Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients

Drug repurposing to tackle parainfluenza 3 based on multi-similarities and network proximity analysis

Given that there is currently no clinically approved drug or vaccine for parainfluenza 3 (PIV3), we applied a drug repurposing method based on disease similarity and chemical similarity to screen 2,585 clinically approved chemical drugs using PIV3 potential drugs BCX-2798 and zanamivir as our controls. Twelve candidate drugs were obtained after being screened with good disease similarity and chemical similarity (S > 0.50, T > 0.56). When docking them with the PIV3 target protein, hemagglutinin-neuraminidase (HN), only oseltamivir was docked with a better score than BCX-2798, which indicates that oseltamivir has an inhibitory effect on PIV3. After the distance (Z d c ) between the drug target of 14 drugs and the PIV3 disease target was measured by the network proximity method based on the PIV3 disease module, it was found that theZ d c values of amikacin, oseltamivir, ribavirin, and streptomycin were less than those of the control. Thus, oseltamivir is the best potential drug because it met all the above screening requirements. Additionally, to explore whether oseltamivir binds to HN stably, molecular dynamics simulation of the binding of oseltamivir to HN was carried out, and the results showed that the RMSD value of the complex tended to be stable within 100 ns, and the binding free energy of the complex was low (-10.60 kcal/mol). It was proved that oseltamivir screened by our drug repurposing method had the potential feasibility of treating PIV3.

Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers

The assessment of the allergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Network-based analysis identifies potential therapeutic ingredients of Chinese medicines and their mechanisms toward lung cancer

Lung cancer is one of the most common malignant tumors around the world, which has the highest mortality rate among all cancers. Traditional Chinese medicine (TCM) has attracted increased attention in the field of lung cancer treatment. However, the abundance of ingredients in Chinese medicines presents a challenge in identifying promising ingredient candidates and exploring their mechanisms for lung cancer treatment. In this work, two network-based algorithms were combined to calculate the network relationships between ingredient targets and lung cancer targets in the human interactome. Based on the enrichment analysis of the constructed disease module, key targets of lung cancer were identified. In addition, molecular docking and enrichment analysis of the overlapping targets between lung cancer and ingredients were performed to investigate the potential mechanisms of ingredient candidates against lung cancer. Ten potential ingredients against lung cancer were identified and they may have similar effect on the development of lung cancer. The results obtained from this study offered valuable insights and provided potential avenues for the development of novel drugs aimed at treating lung cancer.

Unveiling the mechanisms of nephrotoxicity caused by nephrotoxic compounds using toxicological network analysis

Billions of people worldwide have experienced irreversible kidney injuries, which is mainly attributed to the complexity of drug-induced nephrotoxicity. Consequently, there is an urgent need for uncovering the mechanisms of nephrotoxicity caused by compounds. In the present study, a network-based methodology was applied to explore the mechanisms of nephrotoxicity induced by specific compounds. Initially, a total of 42 nephrotoxic compounds and 60 kinds of syndromes associated with nephrotoxicity were collected from public resources. Afterward, network localization and separation algorithms were used to map the targets of compounds and diseases into the human interactome. By doing so, 199 statistically significant nephrotoxic networks displaying the interaction between compound targets and disease genes were obtained, which played pivotal roles in compounds-induced nephrotoxicity. Subsequently, enrichment analysis pinpointed core Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways that highlight commonalities in nephrotoxicity induced by nephrotoxic compounds. It was found that nephrotoxic compounds primarily induce nephrotoxicity by mediating the advanced glycosylation end products-receptor for advanced glycosylation end products signaling pathway in diabetic complications, human cytomegalovirus infection, lipid and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, apoptosis, and the phosphatidylinositol 3-kinase-Akt pathways. These results provide valuable insights for preventing drug-induced nephrotoxicity. Furthermore, the approaches we used are also helpful in conducting research on other kinds of toxicities.

Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine

Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at the level of biological targets and pathways. The effective mechanism study of traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, and integrative efficacy, perfectly corresponds to the application of network pharmacology. Currently, network pharmacology has been widely utilized to clarify the mechanism of the physiological activity of TCM. In this review, we comprehensively summarize the application of network pharmacology in TCM to reveal its potential of verifying the phenotype and underlying causes of diseases, realizing the personalized and accurate application of TCM. We searched the literature using "TCM network pharmacology" and "network pharmacology" as keywords from Web of Science, PubMed, Google Scholar, as well as Chinese National Knowledge Infrastructure in the last decade. The origins, development, and application of network pharmacology are closely correlated with the study of TCM which has been applied in China for thousands of years. Network pharmacology and TCM have the same core idea and promote each other. A well-defined research strategy for network pharmacology has been utilized in several aspects of TCM research, including the elucidation of the biological basis of diseases and syndromes, the prediction of TCM targets, the screening of TCM active compounds, and the decipherment of mechanisms of TCM in treating diseases. However, several factors limit its application, such as the selection of databases and algorithms, the unstable quality of the research results, and the lack of standardization. This review aims to provide references and ideas for the research of TCM and to encourage the personalized and precise use of Chinese medicine.

Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis .

(R)-(+)-Rosmarinic Acid as an Inhibitor of Herpes and Dengue Virus Replication: an In Silico Assessment

Since ancient times, viruses such as dengue, herpes, Ebola, AIDS, influenza, chicken meat, and SARS have been roaming around causing great health burdens. Currently, the prescribed antiviral drugs have not cured the complications caused by viruses, whereas viral replication was not controlled by them. The treatments suggested are not only ineffectual, but also sometimes inefficient against viruses at all stages of the viral cycle as well. To fight against these contagious viruses, people rely heavily on medicinal plants to enhance their innate and adaptive immune systems. In this research, the preparation of ligands and proteins was performed using the Maestro V.13.2 module tool. This software, consisting of LigPrep, Grid Generation, SiteMap, and Glide XP, has each contributed significantly to the preparation of ligands and proteins. Ultimately, the research found that (R)-(+)-rosmarinic acid was found to have significant docking scores of - 10.847 for herpes virus, of - 10.033 for NS5, and - 7.259 for NS1. In addition, the Prediction of Activity Spectra for Substances (PASS) server indicates that rosmarinic acid possesses a diverse spectrum of enzymatic activities, as probability active (Pa) values start at > 0.751, whereas it has fewer adverse effects than the drugs prescribed for viruses. Accordingly, it was found the rate of acute toxicity values of (R)-(+)-rosmarinic acid at doses LD50 log10 (mmol/g) and LD50 (mg/g) in different routes of administration, such as intraperitoneal, intravenous, oral, and subcutaneous. Ultimately, the present study concluded that (R)-(+)-rosmarinic acid would expose significant antiviral effects in in vitro and in vivo experiments, and this research would be a valuable asset for the future, especially for those who wish to discover a drug molecule for a variety of viruses.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s43450-023-00381-y.

Kaempferol has potential anti-coronavirus disease 2019 (COVID-19) targets based on bioinformatics analyses and pharmacological effects on endotoxin-induced cytokine storm

COVID-19 has infected 272 million patients and caused 5.33 million deaths around the world, and it remains the main global threat. Previous studies revealed that Chinese traditional medicine is an effective treatment for COVID-19 infection. This study aims to reveal the pharmacological effects of kaempferol, which is the active component of Radix Bupleuri and Tripterygii Radix, and potential mechanisms for the treatment of COVID-19. Here, we employed the bioinformatics methods to filter the anti-COVID-19 candidate genes of kaempferol, which mainly enriched in inflammation (TNF, JUN, etc.) and virus infection (AKT1, JNK, etc.). The Transcription levels of AKT1, JNK and JUN were significantly reduced by kaempferol treatment in the LPS-activated macrophages. In addition, kaempferol reduced the secretion of inflammatory factors by LPS-stimulated macrophages, inhibited MAPK/NF-κB signaling and regulated macrophage polarization to M2 type in vitro, and suppressed endotoxin-induced cytokine storm and improved survival in mice. Molecular docking analysis demonstrated that kaempferol was probable to bind the COVID-19 protein 5R84 and formatted hydrogen bond with the residues, the free binding energy of which was lower than the original ligand. In summary, our current work indicates that kaempferol has anti-COVID-19 potential through the reduction of COVID-19-induced body dysfunction and molecule-protein interaction, and bioinformatics results clarify that some of these key target genes might serve as potential molecular markers for detecting COVID-19.

Systems biology approach reveals a common molecular basis for COVID-19 and non-alcoholic fatty liver disease (NAFLD)

Background

Patients with non-alcoholic fatty liver disease (NAFLD) may be more susceptible to coronavirus disease 2019 (COVID-19) and even more likely to suffer from severe COVID-19. Whether there is a common molecular pathological basis for COVID-19 and NAFLD remains to be identified. The present study aimed to elucidate the transcriptional alterations shared by COVID-19 and NAFLD and to identify potential compounds targeting both diseases.

Methods

Differentially expressed genes (DEGs) for COVID-19 and NAFLD were extracted from the GSE147507 and GSE89632 datasets, and common DEGs were identified using the Venn diagram. Subsequently, we constructed a protein–protein interaction (PPI) network based on the common DEGs and extracted hub genes. Then, we performed gene ontology (GO) and pathway analysis of common DEGs. In addition, transcription factors (TFs) and miRNAs regulatory networks were constructed, and drug candidates were identified.

Results

We identified a total of 62 common DEGs for COVID-19 and NAFLD. The 10 hub genes extracted based on the PPI network were IL6, IL1B, PTGS2, JUN, FOS, ATF3, SOCS3, CSF3, NFKB2, and HBEGF. In addition, we also constructed TFs–DEGs, miRNAs–DEGs, and protein–drug interaction networks, demonstrating the complex regulatory relationships of common DEGs.

Conclusion

We successfully extracted 10 hub genes that could be used as novel therapeutic targets for COVID-19 and NAFLD. In addition, based on common DEGs, we propose some potential drugs that may benefit patients with COVID-19 and NAFLD.

Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review

Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.

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.

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.