SARS-COV-2 qRT-PCR CT VALUE THRESHOLD DETERMINES THE SUCCESS OF WHOLE GENOME SEQUENCING OF BIOLOGICAL SAMPLES OBTAINED FROM PATIENTS AND CADAVERS

Introduction: Throughout the COVID-19 pandemic, real-time quantitative reverse-transcription PCR (qRT-PCR) and whole genome sequencing (WGS) have emerged as invaluable tools for detecting SARS-CoV-2 in patients and identifying new variants. Further, in academic settings, it is vital for medical students and faculty to handle cadavers that do not harbor SARS-CoV-2. However, not every biological specimen is ideal for WGS, as there are many technical and clinical variables that determine the feasibility of WGS for a specimen. qRT-PCR and WGS are costly endeavors, therefore it is essential to develop techniques that can predict the success of WGS in a specimen. The aim of this study was to evaluate the correlation between qRT-PCR Ct values and the success of WGS in respiratory swabs (ie. nasal [NS], nasopharyngeal [NPS], and oropharyngeal [OPS] swabs) and/or anal swabs (AS) collected from patients and cadavers. Methods: NS, NPS, or OPS collected from patients confirmed as SARS-CoV-2 PCR positive were obtained from several clinical laboratories on Oahu. NS, NPS, OPS, and/or AS obtained from cadavers donated through the JABSOM Willed Body Program were tested to ensure the safety of medical students prior to anatomy labs. Following RNA extraction, the CDC 2019-nCoV qRT-PCR Diagnostic Panel, consisting of two viral targets to the nucleocapsid gene (N1 and N2), was used to detect SARS-CoV-2. For WGS, total RNA was extracted and the entire SARS-CoV-2 genome was amplified using the ARTIC Network V3 primer pools. Purified PCR products were submitted to the Advanced Studies in Genomics, Proteomics and Bioinformatics (ASGPB) facility at UH Manoa and sequenced using the Illumina MiSeq platform. Sequencing reads were mapped to the original Wuhan sequence (MN908947.3) and assembled into whole genomes using the iVar workflow. Assembled sequences were submitted to the GenBank. Results: The average Ct value for all 24 samples amplified using qRT-PCR was 31.40, ranging from 17.15 to 40.49. The average Ct values for sequenced samples submitted and not submitted to GenBank were 24.27 (range 17.15-32.53) and 34.96 (28.10-40.49), respectively. All 52 cadaver samples tested were negative with Ct values >40.00 (40.77-44.23) for N1 and/or N2 genes or had undetermined Ct values. Out of the 24 patient samples processed for WGS, 8 samples (33%) gave high quality WGS. 100% (6/6) samples with Ct values 28.01 gave high quality WGS for GenBank submission. Conclusion: All 52 cadaver samples were clearly negative compared to patient samples. Our qRT-PCR assay had a Ct cutoff of 28.00 as patient samples that exhibited Ct values 28.01. Therefore, Ct values can be used as an accurate and cost-effective parameter for prioritizing samples that can proceed for WGS. Further data analysis using GraphPad Prism is ongoing to identify the threshold Ct value for efficient WGS for our sample population. Acknowledgements: This research was supported by a COBRE grant (P30GM114737) from the Pacific Center for Emerging Infectious Diseases Research, a grant (P20GM103466) from the INBRE, NIGMS, and a grant (T37MD008636) from the NIMHD, NIH. We thank Dr. Jennifer Saito at the ASGPB for her expertise with WGS, and Dr. Eileen Nakano and Dr. Sandra Chang for their assistance with sample procurement. We also thank the Kaiser Permanente Clinical Laboratory, National Kidney Foundation of Hawaii and UH Clinic at Kaka’ako for providing de-identified patient samples, and the Willed Body Program at JABSOM for access to cadaver samples.COBRE grant (P30GM114737) from the Pacific Center for Emerging Infectious Diseases Research, a grant (P20GM103466) from the INBRE, NIGMS, and a grant (T37MD008636) from the NIMHD, NIH

OPTIMIZED WORKFLOW FOR SARS-COV-2 WHOLE GENOME SEQUENCING: THREE-WAY ANALYSIS BETWEEN DNA CONCENTRATION, GEL ELECTROPHORESIS IMAGING AND SUCCESSFUL SUBMISSION OF SEQUENCES TO GENBANK

Introduction: Whole Genome sequencing (WGS) is essential for monitoring mutations and detecting the emergence of SARS-CoV-2 variants as the virus makes its way through the population. However, WGS of every specimen is not feasible due to several clinical and technical variables. Further, WGS is a time consuming and expensive technique, which requires highly trained personnel, therefore it is not practical to conduct WGS on every sample. Laboratories must select the ideal samples for WGS based on time post infection, collection media including swabs, sample quality and quantity, primers, and CT value. Objective: The objective of this study was to correlate variables such as DNA concentration and gel electrophoresis based image analysis, prior to conducting viral cDNA library, to identify ideal samples for successful WGS and submission to GenBank. Methods: Nasal swabs, mid-turbinate swabs, and nasopharyngeal swabs were collected from individuals confirmed to be SARS-CoV-2 RT-PCR positive, from clinical laboratories on Oahu. Total RNA from the samples was extracted and the entire SARS-CoV-2 genome was amplified using the ARTIC Network V3 primer pools and RT-PCR. Gel electrophoresis was conducted on purified PCR products to verify band size and quality followed by image analysis using GelAnalyzer. Further, DNA concentration of PCR products were measured using the Quant-iT PicoGreen dsDNA Assay. PCR products were submitted to the Advanced Studies in Genomics, Proteomics and Bioinformatics (ASGPB) facility at UH Manoa and sequenced using the Illumina MiSeq platform. Sequencing reads were mapped to the original Wuhan sequence (MN908947.3), assembled into whole genomes using the iVar workflow, and submitted to GenBank. To determine ideal samples for successful WGS, three-way correlation analysis was conducted using DNA concentration, an arbitrary unit assigned by the GelAnalyzer and sequences submitted to the GenBank using GraphPad Prism. Results: Of the 149 samples submitted for WGS, 99 (66.4%) samples were successfully sequenced and submitted to the GenBank. DNA concentration (ng/µL) of samples successfully submitted to GenBank was significantly higher (median 27.24, IQR 11.73-61.36, ps = 0.91). Conclusion: These data demonstrate that samples successfully submitted to the GenBank, have a minimum band to ladder ratio of 0.167 and/or a DNA concentration of at least 1.096 ng/µL. Beta testing of 100 samples using the techniques mentioned above is ongoing. These data will assist in determining the ideal samples for which WGS and subsequent GenBank submission will be most successful, to conserve precious reagents, personnel resources, and to cut down on sequencing time. Acknowledgments: This research was supported by a COBRE grant (P30GM114737) from the Pacific Center for Emerging Infectious Diseases Research, a grant (P20GM103466) from the INBRE, NIGMS, and a grant (T37MD008636) from the NIMHD, NIH. We thank Dr. Jennifer Saito at the ASGPB Core, UH Manoa for her expertise with WGS, and Dr. Eileen Nakano and Dr. Sandra Chang for their assistance with sample procurement. We also thank the Tropical Medicine Clinical Laboratory, Kaiser Permanente Clinical Laboratory, and National Kidney Foundation of Hawaii for providing de-identified patient samples.COBRE grant (P30GM114737) from the Pacific Center for Emerging Infectious Diseases Research, a grant (P20GM103466) from the INBRE, NIGMS, and a grant (T37MD008636) from the NIMHD, NIH

SANGER SEQUENCING TO DETERMINE THE ACCURACY OF BIOINFORMATIC SOFTWARE FOR CONFIRMING DROPOUT MUTATIONS IN THE SARS-COV-2 SPIKE GENE OBTAINED USING WHOLE GENOME SEQUENCING

Abstract of a poster to be presented at the 2022 JABSOM Biomedical SymposiumIntroduction: Whole genome sequencing (WGS) is a powerful tool that can be used to track SARS-CoV-2 variants and their spread through a population. New mutations that have led to the emergence of new variants have occurred in genes that encode important viral proteins such as the spike protein, resulting in dropout regions. Bioinformatic analysis can be used to predict these regions of dropout based on reference sequences, however the accuracy of these predictions are questionable. Therefore, the objective of this study is to conduct Sanger sequencing to determine the sequences of the dropout regions by using primers designed to target these regions. Methods: Nasal swabs collected from individuals confirmed to be SARS-CoV-2 PCR positive were obtained from various CLIA approved clinical laboratories across Oahu, Hawaii (UH IRB#21-07-820-21-1A). RNA extraction and RT-PCR using the ARTIC Network V3 primer pools were performed to amplify the whole genome of SARS-CoV-2. The purified PCR products were then processed for WGS at the Advanced Studies in Genomics, Proteomics and Bioinformatics (ASGPB) facility at the University of Hawaii at Manoa. The sequencing reads were mapped to the original Wuhan sequence (MN908947.3) and assembled into whole genomes using iVar workflow. To fill in the ambiguous bases, sequences from the GISAID database from the same lineage and similar collection dates were used as references. Spike gene consensus primers were designed to Sanger sequence the ARTIC primer pool binding sites which frequently contained the dropout. The final step is to compare the Sanger sequences with the predicted sequences based on the consensus reference sequences to determine the accuracy of the prediction. Results: Analysis of whole genome sequence reads showed that the region of the genome that was sequenced using primers 72 and 73 from the ARTIC primer pool frequently contained ambiguous bases, indicating dropouts in the region. ARTIC primers 72 and 73 bind to regions of the SARS-CoV-2 spike protein. Sanger sequencing is ongoing to confirm the dropout sequences. Discussion: WGS is used to track SARS-CoV-2 mutations that are integral to the development of diagnostics, therapeutics, and vaccines. Therefore, the accuracy of the sequences obtained using WGS is critical for patient care. Due to the high mutation rate of SARS-CoV-2, primers used in WGS can quickly become obsolete as the virus mutates and are unable to bind to highly variable regions of the genome, resulting in regions of dropout. Reference sequences can be used to fill in these ambiguous bases, however, this method is fallible. Sanger sequencing provides a reliable method to verify the accuracy of WGS when combined with bioinformatic predictions. Once validated, bioinformatic predictions can ultimately be used to reduce time and cost needed for efficient WGS. Acknowledgements: This research was supported by a COBRE grant (P30GM114737) from the Pacific Center for Emerging Infectious Diseases Research, a grant (P20GM103466) from the INBRE, National Institute of General Medical Sciences, and a grant (T37MD008636) from the National Institute on Minority Health and Health Disparities, NIH. We thank Dr. Jennifer Saito at the ASGPB, UH Manoa for her expertise with WGS, and Dr. Eileen Nakano and Dr. Sandra Chang for their assistance with sample procurement. We also thank the Tropical Medicine Clinical Laboratory, Kaiser Permanente Clinical Laboratory, and National Kidney Foundation of Hawaii for providing de-identified patient samples.COBRE grant (P30GM114737) from the Pacific Center for Emerging Infectious Diseases Research, a grant (P20GM103466) from the INBRE, National Institute of General Medical Sciences, and a grant (T37MD008636) from the National Institute on Minority Health and Health Disparities, NI