Clinical, Virologic, and Immunologic Evaluation of Symptomatic Coronavirus Disease 2019 Rebound Following Nirmatrelvir/Ritonavir Treatment.

BACKGROUND: Nirmatrelvir/ritonavir, the first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease inhibitor, reduces the risk of hospitalization and death by coronavirus disease 2019 (COVID-19) but has been associated with symptomatic rebound after therapy completion. METHODS: Six individuals with relapse of COVID-19 symptoms after treatment with nirmatrelvir/ritonavir, 2 individuals with rebound symptoms without prior antiviral therapy and 7 patients with acute Omicron infection (controls) were studied. Soluble biomarkers and serum SARS-CoV-2 nucleocapsid protein were measured. Nasal swabs positive for SARS-CoV-2 underwent viral isolation and targeted viral sequencing. SARS-CoV-2 anti-spike, anti-receptor-binding domain, and anti-nucleocapsid antibodies were measured. Surrogate viral neutralization tests against wild-type and Omicron spike protein, as well as T-cell stimulation assays, were performed. RESULTS: High levels of SARS-CoV-2 anti-spike immunoglobulin G (IgG) antibodies were found in all participants. Anti-nucleocapsid IgG and Omicron-specific neutralizing antibodies increased in patients with rebound. Robust SARS-CoV-2-specific T-cell responses were observed, higher in rebound compared with early acute COVID-19 patients. Inflammatory markers mostly decreased during rebound. Two patients sampled longitudinally demonstrated an increase in activated cytokine-producing CD4+ T cells against viral proteins. No characteristic resistance mutations were identified. SARS-CoV-2 was isolated by culture from 1 of 8 rebound patients; Polybrene addition increased this to 5 of 8. CONCLUSIONS: Nirmatrelvir/ritonavir treatment does not impede adaptive immune responses to SARS-CoV-2. Clinical rebound corresponds to development of a robust antibody and T-cell immune response, arguing against a high risk of disease progression. The presence of infectious virus supports the need for isolation and assessment of longer treatment courses. Clinical trials registration. NCT04401436.

Successful lung transplantation using an allograft from a COVID-19–recovered donor: a potential role for subgenomic RNA to guide organ utilization

Although the risk of SARS-CoV-2 transmission through lung transplantation from acutely infected donors is high, the risks of virus transmission and long-term lung allograft outcomes are not as well described when using pulmonary organs from COVID-19–recovered donors. We describe successful lung transplantation for a COVID-19–related lung injury using lungs from a COVID-19–recovered donor who was retrospectively found to have detectable genomic SARS-CoV-2 RNA in the lung tissue by multiple highly sensitive assays. However, SARS-CoV-2 subgenomic RNA (sgRNA), a marker of viral replication, was not detectable in the donor respiratory tissues. One year after lung transplantation, the recipient has a good functional status, walking 1 mile several times per week without the need for supplemental oxygen and without any evidence of donor-derived SARS-CoV-2 transmission. Our findings highlight the limitations of current clinical laboratory diagnostic assays in detecting the persistence of SARS-CoV-2 RNA in the lung tissue. The persistence of SARS-CoV-2 RNA in the donor tissue did not appear to represent active viral replication via sgRNA testing and, most importantly, did not negatively impact the allograft outcome in the first year after lung transplantation. sgRNA is easily performed and may be a useful assay for assessing viral infectivity in organs from donors with a recent infection.

Accurate virus identification with interpretable Raman signatures by machine learning.

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups­for example, amide, amino acid, and carboxylic acid­we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

Yearlong COVID-19 Infection Reveals Within-Host Evolution of SARS-CoV-2 in a Patient With B-Cell Depletion.

B-cell-depleting therapies may lead to prolonged disease and viral shedding in individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and this viral persistence raises concern for viral evolution. We report sequencing of early and late samples from a 335-day infection in an immunocompromised patient. The virus accumulated a unique 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. Unique viral mutations found in this study highlight the importance of analyzing viral evolution in protracted SARS-CoV-2 infection, especially in immunosuppressed hosts.