Variant Prediction by Analyzing RdRp/S Gene Double or Low Amplification Pattern in Allplex SARS-CoV-2 Assay

The spread of delta variants (B.1.671.2) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe global threat. Multiplex real-time PCR is a common method for confirming SARS-CoV-2 infection, however, additional tests, such as whole genomic sequencing, are required to reveal the presence or type of viral mutation. Moreover, applying whole genomic sequencing to all SARS-CoV-2 positive samples is challenging due to time and cost constraints. Here, we report that the double or low amplification curve observed during RNA-dependent RNA polymerase (RdRp) gene/S gene amplification in the Allplex SARS-CoV-2 Assay is related to delta/alpha variants. We analyzed the RdRp/S gene amplification curve using 94 samples confirmed as SARS-CoV-2 infection by the Allplex SARS-CoV-2 Assay from January to August, 2021. These positive samples identified variant types using the Novaplex SARS-CoV-2 Variants I and IV Assays. Overall, 17 samples showing a double curve and 11 samples showing a low amplification pattern were associated with alpha-/delta-type strains with variants in the P681 region. The double or low curve shown in the RdRp gene amplification curve had 100% sensitivity and 100% specificity for diagnosing delta/alpha variants. During the SARS-CoV-2 virus diagnostic RT-PCR test using the Allplex SARS-CoV-2 Assay, we could consider the presence of delta/alpha variants in the samples with double or low amplification curve of the RdRp/S gene channel. This PCR amplification curve abnormality enables rapid and cost-effective variant type prediction during SARS-CoV-2 diagnostic testing in clinical laboratories.

shinyCurves, a shiny web application to analyse multisource qPCR amplification data: a COVID-19 case study

Background Quantitative, reverse transcription PCR (qRT-PCR) is currently the gold-standard for SARS-CoV-2 detection and it is also used for detection of other virus. Manual data analysis of a small number of qRT-PCR plates per day is a relatively simple task, but automated, integrative strategies are needed if a laboratory is dealing with hundreds of plates per day, as is being the case in the COVID-19 pandemic. Results Here we present shinyCurves, an online shiny-based, free software to analyze qRT-PCR amplification data from multi-plate and multi-platform formats. Our shiny application does not require any programming experience and is able to call samples Positive, Negative or Undetermined for viral infection according to a number of user-defined settings, apart from providing a complete set of melting and amplification curve plots for the visual inspection of results. Conclusions shinyCurves is a flexible, integrative and user-friendly software that speeds-up the analysis of massive qRT-PCR data from different sources, with the possibility of automatically producing and evaluating melting and amplification curve plots.

Assessment of a Multiplex LAMP Assay (Eazyplex® CSF Direct M) for Rapid Molecular Diagnosis of Bacterial Meningitis: Accuracy and Pitfalls

Background: Automated molecular panels are attractive tools for improving early meningitis diagnosis. This study assessed the Eazyplex® CSF direct M panel (EP), a multiplex real-time Loop-Mediated Isothermal Amplification assay. Methods: From December 2016 to December 2019, cerebrospinal fluid (CSF) samples were routinely tested with the EP V1.0. CSF parameters and microbiological and clinical data were retrospectively collected. Results: Out of 230 CSF samples, the EP yielded positive, negative, and invalid results for 32 (13.9%) (16 N. meningitidis, nine S. pneumoniae, two S. agalactiae, two E. coli, two H. influenzae, one L. monocytogenes), 182 (79.1%), and 16 (7%) samples, respectively. Among the positive samples, 14 (44%) remained negative in culture (antibiotic therapy before lumbar puncture (n = 11), meningococcal meningitis (n = 3)). High CSF protein concentrations and cellularity were associated with LAMP inhibition, counteracted by centrifugation. The automated software yielded 13 false positive and five false negative results. Amplification curve analysis was necessary and enabled the attainment of positive (PPA) and negative percentage agreement and positive and negative predictive values of 91.4%, 100%, 100%, and 98.3%. Three false negative results remained (two E. coli and one N. meningitidis). E. coli presented the poorest PPA (50%). Conclusion: This work confirms the strong performance of the EP, of particular interest in cases of antibiotic therapy before lumbar puncture.

Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data

Background The analyses of amplification and melting curves have been shown to provide valuable information on the quality of the individual reactions in quantitative PCR (qPCR) experiments and to result in more reliable and reproducible quantitative results. Implementation The main steps in the amplification curve analysis are (1) a unique baseline subtraction, not using the ground phase cycles, (2) PCR efficiency determination from the exponential phase of the individual reactions, (3) setting a common quantification threshold and (4) calculation of the efficiency-corrected target quantity with the common threshold, efficiency per assay and Cq per reaction. The melting curve analysis encompasses smoothing of the observed fluorescence data, normalization to remove product-independent fluorescence loss, peak calling and assessment of the correct peak by comparing its melting temperature with the known melting temperature of the intended amplification product. Results The LinRegPCR web application provides visualization and analysis of a single qPCR run. The user interface displays the analysis results on the amplification curve analysis and melting curve analysis in tables and graphs in which deviant reactions are highlighted. The annotated results in the tables can be exported for calculation of gene-expression ratios, fold-change between experimental conditions and further statistical analysis. Web-based LinRegPCR addresses two types of users, wet-lab scientists analyzing the amplification and melting curves of their own qPCR experiments and bioinformaticians creating pipelines for analysis of series of qPCR experiments by splitting its functionality into a stand-alone back-end RDML (Real-time PCR Data Markup Language) Python library and several companion applications for data visualization, analysis and interactive access. The use of the RDML data standard enables machine independent storage and exchange of qPCR data and the RDML-Tools assist with the import of qPCR data from the files exported by the qPCR instrument. Conclusions The combined implementation of these analyses in the newly developed web-based LinRegPCR (https://www.gear-genomics.com/rdml-tools/) is platform independent and much faster than the original Windows-based versions of the LinRegPCR program. Moreover, web-based LinRegPCR includes a novel statistical outlier detection and the combination of amplification and melting curve analyses allows direct validation of the amplification product and reporting of reactions that amplify artefacts.

PRIMARY SLX TEST USING REAL-TIME PCR BASED ON HIGH RESOLUTION MELTING (HRM) ON MICROFILARIA EXAMINATION

Background: Filariasis patients can be a source of transmission if their blood still contains microfilariae. One of the Polymerase Chain Reaction (PCR) methods used is High Resolution Melting (HRM), using primary specificity testing. Purpose: To test the specificity of SLX primer. The samples used for this test were isolates of Salmonella., Klebsiella, Pseudomonas, negative and positive controls for Brugia malayi and Wuchereria bancrofti. Method: The design in this study is a quasi-experiment by testing the specificity of SLX primer using HRMbased real-time PCR based on the Cycle Threshold (CT) value observed through the amplification curve. Result: The real-time PCR results showed that no CT was released in the bacterial samples, and there was a CT value in the positive control. The results of this study indicate that specific SLX primer can be used in identifying microfilariae. Conclusion: SLX primer have a reasonable specificity because they cannot detect the existence of microorganisms in the samples other than microfilariae.

Use and Misuse of Cq in qPCR Data Analysis and Reporting

In the analysis of quantitative PCR (qPCR) data, the quantification cycle (Cq) indicates the position of the amplification curve with respect to the cycle axis. Because Cq is directly related to the starting concentration of the target, and the difference in Cq values is related to the starting concentration ratio, the only results of qPCR analysis reported are often Cq, ΔCq or ΔΔCq values. However, reporting of Cq values ignores the fact that Cq values may differ between runs and machines, and, therefore, cannot be compared between laboratories. Moreover, Cq values are highly dependent on the PCR efficiency, which differs between assays and may differ between samples. Interpreting reported Cq values, assuming a 100% efficient PCR, may lead to assumed gene expression ratios that are 100-fold off. This review describes how differences in quantification threshold setting, PCR efficiency, starting material, PCR artefacts, pipetting errors and sampling variation are at the origin of differences and variability in Cq values and discusses the limits to the interpretation of observed Cq values. These issues can be avoided by calculating efficiency-corrected starting concentrations per reaction. The reporting of gene expression ratios and fold difference between treatments can then easily be based on these starting concentrations.

Accounting for Epistemic Uncertainty in Site Effects in Probabilistic Seismic Hazard Analysis

ABSTRACT A probabilistic seismic hazard analysis performed for rock conditions and modified for soil conditions using deterministic site amplification factors does not account for uncertainty in site effects, which can be significant. One approach to account for such uncertainty is to compute a weighted average amplification curve using a logic tree that accounts for several possible scenarios with assigned weights corresponding to their relative likelihood or confidence. However, this approach can lead to statistical smoothing of the amplification curve and possibly to decreased computed hazard as epistemic uncertainty increases. This is against the expected trend that higher uncertainty leads to higher computed hazard, thus reducing the incentive for practitioners to characterize soil properties at a site. This study proposes a modified approach in which the epistemic uncertainty is captured in a plot of amplification factors versus period. Using a case history, the proposed method is shown to improve the issue with the weighted average method for at least two oscillator periods and to yield similar results for two other periods in which the highlighted issue is less significant.

PCRedux: A Data Mining and Machine Learning Toolkit for qPCR Experiments

MotivationQuantitative Real-time PCR (qPCR) is a widely used -omics method for the precise quantification of nucleic acids, in which the result is associated with the presence/absence or quantity of a specific nucleic acid sequence. As the amount of qPCR data increases worldwide, the manual assessment of results becomes challenging and difficult to reproduce. To overcome this, some automatable characteristics of amplification curves have been described in the literature, often with an appropriate “rule of thumb”.ResultsWe developed PCRedux to analyze and calculate 90 numerical qPCR amplification curve descriptors (‘‘features”) from large datasets of qPCR amplification curves that are aimed for interpretable machine learning and development of decision support systems. In a case study of a diverse dataset with 3181 positive, negative and ambiguous amplification curves, as assessed by three human raters, we demonstrate a sensitivity >99 % and specificity >97 % in detecting positive and negative amplification. PCRedux is unique as it goes beyond traditional qPCR analysis to capture curvature properties that improve the characterization and classification of amplification curves. The calculation of the features is reproducible and objective, since R is used as a controllable working environment. PCRedux is not a black box, but open source software following on the principle of mathematically interpretable features. These can be combined with user-defined labels for automatic multi-category classification and regression in machine learning.Availabilityhttps://cran.r-project.org/package=PCRedux. Web server: http://shtest.evrogen.net/PCRedux-app/. Documentation: https://PCRuniversum.github.io/PCRedux/.

A straightforward molecular strategy to retrospectively investigate the spread of SARS-CoV-2 VOC202012/01 B.1.1.7 variant

The spread of new SARS-CoV-2 variants represents a serious threat worldwide, thus rapid and cost-effective methods are required for their identification. Since November 2020, the TaqPath COVID-19 assay (Thermo Fisher Scientific) has been used to identify viral strains of the new lineage B.1.1.7, since it fails to detect the S-gene with the ∆69/70 deletion. Here, we proposed S-gene mutations screening with the Allplex SARS-CoV-2 assay (Seegene), another widely used RT-PCR test that targets Sarbecovirus E, SARS-CoV-2 N, and RdRp/S genes. Accordingly, we evaluated the S gene amplification curve pattern compared to those of the other genes. Exploiting an Allplex assay-generated dataset, we screened 663 RT-PCR digital records, including all SARS-CoV-2 respiratory samples tested in our laboratory with the Allplex assay between January 1st and February 25th, 2021. This approach enabled us to detect 64 samples with peculiar non-sigmoidal amplification curves. Sequencing a selected group of 4 RNA viral genomes demonstrated that those curves were associated with B.1.1.7 variant strains. Our results strongly suggest that B.1.1.7 variant spread has begun in this area at least since January and imply the potential of these analytical methods to track and characterize the spread of B.1.1.7 strains in those areas where Allplex SARS-CoV-2 datasets have been previously recorded.