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Normalization of SARS-CoV-2 viral load via RT-qPCR provides higher-resolution data for comparison across time and between patients.

ABSTRACT: The 2020 pandemic has transformed the world and elicited thousands of studies to better understand the SARS-CoV-2 virus. Viral load has been a common measure to monitor treatment therapies and associate viral dynamics with patient outcomes; however, methods associated with viral load have varied across studies. These variations have the potential to sacrifice the accuracy of findings as they often do not account for inter-assay variation or variation across samples. In a retrospective study of nasopharyngeal samples, we found a significant amount of variation within the DNA and RNA targets; for example, across time within a single patient, there was an average of a 32-fold change. Further, we explore the impacts of host normalization on 94 clinical samples using the TGen Quantitative SARS-CoV-2 assay, finding that without host normalization samples with the same viral concentration can have up to 100-fold variation in the viral load.

SUBMITTER: Porter WT

PROVIDER: S-EPMC8519666 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Normalization of SARS-CoV-2 viral load via RT-qPCR provides higher-resolution data for comparison across time and between patients.

Porter W Tanner WT Kelley Erin J EJ Bowers Jolene R JR Engelthaler David M DM

Virus research 20211016

The 2020 pandemic has transformed the world and elicited thousands of studies to better understand the SARS-CoV-2 virus. Viral load has been a common measure to monitor treatment therapies and associate viral dynamics with patient outcomes; however, methods associated with viral load have varied across studies. These variations have the potential to sacrifice the accuracy of findings as they often do not account for inter-assay variation or variation across samples. In a retrospective study of n ...[more]

PMID: 34662682

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Project description:ObjectivesTo exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL.MethodsA small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression model was built using a training subset, and its accuracy was assessed in the remaining evaluation subset. The model was then used to convert the stored cycle threshold values of a large dataset of 6208 diagnostic samples into copies/μL of SARS-CoV-2. The calculated viral load was used for a single cohort retrospective study. Finally, the cohort was randomly divided into a training set (n = 3095) and an evaluation set (n = 3113) to establish a logistic regression model for predicting case-fatality and to assess its accuracy.ResultsThe model for converting the Ct values into copies/μL was suitably accurate. The calculated viral load over time in the cohort of Covid-19 positive samples showed very low viral loads during the summer inter-epidemic waves in Italy. The calculated viral load along with gender and age allowed building a predictive model of case-fatality probability which showed high specificity (99.0%) and low sensitivity (21.7%) at the optimal threshold which varied by modifying the threshold (i.e. 75% sensitivity and 83.7% specificity). Alternative models including categorised cVL or raw cycle thresholds obtained by the same diagnostic method also gave the same performance.ConclusionThe modelling of the cycle threshold values using digital PCR had the potential of fostering studies addressing issues regarding Sars-CoV-2; furthermore, it may allow setting up predictive tools capable of early identifying those patients at high risk of case-fatality already at diagnosis, irrespective of the diagnostic RT-qPCR platform in use. Depending upon the epidemiological situation, public health authority policies/aims, the resources available and the thresholds used, adequate sensitivity could be achieved with acceptable low specificity.

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Project description:The COVID-19 pandemic interrupted routine care for individuals living with HIV, putting them at risk of virologic failure and HIV-associated illness. Often this population is at high risk for exposure to SARS-CoV-2 infection, and once infected, for severe disease. Therefore, close monitoring of HIV plasma viral load (VL) and screening for SARS-CoV-2 infection are needed. We developed a non-proprietary method to isolate RNA from plasma, nasal secretions (NS), or both. The extracted RNA is then submitted to RT-qPCR to estimate the VL and classify HIV/SARS-CoV-2 status (i.e., HIV virologic failure or suppressed; SARS-CoV-2 as positive, presumptive positive, negative, or indeterminate). In contrived samples, the in-house RNA extraction workflow achieved a detection limit of 200-copies per mL for HIV RNA in plasma and 100-copies per mL for SARS-CoV-2 RNA in NS. Similar detection limits were observed for HIV and SARS-CoV-2 in pooled plasma/NS contrived samples. When comparing in-house with standard extraction methods, we found high agreement (>0.91) between input and measured RNA copies for HIV LTR in contrived plasma; SARS-CoV-2 N1/N2 in contrived NS; and LTR, N1, and N2 in pooled plasma/NS samples. We further evaluated this workflow on 133 clinical specimens: 40 plasma specimens (30 HIV-positive), 67 NS specimens (31 SARS-CoV-2-positive), and 26 combined plasma/NS specimens (26 HIV-positive with 10 SARS-CoV-2-positive), and compared the results obtained using the in-house RNA extraction to those using a commercial kit (standard extraction method). The in-house extraction and standard extraction of clinical specimens were positively correlated: plasma HIV VL (R2 of 0.81) and NS SARS-CoV-2 VL (R2 of 0.95 and 0.99 for N1 and N2 genes, respectively); and pooled plasma/NS HIV VL (R2 of 0.71) and SARS-CoV-2 VL (R2 of 1 both for N1 and N2 genes). Our low-cost molecular test workflow ($1.85 per pooled sample extraction) for HIV RNA and SARS-CoV-2 RNA could serve as an alternative to current standard assays ($12 per pooled sample extraction) for laboratories in low-resource settings.

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Normalization of SARS-CoV-2 viral load via RT-qPCR provides higher-resolution data for comparison across time and between patients.

Publications

Normalization of SARS-CoV-2 viral load via RT-qPCR provides higher-resolution data for comparison across time and between patients.

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