Mast cell degranulation-triggered by SARS-CoV-2 induces tracheal-bronchial epithelial inflammation and injury.

SARS-CoV-2 infection-induced hyper-inflammation is a key pathogenic factor of COVID-19. Our research, along with others', has demonstrated that mast cells (MCs) play a vital role in the initiation of hyper-inflammation caused by SARS-CoV-2. In previous study, we observed that SARS-CoV-2 infection induced the accumulation of MCs in the peri-bronchus and bronchioalveolar-duct junction in humanized mice. Additionally, we found that MC degranulation triggered by the spike protein resulted in inflammation in alveolar epithelial cells and capillary endothelial cells, leading to subsequent lung injury. The trachea and bronchus are the routes for SARS-CoV-2 transmission after virus inhalation, and inflammation in these regions could promote viral spread. MCs are widely distributed throughout the respiratory tract. Thus, in this study, we investigated the role of MCs and their degranulation in the development of inflammation in tracheal-bronchial epithelium. Histological analyses showed the accumulation and degranulation of MCs in the peri-trachea of humanized mice infected with SARS-CoV-2. MC degranulation caused lesions in trachea, and the formation of papillary hyperplasia was observed. Through transcriptome analysis in bronchial epithelial cells, we found that MC degranulation significantly altered multiple cellular signaling, particularly, leading to upregulated immune responses and inflammation. The administration of ebastine or loratadine effectively suppressed the induction of inflammatory factors in bronchial epithelial cells and alleviated tracheal injury in mice. Taken together, our findings confirm the essential role of MC degranulation in SARS-CoV-2-induced hyper-inflammation and the subsequent tissue lesions. Furthermore, our results support the use of ebastine or loratadine to inhibit SARS-CoV-2-triggered degranulation, thereby preventing tissue damage caused by hyper-inflammation.

CCL4 participates in the reprogramming of glucose metabolism induced by ALV-J infection in chicken macrophages.

Interferon and chemokine-mediated immune responses are two general antiviral programs of the innate immune system in response to viral infections and have recently emerged as important players in systemic metabolism. This study found that the chemokine CCL4 is negatively regulated by glucose metabolism and avian leukosis virus subgroup J (ALV-J) infection in chicken macrophages. Low expression levels of CCL4 define this immune response to high glucose treatment or ALV-J infection. Moreover, the ALV-J envelope protein is responsible for CCL4 inhibition. We confirmed that CCL4 could inhibit glucose metabolism and ALV-J replication in chicken macrophages. The present study provides novel insights into the antiviral defense mechanism and metabolic regulation of the chemokine CCL4 in chicken macrophages.

Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China.

Hepatitis E has placed a heavy burden on China, especially in Jiangsu Province, so accurately predicting the incidence of hepatitis E benefits to alleviate the medical burden. In this paper, we propose a new attentive bidirectional long short-term memory network (denoted as BiLSTM-Attention) to predict the incidence of hepatitis E for all 13 cities in Jiangsu Province, China. Besides, we also explore the performance of adding meteorological factors and the Baidu (the most widely used Chinese search engine) index as additional training data for the prediction of our BiLSTM-Attention model. SARIMAX, GBDT, LSTM, BiLSTM, and BiLSTM-Attention models are tested in this study, based on the monthly incidence rates of hepatitis E, meteorological factors, and the Baidu index collected from 2011 to 2019 for the 13 cities in Jiangsu province, China. From January 2011 to December 2019, a total of 29,339 cases of hepatitis E were detected in all cities in Jiangsu Province, and the average monthly incidence rate for each city is 0.359 per 100,000 persons. Root mean square error (RMSE) and mean absolute error (MAE) are used for model selection and performance evaluation. The BiLSTM-Attention model considering meteorological factors and the Baidu index has the best performance for hepatitis E prediction in all cities, and it gets at least 10% improvement in RMSE and MAE for all 13 cities in Jiangsu province, which means the model has significantly improved the learning ability, generalizability, and prediction accuracy when comparing with others.