Advances in the detection of antimicrobial resistance in aquatic environments: a methodological perspective.

Abstract

Antimicrobial resistance (AMR) is a global health and environmental challenge, driven by complex interactions among microbial communities, resistance genes, and selective pressures in various ecological niches. Traditional surveillance procedures often fall short in capturing the full diversity and dynamics of resistance reservoirs in the environment. This review examines the integration of artificial intelligence (AI) and machine learning (ML) with next-generation sequencing (NGS) technologies for comprehensive resistome profiling. We discuss advances in multi-omics approaches, particularly metagenomics, microbiome-based analytics, and metatranscriptomics. We also highlight computational workflows that enable high-resolution mapping of resistance genes, their mobile genetic elements, and host associations. The role of AI/ML in resistome prediction, classification, and source tracking, as well as the incorporation of environmental metadata for contextual interpretation is discussed based on the selected literature. Moreover, we assess current challenges and propose future directions for developing standardized, scalable, and interpretable bioinformatic pipelines in AMR surveillance. This review primarily elucidates the potential of integrated AI-omics platforms to revolutionize aquatic environmental AMR monitoring and inform risk assessment and mitigation strategies.