Huashibaidu formula attenuates sepsis-induced acute lung injury via suppressing cytokine storm: Implications for treatment of COVID-19.

BACKGROUND: Acute lung injury (ALI) is a common complication of sepsis with poor effective interventions. Huashibaidu formula (HSBD) showed good therapeutic effects in treating coronavirus disease 2019 (COVID-19) patients. PURPOSE: This study was designed to investigate the therapeutic potential and precise mechanism of HSBD against sepsis-induced ALI based on network pharmacology and animal experiments. MATERIALS AND METHODS: Network pharmacology was used to predict the possible mechanism of HSBD against sepsis. Next, a sepsis-induced ALI rat model via intraperitoneal lipopolysaccharide (LPS) was constructed to evaluate the level of inflammatory cytokines and the degree of lung injury. The expression of inflammation-related signaling pathways, including TLR4/NF-κB and PI3K/Akt was determined by western blot. RESULTS: Network pharmacology analysis indicated that HSBD might have a therapeutic effect on sepsis mainly by affecting inflammatory and immune responses. Animal experiments demonstrated that HSBD protected the lung tissue from LPS-induced injury, and inhibited the levels of inflammatory cytokines such as interleukin (IL)-1ß, granulocyte-macrophage colony-stimulating factor (GM-CSF), interferon (IFN)-γ and tumor necrosis factor (TNF)-α in the serum and IL-1ß, IL-5, IL-6, IL-18, GM-CSF, IFN-γ and TNF-α in the lung tissue. Western blot results revealed that HSBD downregulated the expression of TLR4/NF-κB and upregulated the expression of PI3K/Akt. CONCLUSION: The therapeutic mechanism of HSBD against sepsis-induced ALI mainly involved suppressing cytokine storms and relieving inflammatory symptoms by regulating the expression of TLR4/NF-κB and PI3K/Akt. Our study provides a scientific basis for the mechanistic investigation and clinical application of HSBD in the treatment of sepsis and COVID-19.

Identification of a recombinant equine coronavirus in donkey, China.

Equine coronavirus (ECoV) was first identified in the USA and has been previously described in several countries. In order to test the presence of ECoV in China, we collected 51 small intestinal samples from donkey foals with diarrhoea from a donkey farm in Shandong Province, China between August 2020 and April 2021. Two samples tested positive for ECoV and full-length genome sequences were successfully obtained using next-generation sequencing, one of which was further confirmed by Sanger sequencing. The two strains shared 100% sequence identity at the scale of whole genome. Bioinformatics analyses further showed that the two Chinese strains represent a novel genetic variant of ECoV and shared the highest sequence identity of 97.05% with the first identified ECoV strain - NC99. In addition, it may be a recombinant, with the recombination region around the NS2 gene. To our knowledge, this is the first documented report of ECoV in China, highlighting its risk to horse/donkey breeding. In addition, its potential risk to public health also warrants further investigation.

[Analysis of potential role of Chinese classic prescriptions in treatment of COVID-19 based on TCMATCOV platform].

With the global outbreak of coronavirus disease 2019(COVID-19), screening of effective drugs has became the emphasis of research today; furthermore, screening of Chinese classic prescriptions has became one of the directions for drug development. This study analyzed the application of classic prescriptions in the diagnosis and treatment schemes based on the Diagnosis and Treatment Schemes for Coronavirus Disease at the country, provincial and municipal levels, and further explored its disrobing effect on COVID-19 disease severe phase network, and selected representative prescriptions for core target screening and gene enrichment analysis, so as to reveal its mechanism of action. Among them, 13 prescriptions were found to be used for 10 times or more, including Maxing Shigan Tang, Yinqiao San, Shengjiang San, Dayuan Drink, Xuanbai Chengqi Decoction. In addition, the COVID-19 efficacy prediction analysis platform(TCMATCOV platform) was used to calculate the network disturbances of the Chinese classic prescriptions involved. Based on the prediction results, 68 classic prescriptions were assessed on the COVID-19 disease network robustness disturbance. The average disturbance scores for the interaction confidence scores were ranked to be 0.4, 0.5, and 0.6 from the highest to the lowest. There were 7 prescriptions with a score of 17 or more, and 50 prescriptions with a score of 13 or more. Among them, the top three prescriptions were Ganlu Xiaodu Dan(18.19), Lengxiao Wan(17.74), and Maxing Shigan Tang(17.62). After further mining the action targets of these three prescriptions, it was found that COVID-19 disease-specific factors Ccl2, IL10, IL6 and TNF were all the targets of three prescriptions. Through the enrichment analysis of the biological processes of the core targets, it was found that the three prescriptions may prevent the development of the disease by affecting cell-to-cell adhesion, cytokine-mediated signaling pathway, and chronic inflammatory responses to COVID-19 at the severe phase. This study showed that the TCMATCOV platform could evaluate the disturbance effect of different prescriptions on the COVID-19 disease network, and predict potential effectiveness based on the robustness of drug-interfered pneumonia disease networks, so as to provide a reference for further experiments or clinical verification.

[TCMATCOV--a bioinformatics platform to predict efficacy of TCM against COVID-19].

There is urgent need to discover effective traditional Chinese medicine(TCM) for treating coronavirus disease 2019(COVID-19). The development of a bioinformatic tool is beneficial to predict the efficacy of TCM against COVID-19. Here we deve-loped a prediction platform TCMATCOV to predict the efficacy of the anti-coronavirus pneumonia effect of TCM, based on the interaction network imitating the disease network of COVID-19. This COVID-19 network model was constructed by protein-protein interactions of differentially expressed genes in mouse pneumonia caused by SARS-CoV and cytokines specifically up-regulated by COVID-19. TCMATCOV adopted quantitative evaluation algorithm of disease network disturbance after multi-target drug attack to predict potential drug effects. Based on the TCMATCOV platform, 106 TCM were calculated and predicted. Among them, the TCM with a high disturbance score account for a high proportion of the classic anti-COVID-19 prescriptions used by clinicians, suggesting that TCMATCOV has a good prediction ability to discover the effective TCM. The five flavors of Chinese medicine with a disturbance score greater than 1 are mainly spicy and bitter. The main meridian of these TCM is lung, heart, spleen, liver, and stomach meridian. The TCM related with QI and warm TCM have higher disturbance score. As a prediction tool for anti-COVID-19 TCM prescription, TCMATCOV platform possesses the potential to discovery possible effective TCM against COVID-19.