Quantifying Workflow Bias in Antimicrobial Resistance Gene Wastewater Surveillance via Metagenomics Workflows

Wastewater based surveillance (WBS) enables community level disease monitoring and recent initiatives have proposed using WBS for monitoring antimicrobial resistance genes (ARGs) to gain insights into circulating community resistances. Untargeted shotgun metagenomics enables an assessment of all ARGs in a sample, enabling novel gene detection and surveillance of thousands of genes simultaneously. This has great promise for monitoring as it would enable the detection of newly evolved or emergent ARGs to be detected however, understanding the impact that workflow decisions have on a resulting wastewater metagenome and resistome is critical to for an untargeted surveillance system. Here, we systematically reanalyzed 1 931 publicly available wastewater metagenomes from 89 studies to evaluate how sample processing workflows bias both taxonomic and ARG profiles. Our results demonstrate that although certain core microbiome genera and ARGs are consistently detected across studies, workflow decisions, such as concentration method, DNA extraction, and sequencing strategy, significantly impact both microbiome and resistome composition. Workflow variation was confounded with geographic location, and no included dataset employed internal standards or multi-workflow comparisons on shared samples. These findings highlight the urgent need for workflow standards and positive controls to enable robust inter-study comparisons in global AMR wastewater surveillance.