A hierarchical genotyping framework using DNA melting temperatures applied to adenovirus species typing

Abstract

Known genetic variation for a pathogen, in conjunction with post-PCR melting curve analysis, can be leveraged to provide increased taxonomic detail for pathogen identification in commercial molecular diagnostic tests. The increased taxonomic detail may be used by clinicians and public health decision makers to observe circulation patterns, monitor for outbreaks, and inform local testing practices. We propose a method for expanding the taxonomic resolution of PCR diagnostic systems by incorporating a priori knowledge of the assay design and publicly available sequence information into a genotyping classification model. For multiplexed PCR systems, this framework is generalized to incorporate information from multiple assays that react with different gene targets of the same pathogen to increase classification accuracy. To illustrate the method, a hierarchical classification model is developed for the BioFire(R) Respiratory 2.1 Panel and the BioFire(R) Respiratory 2 Panel (collectively the BioFire Respiratory Panels - highly multiplexed PCR diagnostic tests) to predict the species of human adenovirus (HAdV) from Adenovirus Detected test results. Performance of the classification model was characterized via a 10-fold cross-validation on a labeled dataset and exhibited 95% {+/-} 4% accuracy. The model was then applied to the BioFire(R) Syndromic Trends dataset, which contains deidentified patient test data from BioFire Respiratory Panels collected at over 100 sites across the globe since 2015. Adenovirus Detected test results in BioFire Syndromic Trends were classified to produce predicted prevalence of each HAdV species within the United States from 2018 through 2021. These results show a marked change in both the predicted prevalence for HAdV and the species makeup with the onset of the COVID-19 pandemic. In particular, HAdV-B decreased from a pre-pandemic predicted prevalence of up to 40% to less than 5% in 2021, while HAdV-A and HAdV-F species both increased in predicted prevalence. Author SummaryIn the diagnosis of infectious disease there is often a trade-off between time to result, cost, and the amount of information gained about the pathogen. We develop a method that can provide increased genotypic information about a pathogen in a test result, with no additional time or cost. Our method is applied to the BioFire Respiratory(R) 2.1 Panel and the BioFire Respiratory(R) 2 Panel to speciate positive test results for adenovirus. The method shows robust performance in correctly identifying the adenovirus species from the test results. We estimate the prevalence of each adenovirus species in the United States by applying this method to adenovirus positive test results in the BioFire Syndromic Trends network. This information can be used in near real-time to augment the Centers for Disease Control and Prevention efforts to monitor adenovirus species circulation. Our mathematical framework can be generalized to other genotyping applications.