Integrated bioinformatics and network pharmacology to identify the therapeutic target and molecular mechanisms of Huangqin decoction on ulcerative Colitis

Huangqin decoction (HQD) is a Traditional Chinese Medicine formula for ulcerative colitis. However, the pharmacology and molecular mechanism of HQD on ulcerative colitis is still unclear. Combined microarray analysis, network pharmacology, and molecular docking for revealing the therapeutic targets and molecular mechanism of HQD against ulcerative colitis. TCMSP, DrugBank, Swiss Target Prediction were utilized to search the active components and effective targets of HQD. Ulcerative colitis effective targets were obtained by microarray data from the GEO database (GSE107499). Co-targets between HQD and ulcerative colitis are obtained by Draw Venn Diagram. PPI (Protein–protein interaction) network was constructed by the STRING database. To obtain the core target, topological analysis is exploited by Cytoscape 3.7.2. GO and KEGG enrichment pathway analysis was performed to Metascape platform, and molecular docking through Autodock Vina 1.1.2 finished. 161 active components with 486 effective targets of HQD were screened. 1542 ulcerative colitis effective targets were obtained with |Log2FC|> 1 and adjusted P-value < 0.05. The Venn analysis was contained 79 co-targets. Enrichment analysis showed that HQD played a role in TNF signaling pathway, IL-17 signaling pathway, Th17 cell differentiation, etc. IL6, TNF, IL1B, PTGS2, ESR1, and PPARG with the highest degree from PPI network were successfully docked with 19 core components of HQD, respectively. According to ZINC15 database, quercetin (ZINC4175638), baicalein (ZINC3871633), and wogonin (ZINC899093) recognized as key compounds of HQD on ulcerative colitis. PTGS2, ESR1, and PPARG are potential therapeutic targets of HQD. HQD can act on multiple targets through multi-pathway, to carry out its therapeutic role in ulcerative colitis.

Gut Microbiome Distinguishes Patients With Epilepsy From Healthy Individuals

Objective: The gut microecosystem is the largest microecosystem in the human body and has been proven to be linked to neurological diseases. The main objective of this study was to characterize the fecal microbiome, investigate the differences between epilepsy patients and healthy controls, and evaluate the potential efficacy of the fecal microbiome as a diagnostic tool for epilepsy.Design: We collected 74 fecal samples from epilepsy patients (Eps, n = 24) and healthy controls (HCs, n = 50) in the First Affiliated Hospital of Zhengzhou University and subjected the samples to 16S rRNA MiSeq sequencing and analysis. We set up a train set and a test set, identified the optimal microbial markers for epilepsy after characterizing the gut microbiome in the former and built a diagnostic model, then validated it in the validation group.Results: There were significant differences in microbial communities between the two groups. The α-diversity of the HCs was higher than that of the epilepsy group, but the Venn diagram showed that there were more unique operational taxonomic unit (OTU) in the epilepsy group. At the phylum level, Proteobacteria and Actinobacteriota increased significantly in Eps, while the relative abundance of Bacteroidota increased in HCs. Compared with HCs, Eps were enriched in 23 genera, including Faecalibacterium, Escherichia-Shigella, Subdoligranulum and Enterobacteriaceae-unclassified. In contrast, 59 genera including Bacteroides, Megamonas, Prevotella, Lachnospiraceae-unclassified and Blautia increased in the HCs. In Spearman correlation analysis, age, WBC, RBC, PLT, ALB, CREA, TBIL, Hb and Urea were positively correlated with most of the different OTUs. Seizure-type, course and frequency are negatively correlated with most of the different OTUs. In addition, twenty-two optimal microbial markers were identified by a fivefold cross-validation of the random forest model. In the established train set and test set, the area under the curve was 0.9771 and 0.993, respectively.Conclusion: Our study was the first to characterize the gut microbiome of Eps and HCs in central China and demonstrate the potential efficacy of microbial markers as a noninvasive biological diagnostic tool for epilepsy.

Identification of a Key Glioblastoma Candidate Gene, FUBP3, Based on Weighted Gene Co-expression Network Analysis

Background: Glioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene.Methods: Two GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was used to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was used for survival analysis to determine the significant gene, FUBP3. OS,DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis.Results: GSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4+ T cells, CD8+ T cells, and macrophages) and FUBP3.Conclusion: FUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

Study on the Mechanism of Compound Kidney-Invigorating Granule for Osteoporosis based on Network Pharmacology and Experimental Verification

Background. This study used a combination of network pharmacology and experimental confirmation to clarify the mechanism of the compound kidney-invigorating granule (CKG) in treating osteoporosis (OP). Methods. The main bioactive compounds and corresponding targets of CKG were collected and screened via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Yet another Traditional Chinese Medicine (YaTCM), and UniProt databases. Disease targets of OP were summarized in GeneCards and the Comparative Toxicogenomics Database (CTD). Targets of CKG for OP were obtained by Venn diagram. The protein-protein interaction (PPI) network was constructed by the STRING database and then screened for hub genes through Cytoscape 3.7.2 software. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed and visualized by R software. Then, CB-Dock was used for molecular docking verification. Finally, we confirmed the antiosteoporosis effect of CKG through animal and cell experiments. Results. A total of 250 putative targets were obtained from 65 bioactive compounds in CKG. Among them, 140 targets were related to OP. Topological analysis of the PPI network yielded 23 hub genes. Enrichment analysis showed the targets of CKG in treating OP might concentrate on the MAPK signaling pathway, the TNF signaling pathway, the PI3K-Akt signaling pathway, etc. The results of molecular docking showed the bioactive components in CKG had good binding ability with the key targets. The experimental results showed that CKG-medicated serum had a promoting effect on proliferating hBMSCs, increasing the expression of AKT, PI3K, ERK1, and IkB in cells and decreasing the expression of IKK in cells. Conclusion. CKG has a complex of multicomponent, multitarget, and multipathway. This study lays the theoretical foundation for further in vitro and in vivo experimental studies and further expands the clinical applications of CKG.

Target Deconvolution of Fenofibrate in Nonalcoholic Fatty Liver Disease Using Bioinformatics Analysis

Background. Nonalcoholic fatty liver disease (NAFLD) is a prevalent form of liver damage, affecting ~25% of the global population. NAFLD comprises a spectrum of liver pathologies, from hepatic steatosis to nonalcoholic steatohepatitis (NASH), and may progress to liver fibrosis and cirrhosis. The presence of NAFLD correlates with metabolic disorders such as hyperlipidemia, obesity, blood hypertension, cardiovascular, and insulin resistance. Fenofibrate is an agonist drug for peroxisome proliferator-activated receptor alpha (PPARα), used principally for treatment of hyperlipidemia. However, fenofibrate has recently been investigated in clinical trials for treatment of other metabolic disorders such as diabetes, cardiovascular disease, and NAFLD. The evidence to date indicates that fenofibrate could improve NAFLD. While PPARα is considered to be the main target of fenofibrate, fenofibrate may exert its effect through impact on other genes and pathways thereby alleviating, and possibly reversing, NAFLD. In this study, using bioinformatics tools and gene-drug, gene-diseases databases, we sought to explore possible targets, interactions, and pathways involved in fenofibrate and NAFLD. Methods. We first determined significant protein interactions with fenofibrate in the STITCH database with high confidence (0.7). Next, we investigated the identified proteins on curated targets in two databases, including the DisGeNET and DISEASES databases, to determine their association with NAFLD. We finally constructed a Venn diagram for these two collections (curated genes-NAFLD and fenofibrate-STITCH) to uncover possible primary targets of fenofibrate. Then, Gene Ontology (GO) and KEGG were analyzed to detect the significantly involved targets in molecular function, biological process, cellular component, and biological pathways. A P value < 0.01 was considered the cut-off criterion. We also estimated the specificity of targets with NAFLD by investigating them in disease-gene associations (STRING) and EnrichR (DisGeNET). Finally, we verified our findings in the scientific literature. Results. We constructed two collections, one with 80 protein-drug interactions and the other with 95 genes associated with NAFLD. Using the Venn diagram, we identified 11 significant targets including LEP, SIRT1, ADIPOQ, PPARA, SREBF1, LDLR, GSTP1, VLDLR, SCARB1, MMP1, and APOC3 and then evaluated their biological pathways. Based on Gene Ontology, most of the targets are involved in lipid metabolism, and KEGG enrichment pathways showed the PPAR signaling pathway, AMPK signaling pathway, and NAFLD as the most significant pathways. The interrogation of those targets on authentic disease databases showed they were more specific to both steatosis and steatohepatitis liver injury than to any other diseases in these databases. Finally, we identified three significant genes, APOC3, PPARA, and SREBF1, that showed robust drug interaction with fenofibrate. Conclusion. Fenofibrate may exert its effect directly or indirectly, via modulation of several key targets and pathways, in the treatment of NAFLD.

Network Pharmacology-Based Prediction and Verification of Shikonin for the mechanism treating colorectal cancer

Background: Shikonin (SKN), a naturally occurring naphthoquinone, is a major active chemical component isolated from Lithospermum erythrorhizon Sieb Zucc, Arnebia euchroma (Royle) Johnst, or Arnebia guttata Bunge, and commonly used to treat viral infection, inflammation, and cancer. However, the underlying mechanism has not been elucidated Objective: This study aims to explore the antitumor mechanism of SKN in colorectal cancer (CRC) through network pharmacology and cell experiments. Methods: Using SymMap database and Genecards to predict the potential targets of SKN and CRC, while the cotargets were obtained by Venn diagram. The cotargets were imported into website of String and DA DAVID, constructing the protein-protein interaction (PPI) network, performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, the Compound-Target-Pathway (C-T-P) network was generated by connecting potential pathways with the corresponding targets. Results: According to the results of network pharmacological analysis, the cell experiments were used to verify the key signal pathway. The most relevant target of SKN for the treatment of CRC was PI3K/Akt signaling pathway. SKN inhibited CRC cells (HT29 and HCT116) proliferation, migration, and invasion, and promoted cell apoptosis by targeting IL6 and inhibiting the IL6R/PI3K/Akt signaling pathway. SKN promotes apoptosis and suppresses CRC cells (HT29 and HCT116) activity through the PI3K-Akt signaling pathway. Conclusion: This research not only provides a theoretical and experimental basis for more in-depth studies but also offers an efficient method for the rational utilization of a series of Traditional Chinese medicines as anti-CRC drugs.

Gene expression trend changes in breast cancer populations over two decades: insights from The Cancer Genome Atlas database

Background Breast cancer has remained the most common malignancy in women over the past two decades. As lifestyle and living environments have changed, alterations to the disease spectrum have inevitably occurred in this time. As molecular profiling has become a routine diagnostic and objective indicator of breast cancer etiology, we analyzed changes in gene expression in breast cancer populations over two decades using The Cancer Genome Atlas (TCGA). Methods We performed Heatmap and Venn diagram analyses to identify constantly up- and down-regulated genes in this cohort. We used Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to visualize associated functional pathways. Results We determined that three oncogenes, PD-L2, ETV5, and MTOR and 113 long intergenic non-coding RNAs (lincRNAs) were constantly up-regulated, whereas two oncogenes, BCR and GTF2I, one tumor suppression gene (TSG) MEN1, and 30 lincRNAs were constantly down-regulated. Up-regulated genes were enriched in “focal adhesion” and “PI3K-Akt signaling” pathways, et al, and down-regulated genes were significantly enriched in “metabolic pathways” and “viral myocarditis”. Eight up-regulated genes exhibited doubled or higher expression, and the expression of three down-regulated genes was halved or lowered and correlated with long-term survival. Conclusions In this study, we determined that genes and molecular pathways are constantly changing, importantly, some altered genes were associated with prognostics and are potential therapeutic targets, suggesting molecular typing technologies must keep pace with this dynamic situation.

Zoledronic acid promotes osteoclasts ferroptosis by inhibiting FBXO9-mediated p53 ubiquitination and degradation

Bisphosphonates (BPs)-related osteonecrosis of jaw (BRONJ) is a severe complication of the long-term administration of BPs. The development of BRONJ is associated with the cell death of osteoclasts, but the underlying mechanism remains unclear. In the current study, the role of Zoledronic acid (ZA), a kind of bisphosphonates, in suppressing the growth of osteoclasts was investigated and its underlying mechanism was explored. The role of ZA in regulating osteoclasts function was evaluated in the RANKL-induced cell model. Cell viability was assessed by cell counting kit-8 (CCK-8) assay and fluorescein diacetate (FDA)-staining. We confirmed that ZA treatment suppressed cell viability of osteoclasts. Furthermore, ZA treatment led to osteoclasts death by facilitating osteoclasts ferroptosis, as evidenced by increased Fe2+, ROS, and malonyldialdehyde (MDA) level, and decreased glutathione peroxidase 4 (GPX4) and glutathione (GSH) level. Next, the gene expression profiles of alendronate- and risedronate-treated osteoclasts were obtained from Gene Expression Omnibus (GEO) dataset, and 18 differentially expressed genes were identified using venn diagram analysis. Among these 18 genes, the expression of F-box protein 9 (FBXO9) was inhibited by ZA treatment. Knockdown of FBXO9 resulted in osteoclasts ferroptosis. More important, FBXO9 overexpression repressed the effect of ZA on regulating osteoclasts ferroptosis. Mechanistically, FBXO9 interacted with p53 and decreased the protein stability of p53. Collectively, our study showed that ZA induced osteoclast cells ferroptosis by triggering FBXO9-mediated p53 ubiquitination and degradation.

Some Possibilities of Modeling Colored Petri Nets

Petri nets are a mathematical apparatus for modelling dynamic discrete systems. Their feature is the ability to display parallelism, asynchrony and hierarchy. First was described by Karl Petri in 1962 [1,2,8]. The Petri net is a bipartite oriented graph consisting of two types of vertices - positions and transitions connected by arcs between each other; vertices of the same type cannot be directly connected. Positions can be placed by tags (markers) that can move around the network. [2] Petri Nets (PN) used for modelling real systems is sometimes referred to as Condition/Events nets. Places identify the conditions of the parts of the system (working, idling, queuing, and failing), and transitions describe the passage from one state to another (end of a task, failure, repair...). An event occurs (a transition fire) when all the conditions are satisfied (input places are marked) and give concession to the event. The occurrence of the event entirely or partially modifies the status of the conditions (marking). The number of tokens in a place can be used to identify the number of resources lying in the condition denoted by that place [1,2,8]. Coloured Petri nets (CPN) is a graphical oriented language for design, specification, simulation and verification of systems [3-6,9,15]. It is in particular well-suited for systems that consist of several processes which communicate and synchronize. Typical examples of application areas are communication protocols, distributed systems, automated production systems, workflow analysis and VLSI chips. In the Classical Petri Net, tokens do not differ; we can say that they are colourless. Unlike standard Petri nets in Colored Petri Net of a position can contain tokens of arbitrary complexity, such as lists, etc., that enables modelling to be more reliable. The article is devoted to the study of the possibilities of modelling Colored Petri nets. The article discusses the interrelation of languages of the Colored Petri nets and traditional formal languages. The Venn diagram, which the author has modified, shows the relationship between the languages of the Colored Petri nets and some traditional languages. The language class of the Colored Petri nets includes a whole class of Context-free languages and some other classes. The paper shows modelling the task synchronization Patil using Colored Petri net, which can't be modeled using well- known operations P and V or by classical Petri network, since the operations P and V and classical Petri networks have limited mathematical properties which do not allow to model the mechanisms in which the process should be synchronized with the optimal allocation of resources.