ABSTRACT
Clinical rebound of COVID-19 after nirmatrelvir/ritonavir treatment has been reported. We performed clinical, virologic, and immune measurements in seven patients with symptomatic rebound, six after nirmatrelvir/ritonavir treatment and one without previous treatment. There was no evidence of severe disease or impaired antibody and T-cell responses in people with rebound symptoms.
ABSTRACT
BackgroundB-cell depleting therapies may lead to protracted disease and prolonged viral shedding in individuals infected with SARS-CoV-2. Viral persistence in the setting of immunosuppression raises concern for viral evolution. MethodsAmplification of sub-genomic transcripts for the E gene (sgE) was done on nasopharyngeal samples over the course of 355 days in a patient infected with SARS-CoV-2 who had previously undergone CAR T cell therapy and had persistently positive SARS-CoV-2 nasopharyngeal swabs. Whole genome sequencing was performed on samples from the patients original presentation and 10 months later. ResultsOver the course of almost a year, the virus accumulated a unique in-frame deletion in the amino-terminal domain of the spike protein, and complete deletion of ORF7b and ORF8, the first report of its kind in an immunocompromised patient. Also, minority variants that were identified in the early samples--reflecting the heterogeneity of the initial infection--were found to be fixed late in the infection. Remdesivir and high-titer convalescent plasma treatment were given, and the infection was eventually cleared after 335 days of infection. ConclusionsThe unique viral mutations found in this study highlight the importance of analyzing viral evolution in protracted SARS-CoV-2 infection, especially in immunosuppressed hosts, and the implication of these mutations in the emergence of viral variants. SummaryWe report an immunocompromised patient with persistent symptomatic SARS-CoV-2 infection for 335 days. During this time, the virus accumulated a unique in-frame deletion in the spike, and a complete deletion of ORF7b and ORF8 which is the first report of its kind in an immunocompromised patient.
ABSTRACT
High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller, and use of replicate sequencing have the most significant impact on single nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false negative rates. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intrahost viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intrahost variation, viral diversity, and viral evolution. IMPORTANCEWhen viruses replicate inside a host, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus, nor strongly beneficial, can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in inclusion of false positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant calling tools. We used simulated and synthetic data to test their performance against a true set of variants, and then used these studies to inform variant identification in data from clinical SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.
