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Background It is estimated that up to 80% of infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are asymptomatic and asymptomatic patients can still effectively transmit the virus and cause disease. While much of the effort has been placed on decoding single nucleotide variation in SARS-CoV-2 genomes, considerably less is known about their transcript variation and any correlation with clinical severity in human hosts, as defined here by the presence or absence of symptoms. Methods To assess viral genomic signatures of disease severity, we conducted a systematic characterization of SARS-CoV-2 transcripts and genetic variants in 81 clinical specimens collected from symptomatic and asymptomatic individuals using multi-scale transcriptomic analyses including amplicon-seq, short-read metatranscriptome and long-read Iso-seq. Results Here we show a highly coordinated and consistent pattern of sgRNA expression from individuals with robust SARS-CoV-2 symptomatic infection and their expression is significantly repressed in the asymptomatic infections. We also observe widespread inter- and intra-patient variants in viral RNAs, known as quasispecies frequently found in many RNA viruses. We identify unique sets of deletions preferentially found primarily in symptomatic individuals, with many likely to confer changes in SARS-CoV-2 virulence and host responses. Moreover, these frequently occurring structural variants in SARS-CoV-2 genomes serve as a mechanism to further induce SARS-CoV-2 proteome complexity. Conclusions Our results indicate that differential sgRNA expression and structural mutational burden are highly correlated with the clinical severity of SARS-CoV-2 infection. Longitudinally monitoring sgRNA expression and structural diversity could further guide treatment responses, testing strategies, and vaccine development.

Reduced subgenomic RNA expression is a molecular indicator of asymptomatic SARS-CoV-2 infection