Discovery of Vibrio cholerae in urban sewage in Copenhagen, Denmark

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Remarkably, no environmental or locally transmitted clinical case of V. cholerae has been reported in Denmark for more than 100 years. We, however, have recovered a near-complete genome out of 115 sewage samples taken over the past 8 years, despite the extremely low relative abundance of 1 V. cholerae read out of 500.000 sequenced reads. Due to the very low relative abundance, routine screening of the individual samples did not reveal V. cholerae . The recovered genome lacks the gene responsible for cholerae toxin production, but although this strain may not pose an immediate public health risk, our finding illustrates the importance, challenges and effectiveness of wastewater-based pathogen surveillance. Vibrio cholerae Metagenomic genome recovery Sewage surveillance Pathogen detection Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Metagenomic analysis of sewage offers affordable and rapid surveillance of pathogens in an unbiased and predominantly healthy cross section of a large human population. In recent years, genome sequencing of sewage has been used to monitor the prevalence of SARS-CoV-2 in urban populations [ 1 ], the distribution of antimicrobial resistance (AMR) genes across the world [ 2 ], [ 3 ], and the entire virome [ 4 ]. While sewage is easily obtainable, detecting low abundance pathogens, using short-read metagenomic shotgun sequencing, is a challenge. That is because each sample besides fecal matter from thousands of randomly chosen individuals also contains a significant environmental component, as well as material originating from sewer dwelling animals and organic waste material that are spilled into sinks and drains from, e.g., medical facilities, industries, and ordinary households. It is further complicated by the fact that short reads randomly sequenced from a given microorganism might share sequence similarities to several different, not necessarily closely related, microorganisms and thus, be mistakenly assigned. Long-read sequencing makes it considerably easier to correctly assign reads to a species, but the lower read yield and higher error rate makes it hard to reliably detect low-abundance organisms. Sewage samples have been collected from three different sewage treatment plants in Copenhagen since 2015. The first batch of samples from 2015–2018 were published by Brinch et al. [ 5 ], in a study that described the Copenhagen resistome. A second batch of samples obtained between 2019 and 2021 was published by Becsei & Fuschi [ 6 ], also with focus on AMR. Here we focus on a subset of samples from the Avedøre site, which is located approximately 10 km from the city center of Copenhagen, Denmark (55.6086, 12.450). We report the unexpected discovery of a constant presence] over at least a decade of Vibrio cholerae using qPCR not targeting the ctxA gene. This was confirmed with subsequent metagenomic sequencing of very low abundant V. cholerae in the inlet to this specific sewage treatment plant in Copenhagen. The finding is surprising and unusual because V. cholerae naturally occur in salt- or brackish water and is not endemic to northern Europe (CDC Yellow Book 2024). Not a single clinical case of locally transmitted cholera has been reported in Copenhagen since the 1850s. V. cholerae was not detectable when analyzing only single samples due to a large number of sequencing reads with similarities to all Vibrio species. Combining the sequencing reads from hundreds of samples, we were able to detect and recover much of its genome. It is well-known that analyses of metagenomic data can result in the identification of false-positive detection of pathogens mainly because of reads mapping to conserved regions in genomes [ 7 ]. We had previously detected few reads mapping to a variety of Vibrio species, including V. cholerae , in the sewage samples from Copenhagen [ 3 ], [ 5 ], but have hitherto ignored these and they were considered false positives. Previous attempts to verify the findings did not identify any reads mapping to the cholera toxin genes or other unique regions. Materials and Methods Sample collection Untreated sewage has been collected regularly from three different wastewater treatment plants (BIOFOS Rensesanlæg: Avedøre (RA), Damshusåen (RD), and Lynetten (RL)) in Copenhagen, Denmark since late 2015. We here include 11 additional samples, 8 from the inlet of the RA treatment plant and 3 upstream samples, collected in 2022 and 2023. They are previously unpublished. The total number of RA samples amounts to 115 plus 3 from the catchment area. In all cases, 0.5 l of untreated and unfiltered sewage was collected at the inlet to the treatment plant over the course of 24 hours using a flow-proportional sampler. The samples were frozen and stored at -80° C until DNA extraction. Sample preparation and sequencing Samples have been collected over the course of nearly 10 years (Table 1 ). Both laboratory procedures and sequencing technology have undergone changes over that period. Thus, not all samples have been prepared and sequenced using the exact same. For details on the sample preparation and sequencing procedure of the published data, please refer to [ 5 ] and [ 6 ]. For the unpublished samples, 10 sewage samples were prepared like in [ 2 ], while one was sequenced using Oxford Nanopore Technology (ONT) with the Ligation kit (LSK114) [ 8 ] after DNA extraction using Quick-DNA HMW MagBead kit (Zymo Research, Irvine, United States). Quick-DNA HMW MagBead kit (Zymo Research, Irvine, United States). Table 1 Overview of the sample sequencing campaign. Batch Period Platform Number of samples Number of bases [Gbp] Mean fragment count [millions] Standard deviation [millions] Published in 1 09/01/2016–05/03/2017 MiSeq 24 18.50 2.31 1.64 Brinch et al. (2020) 2 23/11/2015–18/10/2018 NextSeq 31 194.36 23.88 8.33 3 03/02/2016 HiSeq 1 14.02 52.24 4 20/01/2019–28/09/2020 NovaSeq 51 583.98 39.58 17.93 Becsei & Fuschi et al. (2024) 5 09/11/2022–25/01/2023 NextSeq 6 22.04 15.12 7.27 This study 6 19/04/2023 NextSeq 1 127.31 469.76 7 11/01/2023 Oxford Nanopore 1 4.95 0.25 Upstream 19/04/2023 NextSeq 3 312.33 371.37 160.86 Total 23/11/2015–19/04/2023 118 1277.49 38.49 V. cholerae detection using qPCR As part of a broader study comparing the sewage microbiomes across five European cities, 117 samples from the three sites in Copenhagen were investigated by real-time PCR for the detection of V. cholerae . The reaction was conducted in six multiplex reactions of 20 µl volume with SensiFast (Bioline, GC Biotech, Waddinxveen, Netherlands) mastermix, primer and probe (Table 2 ) concentrations of 0,5 µM and 0,25 µM respectively. PCRs were run on a Lightcycler 480 II instrument (Roche Diagnostics, Basel, Switzerland) using 45 cycles of 5 seconds of denaturation at 95°C and 30 seconds annealing at 60°C using Phocid herpesvirus as internal control. Table 2 qPCR probe and primer sequences. toxR Vibrio cholerae Schets et al. ToxRf gtgccttcatcagccactgtag ToxRr agcagtcgattccccaagtttg ToxRp TexRed-caccgcagccagccaatgtcgt-BHQ1 V. cholerae detection using PCR Sewage samples from the Avedøre site were also tested for V. cholerae presence using culture-based techniques [ 9 ]. Briefly, sewage samples were enriched in alkaline buffered peptone water (ABPW, pH 8.6 ± 0.2) followed by Vibrio selective media culturing on Thiosulphate Citrate Bile Sucrose agar (Sigma). Yellow colonies (presumably V. cholerae according to the manufacturer’s guidelines) were selected for biochemical identification using API20E system (bioMérieux, Marcy L’Etoile, France). For rapid PCR-based identification, DNA yields were also extracted from the yellow colonies, and Avedøre sewage samples, and used for V. cholerae detection using PCR amplification of toxR and ctxA genes as described in [ 9 ]. Bioinformatics tools A wide range of bioinformatics tools have been used. All short-read mapping and alignment was done with KMA version 1.4.11 [ 10 ]. Initial metagenomic assembly was done using MEGAHIT version 1.2.9 [ 11 ] and after that, all short-read de novo assembly was done with SPAdes version 3.15.5 [ 12 ] and Flye version 2.9.3 for long-read assembly [ 13 ]. Genome annotation was done with Prokka version 1.11 [ 14 ] and genome completeness was estimated with CheckM2 version 0.1.3 [ 15 ]. Phylogenetic analyses were done with CSI Phylogeny version 7.31.0 [ 16 ]. Metabat2 version 2.12.1 [ 17 ] was used to bin the initial metagenomic assembly and Kraken2 version 2.0.7-beta [ 18 ] was used to identify the V. cholerae bin. All hand-editing (breaking and patching contigs based on read depth variations and coverage discontinuities), visualization, quality testing, and contig management was done using Geneious Prime (Geneious version 2023.2 created by Biomatters. Available from https://www.geneious.com ). Recovery of the Copenhagen V. cholerae genome Constructing the genome was a long iterative process which involved many qualitative decisions along the way. We started with the metagenomic co-assembly from Avedøre (RA) previously reported [ 6 ]. Briefly, the co-assembly was the result of the MEGAHIT assembler which was run on the 51 NovaSeq samples. The resulting contigs were taxonomically assigned using kraken2. We extracted all these V. cholerae -annotated contigs (n = 1,428) and extended them by aligning all Illumina reads against them, with KMA using a very stringent setting, requiring 95% of the bases in the reads to match (KMA flags: -mrs 0.95 -mrc 0.95). The aligning reads were recovered and re-assembling them de novo with spades using the previous contigs as “untrusted contigs” (spades flags: --cov-cutoff off --careful --untrusted-contigs). Original MEGAHIT contigs that did not align to a contig in the new set were discarded, thereby weeding out contamination. This process was repeated several times to extend the contigs as much as possible. In some cases, contigs were edited by hand, by breaking and patching contigs based on visual inspection of anomalies and discontinuities in read alignments. Eventually we had 898 contigs that we could not extend any further. When the contigs could not be extended any further, ONT sequences were then aligned against them and the aligning ONT reads were assembled using Flye. This resulted in a set of 102 ONT contigs which were polished with the short reads from the Illumina sequenced samples. Three of the 898 Illumina contigs did not align to any of the long-read contigs, so these three contigs were included in the set of contigs that makes up the final genome consisting of 105 contigs (length = 3,577,379, N50 = 71,987). To calculate the relative abundance of V. cholerae , we used the CLR transformation, which is defined as the logarithm to the ratio of each part to the geometric mean of parts. We considered the two-part composition, with one part being the count of reads aligning to V. cholerae and the other part being the count of reads aligning to any other bacterial genome, i.e., the bacteriome. The CLR abundance of V. cholerae is therefore interpretable as how log-fold under abundant V. cholerae is with respect to the bacteriome. A V. cholerae CLR abundance of -6 means that it is e 12 ≈ 163,000 times less abundant than the bacteriome or, in other words, that out of 163,000 bacterial reads, 1 will come from V. cholerae . Results and Discussion Out of the 117 samples from Copenhagen, 42 were positive for V. cholerae with qPCR; all from Avedøre, which promoted further metagenomics investigations into this. The samples from the early part of the sewage surveillance programme were sequenced on Illumina MiSeq and NextSeq, obtaining an average of 2 and 20 million paired-end reads, respectively. The shallow sequencing did not constantly produce alignments to the V. cholera 16S rRNA. While Vibrio reads were sporadic and had very low abundances in Copenhagen sewage [ 5 ], V. cholerae was detected in Copenhagen with traditional qPCR pathogen screening among sewage samples from several European cities. This prompted the search for V. cholerae in a new sequencing campaign. From about 2019 and onward, additional samples have been obtained and sequenced to a greater depth than previously reported (up to 30–50 million reads per sample). These metagenomes, when aligned against a bacterial whole genome database, consistently revealed reads that align to V. cholerae , but only from Avedøre site (RA), in agreement with the qPCR results. The two other Copenhagen sites (RD, RL) were consistently negative for V. cholerae using both metagenomics and qPCR approaches. When all samples, including the older samples, were revisited following the positive qPCR results, using the full bacterial genome database, they had a similar abundance yet at much lower count rates, consistent with the lower sequencing depth. Some of the earliest MiSeq samples still had no reads that aligned to V. cholerae , but given the low abundance and shallow sequencing, we would not even expect a single read in those. Toward the end of the sampling campaign in April 2023, four samples were sequenced to a markedly higher depth (between 100 and 130 million reads). Three of which were sampled upstream of the treatment plant (4–10 kilometers) from the three main lines leading into the inlet, and these were all negative. and these were all negative. To confirm our tentative finding, several parallel approaches were tested. PCR using standard V. cholerae primers of toxR and ctxA genes were negative, and culturing on selective media only showed Vibrio metschnikovii growth (API20E biochemical results). This was probably due to the very low relative abundance of V. cholerae in the samples and lack of virulence or toxin genes. 1,428 contigs from the deep co-assembly of Avedøre site were taxonomically assigned to V. cholerae . This set of contigs was estimated to represent a 62% complete and 8% contaminated genome, according to CheckM2. After having iteratively extended the length and reduced the number of contigs as described above, we ended up with 898 contigs, which were estimated to be 90% complete and 3% contaminated. Adding the ONT sample further reduced the number of contigs to 102 and this genome was 95% complete and 0.06% contaminated. Mauve alignment of the final genome against the V. cholerae reference genome (Fig. 1 , Genome assembly ASM836960v1, [ 19 ]) shows that the nearly complete genome could align well. No alignments were found for the cholera toxin ctxA gene primers or probe [ 9 ], but both primer and probe sequences for the central regulatory protein toxR aligned to Contig 88 (positions 59,980 and 60,080 – Fig. 2 ). Blasting the region around the alignment resulted in a 99.2% grade hit to toxR from V. cholerae . It is difficult to quantify the abundance of rare species in a complex metagenome, due to the potential presence of regions that are conserved across many species. When mapping our reads to the V. cholerae contigs, many reads that do not originate from V. cholerae will align to these regions, forming depth “towers” that can have up to a thousand times more depth than the other parts of the genome. It is markedly difficult to decide if a read that maps to one of these depth towers originates from V. cholerae or some other unrelated species. Therefore, we statistically adjusted the number of reads in the tower regions by the depth difference between the tower and the baseline read depth. This was acceptable except when the abundance was extremely low or zero, that is, when all tower reads were not from V. cholerae. For instance, after depth correction, 2, 2, and 6 reads mapped statistically to the three upstream samples, respectively. These reads are all aligning to tower regions and not a single read was found outside the towers. Hence, we assume that these 10 reads were not V. cholerae reads, and therefore, that the upstream samples are negative. After depth corrections, we used the read counts to calculate CLR abundance between V. cholerae and any bacterial genome, which is a measure of the fraction of the bacterial reads that belong to V. cholerae (Fig. 3 ). The noise level is defined as the CLR value one would get if exactly 1 read aligns, given the sequencing depth. Hence, samples that fall below the gray line have zero reads that align to V. cholerae , and those points can therefore be considered upper limits. The median abundance of V. cholerae in the 114 Illumina sequenced samples, for which V. cholerae reads were found, was estimated to be approximately 1 V. cholerae read in 140,000 bacterial reads. The average percentage across all samples of reads that are aligned to a bacterial genome is 30%, which means that, on average, to find 1 V. cholerae read, one must sequence approximately half a million reads. Interestingly, we find that even though V. cholerae seems to be consistently present over the entire sampling period, some individual samples have very low abundance, to the point where we hardly detect it. This could be explained by other bacterial species increasing their abundance tremendously for a short period of time, an effect which has been documented in [ 6 ] and this would make V. cholerae seem to disappear due to the compositional nature of metagenomics. A phylogenetic tree was constructed to compare our V. cholerae genome to the 135 complete V. cholerae genomes found on NCBI (at the time of our analysis). The tree was rooted at the reference genome (Fig. 4 ). The genomes have been colored according to whether they have the ctxA gene, where blue is CTX-negative, and red is CTX-positive. Our recovered genome is shown in green, and it does not appear to be special or different when compared to the neighboring genomes. The closest relative is recent and isolated from a human by the CDC [ 20 ]. Globally, many initiatives to utilize wastewater for surveillance of potentially all infectious pathogens are underway [ 21 ], [ 22 ], [ 23 ]. This includes the possible use of several different methods including qPCR and arrays, but also metagenomics that agnostically will sequence any DNA (or cDNA from RNA) indiscriminately, potentially usable to detect all pathogens including those that are still unknown. This also comes with potential challenges of false-positive signals especially when handling low abundance pathogens. In our case we originally ignored the first alignments to V. cholerae since we also found many reads clearly aligning to conserved “tower” regions. The actual presence was only detectable when combining data from many samples. Thus, for future sewage-based surveillance using metagenomics will probably, in most cases, be necessary to establish site specific signatures as a baseline to which subsequent data can be compared and thus improve the sensitivity and specificity of the surveillance. Our finding of non-toxic but constantly colonizing V. cholerae in urban sewage at the inlet of a single site, but not further upstream also show that V. cholerae can establish itself in environments with an average annual temperature of 9° C. This also has implications for future reservoirs and potential transmission of V. cholerae since more frequent heavy rainfalls are expected in the future resulting in more frequent floodings. In our case the specific strain was CTX-negative, but this might not always be the case. Theoretically, the presence of V. cholerae could be explained by a constant source, delivering the bacteria to the sewer, rather than having it colonizing the sewer itself. This scenario, however, is very unlikely given how stable the abundance of V. cholerae is over time and how much the fecal relative contribution to sewage fluctuates from week to week [ 6 ]. It is normally assumed that V. cholerae do not grow as planktonic cells in the environment and several studies have suggested chitin rich aquatic fauna as a reservoir [ 24 ], [ 25 ]. Our study, however, could indicate that cyanobacteria might be a potential reservoir of V. cholerae [ 26 ], since different cyanobacteria are highly abundant in the sewer system while copepods are not. Besides the biological observation our study also illustrates the challenges of using metagenomic sequencing for detection of pathogens. In our case we originally ignored true low abundance signals because they were masked as false-positive reads and further investigations utilizing deep co-assemblies of many metagenomes and contig screening, was not initiated until prompted by the qPCR results. Declarations Competing Interests None Funding This work was supported by the Novo Nordisk Foundation (NNF16OC0021856) and Horizon 2020 grant VEO (874735). Author Contribution Study design and securing of funding was done by F.M.A. qPCR was facilitated by M.v.d.B. and E.F. Sample handling, DNA extraction, PCR, and sequencing was done by S.O. All bioinformatics analysis was done by P.M. and C.B. First draft of the manuscript was written by C.B. All authors contributed to the final version. All authors read and approved the final manuscript. Data Availability Sequencing reads are available from ENA under project numbers PRJEB34633, PRJEB68319, and PRJEB76456. The genome is available from NCBI under BioProject number PRJNA1121193. Code, scripts, and pipelines are available upon request. 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Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2024 Read the published version in Microbial Ecology → Version 1 posted Editorial decision: Revision requested 01 Jul, 2024 Reviews received at journal 01 Jul, 2024 Reviews received at journal 26 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers invited by journal 14 Jun, 2024 Editor assigned by journal 13 Jun, 2024 Submission checks completed at journal 13 Jun, 2024 First submitted to journal 13 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4575730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321407558,"identity":"16e5d4eb-2861-40c9-886f-53a96b9d5682","order_by":0,"name":"Christian Brinch","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDACCQaGAx9sgIwDDAZAktmAKC0HZ6SBtRg2EK2FmYckLfyzex8etkmwkeM7wLz9MQ+DtTFhS+4cNzick5BmLHmArbCZhyHdjKAWA4k0hsO5Pw4nbjjAYwjUctiGOC0WCSRrYUDSQthhEneOMRzsAfnlMFvhzDkG6YS9zz+7jfnDD1CIHW/e8OFNhTUopIkFzGB3Eq9+FIyCUTAKRgEeAABfRzqC90xNWgAAAABJRU5ErkJggg==","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":true,"prefix":"","firstName":"Christian","middleName":"","lastName":"Brinch","suffix":""},{"id":321407559,"identity":"e17a0785-c578-4b70-b001-0a96f3f30b8e","order_by":1,"name":"Saria Otani","email":"","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Saria","middleName":"","lastName":"Otani","suffix":""},{"id":321407560,"identity":"734c82ae-70e4-4352-a296-d7b78224d7a0","order_by":2,"name":"Patrick Munk","email":"","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Munk","suffix":""},{"id":321407561,"identity":"7a1cd33c-6c9a-4cc3-898e-2dbab7a8a3ec","order_by":3,"name":"Maaike Beld","email":"","orcid":"","institution":"National Institute for Public Health and the Environment","correspondingAuthor":false,"prefix":"","firstName":"Maaike","middleName":"","lastName":"Beld","suffix":""},{"id":321407562,"identity":"0080bbb8-db70-4f14-8492-90fd003a3892","order_by":4,"name":"Eelco Franz","email":"","orcid":"","institution":"National Institute for Public Health and the Environment","correspondingAuthor":false,"prefix":"","firstName":"Eelco","middleName":"","lastName":"Franz","suffix":""},{"id":321407565,"identity":"45ccb3cb-0f17-4b92-ab9b-7a7a7de5066f","order_by":5,"name":"Frank M. Aarestrup","email":"","orcid":"","institution":"Technical University of Denmark","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"M.","lastName":"Aarestrup","suffix":""}],"badges":[],"createdAt":"2024-06-13 11:06:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4575730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4575730/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00248-024-02419-7","type":"published","date":"2024-07-31T00:35:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59598593,"identity":"677c561d-0fa6-4c90-9133-a012d114a1f7","added_by":"auto","created_at":"2024-07-03 16:24:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1083072,"visible":true,"origin":"","legend":"\u003cp\u003eA Mauve alignment of our recovered genome against each of the two chromosomes of the \u003cem\u003eV. cholerae\u003c/em\u003e reference genome.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575730/v1/f41ed0733785397ad87c5417.jpg"},{"id":59598592,"identity":"3433b115-ea3e-47fb-9c00-b96ce66f4f9b","added_by":"auto","created_at":"2024-07-03 16:24:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":285851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003etoxR\u003c/em\u003e primer and probe sequences aligned against contig 88 from the recovered \u003cem\u003eV. cholerae\u003c/em\u003e genome.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575730/v1/f2ca03eed6d64fa3f436707a.jpg"},{"id":59598594,"identity":"a91fd6a9-7dfe-4ee1-931e-05aecc8d89f2","added_by":"auto","created_at":"2024-07-03 16:24:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":346825,"visible":true,"origin":"","legend":"\u003cp\u003eObserved \u003cem\u003eV. cholerae\u003c/em\u003e abundance (CLR) over time. The red horizontal bars indicate the corresponding proportions as 1 \u003cem\u003eV. cholerae\u003c/em\u003e read in n bacterial reads. The gray line indicates the noise floor defined as the CLR of 1 read. The noise floor is based on the running average sequence depth. The 3 points plotted in green are upstream samples.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575730/v1/6319f0bbe07b3f7f213d7c12.jpg"},{"id":59599385,"identity":"6baf7006-9055-4070-b37b-6d06978686dd","added_by":"auto","created_at":"2024-07-03 16:32:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":678739,"visible":true,"origin":"","legend":"\u003cp\u003eA phylogenetic tree showing the genomic context of our recovered genome. Each leaf represents a complete \u003cem\u003eV. cholerae\u003c/em\u003e genome. Blue indicates a CTX-negative genome and red indicates a CTX-positive genome. Our genome is shown in green.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575730/v1/7efa757d03691eee7d629b57.jpg"},{"id":61533429,"identity":"73f065b0-5700-4b7f-ac46-f8d3cddf8cb4","added_by":"auto","created_at":"2024-08-01 00:35:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2833910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4575730/v1/8877cdb5-6880-47da-b3cd-f560e87eb979.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovery of Vibrio cholerae in urban sewage in Copenhagen, Denmark","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetagenomic analysis of sewage offers affordable and rapid surveillance of pathogens in an unbiased and predominantly healthy cross section of a large human population. In recent years, genome sequencing of sewage has been used to monitor the prevalence of SARS-CoV-2 in urban populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the distribution of antimicrobial resistance (AMR) genes across the world [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the entire virome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While sewage is easily obtainable, detecting low abundance pathogens, using short-read metagenomic shotgun sequencing, is a challenge. That is because each sample besides fecal matter from thousands of randomly chosen individuals also contains a significant environmental component, as well as material originating from sewer dwelling animals and organic waste material that are spilled into sinks and drains from, e.g., medical facilities, industries, and ordinary households. It is further complicated by the fact that short reads randomly sequenced from a given microorganism might share sequence similarities to several different, not necessarily closely related, microorganisms and thus, be mistakenly assigned. Long-read sequencing makes it considerably easier to correctly assign reads to a species, but the lower read yield and higher error rate makes it hard to reliably detect low-abundance organisms.\u003c/p\u003e \u003cp\u003eSewage samples have been collected from three different sewage treatment plants in Copenhagen since 2015. The first batch of samples from 2015\u0026ndash;2018 were published by Brinch et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], in a study that described the Copenhagen resistome. A second batch of samples obtained between 2019 and 2021 was published by Becsei \u0026amp; Fuschi [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], also with focus on AMR.\u003c/p\u003e \u003cp\u003eHere we focus on a subset of samples from the Aved\u0026oslash;re site, which is located approximately 10 km from the city center of Copenhagen, Denmark (55.6086, 12.450). We report the unexpected discovery of a constant presence] over at least a decade of \u003cem\u003eVibrio cholerae\u003c/em\u003e using qPCR not targeting the \u003cem\u003ectxA\u003c/em\u003e gene. This was confirmed with subsequent metagenomic sequencing of very low abundant \u003cem\u003eV. cholerae\u003c/em\u003e in the inlet to this specific sewage treatment plant in Copenhagen. The finding is surprising and unusual because \u003cem\u003eV. cholerae\u003c/em\u003e naturally occur in salt- or brackish water and is not endemic to northern Europe (CDC Yellow Book 2024). Not a single clinical case of locally transmitted cholera has been reported in Copenhagen since the 1850s. \u003cem\u003eV. cholerae\u003c/em\u003e was not detectable when analyzing only single samples due to a large number of sequencing reads with similarities to all \u003cem\u003eVibrio\u003c/em\u003e species. Combining the sequencing reads from hundreds of samples, we were able to detect and recover much of its genome.\u003c/p\u003e \u003cp\u003eIt is well-known that analyses of metagenomic data can result in the identification of false-positive detection of pathogens mainly because of reads mapping to conserved regions in genomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We had previously detected few reads mapping to a variety of \u003cem\u003eVibrio\u003c/em\u003e species, including \u003cem\u003eV. cholerae\u003c/em\u003e, in the sewage samples from Copenhagen [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], but have hitherto ignored these and they were considered false positives. Previous attempts to verify the findings did not identify any reads mapping to the cholera toxin genes or other unique regions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eUntreated sewage has been collected regularly from three different wastewater treatment plants (BIOFOS Rensesanl\u0026aelig;g: Aved\u0026oslash;re (RA), Damshus\u0026aring;en (RD), and Lynetten (RL)) in Copenhagen, Denmark since late 2015. We here include 11 additional samples, 8 from the inlet of the RA treatment plant and 3 upstream samples, collected in 2022 and 2023. They are previously unpublished. The total number of RA samples amounts to 115 plus 3 from the catchment area. In all cases, 0.5 l of untreated and unfiltered sewage was collected at the inlet to the treatment plant over the course of 24 hours using a flow-proportional sampler. The samples were frozen and stored at -80\u0026deg; C until DNA extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample preparation and sequencing\u003c/h2\u003e \u003cp\u003eSamples have been collected over the course of nearly 10 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Both laboratory procedures and sequencing technology have undergone changes over that period. Thus, not all samples have been prepared and sequenced using the exact same. For details on the sample preparation and sequencing procedure of the published data, please refer to [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For the unpublished samples, 10 sewage samples were prepared like in [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], while one was sequenced using Oxford Nanopore Technology (ONT) with the Ligation kit (LSK114) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] after DNA extraction using Quick-DNA HMW MagBead kit (Zymo Research, Irvine, United States). Quick-DNA HMW MagBead kit (Zymo Research, Irvine, United States).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of the sample sequencing campaign.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of bases [Gbp]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean fragment count [millions]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard deviation [millions]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePublished in\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e09/01/2016\u0026ndash;05/03/2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBrinch et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23/11/2015\u0026ndash;18/10/2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNextSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e194.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e03/02/2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHiSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20/01/2019\u0026ndash;28/09/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovaSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e583.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBecsei \u0026amp; Fuschi et al.\u003c/p\u003e \u003cp\u003e(2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e09/11/2022\u0026ndash;25/01/2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNextSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19/04/2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNextSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e127.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e469.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11/01/2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOxford Nanopore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19/04/2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNextSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e312.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e371.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e160.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23/11/2015\u0026ndash;19/04/2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1277.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eV. cholerae detection using qPCR\u003c/h2\u003e \u003cp\u003eAs part of a broader study comparing the sewage microbiomes across five European cities, 117 samples from the three sites in Copenhagen were investigated by real-time PCR for the detection of \u003cem\u003eV. cholerae\u003c/em\u003e. The reaction was conducted in six multiplex reactions of 20 \u0026micro;l volume with SensiFast (Bioline, GC Biotech, Waddinxveen, Netherlands) mastermix, primer and probe (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) concentrations of 0,5 \u0026micro;M and 0,25 \u0026micro;M respectively. PCRs were run on a Lightcycler 480 II instrument (Roche Diagnostics, Basel, Switzerland) using 45 cycles of 5 seconds of denaturation at 95\u0026deg;C and 30 seconds annealing at 60\u0026deg;C using Phocid herpesvirus as internal control.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eqPCR probe and primer sequences.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003etoxR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVibrio cholerae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSchets et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eToxRf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003egtgccttcatcagccactgtag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eToxRr\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eagcagtcgattccccaagtttg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eToxRp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTexRed-caccgcagccagccaatgtcgt-BHQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eV. cholerae detection using PCR\u003c/h2\u003e \u003cp\u003eSewage samples from the Aved\u0026oslash;re site were also tested for \u003cem\u003eV. cholerae\u003c/em\u003e presence using culture-based techniques [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Briefly, sewage samples were enriched in alkaline buffered peptone water (ABPW, pH 8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) followed by \u003cem\u003eVibrio\u003c/em\u003e selective media culturing on Thiosulphate Citrate Bile Sucrose agar (Sigma). Yellow colonies (presumably \u003cem\u003eV. cholerae\u003c/em\u003e according to the manufacturer\u0026rsquo;s guidelines) were selected for biochemical identification using API20E system (bioM\u0026eacute;rieux, Marcy L\u0026rsquo;Etoile, France). For rapid PCR-based identification, DNA yields were also extracted from the yellow colonies, and Aved\u0026oslash;re sewage samples, and used for \u003cem\u003eV. cholerae\u003c/em\u003e detection using PCR amplification of \u003cem\u003etoxR\u003c/em\u003e and \u003cem\u003ectxA\u003c/em\u003e genes as described in [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics tools\u003c/h2\u003e \u003cp\u003eA wide range of bioinformatics tools have been used. All short-read mapping and alignment was done with KMA version 1.4.11 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Initial metagenomic assembly was done using MEGAHIT version 1.2.9 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and after that, all short-read \u003cem\u003ede novo\u003c/em\u003e assembly was done with SPAdes version 3.15.5 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and Flye version 2.9.3 for long-read assembly [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Genome annotation was done with Prokka version 1.11 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and genome completeness was estimated with CheckM2 version 0.1.3 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Phylogenetic analyses were done with CSI Phylogeny version 7.31.0 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Metabat2 version 2.12.1 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was used to bin the initial metagenomic assembly and Kraken2 version 2.0.7-beta [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was used to identify the \u003cem\u003eV. cholerae\u003c/em\u003e bin. All hand-editing (breaking and patching contigs based on read depth variations and coverage discontinuities), visualization, quality testing, and contig management was done using Geneious Prime (Geneious version 2023.2 created by Biomatters. Available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geneious.com\u003c/span\u003e\u003cspan address=\"https://www.geneious.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRecovery of the Copenhagen V. cholerae genome\u003c/h2\u003e \u003cp\u003eConstructing the genome was a long iterative process which involved many qualitative decisions along the way. We started with the metagenomic co-assembly from Aved\u0026oslash;re (RA) previously reported [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Briefly, the co-assembly was the result of the MEGAHIT assembler which was run on the 51 NovaSeq samples. The resulting contigs were taxonomically assigned using kraken2. We extracted all these \u003cem\u003eV. cholerae\u003c/em\u003e-annotated contigs (n\u0026thinsp;=\u0026thinsp;1,428) and extended them by aligning all Illumina reads against them, with KMA using a very stringent setting, requiring 95% of the bases in the reads to match (KMA flags: -mrs 0.95 -mrc 0.95). The aligning reads were recovered and re-assembling them de novo with spades using the previous contigs as \u0026ldquo;untrusted contigs\u0026rdquo; (spades flags: --cov-cutoff off --careful --untrusted-contigs). Original MEGAHIT contigs that did not align to a contig in the new set were discarded, thereby weeding out contamination. This process was repeated several times to extend the contigs as much as possible. In some cases, contigs were edited by hand, by breaking and patching contigs based on visual inspection of anomalies and discontinuities in read alignments.\u003c/p\u003e \u003cp\u003eEventually we had 898 contigs that we could not extend any further. When the contigs could not be extended any further, ONT sequences were then aligned against them and the aligning ONT reads were assembled using Flye. This resulted in a set of 102 ONT contigs which were polished with the short reads from the Illumina sequenced samples. Three of the 898 Illumina contigs did not align to any of the long-read contigs, so these three contigs were included in the set of contigs that makes up the final genome consisting of 105 contigs (length\u0026thinsp;=\u0026thinsp;3,577,379, N50\u0026thinsp;=\u0026thinsp;71,987).\u003c/p\u003e \u003cp\u003eTo calculate the relative abundance of \u003cem\u003eV. cholerae\u003c/em\u003e, we used the CLR transformation, which is defined as the logarithm to the ratio of each part to the geometric mean of parts. We considered the two-part composition, with one part being the count of reads aligning to \u003cem\u003eV. cholerae\u003c/em\u003e and the other part being the count of reads aligning to any other bacterial genome, i.e., the bacteriome. The CLR abundance of \u003cem\u003eV. cholerae\u003c/em\u003e is therefore interpretable as how log-fold under abundant \u003cem\u003eV. cholerae\u003c/em\u003e is with respect to the bacteriome. A \u003cem\u003eV. cholerae\u003c/em\u003e CLR abundance of -6 means that it is e\u003csup\u003e12\u003c/sup\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;163,000 times less abundant than the bacteriome or, in other words, that out of 163,000 bacterial reads, 1 will come from \u003cem\u003eV. cholerae\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eOut of the 117 samples from Copenhagen, 42 were positive for \u003cem\u003eV. cholerae\u003c/em\u003e with qPCR; all from Aved\u0026oslash;re, which promoted further metagenomics investigations into this. The samples from the early part of the sewage surveillance programme were sequenced on Illumina MiSeq and NextSeq, obtaining an average of 2 and 20\u0026nbsp;million paired-end reads, respectively. The shallow sequencing did not constantly produce alignments to the \u003cem\u003eV. cholera\u003c/em\u003e 16S rRNA. While \u003cem\u003eVibrio\u003c/em\u003e reads were sporadic and had very low abundances in Copenhagen sewage [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], \u003cem\u003eV. cholerae\u003c/em\u003e was detected in Copenhagen with traditional qPCR pathogen screening among sewage samples from several European cities. This prompted the search for \u003cem\u003eV. cholerae\u003c/em\u003e in a new sequencing campaign. From about 2019 and onward, additional samples have been obtained and sequenced to a greater depth than previously reported (up to 30\u0026ndash;50\u0026nbsp;million reads per sample). These metagenomes, when aligned against a bacterial whole genome database, consistently revealed reads that align to \u003cem\u003eV. cholerae\u003c/em\u003e, but only from Aved\u0026oslash;re site (RA), in agreement with the qPCR results. The two other Copenhagen sites (RD, RL) were consistently negative for \u003cem\u003eV. cholerae\u003c/em\u003e using both metagenomics and qPCR approaches. When all samples, including the older samples, were revisited following the positive qPCR results, using the full bacterial genome database, they had a similar abundance yet at much lower count rates, consistent with the lower sequencing depth. Some of the earliest MiSeq samples still had no reads that aligned to \u003cem\u003eV. cholerae\u003c/em\u003e, but given the low abundance and shallow sequencing, we would not even expect a single read in those. Toward the end of the sampling campaign in April 2023, four samples were sequenced to a markedly higher depth (between 100 and 130\u0026nbsp;million reads). Three of which were sampled upstream of the treatment plant (4\u0026ndash;10 kilometers) from the three main lines leading into the inlet, and these were all negative. and these were all negative.\u003c/p\u003e \u003cp\u003eTo confirm our tentative finding, several parallel approaches were tested. PCR using standard \u003cem\u003eV. cholerae\u003c/em\u003e primers of \u003cem\u003etoxR\u003c/em\u003e and \u003cem\u003ectxA\u003c/em\u003e genes were negative, and culturing on selective media only showed \u003cem\u003eVibrio metschnikovii\u003c/em\u003e growth (API20E biochemical results). This was probably due to the very low relative abundance of \u003cem\u003eV. cholerae\u003c/em\u003e in the samples and lack of virulence or toxin genes.\u003c/p\u003e \u003cp\u003e1,428 contigs from the deep co-assembly of Aved\u0026oslash;re site were taxonomically assigned to \u003cem\u003eV. cholerae\u003c/em\u003e. This set of contigs was estimated to represent a 62% complete and 8% contaminated genome, according to CheckM2. After having iteratively extended the length and reduced the number of contigs as described above, we ended up with 898 contigs, which were estimated to be 90% complete and 3% contaminated. Adding the ONT sample further reduced the number of contigs to 102 and this genome was 95% complete and 0.06% contaminated. Mauve alignment of the final genome against the \u003cem\u003eV. cholerae\u003c/em\u003e reference genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Genome assembly ASM836960v1, [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) shows that the nearly complete genome could align well. No alignments were found for the cholera toxin \u003cem\u003ectxA\u003c/em\u003e gene primers or probe [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but both primer and probe sequences for the central regulatory protein \u003cem\u003etoxR\u003c/em\u003e aligned to Contig 88 (positions 59,980 and 60,080 \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Blasting the region around the alignment resulted in a 99.2% grade hit to \u003cem\u003etoxR\u003c/em\u003e from \u003cem\u003eV. cholerae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is difficult to quantify the abundance of rare species in a complex metagenome, due to the potential presence of regions that are conserved across many species. When mapping our reads to the \u003cem\u003eV. cholerae\u003c/em\u003e contigs, many reads that do not originate from \u003cem\u003eV. cholerae\u003c/em\u003e will align to these regions, forming depth \u0026ldquo;towers\u0026rdquo; that can have up to a thousand times more depth than the other parts of the genome. It is markedly difficult to decide if a read that maps to one of these depth towers originates from \u003cem\u003eV. cholerae\u003c/em\u003e or some other unrelated species. Therefore, we statistically adjusted the number of reads in the tower regions by the depth difference between the tower and the baseline read depth. This was acceptable except when the abundance was extremely low or zero, that is, when \u003cem\u003eall\u003c/em\u003e tower reads were not from \u003cem\u003eV. cholerae.\u003c/em\u003e For instance, after depth correction, 2, 2, and 6 reads mapped statistically to the three upstream samples, respectively. These reads are all aligning to tower regions and not a single read was found outside the towers. Hence, we assume that these 10 reads were not \u003cem\u003eV. cholerae\u003c/em\u003e reads, and therefore, that the upstream samples are negative. After depth corrections, we used the read counts to calculate CLR abundance between \u003cem\u003eV. cholerae\u003c/em\u003e and any bacterial genome, which is a measure of the fraction of the bacterial reads that belong to \u003cem\u003eV. cholerae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The noise level is defined as the CLR value one would get if exactly 1 read aligns, given the sequencing depth. Hence, samples that fall below the gray line have zero reads that align to \u003cem\u003eV. cholerae\u003c/em\u003e, and those points can therefore be considered upper limits. The median abundance of \u003cem\u003eV. cholerae\u003c/em\u003e in the 114 Illumina sequenced samples, for which \u003cem\u003eV. cholerae\u003c/em\u003e reads were found, was estimated to be approximately 1 \u003cem\u003eV. cholerae\u003c/em\u003e read in 140,000 bacterial reads. The average percentage across all samples of reads that are aligned to a bacterial genome is 30%, which means that, on average, to find 1 \u003cem\u003eV. cholerae\u003c/em\u003e read, one must sequence approximately half a million reads.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, we find that even though \u003cem\u003eV. cholerae\u003c/em\u003e seems to be consistently present over the entire sampling period, some individual samples have very low abundance, to the point where we hardly detect it. This could be explained by other bacterial species increasing their abundance tremendously for a short period of time, an effect which has been documented in [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and this would make \u003cem\u003eV. cholerae\u003c/em\u003e seem to disappear due to the compositional nature of metagenomics.\u003c/p\u003e \u003cp\u003eA phylogenetic tree was constructed to compare our \u003cem\u003eV. cholerae\u003c/em\u003e genome to the 135 complete \u003cem\u003eV. cholerae\u003c/em\u003e genomes found on NCBI (at the time of our analysis). The tree was rooted at the reference genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The genomes have been colored according to whether they have the \u003cem\u003ectxA\u003c/em\u003e gene, where blue is CTX-negative, and red is CTX-positive. Our recovered genome is shown in green, and it does not appear to be special or different when compared to the neighboring genomes. The closest relative is recent and isolated from a human by the CDC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGlobally, many initiatives to utilize wastewater for surveillance of potentially all infectious pathogens are underway [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This includes the possible use of several different methods including qPCR and arrays, but also metagenomics that agnostically will sequence any DNA (or cDNA from RNA) indiscriminately, potentially usable to detect all pathogens including those that are still unknown. This also comes with potential challenges of false-positive signals especially when handling low abundance pathogens. In our case we originally ignored the first alignments to \u003cem\u003eV. cholerae\u003c/em\u003e since we also found many reads clearly aligning to conserved \u0026ldquo;tower\u0026rdquo; regions. The actual presence was only detectable when combining data from many samples. Thus, for future sewage-based surveillance using metagenomics will probably, in most cases, be necessary to establish site specific signatures as a baseline to which subsequent data can be compared and thus improve the sensitivity and specificity of the surveillance.\u003c/p\u003e \u003cp\u003eOur finding of non-toxic but constantly colonizing \u003cem\u003eV. cholerae\u003c/em\u003e in urban sewage at the inlet of a single site, but not further upstream also show that \u003cem\u003eV. cholerae\u003c/em\u003e can establish itself in environments with an average annual temperature of 9\u0026deg; C. This also has implications for future reservoirs and potential transmission of \u003cem\u003eV. cholerae\u003c/em\u003e since more frequent heavy rainfalls are expected in the future resulting in more frequent floodings. In our case the specific strain was CTX-negative, but this might not always be the case. Theoretically, the presence of \u003cem\u003eV. cholerae\u003c/em\u003e could be explained by a constant source, delivering the bacteria to the sewer, rather than having it colonizing the sewer itself. This scenario, however, is very unlikely given how stable the abundance of \u003cem\u003eV. cholerae\u003c/em\u003e is over time and how much the fecal relative contribution to sewage fluctuates from week to week [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is normally assumed that \u003cem\u003eV. cholerae\u003c/em\u003e do not grow as planktonic cells in the environment and several studies have suggested chitin rich aquatic fauna as a reservoir [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our study, however, could indicate that cyanobacteria might be a potential reservoir of \u003cem\u003eV. cholerae\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], since different cyanobacteria are highly abundant in the sewer system while copepods are not.\u003c/p\u003e \u003cp\u003eBesides the biological observation our study also illustrates the challenges of using metagenomic sequencing for detection of pathogens. In our case we originally ignored true low abundance signals because they were masked as false-positive reads and further investigations utilizing deep co-assemblies of many metagenomes and contig screening, was not initiated until prompted by the qPCR results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Novo Nordisk Foundation (NNF16OC0021856) and Horizon 2020 grant VEO (874735).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy design and securing of funding was done by F.M.A. qPCR was facilitated by M.v.d.B. and E.F. Sample handling, DNA extraction, PCR, and sequencing was done by S.O. All bioinformatics analysis was done by P.M. and C.B. First draft of the manuscript was written by C.B. All authors contributed to the final version. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequencing reads are available from ENA under project numbers PRJEB34633, PRJEB68319, and PRJEB76456. The genome is available from NCBI under BioProject number PRJNA1121193. Code, scripts, and pipelines are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCrits-Christoph A et al (2021) Genome sequencing of sewage detects regionally prevalent SARS-CoV-2 variants. mBio 12(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/MBIO.02703-20/ASSET/1FCC5B74-F52F-4B47-82D7-4EC4764697A2/ASSETS/GRAPHIC/MBIO.02703-20-F0003.JPEG\u003c/span\u003e\u003cspan address=\"10.1128/MBIO.02703-20/ASSET/1FCC5B74-F52F-4B47-82D7-4EC4764697A2/ASSETS/GRAPHIC/MBIO.02703-20-F0003.JPEG\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHendriksen RS et al (Dec. 2019) Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. 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Vaccine 38(1):A52\u0026ndash;A62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.VACCINE.2019.06.033\u003c/span\u003e\u003cspan address=\"10.1016/J.VACCINE.2019.06.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Vibrio cholerae, Metagenomic genome recovery, Sewage surveillance, Pathogen detection","lastPublishedDoi":"10.21203/rs.3.rs-4575730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4575730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe report the unexpected discovery of a persistent presence of \u003cem\u003eVibrio cholerae\u003c/em\u003e at very low abundance in the inlet of a single wastewater treatment plant in Copenhagen, Denmark at least since 2015. Remarkably, no environmental or locally transmitted clinical case of \u003cem\u003eV. cholerae\u003c/em\u003e has been reported in Denmark for more than 100 years. We, however, have recovered a near-complete genome out of 115 sewage samples taken over the past 8 years, despite the extremely low relative abundance of 1 \u003cem\u003eV. cholerae\u003c/em\u003e read out of 500.000 sequenced reads. Due to the very low relative abundance, routine screening of the individual samples did not reveal \u003cem\u003eV. cholerae\u003c/em\u003e. The recovered genome lacks the gene responsible for cholerae toxin production, but although this strain may not pose an immediate public health risk, our finding illustrates the importance, challenges and effectiveness of wastewater-based pathogen surveillance.\u003c/p\u003e","manuscriptTitle":"Discovery of Vibrio cholerae in urban sewage in Copenhagen, Denmark","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 16:24:06","doi":"10.21203/rs.3.rs-4575730/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-02T01:24:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-01T21:24:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-26T08:57:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134996205435469178781609114077448915175","date":"2024-06-24T15:14:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29974873949344465371117393466880414059","date":"2024-06-23T00:52:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336687551477027935782023322960887881169","date":"2024-06-22T22:38:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T16:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74542190891017391122923611925386919454","date":"2024-06-19T03:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148047079152569496821652245100951237940","date":"2024-06-18T16:11:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206435172608257344821947149960421187304","date":"2024-06-18T06:09:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40554839651811523230049718500711016158","date":"2024-06-18T05:46:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280710643869150600237316257620901985848","date":"2024-06-18T03:06:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109528709646672934866158108561007893756","date":"2024-06-17T20:12:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95886024288101575863423131430413877878","date":"2024-06-14T18:22:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-14T18:20:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-14T03:42:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-14T03:41:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2024-06-13T11:04:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8ffa9cfd-65e4-4927-842d-076ae13ef0e0","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T00:35:47+00:00","versionOfRecord":{"articleIdentity":"rs-4575730","link":"https://doi.org/10.1007/s00248-024-02419-7","journal":{"identity":"microbial-ecology","isVorOnly":false,"title":"Microbial Ecology"},"publishedOn":"2024-07-31 00:35:47","publishedOnDateReadable":"July 31st, 2024"},"versionCreatedAt":"2024-07-03 16:24:06","video":"","vorDoi":"10.1007/s00248-024-02419-7","vorDoiUrl":"https://doi.org/10.1007/s00248-024-02419-7","workflowStages":[]},"version":"v1","identity":"rs-4575730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4575730","identity":"rs-4575730","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}