No abstract

A major unanswered question in the current global coronavirus disease 2019 (COVID-19) outbreak is why severe disease develops in a small minority of infected individuals. In the current article, we report that homozygosity for the C allele of rs12252 in the interferon-induced transmembrane protein 3 (IFITM3) gene is associated with more severe disease in an age-dependent manner. This supports a role for IFITM3 in disease pathogenesis and the opportunity for early targeted intervention in at-risk individuals.

The COVID-19 pandemic has emerged as a global health emergency due to its association with severe pneumonia and relative high mortality. However, the molecular characteristics and pathological features underlying COVID-19 pneumonia remain largely unknown. To characterize molecular mechanisms underlying COVID-19 pathogenesis in the lung tissue using a proteomic approach, fresh lung tissues were obtained from newly deceased patients with COVID-19 pneumonia. After virus inactivation, a quantitative proteomic approach combined with bioinformatics analysis was used to detect proteomic changes in the SARS-CoV-2-infected lung tissues. We identified significant differentially expressed proteins involved in a variety of fundamental biological processes including cellular metabolism, blood coagulation, immune response, angiogenesis, and cell microenvironment regulation. Several inflammatory factors were upregulated, which was possibly caused by the activation of NF-κB signaling. Extensive dysregulation of the lung proteome in response to SARS-CoV-2 infection was discovered. Our results systematically outlined the molecular pathological features in terms of the lung response to SARS-CoV-2 infection, and provided the scientific basis for the therapeutic target that is urgently needed to control the COVID-19 pandemic.

Background: A novel enveloped RNA beta coronavirus, Corona Virus Disease 2019 (COVID-19) caused severe and even fetal pneumonia in China and other countries from December 2019. Early detection of severe patients with COVID-19 is of great significance to shorten the disease course and reduce mortality.Methods: We assembled a retrospective cohort of 80 patients (including 56 mild and 24 severe) with COVID-19 infection treated at Beijing You'an Hospital. We used univariable and multivariable logistic regression analyses to select the risk factors of severe and even fetal pneumonia and build scoring system for prediction, which was validated later on in a group of 22 COVID-19 patients.Results: Age, white blood cell count, neutrophil, glomerular filtration rate, and myoglobin were selected by multivariate analysis as candidates of scoring system for prediction of disease severity in COVID-19. The scoring system was applied to calculate the predictive value and found that the percentage of ICU admission (20%, 6/30) and ventilation (16.7%, 5/30) in patients with high risk was much higher than those (2%, 1/50; 2%, 1/50) in patients with low risk (p = 0.009; p = 0.026). The AUC of scoring system was 0.906, sensitivity of prediction is 70.8%, and the specificity is 89.3%. According to scoring system, the probability of patients in high risk group developing severe disease was 20.24 times than that in low risk group. Conclusions:The possibility of severity in COVID-19 infection predicted by scoring system could help patients to receiving different therapy strategies at a very early stage.