Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology

Anatolian medicinal plants as potential antiviral agents: bridging traditional knowledge and modern science in the fight against COVID-19 and related viral infections

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was the cause of the coronavirus 2019 (COVID-19), commonly known as the coronavirus pandemic. Since December 2020, COVID-19 vaccines have been extensively administered in numerous countries. In addition to new antiviral medications, the treatment regimen encompasses symptom management. Despite sustained research efforts, the outbreak remains uncontrolled, with affected patients still lacking proper treatment. This review is a valuable asset for researchers and practitioners aiming to delve into the yet unexplored potential of Anatolian flora in the fight against COVID-19 and other viral infections. Numerous medicinal plants in Anatolia, such as thyme, sage, cannabis, oregano, licorice root, and Origanum sp., contain bioactive compounds with proven antiviral properties that have been used in the region for centuries. The rich legacy of traditional Anatolian medicine (TAM), has significantly influenced modern medicine; thus, the profusion of medicinal plants native to Anatolia holds promise for antiviral drug development, making this review essential for researchers and practitioners.

Mechanistic insights into targeting caspase-3 activation and alveolar macrophage pyroptosis by Ephedra and bitter almond compounds for treating pediatric pneumonia via network pharmacology and bioinformatics

This study investigates the molecular mechanism of Ma Huang-Ku Xing Ren, a traditional Chinese medicine formula, in treating pediatric pneumonia. The focus is on the regulation of caspase-3 activation and reduction of alveolar macrophage necrosis through network pharmacology and bioinformatics analyses of Ephedra and bitter almond components. Active compounds and targets from ephedrine and bitter almond were obtained using TCMSP, TCMID, and GeneCards databases, identifying pediatric pneumonia-related genes. A protein-protein interaction (PPI) network was constructed, and core targets were screened. GO and KEGG pathway enrichment analyses identified relevant genes and pathways. An acute pneumonia mouse model was created using the lipopolysaccharide (LPS) inhalation method, with caspase-3 overexpression induced by a lentivirus. The mice were treated with Ephedra and bitter almond through gastric lavage. Lung tissue damage, inflammatory markers (IL-18 and IL-1β), and cell death-related gene activation were assessed through H&E staining, ELISA, western blot, flow cytometry, and immunofluorescence. The study identified 128 active compounds and 121 gene targets from Ephedra and bitter almond. The PPI network revealed 13 core proteins, and pathway analysis indicated involvement in inflammation, apoptosis, and cell necrosis, particularly the caspase-3 pathway. In vivo results showed that Ephedra and bitter almond treatment significantly mitigated LPS-induced lung injury in mice, reducing lung injury scores and inflammatory marker levels. It also decreased caspase-3 activity and cell death in alveolar macrophages. In conclusion, the active ingredients of Ma Huang-Ku Xing Ren, particularly targeting caspase-3, may effectively treat pediatric pneumonia by reducing apoptosis in alveolar macrophages, as demonstrated by both network pharmacology, bioinformatics analyses, and experimental data.

Herbal combinations against COVID-19: A network pharmacology, molecular docking and dynamics study

OBJECTIVE

The aim of this study is to identify molecules from traditional Chinese medicine (TCM) with potential activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants.

METHODS

We applied the Apriori algorithm to identify important combinations of herbs in the TCM prescriptions for the treatment of coronavirus disease 2019 (COVID-19). Then, we explored the active components and core targets using network pharmacology. In addition, the molecular docking approach was performed to investigate the interaction of these components with the main structural and non-structural proteins, as well as the mutants. Furthermore, their stability in the binding pockets was further evaluated with the molecular dynamics approach.

RESULTS

A combination of Amygdalus Communis Vas., Ephedra Herba and Scutellaria baicalensis Georgi was selected as the important herbal combination, and 11 main components and 20 core targets against COVID-19 were obtained. These components, including luteolin, naringenin, stigmasterol, baicalein, and so on, were the potentially active compounds against COVID-19. The binding affinity of these compounds with the potential targets was as high as the positive controls. Among them, baicalein could interfere with multiple targets simultaneously, and it also interfered with the interaction between spike protein and angiotensin-converting enzyme 2 receptor. Additionally, almost all the systems reached stability during dynamics simulation.

CONCLUSION

The combination of A. communis, Ephedra Herba and S. baicalensis was the most important herbal combination for the treatment of COVID-19. Baicalein may be a potential candidate against SARS-CoV-2 and its variants. Please cite this article as: Song JB, Zhao LQ, Wen HP, Li YP. Herbal combinations against COVID-19: A network pharmacology, molecular docking and dynamics study. J Integr Med. 2023;21(6):593-604.

Xuanfei Baidu decoction in the treatment of coronavirus disease 2019 (COVID-19): Efficacy and potential mechanisms

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide and become a major global public health concern. Although novel investigational COVID-19 antiviral candidates such as the Pfizer agent PAXLOVID™, molnupiravir, baricitinib, remdesivir, and favipiravir are currently used to treat patients with COVID-19, there is still a critical need for the development of additional treatments, as the recommended therapeutic options are frequently ineffective against SARS-CoV-2. The efficacy and safety of vaccines remain uncertain, particularly with the emergence of several variants. All 10 versions of the National Health Commission's diagnosis and treatment guidelines for COVID-19 recommend using traditional Chinese medicine. Xuanfei Baidu Decoction (XFBD) is one of the "three Chinese medicines and three Chinese prescriptions" recommended for COVID-19. This review summarizes the clinical evidence and potential mechanisms of action of XFBD for COVID-19 treatment. With XFBD, patients with COVID-19 experience improved clinical symptoms, shorter hospital stay, prevention of the progression of their symptoms from mild to moderate and severe symptoms, and reduced mortality in critically ill patients. The mechanisms of action may be associated with its direct antiviral, anti-inflammatory, immunomodulatory, antioxidative, and antimicrobial properties. High-quality clinical and experimental studies are needed to further explore the clinical efficacy and underlying mechanisms of XFBD in COVID-19 treatment.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Deciphering suppressive effects of Lianhua Qingwen Capsule on COVID-19 and synergistic effects of its major botanical drug pairs

The COVID-19 pandemic has resulted in excess deaths worldwide. Conventional antiviral medicines have been used to relieve the symptoms, with limited therapeutic effect. In contrast, Lianhua Qingwen Capsule is reported to exert remarkable anti-COVID-19 effect. The current review aims to: 1) uncover the main pharmacological actions of Lianhua Qingwen Capsule for managing COVID-19; 2) verify the bioactive ingredients and pharmacological actions of Lianhua Qingwen Capsule by network analysis; 3) investigate the compatibility effect of major botanical drug pairs in Lianhua Qingwen Capsule; and 4) clarify the clinical evidence and safety of the combined therapy of Lianhua Qingwen Capsule and conventional drugs. Numerous bioactive ingredients in Lianhu Qingwen, such as quercetin, naringenin, β-sitosterol, luteolin, and stigmasterol, were identified to target host cytokines, and to regulate the immune defence in response to COVID-19. Genes including androgen receptor (AR), myeloperoxidase (MPO), epidermal growth factor receptor (EGFR), insulin (INS), and aryl hydrocarbon receptor (AHR) were found to be significantly involved in the pharmacological actions of Lianhua Qingwen Capsule against COVID-19. Four botanical drug pairs in Lianhua Qingwen Capsule were shown to have synergistic effect for the treatment of COVID-19. Clinical studies demonstrated the medicinal effect of the combined use of Lianhua Qingwen Capsule and conventional drugs against COVID-19. In conclusion, the four main pharmacological mechanisms of Lianhua Qingwen Capsule for managing COVID-19 are revealed. Therapeutic effect has been noted against COVID-19 in Lianhua Qingwen Capsule.

The use of Ephedra herbs in the treatment of COVID-19

Objective

Ephedra herbs are the only extant genus in its family, Ephedraceae, and order, Ephedrales. It has been prescribed in traditional medicine for improving headaches and respiratory infections. On the other hand, because the coronavirus disease 2019 (COVID-19) causes respiratory problems and COVID-19 pandemic is the most widespread outbreak that has affected humanity in the last century, the current review aims using literature search to investigate the effects of the Ephedra herbs compounds on COVID-19 to supply a reference for its clinical application in the inhibition and remedy of COVID-19.

Materials and Methods

This review was performed using articles published in various databases, including Web of Science, PubMed, Scopus, and Google Scholar, without a time limit. For this paper, the following keywords were used: "Ephedra", "coronavirus disease 2019", "COVID-19", "Severe acute respiratory syndrome coronavirus 2" or "SARS CoV 2".

Results

The results of this review show that the Ephedra herbs have effectiveness on COVID-19 and its compounds can bind to angiotensin-converting enzyme 2 (ACE2) with a high affinity and act as a blocker and prevent the binding of the virus.

Conclusion

Some plants used in traditional medicine, including the Ephedra herbs, with their active compounds, can be considered a candidate with high potential for the control and prevention of COVID-19.

Herbal medicines exhibit a high affinity for ACE2 in treating COVID-19

Coronavirus Disease 2019 (COVID-19) has been an unprecedented disaster for people around the world. A point particularly worth noting is that herbal medicines have made great contributions to the prevention and treatment of COVID-19 in China. Angiotensin converting enzyme 2 (ACE2) has been identified as the critical functional receptor for SARS-CoV-2. It can bind to the receptor-binding domain (RBD) of the spike protein (S protein), which is responsible for the entry of the coronavirus into host cells. Therefore, ACE2 can be regarded as an important intervention target for COVID-19. Recently, many herbal medicines have exhibited a high affinity for ACE2 in treating COVID-19. The current work summarized these herbal medicines including formulas (such as Lianhua Qingwen capsules, Xuebijing injection, Qingfei Paidu Decoction, Huashi Baidu formula, Shufeng Jiedu capsules, and Maxing Shigan decoction), single herbs including Ephedra sinica Stapf (Mahuang), Scutellariae radix (Huangqin), Lonicera japonica (Jinyinhua), and Houttuynia cordata (Yuxingcao), and active ingredients (such as ursodeoxycholic acid, glycyrrhizic acid, glycyrrhizin, salvianolic acid, quercetin, and andrographidine C), which have exhibited a high affinity for ACE2 in treating COVID-19. We hope this work may provide meaningful and useful information on further research to investigate the mechanisms of herbal medicines against SARS-CoV-2 and follow-up drug discovery.

Ephedrae Herba: A Review of Its Phytochemistry, Pharmacology, Clinical Application, and Alkaloid Toxicity

Ephedrae Herba (Ephedra), known as "MaHuang" in China, is the dried straw stem that is associated with the lung and urinary bladder meridians. At present, more than 60 species of Ephedra plants have been identified, which contain more than 100 compounds, including alkaloids, flavonoids, tannins, sugars, and organic phenolic acids. This herb has long been used to treat asthma, liver disease, skin disease, and other diseases, and has shown unique efficacy in the treatment of COVID-19 infection. Because alkaloids are the main components causing toxicity, the safety of Ephedra must be considered. However, the nonalkaloid components of Ephedra can be effectively used to replace ephedrine extracts to treat some diseases, and reasonable use can ensure the safety of Ephedra. We reviewed the phytochemistry, pharmacology, clinical application, and alkaloid toxicity of Ephedra, and describe prospects for its future development to facilitate the development of Ephedra.

Potential Targets and Mechanisms of Bitter Almond-Licorice for COVID-19 Treatment Based on Network Pharmacology and Molecular Docking

BACKGROUND

The outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in millions of infections and raised global attention. Bitter almonds and licorice are both Traditional Chinese Medicines (TCM), often used in combination to treat lung diseases. Several prescriptions in the guidelines for the diagnosis and treatment of coronavirus disease 2019 (trial version ninth) contained bitter almond-licorice, which was effective in the treatment of COVID-19. However, the active ingredients, drug targets and therapeutic mechanisms of bitter almonds-licorice for the treatment of COVID-19 remain to be elucidated.

METHODS

The active ingredients and targets were derived from the Traditional Chinese Medicine Systems Pharmacology (TCMSP). Meanwhile, targets associated with COVID-19 were obtained from the GeneCards database, PharmGkb database and DrugBank database. Then, the potential targets of bitter almond-licorice against COVID-19 were screened out. Protein-protein interaction (PPI) networks and core targets were analyzed through the String database and Cytoscape software. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed based on potential targets using R statistical software. Finally, molecular docking was used to validate the binding of the active ingredients to the core targets.

RESULTS

The results of the TCMSP database showed that the bitter almond-licorice had 89 active components against COVID-19, involving 102 targets. PPI network and core target analysis indicated that IL-6, TNF, MAPK1, and IL1B were the key targets against COVID-19. In addition, GO and KEGG enrichment analysis showed that the bitter almond-licorice were involved in various biological processes through inflammation-related pathways such as TNF signaling pathway and IL-17 signaling pathway. Finally, molecular docking approaches confirmed the affinity between the active components of the bitter almond-licorice and the therapeutic targets.

CONCLUSION

The bitter almond-licorice could be used to treat COVID-19 by inhibiting inflammatory responses and regulating cellular stress. This work is based on data mining and molecular docking, and the findings need to be interpreted with caution.

Potential Treatment of COVID-19 with Traditional Chinese Medicine: What Herbs Can Help Win the Battle with SARS-CoV-2?

Traditional Chinese medicine (TCM) has been successfully applied worldwide in the treatment of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the pharmacological mechanisms underlying this success remain unclear. Hence, the aim of this review is to combine pharmacological assays based on the theory of TCM in order to elucidate the potential signaling pathways, targets, active compounds, and formulas of herbs that are involved in the TCM treatment of COVID-19, which exhibits combatting viral infections, immune regulation, and amelioration of lung injury and fibrosis. Extensive reports on target screening are elucidated using virtual prediction via docking analysis or network pharmacology based on existing data. The results of these reports indicate that an intricate regulatory mechanism is involved in the pathogenesis of COVID-19. Therefore, more pharmacological research on the natural herbs used in TCM should be conducted in order to determine the association between TCM and COVID-19 and account for the observed therapeutic effects of TCM against COVID-19.

Network analysis-based strategy to investigate the protective effect of cepharanthine on rat acute respiratory distress syndrome

Combined with Network Analysis (NA) and in vivo experimental methods, we explored and verified the mechanism of Cepharanthine (CEP) involved in the treatment of acute respiratory distress syndrome (ARDS). Potential targets of CEP were searched using the SwissTargetPrediction database. The pathogenic genes related to ARDS were obtained using the DisGeNET database. A protein-protein interaction network of common target genes of disease-compound was subsequently built and visualised. Functional enrichment analysis was performed through the Enrichr database. Finally, for in vivo experimental verification, we established an oleic acid-induced ARDS rat model, mainly through histological evaluation and the ELISA method to evaluate both the protective effect of CEP on ARDS and its effect on inflammation. A total of 100 genes were found to be CEP targeted genes, while 153 genes were found to be associated with ARDS. The PPI network was used to illustrate the link and purpose of the genes associated with CEP and ARDS, which contained 238 nodes and 2,333 links. GO and KEGG analyses indicated that inflammatory response and its related signalling pathways were closely associated with CEP-mediated ARDS treatment. Thus, a key CEP–gene–pathway-ARDS network was constructed through network analysis, including 152 nodes (5 targets and 6 pathways) and 744 links. The results of in vivo experiments showed that CEP could alleviate histopathological changes and pulmonary edema related to ARDS, in addition to reducing neutrophil infiltration and secretion of inflammatory cytokines, whilst increasing serum contents of ResolvinD1 and ResolvinE1. Thus, these effects enhance the anti-inflammatory responses. Thus, our results show that CEP can treat oleic acid-induced ARDS in rats via ResolvinE1 and ResolvinD1 signalling pathways that promote inflammation resolution, providing a new avenue to explore for the clinical treatment of ARDS.

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.

Application of network pharmacology in the study of the mechanism of action of traditional chinese medicine in the treatment of COVID-19

Network pharmacology was rapidly developed based on multidisciplinary holistic analysis of biological systems, which has become a popular tool in traditional Chinese medicine (TCM) research in recent years. Its characteristics of integrity and systematization provide a new approach for the study on complex TCM systems, which has many similarities with the holistic concept of TCM. It has been widely used to explain the mechanism of TCM treatment of diseases, drug repositioning, and interpretation of compatibility of TCM prescriptions, to promote the modernization of TCM. The use of TCM have provided crucial support on prevention and treatment of diseases such as the famous “three medicines and three prescriptions”. Furthermore, TCM has become an important part of the treatment of COVID-19 and is one of the main contents of the “Chinese plan” to fight the epidemic. The current review demonstrated the role of TCM in treating diseases with multiple components, multiple targets, and multiple pathways, interprets the connotation of TCM treatment method selection based on pathogenesis and also discusses the application of network pharmacology in the study of COVID-19 treatment in TCM including single drug and prescription. However, there are still some shortcomings such as the lack of experimental verification and regular upgrading of the TCM pharmacology network. Therefore, we must pay attention to the characteristics of TCM and develop a network pharmacology method suitable for TCM system research when applying network pharmacology to TCM research.

Clinical data mining reveals Gancao-Banxia as a potential herbal pair against moderate COVID-19 by dual binding to IL-6/STAT3

BACKGROUND

Coronavirus disease 2019 (COVID-19) keeps spreading globally. Chinese medicine (CM) exerts a critical role for the prevention or therapy of COVID-19 in an integrative and holistic way. However, mining and development of early, efficient, multisite binding CMs that inhibit the cytokine storm are imminent.

METHODS

The formulae were extracted retrospectively from clinical records in Hunan Province. Clinical data mining analysis and association rule analysis were employed for mining the high-frequency herbal pairs and groups from formulae. Network pharmacology methods were applied to initially explore the most critical pair's hub targets, active ingredients, and potential mechanisms. The binding power of active ingredients to the hub targets was verified by molecular docking.

RESULTS

Eight hundred sixty-two prescriptions were obtained from 320 moderate COVID-19 through the Hunan Provincial Health Commission. Glycyrrhizae Radix et Rhizoma (Gancao) and Pinelliae Rhizoma (Banxia) were used with the highest frequency and support. There were 49 potential genes associated with Gancao-Banxia pair against moderate COVID-19 patients. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that Gancao-Banxia might act via inflammatory response, viral defense, and immune responses signaling pathways. IL-6 and STAT3 were the two most hub targets in the protein-protein interaction (PPI) network. The binding of five active ingredients originated from Gancao-Banxia to IL-6-STAT3 was verified by molecular docking, namely quercetin, coniferin, licochalcone a, Licoagrocarpin and (3S,6S)-3-(benzyl)-6-(4-hydroxybenzyl)piperazine-2,5-quinone, maximizing therapeutic efficacy.

CONCLUSIONS

This work provided some potential candidate Chinese medicine formulas for moderate COVID-19. Among them, Gancao-Banxia was considered the most potential herbal pair. Bioinformatic data demonstrated that Gancao-Banxia pair may achieve dual inhibition of IL-6-STAT3 via directly interacting with IL-6 and STAT3, suppressing the IL-6 amplifier. SARS-CoV-2 models will be needed to validate this possibility in the future.

Investigation on anti-Corona viral potential of Yarrow tea

Achillea millefolium (Yarrow) is a herbaceous plant of Greek origin noted to treat pneumonia, common cold, cough, and other respiratory disorders. The flowers and leaves are the core part used to prepare herbal tea that gains the world's recognition as medicinal tea. Coronavirus disease is spreading across the globe, and numerous approaches are lodged to treat virus-induced lung inflammation. Here, we used the network pharmacology, metabolite analysis, docking and molecular simulation and MM-PBSA analysis to comprehend the biochemical basis of the health-boosting impact of Yarrow tea. Next, we performed the microscopic and dynamic light scattering (DLS) analysis of yarrow-treated ChAdOx1 nCoV-19 to evaluate the virucidal activity of the Yarrow. The present study investigates the druggability, metabolites and potential interaction of the title tea with genes associated with Covid-19-induced pathogenesis. Towards this, 1022 gene hits were obtained, 30 are mutually shared. Network Pharmacology and microarray gene expression analysis find the connection of PTGS2 in relieving the virus-induced inflammation. Yarrow constituents Luteolin may inhibit or down-regulate the Cyclooxygenase II (PTGS2), a plausible mechanism underlying the Yarrow's anti-inflammatory actions. Further, the Yarrow's virucidal activity was assessed towards Transmission Electron Microscopic (TEM). The Yarrow treated SARS-nCoV-2 cell exhibits the disintegration of the virus membrane. This work provides a scientific basis for further elucidating the mechanism underlying Achillea millefolium's antiviral and anti-inflammatory properties.

Network pharmacology combined with molecular docking to explore the potential mechanisms for the antioxidant activity of Rheum tanguticum seeds

BACKGROUND

Rheum tanguticum (R. tanguticum) is an edible and medicinal plant that exhibits high antioxidant activity. The purpose of the present study was to investigate the bioactive components of its seeds and the potential mechanisms of antioxidant activity to provide a foundation for further developmental work on R. tanguticum seeds as a functional food.

METHODS

In this study, the antioxidant activities of R. tanguticum seeds were measured using DPPH, ABTS and FRAP assays. LC-Q-TOF/MS was used to identify the active compounds in the seeds, and Swiss Target Prediction was used to identify their potential targets. The DisGENET, DrugBank, OMIM and GeneCard databases were used to search for antioxidant-related targets.

RESULTS

The component-target-pathway network was constructed and included 5 compounds and 9 target genes. The hub genes included ESR1, APP, MAPK8, HSP90AA1, AKT1, MMP2, PTGS2, TGFB1 and JUN. The antioxidant activity signaling pathways of the compounds for the treatment of diseases were the cancer signaling pathway, estrogen signaling pathway, colorectal cancer signaling pathway, MAPK signaling pathway, etc. Molecular docking revealed that the compounds in R. tanguticum seeds could inhibit potential targets (AKT1, ESR1 and PTGS2).

CONCLUSION

Molecular docking studies revealed that the binding energy score between liriodenine and PTGS2 was the highest (8.16), followed by that of chrysophanol (7.10). This result supports the potential for PTGS2-targeted drug screening and design.

Revealing Calcium Signaling Pathway as Novel Mechanism of Danhong Injection for Treating Acute Myocardial Infarction by Systems Pharmacology and Experiment Validation

Introduction: Danhong injection (DHI) is a traditional Chinese medicine preparation commonly used in the clinical treatment of acute myocardial infarction (AMI). In this study, the active components of DHI and its mechanism in the treatment of AMI were investigated.

Methods: The chemical components of DHI were detected by the ultra-high-performance liquid chromatography-linear trap quadrupole-orbitrap-tandem mass spectrometry (UHPLC-LTQ-Orbitrap-MS/MS), and the targets and pathways of DHI in the treatment of AMI were analyzed by systems pharmacology, which was verified by molecular docking and animal experiments.

Results: A total of 12 active components of DHI were obtained, and 158 common targets of component and disease were identified by systems pharmacology. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results showed that DHI is closely related to the calcium signaling pathway in the treatment of AMI. Molecular docking showed that the key target protein has good binding affinity to related compounds. The experimental results showed that compared with the model group, LVAWs, EF, and FS significantly (p < 0.05) increased in the DHI group. The percentage of myocardial infarction significantly (p < 0.01) decreased, both in the ventricular and total cardiac regions, and the pathological damage of myocardial tissue also decreased. In addition, the expression of the protein CaMK II decreased (p < 0.01) and the expression of SERCA significantly increased (p < 0.01).

Conclusion: This study revealed that ferulic acid, caffeic acid and rosmarinic acid could inhibit AMI by regulating PLB, CaMK II, SERCA, etc. And mechanistically, calcium signaling pathway was critically involved. Combination of systems pharmacology prediction with experimental validation may provide a scientific basis for in-depth clinical investigation of the material basis of DHI.

Efficacy and mechanisms of traditional Chinese medicine for COVID-19: a systematic review

Since the outbreak of coronavirus disease 2019 (COVID-19), traditional Chinese medicine (TCM) has made an important contribution to the prevention and control of the epidemic. This review aimed to evaluate the efficacy and explore the mechanisms of TCM for COVID-19. We systematically searched 7 databases from their inception up to July 21, 2021, to distinguish randomized controlled trials (RCTs), cohort studies (CSs), and case–control studies (CCSs) of TCM for COVID-19. Two reviewers independently completed the screening of literature, extraction of data, and quality assessment of included studies. Meta-analysis was performed using Review Manager 5.4 software. Eventually, 29 RCTs involving 3060 patients and 28 retrospective studies (RSs) involving 12,460 patients were included. The meta-analysis demonstrated that TCM could decrease the proportion of patients progressing to severe cases by 55% and the mortality rate of severe or critical patients by 49%. Moreover, TCM could relieve clinical symptoms, curtail the length of hospital stay, improve laboratory indicators, and so on. In addition, we consulted the literature and obtained 149 components of Chinese medicinal herbs that could stably bind to antiviral targets or anti-inflammatory or immune-regulating targets by the prediction of molecular docking. It suggested that the mechanisms involved anti-virus, anti-inflammation, and regulation of immunity. Our study made a systematic review on the efficacy of TCM for COVID-19 and discussed the possible mechanisms, which provided clinical reference and theoretical basis for further research on the mechanism of TCM for COVID-19.

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

BACKGROUND

A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19.

METHODS

In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results.

RESULTS

In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs.

CONCLUSIONS

Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

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.

Screening S protein - ACE2 blockers from natural products: Strategies and advances in the discovery of potential inhibitors of COVID-19

The Coronavirus disease, 2019 (COVID-19) is caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), which poses a major threat to human life and health. Given its continued development, limiting the spread of COVID-19 in the population remains a challenging task. Currently, multiple therapies are being tried around the world to deal with SARS-CoV-2 infection, and a variety of studies have shown that natural products have a significant effect on COVID-19 patients. The combination of SARS-CoV-2 S protein with Angiotensin converting enzyme II(ACE2) of host cell to promote membrane fusion is an initial critical step for SARS-CoV-2 infection. Therefore, screening natural products that inhibit the binding of SARS-CoV-2 S protein and ACE2 also provides a feasible strategy for the treatment of COVID-19. Establishment of high throughput screening model is an important basis and key technology for screening S protein-ACE2 blockers. Based on this, the molecular structures of SARS-CoV-2 and ACE2 and their processes in the life cycle of SARS-CoV-2 and host cell infection were firstly reviewed in this paper, with emphasis on the methods and techniques of screening S protein-ACE2 blockers, including Virtual Screening (VS), Surface Plasmon Resonance (SPR), Biochromatography, Biotin-avidin with Enzyme-linked Immunosorbent assay and Gene Chip Technology. Furthermore, the technical principle, advantages and disadvantages and application scope were further elaborated. Combined with the application of the above screening technologies in S protein-ACE2 blockers, a variety of natural products, such as flavonoids, terpenoids, phenols, alkaloids, were summarized, which could be used as S protein-ACE2 blockers, in order to provide ideas for the efficient discovery of S protein-ACE2 blockers from natural sources and contribute to the development of broad-spectrum anti coronavirus drugs.

Anti-Osteogenic Effect of Danshensu in Ankylosing Spondylitis: An in Vitro Study Based on Integrated Network Pharmacology

Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by abnormal bone metabolism, with few effective treatments available. Danshensu [3-(3,4-dihydroxy-phenyl) lactic acid) is a bioactive compound from traditional Chinese medicine with a variety of pharmacologic effects. In the present study, we investigated the pharmacologic effect and molecular mechanism of Danshensu in AS. Potential targets of Danshensu were identified in four drugs-genes databases; and potential pharmacologic target genes in AS were identified in three diseases-genes databases. Differentially expressed genes related to AS were obtained from the Gene Expression Omnibus database. Overlapping targets of Danshensu and AS were determined and a disease–active ingredient–target interaction network was constructed with Cytoscape software. Enrichment analyses of the common targets were performed using Bioconductor. To test the validity of the constructed network, an in vitro model was established by treating osteoblasts from newborn rats with low concentrations of tumor necrosis factor (TNF)-α. Then, the in vitro model and AS fibroblasts were treated with Danshensu (1–10 μM). Osteogenesis was evaluated by alkaline phosphatase staining and activity assay, alizarin red staining, quantitative PCR, and western blotting. We identified 2944 AS-related genes and 406 Danshensu targets, including 47 that were common to both datasets. The main signaling pathways associated with the targets were the c-Jun N-terminal kinase (JNK) and extracellular signal-regulated kinase (ERK) pathways. A low concentration of TNF-α (0.01 ng/ml) promoted the differentiation of osteoblasts; this was inhibited by Danshensu, which had the same effect on AS fibroblasts but had the opposite effect on normal osteoblasts. Danshensu also decreased the phosphorylation of JNK and ERK in AS fibroblasts. There results provide evidence that Danshensu exerts an anti-osteogenic effect via suppression of JNK and ERK signaling, highlighting its therapeutic potential for the treatment of AS.

The effect and mechanism of cypermethrin-induced hippocampal neurotoxicity as determined by network pharmacology analysis and experimental validation

Cypermethrin (CMN) is a widely used artificial synthetic pesticide that causes neurotoxicity in the hippocampus. However, the underlying toxicological targets and mechanisms remain unclear. In this study, network pharmacology analysis and in vitro models were integrated to investigate the effect and mechanism of CMN-induced hippocampal neurotoxicity. A total of 88 targets of CMN-induced hippocampal neurotoxicity were predicted. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG) analyses suggested that these targets were related to multiple GO terms and signaling pathways. To further investigate underlying mechanism, the top 10 hub targets (Akt1, Tnf, Ptgs2, Casp3, Igf1, Sirt1, Jun, Cat, Il10, and Bcl2l1) were screened. Furthermore, cell viability and lactate dehydrogenase (LDH) assays demonstrated that CMN was toxic to HT22 cells in a time- and dose-dependent manner. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining revealed that treatment with CMN increased the proportion of apoptotic cells. In addition, the real-time quantitative polymerase chain reaction (RT-qPCR) results indicated that CMN altered the mRNA expression levels of most of the hub targets, with the exceptions of Igf1 and Jun. The results demonstrated that multiple targets and signaling pathways were involved in CMN-induced hippocampal neurotoxicity. These findings provided reference values for subsequent studies of the toxicological mechanism of CMN.

Transcriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker

Background

Mastitis is one of the major diseases causing economic loss to the dairy industry by reducing the quantity and quality of milk. Thus, the objective of this scientific study was to find new biomarkers based on genes for the early prediction before its severity.

Methods

In the present study, advanced bioinformatics including hierarchical clustering, enrichment analysis, active site prediction, epigenetic analysis, functional domain identification, and protein docking were used to analyze the important genes that could be utilized as biomarkers and therapeutic targets for mastitis.

Results

Four differentially expressed genes (DEGs) were identified in different regions of the mammary gland (teat cistern, gland cistern, lobuloalveolar, and Furstenberg’s rosette) that resulted in 453, 597, 577, and 636 DEG, respectively. Also, 101 overlapped genes were found by comparing 27 different expressed genes. These genes were associated with eight immune response pathways including NOD-like receptor signaling pathway (IL8, IL18, IL1B, PYDC1) and chemokine signaling pathway (PTK2, IL8, NCF1, CCR1, HCK). Meanwhile, 241 protein-protein interaction networks were developed among overlapped genes. Fifty-seven regulatory events were found between miRNAs, expressed genes, and the transcription factors (TFs) through micro-RNA and transcription factors (miRNA-DEG-TF) regulatory network. The 3D structure docking model of the expressed genes proteins identified their active sites and the binding ligands that could help in choosing the appropriate feed or treatment for affected animals.

Conclusions

The novelty of the distinguished DEG and their pathways in this study is that they can precisely improve the detection biomarkers and treatments techniques of cows’ Escherichia coli mastitis disease due to their high affinity with the target site of the mammary gland before appearing the symptoms.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-021-00235-x.

Network Analysis of Herbs Recommended for the Treatment of COVID-19

PURPOSE

In this study, we aimed to identify the pattern and combination of herbs used in the formulae recommended for treating different stages of COVID-19 using a network analysis approach.

METHODS

The herbal formulae recommended by official guidelines for the treatment of COVID-19 are included in the present analysis. To describe the tendency of herbs to form a "herb pair", we computed the mutual information (MI) value and distance-based mutual information model (DMIM) score. We also performed modularity, degree, betweenness, and closeness centrality analysis. Network analyses were performed and visualized for each disease stage.

RESULTS

A total of 142 herbal formulae comprising 416 herbs were analyzed. All possible herbal pairs were examined, and the top frequently used herbal pairs were identified for each disease stage. The herb Glycyrrhizae radix et rhizoma is only identified in one herb pair, even though this herb is identified as one of the herbs with high frequency of use for every disease stage. This suggests that the DMIM score could be used to identify the optimal combination rule of herbal formulae by achieving a balance among the herbs' frequency and relative distance in herbal formulae.

CONCLUSION

Our results presented the prescription patterns and herbal combinations of the herbal formulae recommended for the treatment of COVID-19. This study may provide new insights and ideas for clinical research in the future.