Towards a Comprehensive Wastewater Virome Atlas for Pathogen Monitoring

Towards a Comprehensive Wastewater Virome Atlas for Pathogen Monitoring | 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 Research Article Towards a Comprehensive Wastewater Virome Atlas for Pathogen Monitoring Nitin Shukla, Jinal Thakor, Priyank Chavda, Harshal Purohit, Harshil Patel, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8093831/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Wastewater represents a complex unstructured mixture of biological material contributed by humans, animals, and other organisms, making it a valuable resource for pathogen surveillance. Capturing the virome using a probe-based approach enables a broader understanding of viral community distribution, allows for the detection of multiple viral strains, and facilitates the identification of viral nucleic acids present in very low amounts in wastewater samples. This study presents a comprehensive virome analysis conducted over one year across four metropolitan cities, with samples collected fortnightly from 24 different sites. Results We identified over 170 DNA and 135 RNA viral species, including viral strains associated with multiple hosts humans, animals, plants, and insects. Notably, we detected 107 DNA viruses and 495 RNA viral genomes/segments with over 90% genome coverage. Beyond detection, we explored temporal patterns, revealing that viral families such as Astroviridae , Orthomyxoviridae , and Picornaviridae exhibited seasonal trends, while Adenoviridae and Sedoreoviridae were consistently detected throughout the year. Shannon diversity was higher and more variable at several sites in Ahmedabad, likely due to its larger population size, landscape, and heavy footfall. Bray-Curtis dissimilarity analysis showed significant variation in viral communities across cities, sites, and sampling time points. The prevalence of SARS-CoV-2 and Rotavirus A from NGS showed a significant positive correlation with digital PCR (dPCR) quantification (p < 0.05). Additionally, trends in Hepatitis A virus abundance aligned with monthly reported cases, further validating the reliability of sequencing-based surveillance. Conclusions This large-scale, longitudinal study demonstrates that NGS-based wastewater virome profiling is a reliable and scalable method for detecting both circulating and sporadic viral strains. The ability to detect low-abundance viral genomes supports early variant identification and offers insights into viral prevalence in the community. The findings offer valuable insights for early public health interventions. Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Wastewater-based surveillance (WBS), or wastewater-based epidemiology (WBE) 1 , has opened new frontiers in pathogen monitoring. It has emerged as a cost-effective, non-invasive 2 , 3 , crucial public health tool 4 , 5 , for monitoring infectious diseases temporally & geographically 6 , 7 offering early detection 4 , providing real-time insights into community health 8 . The One Health approach recognizes the shared environment inhabited by humans, animals, and plants, emphasizing their interconnected health 9 . Thus, monitoring pathogens in sewage wastewater, which captures both human and animal activity in rural and urban environment 10 , provides early warning signals and enhances the traceability of infectious diseases. This, in turn, can facilitate rapid responses to public health threats posed by zoonotic as well as re-emerging pathogens 11 . Besides genomic sequencing of pathogens from wastewater, it has proven to be an effective method for monitoring viral variants 12 , 13 , offering valuable insights to clinicians and public health organizations about the variants circulating within the community 5 . Viral diversity is influenced by seasonal changes 14 . These include respiratory viruses such as Respiratory syncytial virus (RSV), Influenza virus, and Human Metapneumovirus, which typically peak during winter, although some peaks are also observed in summer 15 , 16 . Enteric viruses 17 , such as Norovirus GII, tend to peak during colder seasons 18 . In contrast, certain viruses like Human Adenovirus, Human Parainfluenza virus, and Human Coronavirus do not exhibit strong seasonality and are detected throughout the year 16 , with continuous circulation reported in various studies 14 . Interestingly, SARS-CoV-2 abundance in wastewater has been reported to be higher in winter compared to the monsoon season 19 . The seasonal influenza viruses are detected year-round; their circulation is significantly higher during the autumn and winter months 20 . The seasonality of viral circulation is influenced by environmental factors such as temperature, relative humidity, and rainfall, as well as by human behaviour and its interaction with natural surroundings 14 , 16 , 19 . Wastewater contains a wide range of biological wastes, including stool, urine, bodily fluids, wound exudates, and nasal secretions, as well as viruses shed by infected individuals both symptomatic and asymptomatic 4 , 21 , 22 . This complex composition contributes to extensive viral fragmentation from the point of excretion to sample processing, which can significantly affect the detectable copies of viral genomes 23 . The genomic background of wastewater is highly complex and typically dominated by commonly occurring microorganisms, while target pathogens are often present only in trace amounts 24 . To address these challenges, several strategies have been adopted for viral detection in wastewater. PCR-based amplification has been widely used to detect known viruses, such as SARS-CoV-2, in wastewater samples 25 . However, broader community-level surveillance requires more robust approaches, such as probe capture sequencing, which enables the enrichment of low-abundance targets. Other methods include shotgun metagenomic sequencing, targeted amplicon sequencing, and viral particle enrichment 24 , 26 – 28 . Hybridization-based capture probes can increase the representation of target sequences by several orders of magnitude 26 . Notably, the probe hybridization method has been reported to significantly enhance sensitivity in viral metagenomics and is particularly well-suited for pathogen surveillance in low-biomass samples such as wastewater 29 . In our previous studies during the COVID-19 pandemic, we tracked SARS-CoV-2 in four cities, Ahmedabad, Gandhinagar, Rajkot, and Vadodara in Gujarat 30 – 32 . We demonstrated that trends in wastewater SARS-CoV-2 RNA anticipated case surges and supported hotspot zoning 33 , establishing operational feasibility of such surveillance across large urban catchments. Recent global studies have further supported the use of wastewater surveillance for the detection of influenza viruses, including H5N1 34 has broadened the virus traceability beyond a single virus to multi-pathogens, enabling normalization and trend analytics 35 , 36 . Based on these findings, the present study aimed to perform a year-round profiling of the total virome in four cities of Gujarat. Our goal was to understand the structure and dynamics of wastewater virome, observe how it changes over time and across different seasons, and evaluate whether multi-pathogen surveillance data correlate with single-pathogen surveillance dPCR and clinical case trends. To our knowledge, this is one of the first studies from India to conduct longitudinal virome profiling from wastewater samples collected across multiple sites over the span of an entire year. RESULTS Comprehensive virome taxonomic insights Following the COVID-19 pandemic, the urgency and importance of understanding community virome profiles has become increasingly evident. The study aimed to perform year-round wastewater virome surveillance from four cities in Gujarat, which covered an area of ~ 89.77 km 2 . The sampling sites were selected to represent the demographic distribution of the local population with an estimated size of ~ 16.57 million. The samples were collected from 24 different sites (STPs/SPSs) that represent the major working population of Gujarat, which makes them ideal for understanding the state-wide virome distribution and its implications for public health. In total, we collected 583 wastewater samples and prepared 1,166 sequencing libraries, comprising both DNA and RNA. Sequencing was carried out across 13 NovaSeq 6000 runs, each yielding ~ 245.52 gigabases (Gb) and ~ 1.6 billion reads per run (Table S1 ). Eighteen samples did not meet library quality control (QC) standards due to poor library yield, low quality or low sequencing data and were therefore excluded from downstream analysis. As a result, the final dataset used for analysis included 1,124 libraries (562 DNA and 562 RNA) derived from 565 samples. Viruses were classified on nucleic acid type (DNA or RNA) using the Virus Metadata Resource (VMR) from the International Committee on Taxonomy of Viruses (ICTV) ( https://ictv.global/vmr ). Further classification by host i.e., human, animal, plant, or insect was performed using metadata from Virus-Host DB ( https://virus-world.org ). The extended information of host and nucleic acid type of viruses included in Twist Comprehensive Viral Research Panel is available at ( https://github.com/nitinShukla1912/wastewater_virome ). In total, we detected 10 DNA virus families and 23 RNA virus families. This included 47 DNA virus genera and 55 RNA virus genera, encompassing 170 DNA viral species and 135 RNA viral species. Furthermore, we identified 343 strains of DNA viruses and 469 strains of RNA viruses (Fig. 1 A). Of these, 223 strains were associated with animals, while 549 strains were specific to humans (Fig. 1 B). To evaluate the viral species richness and determine whether the sequencing depth was adequate to capture the full viral diversity, rarefaction curves were generated stratified by city and site. The overall curve for total samples showed a continued but plateauing increase in the number of unique strains, indicating that while a large portion of the viral diversity has been captured, additional sampling may still yield novel strains. City-wise analysis revealed variability in richness, with Ahmedabad and Gandhinagar exhibiting higher saturation levels compared to Rajkot and Vadodara. Site-level rarefaction curves indicated that for individual sites additional sequencing may still uncover a few more viral taxa but is unlikely to drastically change the overall virome profile (Fig. 1 C). Viral species richness and evenness, assessed using the Shannon diversity index, also varied considerably across sites. Ahmedabad exhibited the highest intra-city variability in viral diversity, with sites such as Jamalpur, Pirana, and Ranip consistently showing elevated diversity levels. In Gandhinagar, Jaspur recorded the highest diversity, while other sites displayed relatively uniform profiles. In contrast, diversity patterns in Rajkot and Vadodara were more heterogeneous, with notable median diversity observed at Raiya and Tarsali (Fig. 1 D). These findings underscore the utility of capture based deep sequencing for comprehensive viral surveillance, not only for abundance profiling but also for identifying the breadth of viral strains present in wastewater. In addition to profiling viral abundance, we aimed to reconstruct near-complete viral genomes/segments. A median of 40.8 viral genomes/segments with sequencing reads covering more than 90 percent of alignment length, and 54.7 median genomes/segments with coverage exceeding 50 percent were recovered (Fig. 1 E). Beyond strain-level detection, we successfully identified over 107 DNA viruses and 495 RNA viruses with genome/segment completeness exceeding 90% (Fig. 1 F). Across different cities and sampling sites, the number of recovered viral strains varied. Ahmedabad (Paldi) showed the lowest median 24.5 strains per sample, while Rajkot (Raiya) had the highest, at 73.5 strains per sample. Other top sites with high median strain include Vadodara (Channi) 72.5 strains, Ahmedabad (Jamalpur) 68 strains, and Gandhinagar (Raysan) 67 strains per sample (Fig. 1 G). Spatiotemporal dynamics of wastewater virome To capture the dynamics of total virome diversity across space and time, the study employed a year-round, fortnightly sampling strategy. This design aimed to systematically investigate how viral community structure varies across cities, different sites within cities, and sampling intervals. Spatiotemporal variation in viral communities from wastewater was assessed using beta diversity (Bray-Curtis dissimilarity) across cities and sampling sites over time. In parallel, we assessed temporal variability in viral community composition by performing pairwise Bray-Curtis comparisons of all samples collected from the same site at different time points. Across most sites, dissimilarity remained consistently high (median Bray-Curtis > 0.7, p -value: 2.2e-16), indicating that wastewater virome exhibit substantial short-term fluctuations, potentially driven by seasonal dynamics or intermittent viral shedding patterns within the host population. Despite this high baseline variability, the rate of change in community composition over time differed across sites. At several locations, such as Paldi and Vasna (MLD & SPS) in Ahmedabad, Jaspur in Gandhinagar, and Popatpara in Rajkot dissimilarity slightly increased with longer intervals i.e., samples collected at distant time points, suggesting ongoing viral community turnover. In contrast, sites like Kothariya in Rajkot and Chhani in Vadodara displayed relatively flat trends over time, implying a more stable virome (Fig. 2 A). These patterns highlight that temporal dynamics in wastewater viromes vary spatially and are potentially influenced by differences in catchment population and local environmental factors. We compared Bray-Curtis dissimilarities between samples collected from the same site, different sites within the same city, and across different cities to assess spatial patterns in virome community structure (Fig. 2 B). Contrary to expectations, intra-site comparisons exhibited slightly higher Bray-Curtis dissimilarity than inter-site and inter-city comparisons. This may be attributed to seasonal turnover in viral community composition over time and across individual sites. While spatial differences were anticipated, our year-long dataset revealed that temporal variation potentially driven by seasonal viral shedding patterns plays a dominant role in shaping community dissimilarity in wastewater viromes. Time and location are important variables that define the structure of the viral community, as visualized using t-distributed stochastic neighbor embedding (t-SNE). As expected, two distinct clusters were observed, separating DNA and RNA viruses (Figure S1 ). We stratified the libraries separately and observed that the samples were distributed randomly by collection date and among cities (Fig. 2 C). This could be due to a similar viral community structure shared across the four different cities during that time. Viral families showed a similar pattern in median strain count and mean prevalence, with the highest observed in Vadodara, particularly for Astroviridae , Adenoviridae , and Parvoviridae (Fig. 2 D). Seasonal changes influence viral diversity To assess the influence of season on wastewater virome, we used linear mixed effect model on the alpha diversity (Shannon indices), by incorporating meteorological seasons ( https://mausam.imd.gov.in/ahmedabad/mcdata/climate.pdf ), months and weeks as fixed effects, and city-site as a random intercept. We found that seasons and months both had a significant effect on viral alpha diversity (Season p -value: 0.0002028, Months p -value: 6.2 × 10 − 5 ). Viral diversity was highest during monsoon (Sep23-Nov23) and remained comparable during winter (Dec23-Feb23). However, a marked decline was observed during summer (Mar24-May24) indicating a temporary suppression in the viral diversity which subsequently rebounded with the onset of the following monsoon (Fig. 2 E). Notably, when we analysed diversity at weekly scale (Season p -value: 0.64, weeks p -value: 0.01), a smooth trend line revealed a gradual recovery in alpha diversity across Monsoon 2024, particularly after week 36. This suggests that while seasonal grouping showed reduced diversity during Monsoon 2024, weekly-level resolution captured an increasing trajectory similar to the monsoon from the previous year i.e., Sept-23 (Fig. 2 F). These findings highlight the importance of multi-scale temporal analysis that uncovers the virome diversity influenced by time and season. Although October and November are typically categorized as post-monsoon months by the India Meteorological Department (IMD), Ahmedabad, they were grouped under monsoon in this analysis to simplify seasonal comparisons. The prevalence of virus families exhibited distinct seasonal variations. Polyomaviridae remained consistently abundant across all seasons, with the highest relative abundance observed during the monsoon 2023 (16.2%), and maintained levels above 10% in the subsequent seasons, followed by Adenoviridae (11.9%) in monsoon 2024. During the winter, Dicostaviridae (11.8%), Parvoviridae (10.3%), and Coronaviridae (4%) were more predominant. Interestingly, some families like Astroviridae , Retroviridae , and Herpesviridae appeared intermittently across seasons, suggesting episodic shedding. Notably, Picornaviridae (13.9%) and Parvoviridae (12.3%) showed sharp increases during Summer 2024. This suggests the taxonomic architecture and seasonal shifts in viral families are persistent and well aligned (Fig. 2 G). As the seasonal variations were evident, we observed during the summer, Betapolyomavirus secuhominis (JC Polyomavirus) was more prevalent, whereas Betapolyomavirus hominis (BK Polyomavirus) was dominant in other seasons. Astrovirus MLB1 was predominant in summer, while Masmatrovirus 1 was detected year-round (Figure S2 ). The abundance of Salivirus A ( Picornaviridae ) increased steadily over time, whereas Kobuvirus species were predominantly observed during the monsoon and winter seasons. Within the Circoviridae family, Pigeon Circovirus from avian species showed high abundance during winter and summer, along with Beak and Feather Disease Virus was prominently observed during monsoon and summer. A notable and sustained increase in Human faecal virus Jorvi2 was recorded from week 21 (May 2024) through week 39 (September 2024) higher in monsoon season (Figure S3). Rotavirus A ( Sedoreoviridae ) was consistently dominant across the year. Among the Coronaviridae family, we detected five species with abundances exceeding 1%, including Alphacoronavirus, Avian Coronavirus, Betacoronavirus 1, and SARS-CoV-2 (Figure S4). Profiling human pathogenic viruses from wastewater Distinct temporal trends were observed for several human pathogenic viruses (Fig. 3 A). Among RNA viruses, Influenza A virus, Enterovirus, Hepatitis A virus, Parainfluenza virus, Norovirus, Respiratory syncytial virus (RSV), Mumps, Measles, SARS-CoV-2, Rhinovirus, and Rotavirus exhibited either consistent trend or sporadic occurrences across cities over different months (Fig. 3 B). Similarly, among DNA viruses, notable patterns were seen for JC Polyomavirus, BK Polyomavirus, Human Mastadenovirus, Adeno-associated Dependoparvovirus, and Mupapillomavirus (Fig. 3 C). Members of the Polyomaviridae family infect both humans and animals and can be shed into wastewater via urine, stool, and skin 14 , 37 – 39 . Notably, JC and BK Polyomaviruses are clinically relevant in immunocompromised individuals, such as those undergoing chemotherapy or infected with HIV, where they are associated with conditions like haemorrhagic cystitis and progressive multifocal leukoencephalopathy (PML) 37 , 40 . Both JC and BK Polyomavirus were consistently detected across all cities and throughout the year. These viruses exhibited high genome coverage, with more than 90% of their genomes recovered in 216 samples and 298 samples, respectively. A representative genome coverage plot is shown in (Figure S5). Additionally, other polyomaviruses such as Merkel Cell Polyomavirus, HPyV6, HPyV7, and WU Polyomavirus were also detected in the wastewater samples. These findings indicate that wastewater surveillance can effectively identify circulating polyomaviruses within the community, which can further be classified into their specific genotypes. Enteric viruses, which are primarily associated with gastroenteritis, were frequently detected. These viruses are transmitted via the faecal-oral route and are known for their environmental stability, persisting from days to weeks in wastewater 41 – 44 . They pose a significant public health risk, particularly to children, and are linked to acute gastrointestinal illness (AGI), which may lead to high morbidity and mortality. Detection of these viruses in wastewater provides an opportunity for early intervention, potentially preventing outbreaks of AGI and Hepatitis A 45 . Symptomatic diseases caused by these viruses include gastroenteritis, acute hepatitis, and central nervous system infections such as meningitis, encephalitis, paralysis, as well as conjunctivitis and respiratory illnesses. The major viral genera responsible for such illnesses include Adenovirus, Enterovirus, Parechovirus, Norovirus, Rotavirus, Hepatitis A Virus, and Hepatitis E Virus. These viruses replicate in the gastrointestinal tract and are shed in large quantities via faeces often for weeks regardless of symptom status. Due to their high environmental resistance, they can be transmitted through drinking water, and foods contaminated by wastewater or effluents from wastewater treatment plants (WWTPs) 46 . Animal-infecting and zootnotic viruses from wastewater Among animal-associated viruses, we detected Avian coronavirus, Avian dependoparvovirus 1, Beak and feather disease virus, Carnivore protoparvovirus 1, Canine morbillivirus, Equine rotavirus, Feline rotavirus, Gyrovirus galga1, Pigeon circovirus, and Primate norovirus. Zoonotic viruses currently pose a major threat to public health across worldwide. Notably, some of the detected viruses also exhibited zoonotic potential, including Porcine torovirus, Rocahepevirus ratti, Rosavirus, and Hepatitis E virus, which pose a risk of cross-species transmission 10 (Figure S6 and Table S2 ). Correlation of wastewater virome data with clinical cases and dPCR data As a pooled representation of the community, wastewater effectively captures circulating viral infections, including both symptomatic and asymptomatic cases. To compare sequencing-based estimates with dPCR data, we used RPKMF for viral abundance and compared it with RNA copies/L of wastewater. We validated Human Rotavirus A (NSP3 gene) 47 abundance using dPCR. The comparison revealed significant positive correlation for Ahmedabad (R = 0.17, p -value = 0.019), Gandhinagar (0.31, p -value = 0.0019), Vadodara (R = 0.26, p -value = 0.017), however not for Rajkot (R = 0.079, p -value = 0.49) (Fig. 4 A, Table S3). Besides, we continuously monitored SARS-CoV-2 RNA copies in wastewater using dPCR as part of a statewide surveillance initiative covering multiple cities in Gujarat. We observed a significant positive correlation between the two independent methods for Ahmedabad (R = 0.6, p -value = 0.01) and Gandhinagar (R = 0.58, p -value = 0.02) (Fig. 4 B). These results suggest that sequencing-derived viral abundance trends align well with dPCR based virus estimation, supporting the utility of metagenomic sequencing as a reliable method for monitoring multiple viral loads simultaneously in wastewater. Alongside, we observed a positive correlation between average monthly clinical cases of Hepatitis A virus obtained from the State Integrated Disease Surveillance Programme (IDSP), Gujarat and wastewater (RPKMF) (R = 0.62, p -value = 0.04) (Fig. 4 C). This indicates that viral abundance detected in wastewater closely mirrored trends in reported clinical cases of Hepatitis A Virus, further highlighting the significance of wastewater-based surveillance. This surveillance strategy has enabled us to gain valuable insights into the community-level prevalence of both human and animal viruses, reinforcing its relevance in a One Health framework. It highlights the potential of wastewater-based monitoring for detecting emerging threats and enhancing public health preparedness and response efforts. DISCUSSION The need for a global surveillance network for early warning against emerging and re-emerging pathogens is rapidly increasing 48 . To address this, GLOWACON represents one such initiative, a global consortium aimed at establishing an international sentinel system for pathogen detection and monitoring 49 . Wastewater surveillance strategies have proven to be cost-effective and non-invasive methods for monitoring pathogen circulation at the community level 4 . This method offers a real-time tool to track the pathogens and serves as an early warning system for the detection of both known and emerging threats, including monkeypox (Mpox) 50 , Avian Influenza (H5N1) 34 , and West Nile 51 . By capturing a wide range of viral signatures shed into sewage from both human and animal sources, wastewater reflects a co-existing virome and can uncover potential zoonotic spillovers, helping to identify infections transmitted not only from human to human but also from animals to humans. As a community-driven health monitoring strategy, it plays a vital role in public health preparedness by enabling early detection, informing risk mitigation strategies, and supporting the development of responsive health frameworks. Such global surveillance efforts contribute significantly to pandemic prevention, outbreak management, and the timely implementation of public health measures 52 , 53 . We aimed to establish a baseline understanding of the prevalence and diversity of human and animal viruses in wastewater, contributing to the broader goal of One Health. Importantly, the method was able to capture seasonal and temporal trends in the circulation of key viruses such as Adenovirus, Polyomavirus, Influenza A virus, SARS-CoV-2, Hepatitis E, Rotavirus, and Norovirus 36 , 54 . Furthermore, the detection of clinically relevant pathogens like Respiratory syncytial virus (RSV), Mumps orthorubulavirus, and Measles virus highlight the potential for early warning and disease preparedness. It also offers a way to monitor circulating viral strains that could trigger future outbreaks, such as SARS-CoV-2 variants 36 . When implemented across broader demographics and supported by sustained governmental funding, this NGS-based surveillance strategy can serve as a proactive framework for infectious disease monitoring and outbreak preparedness. The hybridization-based capture probe method proved highly effective in detecting viruses and viral strains, even in samples with lower copies 55 . It is scalable for capturing a diverse range of viruses during both endemic and outbreak conditions 56 . For SARS-CoV-2, and Human Rotavirus A we observed a positive correlation between capture probe-based sequencing and digital PCR (dPCR), validating its effectiveness. This technique also demonstrated remarkable utility beyond SARS-CoV-2, for instance, we successfully recovered nearly the complete genome of Respiratory syncytial virus (RSV) with over 89% coverage, highlighting the method’s sensitivity, such resolution is effective for tracking virus evolution and monitoring the emergence of new variants. These findings are crucial for predicting future outbreaks based on sewage surveillance data. Although the overall virome composition was broadly similar across all cities, certain viral families exhibited distinct patterns, highlighting city-specific variations in viral prevalence. In Vadodara, high prevalence was observed for members of the Astroviridae family (Mamastrovirus 1, Human astrovirus 2, Human astrovirus 4, and Astrovirus MLB1), Adenoviridae (Human mastadenovirus F, Human adenovirus 19, and Human adenovirus 41), Polyomaviridae , and Parvoviridae . The Coronaviridae family SARS-CoV-2 showed consistent detection in Gandhinagar and Rajkot, with the highest prevalence in Vadodara, followed by Ahmedabad. Certain viruses such as Chicken anemia virus and Avian gyrovirus 2 were more prevalent in Ahmedabad. Additional families, including Luteoviridae (Bat luteovirus), Flaviviridae , and Hepeviridae (Hepatitis E virus type 1), were also detected in Ahmedabad, indicating the presence of both human and animal viruses in the community. Our findings also suggest that some viruses exhibit seasonality. Members of the Astroviridae family, which are a major cause of gastroenteritis, particularly in children 57 , 58 , showed lower abundance during the monsoon season and increased prevalence in winter and summer, followed by a decline with the onset of the next monsoon. However, contrasting studies have reported higher astrovirus cases during the rainy season 59 . This could be due to regional climatic differences and dilution effects in wastewater caused by heavy rainfall, which may reduce detectable viral loads despite ongoing transmission in the population 60 . Norovirus I and II, from the Caliciviridae family, exhibited similar seasonal trends, with reported peaks during colder months 61 , 62 . The Adenoviridae family showed higher abundance during the winter season 63 and appeared to follow a cyclic pattern. Interestingly, Influenza A virus (H1N1, H3N2) from the Orthomyxoviridae family displayed higher proportions during the summer, suggesting off-season spikes. A study from China similarly reported increased activity of Influenza A (H3N2) during summer and autumn months 64 . These findings support the notion that viruses exhibit seasonal patterns, and that their diversity and proportions vary throughout the year, likely influenced by environmental factors 65 . Understanding the trends of viral circulation and the mutations they acquire over time can greatly contribute to the development of vaccines and preparedness strategies to prevent future disease outbreaks. CONCLUSION In conclusion, this study provides a comprehensive, year-long surveillance of the wastewater virome across four major urban catchments, revealing the prevalence, diversity, and spatiotemporal patterns of clinically relevant viruses. The use of a capture probe-based sequencing strategy enabled the detection of both DNA and RNA viral genomes, including those present at low abundance, with several genomes recovered at > 90% coverage. We demonstrated that wastewater virome trends often mirrored clinical case data and dPCR validation, confirming the reliability of this approach for public health surveillance accompanying multi-pathogen surveillance. The observed seasonal variation in viral composition and the detection of both persistent and emerging viruses highlight the spatiotemporal dynamics of virome. Integrating such environmental monitoring with clinical datasets can support proactive decision-making, enhance outbreak preparedness, and advance the goals of One Health. METHODS Geo-location and sample collection Pre-treated wastewater samples were collected fortnightly from September 2023 to September 2024 across four cities in Gujarat which include Ahmedabad, Gandhinagar, Rajkot, and Vadodara covering a total of 24 sampling sites (Table S4). The sample collection sites include sewage treatment plants (STPs) and sewage pumping stations (SPSs). These cities & sites were selected for wastewater sample collection to provide a broader understanding of virome diversity, as they represent a major diverse population and are likely to capture the variability in virome profile. We collected 2 L of raw, untreated wastewater in sterile containers and stored the samples at 4°C until further processing. From the collected volume, a total of 400 mL was used for nucleic acid extraction, 200 mL was processed for DNA and 200 mL for RNA isolation. For each 200 mL aliquot of raw sewage, 25 mL of Glycine buffer (0.05 M glycine, 3% beef extract, pH 9.6) was added 66 and vortexed to detach the virus particles from the organic matter. The mixture was then centrifuged at 8,000 × g for 20 minutes to settle down bacteria, fungi, other organisms, and large debris. The supernatant was collected and filtered through a 0.22 µm cellulose acetate filter to remove any remaining debris and other microbial cells. To the filtrate, 8% polyethylene glycol 8000 (PEG 8000) and 1.74% NaCl, was added and vortexed until fully dissolved, followed by overnight incubation at 4°C to facilitate viral aggregation. After incubation, the samples were centrifuged at 12,000 × g for 90 minutes at 4°C, and the resulting pellet was resuspended in 200 µl of nuclease-free water. DNA and RNA were then isolated using the QIAamp DNA Mini Kit and the QIAamp Viral RNA Kit (QIAGEN, Hilden, Germany), respectively. Library preparation and next-generation sequencing Quantification of DNA and RNA was performed using the Cytation 5 (BioTek, Winooski, USA) and the Qubit 4 fluorometer (Thermo Fisher Scientific, USA). For double stranded cDNA synthesis, extracted RNA was reverse transcribed using Random Primer 6 (New England Biolabs Inc., MA, USA) and the Protoscript II First Strand cDNA Synthesis Kit (New England Biolabs Inc., MA, USA), followed by second-strand synthesis using the NEBNext Ultra II Non-Directional RNA Second Strand Module (New England Biolabs Inc., MA, USA), following manufacturer’s protocol. Subsequently, shotgun libraries were prepared using the Twist Library Preparation EF 2.0 Kit and the Twist Universal Adaptor System (Twist Biosciences, CA, USA). From the shotgun libraries, virus fraction was enriched using the Twist Comprehensive Viral Research Panel (Twist Bioscience, CA, USA). The panel has over one million probes that can capture 3,153 species and 15,488 different strains of virus from human and non-human hosts. Paired-end 2×100 bp sequencing was carried out using SP flow cells on the NovaSeq 6000 platform (Illumina, CA, USA). The base call files (bcl) were converted to FASTQ format using the on-premises DRAGEN server (v. 07.021.645.4.0.3). Data quality check, metagenomic profiling, and genome identification The raw sequencing reads were first processed for quality filtering/trimming using Trimmomatic (v. 0.39) 67 to remove low-quality reads and adaptors if any. The clean reads were then processed using the EsViritu pipeline (v0.2.3) 36 , which was employed to map the reads against the Virus Pathogen Database (v2.0.2). This database contains curated viral genomes from NCBI GenBank, and the mapping was performed with minimap2 (v.2.26-r1175) 68 to identify the presence of viral genomes/segments in the samples. A combined batch summary was prepared along with the metadata which was used for downstream analysis in RStudio (v.2024.09.0) and R (v.4.3.2). Declarations ACKNOWLEDGMENTS The authors are also grateful to the Department of Science and Technology-GoG for the support and municipalities of all four cities for their constant and continued support in sample collection. We are thankful to State Integrated Disease Surveillance Program (IDSP), National Health Mission, Gujarat, India for providing clinical case data. FUNDING This study was supported by Gujarat State Biotechnology Mission (GSBTM), Govt. of Gujarat, (Grant Id: GSBTM/JD(R&D)/662/2022-23/00292909). AUTHOR INFORMATION Authors and Affiliations Gujarat Biotechnology Research Centre (GBRC), Department of Science and Technology, Government of Gujarat, Gandhinagar-382010 India Nitin Shukla, Jinal Thakor, Priyank Chavda, Harshal Purohit, Harshil Patel, Niraj Kumar Singh, Snehal Bagatharia, Chaitanya Joshi, Madhvi Joshi, Ramesh Pandit GTU-School of Applied Sciences and Technology (GTU-SAST), Gujarat Technological University, Ahmedabad, Gujarat, India Nitin Shukla, Chandrashekar Mootapally Gujarat Biotechnology University, Nr. Gujarat International Finance Tec-City, Gandhinagar-382355, Gujarat, India Saurabh Thakar, Aesha Raval, Jinal Prajapati, Jay Solanki, Deeparati Datta, Harshita Karri, Abhijeet Mondal, Varun Shah Contributions N.S. wrote the original manuscript, performed data analysis and prepared figures and tables. N.S., J.T., P.C., H.P. and H.P. performed the experiments and validation. S.T., A.R., J.P., J.S., D.D., H.K. and A.M. involved in methodology. NK.S. and S.B. contributed to investigation and supervision. C.J. and C.M. did conceptualization, investigation and supervised. M.J., V.S. and R.P. conceptualized the study, funding acquisition and reviewed-edited the draft manuscript. All authors read and approved the final version of the manuscript. Corresponding authors Correspondence to Ramesh Pandit, Varun Shah, Madhvi Joshi and Chandrashekar Mootapally. CONFLICT OF INTERESTS STATEMENT The authors declare no conflicts of interest. ETHICS STATEMENT Not applicable. DATA AVAILABILITY The raw sequencing data supporting this study is available under BioProject accession number PRJEB102331 (accessible at https://ibdc.dbtindia.gov.in/inda/submittedStudyHome) and INDA accession number INRP000499 (accessible at https://ibdc.dbtindia.gov.in/inda/completeStudyDetailsById?studyid=INRP000499). The data have been deposited in the India Biological Data Centre (IBDC) under the Indian Nucleotide Data Archive (INDA). 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08:39:52","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":493768,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/5c1e3931f35011813ac6e919.png"},{"id":96249082,"identity":"40cd0bfd-6b6a-45c1-b518-8eb16c3ee9be","added_by":"auto","created_at":"2025-11-19 07:30:14","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":588077,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/a8fc40b619b0bdffdb05240c.png"},{"id":96247359,"identity":"412b5981-3230-4e12-810c-b4d8a8c511df","added_by":"auto","created_at":"2025-11-19 07:27:25","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1160926,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/d0b28c762458267eacfaff7c.png"},{"id":96061727,"identity":"c882882c-fc7c-414c-af50-f909facdbdb6","added_by":"auto","created_at":"2025-11-17 08:39:53","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":595848,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/4d358a31ea3d1873e0473dad.png"},{"id":96061726,"identity":"c4b7ad36-6bf7-4b78-84d8-a02c15cb046b","added_by":"auto","created_at":"2025-11-17 08:39:53","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":410277,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/8fbc4743880a5b5870d2a4fb.png"},{"id":96061718,"identity":"398a5a60-c826-4668-95fa-1cfd539790b7","added_by":"auto","created_at":"2025-11-17 08:39:52","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141615,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/0bea7e7bd3d10cc353631f55.png"},{"id":96247364,"identity":"927417e8-3fb2-4337-8363-bc6c0e1ccb27","added_by":"auto","created_at":"2025-11-19 07:27:25","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200526,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/10585581e442205340f56d98.png"},{"id":96061724,"identity":"04d74aca-bebd-49da-9ead-69698f32dbc6","added_by":"auto","created_at":"2025-11-17 08:39:52","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":327098,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/2d1aed7d465d8ea299d8d996.png"},{"id":96246504,"identity":"3968f7fc-0cc2-4481-926a-56284efd9a6c","added_by":"auto","created_at":"2025-11-19 07:26:08","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209788,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/d12d8184175c383e1c27363c.png"},{"id":96061732,"identity":"3d74fe70-9b85-4a33-aad1-85294ed7c0ff","added_by":"auto","created_at":"2025-11-17 08:39:53","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148900,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/ee9ff7b2db271b3785e3011e.png"},{"id":96061731,"identity":"7fff868d-b7ba-4083-8ad3-d04577c163ac","added_by":"auto","created_at":"2025-11-17 08:39:53","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138061,"visible":true,"origin":"","legend":"","description":"","filename":"d44940167435410d928a981c0c0c56ff1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/204fa1c2bcea48f883b99d9a.xml"},{"id":96061733,"identity":"1a57425f-82ef-48bf-89b7-adcce65263c0","added_by":"auto","created_at":"2025-11-17 08:39:53","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156833,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/fc49f5356c97044a7fc667d0.html"},{"id":96246483,"identity":"1a6c6a00-2f94-4159-bb71-b7c75338dcb3","added_by":"auto","created_at":"2025-11-19 07:26:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":724525,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of wastewater virome from four cities of Gujarat, total viruses identified, categorized by nucleic acid type and host origin, viral genome coverage, and spatiotemporal distribution.\u003cstrong\u003e \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eTaxonomic classification of DNA and RNA viruses, with bars representing the number of viruses identified at each taxonomic rank. (B) Taxonomic resolution of viral species and strains detected in the wastewater virome, grouped by host origin, including human, animal, plant, and insect-associated viruses. Each bar represents the number of species and strains identified for the respective host group. (C) Rarefaction curves showing the number of unique viral strains detected: (left) cumulative across all samples, (middle) grouped by city, and (right) by individual sampling sites. (D) Boxplot showing variation in alpha diversity (Shannon index) calculated per sample for each sampling site. The center line represents the median, while the lower and upper bounds of the box denote the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles, respectively. (E) Violin plots showing the number of viral genomes or segments detected per sample across three genome coverage categories: partial (10–50%), moderate (50–90%), and near-complete (\u0026gt;90%). Median values are annotated for each category. (F) Genome/segment assemblies of detected viruses categorized into three tiers based on percent coverage: partial (10–50%), moderate (50–90%), and near-complete (\u0026gt;90%). (G) Site-wise distribution of viral strain counts per sample across all sampling locations, with median represented with horizontal line.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/241d4850d111dacad4467a17.jpg"},{"id":96061711,"identity":"a5231de3-f561-4428-9641-c870e9e38dfa","added_by":"auto","created_at":"2025-11-17 08:39:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1044152,"visible":true,"origin":"","legend":"\u003cp\u003eVirome dissimilarity and prevalence across seasons and changes in community composition. (A) Temporal variation in Bray–Curtis dissimilarity across all sampling sites. Each subplot represents a single site, with dissimilarity plotted against the number of days between sampling events. Each dot represents a comparison between two samples. (B) Boxplots comparing Bray-Curtis dissimilarity observed for samples compared between inter-city and inter-site (padj: 2.76E-20), inter-city and intra-site (padj: 2.66E-10), inter-Site and intra-site (padj: 9.18E-21). (C) t-SNE plots illustrating the spatial distribution of samples by city (left) and temporal trends over the sampling period (right). Points are colored by library type (DNA or RNA) and sampling date. (D) Mean prevalence of viral families detected across the four cities. Dots represent the average detection frequency of each family in Ahmedabad, Gandhinagar, Rajkot, and Vadodara, highlighting both common and city-specific viral signatures. Alpha diversity (Shannon index) of viral communities plotted by month (E) and week (F) across three meteorological seasons: Monsoon 2023, Winter 2023–2024, Summer 2024, and Monsoon 2024. Each point represents an individual sample, with loess smoothing indicating temporal trends in alpha diversity. \u0026nbsp;(G) Relative abundance of dominant viral families across the four seasons, revealing shifts in taxonomic composition. Each color represents a distinct viral family; stacked area plots illustrate seasonal fluctuations in family-level abundance.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/ccbb5fa9e63e2c0f02c6a7c9.jpg"},{"id":96061707,"identity":"77a887d3-8ef4-45e7-8dc9-ba067d65fa29","added_by":"auto","created_at":"2025-11-17 08:39:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":993319,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal dynamics of major viral pathogens in wastewater across four cities. (A) Line plots illustrate the temporal trends (fortnightly moving average of RPKMF) of key clinically relevant viruses across the four cities. Distinct peaks in viral abundance indicate episodic shedding and potential seasonally linked outbreaks, highlighting spatial and temporal differences in virome dynamics. (B) Heatmap displaying the detection and relative abundance (log₁₀ RPKMF) of RNA viruses across monthly timepoints in Ahmedabad, Gandhinagar, Rajkot, and Vadodara. The x-axis represents time (in months), and the y-axis indicates viral abundance (log₁₀ RPKMF). (C) Heatmap showing detection and relative abundance (log₁₀ RPKMF) of DNA viruses over the same time span and locations.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/c4bd68cd73c738c7f5250b98.jpg"},{"id":96246460,"identity":"6181ba78-a5a1-426d-b95b-bd18786e0c79","added_by":"auto","created_at":"2025-11-19 07:26:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":751786,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of wastewater virome abundance with dPCR quantification and clinical data.\u003cstrong\u003e \u003c/strong\u003e(A) Scatter plot and linear regression showing the correlation between RNA copy numbers (copies/L) (dPCR) and RPKMF values for Human Rotavirus A across four cities. Corresponding time-series plots display monthly trends of dPCR and sequencing-based abundance in Ahmedabad, Gandhinagar, Rajkot, and Vadodara. (B) Comparison of SARS-CoV-2 detection in wastewater using two independent methods: relative abundance (RPKMF) from sequencing and RNA copy numbers (copies/L) measured via digital PCR (dPCR) in Ahmedabad and Gandhinagar. (C) Correlation between the average monthly clinical cases of Hepatitis A Virus and its abundance in wastewater (RPKMF). A positive correlation was observed (\u003cem\u003eR\u003c/em\u003e= 0.62, \u003cem\u003ep\u003c/em\u003e = 0.042), demonstrating the relevance of wastewater surveillance for tracking community infection trends.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/aa2423423403b5786cc98f44.jpg"},{"id":97900252,"identity":"a51bbab9-7f61-4f22-b054-437fc52389de","added_by":"auto","created_at":"2025-12-10 15:45:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4387847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/5afa91cb-6cbf-4444-bd37-4ba2c0dfe83e.pdf"},{"id":96247162,"identity":"d27605c5-66d3-4ad5-9e2d-436db7e1cb4a","added_by":"auto","created_at":"2025-11-19 07:27:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14628841,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/fdec87d17eee6a86d2309aec.docx"},{"id":96061735,"identity":"22d19271-df83-45b9-b5af-aaf821fe83ee","added_by":"auto","created_at":"2025-11-17 08:39:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22301747,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/91f1910598cfbad70e154760.xlsx"},{"id":96061712,"identity":"e8855cb6-5c32-4074-ae22-f8d36e6592f5","added_by":"auto","created_at":"2025-11-17 08:39:52","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1167945,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8093831/v1/532c5068081bfde425023219.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards a Comprehensive Wastewater Virome Atlas for Pathogen Monitoring","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWastewater-based surveillance (WBS), or wastewater-based epidemiology (WBE)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, has opened new frontiers in pathogen monitoring. It has emerged as a cost-effective, non-invasive\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, crucial public health tool\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, for monitoring infectious diseases temporally \u0026amp; geographically\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e offering early detection\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, providing real-time insights into community health\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The One Health approach recognizes the shared environment inhabited by humans, animals, and plants, emphasizing their interconnected health\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Thus, monitoring pathogens in sewage wastewater, which captures both human and animal activity in rural and urban environment\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, provides early warning signals and enhances the traceability of infectious diseases. This, in turn, can facilitate rapid responses to public health threats posed by zoonotic as well as re-emerging pathogens\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Besides genomic sequencing of pathogens from wastewater, it has proven to be an effective method for monitoring viral variants\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, offering valuable insights to clinicians and public health organizations about the variants circulating within the community\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eViral diversity is influenced by seasonal changes\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These include respiratory viruses such as Respiratory syncytial virus (RSV), Influenza virus, and Human Metapneumovirus, which typically peak during winter, although some peaks are also observed in summer\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Enteric viruses\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, such as Norovirus GII, tend to peak during colder seasons\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In contrast, certain viruses like Human Adenovirus, Human Parainfluenza virus, and Human Coronavirus do not exhibit strong seasonality and are detected throughout the year\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, with continuous circulation reported in various studies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Interestingly, SARS-CoV-2 abundance in wastewater has been reported to be higher in winter compared to the monsoon season\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The seasonal influenza viruses are detected year-round; their circulation is significantly higher during the autumn and winter months\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The seasonality of viral circulation is influenced by environmental factors such as temperature, relative humidity, and rainfall, as well as by human behaviour and its interaction with natural surroundings\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWastewater contains a wide range of biological wastes, including stool, urine, bodily fluids, wound exudates, and nasal secretions, as well as viruses shed by infected individuals both symptomatic and asymptomatic\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This complex composition contributes to extensive viral fragmentation from the point of excretion to sample processing, which can significantly affect the detectable copies of viral genomes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The genomic background of wastewater is highly complex and typically dominated by commonly occurring microorganisms, while target pathogens are often present only in trace amounts\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo address these challenges, several strategies have been adopted for viral detection in wastewater. PCR-based amplification has been widely used to detect known viruses, such as SARS-CoV-2, in wastewater samples\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, broader community-level surveillance requires more robust approaches, such as probe capture sequencing, which enables the enrichment of low-abundance targets. Other methods include shotgun metagenomic sequencing, targeted amplicon sequencing, and viral particle enrichment\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Hybridization-based capture probes can increase the representation of target sequences by several orders of magnitude\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Notably, the probe hybridization method has been reported to significantly enhance sensitivity in viral metagenomics and is particularly well-suited for pathogen surveillance in low-biomass samples such as wastewater\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn our previous studies during the COVID-19 pandemic, we tracked SARS-CoV-2 in four cities, Ahmedabad, Gandhinagar, Rajkot, and Vadodara in Gujarat\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We demonstrated that trends in wastewater SARS-CoV-2 RNA anticipated case surges and supported hotspot zoning\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, establishing operational feasibility of such surveillance across large urban catchments. Recent global studies have further supported the use of wastewater surveillance for the detection of influenza viruses, including H5N1\u003csup\u003e34\u003c/sup\u003e has broadened the virus traceability beyond a single virus to multi-pathogens, enabling normalization and trend analytics\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on these findings, the present study aimed to perform a year-round profiling of the total virome in four cities of Gujarat. Our goal was to understand the structure and dynamics of wastewater virome, observe how it changes over time and across different seasons, and evaluate whether multi-pathogen surveillance data correlate with single-pathogen surveillance dPCR and clinical case trends. To our knowledge, this is one of the first studies from India to conduct longitudinal virome profiling from wastewater samples collected across multiple sites over the span of an entire year.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eComprehensive virome taxonomic insights\u003c/h2\u003e\u003cp\u003eFollowing the COVID-19 pandemic, the urgency and importance of understanding community virome profiles has become increasingly evident. The study aimed to perform year-round wastewater virome surveillance from four cities in Gujarat, which covered an area of ~\u0026thinsp;89.77 km\u003csup\u003e2\u003c/sup\u003e. The sampling sites were selected to represent the demographic distribution of the local population with an estimated size of ~\u0026thinsp;16.57\u0026nbsp;million. The samples were collected from 24 different sites (STPs/SPSs) that represent the major working population of Gujarat, which makes them ideal for understanding the state-wide virome distribution and its implications for public health.\u003c/p\u003e\u003cp\u003eIn total, we collected 583 wastewater samples and prepared 1,166 sequencing libraries, comprising both DNA and RNA. Sequencing was carried out across 13 NovaSeq 6000 runs, each yielding\u0026thinsp;~\u0026thinsp;245.52 gigabases (Gb) and ~\u0026thinsp;1.6\u0026nbsp;billion reads per run (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Eighteen samples did not meet library quality control (QC) standards due to poor library yield, low quality or low sequencing data and were therefore excluded from downstream analysis. As a result, the final dataset used for analysis included 1,124 libraries (562 DNA and 562 RNA) derived from 565 samples.\u003c/p\u003e\u003cp\u003eViruses were classified on nucleic acid type (DNA or RNA) using the Virus Metadata Resource (VMR) from the International Committee on Taxonomy of Viruses (ICTV) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ictv.global/vmr\u003c/span\u003e\u003cspan address=\"https://ictv.global/vmr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Further classification by host i.e., human, animal, plant, or insect was performed using metadata from Virus-Host DB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://virus-world.org\u003c/span\u003e\u003cspan address=\"https://virus-world.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The extended information of host and nucleic acid type of viruses included in Twist Comprehensive Viral Research Panel is available at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nitinShukla1912/wastewater_virome\u003c/span\u003e\u003cspan address=\"https://github.com/nitinShukla1912/wastewater_virome\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In total, we detected 10 DNA virus families and 23 RNA virus families. This included 47 DNA virus genera and 55 RNA virus genera, encompassing 170 DNA viral species and 135 RNA viral species. Furthermore, we identified 343 strains of DNA viruses and 469 strains of RNA viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Of these, 223 strains were associated with animals, while 549 strains were specific to humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTo evaluate the viral species richness and determine whether the sequencing depth was adequate to capture the full viral diversity, rarefaction curves were generated stratified by city and site. The overall curve for total samples showed a continued but plateauing increase in the number of unique strains, indicating that while a large portion of the viral diversity has been captured, additional sampling may still yield novel strains. City-wise analysis revealed variability in richness, with Ahmedabad and Gandhinagar exhibiting higher saturation levels compared to Rajkot and Vadodara. Site-level rarefaction curves indicated that for individual sites additional sequencing may still uncover a few more viral taxa but is unlikely to drastically change the overall virome profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Viral species richness and evenness, assessed using the Shannon diversity index, also varied considerably across sites. Ahmedabad exhibited the highest intra-city variability in viral diversity, with sites such as Jamalpur, Pirana, and Ranip consistently showing elevated diversity levels. In Gandhinagar, Jaspur recorded the highest diversity, while other sites displayed relatively uniform profiles. In contrast, diversity patterns in Rajkot and Vadodara were more heterogeneous, with notable median diversity observed at Raiya and Tarsali (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). These findings underscore the utility of capture based deep sequencing for comprehensive viral surveillance, not only for abundance profiling but also for identifying the breadth of viral strains present in wastewater.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition to profiling viral abundance, we aimed to reconstruct near-complete viral genomes/segments. A median of 40.8 viral genomes/segments with sequencing reads covering more than 90 percent of alignment length, and 54.7 median genomes/segments with coverage exceeding 50 percent were recovered (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Beyond strain-level detection, we successfully identified over 107 DNA viruses and 495 RNA viruses with genome/segment completeness exceeding 90% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Across different cities and sampling sites, the number of recovered viral strains varied. Ahmedabad (Paldi) showed the lowest median 24.5 strains per sample, while Rajkot (Raiya) had the highest, at 73.5 strains per sample. Other top sites with high median strain include Vadodara (Channi) 72.5 strains, Ahmedabad (Jamalpur) 68 strains, and Gandhinagar (Raysan) 67 strains per sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpatiotemporal dynamics of wastewater virome\u003c/h3\u003e\n\u003cp\u003eTo capture the dynamics of total virome diversity across space and time, the study employed a year-round, fortnightly sampling strategy. This design aimed to systematically investigate how viral community structure varies across cities, different sites within cities, and sampling intervals. Spatiotemporal variation in viral communities from wastewater was assessed using beta diversity (Bray-Curtis dissimilarity) across cities and sampling sites over time. In parallel, we assessed temporal variability in viral community composition by performing pairwise Bray-Curtis comparisons of all samples collected from the same site at different time points. Across most sites, dissimilarity remained consistently high (median Bray-Curtis\u0026thinsp;\u0026gt;\u0026thinsp;0.7, \u003cem\u003ep\u003c/em\u003e-value: 2.2e-16), indicating that wastewater virome exhibit substantial short-term fluctuations, potentially driven by seasonal dynamics or intermittent viral shedding patterns within the host population. Despite this high baseline variability, the rate of change in community composition over time differed across sites. At several locations, such as Paldi and Vasna (MLD \u0026amp; SPS) in Ahmedabad, Jaspur in Gandhinagar, and Popatpara in Rajkot dissimilarity slightly increased with longer intervals i.e., samples collected at distant time points, suggesting ongoing viral community turnover. In contrast, sites like Kothariya in Rajkot and Chhani in Vadodara displayed relatively flat trends over time, implying a more stable virome (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These patterns highlight that temporal dynamics in wastewater viromes vary spatially and are potentially influenced by differences in catchment population and local environmental factors. We compared Bray-Curtis dissimilarities between samples collected from the same site, different sites within the same city, and across different cities to assess spatial patterns in virome community structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Contrary to expectations, intra-site comparisons exhibited slightly higher Bray-Curtis dissimilarity than inter-site and inter-city comparisons. This may be attributed to seasonal turnover in viral community composition over time and across individual sites. While spatial differences were anticipated, our year-long dataset revealed that temporal variation potentially driven by seasonal viral shedding patterns plays a dominant role in shaping community dissimilarity in wastewater viromes.\u003c/p\u003e\u003cp\u003eTime and location are important variables that define the structure of the viral community, as visualized using t-distributed stochastic neighbor embedding (t-SNE). As expected, two distinct clusters were observed, separating DNA and RNA viruses (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We stratified the libraries separately and observed that the samples were distributed randomly by collection date and among cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This could be due to a similar viral community structure shared across the four different cities during that time. Viral families showed a similar pattern in median strain count and mean prevalence, with the highest observed in Vadodara, particularly for \u003cem\u003eAstroviridae\u003c/em\u003e, \u003cem\u003eAdenoviridae\u003c/em\u003e, and \u003cem\u003eParvoviridae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSeasonal changes influence viral diversity\u003c/h3\u003e\n\u003cp\u003eTo assess the influence of season on wastewater virome, we used linear mixed effect model on the alpha diversity (Shannon indices), by incorporating meteorological seasons (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mausam.imd.gov.in/ahmedabad/mcdata/climate.pdf\u003c/span\u003e\u003cspan address=\"https://mausam.imd.gov.in/ahmedabad/mcdata/climate.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), months and weeks as fixed effects, and city-site as a random intercept. We found that seasons and months both had a significant effect on viral alpha diversity (Season \u003cem\u003ep\u003c/em\u003e-value: 0.0002028, Months \u003cem\u003ep\u003c/em\u003e-value: 6.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Viral diversity was highest during monsoon (Sep23-Nov23) and remained comparable during winter (Dec23-Feb23). However, a marked decline was observed during summer (Mar24-May24) indicating a temporary suppression in the viral diversity which subsequently rebounded with the onset of the following monsoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Notably, when we analysed diversity at weekly scale (Season \u003cem\u003ep\u003c/em\u003e-value: 0.64, weeks \u003cem\u003ep\u003c/em\u003e-value: 0.01), a smooth trend line revealed a gradual recovery in alpha diversity across Monsoon 2024, particularly after week 36. This suggests that while seasonal grouping showed reduced diversity during Monsoon 2024, weekly-level resolution captured an increasing trajectory similar to the monsoon from the previous year i.e., Sept-23 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). These findings highlight the importance of multi-scale temporal analysis that uncovers the virome diversity influenced by time and season. Although October and November are typically categorized as post-monsoon months by the India Meteorological Department (IMD), Ahmedabad, they were grouped under monsoon in this analysis to simplify seasonal comparisons.\u003c/p\u003e\u003cp\u003eThe prevalence of virus families exhibited distinct seasonal variations. \u003cem\u003ePolyomaviridae\u003c/em\u003e remained consistently abundant across all seasons, with the highest relative abundance observed during the monsoon 2023 (16.2%), and maintained levels above 10% in the subsequent seasons, followed by \u003cem\u003eAdenoviridae\u003c/em\u003e (11.9%) in monsoon 2024. During the winter, \u003cem\u003eDicostaviridae\u003c/em\u003e (11.8%), \u003cem\u003eParvoviridae\u003c/em\u003e (10.3%), and \u003cem\u003eCoronaviridae\u003c/em\u003e (4%) were more predominant. Interestingly, some families like \u003cem\u003eAstroviridae\u003c/em\u003e, \u003cem\u003eRetroviridae\u003c/em\u003e, and \u003cem\u003eHerpesviridae\u003c/em\u003e appeared intermittently across seasons, suggesting episodic shedding. Notably, \u003cem\u003ePicornaviridae\u003c/em\u003e (13.9%) and \u003cem\u003eParvoviridae\u003c/em\u003e (12.3%) showed sharp increases during Summer 2024. This suggests the taxonomic architecture and seasonal shifts in viral families are persistent and well aligned (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eAs the seasonal variations were evident, we observed during the summer, Betapolyomavirus secuhominis (JC Polyomavirus) was more prevalent, whereas Betapolyomavirus hominis (BK Polyomavirus) was dominant in other seasons. Astrovirus MLB1 was predominant in summer, while Masmatrovirus 1 was detected year-round (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The abundance of Salivirus A (\u003cem\u003ePicornaviridae\u003c/em\u003e) increased steadily over time, whereas Kobuvirus species were predominantly observed during the monsoon and winter seasons. Within the \u003cem\u003eCircoviridae\u003c/em\u003e family, Pigeon Circovirus from avian species showed high abundance during winter and summer, along with Beak and Feather Disease Virus was prominently observed during monsoon and summer. A notable and sustained increase in Human faecal virus Jorvi2 was recorded from week 21 (May 2024) through week 39 (September 2024) higher in monsoon season (Figure S3). Rotavirus A (\u003cem\u003eSedoreoviridae\u003c/em\u003e) was consistently dominant across the year. Among the \u003cem\u003eCoronaviridae\u003c/em\u003e family, we detected five species with abundances exceeding 1%, including Alphacoronavirus, Avian Coronavirus, Betacoronavirus 1, and SARS-CoV-2 (Figure S4).\u003c/p\u003e\n\u003ch3\u003eProfiling human pathogenic viruses from wastewater\u003c/h3\u003e\n\u003cp\u003eDistinct temporal trends were observed for several human pathogenic viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Among RNA viruses, Influenza A virus, Enterovirus, Hepatitis A virus, Parainfluenza virus, Norovirus, Respiratory syncytial virus (RSV), Mumps, Measles, SARS-CoV-2, Rhinovirus, and Rotavirus exhibited either consistent trend or sporadic occurrences across cities over different months (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, among DNA viruses, notable patterns were seen for JC Polyomavirus, BK Polyomavirus, Human Mastadenovirus, Adeno-associated Dependoparvovirus, and Mupapillomavirus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eMembers of the \u003cem\u003ePolyomaviridae\u003c/em\u003e family infect both humans and animals and can be shed into wastewater via urine, stool, and skin\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Notably, JC and BK Polyomaviruses are clinically relevant in immunocompromised individuals, such as those undergoing chemotherapy or infected with HIV, where they are associated with conditions like haemorrhagic cystitis and progressive multifocal leukoencephalopathy (PML)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Both JC and BK Polyomavirus were consistently detected across all cities and throughout the year. These viruses exhibited high genome coverage, with more than 90% of their genomes recovered in 216 samples and 298 samples, respectively. A representative genome coverage plot is shown in (Figure S5). Additionally, other polyomaviruses such as Merkel Cell Polyomavirus, HPyV6, HPyV7, and WU Polyomavirus were also detected in the wastewater samples. These findings indicate that wastewater surveillance can effectively identify circulating polyomaviruses within the community, which can further be classified into their specific genotypes.\u003c/p\u003e\u003cp\u003eEnteric viruses, which are primarily associated with gastroenteritis, were frequently detected. These viruses are transmitted via the faecal-oral route and are known for their environmental stability, persisting from days to weeks in wastewater\u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. They pose a significant public health risk, particularly to children, and are linked to acute gastrointestinal illness (AGI), which may lead to high morbidity and mortality. Detection of these viruses in wastewater provides an opportunity for early intervention, potentially preventing outbreaks of AGI and Hepatitis A\u003csup\u003e45\u003c/sup\u003e. Symptomatic diseases caused by these viruses include gastroenteritis, acute hepatitis, and central nervous system infections such as meningitis, encephalitis, paralysis, as well as conjunctivitis and respiratory illnesses. The major viral genera responsible for such illnesses include Adenovirus, Enterovirus, Parechovirus, Norovirus, Rotavirus, Hepatitis A Virus, and Hepatitis E Virus. These viruses replicate in the gastrointestinal tract and are shed in large quantities via faeces often for weeks regardless of symptom status. Due to their high environmental resistance, they can be transmitted through drinking water, and foods contaminated by wastewater or effluents from wastewater treatment plants (WWTPs)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eAnimal-infecting and zootnotic viruses from wastewater\u003c/h3\u003e\n\u003cp\u003eAmong animal-associated viruses, we detected Avian coronavirus, Avian dependoparvovirus 1, Beak and feather disease virus, Carnivore protoparvovirus 1, Canine morbillivirus, Equine rotavirus, Feline rotavirus, Gyrovirus galga1, Pigeon circovirus, and Primate norovirus. Zoonotic viruses currently pose a major threat to public health across worldwide. Notably, some of the detected viruses also exhibited zoonotic potential, including Porcine torovirus, Rocahepevirus ratti, Rosavirus, and Hepatitis E virus, which pose a risk of cross-species transmission\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (Figure S6 and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation of wastewater virome data with clinical cases and dPCR data\u003c/h2\u003e\u003cp\u003eAs a pooled representation of the community, wastewater effectively captures circulating viral infections, including both symptomatic and asymptomatic cases. To compare sequencing-based estimates with dPCR data, we used RPKMF for viral abundance and compared it with RNA copies/L of wastewater. We validated Human Rotavirus A (NSP3 gene)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e abundance using dPCR. The comparison revealed significant positive correlation for Ahmedabad (R\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.019), Gandhinagar (0.31, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.0019), Vadodara (R\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.017), however not for Rajkot (R\u0026thinsp;=\u0026thinsp;0.079, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.49) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table S3). Besides, we continuously monitored SARS-CoV-2 RNA copies in wastewater using dPCR as part of a statewide surveillance initiative covering multiple cities in Gujarat. We observed a significant positive correlation between the two independent methods for Ahmedabad (R\u0026thinsp;=\u0026thinsp;0.6, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.01) and Gandhinagar (R\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These results suggest that sequencing-derived viral abundance trends align well with dPCR based virus estimation, supporting the utility of metagenomic sequencing as a reliable method for monitoring multiple viral loads simultaneously in wastewater. Alongside, we observed a positive correlation between average monthly clinical cases of Hepatitis A virus obtained from the State Integrated Disease Surveillance Programme (IDSP), Gujarat and wastewater (RPKMF) (R\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This indicates that viral abundance detected in wastewater closely mirrored trends in reported clinical cases of Hepatitis A Virus, further highlighting the significance of wastewater-based surveillance.\u003c/p\u003e\u003cp\u003eThis surveillance strategy has enabled us to gain valuable insights into the community-level prevalence of both human and animal viruses, reinforcing its relevance in a One Health framework. It highlights the potential of wastewater-based monitoring for detecting emerging threats and enhancing public health preparedness and response efforts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe need for a global surveillance network for early warning against emerging and re-emerging pathogens is rapidly increasing\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. To address this, GLOWACON represents one such initiative, a global consortium aimed at establishing an international sentinel system for pathogen detection and monitoring\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Wastewater surveillance strategies have proven to be cost-effective and non-invasive methods for monitoring pathogen circulation at the community level\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This method offers a real-time tool to track the pathogens and serves as an early warning system for the detection of both known and emerging threats, including monkeypox (Mpox)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, Avian Influenza (H5N1)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and West Nile\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. By capturing a wide range of viral signatures shed into sewage from both human and animal sources, wastewater reflects a co-existing virome and can uncover potential zoonotic spillovers, helping to identify infections transmitted not only from human to human but also from animals to humans. As a community-driven health monitoring strategy, it plays a vital role in public health preparedness by enabling early detection, informing risk mitigation strategies, and supporting the development of responsive health frameworks. Such global surveillance efforts contribute significantly to pandemic prevention, outbreak management, and the timely implementation of public health measures\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe aimed to establish a baseline understanding of the prevalence and diversity of human and animal viruses in wastewater, contributing to the broader goal of One Health. Importantly, the method was able to capture seasonal and temporal trends in the circulation of key viruses such as Adenovirus, Polyomavirus, Influenza A virus, SARS-CoV-2, Hepatitis E, Rotavirus, and Norovirus\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Furthermore, the detection of clinically relevant pathogens like Respiratory syncytial virus (RSV), Mumps orthorubulavirus, and Measles virus highlight the potential for early warning and disease preparedness. It also offers a way to monitor circulating viral strains that could trigger future outbreaks, such as SARS-CoV-2 variants\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. When implemented across broader demographics and supported by sustained governmental funding, this NGS-based surveillance strategy can serve as a proactive framework for infectious disease monitoring and outbreak preparedness.\u003c/p\u003e\u003cp\u003eThe hybridization-based capture probe method proved highly effective in detecting viruses and viral strains, even in samples with lower copies\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. It is scalable for capturing a diverse range of viruses during both endemic and outbreak conditions\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. For SARS-CoV-2, and Human Rotavirus A we observed a positive correlation between capture probe-based sequencing and digital PCR (dPCR), validating its effectiveness. This technique also demonstrated remarkable utility beyond SARS-CoV-2, for instance, we successfully recovered nearly the complete genome of Respiratory syncytial virus (RSV) with over 89% coverage, highlighting the method\u0026rsquo;s sensitivity, such resolution is effective for tracking virus evolution and monitoring the emergence of new variants. These findings are crucial for predicting future outbreaks based on sewage surveillance data.\u003c/p\u003e\u003cp\u003eAlthough the overall virome composition was broadly similar across all cities, certain viral families exhibited distinct patterns, highlighting city-specific variations in viral prevalence. In Vadodara, high prevalence was observed for members of the \u003cem\u003eAstroviridae\u003c/em\u003e family (Mamastrovirus 1, Human astrovirus 2, Human astrovirus 4, and Astrovirus MLB1), \u003cem\u003eAdenoviridae\u003c/em\u003e (Human mastadenovirus F, Human adenovirus 19, and Human adenovirus 41), \u003cem\u003ePolyomaviridae\u003c/em\u003e, and \u003cem\u003eParvoviridae\u003c/em\u003e. The \u003cem\u003eCoronaviridae\u003c/em\u003e family SARS-CoV-2 showed consistent detection in Gandhinagar and Rajkot, with the highest prevalence in Vadodara, followed by Ahmedabad. Certain viruses such as Chicken anemia virus and Avian gyrovirus 2 were more prevalent in Ahmedabad. Additional families, including \u003cem\u003eLuteoviridae\u003c/em\u003e (Bat luteovirus), \u003cem\u003eFlaviviridae\u003c/em\u003e, and \u003cem\u003eHepeviridae\u003c/em\u003e (Hepatitis E virus type 1), were also detected in Ahmedabad, indicating the presence of both human and animal viruses in the community.\u003c/p\u003e\u003cp\u003eOur findings also suggest that some viruses exhibit seasonality. Members of the \u003cem\u003eAstroviridae\u003c/em\u003e family, which are a major cause of gastroenteritis, particularly in children\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, showed lower abundance during the monsoon season and increased prevalence in winter and summer, followed by a decline with the onset of the next monsoon. However, contrasting studies have reported higher astrovirus cases during the rainy season\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. This could be due to regional climatic differences and dilution effects in wastewater caused by heavy rainfall, which may reduce detectable viral loads despite ongoing transmission in the population\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Norovirus I and II, from the \u003cem\u003eCaliciviridae\u003c/em\u003e family, exhibited similar seasonal trends, with reported peaks during colder months\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eAdenoviridae\u003c/em\u003e family showed higher abundance during the winter season\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e and appeared to follow a cyclic pattern. Interestingly, Influenza A virus (H1N1, H3N2) from the \u003cem\u003eOrthomyxoviridae\u003c/em\u003e family displayed higher proportions during the summer, suggesting off-season spikes. A study from China similarly reported increased activity of Influenza A (H3N2) during summer and autumn months\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. These findings support the notion that viruses exhibit seasonal patterns, and that their diversity and proportions vary throughout the year, likely influenced by environmental factors\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Understanding the trends of viral circulation and the mutations they acquire over time can greatly contribute to the development of vaccines and preparedness strategies to prevent future disease outbreaks.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, this study provides a comprehensive, year-long surveillance of the wastewater virome across four major urban catchments, revealing the prevalence, diversity, and spatiotemporal patterns of clinically relevant viruses. The use of a capture probe-based sequencing strategy enabled the detection of both DNA and RNA viral genomes, including those present at low abundance, with several genomes recovered at \u0026gt; 90% coverage. We demonstrated that wastewater virome trends often mirrored clinical case data and dPCR validation, confirming the reliability of this approach for public health surveillance accompanying multi-pathogen surveillance. The observed seasonal variation in viral composition and the detection of both persistent and emerging viruses highlight the spatiotemporal dynamics of virome. Integrating such environmental monitoring with clinical datasets can support proactive decision-making, enhance outbreak preparedness, and advance the goals of One Health.\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch2\u003eGeo-location and sample collection\u003c/h2\u003e\u003cp\u003ePre-treated wastewater samples were collected fortnightly from September 2023 to September 2024 across four cities in Gujarat which include Ahmedabad, Gandhinagar, Rajkot, and Vadodara covering a total of 24 sampling sites (Table S4). The sample collection sites include sewage treatment plants (STPs) and sewage pumping stations (SPSs). These cities \u0026amp; sites were selected for wastewater sample collection to provide a broader understanding of virome diversity, as they represent a major diverse population and are likely to capture the variability in virome profile.\u003c/p\u003e\u003cp\u003eWe collected 2 L of raw, untreated wastewater in sterile containers and stored the samples at 4°C until further processing. From the collected volume, a total of 400 mL was used for nucleic acid extraction, 200 mL was processed for DNA and 200 mL for RNA isolation. For each 200 mL aliquot of raw sewage, 25 mL of Glycine buffer (0.05 M glycine, 3% beef extract, pH 9.6) was added\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e and vortexed to detach the virus particles from the organic matter. The mixture was then centrifuged at 8,000 × g for 20 minutes to settle down bacteria, fungi, other organisms, and large debris. The supernatant was collected and filtered through a 0.22 µm cellulose acetate filter to remove any remaining debris and other microbial cells. To the filtrate, 8% polyethylene glycol 8000 (PEG 8000) and 1.74% NaCl, was added and vortexed until fully dissolved, followed by overnight incubation at 4°C to facilitate viral aggregation. After incubation, the samples were centrifuged at 12,000 × g for 90 minutes at 4°C, and the resulting pellet was resuspended in 200 µl of nuclease-free water. DNA and RNA were then isolated using the QIAamp DNA Mini Kit and the QIAamp Viral RNA Kit (QIAGEN, Hilden, Germany), respectively.\u003c/p\u003e\u003ch2\u003eLibrary preparation and next-generation sequencing\u003c/h2\u003e\u003cp\u003eQuantification of DNA and RNA was performed using the Cytation 5 (BioTek, Winooski, USA) and the Qubit 4 fluorometer (Thermo Fisher Scientific, USA). For double stranded cDNA synthesis, extracted RNA was reverse transcribed using Random Primer 6 (New England Biolabs Inc., MA, USA) and the Protoscript II First Strand cDNA Synthesis Kit (New England Biolabs Inc., MA, USA), followed by second-strand synthesis using the NEBNext Ultra II Non-Directional RNA Second Strand Module (New England Biolabs Inc., MA, USA), following manufacturer’s protocol. Subsequently, shotgun libraries were prepared using the Twist Library Preparation EF 2.0 Kit and the Twist Universal Adaptor System (Twist Biosciences, CA, USA). From the shotgun libraries, virus fraction was enriched using the Twist Comprehensive Viral Research Panel (Twist Bioscience, CA, USA). The panel has over one million probes that can capture 3,153 species and 15,488 different strains of virus from human and non-human hosts. Paired-end 2×100 bp sequencing was carried out using SP flow cells on the NovaSeq 6000 platform (Illumina, CA, USA). The base call files (bcl) were converted to FASTQ format using the on-premises DRAGEN server (v. 07.021.645.4.0.3).\u003c/p\u003e\u003ch2\u003eData quality check, metagenomic profiling, and genome identification\u003c/h2\u003e\u003cp\u003eThe raw sequencing reads were first processed for quality filtering/trimming using Trimmomatic (v. 0.39)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e to remove low-quality reads and adaptors if any. The clean reads were then processed using the EsViritu pipeline (v0.2.3)\u003csup\u003e36\u003c/sup\u003e, which was employed to map the reads against the Virus Pathogen Database (v2.0.2). This database contains curated viral genomes from NCBI GenBank, and the mapping was performed with minimap2 (v.2.26-r1175)\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e to identify the presence of viral genomes/segments in the samples. A combined batch summary was prepared along with the metadata which was used for downstream analysis in RStudio (v.2024.09.0) and R (v.4.3.2).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are also grateful to the Department of Science and Technology-GoG for the support and municipalities of all four cities for their constant and continued support in sample collection. We are thankful to State Integrated Disease Surveillance Program (IDSP), National Health Mission, Gujarat, India for providing clinical case data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Gujarat State Biotechnology Mission (GSBTM), Govt. of Gujarat, (Grant Id: GSBTM/JD(R\u0026amp;D)/662/2022-23/00292909).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGujarat Biotechnology Research Centre (GBRC), Department of Science and Technology, Government of Gujarat, Gandhinagar-382010 India\u003c/p\u003e\n\u003cp\u003eNitin Shukla, Jinal Thakor, Priyank Chavda, Harshal Purohit, Harshil Patel, Niraj Kumar Singh, Snehal Bagatharia, Chaitanya Joshi, Madhvi Joshi, Ramesh Pandit\u003c/p\u003e\n\u003cp\u003eGTU-School of Applied Sciences and Technology (GTU-SAST), Gujarat Technological University, Ahmedabad, Gujarat, India\u003c/p\u003e\n\u003cp\u003eNitin Shukla, Chandrashekar\u0026nbsp;Mootapally\u003c/p\u003e\n\u003cp\u003eGujarat Biotechnology University, Nr. Gujarat International Finance Tec-City, Gandhinagar-382355, Gujarat, India\u003c/p\u003e\n\u003cp\u003eSaurabh Thakar, Aesha Raval, Jinal Prajapati, Jay Solanki, Deeparati Datta, Harshita Karri, Abhijeet Mondal, Varun Shah\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.S. wrote the original manuscript, performed data analysis and prepared figures and tables. \u0026nbsp;N.S., J.T., P.C., H.P. and H.P. performed the experiments and validation. S.T., A.R., J.P., J.S., D.D., H.K. and A.M. involved in methodology. NK.S. and S.B. contributed to investigation and supervision. C.J. and C.M. did conceptualization, investigation and supervised. M.J., V.S. and R.P. conceptualized the study, funding acquisition and reviewed-edited the draft manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Ramesh Pandit, Varun Shah, Madhvi Joshi and Chandrashekar\u0026nbsp;Mootapally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTERESTS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data supporting this study is available under BioProject accession number PRJEB102331 (accessible at https://ibdc.dbtindia.gov.in/inda/submittedStudyHome) and INDA accession number INRP000499 (accessible at https://ibdc.dbtindia.gov.in/inda/completeStudyDetailsById?studyid=INRP000499). The data have been deposited in the India Biological Data Centre (IBDC) under the Indian Nucleotide Data Archive (INDA). The dataset is also synchronized with the International Nucleotide Sequence Database Collaboration (INSDC), which includes NCBI, ENA, and DDBJ. The scripts used to generate plots are saved in https://github.com/nitinShukla1912/wastewater_virome.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParkins, M. 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Minimap2: pairwise alignment for nucleotide sequences. \u003cem\u003eBioinformatics\u003c/em\u003e 34, 3094\u0026ndash;3100 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8093831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8093831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWastewater represents a complex unstructured mixture of biological material contributed by humans, animals, and other organisms, making it a valuable resource for pathogen surveillance. Capturing the virome using a probe-based approach enables a broader understanding of viral community distribution, allows for the detection of multiple viral strains, and facilitates the identification of viral nucleic acids present in very low amounts in wastewater samples. This study presents a comprehensive virome analysis conducted over one year across four metropolitan cities, with samples collected fortnightly from 24 different sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified over 170 DNA and 135 RNA viral species, including viral strains associated with multiple hosts humans, animals, plants, and insects. Notably, we detected 107 DNA viruses and 495 RNA viral genomes/segments with over 90% genome coverage. Beyond detection, we explored temporal patterns, revealing that viral families such as \u003cem\u003eAstroviridae\u003c/em\u003e, \u003cem\u003eOrthomyxoviridae\u003c/em\u003e, and \u003cem\u003ePicornaviridae\u003c/em\u003e exhibited seasonal trends, while \u003cem\u003eAdenoviridae\u003c/em\u003e and \u003cem\u003eSedoreoviridae\u003c/em\u003e were consistently detected throughout the year. Shannon diversity was higher and more variable at several sites in Ahmedabad, likely due to its larger population size, landscape, and heavy footfall. Bray-Curtis dissimilarity analysis showed significant variation in viral communities across cities, sites, and sampling time points. The prevalence of SARS-CoV-2 and Rotavirus A from NGS showed a significant positive correlation with digital PCR (dPCR) quantification (p \u0026lt; 0.05). Additionally, trends in Hepatitis A virus abundance aligned with monthly reported cases, further validating the reliability of sequencing-based surveillance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis large-scale, longitudinal study demonstrates that NGS-based wastewater virome profiling is a reliable and scalable method for detecting both circulating and sporadic viral strains. The ability to detect low-abundance viral genomes supports early variant identification and offers insights into viral prevalence in the community. The findings offer valuable insights for early public health interventions.\u003c/p\u003e","manuscriptTitle":"Towards a Comprehensive Wastewater Virome Atlas for Pathogen Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 08:39:47","doi":"10.21203/rs.3.rs-8093831/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1542bd1-b2d0-4b7e-b1dd-d2089b7dc66d","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T09:39:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 08:39:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8093831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8093831","identity":"rs-8093831","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}