Interactive network pharmacology and electrochemical analysis reveals electron transport-mediating characteristics of Chinese medicine formula Jing Guan Fang.

Background: Jing Guan Fang (JGF) is an anti-COVID-19 Chinese Medicine decoction comprised of five medicinal herbs to possess anti-inflammatory and antiviral properties for treatment. This study aims to electrochemically decipher the anti-coronavirus activity of JGF and show that microbial fuel cells may serve as a platform for screening efficacious herbal medicines and providing scientific bases for the mechanism of action (MOA) of TCMs. Methods: Electrochemical techniques (e.g., cyclic voltammetry) and MFCs were adopted as the bioenergy-based platforms to assess the bioenergy-stimulating characteristics of JGF. Phytochemical analysis correlated polyphenolic and flavonoid content with antioxidant activity and bioenergy-stimulating properties. Network pharmacology on the active compounds was employed to identify anti-inflammatory and anti-COVID-19 protein targets, and molecular docking validated in silico results. Significant findings: This first-attempt results show that JGF possesses significant reversible bioenergy-stimulation (amplification 2.02 ± 0.04) properties suggesting that its antiviral efficacy is both bioenergy-steered and electron mediated. Major flavonoids and flavone glycosides identified by HPLC (e.g., baicalein and baicalin, respectively) possess electron-shuttling (ES) characteristics that allow herbal medicines to treat COVID-19 via (1) reversible scavenging of ROS to lessen inflammation; (2) inhibition of viral proteins; and (3) targeting of immunomodulatory pathways to stimulate the immune response according to network pharmacology.

Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19.

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

Interactive deciphering electron-shuttling characteristics of Coffea arabica leaves and potential bioenergy-steered anti-SARS-CoV-2 RdRp inhibitor via microbial fuel cells.

Due to the pandemics of COVID-19, herbal medicine has recently been explored for possible antiviral treatment and prevention via novel platform of microbial fuel cells. It was revealed that Coffea arabica leaves was very appropriate for anti-COVID-19 drug development. Antioxidant and anti-inflammatory tests exhibited the most promising activities for C. arabica ethanol extracts and drying approaches were implemented on the leaf samples prior to ethanol extraction. Ethanol extracts of C. arabica leaves were applied to bioenergy evaluation via DC-MFCs, clearly revealing that air-dried leaves (CA-A-EtOH) exhibited the highest bioenergy-stimulating capabilities (ca. 2.72 fold of power amplification to the blank). Furthermore, molecular docking analysis was implemented to decipher the potential of C. arabica leaves metabolites. Chlorogenic acid (-6.5 kcal/mol) owned the highest binding affinity with RdRp of SARS-CoV-2, showing a much lower average RMSF value than an apoprotein. This study suggested C. arabica leaves as an encouraging medicinal herb against SARS-CoV-2.