ABSTRACT
This study presents the successful optimization of enteric RT-qPCR multiplex assays for detecting Norovirus GII, Enterovirus, and Coxsackievirus A6 or Enterovirus D68 in municipal wastewater samples. Additionally, optimization of a respiratory RT-qPCR multiplex assay to detect influenza A, respiratory syncytial virus, and SARS-CoV-2 was attempted. The enteric multiplex assays successfully detected Coxsackievirus A6 in wastewater during community outbreaks of hand-foot-mouth disease. Enterovirus D68 was also successfully detected in wastewater samples (Summer/Fall, 2022), which coincided with provincial public health reports of Enterovirus D68 cases. Attempting to optimize the respiratory multiplex assay resulted in challenges due to oligonucleotide cross-reactivity and cross-talk. Specifically, when Texas Red and FAM probes detected higher abundance targets, they interfered with the Cy5 and HEX fluorophore probes that detected lower-abundance targets. In contrast, selecting probes with Cy5/HEX for high-abundance targets and Texas Red/FAM for lower-abundance targets provided more robust results.
HIGHLIGHTS
Triplex assays to detect enteric viruses in wastewater.
Monitored levels of Coxsackievirus A6, Enterovirus D68, Norovirus GII, and Enterovirus in wastewater throughout 2022.
Detected Enterovirus D68 and Coxsackievirus A6 in wastewater during periods of reported outbreaks.
Relative gene target abundance is important for fluorophore selection.
INTRODUCTION
Wastewater surveillance (WWS) has seen widespread adoption as an effective tool to track changes in the prevalence of SARS-CoV-2 viral ribonucleic acid (RNA) in wastewater during the COVID-19 pandemic (Ahmed et al. 2020; Wu et al. 2022; Islam et al. 2024). Wastewater data correlated well with changes in disease prevalence as measured by traditional clinical surveillance (McMahan et al. 2021; D'Aoust et al. 2022; Schill et al. 2022; Acosta et al. 2023), demonstrating the utility of WWS in predicting community incidences. Not only has WWS been demonstrated to track disease prevalence in a community effectively, but WWS is also cost-effective compared to traditional clinical surveillance. While both methods require similar amounts of reverse-transcription quantitative polymerase chain reaction (RT-qPCR) reagents per sample, a wastewater sample requires more pre- and post-processing. Still, wastewater is representative of populations of individuals at various scales (local to regional). In contrast, a clinical sample reflects only one person's condition. Furthermore, wastewater can contain nucleic acids - including Ribonucleic acid (RNA) and Deoxyribonucleic acid (DNA) - shed from both symptomatic and asymptomatic individuals, thereby capturing a more representative infection status of the community.
As global WWS networks continue to expand their efforts to detect more pathogens, it is important to not only monitor pathogens that present immediate risks to the community but also leverage the cost-effectiveness of WWS to monitor non-life-threatening pathogens that diminish the quality of life and impose economic burden. Respiratory viruses such as SARS-CoV-2, respiratory syncytial virus (RSV), and influenza A have been shown to pose immediate risks to public health and healthcare systems (Domachowske et al. 2018; Waterlow et al. 2023). Similarly, Enterovirus, Norovirus GII, Coxsackievirus A6 (CA6) and Enterovirus D68 (D68) have the potential to pose immediate risks to public health (Said et al. 2008; Zhao et al. 2020; Jartti et al. 2024), but they are deemed to be of lower overall risk, evidenced by the fact they are not notifiable diseases in Ontario, Canada, and their mortality rates and hospitalization rates are generally lower. This is because their symptoms are more manageable than those associated with SARS-CoV-2 and influenza A.
Enterovirus, Norovirus GII, CA6, and D68 are valuable to monitor because they can spread rapidly throughout a population and cause large numbers of people to become ill. Furthermore, the aetiologies caused by these viruses, like vomiting, diarrhea, or hand-foot-mouth disease (HFMD), often compel people to stay home during peak infection, leading to a potential financial loss and learning disruptions (Nhan et al. 2019). When children are infected, their symptoms may prevent them from attending school, fomenting gaps in their development (Araújo et al. 2021). Symptoms associated with gastroenteritis often act antagonistically to the spread of a pathogen but could exacerbate co-morbidities in vulnerable people. Gastrointestinal viruses such as Norovirus GII strains are so diverse that they can infect individuals who have recently recovered from a different strain. Thus, immunity is typically not long-lasting (Newman & Leon 2015).
Enteroviruses can be transmitted through respiratory droplets or via the fecal-oral route and have varied seasonality. D68 is often seen in the fall, causing respiratory infections, but on occasion can cause acute flaccid myelitis, whereas Coxsackieviruses (including CA6) can cause HFMD, usually seen in the summer. Most Enteroviruses, including CA6 and D68, disproportionately affect the pediatric population, while Norovirus GII affects both pediatric and older adult populations. Monitoring diseases that affect the pediatric population is crucial to reduce further impacts on learning that resulted from the COVID-19 pandemic. Additionally, when younger children are affected, their parents may be required to take time off work to care for them, causing further labour disruptions and financial losses (Bartsch et al. 2016; Putri et al. 2018; Nhan et al. 2019; Bartsch et al. 2020). Nations such as Sweden have recognised this burden and have implemented policies to help mitigate financial losses of parents when their children are sick (Boye 2024).
Expanding a WWS network to include a suite of pathogens can be straightforward if the assays to detect them are in a singleplex (single signal detection) assay configuration; however, this will quickly accrue additional costs as this requires higher additional sample extractions and additional costly reagents. Multiplexing (multiple signals detected simultaneously) allows the expansion of a WWS network while reducing the number of sample extractions and the associated costs of RT-qPCR reagents and labour. Multiplexing with RT-qPCR has unique challenges, including oligonucleotide cross-reactivity, cross-talk, other polymerase chain reaction (PCR) primer/probe design requirements, and specificity/sensitivity. Ensuring a sensitive/specific assay as well as abiding by traditional PCR primer/probe design requirements are well established and applicable to WWS. However, oligonucleotide cross-reactivity and cross-talk are more prevalent in WWS and worth closer examination.
Oligonucleotide cross-reactivity results from a high degree of similarity between one or more oligos, creating favourable conditions for oligos to bind to themselves or other oligos rather than their complementary sequence. This is exacerbated when the concentration of the complementary sequence is low, decreasing the probability of oligos interacting with the complementary sequence and leading to an increased likelihood of unfavourable interactions (Markoulatos et al. 2002). Tailoring the concentration of primers to be proportional to the amount of the complementary sequence helps to fix this issue, but it is very difficult to do when performing surveillance, where levels of targets change depending on population infection prevalence.
Emission spectra of fluorophores and their respective detection windows on the Bio-Rad CFX Opus. The figure was created using Biorender.com based on data from Bio-Rad. This figure is for demonstrative purposes only.
Emission spectra of fluorophores and their respective detection windows on the Bio-Rad CFX Opus. The figure was created using Biorender.com based on data from Bio-Rad. This figure is for demonstrative purposes only.
This one-sided nature of cross-talk is exacerbated further by the photophysical properties of each fluorophore, which in turn reduces the conversion rate for energy used to excite and the energy emitted (quantum yield). Poor quantum yields and increased susceptibility to cross-talk are especially prominent in higher wavelength (near infrared) fluorophores, such as cyanine fluorophores, including Cy5 (Sauer et al. 2011).
Cyanine fluorophores possess a reduced quantum yield because the fluorophores are larger and more flexible. This reduces quantum yield because more energy is lost to vibration, resonance, and the rotation of bonds (Sauer et al. 2011). Additionally, other photophysical properties of Cy5 include cis-trans isomerization, where both isomers (present 1:1) can absorb light, but only the cis isomer can emit light (Sauer et al. 2011). Ultimately, this results in a further reduction in quantum yield – which, in tandem with cross-talk from neighbouring channels, could create instances where there is not only a loss in sensitivity but also specificity.
Challenges regarding cross-talk, oligonucleotide cross-reactivity, primer/probe design, and sensitivity/specificity are inherent to multiplexing in RT-qPCR, and understanding their specific implications when it comes to expanding WWS is of utmost importance. MIQE guidelines (Bustin et al. 2009) largely solve fundamental technical concerns when expanding by providing a reasonable framework; however, ethical concerns remain, especially when targeting infectious pediatric diseases. However, rights- and ethics-based frameworks have been discussed as being reasonable approaches to address ethical concerns regarding WWS networks (Nainani et al. 2024).
Multiplex assay optimization for WWS when expanding assays to include additional pathogen targets that vary in abundance and frequency is quite nuanced. This paper presents the optimization of RT-qPCR multiplex assays for enteric (Norovirus GII, Enterovirus, and Coxsackievirus A6 or Enterovirus D68) and respiratory (SARS-CoV-2, RSV, and influenza) viruses in domestic wastewater. Cross-talk and cross-reactivity were addressed within the context of detecting and amplifying targets from complex wastewater matrices. Results are presented for both optimized (enteric) and unoptimized (respiratory) multiplex assays, followed by a discussion of remaining challenges and opportunities for further multiplex assay development.
METHODS
Wastewater sample collection and PEG–NaCl viral concentration
Post-grit primary influent samples were collected thrice weekly from a pumping station located in a suburban community in the Greater Toronto Area in 2022. Some notable characteristics of the pumping station include a mean flow rate of 35.96 ML/day in 2022, an estimated sewershed population of 146,563 and separated stormwater and wastewater systems. Concentrations of biochemical oxygen demand and ammonium are unknown.
Wastewater samples were transported to Ontario Tech University and immediately processed using the same method outlined in previous publications (de Melo et al. 2023; Islam et al. 2024). Upon arrival, 30 mL of wastewater was added to pre-weighed Nalgene™ Oak Ridge High-Speed PPCO Centrifuge Tubes (Thermo Fisher Scientific, MA, USA) containing 10 mL of 4X PEG–NaCl buffer (40% w/v PEG 8,000 and 1.5 M NaCl), vortexed briefly, and centrifuged using a SORVALL Lynx 4000 Centrifuge (Thermo Fisher Scientific, MA, USA) at 12,000 × g for 2 h at 4 °C. Upon completion, the supernatant was decanted, and the pellet was re-centrifuged at 12,000 × g for 10 min at 4 °C; any remaining supernatant was decanted, leaving a pellet. Pellets were weighed and adjusted when necessary to prevent overloading of the spin column during the extraction process.
RNA extraction
Total RNA was extracted from the pellets using the RNeasy® PowerMicrobiome® Kit (Qiagen, Germantown, MD) with minor alterations. Namely, the addition of 100 μL of phenol–chloroform–isoamyl alcohol (25:24:1, pH 6.5–8, Thermo Fisher Scientific, MA, USA) and 2-Mercaptoethanol (Catalog #: 125472500, Thermo Fisher Scientific, MA, USA) to the bead beating tube. Subsequent steps were followed as per the manufacturer's recommended protocol with a final elution volume of 100 μL. Additionally, human coronavirus 229-E (HCoV-229E) viral stock was used as a whole process control (Chik et al. 2021).
RT-qPCR
RT-qPCR runs were performed using the CFX Opus 96 System (Catalog #: 12011319, Bio-Rad Laboratories, CA, USA) and the associated CFX Maestro software (Version 2.3). RT-qPCR reactions utilized the Reliance One-Step Multiplex RT-qPCR Supermix (BioRad, Hercules, CA), TaqMan probes (IDT, Iowa, USA), and custom primers (IDT, Iowa, USA).
The RT-qPCR analysis was validated with no-template controls using PCR grade water instead of RNA/DNA, no-reverse transcriptase controls, and a 1:10 diluted sample of pepper mild mottle virus (PMMoV) as an internal inhibition control to monitor the presence of PCR inhibitors. All samples analyzed were quantified according to the MIQE recommendations (Bustin et al. 2009). Primer efficiencies for each target ranged from 90 to 110%. The R2 value was ≥0.99, and the slope of the standard curve was ∼3.1–3.5. Any quantification cycles (Cq) values above 40 cycles were deemed as non-detects.
The enteric triplexes (see Table 1) targeted the 5′-untranslated region (5′-UTR) of Enterovirus, the viral protein 1 (VP1) of Norovirus GII, and the viral protein 1 (VP1) of Enterovirus D68 or Coxsackievirus A6. The respiratory triplexes (see Table 2) targeted the SARS-CoV-2 viral nucleocapsid (N2) gene, the Influenza A matrix (M) gene, and the RSV nucleocapsid (N) gene.
Primer/probe sequences of viral targets for the enteric triplexes (CA6 and D68)
Viral target . | Primer/Probe . | Sequence (5′ → 3′) . | Gene target . | Final concentration (nM) . | Source . |
---|---|---|---|---|---|
Universal Enterovirus (Cy5) | EntQuant For | ACAWGGTGYGAAGAGYCTAYTGWGCT | 5′-UTR | 500 | Dierssen et al. (2008) |
EntQuant Rev | CCAAAGTAGTYGGTTCCGC | 500 | |||
EntQuant Probe | Cy5-TCCGGCCCCTGAATGCGGCTAAT-IAbRQSp | 250 | |||
Norovirus GII (HEX) | QNIF2d | ATGTTCAGNTGGATGAGNTTCTCNGA | VP1 | 500 | Loisy et al. (2005) |
COG2R | TCGACGCCATCTTCATTCACA | 500 | |||
NVGII Probe | HEX-AGCACNTGGGAGGGCGATCG-IABkFQ | 250 | |||
Coxsackievirus A6 (TxRED) | CA6 For | CAAGCYGCAGAAACGGGAG | VP1 | 500 | Zhang et al. (2012) |
CA6 Rev | GYTYTACACTCGCCTCATT | 500 | |||
CA6 Probe | TxRED-ACCCCGTTTCGATTCATCACACA-IAbRQSp | 250 | |||
Enterovirus D68 (FAM) | D68 For | CACTGAACCAGAAGAAGCYA | VP1 | 500 | Wylie et al. (2015) |
D68 Rev | CCAAAGCTGCTCTRCTGAGAAA | 500 | |||
D68 Probe | FAM-TCGCACAGTGATAAATCARCACRG-IABkFQ | 250 |
Viral target . | Primer/Probe . | Sequence (5′ → 3′) . | Gene target . | Final concentration (nM) . | Source . |
---|---|---|---|---|---|
Universal Enterovirus (Cy5) | EntQuant For | ACAWGGTGYGAAGAGYCTAYTGWGCT | 5′-UTR | 500 | Dierssen et al. (2008) |
EntQuant Rev | CCAAAGTAGTYGGTTCCGC | 500 | |||
EntQuant Probe | Cy5-TCCGGCCCCTGAATGCGGCTAAT-IAbRQSp | 250 | |||
Norovirus GII (HEX) | QNIF2d | ATGTTCAGNTGGATGAGNTTCTCNGA | VP1 | 500 | Loisy et al. (2005) |
COG2R | TCGACGCCATCTTCATTCACA | 500 | |||
NVGII Probe | HEX-AGCACNTGGGAGGGCGATCG-IABkFQ | 250 | |||
Coxsackievirus A6 (TxRED) | CA6 For | CAAGCYGCAGAAACGGGAG | VP1 | 500 | Zhang et al. (2012) |
CA6 Rev | GYTYTACACTCGCCTCATT | 500 | |||
CA6 Probe | TxRED-ACCCCGTTTCGATTCATCACACA-IAbRQSp | 250 | |||
Enterovirus D68 (FAM) | D68 For | CACTGAACCAGAAGAAGCYA | VP1 | 500 | Wylie et al. (2015) |
D68 Rev | CCAAAGCTGCTCTRCTGAGAAA | 500 | |||
D68 Probe | FAM-TCGCACAGTGATAAATCARCACRG-IABkFQ | 250 |
Note. W = A/T, Y = C/T, N = A/T/G/C, R = G/A.
Primer/probe sequences of viral targets for the respiratory triplex (CDC and WHO)
Viral target . | Primer/Probe . | Sequence (5′ → 3′) . | Gene target . | Final Concentration (nM) . | Source . |
---|---|---|---|---|---|
SARS-CoV-2 (FAM) | 2019-nCoV_N2 F | TTACAAACATTGGCCGCAAA | N2 | 500 | Lu et al. (2020) |
2019-nCoV_N2 R | GCGCGACATTCCGAAGAA | 500 | |||
2019-nCoV_N2 Probe | FAM-ACAATTTGCCCCCAGCGCTTCAG-MGBNFQ | 250 | |||
RSV (HEX or Cy5) | RSV F | CTCCAGAATAYAGGCATGAYTCTCC | N | 500 | Hughes et al. (2022) |
RSV R | GCYCTYCTAATYACWGCTGTAAGAC | 500 | |||
RSV HEX probe | HEX-TAACCAAATTAGCAGCAGGRGATAGATCWG-IABkFQ | 250 | |||
RSV Cy5 probe | Cy5-TAACCAAATTAGCAGCAGGRGATAGATCWG-IAbRQSp | 250 | |||
WHO InfA (TxRED) | MP-39-67For | CCMAGGTCGAAACGTAYGTTCTCTCTATC | M1 | 500 | WHO (2021) |
MP-183-153Rev | TGACAGRATYGGTCTTGTCTTTAGCCAYTCCA | 500 | |||
MP-96-75ProbeAs | TxRED-ATTTCGGCTTTGAGGGGGCCTG-IAbRQSp | 250 | |||
CDC InfA (TxRED) | CDC InfA F | CAAGACCAATCYTGTCACCTCTGAC | M1 | 500 | Sahoo et al. (2024) |
CDC InfA R | GCATTYTGGACAAAVCGTCTACG | 500 | |||
CDC InfA Probe | TxRED-TGCAGTCCTCGCTCACTGGGCACG-IAbRQSp | 250 |
Viral target . | Primer/Probe . | Sequence (5′ → 3′) . | Gene target . | Final Concentration (nM) . | Source . |
---|---|---|---|---|---|
SARS-CoV-2 (FAM) | 2019-nCoV_N2 F | TTACAAACATTGGCCGCAAA | N2 | 500 | Lu et al. (2020) |
2019-nCoV_N2 R | GCGCGACATTCCGAAGAA | 500 | |||
2019-nCoV_N2 Probe | FAM-ACAATTTGCCCCCAGCGCTTCAG-MGBNFQ | 250 | |||
RSV (HEX or Cy5) | RSV F | CTCCAGAATAYAGGCATGAYTCTCC | N | 500 | Hughes et al. (2022) |
RSV R | GCYCTYCTAATYACWGCTGTAAGAC | 500 | |||
RSV HEX probe | HEX-TAACCAAATTAGCAGCAGGRGATAGATCWG-IABkFQ | 250 | |||
RSV Cy5 probe | Cy5-TAACCAAATTAGCAGCAGGRGATAGATCWG-IAbRQSp | 250 | |||
WHO InfA (TxRED) | MP-39-67For | CCMAGGTCGAAACGTAYGTTCTCTCTATC | M1 | 500 | WHO (2021) |
MP-183-153Rev | TGACAGRATYGGTCTTGTCTTTAGCCAYTCCA | 500 | |||
MP-96-75ProbeAs | TxRED-ATTTCGGCTTTGAGGGGGCCTG-IAbRQSp | 250 | |||
CDC InfA (TxRED) | CDC InfA F | CAAGACCAATCYTGTCACCTCTGAC | M1 | 500 | Sahoo et al. (2024) |
CDC InfA R | GCATTYTGGACAAAVCGTCTACG | 500 | |||
CDC InfA Probe | TxRED-TGCAGTCCTCGCTCACTGGGCACG-IAbRQSp | 250 |
Note. W = A/T, Y = C/T, N = A/T/G/C, R = G/A.
For each wastewater sample, technical replicates were run in triplicate. Each reaction contained 5 μL of template, 500 nM of each forward and reverse primer, 250 nM probe, and 5 μL of the Reliance supermix, in a final reaction volume of 20 μL. For the respiratory assays, reactions began with a reverse transcription (RT) step at 50 °C for 10 min, followed by a polymerase activation at 95 °C for 10 min, and then 45 cycles of denaturation and annealing/extension at 95 °C for 10 s, then 60 °C for 30 s. The enteric triplex assays were run with a similar protocol but with a 45 s annealing time at 60 °C.
A mix of DNA and RNA standards was used for optimization. The standards used for Universal Enterovirus, CA6, D68, and RSV were all DNA gBlock gene fragments (IDT, Iowa, USA) of the target genes taken from reference sequences. While for SARS-CoV-2, Influenza A, and Norovirus GII RNA standards were used. More specifically for SARS-CoV-2, the Exact Diagnostics (EDX) SARS-CoV-2 RNA standard (COV019) was used. For influenza a Twist Bioscience Synthetic Influenza H3N2 RNA control (103002) was used, and finally for Norovirus GII, the Quantitative Synthetic RNA from Norovirus G2 from ATCC (VR-3235SD) was used.
Data analysis
The above formula is used to calculate the coefficient of variation (CV) at a given dilution point. E is the efficiency of the standard, SD(Cq) is the standard deviation at a given dilution point
The lower limit of quantification (LLOQ) for each assay was calculated according to Equation (1), using a method outlined by the Ontario Ministry of Environnmental Conservation and Park in a technical guidance protocol for participants in the Wastewater Surveillance Initiative (Ontario Ministry of Environmental Conservation and Parks 2022). A target CV of 0.35 was used. In both cases, 15 technical replicates were included.
Two-tailed paired t-tests (α = 0.05) were performed using Microsoft Excel 2021 (WA, USA) to compare the average Cq at each dilution between singleplex and triplex. The same analysis was used to compare the LLOQ between singleplex and triplex.
Mixed mock control experiment comparing HEX and Cy5.
To investigate the reproducibility of the respiratory triplex and compare RSV HEX and Cy5 probes' ability to detect low amounts of RSV in a wastewater matrix, sample extracts were originally utilized. However, since many of the wastewater RNA extracts contained undetectable concentrations of one or more of the three respiratory targets (SARS-CoV-2, RSV, and influenza A), mixed mock controls were prepared using the standards. For SARS-CoV-2 and influenza A, the amount of standard used ranged from 1,000 to ∼37 gc/mL of influent, while the amount of RSV was kept constant at ∼50 gc/mL of influent. These values were chosen because SARS-CoV-2 and influenza A concentrations observed in wastewater ranged from high to low values, while approximately ∼50 gc/mL of RSV represented one of the highest values observed using the singleplex assay. Therefore, using that value would represent a best-case scenario for RSV to maximize the likelihood of successfully detecting RSV in a triplex configuration. Should that have been successful, the amount of RSV would be lowered similarly to SARS-CoV-2 and influenza A to represent the ranges observed in wastewater.
RESULTS AND DISCUSSION
Multiplex assay optimization
Optimizing multiplex RT-qPCR assays comes with inherent challenges regardless of sample type, such as oligo cross-reactivity, cross-talk, and managing different annealing/melting temperatures. These challenges remain when optimizing assays for use on wastewater samples; however, the lower quantities detected in wastewater exacerbate these challenges.
Triplex vs. singleplex standard curves for targets used in the respiratory Triplex, WHO InfA configuration.
Triplex vs. singleplex standard curves for targets used in the respiratory Triplex, WHO InfA configuration.
Triplex vs. singleplex standard curves for targets used in the respiratory Triplex, CDC InfA configuration.
Triplex vs. singleplex standard curves for targets used in the respiratory Triplex, CDC InfA configuration.
Triplex vs. singleplex standard curves for targets used in the Enteric triplex, D68 configuration.
Triplex vs. singleplex standard curves for targets used in the Enteric triplex, D68 configuration.
Triplex vs. singleplex standard curves for targets used in the Enteric triplex, CA6 configuration.
Triplex vs. singleplex standard curves for targets used in the Enteric triplex, CA6 configuration.
RSV primers/probes in both the WHO and CDC configurations of the respiratory triplex performed worse in the triplex compared to the singleplex. This is also highlighted by the reduction in efficiency observed for RSV in both triplex configurations compared to singleplex (see Figures 2 and 3). A significant difference in overall performance (p-value < 0.05) between RSV in singleplex and triplex was observed (see Table 3). Still, the performance of the RSV assay was within the acceptable MIQE guidelines (Bustin et al. 2009). Additionally, the respiratory (CDC) triplex performed worse overall compared to the respiratory (WHO) triplex, where a larger drop in efficiency was seen in CDC InfA (triplex vs. singleplex) compared to WHO InfA (triplex vs. singleplex) (see Figures 2 and 3). Table 3 also shows that 2/3 of targets for the respiratory (CDC) triplex were significantly different in overall performance (p-value < 0.05) than their singleplex counterparts.
Overall performance of the assays was determined using a two-tailed paired t-test comparing singleplex vs. triplex average Cq values obtained at each dilution point on the standard for each assay
Assay . | Target . | Overall performance (p-value) . | Singleplex LLOQ (gc/rxn) . | Triplex LLOQ (gc/rxn) . | Relative sensitivity (p-value) . |
---|---|---|---|---|---|
Enteric (D68) | D68 | 0.36 | 13.8 | 14.9 | 0.88 |
Norovirus GII | 0.45 | 11.0 | 13.3 | ||
Enterovirus | 0.07 | 14.8 | 12.2 | ||
Enteric (CA6) | CA6 | 0.35 | 6.7 | 5.0 | 0.92 |
Norovirus GII | 0.23 | 17.9 | 12.5 | ||
Enterovirus | 0.20 | 3.7 | 9.7 | ||
Respiratory (WHO) | WHO InfA | 0.08 | 11.3 | 12.3 | 0.46 |
RSV | 0.002 | 9.2 | 7.3 | ||
SARS-CoV-2 | 0.70 | 6.5 | 4.8 | ||
Respiratory (CDC) | CDC InfA | 0.28 | 23.6 | 29.9 | 0.73 |
RSV | 3.6E-04 | 16.4 | 15.3 | ||
SARS-CoV-2 | 0.02 | 6.9 | 5.0 |
Assay . | Target . | Overall performance (p-value) . | Singleplex LLOQ (gc/rxn) . | Triplex LLOQ (gc/rxn) . | Relative sensitivity (p-value) . |
---|---|---|---|---|---|
Enteric (D68) | D68 | 0.36 | 13.8 | 14.9 | 0.88 |
Norovirus GII | 0.45 | 11.0 | 13.3 | ||
Enterovirus | 0.07 | 14.8 | 12.2 | ||
Enteric (CA6) | CA6 | 0.35 | 6.7 | 5.0 | 0.92 |
Norovirus GII | 0.23 | 17.9 | 12.5 | ||
Enterovirus | 0.20 | 3.7 | 9.7 | ||
Respiratory (WHO) | WHO InfA | 0.08 | 11.3 | 12.3 | 0.46 |
RSV | 0.002 | 9.2 | 7.3 | ||
SARS-CoV-2 | 0.70 | 6.5 | 4.8 | ||
Respiratory (CDC) | CDC InfA | 0.28 | 23.6 | 29.9 | 0.73 |
RSV | 3.6E-04 | 16.4 | 15.3 | ||
SARS-CoV-2 | 0.02 | 6.9 | 5.0 |
Note. Relative sensitivity was also evaluated by first calculating the LLOQ for singleplex and triplex using Equation (1) and performing a two-tailed paired t-test comparing LLOQs obtained in singleplex and triplex. Overall performance and relative sensitivity should not be significantly different (p-value > 0.05). Significance level (α = 0.05).
Oligonucleotide cross-reactivity
Oligonucleotide cross-reactivity is exacerbated when the concentration of the complementary sequence is low, as the chances of unfavourable interactions increase (Markoulatos et al. 2002). Thus, it is assumed that relatively small changes in ΔG (cross-reactivity) between two oligos could have an oversized effect on PCR efficiencies for multiplex assays operating at low concentrations such as those observed in wastewater.
The efficiencies of the WHO/CDC InfA and RSV were worse in the triplex compared to the singleplex assay (see Figures 2 and 3). More specifically, the WHO InfA primer-probe set in the triplex assay had a 5.75% loss in efficiency and a 1% loss in RSV for the respiratory (WHO) triplex. The respiratory (CDC) triplex had a worse performance; the CDC InfA primer-probe set had a 16% loss in efficiency and a 3% loss in RSV. This result is expected when evaluating the ΔG values between the CDC/WHO InfA probes and the RSV probes, where the WHO InfA and RSV probes exhibit potential for cross-reactivity with three dimers possessing ΔG values less than −5 kcal/mol, and the most negative ΔG is −6.62 kcal/mol. However, the dimer associated with the lowest ΔG occurs because of an ambiguous nucleotide (W = A or T, 1:1) within the probe sequence, meaning only 50% of the probes can form this dimer. Conversely, the CDC InfA and RSV probes exhibit a slightly higher potential for cross-reactivity, having four ΔG values less than −5 kcal/mol and the most negative ΔG being −6.99 kcal/mol – and like the WHO InfA probe, this dimer forms due to an ambiguous nucleotide. While a difference of 0.37 kcal/mol may seem negligible, there was a sizable apparent impact in this case.
The original objective was for the enteric assay to be a quadruplex; however, interactions between the CA6 and D68 probes prevented that from being feasible. The CA6 and D68 probe sequences exhibited a potential for dimers to form, possessing two ΔG values less than −5 kcal/mol with the most negative ΔG being −6.46 kcal/mol. Attempts to multiplex these targets together were unsuccessful.
Since the singleplex assays performed reliably and the problematic targets (CA6 and D68) had minimal overlap in their seasonality, it was decided to use two triplex configurations instead of redesigning the probes to make a quadruplex possible. Redesigning probes would be an option, but to ensure sufficient specificity to the target viruses, multiple reference standards would be needed for the target virus and other closely related species.
Overall, ambiguous nucleotides add flexibility for detecting genetic variations but also increase the risk of unintended secondary structures forming between probes and primers. These structures are often stable (low ΔG values) and can interfere with PCR efficiency and accuracy, especially in multiplex assays.
Effect of varying target abundances on cross-talk at high quantification cycles
Amplification curves obtained using CFX Maestro (Bio-Rad Laboratories, CA, USA) represent a mixed mock control to assess fluorophore performance for RSV target in the respiratory (WHO) triplex assay. (a) Orange curves are from WHO InfA (TxRED), and red curves are from RSV (Cy5). (b) Orange curves are from WHO InfA (TxRED), and dark red curves are the background fluorescence of an empty cy5 channel. (c) Blue curves are SARS-CoV-2 (N2, FAM), and green curves are RSV (HEX). (d) Blue curves are SARS-CoV-2 (N2, FAM) and dark green curves are the background fluorescence of an empty HEX channel.
Amplification curves obtained using CFX Maestro (Bio-Rad Laboratories, CA, USA) represent a mixed mock control to assess fluorophore performance for RSV target in the respiratory (WHO) triplex assay. (a) Orange curves are from WHO InfA (TxRED), and red curves are from RSV (Cy5). (b) Orange curves are from WHO InfA (TxRED), and dark red curves are the background fluorescence of an empty cy5 channel. (c) Blue curves are SARS-CoV-2 (N2, FAM), and green curves are RSV (HEX). (d) Blue curves are SARS-CoV-2 (N2, FAM) and dark green curves are the background fluorescence of an empty HEX channel.
Log10-transformed abundances of each target virus from each of the triplex assays. Concentrations are from a pumping station in Ajax, Ontario, in 2022. Enteric abundances (left panel) for Norovirus GII/Enterovirus in both configurations represent the average concentration obtained using both configurations. CA6 and D68 values were obtained using their respective triplexes. Respiratory abundances (right panel) were obtained using singleplex reactions. Data were log10-transformed to ensure similar scaling of the plots.
Log10-transformed abundances of each target virus from each of the triplex assays. Concentrations are from a pumping station in Ajax, Ontario, in 2022. Enteric abundances (left panel) for Norovirus GII/Enterovirus in both configurations represent the average concentration obtained using both configurations. CA6 and D68 values were obtained using their respective triplexes. Respiratory abundances (right panel) were obtained using singleplex reactions. Data were log10-transformed to ensure similar scaling of the plots.
Fluorophore-specific susceptibility
Figure 6 displays amplification curves generated from mixed mock controls. Created using standard materials to replicate the observed values for SARS-CoV-2, influenza A, and RSV to validate the impact of cross-talk on quantification using known amounts. It was observed that when no fluorophore was added to the reactions, the Cy5 and HEX channels still exhibited similar amplification curves as when loaded. Namely, a sudden increase in background fluorescence around Cq 30, followed by a sudden decrease for Cy5 and a subtle increase around Cq 40 for HEX. Artefacts of this kind can occur as the software attempts to filter out fluorescence from neighbouring channels with overlapping emission ranges, diminishing the ability to detect targets with low abundance (Cts >35), a common occurrence when analyzing wastewater samples. Cy5 performed noticeably worse for RSV compared to when the RSV probe used HEX, even though WHO InfA used TxRED, which had lower amounts of standard material added – compared to when RSV with HEX was neighbouring SARS-CoV-2 with FAM.
After review (Sauer et al. 2011), it became clear that Cy5 and other cyanine dyes are susceptible to poor quantum yields because of various complex photobleaching pathways, including cis-trans isomerization, and loss of energy due to rotational and vibrational degrees of freedom caused by the polymethine chain connecting the two heads of cyanine dyes. In contrast, when Enterovirus was run in the Cy5 channel, the enteric assay performed well. This was likely because Enterovirus is highly abundant, and its neighbouring channel was either unoccupied (D68 configuration) or occupied by a low-abundance target (CA6 configuration).
Observed viral abundances in wastewater
To visualize the orders of magnitude difference in abundance between targets, Figure 7 shows violin plots for the observed values of each target in each assay (apart from CDC InfA). Values for the enteric abundances were obtained using the triplex assay, while the respiratory abundances were obtained in singleplex during routine monitoring. The enteric (D68), enteric (CA6), and respiratory (WHO) triplex targets had a maximum difference in medians of approximately 1-log10, 2-log10, and 1-log10 times more in each assay, respectively (see Figure 7).
Log10-transformed daily WWS data generated using the enteric triplex assays in Ajax, Ontario, during 2022. Lines represent a 7-day midpoint rolling average. Data was log10-transformed to scale the plots.
Log10-transformed daily WWS data generated using the enteric triplex assays in Ajax, Ontario, during 2022. Lines represent a 7-day midpoint rolling average. Data was log10-transformed to scale the plots.
All enteric targets were detectable in municipal wastewater throughout the surveillance period, including the lower-abundance targets of Coxsackievirus A6 (CA6) and Enterovirus D68 (D68). Unfortunately, no local/regional case data was available to corroborate our observations for any enteric target in wastewater; however, Public Health Ontario has produced a provincial report that outlines an outbreak of D68 (from August to November 2022) across Ontario that coincides with the period where it was detected in this study (see Figure 7) (Public Health Ontario 2022). Furthermore, no case data were available for CA6; however, there were media reports of outbreaks of HFMD (in early July 2022) that also coincided with the period when CA6 was detectable in wastewater (see Figure 8) (Pasieka 2022). Focusing on the month of July, a large discrepancy (average difference of ∼1.5-log10) between the CA6 signal and the Universal Enterovirus signal was observed. This could have been caused by differing assay efficiencies, different prevalences of VP1 vs. 5′-UTR, and the presence of other Enterovirus species. The presence of other Enterovirus species (likely other Coxsackievirus) seems to be the most likely cause. To confirm this hypothesis, optimization of an RT-PCR assay to amplify the VP1 regions of Enterovirus to serotype using nanopore sequencing is underway.
Figure 8 highlights that a multitude of different viruses of various aetiologies and abundances can be monitored through WWS, showing the potential to help inform public health decisions from public health units, but can also provide actionable information to those in the public who may be vulnerable to protect themselves. This work provides a solid foundation for detection methodologies that can be applied elsewhere, where clinical data is available to elucidate the clinical significance of WWS data.
CONCLUSION
Optimizing methods for wastewater-based surveillance improves the capacity of local public health departments to track emerging infectious diseases. Managing disease outbreaks at their early stages can result in tangible public health benefits such as keeping children in school and safeguarding the productivity of the current workforce. Consequently, WWS provides an opportunity for public health to be more holistic in their approach, surveilling not just the pathogens with pandemic potential but also those that reduce the quality of life and diminish the health of the community. Doing so could improve our understanding of human health to better facilitate interconnections between human, animal, and environmental health.
While optimization and development of multiplex RT-qPCR assays that detect pathogens of varying abundances in wastewater are advantageous, fluorophore selection must be considered in addition to the standard precautions when developing RT-qPCR assays. Multiplex assays for WWS should reserve the Cy5 (and other near-Infrared (IR) fluorophores) for highly abundant targets such as common gastrointestinal viruses or normalizing factors such as PMMoV. Furthermore, it is optimal to use an ordinal approach to select the remaining fluorophores – for example, Channel 1 (FAM) by the lowest abundance, Channel 2 (HEX) by the second highest abundance, and Channel 3 (TxRED) by the second lowest abundance.
While wastewater presents new challenges for multiplexed assays, the success of our enteric triplex assays demonstrates that it is possible. Additionally, these enteric triplex assays demonstrate the ability to detect these viruses, laying the foundation to potentially track changes in the prevalence of disease in the community – although future work will be focused on demonstrating this with statistical rigour.
DATA AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.
CONFLICT OF INTEREST
The authors declare there is no conflict.