Unveiling the global urban virome: insights from wastewater metagenomics

Unveiling the global urban virome: insights from wastewater metagenomics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Unveiling the global urban virome: insights from wastewater metagenomics Miranda de Graaf, Nathalie Worp, David Nieuwenhuijse, Ray Izquierdo Lara, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6048078/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Understanding global viral dynamics is critical for public health1. Traditional infectious disease surveillance primarily focuses on individual pathogens and relies on the identification and reporting of symptomatic cases, which may not capture asymptomatic infections or newly emerging viruses, leading to delayed detection and response.2–4 Wastewater-based epidemiology has been used to track individual pathogens through targeted molecular approaches, but its reliance on predefined targets limits the ability to capture the full spectrum of circulating viruses.5–7 Here, we analyzed longitudinal and biannual wastewater samples from 62 major cities across six continents (2017-2019) using shotgun metagenomics and capture-based sequencing targeting viruses associated with gastrointestinal disease. Over 2,500 viral species spanning 122 families were detected, many with human, animal, or plant health relevance. The Bacteriophage families Microviridae and Virgaviridae dominated the metagenomic dataset, while in the enriched dataset Astroviridae and Picornaviridae were the most prevalent. Virus distributions were broadly similar across continents, but distinct city-level fingerprints emerged, reflecting spatiotemporal variation of viruses like astrovirus, and enterovirus. Global wastewater-based epidemiology enabled the early detection of several emerging viruses, including Echovirus E30 in Europe, and Tomato brown rugose fruit virus before agricultural outbreaks. These findings highlight the potential of wastewater metagenomics for early detection of emerging viruses and population-wide virome monitoring across diverse hosts. Biological sciences/Microbiology/Environmental microbiology/Water microbiology Biological sciences/Microbiology/Virology/Viral epidemiology virome gastroenteritis metagenomics global wastewater enteric viruses Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction In recent decades, the frequency and scale of infectious disease outbreaks have increased, particularly those emerging after spillover of animal viruses. 8 The emergence and spread of these diseases have been driven by a range of interconnected factors, including urbanization 9 , the development of megacities, 10 deforestation 11,12 , growing food demands 13 , and increasing global connectivity. Migration from rural to urban areas has contributed to population growth in cities, resulting in increased close-contact interactions that facilitate disease transmission 10 . By 2017, 55% of the world’s population lived in urban areas 14 , making these high-density cities critical hubs for the spread of infectious diseases, and therefore strategic sites for monitoring and responding to both novel and established public health threats. Current infectious disease surveillance primarily relies on the identification and reporting of symptomatic cases 15 , which has limitations, particularly for viruses that frequently cause asymptomatic or mild infections, or diseases that are not non-reportable. As a result, novel infectious diseases caused by emerging viruses or variants of known viruses can evade detection, spreading silently before being recognized. This phenomenon was exemplified by the introduction of Zika virus in South America 2 and the early undetected spread of SARS-CoV-2 into Europe 3,4 and the USA 16 . To better understand the prevalence and spread of viral infections, alternative surveillance strategies beyond symptom-based reporting are needed to capture the full spectrum of viral transmission dynamics and enhance early detection and response to emerging infectious disease threats. Wastewater-based epidemiology (WBE) involves the detection and monitoring of chemicals, drugs and substances 17,18 , genes 19 , pathogens 20 and vectors 21 within wastewater to assess community health or disease risk. Recent detections of poliovirus in wastewater from major urban cities like New York 22 , London 23 , and poliovirus essential facilities 24 highlight the potential of WBE in identifying circulation hotspots and guiding targeted interventions, such as enhancing surveillance and launching of vaccine campaigns. 17 During the COVID-19 pandemic, quantitative reverse transcription PCR-based methods were used to assess the viral load in wastewater, with viral concentrations following the number of positive cases at the community level and providing insights into infection dynamics over time 19,20 . Amplicon-based sequencing was used in parallel to identify circulating viral variants, enhancing early detection capabilities in wastewater if used in combination with pathogen specific bioinformatic pipelines 25,26 . Utilizing targeted molecular methods has extended WBE to track other pathogenic viruses like influenza 5 , respiratory syncytial virus 5 , mpox 6 , arboviruses 27 , and non-polio enteroviruses (EV) such as EV-D68 28 . Among viruses shed into wastewater, enteric viruses pose a significant public health threat. Enteric viruses mainly transmit through the faecal-oral route, cause a wide range of gastrointestinal diseases and are a potential source of community-wide outbreaks. 29 Detection is complicated by their extensive diversity, non-specific clinical symptoms that hamper diagnosis, high rate of secondary transmission which may mask initial introductions, and gaps in surveillance systems. As the number of viral targets of interest grows, the question arises whether surveillance efforts focused on individual pathogens can be combined in a more comprehensive approach. In this context, metagenomic sequencing can be a promising technique that can address this need. 30 A significant advantage of metagenomic sequencing is its ability to simultaneously detect and characterize multiple viral agents present within a single (wastewater) sample including non-target (novel) viruses. 31,32 In this study, we explore the potential of metagenomic sequencing and analysis as a tool for population-level monitoring of virus circulation across hosts in high-density urban areas. We sequenced and characterized the wastewater virome of 62 major cities worldwide using both a shotgun viral metagenomic and a capture-based sequencing method targeting enteric viruses, to compare viromes and viral dynamics across the world. Results Virome data overview To investigate the worldwide urban wastewater virome, wastewater samples were collected from 62 cities across 47 countries on 6 continents (Figure 1). 33 For all cities, samples were acquired biannually in June and November over a two-year period spanning 2017 to 2019 (biannual samples). Additionally, to investigate temporal changes, monthly samples were obtained from eight cities in different countries in six different continents during the same period. Wastewater samples were analysed using both shotgun viral metagenomic sequencing to determine the viral metagenome and sequencing following capture using a custom capture probe set (GastroCap, Supplementary Figure S1), designed to enrich for enteric virus sequences. The median number of reads generated after sequencing was 9.8×10 5 (range: 3 x 10 3 -5.3 x 10 6 ) per sample for the metagenomic sequence dataset and 7.4x10 5 (range: 1.4 ×10 2 -4.5 x 10 6 ) for the capture sequence dataset (Supplementary Figure S2). The median percentage of reads per sample that could be annotated as viral was 11% (range: 0.03%-76%) for the metagenomic sequence dataset and 26% (range: 0.04%-97%) for the capture sequence dataset. Principal component analysis (PCA) of centred log-ratio (CLR) transformed read count data at the superkingdom level revealed distinct clustering between the two datasets and confirmed the enrichment of viral reads using GastroCap (Supplementary Figure S3). Composition of the Global Urban Wastewater Virome To establish a baseline understanding of the urban wastewater virome, we first examined the diversity of viruses at the family level. Overall, viral reads were mapped to a total of 122 families, 1,367 genera and 2,546 species (Figure 2). These findings highlight the broad diversity of viruses associated with vertebrates, plants, microbes, and other life forms within urban environments. A large fraction of the metagenomic virome consisted of bacteriophages and plant viruses (Figure 2). Viral families linked to human disease, such as Astroviridae, Parvoviridae, and Picornaviridae, were frequently detected in high abundances. In contrast, other important human-associated families, including Adenoviridae, Caliciviridae, Hepeviridae, and Sedoreoviridae, were also detected but less frequently. Of the eight viral families targeted by GastroCap ( Adenoviridae , Astroviridae , Caliciviridae , Hepeviridae , Parvoviridae , Picornaviridae , Spinareoviridae and Sedoreoviridae ) which include 72 genera and 1038 species, a total of 72 genera and 165 species were identified (Figure 2). GastroCap-based sequencing resulted in a substantial increase in both the total and relative number of reads for the targeted viral families. The median fold increase per sample, based on relative abundance in reads per million (rpm), was as follows: Adenovirida e (545-fold), Astroviridae (470-fold), Caliciviridae (5094-fold), Hepeviridae (22-fold), Parvoviridae (2-fold), Picornaviridae (132-fold), Sedoreoviridae (193-fold) and Spinareoviridae (21-fold). Viral genera associated with human gastrointestinal disease, including those commonly targeted by surveillance, such as Mamastrovirus, Bocaparvovirus, Salivirus, Kobuvirus, Norovirus, Sapovirus, Enterovirus, Mastadenovirus, Rotavirus, Paslahepevirus, Parechovirus, Cardiovirus were detected on every continent. However, while Mamastrovirus was highly abundant in most samples across all continents (Figure 3A), Enterovirus was mainly detected at high levels in samples from Africa, a few cities in Europe (Belgrade, Bratislava), North America (Sisimiut, Chapel Hill), and South America (Quito) (Figure 3B). Conversely, Paslahepevirus , to which Hepatitis E belongs, was more prevalent in Europe than in other regions. The longitudinal samples further revealed distinct spatial and temporal patterns in the prevalence of certain gastrointestinal viruses. For example, in Regina (Canada), Enterovirus levels showed a relative increase during summer months, whereas Mastadenovirus abundance was higher in the winter months. Principal Component Analysis revealed that cities differ in virome composition To identify broader patterns and potential geographical signatures in virome composition, we applied principal component analysis (PCA) on both the metagenomic and GastroCap sequence datasets. The metagenomic sequence data revealed no clear clustering patterns across continents at either the family or genus level, suggesting that the viral community structures were broadly similar worldwide (Figure 4A and B). The limited variance between locations was primarily driven by the genera Tobamovirus (a group of plant viruses within the Virgaviridae family) and Gemykibivirus (associated with multiple hosts, including plants, insects, mammals, and occasional human samples 34 ), both of which were more prevalent in samples from Asia. However, when comparing the virome composition by city, more pronounced differences were observed in both biannual and longitudinal samples (Figure S4 and Figure 4C and D). Focusing on the enteric virus families in the GastroCap sequence dataset, PCA revealed a broadly similar composition of these viral families across all continents, as indicated by overlapping clusters in the plot (Figure 5A). The largest differences were observed for the Astroviridae and Picornaviridae families. However, at the genus level, more distinct differences in viral composition were observed between continents (Figure 5B). Notably, Europe and Africa showed overlapping compositions, while the dataset for Oceania was distinctly separated. At the city level, even for the biannually sampled cities, PCA revealed clear distinct clustering, indicating significant variation in viral compositions between cities (Figure 5C and D, Supplementary Figure S5). These analyses identified the genus Mamastrovirus ( Astroviridae) as the primary driver ofbeta-diversity. Norovirus and Sapovirus showed covariation, as reflected by the small angle between their loading vectors, indicating similar contributions to the observed variation. Within the Picornaviridae family, genera such as Enterovirus, and Cardiovirus, also displayed some degree of covariation. The cities with longitudinal sampling showed that over time, Seattle (USA) and Copenhagen (Denmark) exhibited tightly clustered sample groupings at the family level, suggesting a stable composition of enteric virus families throughout the study period. In contrast, the virome composition in Regina (Canada), Kuala Lumpur (Malaysia), Guangzhou (China), and Yaoundé (Cameroon) showed greater variability, reflecting a more dynamic and heterogeneous composition over time. At the genus level, the enteric virome composition in Melbourne (Australia) was distinct from that of all other cities, primarily due to a high abundance of Mamastroviruses (Figure 5D). Species and genotype-level analysis of selected viral genera We further explored the value of wastewater for monitoring viruses at finer taxonomic resolution, at the level of species and genotypes to capture fine-scale spatiotemporal patterns of viral diversity. The genera Tobamovirus (Virgaviridae) from the metagenomic sequence data and Mamastrovirus ( Astroviridae) and Enterovirus (Picornaviridae ) from the GastroCap data had the largest influence on virome compositions across the biannual samples in the PCA. Due to their relevance to plant, public, and animal health, these genera were selected for further investigation. Shotgun metagenomic sequencing reveals Tobamovirus diversity in wastewater Tobamoviruses are of major concern because they can cause disease in a wide range of agriculturally important plant species, affecting tomato, pepper and cucumber plants. We identified a total of 5,204 Tobamovirus contigs in the metagenomic dataset of which 32% (1,653 contigs) were assigned to 17 different species (Figure 6), including both widely distributed, well-known viruses and emerging viruses with increasing agricultural and environmental relevance. Analysis of the sequences at the species level showed large compositional differences between samples and locations. Overall Cucumber Green Mild Mottle virus (CGMMV) and Pepper Mild mottle virus (PMMoV) – both well-studied and widely distributed viruses - were highly prevalent in urban sewage samples worldwide. PPMoV did not exhibit temporal variation, but its relative abundance was lower in Asia (Figure 6A and B). The relative abundance of CGMMV was highest in samples from Europe, Asia and North America (Figure 6A and B). Tobacco mosaic virus (TMV) was predominantly found in high abundance in samples from Asia and Africa, while Tobacco mild green mosaic virus (TMGMV) was primarily observed in samples from Asia (Figure 6, A and B). Additionally, we detected emerging viruses of concern, such as the rapidly spreading Tomato brown rugose fruit virus (ToBRFV), which was detected in wastewater samples from multiple locations including Rome (Italy) in 2017 and Athens (Greece), Vancouver (Canada), Be’er Sheva (Israel) and Seattle (USA) in 2018 (Figure 6, A and B). Global diversity and phylogenetic analysis of Mamastrovirus in wastewater Variation in the relative abundance of the genus Mamastrovirus contributed most to the differentiation of samples in the PCA in the GastroCap sequence dataset (Figure 5B). To explore if these differences were associated to specific Mamastrovirus types, we developed a custom typing workflow using reference sequences from a phylogenetic study on astrovirus diversity 35 . Our approach identified 10,668 Mamastrovirus contigs of which 14% (1512 contigs) could be assigned a genotype, resulting in the detection of 24 different types. Our analysis revealed both well-established (endemic) and less commonly observed Mamastrovirus genotypes across various global wastewater samples. There was substantial variation in Mamastrovirus genotype distribution between samples. Compositional differences in genotypes did not clearly correlate with specific continents or cities in the biannual dataset (Figure 7A). Human astroviruses (HAstV) formed the major fraction of Mamastrovirus genotypes detected, among these, the classical HAstV type 1–5 and 8 were most frequently detected. HAstV-1, a genotype considered endemic worldwide, was frequently detected across all sampled continents and was the predominant genotype in many samples. HAstV-8, a genotype rarely reported in clinical surveillance, was also detected in high relative abundance across all continents. Additionally, recently discovered astroviruses, including MLB1, VA1/HMO-C, VA2/HMO-A and VA3/HMO-B were detected across multiple continents. The novel human astrovirus BF34, which was identified and exclusively detected in Burkina Faso in 2010 (Africa), was detected in wastewater samples from Cameroon (February and March 2018), Austria (June and November 2018) and Uganda (July 2018) indicating its presence across multiple geographic regions in 2018. While less common in wastewater samples, animal astroviruses like, canine- (CAstV), rat astrovirus (RAstV) and feline astrovirus (FAstV) were detected across continents, with surprisingly high prevalence of CAstV in Regina, Canada. For the cities with longitudinal sampling, temporal shifts in the genotype distribution were observed. PCA analyses showed that Mamastrovirus was a major driver for the distinct clustering of Melbourne's virome (Figure 5D) with consistently high proportions of Mamastrovirus reads detected. In Melbourne HAstV-5 was more prevalent during 2017 and in early 2018, while HAstV-1 was mainly prevalent during the remainder of 2018. Other cities also exhibited peaks in Mamastrovirus reads over the observation period, but the timing of these peaks varied across locations and was not linked to a specific genotype. To investigate the global diversity and circulation patterns of the abundant ‘classical’ human astroviruses in greater detail, we conducted phylogenetic analysis. The retrieval of partial capsid sequences for HAstV type 1, 2, 3, 4, 5 and 8 (Figure 8 and Supplementary Figure S6) from wastewater considerably expands the number of available capsid sequences from previously underrepresented regions. For several types we observed wastewater-specific clusters, indicating that current clinical surveillance does not fully capture the diversity of HAstVs. Additionally, phylogenetic analysis revealed that integrating global wastewater sequences within a limited timeframe provided insights into country and city specific virus circulation. Viral sequences from the same city often clustered together, as did sequences from samples collected closer in time suggesting periodic circulation and replacement of viral lineages over time. Notably, evidence suggestive of region-specific virus circulation was observed for some African HAstV type 4, 5, and 8 clusters. For example, HAstV-5 wastewater sequences from Cameroon (2018), Senegal (2019), and human faecal sequences from Cameroon (2014) formed a distinct cluster. Similarly, a separate HAstV-8 cluster comprised of sequences from clinical samples from Cameroon from 2014 and wastewater sequences from Nigeria (2018) was observed. Global diversity and phylogenetic analysis of enterovirus in wastewater The abundance of Picornaviridae varied across continents, with enteroviruses frequently detected using GastroCap, significantly influencing PCA clustering patterns (Figure 5D). Enteroviruses, a major public health concern due to their potential to cause a wide range of illnesses, including respiratory infections and neurological diseases, were analysed in wastewater to evaluate their surveillance potential in metagenomic datasets. A total of 2,331 contigs were classified within the Enterovirus genus, of which 37% (868 contigs) could be genotyped using the RIVM Enterovirus genotyping tool, corresponding to 62 distinct Enterovirus genotypes. Analysis of prevalence and relative abundance revealed differences in enterovirus distribution across continents (Figure 9A and B). Samples from Africa showed the highest enterovirus abundance compared to other regions. Enterovirus B was the most prevalent species worldwide, with Coxsackievirus (CV) B5 and CV-A9 being the most prevalent types across all regions, suggesting widespread endemicity. In North America Enterovirus A was the most prevalent species, with Coxsackievirus CV-A6 and CV-A4, CV-A10 – endemic enteroviruses and common causes of hand, foot, and mouth disease- among the most detected types. Enterovirus C, though less prevalent, was frequently detected in the African region and was the dominant enterovirus species in longitudinal samples from Ecuador, Malaysia, and Cameroon. While Enterovirus D was detected sporadically, Enterovirus D-68—a clinically significant genotype due to its association with respiratory illness outbreaks and acute flaccid myelitis—was primarily identified toward the end of 2017 and in 2018. In most locations, Enterovirus D-68 Clade B3 was predominant, while Clade A2 was detected in Athens and Taipei. Enterovirus G, associated with infection in pigs, was detected in high abundances in several cities across all continents. Geographical specificity was evident for certain enterovirus genotypes. For example, CV-A20 was exclusively found in Africa and South America, while CV-A1 and CV-A22 were detected across all continents. In Europe, Echovirus type 30 (E-30) displayed an unusually high prevalence, which was not observed in other regions, possibly indicating a recent outbreak or re-emergence after a period of lower endemic circulation. Phylogenetic analysis of partial VP1 sequences revealed that European wastewater E-30 sequences from 2018 were generally more closely related to each other than to sequences from 2017 (Figure 10). Longitudinal analysis revealed dynamic patterns in enterovirus genotype distribution across cities over time (Figure 9B). For instance, Yaoundé (Cameroon) exhibited a broad diversity of circulating genotypes, including vaccine-derived poliovirus type 3. No sequences of the typing region VP1 for other polio types were detected. Longitudinal analysis across sites identified periods of genotype dominance, with CV-C116 – a lesser-known member of Enterovirus C with unknown clinical relevance - prevailing in Quito in 2018 and CV-A1 in Kuala Lumpur during 2017 and early 2018. In Regina, seasonal variations in overall enterovirus presence could be observed, with elevated levels occurring primarily during the summer and fall. Additionally, while multiple genotypes circulated in 2017, CV-A10 was present in higher relative abundance during specific periods, and shifted to CV-A6 in 2018, indicating dynamic changes in circulating enterovirus populations over consecutive years. Additionally, EV-A71 –one of the leading causes of hand, foot and mouth disease and associated with neurological complications - was detected in Regina in July, August and November 2017, coinciding with a period of elevated laboratory confirmed enterovirus/rhinovirus cases in Canada that year according to national surveillance data 36,37 . Discussion This study provides a comprehensive analysis of the urban wastewater virome of 62 major cities worldwide using shotgun metagenomic and capture-based (GastroCap) sequencing. 35 By incorporating both biannual and longitudinal samples, we assessed viral diversity and temporal dynamics. While biannual sampling provided a snapshot of viral diversity within a particular location, more frequent sampling revealed a higher viral diversity per location and enabled the detection of gradual shifts in composition. Shotgun metagenomics, at least with the sequencing depth used in our study, yielded a relatively low proportion of enteric virus reads, making it difficult to effectively monitor enteric viruses in complex wastewater samples. 38 The newly developed GastroCap probe set significantly enriched clinically relevant enteric viruses, whereas metagenomic sequencing primarily detected bacteriophages and plant viruses. Thus, for the surveillance of human pathogenic enteric viruses, which are stable in aquatic environments 39 , wastewater is a valuable source when combined with GastroCap enrichment. Additionally, a large proportion of reads could not be assigned to known virus taxa, highlighting the potential for future discovery of novel viruses. Our findings and those of others demonstrate the utility of wastewater metagenomics as a powerful One Health surveillance tool, capturing the diversity and dynamics of viruses associated with human, animal, and environmental health. Global comparisons at the continent scale revealed homogeneity in viral community composition at high taxonomic levels, consistent with findings that viral communities are often more specific to environmental habitats (e.g. wastewater or marine) rather than geographical location. 40 At the city level, however, we observed geographical partitioning at both the family and genus levels, likely influenced by local factors such as climate, weather, demographics, agricultural practices, diet, population density and previous exposure history 32 . Sampling in South America and Oceania was limited, making the findings less generalizable for these regions. Additionally, an expanded dataset of these wastewater samples -sequenced using a strategy optimized for bacterial and AMR profiling- showed distinct regional variations in the resistome and bacteriome across continents. Specifically, bacterial composition divides the world's regions into roughly two major groups (Europe, Central Asia & North America versus Africa & Middle East), while the resistomes showed more unique regional profiles 19 . By increasing taxonomic resolution to the species and genotype level, our results and those of others demonstrate that metagenomic and phylogenetic analysis of viral communities in wastewater can reveal distinct temporal and geographical patterns. 32 Tobamoviruses were highly prevalent in urban wastewater, consistent with their detection in various water sources 41–43 The widespread detection of CGMMV and PMMoV in our study align with their documented widespread prevalence 44–46 . Notably, PMMoV has been proposed as a potential viral indicator for human faecal contamination in water and wastewater 38,42,43 . However, our data showed significant regional variability, with lower proportions in Asia, suggesting that regional differences should be considered when interpreting PMMoV in water quality assessments. Variations in the composition of Tobamovirus species might reflect differences in agricultural practices or diet. For instance, the detection of TMGMV in China could be related to their substantial tobacco production and consumption 47–49 . Importantly, early evidence of the emerging plant viruses like ToBRFV could also be detected. ToBRFV, which causes severe infections in tomatoes and peppers, was first identified in Jordan in 2015, and later traced back to Israel in 2014 50 . Since then, it has spread widely, with our data indicating its presence in Canada as early as July 2018, more than a year before its first official report in 2019. Similarly, detections in Italy in November 2017 and in Greece in June 2018 precede their reported outbreaks by nearly a year 51,52 . These findings highlight the utility of wastewater monitoring for the early detection and tracking of emerging plant pathogens. Extending our study to viruses impacting animal and human health, we were able to characterize the diversity of astroviruses on a global scale. The prevalence and distribution of HAstV genotypes in our study generally align with trends observed in environmental, epidemiological, and serological studies. The detection of emerging viruses is an important focus of wastewater surveillance. In our study, 'non-classical' astroviruses, such as HAstV-MLB1, HAstV-VA1/HMO-C, HAstV-VA2/HMO-A, and HAstV-VA3/HMO-C which are sometimes associated with neurological disease in immunocompromised individuals 53 were detected in global wastewater. Consistent with clinical reports indicating that novel astroviruses generally have lower detection rates than classical HAstVs, our wastewater data reflect this pattern with fewer samples positive for HAstV-MLB1 and a lower relative abundance compared to classical HAstVs. 53,54 However, in certain samples, novel astroviruses were the predominant type, which may contribute to the high seroprevalences observed in seroepidemiological studies. 55,56 An earlier metagenomic wastewater study reported higher abundances of astroviruses in the northern hemisphere during winter compared to the southern hemisphere 57 , and seasonal peaks in HAstV infections have been observed in colder months in regions such as China, Spain, and the USA 58–60 . However, comprehensive understanding of astrovirus types and their seasonality remains limited, and it is unclear whether types are constantly prevalent throughout the year or if they disappear and re-emerge over time 54 . Our findings showed consistent detection of some astroviruses like HAstV-1 in most locations with longitudinal sampling, while other types such as MLB1, were only detected in specific months. These findings suggest that astrovirus seasonality may be type specific. Like astroviruses, enteroviruses have a major impact on public health, and polioviruses were the earliest viruses to be monitored in wastewater. In global wastewater, enterovirus abundance consistently showed a dominance of EV-B across most regions, and high abundance of Enterovirus C types in African wastewater samples, aligning with trends from clinical surveillance. 61,62 In contrast, Enterovirus A types, such as CV-A6 and CV-A10, which are frequently reported in clinical samples from Asia, were not dominant in wastewater from this region 61 , potentially reflecting biases in clinical surveillance efforts that are targeted towards detection of the enterovirus types causing hand, foot, and mouth disease. EV-D68, another focus of clinical surveillance, was infrequently detected, possibly due to its respiratory transmission route and limited faecal shedding 63 . Its biennial circulation patterns, with even-numbered years from 2014 to 2018 in temperate climates 64,65 , were reflected in our data, in which we detected EV-D68 primarily in 2018. Dynamic changes in enterovirus populations over time could be observed, including for genotypes not commonly detected in clinical surveillance, giving a broader perspective on circulating diversity. In many cases we only recovered partial genomes, complicating taxonomic annotation and limiting the ability to determine the clade, type, subtype or lineage which are often necessary to investigate outbreaks 72,73 . For example, poliovirus type 3 detected in Cameroon likely reflects shedding of oral polio vaccine (OPV3) strains, which can, in rare cases, mutate into vaccine-derived polioviruses (VDPVs) capable of causing outbreaks. However, insufficient genomic resolution in critical regions (e.g., VP1, 5’UTR) prevented confirmation of whether the detected sequences represented vaccine strains or VDPVs. Wastewater monitoring also captured emerging enteroviruses, exemplified by Enterovirus E30. High proportions of E30 were observed in European samples, aligning with increased reporting of E30 across European countries from 2015 to 2017 66 , and a subsequent rise in meningitis and meningoencephalitis cases from April to September 2018 67 . Notably, the detection of E30 in European wastewater as early as June 2017 preceded the clinical observations, suggesting undetected circulation before its recognized outbreak. These findings demonstrate that wastewater monitoring provides a comprehensive view of enterovirus diversity, geographical spread, and temporal changes, enabling the detection of both clinically targeted and under-surveilled types and the (re)emergence of specific genotypes. Interestingly, animal-associated viruses were frequently detected in the global wastewater, including canine- and feline astrovirus and porcine enterovirus G, likely reflecting contamination from pet waste or animal-industry runoff entering sewage systems. The high abundance of canine astrovirus in Regina (Canada) may be linked to local practices encouraging the flushing of dog waste into sewage systems, a method recommended in several Canadian cities since 2017 to reduce environmental pollution 68 . In conclusion, in this study, shotgun metagenomic and capture-based sequencing of wastewater provided insights into the genetic diversity and spatiotemporal patterns of both endemic and emerging viruses. In resource-limited settings, where clinical testing facilities and surveillance infrastructure may be constrained, centralized metagenomic wastewater-based monitoring could provide a cost-effective and scalable alternative to detect viral threats. These findings underscore the value of wastewater metagenomic analysis as a One Health resource, offering insights into infectious disease dynamics. Materials and Methods Urban wastewater sample collection Raw wastewater samples were collected from 62 sites from 6 continents as previously described. 69 Briefly, unfiltered urban wastewater samples prior to the inlet of the wastewater treatment plant or main outlet to rivers were acquired. Where possible, flow proportion sampling over 24h or otherwise three crude point samples were obtained for representative sampling. Samples were stored at -80°C and shipped. Additional metadata containing details on the sampling location and the conditions of the samples such as temperature and sample consistency were gathered. The biannual samples were obtained around June and November in 2017 and 2018 (and in some cases early 2019). Monthly longitudinal samples were collected from eight locations across six continents during the same period. Sample processing Samples were thawed at room temperature and 50 ml of raw wastewater was centrifuged at 4000 rpm for 15 min to remove large debris. The supernatant was filtered using 0,45 µm pore diameter filter to remove bacterial and eukaryotic cells. 30 mL of the supernatant was concentrated using 30kDa Pierce Protein Concentrators PES (Thermo Fisher Scientific). The filtrate was treated with OmniCleave™ Endonuclease (Lucigen Corporation) for the removal of extracellular nucleic acid. Total nucleic acid content was extracted with the High Pure Viral Nucleic Acid Kit, without the use of DNAse treatment (Roche). Metagenomic and capture-based sequencing Library preparation was done using the KAPA HyperPlus kit (Roche) using Superscript IV and random priming to synthesize cDNA from RNA. Subsequently, double-stranded DNA (dsDNA) was generated using Klenow and the samples were subjected to enzymatic fragmentation. End repair and A-tailing was performed and KAPA Dual Indexed adapters were ligated. A post-ligation cleanup step was done using 0,8 x AMPure XP Beads (Beckman Coulter). After that, a double-sided size selection was performed according to the manufacturer’s instruction. Library PCR amplification was conducted using a total of 24 cycles. After purification, the quantity and quality assessment of the libraries was carried out using a Qubit 4 Fluorometer (Thermo Fisher Scientific) and an Agilent 2100 Bioanalyzer, following the respective manufacturers' protocols. The samples were pooled in equimolar proportions and sequenced directly or used as input for the GastroCap following sequencing on the Illumina MiSeq platform to generate 2x300 nt paired end sequences. For target enrichment the GastroCap probe set was used. This probe set was designed to target a manually curated list of vertebrate virus species belonging to the Adenoviridae , Astroviridae , Caliciviridae , Hepeviridae , Parvoviridae , Picornaviridae , Sedoreoviridae and Spinareoviridae families (Supplementary Fig S1). The probes were based on all sequences available in GenBank longer than 500 nucleotides for the selected species. To minimize redundancy, sequences were clustered at 95% nucleotide identity. To enhance representation of less abundant viral species, sequences from genera other than the highly abundant Enterovirus, Mastadenovirus, and Rotavirus were 'boosted' by multiplying their sequence abundance by a factor of three in the input dataset. The GastroCap probe set was designed by Roche using their proprietary Nimble Design workflow. Six library samples were pooled in equimolar amounts and purified using AMPure XP Beads of the KAPA HyperCapture Bead kit (Roche). The GastroCap probes were diluted 1:10, and 4,5 mL was added to 10,5 mL of the sample pools. Hybridization was performed by incubating the samples at 95°C for 5 minutes and then at 47°C for 72 hours. Following hybridization, 50 mL capture beads from the KAPA HyperCapture Bead kit (Roche) were washed according to the manufacturer’s protocol, added to the hybridized sample pools (15 mL) and incubated at 47°C for 15 minutes. The sample pools were then washed, and a post-capture PCR was performed using 14 cycles before purification by AMPure XP Beads. The quantity and quality of the captured library pools were assessed using the Qubit 4 Fluorometer and the Agilent 2100 Bioanalyzer following the manufacturers' protocols. The capture reactions were then pooled equimolarly and sequenced on the Illumina MiSeq platform to generate 2x300 nt paired end sequences. Sequence data analysis The sequence analysis workflow was generated using Snakemake (version 7.19.1). 70 Raw fastq files were quality trimmed using FastP (version 0.23.4). Read ends were trimmed to a mean quality Phred score of 25 with a sliding window of 5. Reads shorter than 30 nucleotides were discarded as well as reads with an average Phred score below 25. To identify and remove PCR duplicates, sequencing reads were deduplicated using CD-HIT 71 , which groups and removes duplicate reads that are identical within the first 150 nucleotides. Reads were aligned to the GRCh38 human genome reference (version GCF_000001405.26) using BWA-MEM (version 0.7.17) 72 and filtered using SAMtools (version 1.6) 73 before the reads were assembled using metaSPAdes (version SP) 74 . Taxonomic annotation was performed using DIAMOND v2.1.10.164 75 using the NCBI non-redundant protein reference database. Taxonomic assignment of each assembled contig was done based on the highest bit score normalized by the length of the contig. Finally, reads were mapped to the contigs using BWA-MEM to obtain read counts. Quantitative analysis: heatmaps of viral diversity For further quantitative analysis, annotated contigs with a length ≥300 nucleotides and an average depth of coverage ≥3 were selected to ensure the inclusion of more reliable hits. The total number of reads assigned to the superkingdom “Viruses” per sample was used to normalize read counts. The total number of reads mapping to a contig (forward and or reverse) were adjusted by dividing by the average genome size per individual viral family. For viral families with large variation in genome sizes the average genus level genome size was used. Relative viral abundance was calculated as reads per million (RPM) according to Eq. (1): The classification of viral families based on their hosts range was obtained from Virus-Host DB, which pairs viruses and hosts using NCBI taxonomy IDs, integrating host information from genome databases like RefSeq and GenBank, along with additional sources including UniProt, ViralZone, and literature 76 . Comparison of viral composition We compared viral composition across different locations using PCA, accounting for the compositional nature of metagenomic data. Therefore, the Centred Log-Ratio (CLR) coefficients were computed for distinct data subsets to preserve the relative relationships between viral taxa 77 . PCA was conducted on the viral fraction of the dataset using genome-size adjusted read counts to calculate CLR values. This normalization was not applied to plots at the superkingdom level (Archaea, Bacteria, Eukaryota (human separate), Viruses and Unknown) due to significant variability in genome sizes at this taxonomic level. For the virome data at family, genus and species level (metagenomic sequence data), only viral taxa with a high CLR median and high variance were retained, excluding those with low abundance and minimal variability between samples. For enteric viruses, all viral families targeted by the GastroCap probe set were included without additional filtering. Zero values were replaced using an Aitchison mean point estimate before applying the CLR transformation. The transformed data were then visualized in a biplot using the pyCoDaMath package in Python (version 3.8). Taxonomic analysis at the species and genotype level for selected viruses For selected viruses, the dataset was filtered to extract contigs belonging to specific genera using DIAMOND annotation with a contig length >=300 and average depth of coverage of >=3x. These contigs were then processed using custom workflows to determine species and genotype. These workflows involved mapping contigs with a blast search against a database of reference sequences from literature or existing typing tools, and assigning species and type based on virus specific ICTV classification criteria. Tobamovirus species assignment A custom blastn species assignment database was generated using sequences from all 37 ICTV-recognized Tobamovirus species. Contigs were assigned to specific species based on the criterion that strains within the same species must share more than 300bp and 90% nucleotide sequence identity across the whole genome, following the species demarcation criteria proposed by ICTV 78 . Mamastrovirus and Enterovirus genotyping and phylogenetic analysis The typing workflow for Mamastrovirus used a nucleotide blast database of complete ORF2 sequences sourced from Donato et al. 35 Annotated Mamastrovirus contigs were aligned against this database using blastn. Contigs that covered at least 10% of ORF2 and exhibiting ≥ 85% nucleotide identity, in line with classification criteria proposed by the ICTV Astroviridae Study Group 54,79 , were included for further analysis. Enterovirus contigs were assigned using the RIVM enterovirus typing tool, which performs a blastn search and phylogenetic analysis of the (partial) VP1 gene, with a minimum overlap of 100bp required for classification. 80 For phylogenetic analysis, sequences covering >500 bp of ORF2 were considered sufficient for reliable alignment and tree-building. Complete and partial Human astrovirus (type 1-8) ORF2 sequences and E-30 VP1 sequences were retrieved from NCBI, combined with our sequences and aligned using MAFFT (version 7.508). 81 76 Phylogenetic trees were generated using IQ-TREE (version 2.3.6), using automated model selection, the -czb option, and 1000 bootstrap replicates 82 . Declarations Acknowledgements This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 874735 (VEO) and the NWO Stevin Prize 2018 awarded to M.K. by the Netherlands Organisation for Scientific Research (NWO). Contributions M.K. and F.M.A. conceived the study, secured funding and provided overall supervision. M.G., & N.W. drafted the initial manuscript with input from M.K.. M.G & B.B.O.M. supervised manuscript preparation and data analysis. C.M.E.S and N.W performed NGS. N.W. and D.F.N performed bioinformatic data analyses. E.E.B.J. and C.B. contributed to the PCA and provided methodological guidance. N.W. produced the figures. D.F.N, R.I.L., C.M.E.S., C.B., E.E.B.J. , P.M., R.S.H., F.M.A., M.K., M.G. & B.B.O.M., critically read and contributed to the manuscript. The Global Sewage Consortium authors carried out sewage sampling, filled in metadata and shipped the samples. 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J Clin Virol 51 , 121–125 (2011). Katoh, K., Misawa, K., Kuma, K. I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30 , 3059–3066 (2002). Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol Biol Evol 32 , 268 (2015). Additional Declarations There is NO Competing Interest. Supplementary Files NatureSupplementaryinformation.docx Supplementary information Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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sampling.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/31a7a50951a760830130d10c.png"},{"id":79176012,"identity":"d77a58b3-c5ec-4074-a72f-f781d6264239","added_by":"auto","created_at":"2025-03-25 09:56:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":973776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eHeatmap of the viral family diversity in the global wastewater samples\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. The heatmap is ordered by RPM value of the GastroCap sequence data and categorized by host association and continent. The colour gradient represents log-transformed relative abundance of reads normalized to genome length. OCE*= Oceania, SA* = South America\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/20808b066a1d94db1b49d923.png"},{"id":79173861,"identity":"bec0f961-69c8-427a-a67f-062f961c6c97","added_by":"auto","created_at":"2025-03-25 09:48:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":461240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrevalence and relative abundance of known human pathogenic enteric viruses in urban wastewater. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eReads per million viral reads (normalized by genome length) per genus are given for the biannual (a) and longitudinal (b) GastroCap sample set. The heatmap is ordered by reads per million viral reads (normalized by genome length) and categorized by continent (a) and city (b). The colour gradient represents log-transformed relative abundance of reads. SA* = South America\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/fb61a76451feb9bba6cfc08c.png"},{"id":79176013,"identity":"c1d47d13-9ad9-417e-a743-7c97a07a4c2a","added_by":"auto","created_at":"2025-03-25 09:56:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":315772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePCA clustering of the urban wastewater virome compositions from 62 cities of the shotgun metagenomic sequence dataset.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe virome compostions of the biannual samples (a, b) coloured by continent and longitudinal samples (c, d) coloured by city at the family (a, c) and genus (b, d) levels. Principal components were derived from genome size-adjusted read counts subjected to a CLR transformation. Arrows represent viral families or genera, with their direction showing their contribution to the principal components and their length indicating the strength of their contribution to the variance in virome composition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/75400178d8397cc1debed522.png"},{"id":79173867,"identity":"a5a6bf93-a651-4115-bd00-f9802c1e77f1","added_by":"auto","created_at":"2025-03-25 09:48:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":289432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePCA clustering of the urban wastewater virome compositions from 62 cities of the GastroCap sequence dataset.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe virome compostions of the biannual samples (a, b) coloured by continent and longitudinal (c, d) samples coloured by city at the family (a, c) and genus (b, d) levels. Principal components were derived from genome size-adjusted read counts subjected to a CLR transformation. Arrows represent viral families or genera, with their direction showing their contribution to the principal components and their length indicating the strength of their contribution to the variance in virome composition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/8fc96c08fcca078b6cc03181.png"},{"id":79173864,"identity":"299a5fcf-f1f3-4fb6-8897-ad6ad01ad8db","added_by":"auto","created_at":"2025-03-25 09:48:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":216012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGlobal prevalence of species within the Tobamovirus genus in urban wastewater.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBiannual (a) and longitudinal data (b) are shown. In each section, the upper plots depict total reads per million viral reads assigned to the Tobamovirus genus (green) and to its specific species (red). The lower plots display a bar chart of the relative abundance of Tobamovirus species. The biannual samples were grouped using hierarchical clustering based on species-level composition. *YoMV = Youcai mosaic virus, TVCV = Turnip Vein-Clearing virus, TSAMV = Tropical soda apple mosaic virus, ToMV = Tomato mosaic virus, ToMMV = tomato mottle mosaic virus, TMV = Tobacco mosaic virus, TMGMV = Tobacco mild green mosaic virus, TBRFV =. Tomato brown rugose virus, RMV = Ribgrass mosaic virus, RheMV = Rehmannia mosaic virus, PMMoV = Pepper mild mottle virus, PaMMV = paprika mild mottle virus, HLSV = Hibiscus latent Singapore virus, HLFPV = Hibiscus latent Fort Pierce virus, CMoV = Cucumber mottle virus, CGMMV= Cucumber green mild mottle virus, BPMV, bell pepper mottle virus.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/cd5b9c68fd07625c81e4a8ad.png"},{"id":79177160,"identity":"f949cc30-f850-46db-abf2-19f273df2e64","added_by":"auto","created_at":"2025-03-25 10:04:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":287041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGlobal prevalence of genotypes within the Mamastrovirus genus in urban wastewater.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBiannual (a) and longitudinal data (b) are shown. In each section, the upper plots in each panel show total reads per million viral reads assigned to the Mamastrovirus genus (green) and to specific genotypes (red). The lower plots show the relative abundance of Mamastrovirus genotypes. The biannual samples were grouped using hierarchical clustering based on genotype composition. *HAstV = Human Astrovirus, RAstV = Rat astrovirus, CAstV = Canine astrovirus, FAstV = Feline astrovirus, ChAstV = Cheetah astrovirus, YakAstV = Yak astrovirus, Other = genotypes detected in fewer than 4 samples.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/61f4441918e01616029e4498.png"},{"id":79173870,"identity":"7d366375-844e-4f0d-81c4-bf3694cbf967","added_by":"auto","created_at":"2025-03-25 09:48:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":403714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMaximum likelihood phylogenetic tree of Human astrovirus type 5 based on partial ORF2 gene (capsid) sequences\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Sequences obtained from GenBank are indicated in coloured squares, while sequences derived from wastewater with a minimum length of 500 bp are denoted in coloured dots. Bootstrap values \u0026gt;70 are shown.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/93d5cf63ef41e06dca76a59f.png"},{"id":79177159,"identity":"6fe15eaa-9f20-41ed-9c3e-e3b485e1792b","added_by":"auto","created_at":"2025-03-25 10:04:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":530509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrevalence of enterovirus genotypes in global urban wastewater.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBiannual (a) and longitudinal data (b) are shown. In each section, the upper plots show the total reads per million viral reads assigned to the Enterovirus genus (green) and those assigned to specific genotypes (red). The middle and lower plots show the relative abundance of Enterovirus species and genotypes, respectively. The biannual samples were grouped using hierarchical clustering based on genotype composition. Other = genotypes detected in fewer than 10 samples, including (vaccine-derived) poliovirus type 3.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/589f8c811c89df3c56795468.png"},{"id":79176015,"identity":"a35ee3f3-44d7-4408-8d08-6d33f9ddc35d","added_by":"auto","created_at":"2025-03-25 09:56:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":330837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMaximum likelihood phylogenetic tree of Echovirus 30 (E30) based on partial VP1 gene (capsid)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Sequences obtained from GenBank are indicated with coloured squares, while sequences derived from wastewater with a minimum length of 500 bp are denoted in coloured dots. Bootstrap values \u0026gt;70 are shown.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/1021bf47faf28f11f40e148f.png"},{"id":97040297,"identity":"f5acd200-2f6d-4f59-8837-73d86c0f4f4a","added_by":"auto","created_at":"2025-11-29 08:11:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5137584,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/2a2b45d5-fa3b-4dea-a515-b351f10ee793.pdf"},{"id":79173871,"identity":"1956e00d-546c-49a2-b77a-096b5de3d3bc","added_by":"auto","created_at":"2025-03-25 09:48:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4685154,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"NatureSupplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6048078/v1/75d4c1993385dfa157f84de7.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Unveiling the global urban virome: insights from wastewater metagenomics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, the frequency and scale of infectious disease outbreaks have increased, particularly those emerging after spillover of animal viruses.\u003csup\u003e8\u003c/sup\u003e The emergence and spread of these diseases have been driven by a range of interconnected factors, including urbanization\u003csup\u003e9\u003c/sup\u003e, the development of megacities,\u003csup\u003e10\u003c/sup\u003e deforestation\u003csup\u003e11,12\u003c/sup\u003e, growing food demands\u003csup\u003e13\u003c/sup\u003e, and increasing global connectivity. Migration from rural to urban areas has contributed to population growth in cities, resulting in increased close-contact interactions that facilitate disease transmission\u003csup\u003e10\u003c/sup\u003e. By 2017, 55% of the world’s population lived in urban areas\u003csup\u003e14\u003c/sup\u003e, making these high-density cities critical hubs for the spread of infectious diseases, and therefore strategic sites for monitoring and responding to both novel and established public health threats.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCurrent infectious disease surveillance primarily relies on the identification and reporting of symptomatic cases\u003csup\u003e15\u003c/sup\u003e, which has limitations, particularly for viruses that frequently cause asymptomatic or mild infections, or diseases that are not non-reportable. As a result, novel infectious diseases caused by emerging viruses or variants of known viruses can evade detection, spreading silently before being recognized. This phenomenon was exemplified by the introduction of Zika virus in South America\u003csup\u003e2\u003c/sup\u003e and the early undetected spread of SARS-CoV-2 into Europe\u003csup\u003e3,4\u003c/sup\u003e and the USA\u003csup\u003e16\u003c/sup\u003e. To better understand the prevalence and spread of viral infections, alternative surveillance strategies beyond symptom-based reporting are needed to capture the full spectrum of viral transmission dynamics and enhance early detection and response to emerging infectious disease threats.\u003c/p\u003e\n\u003cp\u003eWastewater-based epidemiology (WBE) involves the detection and monitoring of chemicals, drugs and substances\u003csup\u003e17,18\u003c/sup\u003e, genes\u003csup\u003e19\u003c/sup\u003e , pathogens\u003csup\u003e20\u003c/sup\u003e and vectors\u003csup\u003e21\u003c/sup\u003e within wastewater to assess community health or disease risk. Recent detections of poliovirus in wastewater from major urban cities like New York\u003csup\u003e22\u003c/sup\u003e, London\u003csup\u003e23\u003c/sup\u003e, and poliovirus essential facilities\u003csup\u003e24\u003c/sup\u003e highlight the potential of WBE in identifying circulation hotspots and guiding targeted interventions, such as enhancing surveillance and launching of vaccine campaigns.\u003csup\u003e17\u003c/sup\u003e During the COVID-19 pandemic, quantitative reverse transcription PCR-based methods were used to assess the viral load in wastewater, with viral concentrations following the number of positive cases at the community level and providing insights into infection dynamics over time\u003csup\u003e19,20\u003c/sup\u003e. Amplicon-based sequencing was used in parallel to identify circulating viral variants, enhancing early detection capabilities in wastewater if used in combination with pathogen specific bioinformatic pipelines\u003csup\u003e25,26\u003c/sup\u003e. Utilizing targeted molecular methods has extended WBE to track other pathogenic viruses like influenza\u003csup\u003e5\u003c/sup\u003e, respiratory syncytial virus\u003csup\u003e5\u003c/sup\u003e, mpox\u003csup\u003e6\u003c/sup\u003e, arboviruses\u003csup\u003e27\u003c/sup\u003e, and non-polio enteroviruses (EV) such as EV-D68\u003csup\u003e28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong viruses shed into wastewater, enteric viruses pose a significant public health threat. Enteric viruses mainly transmit through the faecal-oral route, cause a wide range of gastrointestinal diseases and are a potential source of community-wide outbreaks.\u003csup\u003e29\u003c/sup\u003e Detection is complicated by their extensive diversity, non-specific clinical symptoms that hamper diagnosis, high rate of secondary transmission which may mask initial introductions, and gaps in surveillance systems. As the number of viral targets of interest grows, the question arises whether surveillance efforts focused on individual pathogens can be combined in a more comprehensive approach. In this context, metagenomic sequencing can be a promising technique that can address this need.\u003csup\u003e30\u003c/sup\u003e A significant advantage of metagenomic sequencing is its ability to simultaneously detect and characterize multiple viral agents present within a single (wastewater) sample including non-target (novel) viruses.\u003csup\u003e31,32\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we explore the potential of metagenomic sequencing and analysis as a tool for population-level monitoring of virus circulation across hosts in high-density urban areas. We sequenced and characterized the wastewater virome of 62 major cities worldwide using both a shotgun viral metagenomic and a capture-based sequencing method targeting enteric viruses, to compare viromes and viral dynamics across the world.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVirome data overview\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the worldwide urban wastewater virome, wastewater samples were collected from 62 cities across 47 countries on 6 continents (Figure 1).\u003csup\u003e33\u003c/sup\u003e For all cities, samples were acquired biannually in June and November over a two-year period spanning 2017 to 2019 (biannual samples). Additionally, to investigate temporal changes, monthly samples were obtained from eight cities in different countries in six different continents during the same period. Wastewater samples were analysed using both shotgun viral metagenomic sequencing to determine the viral metagenome and sequencing following capture using a custom capture probe set (GastroCap, Supplementary Figure S1), designed to enrich for enteric virus sequences. The median number of reads generated after sequencing was 9.8\u0026times;10\u003csup\u003e5\u003c/sup\u003e (range: 3 x 10\u003csup\u003e3\u003c/sup\u003e-5.3 x 10\u003csup\u003e6\u003c/sup\u003e) per sample for the metagenomic sequence dataset and 7.4x10\u003csup\u003e5\u003c/sup\u003e (range: 1.4 \u0026times;10\u003csup\u003e2\u003c/sup\u003e-4.5 x 10\u003csup\u003e6\u003c/sup\u003e)\u0026nbsp;for the capture sequence dataset (Supplementary Figure S2).\u0026nbsp;The median percentage of reads per sample that could be annotated as viral was 11% (range: 0.03%-76%) for the metagenomic sequence dataset and 26% (range: 0.04%-97%) for the capture sequence dataset.\u0026nbsp;Principal component analysis (PCA) of centred log-ratio (CLR) transformed read count data at the superkingdom level revealed distinct clustering between the two datasets and confirmed the enrichment of viral reads using GastroCap (Supplementary Figure S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComposition of the Global Urban Wastewater Virome\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish a baseline understanding of the urban wastewater virome, we first examined the diversity of viruses at the family level. Overall, viral reads were mapped to a total of 122 families, 1,367 genera and 2,546 species (Figure 2). These findings highlight the broad diversity of viruses associated with vertebrates, plants, microbes, and other life forms within urban environments. A large fraction of the metagenomic virome consisted of bacteriophages and plant viruses (Figure 2). Viral families linked to human disease, such as \u003cem\u003eAstroviridae, Parvoviridae, and Picornaviridae,\u003c/em\u003e were frequently detected in high abundances. In contrast, other important human-associated families, including \u003cem\u003eAdenoviridae, Caliciviridae, Hepeviridae, and Sedoreoviridae,\u0026nbsp;\u003c/em\u003ewere also detected but less frequently.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf the eight viral families targeted by GastroCap (\u003cem\u003eAdenoviridae\u003c/em\u003e, \u003cem\u003eAstroviridae\u003c/em\u003e, \u003cem\u003eCaliciviridae\u003c/em\u003e, \u003cem\u003eHepeviridae\u003c/em\u003e, \u003cem\u003eParvoviridae\u003c/em\u003e, \u003cem\u003ePicornaviridae\u003c/em\u003e, \u003cem\u003eSpinareoviridae\u003c/em\u003e and \u003cem\u003eSedoreoviridae\u003c/em\u003e) which include 72 genera and 1038 species, a total of 72 genera and 165 species were identified (Figure 2). GastroCap-based sequencing resulted in a substantial increase in both the total and relative number of reads for the targeted viral families. The median fold increase per sample, based on relative abundance in reads per million (rpm), was as follows: \u003cem\u003eAdenovirida\u003c/em\u003ee (545-fold), \u003cem\u003eAstroviridae\u003c/em\u003e (470-fold), \u003cem\u003eCaliciviridae\u003c/em\u003e (5094-fold), \u003cem\u003eHepeviridae\u003c/em\u003e (22-fold), \u003cem\u003eParvoviridae\u003c/em\u003e (2-fold), \u003cem\u003ePicornaviridae\u003c/em\u003e (132-fold), \u003cem\u003eSedoreoviridae\u003c/em\u003e (193-fold) and\u003cem\u003e\u0026nbsp;Spinareoviridae\u0026nbsp;\u003c/em\u003e(21-fold). Viral genera associated with human gastrointestinal disease, including those commonly targeted by surveillance, such as \u003cem\u003eMamastrovirus, Bocaparvovirus, Salivirus, Kobuvirus, Norovirus, Sapovirus, Enterovirus, Mastadenovirus, Rotavirus, Paslahepevirus, Parechovirus, Cardiovirus\u0026nbsp;\u003c/em\u003ewere detected on every continent. However, while \u003cem\u003eMamastrovirus\u0026nbsp;\u003c/em\u003ewas highly abundant in most samples across all continents (Figure 3A),\u003cem\u003e\u0026nbsp;Enterovirus\u0026nbsp;\u003c/em\u003ewas mainly detected at high levels in samples from Africa, a few cities in Europe (Belgrade, Bratislava), North America (Sisimiut, Chapel Hill), and South America (Quito) (Figure 3B). Conversely, \u003cem\u003ePaslahepevirus\u003c/em\u003e, to which Hepatitis E belongs, was more prevalent in Europe than in other regions. The longitudinal samples further revealed distinct spatial and temporal patterns in the prevalence of certain gastrointestinal viruses. For example, in Regina (Canada), \u003cem\u003eEnterovirus\u003c/em\u003e levels showed a relative increase during summer months, whereas \u003cem\u003eMastadenovirus\u003c/em\u003e abundance was higher in the winter months.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrincipal Component Analysis revealed that cities differ in virome composition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify broader patterns and potential geographical signatures in virome composition, we applied principal component analysis (PCA) on both the metagenomic and GastroCap sequence datasets. The metagenomic sequence data revealed no clear clustering patterns across continents at either the family or genus level, suggesting that the viral community structures were broadly similar worldwide (Figure 4A and B). The limited variance between locations was primarily driven by the genera \u003cem\u003eTobamovirus\u003c/em\u003e (a group of plant viruses within the \u003cem\u003eVirgaviridae\u003c/em\u003e family) and \u003cem\u003eGemykibivirus\u003c/em\u003e (associated with multiple hosts, including plants, insects, mammals, and occasional human samples\u003csup\u003e34\u003c/sup\u003e), both of which were more prevalent in samples from Asia. However, when comparing the virome composition by city, more pronounced differences were observed in both biannual and longitudinal samples (Figure S4 and Figure 4C and D).\u003c/p\u003e\n\u003cp\u003eFocusing on the enteric virus families in the GastroCap sequence dataset, PCA revealed a broadly similar composition of these viral families across all continents, as indicated by overlapping clusters in the plot (Figure 5A). The largest differences were observed for the \u003cem\u003eAstroviridae\u003c/em\u003e and \u003cem\u003ePicornaviridae\u0026nbsp;\u003c/em\u003efamilies. However, at the genus level, more distinct differences in viral composition were observed between continents (Figure 5B). Notably, Europe and Africa showed overlapping compositions, while the dataset for Oceania was distinctly separated. At the city level, even for the biannually sampled cities, PCA revealed clear distinct clustering, indicating significant variation in viral compositions between cities (Figure 5C and D, Supplementary Figure S5). These analyses identified the genus \u003cem\u003eMamastrovirus\u0026nbsp;\u003c/em\u003e(\u003cem\u003eAstroviridae)\u0026nbsp;\u003c/em\u003eas the primary driver ofbeta-diversity. \u003cem\u003eNorovirus\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSapovirus\u003c/em\u003e showed covariation, as reflected by the small angle between their loading vectors, indicating similar contributions to the observed variation. Within the \u003cem\u003ePicornaviridae\u003c/em\u003e family, genera such as \u003cem\u003eEnterovirus,\u003c/em\u003e and \u003cem\u003eCardiovirus, also\u0026nbsp;\u003c/em\u003edisplayed some degree of covariation. The cities with longitudinal sampling showed that over time, Seattle (USA) and Copenhagen (Denmark) exhibited tightly clustered sample groupings at the family level, suggesting a stable composition of enteric virus families throughout the study period. In contrast, the virome composition in Regina (Canada), Kuala Lumpur (Malaysia), Guangzhou (China), and Yaound\u0026eacute; (Cameroon) showed greater variability, reflecting a more dynamic and heterogeneous composition over time. At the genus level, the enteric virome composition in Melbourne (Australia) was distinct from that of all other cities, primarily due to a high abundance of \u003cem\u003eMamastroviruses\u003c/em\u003e (Figure 5D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSpecies and genotype-level analysis of selected viral genera\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further explored the value of wastewater for monitoring viruses at finer taxonomic resolution, at the level of species and genotypes to capture fine-scale spatiotemporal patterns of viral diversity. The genera\u0026nbsp;\u003cem\u003eTobamovirus\u003c/em\u003e \u003cem\u003e(Virgaviridae)\u003c/em\u003e from the metagenomic sequence data and \u003cem\u003eMamastrovirus\u003c/em\u003e (\u003cem\u003eAstroviridae)\u003c/em\u003e and \u003cem\u003eEnterovirus (Picornaviridae\u003c/em\u003e) from the GastroCap data had the largest influence on virome compositions across the biannual samples in the PCA. Due to their relevance to plant, public, and animal health, these genera were selected for further investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eShotgun metagenomic sequencing reveals Tobamovirus diversity in wastewater\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTobamoviruses\u003c/em\u003e are of major concern because they can cause disease in a wide range of agriculturally important plant species, affecting tomato, pepper and cucumber plants. We identified a total of 5,204 \u003cem\u003eTobamovirus\u003c/em\u003e contigs in the metagenomic dataset of which 32% (1,653 contigs) were assigned to 17 different species (Figure 6), including both widely distributed, well-known viruses and emerging viruses with increasing agricultural and environmental relevance. Analysis of the sequences at the species level showed large compositional differences between samples and locations. Overall Cucumber Green Mild Mottle virus (CGMMV) and Pepper Mild mottle virus (PMMoV) \u0026ndash; both well-studied and widely distributed viruses - were highly prevalent in urban sewage samples worldwide. PPMoV did not exhibit temporal variation, but its relative abundance was lower in Asia (Figure 6A and B). The relative abundance of CGMMV was highest in samples from Europe, Asia and North America (Figure 6A and B). Tobacco mosaic virus (TMV) was predominantly found in high abundance in samples from Asia and Africa, while Tobacco mild green mosaic virus (TMGMV) was primarily observed in samples from Asia (Figure 6, A and B). Additionally, we detected emerging viruses of concern, such as the rapidly spreading Tomato brown rugose fruit virus (ToBRFV), which was detected in wastewater samples from multiple locations including Rome (Italy) in 2017 and Athens (Greece), Vancouver (Canada), Be\u0026rsquo;er Sheva (Israel) and Seattle (USA) in 2018 (Figure 6, A and B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlobal diversity and phylogenetic analysis of Mamastrovirus in wastewater\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariation in the relative abundance of the genus \u003cem\u003eMamastrovirus\u003c/em\u003e contributed most to the differentiation of samples in the PCA in the GastroCap sequence dataset (Figure 5B). To explore if these differences were associated to specific \u003cem\u003eMamastrovirus\u003c/em\u003e types, we developed a custom typing workflow using reference sequences from a phylogenetic study on astrovirus diversity\u003csup\u003e35\u003c/sup\u003e. Our approach identified 10,668 \u003cem\u003eMamastrovirus\u003c/em\u003e contigs of which 14% (1512 contigs) could be assigned a genotype, resulting in the detection of 24 different types. Our analysis revealed both well-established (endemic) and less commonly observed \u003cem\u003eMamastrovirus\u0026nbsp;\u003c/em\u003egenotypes across various global wastewater samples. There was substantial variation in \u003cem\u003eMamastrovirus\u003c/em\u003e genotype distribution between samples.\u0026nbsp;Compositional differences in genotypes did not clearly correlate with specific continents or cities in the biannual dataset (Figure 7A). Human astroviruses (HAstV) formed\u0026nbsp;the major fraction of Mamastrovirus genotypes detected, among these, the classical HAstV type 1\u0026ndash;5 and 8 were most frequently detected. HAstV-1, a genotype considered endemic worldwide, was frequently detected across all sampled continents and was the predominant genotype in many samples. HAstV-8, a genotype rarely reported in clinical surveillance, was also detected in high relative abundance across all continents. Additionally, recently discovered astroviruses, including MLB1, VA1/HMO-C, VA2/HMO-A and VA3/HMO-B were detected across multiple continents.\u0026nbsp;The novel human astrovirus BF34, which was identified and exclusively detected in Burkina Faso in 2010 (Africa), was detected in wastewater samples from Cameroon (February and March 2018), Austria (June and November 2018) and Uganda (July 2018)\u0026nbsp;indicating its presence across multiple geographic regions in 2018. While less common in wastewater samples, animal astroviruses like, canine- (CAstV), rat astrovirus (RAstV) and feline astrovirus (FAstV) were detected across continents, with surprisingly high prevalence of CAstV in Regina, Canada. For the cities with longitudinal sampling, temporal shifts in the genotype distribution were observed. PCA analyses showed that \u003cem\u003eMamastrovirus\u003c/em\u003e was a major driver for the distinct clustering of Melbourne\u0026apos;s virome (Figure 5D) with consistently high proportions of \u003cem\u003eMamastrovirus\u0026nbsp;\u003c/em\u003ereads detected. In Melbourne HAstV-5 was more prevalent during 2017 and in early 2018, while HAstV-1 was mainly prevalent during the remainder of 2018. Other cities also exhibited peaks in \u003cem\u003eMamastrovirus\u003c/em\u003e reads over the observation period, but the timing of these peaks varied across locations and was not linked to a specific genotype.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the global diversity and circulation patterns of the abundant \u0026lsquo;classical\u0026rsquo; human astroviruses in greater detail, we conducted phylogenetic analysis. The retrieval of partial capsid sequences for HAstV type 1, 2, 3, 4, 5 and 8 (Figure 8 and Supplementary Figure S6) from wastewater considerably expands the number of available capsid sequences from previously underrepresented regions. For several types we observed wastewater-specific clusters, indicating that current clinical surveillance does not fully capture the diversity of HAstVs. Additionally, phylogenetic analysis revealed that integrating global wastewater sequences within a limited timeframe provided insights into country and city specific virus circulation. Viral sequences from the same city often clustered together, as did sequences from samples collected closer in time suggesting periodic circulation and replacement of viral lineages over time. Notably, evidence suggestive of region-specific virus circulation was observed for some African\u0026nbsp;HAstV type 4, 5, and 8 clusters. For example, HAstV-5 wastewater sequences from Cameroon (2018), Senegal (2019), and human faecal sequences from Cameroon (2014) formed a distinct cluster. Similarly, a separate HAstV-8 cluster comprised of sequences from clinical samples from Cameroon from 2014 and wastewater sequences from Nigeria (2018) was observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlobal diversity and phylogenetic analysis of enterovirus in wastewater\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe abundance of \u003cem\u003ePicornaviridae\u003c/em\u003e varied across continents, with enteroviruses frequently detected using GastroCap, significantly influencing PCA clustering patterns (Figure 5D). Enteroviruses, a major public health concern due to their potential to cause a wide range of illnesses, including respiratory infections and neurological diseases, were analysed in wastewater to evaluate their surveillance potential in metagenomic datasets. A total of 2,331 contigs were classified within the \u003cem\u003eEnterovirus\u0026nbsp;\u003c/em\u003egenus, of which 37% (868 contigs) could be genotyped using the RIVM Enterovirus genotyping tool, corresponding to 62 distinct \u003cem\u003eEnterovirus\u0026nbsp;\u003c/em\u003egenotypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of prevalence and relative abundance revealed differences in enterovirus distribution across continents (Figure 9A and B). Samples from Africa showed the highest enterovirus abundance compared to other regions. Enterovirus B was the most prevalent species worldwide, with\u0026nbsp;Coxsackievirus (CV) B5 and CV-A9 being the most prevalent types across all regions, suggesting widespread endemicity. In North America Enterovirus A was the most prevalent species, with Coxsackievirus CV-A6 and CV-A4, CV-A10 \u0026ndash; endemic enteroviruses and common causes of hand, foot, and mouth disease- among the most detected types. Enterovirus C, though less prevalent, was frequently detected in the African region and was the dominant enterovirus species in longitudinal samples from Ecuador, Malaysia, and Cameroon. While Enterovirus D was detected sporadically, Enterovirus D-68\u0026mdash;a clinically significant genotype due to its association with respiratory illness outbreaks and acute flaccid myelitis\u0026mdash;was primarily identified toward the end of 2017 and in 2018. In most locations, Enterovirus D-68 Clade B3 was predominant, while Clade A2 was detected in Athens and Taipei. Enterovirus G, associated with infection in pigs, was detected in high abundances in several cities across all continents.\u003c/p\u003e\n\u003cp\u003eGeographical specificity was evident for certain enterovirus genotypes. For example, CV-A20 was exclusively found in Africa and South America, while CV-A1 and CV-A22 were detected across all continents. In Europe, Echovirus type 30 (E-30) displayed an unusually high prevalence, which was not observed in other regions, possibly indicating a recent outbreak or re-emergence after a period of lower endemic circulation. Phylogenetic analysis of partial VP1 sequences revealed that European wastewater E-30 sequences from 2018 were generally more closely related to each other than to sequences from 2017 (Figure 10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLongitudinal analysis revealed dynamic patterns in enterovirus genotype distribution across cities over time (Figure 9B). For instance, Yaound\u0026eacute; (Cameroon) exhibited a broad diversity of circulating genotypes, including vaccine-derived poliovirus type 3. No sequences of the typing region VP1 for other polio types were detected. Longitudinal analysis across sites identified periods of genotype dominance, with CV-C116 \u0026ndash; a lesser-known member of Enterovirus C with unknown clinical relevance - prevailing in Quito in 2018 and CV-A1 in Kuala Lumpur during 2017 and early 2018.\u0026nbsp;In Regina, seasonal variations in overall enterovirus presence could be observed, with elevated levels occurring primarily during the summer and fall. Additionally, while multiple genotypes circulated in 2017, CV-A10 was present in higher relative abundance during specific periods, and shifted to CV-A6 in 2018,\u0026nbsp;indicating dynamic changes in circulating enterovirus populations over consecutive years. Additionally, EV-A71 \u0026ndash;one of the leading causes of hand, foot and mouth disease and associated with neurological complications - was detected in Regina in July, August and November 2017, coinciding with a period of elevated laboratory confirmed enterovirus/rhinovirus cases in Canada that year according to national surveillance data\u003csup\u003e36,37\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eThis study provides a comprehensive analysis of the urban wastewater virome of 62 major cities worldwide using shotgun metagenomic and capture-based (GastroCap) sequencing.\u003csup\u003e35\u0026nbsp;\u003c/sup\u003eBy incorporating both biannual and longitudinal samples, we assessed viral diversity and temporal dynamics. While biannual sampling provided a snapshot of viral diversity within a particular location, more frequent sampling revealed a higher viral diversity per location and enabled the detection of gradual shifts in composition. Shotgun\u0026nbsp;metagenomics, at least with the sequencing depth used in our study, yielded a relatively low proportion of enteric virus reads, making it difficult to effectively monitor enteric viruses in complex wastewater samples.\u003csup\u003e38\u003c/sup\u003e The newly developed GastroCap probe set significantly enriched clinically relevant enteric viruses, whereas metagenomic sequencing primarily detected bacteriophages and plant viruses. Thus, for the surveillance of human pathogenic enteric viruses, which are stable in aquatic environments\u003csup\u003e39\u003c/sup\u003e, wastewater is a valuable source when combined with GastroCap enrichment. Additionally, a large proportion of reads could not be assigned to known virus taxa, highlighting the potential for future discovery of novel viruses. Our findings and those of others demonstrate the utility of wastewater metagenomics as a powerful One Health surveillance tool, capturing the diversity and dynamics of viruses associated with human, animal, and environmental health.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Global comparisons at the continent scale revealed homogeneity in viral community composition at high taxonomic levels, consistent with findings that viral communities are often more specific to environmental habitats (e.g. wastewater or marine) rather than geographical location.\u003csup\u003e40\u003c/sup\u003e At the city level, however, we observed geographical partitioning at both the family and genus levels, likely influenced by local factors such as climate, weather, demographics, agricultural practices, diet, population density and previous exposure history\u003csup\u003e32\u003c/sup\u003e. Sampling in South America and Oceania was limited, making the findings less generalizable for these regions. Additionally, an expanded dataset of these wastewater samples -sequenced using a strategy optimized for bacterial and AMR profiling- showed\u0026nbsp;distinct regional variations in the resistome and bacteriome across continents. Specifically, bacterial composition divides the world\u0026apos;s regions into roughly two major groups (Europe, Central Asia \u0026amp; North America versus Africa \u0026amp; Middle East), while the resistomes showed more unique regional profiles\u003csup\u003e19\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy increasing taxonomic resolution to the species and genotype level, our results and those of others demonstrate that metagenomic and phylogenetic analysis of viral communities in wastewater can reveal distinct temporal and geographical patterns.\u003csup\u003e32\u003c/sup\u003e \u003cem\u003eTobamoviruses\u003c/em\u003e were highly prevalent in urban wastewater, consistent with their detection in various water sources\u003csup\u003e41\u0026ndash;43\u003c/sup\u003e The widespread detection of CGMMV and PMMoV in our study align with their documented widespread prevalence\u003csup\u003e44\u0026ndash;46\u003c/sup\u003e. Notably,\u0026nbsp;PMMoV has been proposed as a potential viral indicator for human faecal contamination in water and wastewater\u003csup\u003e38,42,43\u003c/sup\u003e. However, our data showed significant regional variability, with lower proportions in Asia, suggesting that regional differences should be considered when interpreting PMMoV in water quality assessments. Variations in the composition of \u003cem\u003eTobamovirus\u003c/em\u003e species might reflect differences in agricultural practices or diet. For instance, the detection of TMGMV in China could be related to their substantial tobacco production and consumption\u003csup\u003e47\u0026ndash;49\u003c/sup\u003e. Importantly, early evidence of the emerging plant viruses like\u0026nbsp;ToBRFV could also be detected. ToBRFV, which causes severe infections in tomatoes and peppers, was first identified in Jordan in 2015, and later traced back to Israel in 2014\u003csup\u003e50\u003c/sup\u003e. Since then, it has spread widely, with our data indicating its presence in Canada as early as July 2018,\u0026nbsp;more than\u0026nbsp;a year before its first official report in 2019. Similarly, detections in Italy in November 2017 and in Greece in June 2018 precede their reported outbreaks by nearly a year\u003csup\u003e51,52\u003c/sup\u003e. These findings highlight the utility of wastewater monitoring for the early detection and tracking of emerging plant pathogens.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExtending our study to viruses impacting animal and human health, we were able to characterize the diversity of astroviruses on a global scale.\u0026nbsp;The prevalence and distribution of HAstV genotypes in our study generally align with trends observed in environmental, epidemiological, and serological studies. The detection of emerging viruses is an important focus of wastewater surveillance. In our study, \u0026apos;non-classical\u0026apos; astroviruses, such as HAstV-MLB1, HAstV-VA1/HMO-C, HAstV-VA2/HMO-A, and HAstV-VA3/HMO-C which are sometimes associated with neurological disease in immunocompromised individuals\u003csup\u003e53\u003c/sup\u003e were detected in global wastewater. Consistent with clinical reports indicating that novel astroviruses generally have lower detection rates than classical HAstVs, our wastewater data reflect this pattern with fewer samples positive for HAstV-MLB1 and a lower relative abundance compared to classical HAstVs.\u003csup\u003e\u0026nbsp;53,54\u003c/sup\u003e However, in certain samples, novel astroviruses were the predominant type, which may contribute to the high seroprevalences observed in seroepidemiological studies.\u003csup\u003e\u0026nbsp;55,56\u0026nbsp;\u003c/sup\u003eAn earlier metagenomic wastewater study reported higher abundances of astroviruses in the northern hemisphere during winter compared to the southern hemisphere\u003csup\u003e57\u003c/sup\u003e, and seasonal peaks in HAstV infections have been observed in colder months in regions such as China, Spain, and the USA\u003csup\u003e58\u0026ndash;60\u003c/sup\u003e. However, comprehensive understanding of astrovirus types and their seasonality remains limited, and it is unclear whether types are constantly prevalent throughout the year or if they disappear and re-emerge over time\u003csup\u003e54\u003c/sup\u003e. Our findings showed consistent detection of some astroviruses like HAstV-1 in most locations with longitudinal sampling, while other types such as MLB1, were only detected in specific months. These findings suggest that astrovirus seasonality may be type specific.\u003c/p\u003e\n\u003cp\u003eLike astroviruses, enteroviruses have a major impact on public health, and polioviruses were the earliest viruses to be monitored in wastewater. In global wastewater, enterovirus abundance consistently showed a dominance of EV-B across most regions, and high abundance of Enterovirus C types in African wastewater samples, aligning with trends from clinical surveillance.\u003csup\u003e61,62\u003c/sup\u003e In contrast, Enterovirus A types, such as CV-A6 and CV-A10, which are frequently reported in clinical samples from Asia, were not dominant in wastewater from this region\u003csup\u003e61\u003c/sup\u003e, potentially reflecting biases in clinical surveillance efforts that are targeted towards detection of the enterovirus types causing \u0026nbsp;hand, foot, and mouth disease. EV-D68, another focus of clinical surveillance, was infrequently detected, possibly due to its respiratory transmission route and limited faecal shedding\u003csup\u003e63\u003c/sup\u003e. Its biennial circulation patterns, with even-numbered years from 2014 to 2018 in temperate climates\u003csup\u003e64,65\u003c/sup\u003e, were reflected in our data, in which we detected EV-D68 primarily in 2018. Dynamic changes in enterovirus populations over time could be observed, including for genotypes not commonly detected in clinical surveillance, giving a broader perspective on circulating diversity. In many cases we only recovered partial genomes, complicating taxonomic annotation and limiting the ability to determine the clade, type, subtype or lineage which are often necessary to investigate outbreaks\u003csup\u003e72,73\u003c/sup\u003e. For example, poliovirus type 3 detected in Cameroon likely reflects shedding of oral polio vaccine (OPV3) strains, which can, in rare cases, mutate into vaccine-derived polioviruses (VDPVs) capable of causing outbreaks. However, insufficient genomic resolution in critical regions (e.g., VP1, 5\u0026rsquo;UTR) prevented confirmation of whether the detected sequences represented vaccine strains or VDPVs.\u003c/p\u003e\n\u003cp\u003eWastewater monitoring also captured emerging enteroviruses, exemplified by Enterovirus E30. High proportions of E30 were observed in European samples, aligning with increased reporting of E30 across European countries from 2015 to 2017\u003csup\u003e66\u003c/sup\u003e, and a subsequent rise in meningitis and meningoencephalitis cases from April to September 2018\u003csup\u003e67\u003c/sup\u003e. Notably, the detection of E30 in European wastewater as early as June 2017 preceded the clinical observations, suggesting undetected circulation before its recognized outbreak. These findings demonstrate that wastewater monitoring provides a comprehensive view of enterovirus diversity, geographical spread, and temporal changes, enabling the detection of both clinically targeted and under-surveilled types and the (re)emergence of specific genotypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly,\u0026nbsp;animal-associated viruses were frequently detected in the global wastewater, including canine- and feline astrovirus and porcine enterovirus G, likely reflecting contamination from pet waste or animal-industry runoff entering sewage systems. The high abundance of canine astrovirus in Regina (Canada) may be linked to local practices encouraging the flushing of dog waste into sewage systems, a method recommended in several Canadian cities since 2017 to reduce environmental pollution\u003csup\u003e68\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, in this study, shotgun metagenomic\u0026nbsp;and capture-based sequencing of wastewater provided insights into the genetic diversity and spatiotemporal patterns of both endemic and emerging viruses. In resource-limited settings, where clinical testing facilities and surveillance infrastructure may be constrained, centralized metagenomic wastewater-based monitoring could provide a cost-effective and scalable alternative to detect viral threats. These findings underscore the value of wastewater metagenomic analysis as a One Health resource, offering insights into infectious disease dynamics.\u003c/p\u003e"},{"header":"Materials and Methods ","content":"\u003cp\u003e\u003cem\u003eUrban wastewater sample collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRaw wastewater samples were collected from 62 sites from 6 continents as previously described.\u003csup\u003e69\u003c/sup\u003e Briefly, unfiltered urban wastewater samples prior to the inlet of the wastewater treatment plant or main outlet to rivers were acquired. Where possible, flow proportion sampling over 24h or otherwise three crude point samples were obtained for representative sampling. Samples were stored at -80\u0026deg;C and shipped. Additional metadata containing details on the sampling location and the conditions of the samples such as temperature and sample consistency were gathered. The biannual samples were obtained around June and November in 2017 and 2018 (and in some cases early 2019). Monthly longitudinal samples were collected from eight locations across six continents during the same period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample processing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSamples were thawed at room temperature and 50 ml of raw wastewater was centrifuged at 4000 rpm for 15 min to remove large debris. The supernatant was filtered using 0,45 \u0026micro;m pore diameter filter to remove bacterial and eukaryotic cells. 30 mL of the supernatant was concentrated using 30kDa Pierce Protein Concentrators PES (Thermo Fisher Scientific). The filtrate was treated with OmniCleave\u0026trade; Endonuclease (Lucigen Corporation) for the removal of extracellular nucleic acid. Total nucleic acid content was extracted with the High Pure Viral Nucleic Acid Kit, without the use of DNAse treatment (Roche).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMetagenomic and capture-based sequencing\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLibrary preparation was done using the KAPA HyperPlus kit (Roche) using Superscript IV and random priming to synthesize cDNA from RNA. Subsequently, double-stranded DNA (dsDNA) was generated using Klenow and the samples were subjected to enzymatic fragmentation. End repair and A-tailing was performed and KAPA Dual Indexed adapters were ligated. A post-ligation cleanup step was done using 0,8 x AMPure XP Beads (Beckman Coulter). After that, a double-sided size selection was performed according to the manufacturer\u0026rsquo;s instruction. Library PCR amplification was conducted using a total of 24 cycles. After purification, the quantity and quality assessment of the libraries was carried out using a Qubit 4 Fluorometer (Thermo Fisher Scientific) and an Agilent 2100 Bioanalyzer, following the respective manufacturers\u0026apos; protocols. The samples were pooled in equimolar proportions and sequenced directly or used as input for the GastroCap following sequencing on the Illumina MiSeq platform to generate 2x300 nt paired end sequences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;For target enrichment the GastroCap probe set was used. This probe set was designed to target a manually curated list of vertebrate virus species belonging to the\u0026nbsp;\u003cem\u003eAdenoviridae\u003c/em\u003e, \u003cem\u003eAstroviridae\u003c/em\u003e, \u003cem\u003eCaliciviridae\u003c/em\u003e, \u003cem\u003eHepeviridae\u003c/em\u003e, \u003cem\u003eParvoviridae\u003c/em\u003e, \u003cem\u003ePicornaviridae\u003c/em\u003e, \u003cem\u003eSedoreoviridae and Spinareoviridae\u003c/em\u003e families (Supplementary Fig S1). The probes were based on all sequences available in GenBank longer than 500 nucleotides for the selected species. To minimize redundancy, sequences were clustered at 95% nucleotide identity. To enhance representation of less abundant viral species, sequences from genera other than the highly abundant \u003cem\u003eEnterovirus, Mastadenovirus,\u003c/em\u003e and \u003cem\u003eRotavirus\u003c/em\u003e were \u0026apos;boosted\u0026apos; by multiplying their sequence abundance by a factor of three in the input dataset. The GastroCap probe set was designed by Roche using their proprietary Nimble Design workflow. Six library samples were pooled in equimolar amounts and purified using AMPure XP Beads of the KAPA HyperCapture Bead kit (Roche). The GastroCap probes were diluted 1:10, and 4,5\u0026nbsp;mL was added to 10,5\u0026nbsp;mL of the sample pools. Hybridization was performed by incubating the samples at 95\u0026deg;C for 5 minutes and then at 47\u0026deg;C for 72 hours. Following hybridization, 50\u0026nbsp;mL capture beads from the KAPA HyperCapture Bead kit (Roche) were washed according to the manufacturer\u0026rsquo;s protocol, added to the hybridized sample pools (15\u0026nbsp;mL) and incubated at 47\u0026deg;C for 15 minutes. The sample pools were then washed, and a post-capture PCR was performed using 14 cycles before purification by AMPure XP Beads. The quantity and quality of the captured library pools were assessed using the Qubit 4 Fluorometer and the Agilent 2100 Bioanalyzer following the manufacturers\u0026apos; protocols. The capture reactions were then pooled equimolarly and sequenced on the Illumina MiSeq platform to generate 2x300 nt paired end sequences.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSequence data analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence analysis workflow was generated using Snakemake (version 7.19.1).\u003csup\u003e70\u003c/sup\u003e Raw fastq files were quality trimmed using FastP (version 0.23.4). Read ends were trimmed to a mean quality Phred score of 25 with a sliding window of 5. Reads shorter than 30 nucleotides were discarded as well as reads with an average Phred score below 25. To identify and remove PCR duplicates, sequencing reads were deduplicated using CD-HIT\u003csup\u003e71\u003c/sup\u003e, which groups and removes duplicate reads that are identical within the first 150 nucleotides. Reads were aligned to the GRCh38 human genome reference (version GCF_000001405.26) using BWA-MEM (version 0.7.17)\u003csup\u003e72\u003c/sup\u003e and filtered using SAMtools (version 1.6)\u003csup\u003e73\u003c/sup\u003e before the reads were assembled using metaSPAdes (version SP)\u003csup\u003e74\u003c/sup\u003e. Taxonomic annotation was performed using DIAMOND v2.1.10.164\u003csup\u003e75\u003c/sup\u003e using the NCBI non-redundant protein reference database. Taxonomic assignment of each assembled contig was done based on the highest bit score normalized by the length of the contig. Finally, reads were mapped to the contigs using BWA-MEM to obtain read counts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuantitative analysis: heatmaps of viral diversity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor further quantitative analysis, annotated contigs with a length \u0026ge;300 nucleotides and an average depth of coverage \u0026ge;3 were selected to ensure the inclusion of more reliable hits. The total number of reads assigned to the superkingdom \u0026ldquo;Viruses\u0026rdquo; per sample was used to normalize read counts. The total number of reads mapping to a contig (forward and or reverse) were adjusted by dividing by the average genome size per individual viral family. For viral families with large variation in genome sizes the average genus level genome size was used. Relative viral abundance was calculated as reads per million (RPM) according to Eq. (1):\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eThe classification of viral families based on their hosts range was obtained from Virus-Host DB, which pairs viruses and hosts using NCBI taxonomy IDs, integrating host information from genome databases like RefSeq and GenBank, along with additional sources including UniProt, ViralZone, and literature\u003csup\u003e76\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison of viral composition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe compared viral composition across different locations using PCA, accounting for the compositional nature of metagenomic data. Therefore, the Centred Log-Ratio (CLR) coefficients were computed for distinct data subsets to preserve the relative relationships between viral taxa\u003csup\u003e77\u003c/sup\u003e. PCA was conducted on the viral fraction of the dataset using genome-size adjusted read counts to calculate CLR values. This normalization was not applied to plots at the superkingdom level (Archaea, Bacteria, Eukaryota (human separate), Viruses and Unknown) due to significant variability in genome sizes at this taxonomic level. For the virome data at family, genus and species level (metagenomic sequence data), only viral taxa with a high CLR median and high variance were retained, excluding those with low abundance and minimal variability between samples. For enteric viruses, all viral families targeted by the GastroCap probe set were included\u003cem\u003e\u0026nbsp;\u003c/em\u003ewithout additional filtering. Zero values were replaced using an Aitchison mean point estimate before applying the CLR transformation. The transformed data were then visualized in a biplot using the pyCoDaMath package in Python (version 3.8).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTaxonomic analysis at the species and genotype level for selected viruses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor selected viruses, the dataset was filtered to extract contigs belonging to specific genera using DIAMOND annotation with a contig length \u0026gt;=300 and average depth of coverage of \u0026gt;=3x. These contigs were then processed using custom workflows to determine species and genotype. These workflows involved mapping contigs with a blast search against a database of reference sequences from literature or existing typing tools, and assigning species and type based on virus specific ICTV classification criteria.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTobamovirus species assignment\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA custom blastn species assignment database was generated using sequences from all 37 ICTV-recognized \u003cem\u003eTobamovirus\u003c/em\u003e species. Contigs were assigned to specific species based on the criterion that strains within the same species must share more than 300bp and 90% nucleotide sequence identity across the whole genome, following the species demarcation criteria proposed by ICTV\u003csup\u003e78\u003c/sup\u003e.\u0026nbsp;\u003cbr\u003e \u003cem\u003eMamastrovirus and Enterovirus genotyping and phylogenetic analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe typing workflow for \u003cem\u003eMamastrovirus\u003c/em\u003e used a nucleotide blast database of complete ORF2 sequences sourced from Donato et al.\u003csup\u003e35\u003c/sup\u003e Annotated \u003cem\u003eMamastrovirus\u003c/em\u003e contigs were aligned against this database using blastn. Contigs that covered at least 10% of ORF2 and exhibiting \u003cstrong\u003e\u0026ge;\u003c/strong\u003e85% nucleotide identity, in line with classification criteria proposed by the ICTV Astroviridae Study Group\u003csup\u003e54,79\u003c/sup\u003e, were included for further analysis. \u003cem\u003eEnterovirus\u0026nbsp;\u003c/em\u003econtigs were assigned using the RIVM enterovirus typing tool, which performs a blastn search and phylogenetic analysis of the (partial) VP1 gene, with a minimum overlap of 100bp required for classification. \u003csup\u003e80\u003c/sup\u003e For phylogenetic analysis, sequences covering \u0026gt;500 bp of ORF2 were considered sufficient for reliable alignment and tree-building. Complete and partial Human astrovirus (type 1-8) ORF2 sequences and E-30 VP1 sequences were retrieved from NCBI, combined with our sequences and aligned using MAFFT (version 7.508).\u003csup\u003e81\u003c/sup\u003e\u003csup\u003e\u0026nbsp;76\u0026nbsp;\u003c/sup\u003ePhylogenetic trees were generated using IQ-TREE (version 2.3.6), using automated model selection, the -czb option, and 1000 bootstrap replicates\u003csup\u003e82\u003c/sup\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation program under grant agreement no. 874735 (VEO) and the NWO\u0026nbsp;Stevin\u0026nbsp;Prize 2018 awarded to M.K. by the Netherlands Organisation for Scientific Research (NWO).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K. and F.M.A. conceived the study, secured funding and provided overall supervision. M.G., \u0026amp; N.W. drafted the initial manuscript with input from M.K.. M.G \u0026amp; B.B.O.M. supervised manuscript preparation and data analysis. C.M.E.S and N.W performed NGS. N.W. and D.F.N performed bioinformatic data analyses. E.E.B.J. and C.B. contributed to the PCA and provided methodological guidance. N.W. produced the figures. D.F.N, R.I.L., C.M.E.S., C.B., E.E.B.J. , P.M., R.S.H., F.M.A., M.K., M.G. \u0026amp; B.B.O.M., critically read and contributed to the manuscript. \u0026nbsp;The Global Sewage Consortium authors carried out sewage sampling, filled in metadata and shipped the samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing datasets will be made available from the European Nucleotide Archive with the accessions X and X.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow will be made available at https://github.com/EMC-Viroscience\u0026nbsp;\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eBaker, R. 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IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 268 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"virome, gastroenteritis, metagenomics, global wastewater, enteric viruses","lastPublishedDoi":"10.21203/rs.3.rs-6048078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6048078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Understanding global viral dynamics is critical for public health1. Traditional infectious disease surveillance primarily focuses on individual pathogens and relies on the identification and reporting of symptomatic cases, which may not capture asymptomatic infections or newly emerging viruses, leading to delayed detection and response.2–4 Wastewater-based epidemiology has been used to track individual pathogens through targeted molecular approaches, but its reliance on predefined targets limits the ability to capture the full spectrum of circulating viruses.5–7 Here, we analyzed longitudinal and biannual wastewater samples from 62 major cities across six continents (2017-2019) using shotgun metagenomics and capture-based sequencing targeting viruses associated with gastrointestinal disease. Over 2,500 viral species spanning 122 families were detected, many with human, animal, or plant health relevance. The Bacteriophage families Microviridae and Virgaviridae dominated the metagenomic dataset, while in the enriched dataset Astroviridae and Picornaviridae were the most prevalent. Virus distributions were broadly similar across continents, but distinct city-level fingerprints emerged, reflecting spatiotemporal variation of viruses like astrovirus, and enterovirus. Global wastewater-based epidemiology enabled the early detection of several emerging viruses, including Echovirus E30 in Europe, and Tomato brown rugose fruit virus before agricultural outbreaks. These findings highlight the potential of wastewater metagenomics for early detection of emerging viruses and population-wide virome monitoring across diverse hosts.","manuscriptTitle":"Unveiling the global urban virome: insights from wastewater metagenomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 09:48:42","doi":"10.21203/rs.3.rs-6048078/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b5fcaaca-9b9d-42bd-b13f-40a4f592256b","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45241957,"name":"Biological sciences/Microbiology/Environmental microbiology/Water microbiology"},{"id":45241958,"name":"Biological sciences/Microbiology/Virology/Viral epidemiology"}],"tags":[],"updatedAt":"2025-11-29T08:11:05+00:00","versionOfRecord":{"articleIdentity":"rs-6048078","link":"https://doi.org/10.1038/s41467-025-65208-x","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-11-28 05:00:00","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-03-25 09:48:42","video":"","vorDoi":"10.1038/s41467-025-65208-x","vorDoiUrl":"https://doi.org/10.1038/s41467-025-65208-x","workflowStages":[]},"version":"v1","identity":"rs-6048078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6048078","identity":"rs-6048078","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}