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.

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.

Deepening of In Silico Evaluation of SARS-CoV-2 Detection RT-qPCR Assays in the Context of New Variants.

For 1 year now, the world is undergoing a coronavirus disease-2019 (COVID-19) pandemic due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The most widely used method for COVID-19 diagnosis is the detection of viral RNA by RT-qPCR with a specific set of primers and probe. It is important to frequently evaluate the performance of these tests and this can be done first by an in silico approach. Previously, we reported some mismatches between the oligonucleotides of publicly available RT-qPCR assays and SARS-CoV-2 genomes collected from GISAID and NCBI, potentially impacting proper detection of the virus. In the present study, 11 primers and probe sets investigated during the first study were evaluated again with 84,305 new SARS-CoV-2 unique genomes collected between June 2020 and January 2021. The lower inclusivity of the China CDC assay targeting the gene N has continued to decrease with new mismatches detected, whereas the other evaluated assays kept their inclusivity above 99%. Additionally, some mutations specific to new SARS-CoV-2 variants of concern were found to be located in oligonucleotide annealing sites. This might impact the strategy to be considered for future SARS-CoV-2 testing. Given the potential threat of the new variants, it is crucial to assess if they can still be correctly targeted by the primers and probes of the RT-qPCR assays. Our study highlights that considering the evolution of the virus and the emergence of new variants, an in silico (re-)evaluation should be performed on a regular basis. Ideally, this should be done for all the RT-qPCR assays employed for SARS-CoV-2 detection, including also commercial tests, although the primer and probe sequences used in these kits are rarely disclosed, which impedes independent performance evaluation.

Use of Whole Genome Sequencing Data for a First in Silico Specificity Evaluation of the RT-qPCR Assays Used for SARS-CoV-2 Detection.

The current COronaVIrus Disease 2019 (COVID-19) pandemic started in December 2019. COVID-19 cases are confirmed by the detection of SARS-CoV-2 RNA in biological samples by RT-qPCR. However, limited numbers of SARS-CoV-2 genomes were available when the first RT-qPCR methods were developed in January 2020 for initial in silico specificity evaluation and to verify whether the targeted loci are highly conserved. Now that more whole genome data have become available, we used the bioinformatics tool SCREENED and a total of 4755 publicly available SARS-CoV-2 genomes, downloaded at two different time points, to evaluate the specificity of 12 RT-qPCR tests (consisting of a total of 30 primers and probe sets) used for SARS-CoV-2 detection and the impact of the virus' genetic evolution on four of them. The exclusivity of these methods was also assessed using the human reference genome and 2624 closely related other respiratory viral genomes. The specificity of the assays was generally good and stable over time. An exception is the first method developed by the China Center for Disease Control and prevention (CDC), which exhibits three primer mismatches present in 358 SARS-CoV-2 genomes sequenced mainly in Europe from February 2020 onwards. The best results were obtained for the assay of Chan et al. (2020) targeting the gene coding for the spiking protein (S). This demonstrates that our user-friendly strategy can be used for a first in silico specificity evaluation of future RT-qPCR tests, as well as verifying that the former methods are still capable of detecting circulating SARS-CoV-2 variants.