Glycyrrhetinic acid blocks SARS-CoV-2 infection by activating the cGAS-STING signalling pathway.

BACKGROUND AND PURPOSE: Traditional Chinese medicine (TCM) played an important role in controlling the COVID-19 pandemic, but the scientific basis and its active ingredients are still weakly studied. This study aims to decipher the underlying anti-SARS-CoV-2 mechanisms of glycyrrhetinic acid (GA). EXPERIMENTAL APPROACH: GA's anti-SARS-CoV-2 effect was verified both in vitro and in vivo. Homogeneous time-resolved fluorescence assays, biolayer interferometry technology, and molecular docking were employed to examine interactions of GA with human stimulator of interferon genes (hSTING). Immunofluorescence staining, western blot, and RT-qPCR were used to investigate nuclear translocation of interferon regulatory factor 3 (IRF3) and levels of STING target genes. Pharmacokinetics of GA was studied in mice. KEY RESULTS: GA could directly bind to Ser162 and Tyr240 residues of hSTING, thus up-regulating downstream targets and activation of the STING signalling pathway. Such activation is crucial for limiting the replication of SARS-CoV-2 Omicron in Calu-3 cells and protecting against lung injury induced by SARS-CoV-2 Omicron infection in K18-ACE2 transgenic mice. Immunofluorescence staining and western blot indicated that GA increased levels of phosphorylated STING, phosphorylated TANK-binding kinase-1, and cyclic GMP-AMP synthase (cGAS). Importantly, GA increased nuclear translocation of IRF3. Pharmacokinetic analysis of GA in mice indicated it can be absorbed into circulation and detected in the lung at a stable level. CONCLUSION AND IMPLICATIONS: Activation of the cGAS-STING pathway through the GA-STING-IRF3 axis is essential for the antiviral activity of GA in mice, providing new insights into the potential translation of GA for treating SARS-CoV-2 in patients.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Identification of active compounds of traditional chinese medicine derived from maxing shigan decoction for COVID-19 treatment: a meta-analysis and in silico study.

BACKGROUND: Coronavirus 2019 (COVID-19) poses a serious threat to human health. In China, traditional Chinese medicine (TCM), mainly based on the Maxing Shigan decoction (MXSGD), is used in conjunction with western medicine to treat COVID-19. RESEARCH DESIGN AND METHODS: We conducted a network meta-analysis to investigate whether MXSGD-related TCM combined with western medicine is more effective in treating COVID-19 compared to western medicine alone. Additionally, using network pharmacology, cross-docking, and molecular dynamics (MD) simulation to explore the potential active compounds and possible targets underlying the therapeutic effects of MXSGD-related TCM. RESULTS: MXSGD-related TCM combined with western medicine was better for treating COVID-19 compared to western medicine alone. Network pharmacological analysis identified 43 shared ingredients in the MXSGD-related TCM prescriptions and 599 common target genes. Cross-docking of the 43 compounds with 154 proteins that matched these genes led to the identification of 60 proteins. Pathway profiling revealed that the active ingredients participated in multiple signaling pathways that contribute to their efficacy. Molecular docking and MD simulation demonstrated that MOL007214, the most promising molecule, could stably bind to the active site of SARS-CoV-2 3CLpro. CONCLUSION: This study demonstrates the important role of MXSGD-related TCM in the treatment of COVID-19.

Global Xenobiotic Profiling of Rat Plasma Using Untargeted Metabolomics and Background Subtraction-Based Approaches: Method Evaluation and Comparison.

BACKGROUND: Global xenobiotic profiling (GXP) is to detect and structurally characterize all xenobiotics in biological samples using mainly liquid chromatography-high resolution mass spectrometry (LC-HRMS) based methods. GXP is highly needed in drug metabolism study, food safety testing, forensic chemical analysis, and exposome research. For detecting known or predictable xenobiotics, targeted LC-HRMS data processing methods based on molecular weights, mass defects and fragmentations of analytes are routinely employed. For profiling unknown xenobiotics, untargeted and LC-HRMS based metabolomics and background subtraction-based approaches are required. OBJECTIVE: This study aimed to evaluate the effectiveness of untargeted metabolomics and the precise and thorough background subtraction (PATBS) in GXP of rat plasma. METHODS: Rat plasma samples collected from an oral administration of nefazodone (NEF) or Glycyrrhizae Radix et Rhizoma (Gancao, GC) were analyzed by LC-HRMS. NEF metabolites and GC components in rat plasma were thoroughly searched and characterized via processing LC-HRMS datasets using targeted and untargeted methods. RESULTS: PATBS detected 68 NEF metabolites and 63 GC components, while the metabolomic approach (MS-DIAL) found 67 NEF metabolites and 60 GC components in rat plasma. The two methods found 79 NEF metabolites and 80 GC components with 96% and 91% successful rates, respectively. CONCLUSION: Metabolomics methods are capable of GXP and measuring alternations of endogenous metabolites in a group of biological samples, while PATBS is more suited for sensitive GXP of a single biological sample. A combination of metabolomics and PATBS approaches can generate better results in the untargeted profiling of unknown xenobiotics.