A Hierarchical Genotyping Framework Using DNA Melting Temperatures Applied to Adenovirus Species Typing

Abstract
Known genetic variation, in conjunction with post-PCR melting curve analysis, can be leveraged to provide increased taxonomic detail for pathogen identification in commercial molecular diagnostic tests. Increased taxonomic detail may be used by clinicians and public health decision-makers to observe circulation patterns, monitor for outbreaks, and inform testing practices. We propose a method for expanding the taxonomic resolution of PCR diagnostic systems by incorporating a priori knowledge of assay design and sequence information into a genotyping classification model. For multiplexed PCR systems, this framework is generalized to incorporate information from multiple assays to increase classification accuracy. An illustrative hierarchical classification model for human adenovirus (HAdV) species was developed and demonstrated ~95% cross-validated accuracy on a labeled dataset. The model was then applied to a near-real-time surveillance dataset in which deidentified adenovirus detected patient test data from 2018 through 2021 were classified into one of six adenovirus species. 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. 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.
Keywords: Bayesian; BioFire; DNA melting curve analysis; adenovirus; bioMérieux; epidemiology; genotyping; surveillance.