Metagenomic Sequencing for Wastewater-Based Surveillance: Modeling and Experimental Approaches for Determining Limit of Detection

Since the COVID-19 pandemic, wastewater-based surveillance (WBS) has emerged as a key approach to assess community-level health and the evolution of pathogens. To date, most established WBS systems focus on polymerase chain reaction (PCR) based detection and targeted sequencing of known pathogens because these approaches are well-accepted and include amplification of pathogen target sequences of interest thereby enabling lower limits of detection. Metagenomic next-generation sequencing (mNGS) is a promising approach to enable pathogen detection and surveillance beyond predefined pathogen lists, but its regular application to WBS has not been yet widely adopted because many key performance characteristics are not well-understood, including limit of detection (LOD) and false positive/negative rates. This paper describes a computational analysis to estimate the operational LOD of various sequencing approaches using a simplified model of a local wastewater (WW) system involving a military base. This paper also presents findings from two types of experiments: 1) laboratory-spiked, those for which Atlantibacter subterraneus (Asub) is introduced into real-world WW samples in a laboratory setting, and 2) system-spiked, those for which Asub is introduced at a source location of a real-world WW system. Findings indicate that mNGS detection performance varies with sequencing method and the data analysis process. In addition, findings indicate that site-specific method characterization should be used when implementing mNGS for WBS because sites can have different WW system configurations, background organisms and sequencing inhibitors.