CRISPR-mediated detection of Pneumocystis transcripts in bronchoalveolar, oropharyngeal, and serum specimens for Pneumocystis pneumonia diagnosis

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Abstract

BACKGROUND.Pneumocystis jirovecii pneumonia (PCP) is a leading cause of fungal pneumonia, but its diagnosis primarily relies on invasive bronchoalveolar lavage (BAL) specimens that are difficult to obtain. Oropharyngeal swabs and serum could improve the PCP diagnostic workflow, and we hypothesized that CRISPR could enhance assay sensitivity to allow robust P. jirovecii diagnosis using swabs and serum. Herein we describe the development of an ultrasensitive RT-PCR-coupled CRISPR assay with high active-infection specificity in infant swabs and adult BAL and serum. METHODS. Mouse analyses employed an RT-PCR CRISPR assay to analyze P. murina transcripts in wild-type and Rag2–/– mouse lung RNA, BAL, and serum at 2-, 4-, and 6-weeks post-infection. Human studies used an optimized RT-PCR CRISPR assay to detect P. jirovecii transcripts in infant oropharyngeal swab samples, adult serum, and adult BAL specimens from P. jirovecii-infected and P. jirovecii-non-infected patients. RESULTS. The P. murina assays sensitively detected Pneumocystis RNA in the serum of infected mice throughout infection. Oropharyngeal swab CRISPR assay results identified infants infected with P. jirovecii with greater sensitivity (96.3% vs. 66.7%) and specificity (100% vs. 90.6%) than RT-qPCR compared to mtLSU standard marker, and CRISPR results achieved higher sensitivity than RT-qPCR results (93.3% vs. 26.7%) in adult serum specimens. CONCLUSION. Since swabs are routinely collected in pediatric pneumonia patients and serum is easier to obtain than BAL, this assay approach could improve the accuracy and timing of pediatric and adult Pneumocystis diagnosis by achieving specificity for active infection and potentially avoiding the requirement for BAL specimens.

Authors

Brady M. Youngquist, Ayanda Trevor Mnguni, Dora Pungan, Rachel P.J. Lai, Guixiang Dai, Chun Fai Ng, Amy Samson, Yasmean Abdelgaliel, Christopher J. Lyon, Bo Ning, Shahid Husain, Sean Wasserman, Jay K. Kolls, Tony Y. Hu