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SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.

ABSTRACT: The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be "severe" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the "mild" category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.

SUBMITTER: Skarzynski M

PROVIDER: S-EPMC9173902 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.

Skarzynski Martin M McAuley Erin M EM Maier Ezekiel J EJ Fries Anthony C AC Voss Jameson D JD Chapleau Richard R RR

Global health, epidemiology and genomics 20220531

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algor ...[more]

PMID: 35707747

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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.

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.

Project description:Deleterious mutations occur frequently in eukaryotes, resulting in individuals carrying multiple alleles that decrease their fitness. At a population level, if unchecked, accumulation of this mutation load can ultimately lead to extinction. How selection counters the accumulation of mutation load, limiting declines in population fitness, is not well understood. Here, we use manipulative experiments in zebrafish (Danio rerio) to investigate the opportunities for selection on mutation load. Inducing high mutation load through mutagenesis, we applied one generation of within-family selection on locomotor performance and characterized both the direct response to this selection and the indirect response of reproductive success. Offspring of slow swimming parents exhibited age-dependent declines in swimming speed, whereas their cousins, with faster swimming parents, did not. This pattern mimics previously documented differences between high and low mutation load populations of zebrafish, suggesting that slow swimming siblings inherited (and transmitted) more mutations than their faster swimming siblings. Crosses among offspring of slow swimming fish had, on average, <75% of the reproductive success of crosses among offspring of fast swimming parents, or crosses of offspring of slow swimmers with offspring of fast swimmers. This evidence of mutationally correlated swimming speed and reproductive success reveals the potential for concordant selection on mutation load through different fitness components. There was no evidence that crosses within families (where parents potentially shared the same mutations inherited from their common ancestor) had lower reproductive success than crosses among families, suggesting that viability selection was not acting predominantly through lethal recessive homozygotes. Rather, patterns of reproductive success are suggestive of effects of mutation number per se on embryo viability. Overall, our results highlight the potential for early life mortality to remove deleterious mutations, and the need to account for this mortality when investigating the evolutionary dynamics of mutation load.

Dataset Information

SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.

Publications

SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.

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