Development of a reverse transcriptase digital droplet polymerase chain reaction-based approach for SARS-CoV-2 variant surveillance in wastewater.

An urgent need for effective surveillance strategies arose due to the global emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although vaccines and antivirals are available, concerns persist about the evolution of new variants with potentially increased infectivity, transmissibility, and immune evasion. Therefore, variant monitoring is crucial for public health decision-making. Wastewater-based surveillance has proven to be an effective tool to monitor SARS-CoV-2 variants within populations. Specific SARS-CoV-2 variants are detected and quantified in wastewater in this study using a reverse transcriptase digital droplet polymerase chain reaction (RT-ddPCR) approach. The 11 designed assays were first validated in silico using a substantial dataset of high-quality SARS-CoV-2 genomes to ensure comprehensive variant coverage. The assessment of the sensitivity and specificity with reference material showed the capability of the developed assays to reliably identify target mutations while minimizing false positives and false negatives. The applicability of the assays was evaluated using wastewater samples from a wastewater treatment plant in Ghent, Belgium. The quantification of the specific mutations linked to the variants of concern present in these samples was calculated using these assays based on the detection of single mutations, which confirms their use for real-world variant surveillance. In conclusion, this study provides an adaptable protocol to monitor SARS-CoV-2 variants in wastewater with high sensitivity and specificity. Its potential for broader application in other viral surveillance contexts highlights its added value for rapid response to emerging infectious diseases. PRACTITIONER POINTS: Robust RT-ddPCR methodology for specific SARS-CoV-2 variants of concern detection in wastewater. Rigorous validation that demonstrates high sensitivity and specificity. Demonstration of real-world applicability using wastewater samples. Valuable tool for rapid response to emerging infectious diseases.

Development of a Droplet Digital PCR to Monitor SARS-CoV-2 Omicron Variant BA.2 in Wastewater Samples.

Wastewater-based surveillance can be used as a complementary method to other SARS-CoV-2 surveillance systems. It allows the emergence and spread of infections and SARS-CoV-2 variants to be monitored in time and place. This study presents an RT-ddPCR method that targets the T19I amino acid mutation in the spike protein of the SARS-CoV-2 genomes, which is specific to the BA.2 variant (omicron). The T19I assay was evaluated both in silico and in vitro for its inclusivity, sensitivity, and specificity. Moreover, wastewater samples were used as a proof of concept to monitor and quantify the emergence of the BA.2 variant from January until May 2022 in the Brussels-Capital Region which covers a population of more than 1.2 million inhabitants. The in silico analysis showed that more than 99% of the BA.2 genomes could be characterized using the T19I assay. Subsequently, the sensitivity and specificity of the T19I assay were successfully experimentally evaluated. Thanks to our specific method design, the positive signal from the mutant probe and wild-type probe of the T19I assay was measured and the proportion of genomes with the T19I mutation, characteristic of the BA.2 mutant, compared to the entire SARS-CoV-2 population was calculated. The applicability of the proposed RT-ddPCR method was evaluated to monitor and quantify the emergence of the BA.2 variant over time. To validate this assay as a proof of concept, the measurement of the proportion of a specific circulating variant with genomes containing the T19I mutation in comparison to the total viral population was carried out in wastewater samples from wastewater treatment plants in the Brussels-Capital Region in the winter and spring of 2022. This emergence and proportional increase in BA.2 genomes correspond to what was observed in the surveillance using respiratory samples; however, the emergence was observed slightly earlier, which suggests that wastewater sampling could be an early warning system and could be an interesting alternative to extensive human testing.

Development of Digital Droplet PCR Targeting the Influenza H3N2 Oseltamivir-Resistant E119V Mutation and Its Performance through the Use of Reverse Genetics Mutants.

The monitoring of antiviral-resistant influenza virus strains is important for public health given the availability and use of neuraminidase inhibitors and other antivirals to treat infected patients. Naturally occurring oseltamivir-resistant seasonal H3N2 influenza virus strains often carry a glutamate-to-valine substitution at position 119 in the neuraminidase (E119V-NA). Early detection of resistant influenza viruses is important for patient management and for the rapid containment of antiviral resistance. The neuraminidase inhibition assay allows the phenotypical identification of resistant strains; however, this test often has limited sensitivity with high variability depending on the virus strain, drugs and assays. Once a mutation such as E119V-NA is known, highly sensitive PCR-based genotypic assays can be used to identify the prevalence of such mutant influenza viruses in clinical samples. In this study, based on an existing reverse transcriptase real-time PCR (RT-qPCR) assay, we developed a reverse transcriptase droplet digital PCR assay (RT-ddPCR) to detect and quantify the frequency of the E119V-NA mutation. Furthermore, reverse genetics viruses carrying this mutation were created to test the performance of the RT-ddPCR assay and compare it to the standard phenotypic NA assay. We also discuss the advantage of using an RT-ddPCR instead of qPCR method in the context of viral diagnostics and surveillance.

A general approach to identify low-frequency variants within influenza samples collected during routine surveillance.

Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 104 genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016-2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.

Optimization and Application of a Multiplex Digital PCR Assay for the Detection of SARS-CoV-2 Variants of Concern in Belgian Influent Wastewater.

Since the beginning of the COVID-19 pandemic, the wastewater-based epidemiology (WBE) of SARS-CoV-2 has been used as a complementary indicator to follow up on the trends in the COVID-19 spread in Belgium and in many other countries. To further develop the use of WBE, a multiplex digital polymerase chain reaction (dPCR) assay was optimized, validated and applied for the measurement of emerging SARS-CoV-2 variants of concern (VOC) in influent wastewater (IWW) samples. Key mutations were targeted in the different VOC strains, including SΔ69/70 deletion, N501Y, SΔ241 and SΔ157. The presented bioanalytical method was able to distinguish between SARS-CoV-2 RNA originating from the wild-type and B.1.1.7, B.1.351 and B.1.617.2 variants. The dPCR assay proved to be sensitive enough to detect low concentrations of SARS-CoV-2 RNA in IWW since the limit of detection of the different targets ranged between 0.3 and 2.9 copies/µL. This developed WBE approach was applied to IWW samples originating from different Belgian locations and was able to monitor spatio-temporal changes in the presence of targeted VOC strains in the investigated communities. The present dPCR assay developments were realized to bring added-value to the current national WBE of COVID-19 by also having the spatio-temporal proportions of the VoC in presence in the wastewaters.

Strategy to Develop and Evaluate a Multiplex RT-ddPCR in Response to SARS-CoV-2 Genomic Evolution.

The worldwide emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since 2019 has highlighted the importance of rapid and reliable diagnostic testing to prevent and control the viral transmission. However, inaccurate results may occur due to false negatives (FN) caused by polymorphisms or point mutations related to the virus evolution and compromise the accuracy of the diagnostic tests. Therefore, PCR-based SARS-CoV-2 diagnostics should be evaluated and evolve together with the rapidly increasing number of new variants appearing around the world. However, even by using a large collection of samples, laboratories are not able to test a representative collection of samples that deals with the same level of diversity that is continuously evolving worldwide. In the present study, we proposed a methodology based on an in silico and in vitro analysis. First, we used all information offered by available whole-genome sequencing data for SARS-CoV-2 for the selection of the two PCR assays targeting two different regions in the genome, and to monitor the possible impact of virus evolution on the specificity of the primers and probes of the PCR assays during and after the development of the assays. Besides this first essential in silico evaluation, a minimal set of testing was proposed to generate experimental evidence on the method performance, such as specificity, sensitivity and applicability. Therefore, a duplex reverse-transcription droplet digital PCR (RT-ddPCR) method was evaluated in silico by using 154 489 whole-genome sequences of SARS-CoV-2 strains that were representative for the circulating strains around the world. The RT-ddPCR platform was selected as it presented several advantages to detect and quantify SARS-CoV-2 RNA in clinical samples and wastewater. Next, the assays were successfully experimentally evaluated for their sensitivity and specificity. A preliminary evaluation of the applicability of the developed method was performed using both clinical and wastewater samples.

Strategy and Performance Evaluation of Low-Frequency Variant Calling for SARS-CoV-2 Using Targeted Deep Illumina Sequencing.

The ongoing COVID-19 pandemic, caused by SARS-CoV-2, constitutes a tremendous global health issue. Continuous monitoring of the virus has become a cornerstone to make rational decisions on implementing societal and sanitary measures to curtail the virus spread. Additionally, emerging SARS-CoV-2 variants have increased the need for genomic surveillance to detect particular strains because of their potentially increased transmissibility, pathogenicity and immune escape. Targeted SARS-CoV-2 sequencing of diagnostic and wastewater samples has been explored as an epidemiological surveillance method for the competent authorities. Currently, only the consensus genome sequence of the most abundant strain is taken into consideration for analysis, but multiple variant strains are now circulating in the population. Consequently, in diagnostic samples, potential co-infection(s) by several different variants can occur or quasispecies can develop during an infection in an individual. In wastewater samples, multiple variant strains will often be simultaneously present. Currently, quality criteria are mainly available for constructing the consensus genome sequence, and some guidelines exist for the detection of co-infections and quasispecies in diagnostic samples. The performance of detection and quantification of low-frequency variants using whole genome sequencing (WGS) of SARS-CoV-2 remains largely unknown. Here, we evaluated the detection and quantification of mutations present at low abundances using the mutations defining the SARS-CoV-2 lineage B.1.1.7 (alpha variant) as a case study. Real sequencing data were in silico modified by introducing mutations of interest into raw wild-type sequencing data, or by mixing wild-type and mutant raw sequencing data, to construct mixed samples subjected to WGS using a tiling amplicon-based targeted metagenomics approach and Illumina sequencing. As anticipated, higher variation and lower sensitivity were observed at lower coverages and allelic frequencies. We found that detection of all low-frequency variants at an abundance of 10, 5, 3, and 1%, requires at least a sequencing coverage of 250, 500, 1500, and 10,000×, respectively. Although increasing variability of estimated allelic frequencies at decreasing coverages and lower allelic frequencies was observed, its impact on reliable quantification was limited. This study provides a highly sensitive low-frequency variant detection approach, which is publicly available at https://galaxy.sciensano.be, and specific recommendations for minimum sequencing coverages to detect clade-defining mutations at certain allelic frequencies. This approach will be useful to detect and quantify low-frequency variants in both diagnostic (e.g., co-infections and quasispecies) and wastewater [e.g., multiple variants of concern (VOCs)] samples.

Whole-genome-based phylogenomic analysis of the Belgian 2016-2017 influenza A(H3N2) outbreak season allows improved surveillance.

Seasonal influenza epidemics are associated with high mortality and morbidity in the human population. Influenza surveillance is critical for providing information to national influenza programmes and for making vaccine composition predictions. Vaccination prevents viral infections, but rapid influenza evolution results in emerging mutants that differ antigenically from vaccine strains. Current influenza surveillance relies on Sanger sequencing of the haemagglutinin (HA) gene. Its classification according to World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC) guidelines is based on combining certain genotypic amino acid mutations and phylogenetic analysis. Next-generation sequencing technologies enable a shift to whole-genome sequencing (WGS) for influenza surveillance, but this requires laboratory workflow adaptations and advanced bioinformatics workflows. In this study, 253 influenza A(H3N2) positive clinical specimens from the 2016-2017 Belgian season underwent WGS using the Illumina MiSeq system. HA-based classification according to WHO/ECDC guidelines did not allow classification of all samples. A new approach, considering the whole genome, was investigated based on using powerful phylogenomic tools including beast and Nextstrain, which substantially improved phylogenetic classification. Moreover, Bayesian inference via beast facilitated reassortment detection by both manual inspection and computational methods, detecting intra-subtype reassortants at an estimated rate of 15 %. Real-time analysis (i.e. as an outbreak is ongoing) via Nextstrain allowed positioning of the Belgian isolates into the globally circulating context. Finally, integration of patient data with phylogenetic groups and reassortment status allowed detection of several associations that would have been missed when solely considering HA, such as hospitalized patients being more likely to be infected with A(H3N2) reassortants, and the possibility to link several phylogenetic groups to disease severity indicators could be relevant for epidemiological monitoring. Our study demonstrates that WGS offers multiple advantages for influenza monitoring in (inter)national influenza surveillance, and proposes an improved methodology. This allows leveraging all information contained in influenza genomes, and allows for more accurate genetic characterization and reassortment detection.

Evaluation of the added value of viral genomic information for predicting severity of influenza infection.

BACKGROUND: The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management. METHODS: 160 A(H3N2) influenza positive samples from the 2016-2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models. RESULTS: The model including viral mutations in addition to the host characteristics had an improved fit ([Formula: see text]=12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732. CONCLUSIONS: Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management.

Next-Generation Sequencing: An Eye-Opener for the Surveillance of Antiviral Resistance in Influenza.

Next-generation sequencing (NGS) can enable a more effective response to a wide range of communicable disease threats, such as influenza, which is one of the leading causes of human morbidity and mortality worldwide. After vaccination, antivirals are the second line of defense against influenza. The use of currently available antivirals can lead to antiviral resistance mutations in the entire influenza genome. Therefore, the methods to detect these mutations should be developed and implemented. In this Opinion, we assess how NGS could be implemented to detect drug resistance mutations in clinical influenza virus isolates.