A flowing database: Harnessing sewage-based surveillance for antimicrobial resistance

Antimicrobial resistance poses a major challenge to modern medicine and jeopardizes the ability to maintain a robust global public health response against persistent infectious disease threats. A 2024 Lancet series on antimicrobial resistance estimated that bacterial antimicrobial resistance was associated with 4.71 million deaths globally in 2021, including 1.14 million deaths directly attributable to resistant infections. If not actively addressed, antimicrobial resistance might result in an annual reduction of the global gross domestic product by $3.4 trillion and push an additional 24 million individuals into extreme poverty within the next decade. Antibiotic resistance genes (ARGs) are the key culprits behind antimicrobial resistance, widely disseminating among bacterial populations, particularly in medical, agricultural, and veterinary sectors. Furthermore, anthropogenic activities, such as wastewater discharge, trade, tourism, and chemical contamination, have reshaped microbial biogeography, creating an unforeseen global platform for ARG mobilization. This exacerbates the existing antimicrobial resistance crisis and enhances risks to global public health and environmental stability.

Conventional wastewater surveillance systems typically involve monitoring community drug use; tracking the consumption of substances such as nicotine, caffeine, and alcohol; and measuring exposure to environmental contaminants. Mounting evidence indicates that wastewater may serve as an optimal environment for epidemiological surveillance. Sewage-based surveillance is an emerging field that systematically examines the presence of infection biomarkers, such as pathogen/virus DNA or RNA shed by infected individuals, in both treated and untreated municipal wastewater using molecular and genomic tools. Hong Kong's city-wide sewage-based surveillance program effectively guided COVID-19 responses by tracking wastewater virus levels in real time. The global spread of the SARS-CoV-2 infection has propelled sewage-based epidemiology as a potent surveillance tool in public health, offering early detection of infectious disease outbreaks within community populations sharing a common sewer system or drainage area; yet, surveillance of ARGs is frequently still in its fledging stages.

Current ARG surveillance relies on the passive reporting of laboratory results for specific pathogens isolated from humans or contaminated environments, incurring substantial time and economic costs. In contrast, sewage-based surveillance aggregates data from various sewer sheds to reflect the collective health status of a community (Figure 1). A strategically designed and comprehensive sewage-based surveillance system has the potential to establish a genomic framework for comparing baseline levels of antimicrobial resistance within a community, identifying concerning trends for prompt intervention, and predicting major developments at local, regional, and national scales. Compared with metagenomics and shotgun sequencing, the surveillance necessitates the use of highly sensitive techniques capable of comprehensively assessing ARG prevalence and abundance, even at low levels undetectable by current metagenomics methodologies. In this context, digital polymerase chain reaction (PCR) and highly parallel quantitative PCR offer high sensitivity and specificity, enabling accurate quantification of nucleic acids even at low levels, and both can be applied in various fields like genomic, clinical diagnosis, etc. The superior accuracy of advanced PCR methods justifies their cost for surveillance, with improving affordability and significant long-term public health returns offsetting initial investments. Though ARGs are environmentally ubiquitous, sewage monitoring of low-level ARGs provides vital public health intelligence by tracking community resistance patterns and emerging threats, enabling proactive interventions. Digital PCR excels in absolute quantification without the need for standard curves and offers high sensitivity for detecting rare targets; however, it has higher costs and lower throughput compared to a highly parallel qPCR. In contrast, highly parallel qPCR analyzes up to 384 primer sets simultaneously in nanoliter volumes, reducing per-sample costs and labor for large-scale studies. Pärnänen et al. conducted a groundbreaking trans-European survey on antimicrobial resistance using the highly parallel qPCR, revealing a strong correlation between environmental and clinical antimicrobial resistance through the analysis of ARG profiles in urban wastewater.