Date of Award

January 2023

Document Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Anne L. Wyllie

Second Advisor

Gregg Gonsalves

Abstract

Current recommendations for the diagnosis of mpox (monkeypox) rely on lesion-swabs as the gold-standard specimen type even though many patients experience symptoms prior to lesion-onset. Earlier detection could bolster the mpox response by mitigating transmission and facilitating access to antiviral treatments. In this study, we validated five PCR assays and compared their detection of mpox virus DNA extracted from 30 saliva specimens collected into Spectrum SDNA-1000 tubes. We found the Logix Smart™ Mpox (2-Gene) RUO assay (Co-Diagnostics, Inc., Salt Lake City, UT) performed statistically significantly better than the other assays. Next, we tested five raw (unsupplemented) known-positive saliva samples in the three workflows (heat and/or proteinase K pre-treatment) of our nucleic acid extraction-free protocol (“SalivaDirect”). Preliminary results from using the SalivaDirect workflows suggest the potential for extraction-free protocols for saliva-based detection of mpox. Having demonstrated the stability of SARS-CoV-2 viral RNA in raw saliva, we tested the stability of mpox virus DNA in the raw, known-positive samples. Importantly, we observed stability for 24-48 hours and through simulated shipping conditions, with little impact of temperature on the change in cycle threshold value. Following this, we successfully sequenced 7 out of 10 saliva specimens and compared the sequences to the primers and probes of the RT-PCR assays. We found that there were nucleotide substitutions in the forward and reverse primer binding regions and a single nucleotide insertion in the probe binding region of the CDC’s General Mpox Assay. Though a limited sample size, findings of this pilot investigation support saliva as a promising specimen for the detection of mpox virus and highlight the need for ongoing diagnostics development, optimization, and evaluation.

Comments

This thesis is restricted to Yale network users only. It will be made publicly available on 05/10/2025

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