Effect of wastewater collection and concentration methods on assessment of viral diversity

In-depth comparison of untargeted and targeted sequencing for detecting virus diversity in wastewater

Sequencing approaches may enable monitoring of a broad range of viruses in wastewater, including potential emerging and non-reportable human viruses. Considering the fact that metagenomic sequencing may be non-specific for low-abundance human viruses, integration of viral amplification and enrichment strategies are proposed to enhance the accurate detection of a broad range of human viruses in municipal wastewater. In this study, we focused on the in-depth comparison analysis of three untargeted amplification methods (Multiple Displace Amplification [MDA], Reverse Transcription - MDA [RT-MDA], and a PCR-based random amplification [PCR-based]) and one targeted method (Twist Comprehensive Viral Research Panel [TWIST]) for detecting virus diversity in wastewater. In addition, we included the comparisons of two extraction kits (Qiagen QIAamp VIRAL RNA Mini Kit and ZymoBIOMICSTM DNA/RNA Minipre Kit) and four virus identification tools (Diamond blast, Kraken2, VirSorter2 and geNomad) for a systematic study. Performances of Qiagen and Zymo extraction kits in recovering viruses and human viruses in wastewater were comparable. By the three untargeted methods we detected 12,808 contigs with lengths longer than 10,000 bp. No contig longer than 10,000 bp was detected by the targeted method. Presence of human viruses were analyzed further by comparing the viral contigs against a custom Swiss-Prot human virus database. There were 45 viruses that are potentially associated with human health found in wastewater, 8 of them were unique to the targeted method and 7 of them were unique to the three untargeted methods. Four enteric viruses Mamastrovirus, Norovirus, Rotavirus and Sapovirus were detected with high abundance in samples prepared with the targeted method. Dimensional scaling analysis demonstrated the divergent virus and human virus communities from the untargeted and targeted methods. Patterns of virus and human virus populations identified by Kraken2 and geNomad were similar. Presence of selected viruses (SARS-CoV-2 [N1&N2], SC2, RSV, Norovirus GI and GII) were confirmed with ddPCR. This work indicates integration of untargeted and targeted sequencing methods, and complementary ddPCR can ensure the accurate detection of known and novel viruses using wastewater surveillance.

A systematic review on the utility of wastewater surveillance for monitoring yellow fever virus and other arboviruses

AIMS

This review aims to examine wastewater surveillance for the detection of yellow fever virus (YFV) and related arboviruses, focusing on concentration and extraction methodology, viral decay kinetics, and quantification techniques.

METHODS

A literature search was conducted across 5 databases: PubMed, Science Direct, Web of Science, Embase, and Google Scholar following the PRISMA guidelines. Studies included were original scientific articles published between April 2014 and April 2024. Human research studies investigating wastewater surveillance and YFV or other arboviruses/flaviviruses were assessed.

RESULTS

A total of 17 studies were included in this review. YFV was not detected in population-based wastewater samples; however, successful detection of similar viruses suggests potential for YFV monitoring with wastewater surveillance. YFV-spiked wastewater studies reveal similar concentration efficiency and decay rates between arboviruses. Effective concentration methods for YFV likely include centrifugation ultrafiltration and solid pellet extraction. YFV and arboviruses decay faster at higher temperatures, though YFV remains detectable for several days at these temperatures.

CONCLUSIONS

Wastewater surveillance presents a promising approach for monitoring YFV and other arboviruses. However, further research is needed to overcome existing limitations and enhance its effectiveness.

Community case study: an academia-industry-government partnership that monitors and predicts outbreaks in Tri-County Detroit area since 2017

The Tri-County Detroit Area (TCDA) is the 12th most populous metropolitan area in the United States with over three million people. Multiple communicable diseases are endemic in the TCDA. In 2017, to explore innovative methods that may provide early warnings of outbreaks affecting populations in the TCDA, an exploratory partnership that was funded by a U.S. National Science Foundation Early-concept Grant for Exploratory Research (EAGER) began. Since 2017, a project team including the College of Engineering at Michigan State University (MSU), the City of Detroit, the Great Lakes Water Authority (GLWA), industry, and local government and health departments, has been testing municipal wastewater from the TCDA to survey and predict surges in communicable diseases in the area. This ongoing effort started years before wastewater-based epidemiology became a widespread method in public health practice, due to the COVID-19 pandemic, and is now supported by the U.S. Centers for Disease Control and Prevention (CDC). The work of the partnership led to significant breakthroughs in the field of wastewater surveillance/wastewater epidemiology. The results of our surveillance efforts are used to assist local health departments in their understanding and response efforts for health issues in the TCDA, facilitating public health messaging for local awareness, targeted clinical testing, and increased vaccination efforts. Our data are available to the local health departments, and our methodological advancements are published and have been used by other communities nationwide and beyond. This paper describes the partnership, lessons learned, significant achievements, and provides a look into the future. The successful implementations and advancements of wastewater surveillance in the TCDA advocate the importance of frequent communications and interactions within the partnership, idea generations from each stakeholder for decision-making, maintenance of scientific rigor, ethical awareness, and more.

The Impact of Viral Concentration Method on Quantification and Long Amplicon Nanopore Sequencing of SARS-CoV-2 and Noroviruses in Wastewater

Wastewater-based surveillance has gained attention in the four years following the start of the COVID-19 pandemic. Accurate pathogen detection, quantification and characterisation rely on the selection of appropriate methodologies. Here, we explore the impact of viral concentration method on RT-qPCR inhibition and quantification of norovirus genogroups I and II (GI and GII), crAssphage, phi6 and SARS-CoV-2. Additionally, their impact on long amplicon sequencing for typing noroviruses and whole-genome sequencing (WGS) SARS-CoV-2 was explored. RT-qPCR inhibition for each viral concentration method was significantly different apart from the two ultrafiltration methods, InnovaPrep® concentrating pipette (IP) and Vivaspin® (VS) centrifugal concentrators. Using an ultrafiltration method reduced inhibition by 62.0% to 96.0% compared to the ammonium sulphate (AS) and polyethylene glycol (PEG) precipitation-based methods. Viral quantification was significantly impacted by concentration method with the highest concentrations (copies/L) observed for VS with 7.2- to 83.2-fold differences from AS depending on the target. Norovirus long amplicon sequencing showed genotype-dependent differences with IP performing best for GI and VS for GII although IP performance gains for GI were relatively small. VS outperformed AS and IP across all metrics during SARS-CoV-2 WGS. Overall, VS performed the best when considering all the areas of investigation.

Genomic mapping of wastewater bacteriophage may predict potential bacterial pathogens infecting the community

Most existing wastewater surveillance studies that focus on viruses have identified a large fraction of bacteriophages. Identifying bacteria by considering bacteriophage-host interactions is a novel method for detecting bacterial pathogens circulating in a community, using wastewater surveillance. This study aims to identify human-related bacterial pathogens in municipal wastewater collected in metro Detroit, using high-throughput sequencing and bioinformatics. Untreated municipal wastewater samples were collected on August 11, 2020, and bacteriophages were concentrated using the VIRus ADsorption-ELution (VIRADEL) method. Bacteriophage-related contigs in samples ranged from 15.53 % to 18.91 %, with 2477 classified and 8853 unclassified contigs. Most identified bacteriophages were from Caudoviricetes and Malgrandaviricetes classes belonging to 19 families. Hosts of bacteriophages were predicted with the PhaBOX (CHERRY) tool. The results indicated that out of the 2477 classified phages, 2373 were associated with known bacterial hosts. Also, out of 8853 unclassified bacteriophages, 8421 were associated with known bacterial hosts, and the remaining 432 were with unknown bacterial hosts. Among all bacteriophage-associated hosts, 399 were identified as pathogenic bacteria at the species level. Approximately, 85 % of the identified pathogenic bacteria are reported to be associated with human diseases. Genome quality assessments showed that 15 bacteriophages had nearly complete genomes, which were further analyzed to understand bacteriophage-bacteria interactions in wastewater. Identified hosts of these complete-genome phages included human pathogens such as Salmonella enterica, Bacillus cereus, Achromobacter xylosoxidans, and Escherichia coli. The S. enterica bacteriophage (k141_1005294) genomic map was annotated, and responsible open reading frames (ORFs) were characterized to illustrate bacteriophage behavior during infection of pathogenic bacteria in untreated wastewater. To the best of our knowledge, this is the first attempt to characterize human bacterial pathogens in wastewater through bacteriophage-pathogen interactions. Novel bioinformatic approaches enhance pathogen detection and improve the understanding of community wastewater microbiomes.

Revealing patterns of SARS-CoV-2 variant emergence and evolution using RBD amplicon sequencing of wastewater

OBJECTIVES

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study aimed to monitor the SARS-CoV-2 variant community dynamics and evolution using receptor-binding domain (RBD) amplicon sequencing of wastewater samples.

METHODS

We sequenced wastewater from El Paso, Texas, over 17 months, compared the sequencing data with clinical genome data, and performed biodiversity analysis to reveal SARS-CoV-2 variant dynamics and evolution.

RESULTS

We identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Comparison with clinical genome sequencing data revealed earlier detection of variants and identification of unreported outbreaks. Our results also showed strong consistency with clinical data for dominant variants at the local, state, and national levels. Alpha diversity analyses revealed significant seasonal variations, with the highest diversity observed in winter. By segmenting the outbreak into lag, growth, stationary, and decline phases, we found higher variant diversity during the lag phase, likely due to lower inter-variant competition preceding outbreak growth.

CONCLUSIONS

Our findings underscore the importance of low transmission periods in facilitating rapid mutation and variant evolution. Our approach, integrating RBD amplicon sequencing with wastewater surveillance, demonstrates effectiveness in tracking viral evolution and understanding variant emergence, thus enhancing public health preparedness.

RBD amplicon sequencing of wastewater reveals patterns of variant emergence and evolution

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study utilized receptor-binding domain (RBD) amplicon sequencing of wastewater samples to monitor the SARS-CoV-2 community dynamics and evolution in El Paso, TX. Over 17 months, we identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Our findings demonstrated early detection of variants and identification of unreported outbreaks, while showing strong consistency with clinical genome sequencing data at the local, state, and national levels. Alpha diversity analyses revealed significant periodical variations, with the highest diversity observed in winter and the outbreak lag phases, likely due to lower competition among variants before the outbreak growth phase. The data underscores the importance of low transmission periods for rapid mutation and variant evolution. This study highlights the effectiveness of integrating RBD amplicon sequencing with wastewater surveillance in tracking viral evolution, understanding variant emergence, and enhancing public health preparedness.

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

A broad wastewater screening and clinical data surveillance for virus-related diseases in the metropolitan Detroit area in Michigan

BACKGROUND

Periodic bioinformatics-based screening of wastewater for assessing the diversity of potential human viral pathogens circulating in a given community may help to identify novel or potentially emerging infectious diseases. Any identified contigs related to novel or emerging viruses should be confirmed with targeted wastewater and clinical testing.

RESULTS

During the COVID-19 pandemic, untreated wastewater samples were collected for a 1-year period from the Great Lakes Water Authority Wastewater Treatment Facility in Detroit, MI, USA, and viral population diversity from both centralized interceptor sites and localized neighborhood sewersheds was investigated. Clinical cases of the diseases caused by human viruses were tabulated and compared with data from viral wastewater monitoring. In addition to Betacoronavirus, comparison using assembled contigs against a custom Swiss-Prot human virus database indicated the potential prevalence of other pathogenic virus genera, including: Orthopoxvirus, Rhadinovirus, Parapoxvirus, Varicellovirus, Hepatovirus, Simplexvirus, Bocaparvovirus, Molluscipoxvirus, Parechovirus, Roseolovirus, Lymphocryptovirus, Alphavirus, Spumavirus, Lentivirus, Deltaretrovirus, Enterovirus, Kobuvirus, Gammaretrovirus, Cardiovirus, Erythroparvovirus, Salivirus, Rubivirus, Orthohepevirus, Cytomegalovirus, Norovirus, and Mamastrovirus. Four nearly complete genomes were recovered from the Astrovirus, Enterovirus, Norovirus and Betapolyomavirus genera and viral species were identified.

CONCLUSIONS

The presented findings in wastewater samples are primarily at the genus level and can serve as a preliminary "screening" tool that may serve as indication to initiate further testing for the confirmation of the presence of species that may be associated with human disease. Integrating innovative environmental microbiology technologies like metagenomic sequencing with viral epidemiology offers a significant opportunity to improve the monitoring of, and predictive intelligence for, pathogenic viruses, using wastewater.