Deciphering Microbiota of Acute Upper Respiratory Infections: A Comparative Analysis of PCR and mNGS Methods for Lower Respiratory Trafficking Potential.
Sadia AlmasRob E CarpenterAnuradha SinghChase RowanVaibhav Kumar TamrakarRahul SharmaPublished in: Advances in respiratory medicine (2023)
Although it is clinically important for acute respiratory tract (co)infections to have a rapid and accurate diagnosis, it is critical that respiratory medicine understands the advantages of current laboratory methods. In this study, we tested nasopharyngeal samples ( n = 29) with a commercially available PCR assay and compared the results with those of a hybridization-capture-based mNGS workflow. Detection criteria for positive PCR samples was Ct < 35 and for mNGS samples it was >40% target coverage, median depth of 1X and RPKM > 10. A high degree of concordance (98.33% PPA and 100% NPA) was recorded. However, mNGS yielded positively 29 additional microorganisms (23 bacteria, 4 viruses, and 2 fungi) beyond PCR. We then characterized the microorganisms of each method into three phenotypic categories using the IDbyDNA Explify ® Platform (Illumina ® Inc, San Diego, CA, USA) for consideration of infectivity and trafficking potential to the lower respiratory region. The findings are significant for providing a comprehensive yet clinically relevant microbiology profile of acute upper respiratory infection, especially important in immunocompromised or immunocompetent with comorbidity respiratory cases or where traditional syndromic approaches fail to identify pathogenicity. Accordingly, this technology can be used to supplement current syndrome-based tests, and data can quickly and effectively be phenotypically characterized for trafficking potential, clinical (co)infection, and comorbid consideration-with promise to reduce morbidity and mortality.
Keyphrases
- respiratory tract
- liver failure
- respiratory failure
- real time pcr
- drug induced
- high throughput
- computed tomography
- electronic health record
- staphylococcus aureus
- risk assessment
- human health
- escherichia coli
- hepatitis b virus
- magnetic resonance
- deep learning
- machine learning
- pseudomonas aeruginosa
- mass spectrometry
- autism spectrum disorder
- intellectual disability
- climate change
- single molecule
- health insurance
- nucleic acid