Systems Pharmacology and Verification of ShenFuHuang Formula in Zebrafish Model Reveal Multi-Scale Treatment Strategy for Septic Syndrome in COVID-19

Chinese Medicine for Treatment of COVID-19: A Review of Potential Pharmacological Components and Mechanisms

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.

Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology

Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.

Role of AKR1B10 in inflammatory diseases

Inflammation is an important pathophysiological process in many diseases; it has beneficial and harmful effects. When exposed to various stimuli, the body triggers an inflammatory response to eliminate invaded pathogens and damaged tissues to maintain homeostasis. However, uncontrollable persistent or excessive inflammatory responses may damage tissues and induce various diseases, such as metabolic diseases (e.g. diabetes), autoimmune diseases, nervous system-related diseases, digestive system-related diseases, and even tumours. Aldo-keto reductase 1B10 (AKR1B10) is an important player in the development and progression of multiple diseases, such as tumours and inflammatory diseases. AKR1B10 is upregulated in solid tumours, such as hepatocellular carcinoma (HCC), non-small cell lung carcinoma, and breast cancer, and is a reliable serum marker. However, information on the role of AKR1B10 in inflammation is limited. In this study, we summarized the role of AKR1B10 in inflammatory diseases, including its expression, functional contribution to inflammatory responses, and regulation of signalling pathways related to inflammation. We also discussed the role of AKR1B10 in glucose and lipid metabolism and oxidative stress. This study provides novel information and increases the understanding of clinical inflammatory diseases.

Shenhuangdan decoction alleviates sepsis-induced lung injury through inhibition of GSDMD-mediated pyroptosis

ETHNOPHARMACOLOGICAL RELEVANCE

Sepsis-induced lung injury is closely associated with it remarkable morbidity and mortality. Shenhuangdan (SHD) decoction, a traditional Chinese medicine prescription, has been clinically proven to be an effective treatment for sepsis. However, the mechanism of SHD decoction in treating sepsis remains unclear.

AIM OF THE STUDY

This study aimed to evaluate the therapeutic effect of SHD decoction on sepsis-induced lung injury and its underlying mechanism.

MATERIALS AND METHODS

In this study, we established a mouse model of sepsis by cecum ligation and puncture (CLP) surgery. Firstly, seven-day survival analysis and histological staining of lung tissue were used to evaluate the therapeutic effect of SHD decoction on lung injury in septic mice. Multifactor microarray was used to detect cytokine expression changes in serum and bronchoalveolar lavage fluid (BALF). Subsequently, the main components in medicated serum of SHD decoction were inspected by Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The material basis of SHD decoction and potential interaction mechanisms were analysed by systemic pharmacology. To confirm the reliability of network pharmacology for predicting outcomes, we used molecular docking techniques to identify interactions between the core components and targets of SHD. Finally, TUNEL staining, immunofluorescence and western blotting were used to explore the mechanism of SHD decoction through the inhibition of GSDMD-mediated pyroptosis in septic mice.

RESULTS

SHD was found to be effective in reducing the mortality and alleviating lung pathological damage in septic mice. Multifactor microarray results showed that SHD can reduce the expression of inflammation factors (IL-18, IL-1β, IL-5, IL-6 and TNF-α) in serum and BALF of septic mice. There were 22 major blood-entry components detected by UPLC-MS/MS. Then, combined with the network pharmacological analysis, it is evident that the main components of SHD for sepsis are Renshen-ginsenoside Rh2, Danshen-tanshinone IIA and Dahuang-rhein. The main targets were IL-1β and caspase-1, which were related to GSDMD-mediated pyroptosis signalling pathway. Molecular docking exhibited that Renshen-ginsenoside Rh2, Danshen-tanshinone IIA and Dahuang-rhein can closely bind to GSDMD, IL-1β and caspase-1. In addition, TUNEL staining and immunohistochemistry demonstrated that SHD alleviated the expression of GSDMD protein. The western blotting showed that SHD significantly inhibited the protein expression levels of NLRP3, GSDMD, GSDMD-N, cleved caspase-1, caspase-1 and ASC in lung tissue.

CONCLUSIONS

Our study revealed that SHD improves CLP-induced lung injury by blocking the GSDMD-mediated pyroptosis signalling pathway in septic mice. This study provides evidence to support that SHD had a potential therapeutic effect on sepsis.

Alterations in the gut microbiome and metabolome profiles of septic mice treated with Shen FuHuang formula

Sepsis has a high mortality rate, and treating sepsis remains a significant challenge worldwide. In former studies, our group found that traditional Chinese medicine, Shen FuHuang formula (SFH), is a promising medicine in treating coronavirus disease 2019 (COVID-19) patients with the septic syndrome. However, the underlying mechanisms remain elusive. In the present study, we first investigated the therapeutic effects of SFH on septic mice. To investigate the mechanisms of SFH-treated sepsis, we identified the gut microbiome profile and exploited untargeted metabolomics analyses. The results demonstrated that SFH significantly enhanced the mice’s 7-day survival rate and hindered the release of inflammatory mediators, i.e., TNF-α, IL-6, and IL-1β. 16S rDNA sequencing further deciphered that SFH decreased the proportion of Campylobacterota and Proteobacteria at the phylum level. LEfSe analysis revealed that the treatment of SFH enriched Blautia while decreased Escherichia_Shigella. Furthermore, serum untargeted metabolomics analysis indicated that SFH could regulate the glucagon signaling pathway, PPAR signaling pathway, galactose metabolism, and pyrimidine metabolism. Finally, we found the relative abundance of Bacteroides, Lachnospiraceae_NK4A136_group, Escherichia_Shigella, Blautia, Ruminococcus, and Prevotella were closely related to the enrichment of the metabolic signaling pathways, including L-tryptophan, uracil, glucuronic acid, protocatechuic acid, and gamma-Glutamylcysteine. In conclusion, our study demonstrated that SFH alleviated sepsis by suppressing the inflammatory response and hence reduced mortality. The mechanism of SFH for treating sepsis may be ascribed to the enrichment of beneficial gut flora and modulation in glucagon signaling pathway, PPAR signaling pathway, galactose metabolism, and pyrimidine metabolism. To sum up, these findings provide a new scientific perspective for the clinical application of SFH in treating sepsis.

A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis

BACKGROUND

Immune-related genes (IRGs) remain poorly understood in their function in the onset and progression of sepsis.

METHODS

GSE65682 was obtained from the Gene Expression Omnibus database. The IRGs associated with survival were screened for subsequent modeling using univariate Cox regression analysis and least absolute shrinkage and selection operator in the training cohort. Then, we assessed the reliability of the 7 IRGs signature's independent predictive value in the training and validation cohorts following the creation of a signature applying multivariable Cox regression analysis. After that, we utilized the E-MTAB-4451 external dataset in order to do an independent validation of the prognostic signature. Finally, the CIBERSORT algorithm and single-sample gene set enrichment analysis was utilized to investigate and characterize the properties of the immune microenvironment.

RESULTS

Based on 7 IRGs signature, patients could be separated into low-risk and high-risk groups. Patients in the low-risk group had a remarkably increased 28-day survival compared to those in the high-risk group (P < 0.001). In multivariable Cox regression analyses, the risk score calculated by this signature was an independent predictor of 28-day survival (P < 0.001). The signature's predictive ability was confirmed by receiver operating characteristic curve analysis with the area under the curve reaching 0.876 (95% confidence interval 0.793-0.946). Moreover, both the validation set and the external dataset demonstrated that the signature had strong clinical prediction performance. In addition, patients in the high-risk group were characterized by a decreased neutrophil count and by reduced inflammation-promoting function.

CONCLUSION

We developed a 7 IRGs signature as a novel prognostic marker for predicting sepsis patients' 28-day survival, indicating possibilities for individualized reasonable resource distribution of intensive care unit.

Traditional Chinese Medicine: A promising strategy to regulate inflammation, intestinal disorders and impaired immune function due to sepsis

Sepsis is described as a dysregulation of the immune response to infection, which leads to life-threatening organ dysfunction. The interaction between intestinal microbiota and sepsis can't be ignored. Furthermore, the intestinal microbiota may regulate the progress of sepsis and attenuate organ damage. Thus, maintaining or restoring microbiota may be a new way to treat sepsis. Traditional Chinese medicine (TCM) assumes a significant part in the treatment of sepsis through multi-component, multi-pathway, and multi-targeting abilities. Moreover, TCM can prevent the progress of sepsis and improve the prognosis of patients with sepsis by improving the imbalance of intestinal microbiota, improving immunity and reducing the damage to the intestinal barrier. This paper expounds the interaction between intestinal microbiota and sepsis, then reviews the current research on the treatment of sepsis with TCM, to provide a theoretical basis for its clinical application.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Network pharmacology approach and molecular docking to explore the potential mechanism of Wu-Wei-Wen-Tong Chubi capsules in rheumatoid arthritis

Network pharmacology, a holistic approach based on the theory of biological network technology, integrates information from biological systems, drugs, and diseases. Here, this theory was used to predict the targets of Wu-Wei-Wen-Tong Chubi capsule (WWWT) to explore the mechanism in the treatment of rheumatoid arthritis (RA). The ingredients of each herbal medicine in WWWT were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the active ingredients were screened through bioavailability (OB) ≥30% and drug-likeness (DL) ≥ 0.18. SwissTargetPrection and TCMSP were utilized to calculate and predict the targets of active ingredients. RA-related targets were obtained by searching the Genecards and OMIM databases. The common targets of RA and WWWT were used for gene ontology (GO), KEGG pathway enrichment, protein-protein interaction (PPI) analysis, and molecular docking. And then, four key genes were screened for subsequent verification experiments. In total, 90 active compounds and 330 potential targets of WWWT, 1310 targets of RA, and 135 intersection targets were found. Additionally, GO and pathway analysis identified 4610 significant GO terms and 147 significant KEGG pathways. Based on the PPI network, 11 key genes including IL-6, MMP-9, and TNF-α were screened out for molecular docking. Molecular docking showed that these key genes have good binding activities to active compounds of WWWT such as oroxylin a, kaempferol, and luteolin. Simultaneously, Western blot experimental validation demonstrated that the protein expressions of IL-6, MMP-9, TNF-α, and VEGFA significantly decreased after WWWT treatment. The mechanism of WWWT in treating RA involves multiple active compounds acting on multiple targets, and multiple pathways, which provides an important reference for further elucidation the mechanism and clinical applications of WWWT in the treatment of RA.

Exploring the Mechanism of Astragalus propinquus Schischkin and Panax Notoginseng (A&P) Compounds in the Treatment of Renal Fibrosis and Chronic Kidney Disease Based on Integrated Network Analysis

Astragalus propinquus Schischkin and Panax notoginseng (A&P) has been widely used in clinical practice to treat chronic kidney disease (CKD) for many years and achieved a remarkable improvement of these outcomes. However, its mechanisms for ameliorating CKD are still poorly obscure. In the current study, integrated network analysis was carried out to analyze the potential active ingredients and molecular mechanism of A&P on CKD, and 39 active ingredients and a total of 570 targets were obtained. Furthermore, the potential disease-related genes were obtained from the NCBI GEO database by integrating 2 microarray datasets, and 24 significant genes were utilized for subsequent analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis displayed that pathways including cell oxidative stress and Akt signaling pathway are medicated by A&P. Of note, Heat Shock Transcription Factor 1 (HSF1) and RELA Proto-Oncogene (RELA) were regarded as hub genes considering their central roles in the gene regulatory network. What's more, the effect of A&P and potential genes was furthermore verified by using unilateral ureteral ligation (UUO) in rodent model. The results showed that the expression of HSF1 and RELA both at transcript and protein level was significantly upregulated in UUO model, but the expression was markedly reversed after A&P intervention. To further guide the interpretation of active ingredients from A&P on the effect of HSF1 and RELA, we performed a molecular docking assay and the results showed that active ingredients such as coptisine docked well into HSF1 and RELA. In total, these results suggest that A&P may improve RF in CKD by regulating HSF1 and RELA, which provides a basis for further understanding the mechanism of A&P in the treatment of RF and CKD.

AKR1B10, One of the Triggers of Cytokine Storm in SARS-CoV2 Severe Acute Respiratory Syndrome

Preventing the cytokine storm observed in COVID-19 is a crucial goal for reducing the occurrence of severe acute respiratory failure and improving outcomes. Here, we identify Aldo-Keto Reductase 1B10 (AKR1B10) as a key enzyme involved in the expression of pro-inflammatory cytokines. The analysis of transcriptomic data from lung samples of patients who died from COVID-19 demonstrates an increased expression of the gene encoding AKR1B10. Measurements of the AKR1B10 protein in sera from hospitalised COVID-19 patients suggests a significant link between AKR1B10 levels and the severity of the disease. In macrophages and lung cells, the over-expression of AKR1B10 induces the expression of the pro-inflammatory cytokines Interleukin-6 (IL-6), Interleukin-1β (IL-1β) and Tumor Necrosis Factor a (TNFα), supporting the biological plausibility of an AKR1B10 involvement in the COVID-19-related cytokine storm. When macrophages were stressed by lipopolysaccharides (LPS) exposure and treated by Zopolrestat, an AKR1B10 inhibitor, the LPS-induced production of IL-6, IL-1β, and TNFα is significantly reduced, reinforcing the hypothesis that the pro-inflammatory expression of cytokines is AKR1B10-dependant. Finally, we also show that AKR1B10 can be secreted and transferred via extracellular vesicles between different cell types, suggesting that this protein may also contribute to the multi-organ systemic impact of COVID-19. These experiments highlight a relationship between AKR1B10 production and severe forms of COVID-19. Our data indicate that AKR1B10 participates in the activation of cytokines production and suggest that modulation of AKR1B10 activity might be an actionable pharmacological target in COVID-19 management.

Integrative COVID-19 biological network inference with probabilistic core decomposition

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

A Systematic Study of the Mechanism of Acacetin Against Sepsis Based on Network Pharmacology and Experimental Validation

Sepsis is a dysregulated systemic response to infection, and no effective treatment options are available. Acacetin is a natural flavonoid found in various plants, including Sparganii rhizoma, Sargentodoxa cuneata and Patrinia scabiosifolia. Studies have revealed that acacetin potentially exerts anti-inflammatory and antioxidative effects on sepsis. In this study, we investigated the potential protective effect of acacetin on sepsis and revealed the underlying mechanisms using a network pharmacology approach coupled with experimental validation and molecular docking. First, we found that acacetin significantly suppressed pathological damage and pro-inflammatory cytokine expression in mice with LPS-induced fulminant hepatic failure and acute lung injury, and in vitro experiments further confirmed that acacetin attenuated LPS-induced M1 polarization. Then, network pharmacology screening revealed EGFR, PTGS2, SRC and ESR1 as the top four overlapping targets in a PPI network, and GO and KEGG analyses revealed the top 20 enriched biological processes and signalling pathways associated with the therapeutic effects of acacetin on sepsis. Further network pharmacological analysis indicated that gap junctions may be highly involved in the protective effects of acacetin on sepsis. Finally, molecular docking verified that acacetin bound to the active sites of the four targets predicted by network pharmacology, and in vitro experiments further confirmed that acacetin significantly inhibited the upregulation of p-src induced by LPS and attenuated LPS-induced M1 polarization through gap junctions. Taken together, our results indicate that acacetin may protect against sepsis via a mechanism involving multiple targets and pathways and that gap junctions may be highly involved in this process.