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May 18, 2026The Lancet Microbe1 citationsOpen Access

Antimicrobial resistance surveillance through wastewater: methodological considerations for metagenomic approaches and public health perspectives

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NHNaomi HughesSSSanchutha SathiananthamoorthyCSChrysi Sergaki

Key Points

  • This work aims to discuss the implications of varying workflows in metagenomic sequencings for antimicrobial resistance (AMR) surveillance.
  • Summarised sequencing workflow considerations for metagenomic study design.
  • Reviewed the limitations of phenotypic and genotypic methods for AMR detection.
  • Reflected on the need for standardization in AMR data generation.
  • Emphasized the importance of workflow standardization to improve data comparability and reproducibility.
  • Identified overlooked mechanisms of AMR resistance that complicate environment burden assessments.
  • Highlighted the direct impact of workflow variations on the interpretation of sequencing results.

Abstract

Antimicrobial resistance (AMR) is a recognised global threat with substantial predicted impact on lives, agriculture, and the economy. Metagenomic sequencing is being increasingly used for AMR surveillance and detection, given its capacity for community-level AMR profiling with high-level resolution. This technology has seen an explosion of surveillance efforts and data generation; however, the variation between workflows has direct implications on the sequencing results and their interpretation. In this Personal View, we summarise aspects of the sequencing workflow that need to be considered during metagenomic study design, for meaningful and reliable population-based surveillance. We reflect on the vital role of standardisation for capturing the ground truth of AMR and data comparability and reproducibility, and in addition, review the limitations of the various phenotypic and genotypic methods of AMR detection. We further highlight complex mechanisms of resistance to antimicrobials that could hinder our ability to confidently assess the true AMR burden in the environment and those that are often overlooked during surveillance.

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Cite This Study

Hughes et al. (2026) studied this question.

synapsesocial.com/papers/6a0aabf55ba8ef6d83b6f85bhttps://doi.org/10.1016/j.lanmic.2026.101400
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