key: cord-0910832-69f8y860
authors: Lou, Esther G.; Sapoval, Nicolae; McCall, Camille; Bauhs, Lauren; Carlson-Stadler, Russell; Kalvapalle, Prashant; Lai, Yanlai; Palmer, Kyle; Penn, Ryker; Rich, Whitney; Wolken, Madeline; Brown, Pamela; Ensor, Katherine B.; Hopkins, Loren; Treangen, Todd J.; Stadler, Lauren B.
title: Direct comparison of RT-ddPCR and targeted amplicon sequencing for SARS-CoV-2 mutation monitoring in wastewater
date: 2022-04-06
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2022.155059
sha: beb3c671479e0f836dc07a3300135c3d07f4f221
doc_id: 910832
cord_uid: 69f8y860

Over the course of the COVID-19 pandemic, variants of SARS-CoV-2 have emerged that are more contagious and more likely to cause breakthrough infections. Targeted amplicon sequencing approach is a gold standard for identification and analysis of variants. However, when applied to environmental samples such as wastewater, it remains unclear how sensitive this method is for detecting variant-associated mutations in environmental samples. Here we directly compare a targeted amplicon sequencing approach (using ARTIC v3; hereafter referred to as sequencing) with RT-ddPCR quantification for the detection of five mutations that are characteristic of variants of concern (VoCs) in wastewater samples. In total, 547 wastewater samples were analyzed using both methods in parallel. When we observed positive mutation detections by RT-ddPCR, 42.6% of the detection events were missed by sequencing, due to negative detection or the limited read coverage at the mutation position. Further, when sequencing reported negative or depth-limited mutation detections, 26.7% of those events were instead positive detections by RT-ddPCR, highlighting the relatively poor sensitivity of sequencing. No or weak associations were observed between quantitative measurements of target mutations determined by RT-ddPCR and sequencing. These findings caution the use of quantitative measurements of SARS-CoV-2 variants in wastewater samples determined solely based on sequencing.

Over the course of the COVID-19 pandemic, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved and numerous lineages have emerged that are more transmissible, cause more severe disease, and/or are better at escaping the immune response system (Garcia-Beltran et al., 2021; Harvey et al., 2021; Q. Li et al., 2020) . Tracking the emergence and spread of these variants of concern (VoCs) and variants of interest (VoIs) has become critical to public health response and mitigation strategies for stopping the spread of SARS-CoV-2. Wastewater-based epidemiology (WBE) is one prominent approach that has been adopted by public health departments and water utilities to track infection dynamics in communities by quantifying the amount of SARS-CoV-2 RNA in wastewater samples (Ahmed et al., 2020; Arora et al., 2020) . WBE can also be used for monitoring VoCs and VoIs in communities Fontenele et al., 2021; Heijnen et al., 2021) .

Variant identification in wastewater samples is challenging because the viral genomes are highly fragmented, dilute, and comprised of mixtures of circulating variants. The most common methods used for wastewater variant screening are: (1) quantifying specific characteristic mutations via RT-qPCR or RT-ddPCR (Ciesielski et al., 2021; Heijnen et al., 2021) ; and (2) enriching and sequencing SARS-CoV-2 genomes in wastewater (Swift et al., 2021; Tyson et al., 2020) . Underlying both approaches is the ability to identify and quantify characteristic mutations that define variants. RT-qPCR is regarded as a gold standard method for routine wastewater surveillance (Alygizakis et al., 2021; Rahman et al., 2021; Van Poelvoorde et al., 2021) .

Compared to RT-qPCR, RT-ddPCR has a superior detection sensitivity (Ahmed et al., 2022; Ciesielski et al., 2021; Flood et al., 2021) and is less sensitive to inhibitors present in wastewater (Cao et al., 2015; Ciesielski et al., 2021) . However, PCR based methods are limited in that the mutations must be known ahead of time for primer and probe design. In addition, in practice, they are limited by the number of targets that can be multiplexed per reaction, and it is difficult to delineate all variants present in the sample by only targeting a few mutations. Next generation sequencing (NGS) enables comprehensive screening of all potential mutations without any prior knowledge, and thus has been frequently applied for characterizing pathogens and viruses (Greninger et al., 2015; Yang et al., 2011) . The unbiased, non-targeted metagenomics sequencing approaches often require high-or ultra-high coverage in order to obtain enough target sequences (Chiara et al., 2021) . On the other hand, targeted sequencing approaches using an enrichment step during library preparation maximize the detection of viruses effectively (Deng et al., 2020) .

For SARS-CoV-2 genome enrichment, multiplex tiling PCR and oligonucleotide capture are the most frequently implemented methods, both demonstrating great performance in terms of genome coverage (Doddapaneni et al., 2021; Tyson et al., 2020) and mutation detection Crits-Christoph et al., 2021) . Targeted amplicon sequencing (i.e., multiplex tiling PCR coupled with amplicon sequencing) is considered the lower-cost and faster approach (Chiara et al., 2021; Lin et al., 2021) . For example, ARTIC Network panels (https://artic.network) are commonly used by laboratories globally to characterize SARS-CoV-2 present in clinical samples (Charre et al., 2020; Mboowa et al., 2021) .

However, targeted amplicon sequencing is frequently limited by coverage and/or quality dropout associated mutations detected in wastewater sample to estimate the relative abundances of different lineages circulating in a community (Ellmen et al., 2021) . However, it is unclear how quantitative mutation AF are that are generated from wastewater genomes via this approach.

Thus, a direct comparison between targeted amplicon sequencing and RT-ddPCR (or RT-qPCR) for mutation detection and quantification using wastewater samples is needed. In this study, we quantified five unique mutations using RT-ddPCR and performed targeted amplicon sequencing (ARTIC v3 based) of SARS-CoV-2 in parallel on 547 wastewater samples. We compare the consistency in the approaches in terms of (1) detection vs. no detection; and (2) quantitative information generated by each method. In addition, we evaluated the impact of mutation concentration, single base coverage at the mutation position, the overall SARS-CoV-2 concentrations, and SARS-CoV-2 genome coverage on mutation detection via targeted amplicon sequencing.

2.1 Wastewater sample collection, concentration, and RNA extraction. We collected weekly wastewater samples from 39 wastewater treatment plants (WWTPs) in Houston covering a service area of approximately 580 square miles and serving over 2.3 million people. Timeweighted composite samples of raw wastewater were collected every 1 hour for 24 hours from the influent of the WWTPs. The sampling campaign was conducted during two separate periods (GISAID, https://www.gisaid.org/). In total, 249 and 298 samples were analyzed during Phase I and Phase II, respectively. SARS-CoV-2 was concentrated in wastewater samples using an electronegative filtration method as previously described (LaTurner et al., 2021) . RNA extraction was performed using a Chemagic™ Prime Viral DNA/RNA 300 Kit H96 (Chemagic, CMG-1433, PerkinElmer) with the PerkinElmer viral RNA/DNA purification protocol and reagents.

Finally, 10 μL of sample extract was used for each RT-ddPCR reaction, and 11 μL of sample extract was used for sequencing library preparation. Detailed concentration procedures, RNA extraction procedures, concentration factors (Table S1) , and associated quality control measurements are provided in the Supplementary materials following the Environmental Microbiology Minimum Information (EMMI) Guidelines (Borchardt et al., 2021) .

mutations. RT-ddPCR was performed on a QX200 AutoDG Droplet Digital PCR System (Bio-Rad) and a C1000 Thermal Cycler (Bio-Rad) in 96-well optical plates. SARS-CoV-2 N1 and N2 gene targets were quantified in wastewater samples as previously described (LaTurner et al., 2021) . Five mutations, namely S:DEL69/70, S:N501Y, S:E484K, S:K417T, S:L452R, were quantified via RT-ddPCR. GT Molecular kits were used for RT-ddPCR quantification of mutations (kit information provided in Table S2 ). S:DEL69/70 and S:N501Y, two characteristic mutations associated with the Alpha lineage B.1.1.7, were quantified during Phase I, and S:L452R, S:K417T and S:E484K were quantified during Phase II. The latter three mutations were selected due to their reducing SARS-CoV-2 susceptibility to convalescent and vaccineelicited sera and mAbs, and their emergence in newly evolved SARS-CoV-2 strains (Jangra et al., 2021; Wilhelm et al., 2021) . These SARS-CoV-2 mutations were quantified using one-step RT-J o u r n a l P r e -p r o o f Journal Pre-proof ddPCR assays according to the manufacturer's protocol (GT Molecular). A detailed description of the methods, including droplet thresholding and limit of detection (LOD) are described in the Supplementary materials (Section 1.4, Table S3 -S5). For all targets (N1, N2, and the five mutations), positive detection (+) was defined as above the LOD, and a negative detection (-) was defined as below the LOD. RT-ddPCR analysis was used to generate: (1) the concentration of the mutation in copies/L-wastewater, and (2) the fraction of SARS-CoV-2 containing the mutation, which was calculated by normalizing the copies of the mutation by the sum of the copies of the mutation and the wild-type.

Amplicon-based sequencing using ARTIC v3 and data analysis. cDNA was generated using 11 μL RNA extract via reverse transcription using the Superscript IV first-strand synthesis system (ThermoFisher Scientific, 18091050) following the manufacturer's protocol. SARS-CoV-2 genome enrichment via multiplexing PCR was conducted using ARTIC v3 protocol (Tyson et al., 2020) . Illumina DNA Prep kit with the manufacturer's manual (DNA Flex) were applied for amplicon tagmentation and flex amplification, followed by library clean-up. Each sample library was then quantitated, normalized, pooled, and diluted to 6 pM. Finally, sequencing was performed on an Illumina MiSeq instrument using MiSeq Reagent Kit v2 (300-cycles, MS-103-To compare targeted amplicon sequencing (hereafter referred to as sequencing) with RT-ddPCR, we first calculated the sequencing read coverage for each target mutation in each sample by averaging the single base coverage across the target region used for quantification in RT-ddPCR. For example, for the N1 and N2 RT-ddPCR assays, the CDC 2019-nCoV_N1 probe and 2019-nCoV_N2 probe were applied to RT-ddPCR assays in this study. These probes align to nt 28318 -28332 and nt 29188 -29210, respectively (the nt coordinates correspond to the Wuhan reference NC_045512.2). Accordingly, sequencing mapped reads for N1 and N2 were checked at each single base position from nt 28318 to 28332 (N1 region, containing 15 positions) and from nt 29188 to 29210 (N2 region, containing 23 positions), respectively. A positive detection (+) was called for N1 and N2 if (1) the single base coverage at each nt position across the target region (nt 28318 -28332 for N1 and nt 29188 -29210 for N2) was at least 1×, and (2) the average single base coverage across the target region was at least 20×; otherwise, a non-detect (ND) was called. For the five mutations, if there was at least 20× read depth at the position that corresponded to the target mutation, a positive detection (+) was called if any reads containing that mutation were observed. A negative detection for sequencing (-) was defined as no reads containing the target mutation were observed, and there was at least 20× read depth at the mutation location. Finally, for sequencing if less than 20 reads mapped to the mutation position, we defined this as "depth limited (DL)". We used a 20× coverage threshold for sequencing analysis based on previous studies that applied tiled PCR and short-read sequencing (Illumina) for SARS-CoV-2 wastewater analysis (Baaijens et al., 2021; Fontenele et al., 2021) .

Welch two sample t-test was applied to compare datasets. Spearman rank correlation analysis was used to study the associations between quantitative results generated by RT-ddPCR and sequencing. For each target mutation, we used Spearman rank correlation to assess the correlations between (1) the mutation concentration as determined by RT-ddPCR (copies/Lwastewater) and the number of reads containing the mutation as determined by sequencing; and

(2) the fraction of SARS-CoV-2 containing the target mutation (target mutation concentration/total SARS-CoV-2 concentration as determined by RT-ddPCR) and the AF of the mutation as determined by sequencing. Strength of correlations were identified based on Spearman's correlation coefficient Rho (R).

3.1 RT-ddPCR was more sensitive than sequencing for mutation detection 547 wastewater samples (249 samples for Phase I, 298 samples for Phase II) were analyzed using both RT-ddPCR (targeting N1, N2, and five mutations) and sequencing. The wastewater concentrations of SARS-CoV-2 (determined by the average of N1 and N2 concentrations) were significantly higher during Phase I than during Phase II ( Figure Table S6 ). Additional information on all samples analyzed, including their detections by RT-ddPCR and sequencing for each target are detailed in Figure S2 .

To compare RT-ddPCR and sequencing, we categorized detection events into different scenarios. There were four possible scenarios for N1 and N2 These results indicate that sequencing detection is less sensitive than RT-ddPCR detection when focusing on commonly targeted N-gene regions of the SARS-CoV-2 genome. J o u r n a l P r e -p r o o f Unsurprisingly, we also found that sequencing was less sensitive than RT-ddPCR for detecting target mutations. We compared 1,354 possible mutation detections using both RT-ddPCR and sequencing (Table S2, Violins represent the distribution of detection events in each scenario. Boxes represent the interquartile range, with dashed lines as means and solid lines as medians. Whiskers represent the standard deviation. "ns", "*", and "****" indicate "not significant (p>0.05)", "p<0.05" and "p<0.0001", respectively, based on a t-test. With the aim of guiding wastewater-based SARS-CoV-2 monitoring in practice, we also attempted to identify whether there was a threshold level of sequencing depth, or the total reads mapped to SARS-CoV-2 reference genome, above which sequencing called positive detections concomitantly with RT-ddPCR ( Figure S4 ). Two groups of samples belonging to scenarios (+/+) and (+/-) were used for this analysis, because there was no significant difference in SARS-CoV-2 concentration or in mutation concentration between them, ensuring that the sample sequencing depth is the only variable that determined mutation detection by sequencing. We were not able to J o u r n a l P r e -p r o o f identify a threshold level of reads that could differentiate the sequencing positive and negative detection events. In other words, there was no clear pattern in the number of reads for samples with sequencing positive versus negative detections ( Figure S4 ).

sequencing were not quantitative representations of the mutation concentration as determined by RT-ddPCR.

We next asked if data generated from sequencing could be used to quantitatively estimate the proportion of SARS-CoV-2 that contained a target mutation. For each target mutation, we compared (1) the mutation concentration as determined by RT-ddPCR (copies/L-wastewater);

and (2) and N2 quantifications in wastewater samples found strong correlations between N1 and N2 signals, reporting correlation coefficients such as 0.952 (Sanjuán & Domingo-Calap, 2021 ) and above 0.99 (Ahmed et al., 2022) . Studies that applied RT-ddPCR for N1 and N2 quantifications in wastewater samples also reported strong correlation coefficients above 0.85 (Feng et al., 2021) J o u r n a l P r e -p r o o f and above 0.90 (D'Aoust et al., 2021) . These results highlight that AF values and the read counts as determined by sequencing for mutations may not vary consistently with one another, and thus are not appropriate for inferring VoC concentrations or relative abundances in wastewater samples.

RT-ddPCR or RT-qPCR should be applied for quantitative analyses due to the great sensitivity and consistency of detection. In addition, RT-ddPCR and RT-qPCR generally have much shorter result turnaround time compared to sequencing (Bloom et al., 2021) , which is critical for real- Yaniv et al., 2021) . Recently, allelespecific and multiplex-compatible RT-qPCR assays targeting mutations T19R, D80A, K417N, T478K and E484Q for quantitative detection and discrimination of the Delta, Delta plus, Kappa and Beta variants in wastewater were developed and validated (Lee et al., 2021) .

The lower detection sensitivity of sequencing ( Figure 2) can be attributed to 1) the low concentration of target mutations in the wastewater sample (Figure 3a) , 2) the lack of sufficient read depth at the mutation position (Figure 3b (Itokawa et al., 2020) , or optimizing library preparation protocols (Coil et al., 2021) . PCR inhibition due to other constituents in wastewater is another factor that may impact the sensitivity of sequencing more than RT-ddPCR, as digital PCR is relatively resilient to PCR inhibition (Ahmed et al., 2022; Ciesielski et al., 2021) . Another approach for increasing sequencing coverage at specific sites is to target a smaller region of the genome for amplification and sequencing, such as by sequencing only the spike protein region of SARS-CoV-2 instead of the whole genome. For example, the receptor binding domain (RBD) on the spike region of SARS-CoV-2, which is involved in the interactions with human angiotensin-converting enzyme-2 (ACE-2) receptor, can be sequenced instead of the entire genome for mutation or variant analysis (Gregory et al., 2021) . The mutations in the RBD are associated with the severity of infection (i.e., ACE-2 binding affinity and virus entry to the host cells) (Andersen et al., 2020; Heald- J o u r n a l P r e -p r o o f Journal Pre-proof Sargent & Gallagher, 2012) and potential antibody-escape affecting antigenicity (Harvey et al., 2021) . In addition, many of the VoCs are defined by mutations to in the Spike region (Baaijens et al., 2021) . Furthermore, the spike region only accounts for approximately 12.8% of the total genome, therefore, may be a more efficient use of sequencing for mutation detection. However, this approach also suffers from amplification and sequencing challenges due to degraded RNA inherent to wastewater samples.

Despite its lower sensitivity and qualitative nature, sequencing still has a clear advantage of being more comprehensive, not limited by a priori knowledge of the target mutations, and enables the discovery of cryptic lineages (Smyth et al., 2022) and emerging lineages of concern (Sapoval et al., 2021) . This can be critical for early detection of variants when the availability of primers and probes is limited or delayed due to supply chain challenges. In addition, sequencing data facilitates retrospective analyses, such as searching for specific mutations or collections of mutations present in samples collected prior to knowledge of the variants in communities (Johnson et al., 2022; La Rosa et al., 2021; Wilton et al., 2021) . In practice, WBE systems can benefit from coupling sequencing with quantitative analyses such as RT-ddPCR or RT-qPCR to achieve a comprehensive picture of circulating mutations (using sequencing), and sensitive, quantitative information on variant-associated mutations (using RT-qPCR/RT-ddPCR).

For WBE work on SARS-CoV-2, sequencing technology has demonstrated irreplaceable advantages in efficient screening and the potentials to detect emerging or cryptic lineages. We performed RT-ddPCR and sequencing analyses in parallel on hundreds of wastewater samples for SARS-CoV-2 monitoring, with a specific focus on mutations associated with VOCs. This is J o u r n a l P r e -p r o o f Journal Pre-proof the first study to directly compare mutation detection consistency between these two methods.

Results first showed the significantly greater detection sensitivity of RT-ddPCR in detecting five mutations as compared to amplicon-based sequencing. Secondly, quantitative results generated from sequencing, including allele frequency (AF) and single base coverage of specific mutations failed to reflect the concentrations of the corresponding mutations in wastewater, showing poor correlations with RT-ddPCR quantification results. Therefore, caution should be exercised in using sequencing for quantitative assessments of mutation abundance in wastewater samples.

RT-ddPCR or RT-qPCR should be applied for quantitative analyses due to the great sensitivity and consistency of detection. 

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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We thank Houston Water for their assistance in the collection of wastewater samples. We 

Ahmed, W., Angel, N., Edson, J., Bibby, K., Bivins, A., O'Brien, J. W., Choi, P. M., Kitajima,