Brizola Toscan Rodolfo, Lesiński Wojciech, Stomma Piotr, Subramanian Balakrishnan, Łabaj Paweł P, Rudnicki Witold R
Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland.
Faculty of Computer Science, University of Białystok, Białystok, Poland.
Front Genet. 2025 Jan 29;16:1460508. doi: 10.3389/fgene.2025.1460508. eCollection 2025.
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.
抗菌素耐药性（AMR）是一个日益引起全球健康关注的问题，由城市化和人为活动驱动。本研究调查了美国六个城市微生物群落中的AMR分布和动态，重点关注耐药基因组、病毒组和移动遗传元件（MGEs）。利用来自2023年CAMDA挑战赛的宏基因组数据，我们应用了AMR++、Bowtie、AMRFinderPlus和RGI等工具进行耐药基因组分析，并结合聚类、归一化和机器学习技术来识别预测标志物。在检测多种AMR标志物方面，AMR++和Bowtie的表现优于其他工具，二元归一化提高了分类准确性。发现MGEs在AMR传播中起关键作用，所有城市共有394个基因。去除与MGE相关的AMR基因会改变耐药基因组图谱并降低模型性能。研究结果揭示了城市微生物群落中AMR的异质性格局，特别是在纽约市，其耐药基因组多样性最高。这些结果强调了MGEs在AMR分析中的重要性，并为设计针对性策略以应对城市环境中的AMR提供了有价值的见解。
