear how the assemblages are influenced by city characteristics, rendering city-specific and microbial-aware urban planning challenging. Here, we analyzed 3,359 UTS microbial samples collected from 16 cities around the world. We found the stochastic process dominated in all UTS microbiota assemblages, with the explanation rate (R(2)) of the neutral community model (NCM) higher than 0.7. Moreover, city characteristics predominantly drove such assemblage, largely responsible for the variation in the stochasticity ratio (50.1%). Furthermore, by utilizing an artificial intelligence model, we quantified the ability of UTS microbes in discriminating between cities and found that the ability was also strongly affected by city characteristics, especially climate and continent. From these, we found that although the NCM R(2) of the New York City UTS microbiota was 0.831, the accuracy of the microbial-based city characteristic classifier was higher than 0.9. This is the first study to demonstrate the effects of city characteristics on the UTS microbiota assemblage, paving the way for city-specific and microbial-aware applications. IMPORTANCE We analyzed the urban transit system microbiota assemblage across 16 cities. The stochastic process was dominant in the urban transit system microbiota assemblage. The urban transit system microbe’s ability in discriminating between cities was quantified using transfer learning based on random forest (RF) methods. Certain urban transit system microbes were strongly affected by city characteristics. |
format | Online Article Text |
id | pubmed-10101094 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-101010942023-04-14 Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics Xiong, Guangzhou Ji, Lei Cheng, Mingyue Ning, Kang Microbiol Spectr Research Article Microbiota residing on the urban transit systems (UTSs) can be shared by travelers and have niche-specific assemblage. However, it remains unclear how the assemblages are influenced by city characteristics, rendering city-specific and microbial-aware urban planning challenging. Here, we analyzed 3,359 UTS microbial samples collected from 16 cities around the world. We found the stochastic process dominated in all UTS microbiota assemblages, with the explanation rate (R(2)) of the neutral community model (NCM) higher than 0.7. Moreover, city characteristics predominantly drove such assemblage, largely responsible for the variation in the stochasticity ratio (50.1%). Furthermore, by utilizing an artificial intelligence model, we quantified the ability of UTS microbes in discriminating between cities and found that the ability was also strongly affected by city characteristics, especially climate and continent. From these, we found that although the NCM R(2) of the New York City UTS microbiota was 0.831, the accuracy of the microbial-based city characteristic classifier was higher than 0.9. This is the first study to demonstrate the effects of city characteristics on the UTS microbiota assemblage, paving the way for city-specific and microbial-aware applications. IMPORTANCE We analyzed the urban transit system microbiota assemblage across 16 cities. The stochastic process was dominant in the urban transit system microbiota assemblage. The urban transit system microbe’s ability in discriminating between cities was quantified using transfer learning based on random forest (RF) methods. Certain urban transit system microbes were strongly affected by city characteristics. American Society for Microbiology 2023-03-14 /pmc/articles/PMC10101094/ /pubmed/36916942 http://dx.doi.org/10.1128/spectrum.00167-23 Text en Copyright © 2023 Xiong et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Xiong, Guangzhou Ji, Lei Cheng, Mingyue Ning, Kang Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics |
title | Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics |
title_full | Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics |
title_fullStr | Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics |
title_full_unstemmed | Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics |
title_short | Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics |
title_sort | niche-based microbial community assemblage in urban transit systems and the influence of city characteristics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101094/ https://www.ncbi.nlm.nih.gov/pubmed/36916942 http://dx.doi.org/10.1128/spectrum.00167-23 |
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