A novel RT-LAMP workflow for rapid salivary diagnostics of COVID-19 and effects of age, gender and time from symptom onset

2021 
ObjectivesRapid diagnostics is pivotal to curb SARS-CoV-2 transmission, and saliva has emerged as a practical alternative to naso/oropharyngeal (NOP) specimens. We aimed to develop a direct RT-LAMP workflow for viral detection in saliva, and to provide more information regarding its potential in COVID-19 diagnostics. MethodsClinical and contrived specimens were used to screen/optimize formulations and sample processing protocols. Salivary viral load was determined in symptomatic patients to evaluate clinical performance (n = 90) and to characterize saliva based on age, gender and time from onset of symptoms (n = 49). ResultsThe devised workflow achieved 93.2% sensitivity, 97% specificity, and 0.895 Kappa for salivas containing >102 copies/L. Further analyses in saliva showed peak viral load in the first days of symptoms and lower viral loads in females, particularly among young individuals (<38 years). NOP RT-PCR data did not yield relevant associations. ConclusionsThis novel saliva RT-LAMP workflow can be applied to point-of-care testing. This work reinforces that saliva better correlates with transmission dynamics than NOP specimens, and reveals gender differences that may reflect higher transmission by males. To maximize detection, testing should be done immediately after symptom onset, especially in females. HIGHLIGHTS- Development of DGS, a dithiothreitol/guanidine-based solution for stabilization of the viral genome that increases sensitivity for SARS-CoV-2 detection in saliva; - Rapid, cost-effective RT-LAMP assay workflow for viral detection in saliva without need of RNA extraction; - Insights into the differences in viral load between saliva and naso-oropharyngeal specimens, and correlation with age, gender and time from symptom onset;
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    44
    References
    1
    Citations
    NaN
    KQI
    []