Analysis of metatranscriptomic methods to enable wastewater-based biosurveillance of all infectious diseases.

Introduction: Wastewater-based surveillance emerged during the COVID-19 pandemic as an efficient way to quickly screen large populations, monitor infectious disease transmission over time, and identify whether more virulent strains are becoming more prevalent in the region without burdening the health care system with individualized testing. Ohio was one of the first states to implement wastewater monitoring through its Ohio Coronavirus Wastewater Monitoring Network (OCWMN), originally tracking the prevalence of COVID-19 by quantitative qPCR from over 67 sites across the state. The OCWMN evolved along with the pandemic to include sequencing the SARS-CoV-2 genome to assess variants of concern circulating within the population. As the pandemic wanes, networks such as OCWMN can be expanded to monitor other infectious diseases and outbreaks of interest to the health department to reduce the burden of communicable diseases. However, most surveillance still utilizes qPCR based diagnostic tests for individual pathogens, which is hard to scale for surveillance of multiple pathogens. Methods: Here we have tested several genomic methods, both targeted and untargeted, for wastewater-based biosurveillance to find the most efficient procedure to detect and track trends in reportable infectious diseases and outbreaks of known pathogens as well as potentially novel pathogens or variants on the rise in our communities. RNA extracts from the OCWMN were provided weekly from 10 sites for 6 weeks. Total RNA was sequenced from the samples on the Illumina NextSeq and on the MinION to identify pathogens present. The MinION long read platform was also used to sequence SARS-CoV-2 with the goal of reducing the complexity of variant calling in mixed populations as occurs with short Illumina reads. Finally, a targeted hybridization approach was tested for compatibility with wastewater RNA samples. Results and discussion: The data analyzed here provides a baseline assessment that demonstrates that wastewater is a rich resource for infectious disease epidemiology and identifies technology gaps and potential solutions to enable this resource to be used by public health laboratories to monitor the infectious disease landscape of the regions they serve.

Feasibility of neighborhood and building scale wastewater-based genomic epidemiology for pathogen surveillance.

The benefits of wastewater-based epidemiology (WBE) for tracking the viral load of SARS-CoV-2, the causative agent of COVID-19, have become apparent since the start of the pandemic. However, most sampling occurs at the wastewater treatment plant influent and therefore monitors the entire catchment, encompassing multiple municipalities, and is conducted using quantitative polymerase chain reaction (qPCR), which only quantifies one target. Sequencing methods provide additional strain information and also can identify other pathogens, broadening the applicability of WBE to beyond the COVID-19 pandemic. Here we demonstrate feasibility of sampling at the neighborhood or building complex level using qPCR, targeted sequencing, and untargeted metatranscriptomics (total RNA sequencing) to provide a refined understanding of the local dynamics of SARS-CoV-2 strains and identify other pathogens circulating in the community. We demonstrate feasibility of tracking SARS-CoV-2 at the neighborhood, hospital, and nursing home level with the ability to detect one COVID-19 positive out of 60 nursing home residents. The viral load obtained was correlative with the number of COVID-19 patients being treated in the hospital. Targeted wastewater-based sequencing over time demonstrated that nonsynonymous mutations fluctuate in the viral population. Clades and shifts in mutation profiles within the community were monitored and could be used to determine if vaccine or diagnostics need to be adapted to ensure continued efficacy. Furthermore, untargeted RNA sequencing identified several other pathogens in the samples. Therefore, untargeted RNA sequencing could be used to identify new outbreaks or emerging pathogens beyond the COVID-19 pandemic.