[Analysis of Nucleotide Changes in RT-PCR Primer/Probe Binding Regions in SARS-CoV-2 Isolates Reported from Turkey]. / Türkiye'den Bildirilen Sars-CoV-2 Izolatlarinda RT-PCR Primer/Prob Baglanma Bölgelerindeki Nükleotit Degisimlerinin Analizi.

The SARS-CoV-2 virus, which caused the COVID-19 epidemic, caused more than 55 million cases and nearly 1.5 million deaths worldwide. For the microbiological diagnosis of the disease, the most valid method is detecting the presence of the viral genome by real-time reverse transcription polymerase chain reaction (rRT-PCR). However, due to the nature of the RNA viruses, frequent mutations may affect the sensitivity of the analyses made on the genetic material of the virus, such as PCR. In this study, we aimed to investigate the mutations in the primer-probe binding regions of the rRT-PCR panels used in COVID-19 diagnosis. SARS-CoV-2 whole genome sequence data (n= 194) isolated from COVID-19 cases in Turkey and uploaded on GISAID database from the centers in Istanbul (n= 78), Ankara (n= 58), Kars (n= 47), Bursa (n= 2), Adiyaman (n= 2), Erciyes (n= 1) and Kocaeli (n= 1) between March 17-September 14, 2020 were analyzed. In order to determine the nucleotide changes, SARS-CoV-2 sequences from Turkey were compared to the reference genome sequence (NC_045512.1) present in "GenBank" website. The constructed data set was aligned using the MAFFT program and was checked manually if the sequences were in the same frame by using the AliView program. Primer-probe binding sites of the thirteen SARS-CoV-2 rRT-PCR panels from seven different institutes (US CDC, China CDC, Charite CDC, Pasteur, HKU, Thailand, NIID) that are being used in COVID-19 diagnosis were evaluated in terms of nucleotide changes within the corresponding regions compared to the reference genome. Sequence diversities in the viral genomes were determined via positional nucleotide numerical calculator and entropy calculator modules and nucleotide and entropy changes in primer-probe binding regions for each rRT-PCR panel were examined. Among thirteen different primer-probe panels, nucleotide changes in the target regions of the seven primer-probe panels were determined. When viral sequences with nucleotide changes in the primer-probe binding regions were examined, the most common changes were observed in the "China CDC" N-forward primer and "US CDC" N3-forward primer binding regions. It is important that the kits to be used as diagnostic tests are designed specific to the regions with less nucleotide changes. Nucleotide changes may not be critical for DNA amplification for most PCR panels, but should be carefully monitored as they may affect the sensitivity of the assay. If the risk of alteration of the designed region is high, the primer - probe binding sites should be checked frequently and updated when necessary.

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples.

INTRODUCTION: Metagenomic sequencing is increasingly being used in clinical settings for difficult to diagnose cases. The performance of viral metagenomic protocols relies to a large extent on the bioinformatic analysis. In this study, the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS) initiated a benchmark of metagenomic pipelines currently used in clinical virological laboratories. METHODS: Metagenomic datasets from 13 clinical samples from patients with encephalitis or viral respiratory infections characterized by PCR were selected. The datasets were analyzed with 13 different pipelines currently used in virological diagnostic laboratories of participating ENNGS members. The pipelines and classification tools were: Centrifuge, DAMIAN, DIAMOND, DNASTAR, FEVIR, Genome Detective, Jovian, MetaMIC, MetaMix, One Codex, RIEMS, VirMet, and Taxonomer. Performance, characteristics, clinical use, and user-friendliness of these pipelines were analyzed. RESULTS: Overall, viral pathogens with high loads were detected by all the evaluated metagenomic pipelines. In contrast, lower abundance pathogens and mixed infections were only detected by 3/13 pipelines, namely DNASTAR, FEVIR, and MetaMix. Overall sensitivity ranged from 80% (10/13) to 100% (13/13 datasets). Overall positive predictive value ranged from 71-100%. The majority of the pipelines classified sequences based on nucleotide similarity (8/13), only a minority used amino acid similarity, and 6 of the 13 pipelines assembled sequences de novo. No clear differences in performance were detected that correlated with these classification approaches. Read counts of target viruses varied between the pipelines over a range of 2-3 log, indicating differences in limit of detection. CONCLUSION: A wide variety of viral metagenomic pipelines is currently used in the participating clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implicating the need for standardization and validation of metagenomic analysis for clinical diagnostic use. Future studies should address the selective effects due to the choice of different reference viral databases.