Currently, emerging infectious diseases present one of the greatest public health challenges. Human 32 noroviruses (NoVs) are recognized as the leading cause of acute gastroenteritis (AGE) for all ages (1). 33 Viral infections are estimated to be associated with 18% of diarrheal diseases and 212,000 deaths 34 annually (2). Due to their high contagiousness, most cluster infections or outbreaks of NoVs have been 35
reported in semienclosed settings such as hospitals, childcare centers, schools, and universities, leading 36 to public panic regarding disease transmission (3). 37 38 Based on the sequence of the VP1 gene, NoVs can be classified into seven genogroups (GI-GVII) and 39 further subdivided into more than 40 genotypes (4). Viruses from the GI, GII, and GIV genogroups are 40 known to infect humans. Currently, there is no drug or licensed vaccine for NoV infections. Disease 41 control has mainly relied on clinical surveillance, which informs us of the emergence and prevalence of 42 different genotypes and their potential risks of causing global outbreaks. Since 1995, GII.4 has been 43 identified as the predominant genotype responsible for 65-80% of NoV infections worldwide (5, 6). 44
The novel GII.4 variant has emerged every 2-5 years and presented amino acid changes in five 45 antigenic sites (7) associated with viral escape from herd immunity (8, 9) . This epochal evolution 46 pattern is regarded as the principal mechanism adopted by GII.4 to sustain its predominance and has 47 120 Norovirus genotype distributions in sewage, 2013-2018 121
We next applied the above method to investigate NoV distribution in Guangzhou sewage from 122 2013-2018. Approximately 1,000,000 reads (range 112,358-2,947,207) were generated from each 123 amplicon (Table S2 ). Genotype distributions in the GI and GII genogroups were determined separately 124 by measuring the number of reads in each genotype cluster. Genotypes with a percentage less than 0.1% 125 of total reads were removed. In total, 9 GI genotypes and 15 GII genotypes were identified in sewage 126 All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted August 22, 2020. . https://doi.org/10.1101/2020.08.20.20173088 doi: medRxiv preprint 6 / 26 water from 2013-2018 (Fig. 3A&3C ). For the GI genotype, GI.2, GI.8 and GI.9 were found to be 127 dominant in sewage samples (percentages were over 30%) but with different temporal distributions 128 ( Fig. 3A & 3B) . For the GI genogroup, GI.2 was continuously circulating in the population from 2013 129 through 2018 (Fig. 3A) , and its predominance was significant in the 2017-2018 winter season (Fig. 3B) . 130
In contrast, the GI.8 and GI.9 genotypes were dominant in 2013 but were rarely detected in 2017 and 131 2018 (Fig. 3A) . 132 133 GII NoV was the dominant genogroup causing AGE outbreaks. The temporal pattern of GII genotypes 134 in sewage was well captured. GII.2, GII.4, and GII.17 were the major circulating genotypes in sewage, 135 and other genotypes were detected with lower prevalence (<25%, Fig. 3C ). More specifically, GII. The emergence and spread of new NoV variants always lead to an increase in NoV outbreaks in the 150 population. One priority of sewage surveillance is to identify the early emergence of novel genetic 151 variants and characterize their dynamic distributions. In this study, we set the sequence similarity 152 threshold at 97% in the clustering method to distinguish the different genetic variants (see Material and 153
Methods for details). Through this method, we obtained a consensus sequence of a genetic cluster and 154 the number of reads in it, which was proportional to the abundance in sewage. To define these genetic 155 clusters, we constructed maximum-likelihood (ML) trees for the GII.2, GII.17 and GII.4 genotypes by 156 combining all consensus sequences recovered from sewage and representative variant sequences from 157 public databases (Fig.S1&S2&S3) . The 350-bp amplicon sequenced in this study represented one of 158 the most divergent parts of the viral genome and is widely used for NoV genotyping. For GII.4, nearly 159 All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted August 22, 2020. with this, the closely related reference sequences for Var2 were identified in Asian-Pacific countries 192 All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted August 22, 2020. November 2016, which was two months after its dominance in the sewage sample. 198 All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. NoV genetic diversity (13). In contrast to the global predominance of GII.4 observed in the past decade, 201 the distribution of NoV genotypes is now varied among countries, indicating that spatial and temporal 202 differences exist. Due to this remarkable change, regional surveillance data are critical for early 203 warning and vaccine design, similar to the actions taken in response to seasonal influenza viruses. To 204 date, extensive and timely clinical surveillance is difficult to achieve in most regions. In this study, we 205 prove that high-throughput sequencing combined with the OTU clustering method could be 206 successfully applied to investigate the dynamic distribution of NoVs in sewage. By comparison with 207 long-term surveillance data from NoV outbreaks, our results highlight that sewage surveillance serves 208 as an efficient tool to capture the NoV distribution in the specific population. 209
The current international or regional NoV surveillance networks mostly focus on AGE outbreaks or 211 sporadic infections from clinics. As a member of CaliciNet (3), the Guangdong provincial surveillance 212 system followed a similar scheme to report NoV-associated AGE outbreaks in 21 prefectural cities. 213
Great efforts have been made toward sequencing and molecular typing to capture genotype 214 distributions, especially for the emerging novel genetic variants in the local population (14, 15) . (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted August 22, 2020. . https://doi.org/10.1101/2020.08.20.20173088 doi: medRxiv preprint regions in 2014-2015 (31-33). A similar pattern was also observed for GII.P16_GII.2, which was 232 almost simultaneously identified in Germany and multiple areas in China at the end of 2016 (15, 34) 233 and led to an exponential increase in NoV outbreaks (Fig. 4C) . From this perspective, the time window 234 for our response to the epidemic is too short when we rely on the current clinical surveillance system. 235
In this study, we discuss the possibility of capturing the NoV distribution in the local population 237 through sewage surveillance in a hub city. Before this study, several groups tried to use Sanger 238 sequencing/cloning or NGS to investigate NoV genotype diversity and temporal dynamics in sewage 239 November highlights its outbreak risk in the following epidemic season (Fig. 4B ). This pattern was 252 repeatedly observed two years later, during the outbreak of GII.P16-GII.2 (Fig. 4C ). More importantly, 253 via the clustering method, we could reveal the evolutionary history of these genetic variants, which was 254 difficult to fully capture by clinical surveillance. As shown in Fig. 4B There remain some limitations to the present study. First, specific PCR amplification is required before 263 high-throughput sequencing, and the PCR bias would lead to low-proportion (<10%) samples being 264 All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted August 22, 2020. . https://doi.org/10.1101/2020.08.20.20173088 doi: medRxiv preprint 11 / 26 overrepresented (Fig. 2B) . However, we need to balance the sensitivity and precision of this 265 high-throughput method. The RT-PCR results showed a relatively low copy of NoVs in the raw sewage 266 (90% samples less than 3.1x10 5 copies/L), and direct metagenomic sequencing could obtain only a few 267 fragments of the most abundant genotype(s). Therefore, PCR amplification is required to analyze the 268 distribution of multiple genotypes in sewage, especially during the nonepidemic season. Second, the 269 modified primer set G2SKF and G2SKR was used in this study to produce an ~350-bp fragment (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted August 22, 2020. Minikit (Qiagen, USA). RT-PCR was performed with Superscript III (Thermo Fisher, USA) by using 320 random primers. The cDNA was then used in the first round of PCR amplification with the primer set 321 described in Table S1 . After 27 cycles of amplification, 2.5 μ l of the first PCR products were used as a 322 template in the second-round PCR. After 35 cycles of amplification, the positive products (~354 323 nucleotides) were subjected to Illumina MiSeq sequencing (PE250), and approximately 1,000,000 324 All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted August 22, 2020. . https://doi.org/10.1101/2020.08.20.20173088 doi: medRxiv preprint 13 / 26 reads were generated for each PCR product. The datasets were quality controlled (QC) using fastp (42) 325 to trim artificial sequences (adapters), to cut low-quality bases (quality scores < 30) and to remove 326 short reads (< 50 bp). Paired-end reads were assembled and PCR primers were trimmed by using 327 cutadapt. Then, the sequences were clustered into OTUs with the following clustering criteria: 1) all 328 pairs of OTU sequences should have <97% pairwise sequence identity; 2) an OTU sequence should be 329 the most abundant within a 97% neighborhood. These criteria have been tested to distinguish the 330 different genetic variants of the NoV genotype, including GII.4, GII.17, and GII.2. After clustering, 331 genotype assignment was performed using blastn (43) by alignment with the local NoV nucleotide 332 database. The abundance of different OTUs and genotypes was calculated with the relative number of 333 raw reads using R scripts. All sequencing metadata generated in this study have been submitted to the 334 National Genomics Data Center (https://bigd.big.ac.cn/) with submission number subCRA003702. 335 336
The phylogenetic analyses were performed by combining the partial VP1 gene sequences generated 338 from sewage samples and the selected reference sequences from the public database (Fig. S1&S2&S3)  339 to classify the different genetic lineages. ML trees were estimated for sequences from GII.4, GII.17, 340 and GII.2 by using IQ-Tree (44). Phylogenetic trees were annotated and visualized with ggtree (45). 341
The dynamic changes in different genetic lineages were visualized using R scripts. 342 All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted August 22, 2020. . https://doi.org/10.1101/2020.08.20.20173088 doi: medRxiv preprint with the same scheme in Fig. 4C . 558 559 All rights reserved. No reuse allowed without permission.
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