# Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers

## TL;DR

This study explores how antimicrobial resistance spreads in urban areas by analyzing microbiomes from six U.S. cities.

## Contribution

The study introduces a novel approach combining metagenomic data and machine learning to identify predictive markers of antimicrobial resistance.

## Key findings

- AMR++ and Bowtie showed superior performance in detecting diverse AMR markers.
- Mobile genetic elements play a critical role in AMR dissemination across cities.
- New York City exhibited the highest resistome diversity among the studied cities.

## Abstract

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.