Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers
Antimicrobial resistance (AMR) is a growing global health concern, driven by urbanization and anthropogenic activities. This study investigated AMR distribution and dynamics across microbiomes from six U.S. cities, focusing on resistomes, viromes, and mobile genetic elements (MGEs). Using metagenomi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1460508/full |
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| author | Rodolfo Brizola Toscan Wojciech Lesiński Piotr Stomma Balakrishnan Subramanian Paweł P. Łabaj Witold R. Rudnicki Witold R. Rudnicki |
| author_facet | Rodolfo Brizola Toscan Wojciech Lesiński Piotr Stomma Balakrishnan Subramanian Paweł P. Łabaj Witold R. Rudnicki Witold R. Rudnicki |
| author_sort | Rodolfo Brizola Toscan |
| collection | DOAJ |
| description | Antimicrobial resistance (AMR) is a growing global health concern, driven by urbanization and anthropogenic activities. This study investigated AMR distribution and dynamics across microbiomes from six U.S. cities, focusing on resistomes, viromes, and mobile genetic elements (MGEs). Using metagenomic data from the CAMDA 2023 challenge, we applied tools such as AMR++, Bowtie, AMRFinderPlus, and RGI for resistome profiling, along with clustering, normalization, and machine learning techniques to identify predictive markers. AMR++ and Bowtie outperformed other tools in detecting diverse AMR markers, with binary normalization improving classification accuracy. MGEs were found to play a critical role in AMR dissemination, with 394 genes shared across all cities. Removal of MGE-associated AMR genes altered resistome profiles and reduced model performance. The findings reveal a heterogeneous AMR landscape in urban microbiomes, particularly in New York City, which showed the highest resistome diversity. These results underscore the importance of MGEs in AMR profiling and provide valuable insights for designing targeted strategies to address AMR in urban settings. |
| format | Article |
| id | doaj-art-947fb25e20174c49ade4449794f14ce1 |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-947fb25e20174c49ade4449794f14ce12025-01-29T06:45:46ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-01-011610.3389/fgene.2025.14605081460508Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markersRodolfo Brizola Toscan0Wojciech Lesiński1Piotr Stomma2Balakrishnan Subramanian3Paweł P. Łabaj4Witold R. Rudnicki5Witold R. Rudnicki6Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, PolandFaculty of Computer Science, University of Białystok, Białystok, PolandFaculty of Computer Science, University of Białystok, Białystok, PolandComputational Center, University of Białystok, Białystok, PolandMałopolska Centre of Biotechnology, Jagiellonian University, Kraków, PolandFaculty of Computer Science, University of Białystok, Białystok, PolandComputational Center, University of Białystok, Białystok, PolandAntimicrobial resistance (AMR) is a growing global health concern, driven by urbanization and anthropogenic activities. This study investigated AMR distribution and dynamics across microbiomes from six U.S. cities, focusing on resistomes, viromes, and mobile genetic elements (MGEs). Using metagenomic data from the CAMDA 2023 challenge, we applied tools such as AMR++, Bowtie, AMRFinderPlus, and RGI for resistome profiling, along with clustering, normalization, and machine learning techniques to identify predictive markers. AMR++ and Bowtie outperformed other tools in detecting diverse AMR markers, with binary normalization improving classification accuracy. MGEs were found to play a critical role in AMR dissemination, with 394 genes shared across all cities. Removal of MGE-associated AMR genes altered resistome profiles and reduced model performance. The findings reveal a heterogeneous AMR landscape in urban microbiomes, particularly in New York City, which showed the highest resistome diversity. These results underscore the importance of MGEs in AMR profiling and provide valuable insights for designing targeted strategies to address AMR in urban settings.https://www.frontiersin.org/articles/10.3389/fgene.2025.1460508/fullantimicrobial resistance (AMR)feature selectiondata sciencePCArandom forestmicrobiome |
| spellingShingle | Rodolfo Brizola Toscan Wojciech Lesiński Piotr Stomma Balakrishnan Subramanian Paweł P. Łabaj Witold R. Rudnicki Witold R. Rudnicki Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers Frontiers in Genetics antimicrobial resistance (AMR) feature selection data science PCA random forest microbiome |
| title | Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers |
| title_full | Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers |
| title_fullStr | Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers |
| title_full_unstemmed | Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers |
| title_short | Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers |
| title_sort | antimicrobial resistance in diverse urban microbiomes uncovering patterns and predictive markers |
| topic | antimicrobial resistance (AMR) feature selection data science PCA random forest microbiome |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1460508/full |
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