The function of SARS-CoV-2 spike protein is impaired by disulfide-bond disruption with mutation at cysteine-488 and by thiol-reactive N-acetyl-cysteine and glutathione.

Viral spike proteins play important roles in the viral entry process, facilitating attachment to cellular receptors and fusion of the viral envelope with the cell membrane. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein binds to the cellular receptor angiotensin converting enzyme-2 (ACE2) via its receptor-binding domain (RBD). The cysteine residue at position 488, consisting of a disulfide bridge with cysteine 480 is located in an important structural loop at ACE2-binding surface of RBD, and is highly conserved among SARS-related coronaviruses. We showed that the substitution of Cys-488 with alanine impaired pseudotyped SARS-CoV-2 infection, syncytium formation, and cell-cell fusion triggered by SARS-CoV-2 spike expression. Consistently, in vitro binding of RBD and ACE2, spike-mediated cell-cell fusion, and pseudotyped viral infection of VeroE6/TMPRSS2 cells were inhibited by the thiol-reactive compounds N-acetylcysteine (NAC) and a reduced form of glutathione (GSH). Furthermore, we demonstrated that the activity of variant spikes from the SARS-CoV-2 alpha and delta strains were also suppressed by NAC and GSH. Taken together, these data indicate that Cys-488 in spike RBD is required for SARS-CoV-2 spike functions and infectivity, and could be a target of anti-SARS-CoV-2 therapeutics.

Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study.

BACKGROUND: Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. METHODS: This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. RESULTS: Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. CONCLUSIONS: Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.

[Important differential diseases that may be "mimics" of COVID-19 pneumonia : Mimics and chameleons of COVID-19]

Summary Recent studies suggest that chest computed tomography (CT) may be useful in diagnosing COVID-19 pneumonia in endemic areas. On the other hand, CT scan has high sensitivity but low specificity in the diagnosis of COVID-19 pneumonia, because there are diseases with similar or identical imaging findings to COVID-19 pneumonia, and patients with underlying diseases such as emphysema, heart failure, and malignancy may have atypical or complex findings. In addition, patients with underlying diseases such as emphysema, heart failure, and malignancy may have atypical or complex findings.