key: cord-0737268-dzxubeaf
authors: Reynolds, Liam J.; Gonzalez, Gabriel; Sala-Comorera, Laura; Martin, Niamh A.; Byrne, Alannah; Fennema, Sanne; Holohan, Niamh; Kuntamukkula, Sailusha Ratnam; Sarwar, Natasha; Nolan, Tristan M.; Stephens, Jayne H.; Whitty, Megan; Bennett, Charlene; Luu, Quynh; Morley, Ursula; Yandle, Zoe; Dean, Jonathan; Joyce, Eadaoin; O'Sullivan, John J.; Cuddihy, John M.; McIntyre, Angeline M.; Robinson, Eve P.; Dahly, Darren; Fletcher, Nicola F.; Carr, Michael; De Gascun, Cillian; Meijer, Wim G.
title: SARS-CoV-2 variant trends in Ireland: Wastewater based epidemiology and clinical surveillance
date: 2022-05-16
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2022.155828
sha: 44c90118a2d2c2788937cf1336b7ec8fe5ebeb87
doc_id: 737268
cord_uid: dzxubeaf

SARS-CoV-2 RNA quantification in wastewater is an important tool for monitoring the prevalence of COVID-19 disease on a community scale which complements case-based surveillance systems. As novel variants of concern (VOCs) emerge there is also a need to identify the primary circulating variants in a community, accomplished to date by sequencing clinical samples. Quantifying variants in wastewater offers a cost-effective means to augment these sequencing efforts. In this study, SARS-CoV-2 N1 RNA concentrations and daily loadings were determined and compared to case-based data collected as part of a national surveillance programme to determine the validity of wastewater surveillance to monitor infection spread in the greater Dublin area. Further, sequencing of clinical samples was conducted to determine the primary SARS-CoV-2 lineages circulating in Dublin. Finally, digital PCR was employed to determine whether SARS-CoV-2 VOCs, Alpha and Delta, were quantifiable from wastewater. No lead or lag time was observed between SARS-CoV-2 wastewater and case-based data and SARS-CoV-2 trends in Dublin wastewater significantly correlated with the notification of confirmed cases through case-based surveillance preceding collection with a 5-day average. This demonstrates that viral RNA in Dublin's wastewater mirrors the spread of infection in the community. Clinical sequence data demonstrated that increased COVID-19 cases during Ireland's third wave coincided with the introduction of the Alpha variant, while the fourth wave coincided with increased prevalence of the Delta variant. Interestingly, the Alpha variant was detected in Dublin wastewater prior to the first genome being sequenced from clinical samples, while the Delta variant was identified at the same time in clinical and wastewater samples. This work demonstrates the validity of wastewater surveillance for monitoring SARS-CoV-2 infections and also highlights its effectiveness in identifying circulating variants which may prove useful when sequencing capacity is limited.

The emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unprecedented global public health emergency (Zhou et al., 2020) .

Infection symptoms can range from fever and lethargy to respiratory impairment in which ventilation is required. Thus, severe burdens both in mortality and morbidity and, also, serious socioeconomic consequences have resulted (Leal Filho et al., 2020) . Although clinical diagnostic testing and close contact tracing continue in many countries, current efforts are also focused on mass vaccination campaigns and developing effective treatments including therapeutic monoclonal antibodies (Twomey et al., 2020) . Vaccines and most passive antibody cocktail therapies target the spike protein (S), a homotrimeric viral envelope protein that mediates host-cell entry via the cellular receptor angiotensin-converting enzyme 2 (ACE2) (Baum et al., 2020; Lan et al., 2020) . Amino acid substitutions in the S protein have effects on protein folding and immune evasion by creating mutants unrecognised by antibodies (Greaney et al., 2021; Harvey et al., 2021) . Therefore, the circulation of variants with mutations in S could have a significant impact upon the protective efficacy of prophylactic vaccines and passive antibody-based treatments.

Among the variants with mutations in S, variants Alpha and Delta have been characterised with enhanced transmissibility and increased odds of causing hospitalisations compared with preceding variants (Funk et al., 2021) . The Alpha variant was initially distinguished by the S substitution N501Y that has been shown to strengthen the binding of the virus receptor binding motif (RBD) to ACE2 (Tian et al., 2021) . The S:L452R associated with the Delta variant has been shown to increase the viral infectivity, promote viral replication and improve cellular immunity evasion (Motozono et al., 2021) . The introduction of both variants to countries has been followed by surges in the number of positive cases.

In addition to being detected in sputum and saliva, SARS-CoV-2 RNA is shed in the faeces of up to 67% of infected patients and is also present in the faeces of asymptomatic individuals J o u r n a l P r e -p r o o f Han et al., 2020) . The presence of SARS-CoV-2 RNA in faeces indicated that wastewater could be used to monitor infection rates in a treatment plant's catchment population. Indeed, from the outset of the COVID-19 pandemic wastewater monitoring had already commenced in a number of laboratories around the world (Ahmed et al., 2020; La Rosa et al., 2020) . Among the first was wastewater in The Netherlands prior to the first clinical cases (Medema et al., 2020) .

According to the University of California, Merced SARS-CoV-2 wastewater monitoring dashboard, there are at least 66 countries engaged in SARS-CoV-2 wastewater surveillance with some, including Ireland, conducting national-scale monitoring.

Wastewater surveillance programmes offer a potentially cost-effective and non-invasive means to monitor pathogens circulating in communities or trends in rates of COVID-19 in the population that complements national testing efforts (Feng et al., 2021; LaTurner et al., 2021) . This is because a single wastewater sample represents a pooled sample from the population contributing to the wastewater treatment plant (WWTP) which can be thousands or millions of people (Shah et al., 2022) . Moreover, typically only symptomatic individuals or close contacts of SARS-CoV-2 positive patients present for clinical testing so a large proportion of infected people may go undiagnosed, particularly when testing capacity is low (Sharif et al., 2021) . Wastewater surveillance is not impacted by this bias as infected individuals in a catchment will contribute passively to the wastewater system whether they are symptomatic or not (Hata and Honda, 2020) . This aspect of wastewater surveillance will be beneficial as infection trends can continue to be monitored as vaccination rates increase which will result in an increased proportion of asymptomatic infections (Polack et al., 2020) .

Monitoring wastewater for SARS-CoV-2 variants also offers a means to augment the J o u r n a l P r e -p r o o f sequencing of clinical samples which is expensive and not possible for every positive case due to limitations on input viral load, laboratory capacity and cost (Viveros et al., 2021) .

The greater Dublin area is served by a WWTP that receives wastewater from 40 % of Ireland's population. Thus, the aim of this study was to determine if SARS-CoV-2 RNA levels in Dublin wastewater mirror trends determined through a case-based surveillance system in Dublin. Furthermore, wastewater samples were analysed for the presence of the Alpha and Delta variants to identify if the presence of these variants in wastewater aligned with their first identification in clinical sequence data in the catchment area.

Between June 2020 and August 2021, 24-hour composite wastewater influent samples (n = 99) were collected twice per week from the Ringsend WWTP that receives influent from the greater Dublin area, Table S1 . Composite samples were time-weighted with subsamples collected every hour. The Ringsend WWTP is the largest WWTP in the Republic of Ireland, receiving a collected load of approximately 1.9 million population equivalents. The flow rate (m 3 /day) for the Ringsend WWTP on the day of sampling was obtained from Irish Water.

Composite wastewater influent samples were concentrated using 100 kDa Centricon Plus-70 filters (Merck, Germany). Briefly, the filter devices were washed by filling with 70 ml of J o u r n a l P r e -p r o o f distilled water and centrifuging at 3,200 g for 5 minutes. Composite wastewater influent samples (200 -250 ml) were centrifuged at 3,200 g to remove solids. The resulting supernatants were then passed through the filters in 70 ml aliquots by centrifuging at 3,200 g for 15-40 minutes at a time. Approximately 500 µl of concentrated wastewater was recovered by inverting the filter into a collection cup and centrifuging at 1,000 g for 2 minutes. RNA was extracted from 250 µl of the wastewater influent concentrates using the RNeasy PowerMicrobiome Kit (Qiagen, Germany) according to the manufacturer's protocols. This protocol was validated using 50 ml of SARS-CoV-2 negative sewage spiked with heat inactivated SARS-CoV-2 (3 x 10 7 gc/ml). The recovery range was found to be between 50 % and 94.7 %, Table S2. 2.3 qPCR Assays to Quantify SARS-CoV-2 RNA Reverse Transcription-quantitative PCR (RT-qPCR) assays were performed on the Roche LightCycler 96 platform (Roche Diagnostics, Germany). SARS-CoV-2 RT-qPCR assays were conducted using the LightCycler Multiplex RNA Virus Master (Roche Diagnostics, Germany) according to the manufacturer's guidelines in 20 µl volumes, containing 0.5 µM each of forward and reverse primers, 0.125 µM probe, water and 5 µl sample RNA (or 5 µl plasmid standard DNA). The primer details and thermocycling conditions are listed in Table S3 . All samples, negative controls and extraction blanks were analysed in duplicate, while standards were included in triplicate in each 96-well plate. The nCoV-CDC-Control plasmid was used a standard for N1 qPCR assays (CDC, 2020; Lu et al., 2020) . Results were expressed as gene copies (gc)/100 ml. Reaction efficiencies for each assay were determined using the E = 10 (-1/slope) equation (Rutledge and Côté, 2003) . The limit of detection (LOD) was J o u r n a l P r e -p r o o f Journal Pre-proof determined as the lowest concentration of DNA detected in 95% or more of replicates and the limit of quantification (LOQ) was determined as the lowest concentration of DNA quantified within 0.5 standard deviations of the log10 concentration, Table S3 . (Rutledge and Stewart, 2008) .

Virus genome sequencing in Ireland was performed in the context of enhanced surveillance in support and to inform public health investigations, so ethical approval was not required.

For this study, only sequences from samples collected from cases resident in County Dublin between June 2020 and August 2021 and made publicly available in GISAID (www.gisaid.org) were used. SARS-CoV-2 whole genome sequences were generated from clinical samples with Ct values ≤25 using the tiled amplicon approach described by Freed et al. and nanopore sequencing on the GridION platform (Oxford Nanopore Technologies, UK) (Freed et al., 2020) . Briefly, 8 μl viral RNA was reverse transcribed with 2 μl of LunaScript (NEB, USA) and cDNA was amplified using two separate primer pools to generate overlapping 1,200 bp tiled amplicons by amplification with Q5 Hot Start High-Fidelity DNA polymerase (NEB, USA). PCR reactions were subsequently combined, and barcodes were assigned using the rapid barcoding kit (SQK-RBK110.96; ONT, UK) as described by the manufacturer. Barcoded samples were pooled in a 2 ml Eppendorf DNA LoBind tube then incubated with SPRI magnetic beads for 10 min on a HulaMixer (Invitrogen, UK). Samples were magnetically separated and then washed twice with 80% ethanol and eluted in buffer EB. Attachment of sequencing adapters of 800 ng of cleaved cDNA was performed by J o u r n a l P r e -p r o o f addition of RAP-F and incubation at room temperature for 5 min then placed on ice.

Libraries were sequenced on FLO-MIN106D flow cells on the GridION platform (ONT, UK).

The lineages of the sequences were assigned using PANGOLIN with the pangoLEARN version 2021-09-28 and grouped according to the variant and super lineage names associated to each lineage (Rambaut et al., 2020a; O'Toole et al., 2021) . To visualize the phylogenetic relation among sequences, a phylogenetic tree was inferred using RAxML with a subset of sequences that were randomly subsampled (n=505) and multiple-sequence aligned with MAFFT with the algorithm FFT-NS-I (Stamatakis, 2006; Nakamura et al., 2018) . The phylogenetic tree was rooted with the reference sequence (accession number: MN908947) (Stamatakis, 2014; Rozewicki et al., 2019 Finally, the time series of first order differences of COVID-19 cases was related to those for viral concentration and load using Spearman rank correlations (ρ). This analysis was conducted using R version 4.0.3 (REF) and GAMs were fit using the mgcv package (R Core Team, 2020; Wood, 2011) . Additionally, a grid search was employed to consider a range of lead and lag times between SARS-CoV-2 wastewater and case-based data ranging from -6 to 6 weeks (LaValle et al., 2004) . For statistical analyses, wastewater samples with levels of SARS-CoV-2 N1 below the detection limit of the assay were given the concentration of 1.25 gc/reaction, one quarter of the quantification limit of this assay.

Composite wastewater samples were collected from the Ringsend WWTP in Dublin between June 2020 and August 2021 in order to monitor the prevalence of infection in the region.

This period encompassed fully the second and third waves of the COVID-19 pandemic in Ireland. During these waves in Dublin, new daily cases peaked at 396 on the 17 th of October 2020 and 3,647 on the 3 rd of January 2021 respectively. The results of monitoring SARS-CoV-2 RNA by RT-qPCR in Ringsend composite influent demonstrated that both N1 gene concentrations and daily loadings from the greater Dublin area mirrored trends in the reported case-based data ( Figure. 1A, B) . Furthermore, peak SARS-CoV-2 N1 gene daily loadings were observed in early November 2020 during the second wave and in January 2021 during the third wave ( Figure. 1A, B) . Following this third wave peak, SARS-CoV-2 levels decreased and remained relatively stable in Ringsend wastewater for the remainder of the study period, although sporadic increases were observed in March and May that were not reflected in new case-based data. The time series of first-order differences in COVID-19 cases in Dublin (preceding sample collection) were correlated with the time-series of first order differences in SARS-CoV-2 N1 daily loadings (Spearman's ρ = 0.50, p < 0.001) and

concentrations (Spearman's ρ = 0.49, p < 0.001), Figure S2 . Furthermore, to determine if there was a lead or lag time between SARS-CoV-2 clinical cases and wastewater signals we used a grid search to consider a range of lag times ranging from -6 to +6 weeks and found the strongest correspondence between wastewater viral loading and cases when there was no lag (lag = 0). (Peccia et al., 2020; Randazzo et al., 2020) . For instance, Peccia et al. identified increasing levels of SARS-CoV-2 RNA to precede clinical cases by 6-8 days. This relationship was not observed during the course of our study, which is likely a result of a number of factors including sewage system residence time and lag time between symptom onset, testing and result confirmation and notification (Krivoňáková et al., 2021; Weidhaas et al., 2021) . Such factors contribute to the variability in lead and lag times that have been reported between clinical case and wastewater data, which has been highlighted in a recent review by Kumar et al. (Kumar et al., 2022) .

However, during the third wave of infections peak daily loadings of SARS-CoV-2 RNA were observed on the 3 rd of January which also had the highest new daily case numbers in Dublin.

The predictive power of wastewater monitoring for SARS-CoV-2 is greater when community testing is limited (Sharif et al., 2021) . As such the overlap between peak wastewater and case-based data indicates the rapidness of the increase in infections during this wave in J o u r n a l P r e -p r o o f Ireland which has high clinical testing capacity. It should also be noted that as wastewater samples were not collected daily any differences between wastewater and case data may be a result of sampling frequencies differing. Following the third wave of infections, SARS-CoV-2 N1 concentrations and daily loadings remained relatively stable and low for the remainder of the study period which was also observed for case-based data. This is likely a result of Although the presence of the SNV A23063T corresponding to S:N501Y was largely associated with the Alpha variant, during the period spanning the third epidemic wave multiple lineages independently acquired this SNV such as variants Beta, Gamma and Mu (source: https://outbreak.info/compare-lineages). Analogously the SNV T22917G (S:L452R) associated with the Delta variant was related to lineages S and Epsilon variants.

Nevertheless, the sequencing of samples during this period showed the low prevalence of these variants in the region. Therefore, such results highlight the synergy between the wastewater analysis and the whole-genome sequencing of samples from the national surveillance efforts as complementary for the proper interpretation of the observations.

A number of RT-qPCR assays have been developed to identify and quantify SARS-CoV-2 variants in wastewater (Yaniv et al., 2021; Johnson et al., 2022) . However, circulating SARS-J o u r n a l P r e -p r o o f CoV-2 variant targets in many cases will be present in concentrations too low to be quantified using traditional RT-qPCR. As such, we used dPCR to quantify the N501Y and L452R SNVs in wastewater samples as proxies for the presence of the Alpha and Delta variants respectively (Borchardt et al., 2021; Ho et al., 2022; Lou et al., 2022) .

Using this method, the Alpha variant was identified in Ringsend wastewater samples in November three weeks prior to its genome identification from clinical samples in early December and two months after its first identification in the UK, Ireland's only bordering neighbour, in September 2020 (Rambaut et al., 2020b) . The Delta variant on the other hand was first identified in wastewater and clinical samples on the same day. These data contrast with Heijnen et al. who found the Alpha variant in Utrecht wastewater two weeks after its first clinical identification (Heijnen et al., 2021) . This observation may be a result of sequence capacity, which is supported by the retrospective identification of the Alpha To conclude, we have demonstrated that SARS-CoV-2 wastewater monitoring data from a single large WWTP in Dublin reflected case data in the greater Dublin area. Moreover, the surveillance of VOCs in this WWTP reflected the results of clinical sample sequencing and also preceded, further demonstrating the potential utility of this approach to SARS-CoV-2 surveillance.

Funding: This work was part funded by the European Regional Development Fund through the Ireland Wales Cooperation programme (Acclimatize), by Science Foundation Ireland (20-CoV-0159) and by the Health Service Executive. Using the same X-axis, percentages of Dublin cases per super-lineage during the sampling period are shown. Sequences are grouped biweekly, and the vertical axis represents the percentage of cases in each super lineage considering all available sequence data (n=9,187).

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