Profiling of SARS-CoV-2 Subgenomic RNAs in Clinical Specimens.

SARS-CoV-2 transcribes a set of subgenomic RNAs (sgRNAs) essential for the translation of structural and accessory proteins to sustain its life cycle. We applied RNA-seq on 375 respiratory samples from individual COVID-19 patients and revealed that the majority of the sgRNAs were canonical transcripts with N being the most abundant (36.2%), followed by S (11.6%), open reading frame 7a (ORF7a; 10.3%), M (8.4%), ORF3a (7.9%), ORF8 (6.0%), E (4.6%), ORF6 (2.5%), and ORF7b (0.3%); but ORF10 was not detected. The profile of most sgRNAs, except N, showed an independent association with viral load, time of specimen collection after onset, age of the patient, and S-614D/G variant with ORF7b and then ORF6 being the most sensitive to changes in these characteristics. Monitoring of 124 serial samples from 10 patients using sgRNA-specific real-time RT-PCR revealed a potential of adopting sgRNA as a marker of viral activity. Respiratory samples harboring a full set of canonical sgRNAs were mainly collected early within 1 to 2 weeks from onset, and most of the stool samples (90%) were negative for sgRNAs despite testing positive by diagnostic PCR targeting genomic RNA. ORF7b was the first to become undetectable and again being the most sensitive surrogate marker for a full set of canonical sgRNAs in clinical samples. The potential of using sgRNA to monitor viral activity and progression of SARS-CoV-2 infection, and hence as one of the objective indicators to triage patients for isolation and treatment should be considered. IMPORTANCE Attempts to use subgenomic RNAs (sgRNAs) of SARS-CoV-2 to identify active infection of COVID-19 have produced diverse results. In this work, we applied next-generation sequencing and RT-PCR to profile the full spectrum of SARS-CoV-2 sgRNAs in a large cohort of respiratory and stool samples collected throughout infection. Numerous known and novel discontinuous transcription events potentially encoding full-length, deleted and frameshift proteins were observed. In particular, the expression profile of canonical sgRNAs was associated with genomic RNA level and clinical characteristics. Our study found sgRNAs as potential biomarkers for monitoring infectivity and progression of SARS-CoV-2 infection, which provides an alternative target for the management and treatment of COVID-19 patients.