Tracing the origin of SARS-CoV-2 omicron-like spike sequences detected in an urban sewershed: a targeted, longitudinal surveillance study of a cryptic wastewater lineage.

BACKGROUND: The origin of novel SARS-CoV-2 spike sequences found in wastewater, without corresponding detection in clinical specimens, remains unclear. We sought to determine the origin of one such cryptic wastewater lineage by tracking and characterising its persistence and genomic evolution over time. METHODS: We first detected a cryptic lineage, WI-CL-001, in municipal wastewater in Wisconsin, USA, in January, 2022. To determine the source of WI-CL-001, we systematically sampled wastewater from targeted sub-sewershed lines and maintenance holes using compositing autosamplers. Viral concentrations in wastewater samples over time were measured by RT digital PCR. In addition to using metagenomic 12s rRNA sequencing to determine the virus's host species, we also sequenced SARS-CoV-2 spike receptor binding domains, and, where possible, whole viral genomes to identify and characterise the evolution of this lineage. FINDINGS: We traced WI-CL-001 to its source at a single commercial building. There we detected the cryptic lineage at concentrations as high as 2·7 × 109 genome copies per L. The majority of 12s rRNA sequences detected in wastewater leaving the identified source building were human. Additionally, we generated over 100 viral receptor binding domain and whole-genome sequences from wastewater samples containing the cryptic lineage collected over the 13 consecutive months this virus was detectable (January, 2022, to January, 2023). These sequences contained a combination of fixed nucleotide substitutions characteristic of Pango lineage B.1.234, which circulated in humans in Wisconsin at low levels from October, 2020, to February, 2021. Despite this, mutations in the spike gene and elsewhere resembled those subsequently found in omicron variants. INTERPRETATION: We propose that prolonged detection of WI-CL-001 in wastewater indicates persistent shedding of SARS-CoV-2 from a single human initially infected by an ancestral B.1.234 virus. The accumulation of convergent omicron-like mutations in WI-CL-001's ancestral B.1.234 genome probably reflects persistent infection and extensive within-host evolution. People who shed cryptic lineages could be an important source of highly divergent viruses that sporadically emerge and spread. FUNDING: The Rockefeller Foundation, Wisconsin Department of Health Services, Centers for Disease Control and Prevention, National Institute on Drug Abuse, and the Center for Research on Influenza Pathogenesis and Transmission.

Wastewater Surveillance Data as a Complement to Emergency Department Visit Data for Tracking Incidence of Influenza A and Respiratory Syncytial Virus - Wisconsin, August 2022-March 2023.

Wastewater surveillance has been used to assist public health authorities in tracking local transmission of SARS-CoV-2. The usefulness of wastewater surveillance to track community spread of other respiratory pathogens, including influenza virus and respiratory syncytial virus (RSV), is less clear. During the 2022-23 respiratory diseases season, concentrations of influenza A virus and RSV in wastewater samples in three major Wisconsin cities were compared with emergency department (ED) visits associated with these pathogens. In all three cities, higher concentrations of influenza A virus and RSV in wastewater were associated with higher numbers of associated ED visits (Kendall's tau range = 0.50-0.63 for influenza-associated illness and 0.30-0.49 for RSV-associated illness). Detections of both influenza A virus and RSV in wastewater often preceded a rise in associated ED visits for each pathogen, and virus material remained detectable in wastewater for up to 3 months after pathogen-specific ED visits declined. These results demonstrate that wastewater surveillance has the potential to complement conventional methods of influenza and RSV surveillance, detecting viral signals earlier and for a longer duration than do clinical data. Continued use of wastewater surveillance as a supplement to established surveillance systems such as ED visits might improve local understanding and response to seasonal respiratory virus outbreaks.

Separating signal from noise in wastewater data: An algorithm to identify community-level COVID-19 surges in real time.

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time.

Guts of the Urban Ecosystem: Microbial Ecology of Sewer Infrastructure.

Microbes have inhabited the oceans and soils for millions of years and are uniquely adapted to their habitat. In contrast, sewer infrastructure in modern cities dates back only ~150 years. Sewer pipes transport human waste and provide a view into public health, but the resident organisms that likely modulate these features are relatively unexplored. Here, we show that the bacterial assemblages sequenced from untreated wastewater in 71 U.S. cities were highly coherent at a fine sequence level, suggesting that urban infrastructure separated by great spatial distances can give rise to strikingly similar communities. Within the overall microbial community structure, temperature had a discernible impact on the distribution patterns of closely related amplicon sequence variants, resulting in warm and cold ecotypes. Two bacterial genera were dominant in most cities regardless of their size or geographic location; on average, Arcobacter accounted for 11% and Acinetobacter 10% of the entire community. Metagenomic analysis of six cities revealed these highly abundant resident organisms carry clinically important antibiotic resistant genes blaCTX-M, blaOXA, and blaTEM. In contrast, human fecal bacteria account for only ~13% of the community; therefore, antibiotic resistance gene inputs from human sources to the sewer system could be comparatively small, which will impact measurement capabilities when monitoring human populations using wastewater. With growing awareness of the metabolic potential of microbes within these vast networks of pipes and the ability to examine the health of human populations, it is timely to increase our understanding of the ecology of these systems. IMPORTANCE Sewer infrastructure is a relatively new habitat comprised of thousands of kilometers of pipes beneath cities. These wastewater conveyance systems contain large reservoirs of microbial biomass with a wide range of metabolic potential and are significant reservoirs of antibiotic resistant organisms; however, we lack an adequate understanding of the ecology or activity of these communities beyond wastewater treatment plants. The striking coherence of the sewer microbiome across the United States demonstrates that the sewer environment is highly selective for a particular microbial community composition. Therefore, results from more in-depth studies or proven engineering controls in one system could be extrapolated more broadly. Understanding the complex ecology of sewer infrastructure is critical for not only improving our ability to treat human waste and increasing the sustainability of our cities but also to create scalable and effective sewage microbial observatories, which are inevitable investments of the future to monitor health in human populations.

A weight-of-evidence approach for identifying potential sources of untreated sewage inputs into a complex urbanized catchment.

The Hawkesbury-Nepean River (HNR) is the largest catchment in the Sydney region and is undergoing unprecedented population growth. The HNR system receives a mix of anthropogenic inputs such as treated sewage, stormwater and agricultural runoff. Combined, these can diminish the ecological system health and pose potential concerns to human health. Of particular concern are inputs of untreated sewage, that can occur due to a range of different reasons including illegal point source discharges, failure of the sewerage network, and overloading of wastewater treatment plants during storm events. Here, we present findings of an intensive assessment across the HNR catchment where we used a weight-of-evidence (WOE) approach to identify untreated sewage contamination in surface waters against the background of treated effluent and diffuse inputs during post high flow conditions. Total nitrogen and phosphorus concentrations were used to assess treated effluent and diffuse inputs, and microbial analysis, including both culture-based traditional methods for E. coli and enterococci and qPCR analysis of Bacteroides and Lachnospiraceae, were used to assess raw sewage contamination. Despite a background of diffuse inputs from recent high flow events and the influence of treated wastewater, we found no gradient of faecal contamination along the HNR system or its tributaries. We observed two sites with evidence of untreated sewage contamination, where the human markers Bacteroides and Lachnospiraceae qPCR copy numbers were high. The biological and chemical approaches suggested these latter two hotspots originate from an industrial runoff source and possibly from a dry weather sewage leak. Our findings demonstrate the potential of a WOE approach in the assessment of human faecal signal in an urban river that can also pinpoint small sources of contamination as a strategy that can reshape the way monitoring is performed and the chemical end-points chosen to provide pertinent information on the potential risks to aquatic system health.

Impact of Escherichia coli from stormwater drainage on recreational water quality: an integrated monitoring and modelling of urban catchment, pipes and lake.

In recent years, recreational waterbodies are increasingly favoured in urban areas. In spite of the growing concerns for maintaining the required bathing water quality, the impacts of stormwater drainage are still poorly controlled. In this context, this study originally develops an integrated urban catchment-pipes-lake monitoring and modelling approach to simulate the impacts of microbial quality from stormwater drainage on recreational water quality. The modelling system consists of three separated components: the urban catchment component, the 3D lake hydrodynamic component and the 3D lake water quality component. A series of processes are simulated in the model, such as rainfall-discharge, build-up, wash-off of Escherichia coli (E. coli) on urban surfaces, sewer flows, hydrothermal dynamics of lake water and transport and mortality of E. coli in the lake. This integrated model is tested for an urban catchment and its related recreational lake located in the Great Paris region. Continuous monitoring and samplings were performed at the stormwater drainage outlet and three different sites in the lake. Comparing the measured data with simulation results over 20 months, the modelling system can correctly represent the E. coli dynamics in the stormwater sewer systems and in the lake. Although uncertainties related to parameter values, pollution sources and E. coli mortality processes could be further discussed, the good performance of this modelling approach emphasizes a promising potential for urban bathing water quality management.

FORENSIC: an Online Platform for Fecal Source Identification.

Sewage overflows, agricultural runoff, and stormwater discharges introduce fecal pollution into surface waters. Distinguishing these sources is critical for evaluating water quality and formulating remediation strategies. With the falling costs of sequencing, microbial community-based water quality assessment tools are under development. However, their application is limited by the need to build reference libraries, which requires extensive sampling of sources and bioinformatic expertise. Here, we introduce FORest Enteric Source IdentifiCation (FORENSIC; https://forensic.sfs.uwm.edu/), an online, library-independent source tracking platform based on random forest classification and 16S rRNA gene amplicon sequences to identify in environmental samples common fecal contamination sources, including humans, domestic pets, and agricultural animals. FORENSIC relies on a broad reference signature database of Bacteroidales and Clostridiales, two predominant bacterial groups that have coevolved with their hosts. As a result, these groups demonstrate cohesive and reliable assemblage patterns within mammalian species or among species sharing the same diet/physiology. We created a scalable and extensible platform that we tested for global applicability using samples collected in distant geographic locations. This Web application offers a fast and intuitive approach for fecal source identification, particularly in sewage-contaminated waters.IMPORTANCE FORENSIC is an online platform to identify sources of fecal pollution without the need to create reference libraries. FORENSIC is based on the ability of random forest classification to extract cohesive source microbial signatures to create classifiers despite individual variability and to detect the signatures in environmental samples. We primarily focused on defining sewage signals, which are associated with a high human health risk in polluted waters. To test for fecal contamination sources, the platform only requires paired-end reads targeting the V4 or V6 regions of the 16S rRNA gene. We demonstrated that we could use V4V5 reads trimmed to the V4 positions to generate the reference signature. The systematic workflow we describe to create and validate the signatures could be applied to many disciplines. With the increasing gap between advancing technology and practical applications, this platform makes sequence-based water quality assessments accessible to the public health and water resource communities.

The unexpected habitat in sewer pipes for the propagation of microbial communities and their imprint on urban waters.

Modern urban sewer pipe infrastructure is a unique niche where microbes can thrive. Arcobacter, Acinetobacter, Aeromonas, and Trichococcus are among the organisms that dominate the microbial community of sewage influent, but are not major members of human fecal microbiome, drinking water, or groundwater. Pipe resident communities in untreated sewage are distinct from sewer biofilm communities. Because of their high biomass, these organisms likely have a role in biotransformation of waste during conveyance and could represent an important inoculum for treatment plants. Studies demonstrate stormwater systems act as direct conduits for sewage to surface waters, releasing organisms propagated in sewer pipes. Frequent occurrence of these pipe residents, in particular Arcobacter, demonstrates the extent that urban infrastructure impacts rivers, lakes, and urban coasts worldwide.

Fecal source identification using random forest.

BACKGROUND: Clostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species. As a result, members of Clostridiales and Bacteroidales are particularly suitable for identifying sources of fecal contamination in environmental samples. However, a comprehensive evaluation of their predictive power and development of computational approaches is lacking. Given the global public health concern for waterborne disease, accurate identification of fecal pollution sources is essential for effective risk assessment and management. Here, we use random forest algorithm and 16S rRNA gene amplicon sequences assigned to Clostridiales and Bacteroidales to identify common fecal pollution sources. We benchmarked the accuracy, consistency, and sensitivity of our classification approach using fecal, environmental, and artificial in silico generated samples. RESULTS: Clostridiales and Bacteroidales classifiers were composed mainly of sequences that displayed differential distributions (host-preferred) among sewage, cow, deer, pig, cat, and dog sources. Each classifier correctly identified human and individual animal sources in approximately 90% of the fecal and environmental samples tested. Misclassifications resulted mostly from false-positive identification of cat and dog fecal signatures in host animals not used to build the classifiers, suggesting characterization of additional animals would improve accuracy. Random forest predictions were highly reproducible, reflecting the consistency of the bacterial signatures within each of the animal and sewage sources. Using in silico generated samples, we could detect fecal bacterial signatures when the source dataset accounted for as little as ~ 0.5% of the assemblage, with ~ 0.04% of the sequences matching the classifiers. Finally, we developed a proxy to estimate proportions among sources, which allowed us to determine which sources contribute the most to observed fecal pollution. CONCLUSION: Random forest classification with 16S rRNA gene amplicons offers a rapid, sensitive, and accurate solution for identifying host microbial signatures to detect human and animal fecal contamination in environmental samples.

Importance of Local and Regional Scales in Shaping Mycobacterial Abundance in Freshwater Lakes.

Biogeographical studies considering the entire bacterial community may underestimate mechanisms of bacterial assemblages at lower taxonomic levels. In this context, the study aimed to identify factors affecting the spatial and temporal dynamic of the Mycobacterium, a genus widespread in aquatic ecosystems. Nontuberculous mycobacteria (NTM) density variations were quantified in the water column of freshwater lakes at the regional scale (annual monitoring of 49 lakes in the Paris area) and at the local scale (2-year monthly monitoring in Créteil Lake) by real-time quantitative PCR targeting the atpE gene. At the regional scale, mycobacteria densities in water samples ranged from 6.7 × 103 to 1.9 × 108 genome units per liter. Density variations were primarily explained by water pH, labile iron, and dispersal processes through the connection of the lakes to a river. In Créteil Lake, no spatial variation of mycobacterial densities was noticed over the 2-year monthly survey, except after large rainfall events. Indeed, storm sewer effluents locally and temporarily increased NTM densities in the water column. The temporal dynamic of the NTM densities in Créteil Lake was associated with suspended solid concentrations. No clear seasonal variation was noticed despite a shift in NTM densities observed over the 2012-2013 winter. Temporal NTM densities fluctuations were well predicted by the neutral community model, suggesting a random balance between loss and gain of mycobacterial taxa within Créteil Lake. This study highlights the importance of considering multiple spatial scales for understanding the spatio-temporal dynamic of bacterial populations in natural environments.

Neutral community model explains the bacterial community assembly in freshwater lakes.

Over the past decade, neutral theory has gained attention and recognition for its capacity to explain bacterial community structure (BCS) in addition to deterministic processes. However, no clear consensus has been drawn so far on their relative importance. In a metacommunity analysis, we explored at the regional and local scale the effects of these processes on the bacterial community assembly within the water column of 49 freshwater lakes. The BCS was assessed using terminal restriction fragment length polymorphism (T-RFLP) of the 16S rRNA genes. At the regional scales, results indicated that the neutral community model well predicted the spatial community structure (R(2) mean = 76%) compared with the deterministic factors - which explained only a small fraction of the BCS total variance (less than 14%). This suggests that the bacterial compartment was notably driven by stochastic processes, through loss and gain of taxa. At the local scale, the bacterial community appeared to be spatially structured by stochastic processes (R(2) mean = 65%) and temporally governed by the water temperature, a deterministic factor, even if some bacterial taxa were driven by neutral dynamics. Therefore, at both regional and local scales the neutral community model appeared to be relevant in explaining the bacterial assemblage structure.

atpE gene as a new useful specific molecular target to quantify Mycobacterium in environmental samples.

BACKGROUND: The environment is the likely source of many pathogenic mycobacterial species but detection of mycobacteria by bacteriological tools is generally difficult and time-consuming. Consequently, several molecular targets based on the sequences of housekeeping genes, non-functional RNA and structural ribosomal RNAs have been proposed for the detection and identification of mycobacteria in clinical or environmental samples. While certain of these targets were proposed as specific for this genus, most are prone to false positive results in complex environmental samples that include related, but distinct, bacterial genera. Nowadays the increased number of sequenced genomes and the availability of software for genomic comparison provide tools to develop novel, mycobacteria-specific targets, and the associated molecular probes and primers. Consequently, we conducted an in silico search for proteins exclusive to Mycobacterium spp. genomes in order to design sensitive and specific molecular targets. RESULTS: Among the 3989 predicted proteins from M. tuberculosis H37Rv, only 11 proteins showed 80% to 100% of similarity with Mycobacterium spp. genomes, and less than 50% of similarity with genomes of closely related Corynebacterium, Nocardia and Rhodococcus genera. Based on DNA sequence alignments, we designed primer pairs and a probe that specifically detect the atpE gene of mycobacteria, as verified by quantitative real-time PCR on a collection of mycobacteria and non-mycobacterial species. The real-time PCR method we developed was successfully used to detect mycobacteria in tap water and lake samples. CONCLUSIONS: The results indicate that this real-time PCR method targeting the atpE gene can serve for highly specific detection and precise quantification of Mycobacterium spp. in environmental samples.