CD147 antibody specifically and effectively inhibits infection and cytokine storm of SARS-CoV-2 variants (preprint)

SARS-CoV-2 and its variants are raging worldwide. Unfortunately, the global vaccination is not efficient enough to attain a vaccine-based herd-immunity and yet no special and effective drug is developed to contain the spread of the disease. Previously we have identified CD147 as a novel receptor for SARS-CoV-2 infection. Here, we demonstrated that CD147 antibody effectively inhibits infection and cytokine storm caused by SARS-CoV-2 variants. In CD147KO VeroE6 cells, infections of SARS-CoV-2, its variants (B.1.1.7, B.1.351) and pseudovirus mutants (B.1.1.7, B.1.351, B.1.525, B.1.526 (S477N), B.1.526 (E484K), P.1, P.2, B.1.617.1, B.1.617.2) were decreased. Meanwhile, CD147 antibody effectively blocked the entry of variants and pseudomutants in VeroE6 cells, and inhibited the expression of cytokines. A model of SARS-CoV-2-infected hCD147 transgenic mice was constructed, which recapitulated the features of exudative diffuse alveolar damage and dynamic immune responses of COVID-19. CD147 antibody could effectively clear the virus and alveolar exudation, resolving the pneumonia. We found the elevated level of cyclophilin A (CyPA) in plasma of severe/critical cases, and identified CyPA as the most important proinflammatory intermediate causing cytokine storm. Mechanistically, spike protein of SARS-CoV-2 bound to CD147 and initiated the JAK-STAT pathway, which induced expression of CyPA. CyPA reciprocally bound to CD147, triggered MAPK pathway and consequently mediated the expression of cytokine and chemokine. In conclusion, CD147 is a critical target for SARS-CoV-2 variants and CD147 antibody is a promising drug to control the new wave of COVID-19 epidemic.

SARS-CoV-2 manipulates the SR-B1-mediated HDL uptake pathway for its entry (preprint)

The recently emerged pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly, leading to a global COVID-19 pandemic. Binding of the viral spike protein (SARS-2-S) to cell surface receptor angiotensin-converting enzyme 2 (ACE2) mediates host cell infection. In the present study, we demonstrate that in addition to ACE2, the S1 subunit of SARS-2-S binds to HDL and that SARS-CoV-2 hijacks the SR-B1-mediated HDL uptake pathway to facilitate its entry. SR-B1 facilitates SARS-CoV-2 entry into permissive cells by augmenting virus attachment. MAb (monoclonal antibody)-mediated blocking of SARS-2-S-HDL binding and SR-B1 antagonists strongly inhibit HDL-enhanced SARS-CoV-2 infection. Notably, SR-B1 is co-expressed with ACE2 in human pulmonary and extrapulmonary tissues. These findings revealed a novel mechanism for SARS-CoV-2 entry and could provide a new target to treat SARS-CoV-2 infection.

Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set (preprint)

Utilizing computed tomography (CT) images to quickly estimate the severity of cases with COVID-19 is one of the most straightforward and efficacious methods. Two tasks were studied in this present paper. One was to segment the mask of intact lung in case of pneumonia. Another was to generate the masks of regions infected by COVID-19. The masks of these two parts of images then were converted to corresponding volumes to calculate the physical proportion of infected region of lung. A total of 129 CT image set were herein collected and studied. The intrinsic Hounsfiled value of CT images was firstly utilized to generate the initial dirty version of labeled masks both for intact lung and infected regions. Then, the samples were carefully adjusted and improved by two professional radiologists to generate the final training set and test benchmark. Two deep learning models were evaluated: UNet and 2.5D UNet. For the segment of infected regions, a deep learning based classifier was followed to remove unrelated blur-edged regions that were wrongly segmented out such as air tube and blood vessel tissue etc. For the segmented masks of intact lung and infected regions, the best method could achieve 0.972 and 0.757 measure in mean Dice similarity coefficient on our test benchmark. As the overall proportion of infected region of lung, the final result showed 0.961 (Pearson's correlation coefficient) and 11.7% (mean absolute percent error). The instant proportion of infected regions of lung could be used as a visual evidence to assist clinical physician to determine the severity of the case. Furthermore, a quantified report of infected regions can help predict the prognosis for COVID-19 cases which were scanned periodically within the treatment cycle.

Severe coronavirus disease 2019: CT changes based on prognosis (preprint)

Purpose: To determine the characteristics of CT changes in patients with severe coronavirus disease 2019 (COVID-19) based on prognosis.Method: Serial CT scans in 47 patients with severe COVID-19 were reviewed. The patterns, distribution and CT score of lung abnormalities were assessed. Scans were classified according to duration in weeks after onset of symptoms. These CT abnormalities were compared between discharged and dead patients.Results: Twenty-six patients were discharged, whereas 21 passed away. Discharged patients were characterized by a rapid rise in CT score in the first 2 weeks followed by a slow decline, presence of reticular and mixed patterns from the second week, and prevalence of subpleural distribution of opacities in all weeks. In contrast, dead patients were characterized by a progressive rise in CT score, persistence of ground-glass opacity and consolidation patterns in all weeks, and prevalence of diffuse distribution from the second week. CT scores of death group were significantly higher than those of discharge group ( P < .05). Significant differences were also noted in abnormality pattern ( P < .05) and opacity distribution (P < .05) between groups.Conclusions: The severe COVID-19 patients presented with characteristic CT changes and the CT changes varied with prognosis.Authors Bin Liang, Lingli Xie contributed equally to this work.

SARS-CoV-2 invades host cells via a novel route: CD147-spike protein (preprint)

Currently, COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely spread around the world; nevertheless, so far there exist no specific antiviral drugs for treatment of the disease, which poses great challenge to control and contain the virus. Here, we reported a research finding that SARS-CoV-2 invaded host cells via a novel route of CD147-spike protein (SP). SP bound to CD147, a receptor on the host cells, thereby mediating the viral invasion. Our further research confirmed this finding. First, in vitro antiviral tests indicated Meplazumab, an anti-CD147 humanized antibody, significantly inhibited the viruses from invading host cells, with an EC50 of 24.86 g/mL and IC50 of 15.16 g/mL. Second, we validated the interaction between CD147 and SP, with an affinity constant of 1.85x10-7M. Co-Immunoprecipitation and ELISA also confirmed the binding of the two proteins. Finally, the localization of CD147 and SP was observed in SARS-CoV-2 infected Vero E6 cells by immuno-electron microscope. Therefore, the discovery of the new route CD147-SP for SARS-CoV-2 invading host cells provides a critical target for development of specific antiviral drugs.

Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia (preprint)

We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.