In Cell reports methods
Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.
Zhang Zijun, Sauerwald Natalie, Cappuccio Antonio, Ramos Irene, Nair Venugopalan D, Nudelman German, Zaslavsky Elena, Ge Yongchao, Gaitas Angelo, Ren Hui, Brockman Joel, Geis Jennifer, Ramalingam Naveen, King David, McClain Micah T, Woods Christopher W, Henao Ricardo, Burke Thomas W, Tsalik Ephraim L, Goforth Carl W, Lizewski Rhonda A, Lizewski Stephen E, Weir Dawn L, Letizia Andrew G, Sealfon Stuart C, Troyanskaya Olga G
RNA splicing, SARS-CoV-2, diagnostic biomarker, host response assays, infectious disease, viral infection