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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1460508
This article is part of the Research Topic Critical Assessment of Massive Data Analysis (CAMDA) Annual Conference 2023 View all 4 articles

Antimicrobial Resistance in Diverse Urban Microbiomes: Uncovering Patterns and Predictive Markers

Provisionally accepted
Rodolfo Brizola Toscan Rodolfo Brizola Toscan 1*Piotr Stomma Piotr Stomma 2Balakrishnan Subramanian Balakrishnan Subramanian 2Wojciech Lesiński Wojciech Lesiński 2Paweł P Łabaj Paweł P Łabaj 1Witold Rudnicki Witold Rudnicki 2
  • 1 Małopolska Center of Biotechnology, Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
  • 2 Institute of Computer Science, University of Białystok, Białystok, Podlaskie, Poland

The final, formatted version of the article will be published soon.

    Antimicrobial resistance (AMR) poses a significant global health threat, exacerbated by urbanization and anthropogenic activities. This study investigates the distribution and dynamics of AMR within urban microbiomes from six major U.S. cities using metagenomic data provided by the CAMDA 2023 challenge. We employed a range of analytical tools to investigate sample resistome, virome, and mobile genetic elements (MGEs) across these urban environments. Our results demonstrate that AMR++ and Bowtie outperform other tools in detecting diverse and abundant AMR genes, with binarization of data enhancing classification performance. The analysis revealed that a portion of resistome markers is closely associated with MGEs, and their removal drastically impacts the resistome profile and the accuracy of resistome modeling. These findings highlight the importance of preserving key MGEs in resistome studies to maintain the integrity and predictive power of AMR profiling models. This study underscores the heterogeneous nature of AMR in urban settings and the critical role of MGEs, providing valuable insights for future research and public health strategies.

    Keywords: AMR, antimicrobial resistance, Feature Selection, data science, PCA, MDFs, SVD random forest, microbiome

    Received: 15 Aug 2024; Accepted: 09 Jan 2025.

    Copyright: © 2025 Brizola Toscan, Stomma, Subramanian, Lesiński, Łabaj and Rudnicki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Rodolfo Brizola Toscan, Małopolska Center of Biotechnology, Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.