ABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) targets mainly the respiratory tract. In addition to respiratory symptoms, many extrapulmonary manifestations were observed in the gastrointestinal tract and reported by SARS-CoV-2 patients, including abdominal pain, nausea, and diarrhea. SARS-CoV-2 binds initially to angiotensin-converting enzyme 2 (ACE2) on the host cell surface via its spike (S) protein before it undergoes endocytosis and fusion with the lysosomal membrane. The spike protein of SARS-CoV-2 is a heavily N- and O-glycosylated trimer. Glycosylation is an essential posttranslational modification in the life cycle of membrane and secretory proteins that affects their structural and functional characteristics as well as their trafficking and sorting patterns. This study aimed at elucidating the impact of glycosylation modulation on the trafficking of both S1 subunit and ACE2 as well as their interaction at the cell surface of intestinal epithelial cells. For this purpose, the S1 protein was expressed in COS-1 cells and its glycosylation modified using N-butyldeoxynojirimycin (NB-DNJ), an inhibitor of ER-located alpha-glucosidases I and II, and or 1-deoxymannojirimycin (dMM), an inhibitor of the Golgi-located alpha-mannosidase I. The intracellular and secreted S1 proteins were analyzed by endoglycosidase H treatment. Similarly, ACE2 trafficking to the brush border membrane of intestinal Caco-2 cells was also assessed in the presence or absence of the inhibitors. Finally, the interaction between the S1 protein and ACE2 was investigated at the surface of Caco-2 cells by co-immunoprecipitation. Our data show that NB-DNJ significantly reduced the secretion of S1 proteins in COS-1 cells, while dMM affected S1 secretion to a lesser extent. Moreover, NB-DNJ and dMM differentially affected ACE2 trafficking and sorting to the brush border membrane of intestinal Caco-2 cells. Strikingly, the interaction between S1 and ACE2 was significantly reduced when both proteins were processed by the glycosylation inhibitors, rendering glycosylation and its inhibitors potential candidates for SARS-CoV-2 treatment. This work has been supported by a grant from the German Research Foundation (DFG) grant NA331/15-1 to HYN. M.K. was supported by a scholarship from the Hannover Graduate School for Veterinary Pathobiology, Neuroinfectiology, and Translational Medicine (HGNI) and by the DFG grant NA331/15-1.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.
ABSTRACT
Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin;elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin;and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.