Potential immune-related therapeutic mechanisms of multiple traditional Chinese medicines on type 2 diabetic nephropathy based on bioinformatics, network pharmacology and molecular docking.

BACKGROUND: The prevalence of type 2 diabetic nephropathy (T2DN) ranges from 20 % to 40 % among individuals with type 2 diabetes. Multiple immune pathways play a pivotal role in the pathogenesis of T2DN. This study aimed to investigate the immunomodulatory effects of active ingredients derived from 14 traditional Chinese medicines (TCMs) on T2DN. METHODS: By removing batch effect on the GSE30528 and GSE96804 datasets, we employed a combination of weighted gene co-expression network analysis, least absolute shrinkage and selection operator analysis, protein-protein interaction network analysis, and the CIBERSORT algorithm to identify the active ingredients of TCMs as well as potential hub biomarkers associated with immune cells. Functional analysis was conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set variation analysis (GSVA). Additionally, molecular docking was employed to evaluate interactions between active ingredients and potential immunotherapy targets. RESULTS: A total of 638 differentially expressed genes (DEGs) were identified in this study, comprising 5 hub genes along with 4 potential biomarkers. Notably, CXCR1, CXCR2, and FOS exhibit significant associations with immune cells while displaying robust or favorable affinities towards the active ingredients kaempferol, quercetin, and luteolin. Furthermore, functional analysis unveiled intricate involvement of DEGs, hub genes and potential biomarkers in pathways closely linked to immunity and diabetes. CONCLUSION: The potential hub biomarkers and immunotherapy targets associated with immune cells of T2DN comprise CXCR1, CXCR2, and FOS. Furthermore, kaempferol, quercetin, and luteolin demonstrate potential immunomodulatory effects in modulating T2DN through the regulation of CXCR1, CXCR2, and FOS expression.

[Identification and functional analysis of jasmonic acid signaling repressor McJAZ8 gene in Mentha canadensis].

Mentha canadensis, as a plant with medicinal and culinary uses, holds significant economic value. Jasmonic acid signaling repressor JAZ protein has a crucial role in regulating plant response to adversity stresses. The M. canadensis McJAZ8 gene is cloned and analyzed for protein characterization, protein interactions, and expression patterns, so as to provide genetic resources for molecular breeding of M. canadensis for stress tolerance. This experiment will analyze the protein structural characteristics, subcellular localization, protein interactions, and gene expression of McJAZ8 using bioinformatics, yeast two-hybrid(Y2H), transient expression in tobacco leaves, qRT-PCR, and other technologies. The results show that:(1)The full length of the McJAZ8 gene is 543 bp, encoding 180 amino acids. The McJAZ8 protein contains conserved TIFY and Jas domains and exhibits high homology with Arabidopsis thaliana AtJAZ1 and AtJAZ2.(2)The McJAZ8 protein is localized in the nucleus and cytoplasm.(3)The Y2H results show that McJAZ8 interacts with itself or McJAZ1/3/4/5 proteins to form homologous or heterologous dimers.(4)McJAZ8 is expressed in different tissue, with the highest expression level in young leaves. In terms of leaf sequence, McJAZ8 shows the highest expression level in the fourth leaf and the lowest expression level in the second leaf.(5) In leaves and roots, the expression of McJAZ8 is upregulated to varying degrees under methyl jasmonate(MeJA), drought, and NaCl treatments. The expression of McJAZ8 shows an initial upregulation followed by a downregulation pattern under CdCl_2 treatment. In leaves, the expression of McJAZ8 tends to gradually decrease under CuCl_2 treatment, while in roots, it initially decreases and then increases before decreasing again. In both leaves and roots, the expression of McJAZ8 is downregulated to varying degrees under AlCl_(3 )treatment. This study has enriched the research on jasmonic acid signaling repressor JAZ genes in M. canadensis and provided genetic resources for the molecular breeding of M. canadensis.

[Screening for Characteristic Genes of Different Traditional Chinese Medicine Syndromes of Psoriasis Vulgaris: A Study Based on Bioinformatics and Machine Learning]. / ç”Ÿç‰©ä¿¡æ¯å­¦å’Œæœºå™¨å­¦ä¹ ç­›é€‰é“¶å±‘ç— ä¸­åŒ»è¯åž‹ç‰¹å¾åŸºå› .

Objective: To screen for the key characteristic genes of the psoriasis vulgaris (PV) patients with different Traditional Chinese Medicine (TCM) syndromes, including blood-heat syndrome (BHS), blood stasis syndrome (BSS), and blood-dryness syndrome (BDS), through bioinformatics and machine learning and to provide a scientific basis for the clinical diagnosis and treatment of PV of different TCM syndrome types. Methods: The GSE192867 dataset was downloaded from Gene Expression Omnibus (GEO). The limma package was used to screen for the differentially expressed genes (DEGs) of PV, BHS, BSS, and BDS in PV patients and healthy populations. In addition, KEGG (Kyoto Encyclopedia of Genes and Genes) pathway enrichment analysis was performed. The DEGs associated with PV, BHS, BSS, and BDS were identified in the screening and were intersected separately to obtain differentially characterized genes. Out of two algorithms, the support vector machine (SVM) and random forest (RF), the one that produced the optimal performance was used to analyze the characteristic genes and the top 5 genes were identified as the key characteristic genes. The receiver operating characteristic (ROC) curves of the key characteristic genes were plotted by using the pROC package, the area under curve (AUC) was calculated, and the diagnostic performance was evaluated, accordingly. Results: The numbers of DEGs associated with PV, BHS, BSS, and BDS were 7699, 7291, 7654, and 6578, respectively. KEGG enrichment analysis was focused on Janus kinase (JAK)/signal transducer and activator of transcription (STAT), cyclic adenosine monophosphate (cAMP), mitogen-activated protein kinase (MAPK), apoptosis, and other pathways. A total of 13 key characteristic genes were identified in the screening by machine learning. Among the 13 key characteristic genes, malectin (MLEC), TUB like protein 3 (TULP3), SET domain containing 9 (SETD9), nuclear envelope integral membrane protein 2 (NEMP2), and BTG anti-proliferation factor 3 (BTG3) were the key characteristic genes of BHS; phosphatase 15 (DUSP15), C1q and tumor necrosis factor related protein 7 (C1QTNF7), solute carrier family 12 member 5 (SLC12A5), tripartite motif containing 63 (TRIM63), and ubiquitin associated protein 1 like (UBAP1L) were the key characteristic genes of BSS; recombinant mouse protein (RRNAD1), GTPase-activating protein ASAP3 Protein (ASAP3), and human myomesin 2 (MYOM2) were the key characteristic genes of BDS. Moreover, all of them showed high diagnostic efficacy. Conclusion: There are significant differences in the characteristic genes of different PV syndromes and they may be potential biomarkers for diagnosing TCM syndromes of PV.

Investigating the potential of Juglans regia phytoconstituents for the treatment of cervical cancer utilizing network biology and molecular docking approach.

The fourth most frequent type of cancer in women and the leading cause of mortality for females worldwide is cervical cancer. Traditionally, medicinal plants have been utilized to treat various illnesses and ailments. The molecular docking method is used in the current study to look into the phytoconstituents of Juglans regia's possible anticancer effects on cervical cancer target proteins. This work uses the microarray dataset analysis of GSE63678 from the NCBI Gene Expression Omnibus database to find differentially expressed genes. Furthermore, protein-protein interactions of differentially expressed genes were constructed using network biology techniques. The top five hub genes (IGF1, FGF2, ESR1, MYL9, and MYH11) are then determined by computing topological parameters with Cytohubba. In addition, molecular docking research was performed on Juglans regia phytocompounds that were extracted from the IMPPAT database versus hub genes that had been identified. Utilizing molecular dynamics, simulation confirmed that prioritized docked complexes with low binding energies were stable.

Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach.

BACKGROUND The gut microbial metabolites demonstrate significant activity against metabolic diseases including osteoporosis (OP) and obesity, but active compounds, targets, and mechanisms have not been fully identified. Hence, the current investigation explored the mechanisms of active metabolites and targets against OP and obesity by using network pharmacology approaches. MATERIAL AND METHODS The gutMGene database was used to collect gut microbial targets-associated metabolites; DisGeNET and OMIM databases were used to identify targets relevant to OP and obesity. A total of 63 and 89 overlapped targets were considered the final OP and obesity targets after creating a Venn diagram of metabolites-related targets and disease-related targets. Furthermore, the top 20% of degrees, betweenness, and closeness were used to form the sub-network of protein-protein interaction of these targets. Finally, the biotransformation-increased receptors and biological mechanisms were identified and validated using ADMET properties analysis, molecular docking, and molecular dynamic simulation. RESULTS GO, KEGG pathway analysis, and protein-protein interactions were performed to establish metabolites and target networks. According to the enrichment analysis, OP and obesity are highly linked to the lipid and atherosclerosis pathways. Moreover, ADMET analysis depicts that the major metabolites have drug-likeliness activity and no or less toxicity. Following that, the molecular docking studies showed that compound K and TP53 target have a remarkable negative affinity (-8.0 kcal/mol) among all metabolites and targets for both diseases. Finally, the conformity of compound K against the targeted protein TP53 was validated by 250ns MD simulation. CONCLUSIONS Therefore, we summarized that compound K can regulate TP53 and could be developed as a therapy option for OP and obesity.

Bidirectional crosstalk between the epithelial-mesenchymal transition and immunotherapy: A bibliometric study.

Immunotherapy has recently attracted considerable attention. However, currently, a thorough analysis of the trends associated with the epithelial-mesenchymal transition (EMT) and immunotherapy is lacking. In this study, we used bibliometric tools to provide a comprehensive overview of the progress in EMT-immunotherapy research. A total of 1,302 articles related to EMT and immunotherapy were retrieved from the Web of Science Core Collection (WOSCC). The analysis indicated that in terms of the volume of research, China was the most productive country (49.07%, 639), followed by the United States (16.89%, 220) and Italy (3.6%, 47). The United States was the most influential country according to the frequency of citations and citation burstiness. The results also suggested that Frontiers in Immunotherapy can be considered as the most influential journal with respect to the number of articles and impact factors. "Immune infiltration," "bioinformatics analysis," "traditional Chinese medicine," "gene signature," and "ferroptosis" were found to be emerging keywords in EMT-immunotherapy research. These findings point to potential new directions that can deepen our understanding of the mechanisms underlying the combined effects of immunotherapy and EMT and help develop strategies for improving immunotherapy.

Molecular insights into gastric cancer: The impact of TGFBR2 and hsa-mir-107 revealed by microarray sequencing and bioinformatics.

BACKGROUND: Gastric carcinoma (GC) remains a significant therapeutic challenge, garnering widespread attention. Oxymatrine (OMT), an active component of the traditional Chinese medicine compound Kushen injection (CKI), has shown promising results in combination with chemotherapy for the treatment of GC. However, the molecular mechanisms underlying OMT's therapeutic effects in GC have yet to be elucidated. METHODS: The transcriptomic expression data of HGC-27 post-OMT intervention were obtained through microarray sequencing, while the miRNA and mRNA sequencing data for GC patients were sourced from the TCGA database. The mechanism of OMT intervention in GC is analyzed in multiple aspects, including Protein-Protein Interactions (PPI), Competitive Endogenous RNA (ceRNA) networks, correlation and co-expression analyses, immune infiltration, and clinical implications. RESULTS: By analyzing key modules, five critical mRNAs were identified, and their interacting miRNAs were predicted to construct a ceRNA network. Among these, TGFBR2 and hsa-miR-107 have correlations or co-expression relationships with other genes in the network. They are differentially expressed in most other cancers, associated with prognosis, and have diagnostic value. TGFBR2 also exhibits immune infiltration phenomena, and its high expression is linked to poor patient prognosis. Low expression of hsa-miR-107 is associated with poor patient prognosis. OMT may act on the TGFß/Smad signaling pathway or negatively regulate the WNT signaling pathway through the hsa-miR-107/BTRC axis, thereby inhibiting the onset and progression of GC. CONCLUSION: The mechanisms of OMT intervention in GC are diverse, TGFBR2 and hsa-miR-107 may serve as prognostic molecular biomarkers or potential therapeutic targets.

Molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania based on network pharmacology and molecular docking: Evidence from computational biology.

BACKGROUND: Quetiapine monotherapy is recommended as the first-line option for acute mania and acute bipolar depression. However, the mechanism of action of quetiapine is unclear. Network pharmacology and molecular docking were employed to determine the molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania. METHODS: Putative target genes for quetiapine were collected from the GeneCard, SwissTargetPrediction, and DrugBank databases. Targets for bipolar depression and bipolar mania were identified from the DisGeNET and GeneCards databases. A protein-protein interaction (PPI) network was generated using the String database and imported into Cytoscape. DAVID and the Bioinformatics platform were employed to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the top 15 core targets. The drug-pathway-target-disease network was constructed using Cytoscape. Finally, molecular docking was performed to evaluate the interactions between quetiapine and potential targets. RESULTS: Targets for quetiapine actions against bipolar depression (126 targets) and bipolar mania (81 targets) were identified. Based on PPI and KEGG pathway analyses, quetiapine may affect bipolar depression by targeting the MAPK and PI3K/AKT insulin signaling pathways via BDNF, INS, EGFR, IGF1, and NGF, and it may affect bipolar mania by targeting the neuroactive ligand-receptor interaction signaling pathway via HTR1A, HTR1B, HTR2A, DRD2, and GRIN2B. Molecular docking revealed good binding affinity between quetiapine and potential targets. LIMITATIONS: Pharmacological experiments should be conducted to verify and further explore these results. CONCLUSIONS: Our findings suggest that quetiapine affects bipolar depression and bipolar mania through distinct biological core targets, and thus through different mechanisms. Furthermore, our results provide a theoretical basis for the clinical use of quetiapine and possible directions for new drug development.

Identification of Hedyotis diffusa Willd-specific mRNA-miRNA-lncRNA network in rheumatoid arthritis based on network pharmacology, bioinformatics analysis, and experimental verification.

Hedyotis diffusa Willd (HDW) possesses heat-clearing, detoxification, anti-cancer, and anti-inflammatory properties. However, its effects on rheumatoid arthritis (RA) remain under-researched. In this study, we identified potential targets of HDW and collected differentially expressed genes of RA from the GEO dataset GSE77298, leading to the construction of a drug-component-target-disease regulatory network. The intersecting genes underwent GO and KEGG analysis. A PPI protein interaction network was established in the STRING database. Through LASSO, RF, and SVM-RFE algorithms, we identified the core gene MMP9. Subsequent analyses, including ROC, GSEA enrichment, and immune cell infiltration, correlated core genes with RA. mRNA-miRNA-lncRNA regulatory networks were predicted using databases like TargetScan, miRTarBase, miRWalk, starBase, lncBase, and the GEO dataset GSE122616. Experimental verification in RA-FLS cells confirmed HDW's regulatory impact on core genes and their ceRNA expression. We obtained 11 main active ingredients of HDW and 180 corresponding targets, 2150 RA-related genes, and 36 drug-disease intersection targets. The PPI network diagram and three machine learning methods screened to obtain MMP9, and further analysis showed that MMP9 had high diagnostic significance and was significantly correlated with the main infiltrated immune cells, and the molecular docking verification also showed that MMP9 and the main active components of HDW were well combined. Next, we predicted 6 miRNAs and 314 lncRNAs acting on MMP9, and two ceRNA regulatory axes were obtained according to the screening. Cellular assays indicated HDW inhibits RA-FLS cell proliferation and MMP9 protein expression dose-dependently, suggesting HDW might influence RA's progression by regulating the MMP9/miR-204-5p/MIAT axis. This innovative analytical thinking provides guidance and reference for the future research on the ceRNA mechanism of traditional Chinese medicine in the treatment of RA.

Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus.

The target and mechanism of ellagic acid (EA) against rotavirus (RV) were investigated by network pharmacology, computational biology, and surface plasmon resonance verification. The target of EA was obtained from 11 databases such as HIT and TCMSP, and RV-related targets were obtained from the Gene Cards database. The relevant targets were imported into the Venny platform to draw a Venn diagram, and their intersections were visualized. The protein-protein interaction networks (PPI) were constructed using STRING, DAVID database, and Cytoscape software, and key targets were screened. The target was enriched by Gene Ontology (GO) and KEGG pathway, and the 'EA anti-RV target-pathway network' was constructed. Schrodinger Maestro 13.5 software was used for molecular docking to determine the binding free energy and binding mode of ellagic acid and target protein. The Desmond program was used for molecular dynamics simulation. Saturation mutagenesis analysis was performed using Schrodinger's Maestro 13.5 software. Finally, the affinity between ellagic acid and TLR4 protein was investigated by surface plasmon resonance (SPR) experiments. The results of network pharmacological analysis showed that there were 35 intersection proteins, among which Interleukin-1ß (IL-1ß), Albumin (ALB), Nuclear factor kappa-B1 (NF-κB1), Toll-Like Receptor 4 (TLR4), Tumor necrosis factor alpha (TNF-α), Tumor protein p53 (TP53), Recombinant SMAD family member 3 (SAMD3), Epidermal growth factor (EGF) and Interleukin-4 (IL-4) were potential core targets of EA anti-RV. The GO analysis consists of biological processes (BP), cellular components (CC), and molecular functions (MF). The KEGG pathways with the highest gene count were mainly related to enteritis, cancer, IL-17 signaling pathway, and MAPK signaling pathway. Based on the crystal structure of key targets, the complex structure models of TP53-EA, TLR4-EA, TNF-EA, IL-1ß-EA, ALB-EA, NF-κB1-EA, SAMD3-EA, EGF-EA, and IL-4-EA were constructed by molecular docking (XP mode of flexible docking). The MMGBS analysis and molecular dynamics simulation were also studied. The Δaffinity of TP53 was highest in 220 (CYS → TRP), 220 (CYS → TYR), and 220 (CYS → PHE), respectively. The Δaffinity of TLR4 was highest in 136 (THR → TYR), 136 (THR → PHE), and 136 (THR → TRP). The Δaffinity of TNF-α was highest in 150 (VAL → TRP), 18 (ALA → GLU), and 144 (PHE → GLY). SPR results showed that ellagic acid could bind TLR4 protein specifically. TP53, TLR4, and TNF-α are potential targets for EA to exert anti-RV effects, which may ultimately provide theoretical basis and clues for EA to be used as anti-RV drugs by regulating TLR4/NF-κB related pathways.

Role of Network Pharmacology in Prediction of Mechanism of Neuroprotective Compounds.

Network pharmacology is an emerging pioneering approach in the drug discovery process, which is used to predict the therapeutic mechanism of compounds using various bioinformatic tools and databases. Emerging studies have indicated the use of network pharmacological approaches in various research fields, particularly in the identification of possible mechanisms of herbal compounds/ayurvedic formulations in the management of various diseases. These techniques could also play an important role in the prediction of the possible mechanisms of neuroprotective compounds. The first part of the chapter includes an introduction on neuroprotective compounds based on literature. Further, network pharmacological approaches are briefly discussed. The use of network pharmacology in the prediction of the neuroprotective mechanism of compounds is discussed in detail with suitable examples. Finally, the chapter concludes with the current challenges and future prospectives.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis.

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

Network pharmacological analysis on the mechanism of Linggui Zhugan decoction for nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD), represents a chronic progressive disease that imposes a significant burden on patients and the healthcare system. Linggui Zhugan decoction (LGZGD) plays a substantial role in treating NAFLD, but its exact molecular mechanism is unknown. Using network pharmacology, this study aimed to investigate the mechanism of action of LGZGD in treating NAFLD. Active ingredients and targets were identified through the integration of data from the TCMSP, GEO, GeneCards, and OMIM databases. Cytoscape 3.9.1 software, in conjunction with the STRING platform, was employed to construct network diagrams and screen core targets. The enrichment analysis of gene ontology and the Kyoto Encyclopedia of Genes and Genomes pathways were conducted by using the R. Molecular docking of the active ingredients and core targets was performed with AutoDock Vina software. We obtained 93 and 112 active ingredients and potential targets using the bioinformatic analysis of LGZGD in treating NAFLD. The primary ingredients of LGZGD included quercetin, kaempferol, and naringenin. The core targets were identified AKT1, MYC, HSP90AA1, HIF1A, ESR1, TP53, and STAT3. Gene ontology function enrichment analysis revealed associations with responses to nutrient and oxygen levels, nuclear receptor activity, and ligand-activated transcription factor activity. Kyoto Encyclopedia of Genes and Genomes signaling pathway analysis implicated the involvement of the PI3K-Akt, IL-17, TNF, Th17 cell differentiation, HIF-1, and TLR signaling pathways. Molecular docking studies indicated strong binding affinities between active ingredients and targets. LGZGD intervenes in NAFLD through a multi-ingredient, multi-target, and multi-pathway approach. Treatment with LGZGD can improve insulin resistance, oxidative stress, inflammation, and lipid metabolism associated with NAFLD.

Mechanisms Involved in Therapeutic Effects of Scutellaria baicalensis Georgi in Oral Squamous Cell Carcinoma Based on Systems Biology and Structural Bioinformatics Approaches.

Objective: Oral squamous cell carcinoma (OSCC) is the most frequent oral cancer, constituting more than 90% of all oral carcinomas. The 5-year survival rate of OSCC patients is not satisfactory, and therefore, there is an urgent need for new practical therapeutic approaches besides the current therapies to overcome OSCC. Scutellaria baicalensis Georgi (SBG) is a plant of the family Lamiaceae with several pharmaceutical properties such as antioxidant, anti-inflammatory, and anticancer effects. Previous studies have demonstrated the curative effects of SBG in OSCC. Methods: A systems biology approach was conducted to identify differentially expressed miRNAs (DEMs) in OSCC patients with a dismal prognosis compared to OSCC patients with a favorable prognosis. A protein interaction map (PIM) was built based on DEMs targets, and the hub genes within the PIM were indicated. Subsequently, the prognostic role of the hubs was studied using Kaplan-Meier curves. Next, the binding affinity of SBG's main components, including baicalein, wogonin, oroxylin-A, salvigenin, and norwogonin, to the prognostic markers in OSCC was evaluated using molecular docking analysis. Results: Survival analysis showed that overexpression of CAV1, SERPINE1, ACTB, SMAD3, HMGA2, MYC, EIF2S1, HSPA4, HSPA5, and IL6 was significantly related to a poor prognosis in OSCC. Besides, molecular docking analysis demonstrated the ΔGbinding and inhibition constant values between SBG's main components and SERPINE1, ACTB, HMGA2, EIF2S1, HSPA4, and HSPA5 were as <-8.00 kcal/mol and nanomolar concentration, respectively. The most salient binding affinity was observed between wogonin and SERPINE1 with a criterion of ΔGbinding < -10.02 kcal/mol. Conclusion: The present results unraveled potential mechanisms involved in therapeutic effects of SBG in OSCC based on systems biology and structural bioinformatics analyses.

Integrated study reveals mechanism of Tripterygium Wilfordii against cholangiocarcinoma based on bioinformatics approaches and molecular dynamics simulation.

BACKGROUND: Tripterygium wilfordii Hook. f. (TW) shows anticancer activity, and no study has comprehensively investigated the effects of TW in treating cholangiocarcinoma (CHOL). This study was designed to identify the therapeutic role and the mechanism of TW against CHOL to obtain anti-CHOL candidate components and targets. METHODS: Ingredients of TW were collected from the Traditional Chinese Medicine System Pharmacology Database and literature. Limma package and weighted gene co-expression network analysis were used to identify the genes related to CHOL. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) was performed by R package Cluster-Profiler and Metascape, respectively. Protein-Protein Interaction (PPI) network was used to select core genes in the treatment of CHOL by TW, followed by GEPIA2, UALCAN database, and ROC curves to assess their diagnostic and prognostic capability. Molecular docking and molecular dynamics simulation were applied to explore the binding affinity and stability of the complex between the bioactive ingredients in TW and core targets. RESULTS: A total of 67 ingredients in TW were collected, and 495 genes were obtained as genes of CHOL. 55 common TW-CHOL targets were identified. 171 biological process terms and 100 KEGG pathways were enriched. 12 genes were regarded as core genes through PPI analysis, such as CYP3A4, CES1, GC, and PLG, whose good diagnostic and prognostic capability were identified. Ten ingredients were selected through the construction of Herb-Components-Targets-Disease network. Molecular docking and molecular dynamics simulation both confirmed the good binding affinity and stability of the ligand-protein complexes. CONCLUSION: This study identified the therapeutic role and predicted the mechanism of TW against CHOL, where TW may combat CHOL through the regulation of metabolic conditions of the body, bile acid secretion, xenobiotics metabolism, and the inflammatory response. Celastrol, triptonide, triptolide and wilforlide A emerged as promising anti-CHOL candidates. So, this study offered a reference for the treatment of CHOL and the development of anti-CHOL drugs.

Exploring the molecular mechanism of Xuebifang in the treatment of diabetic peripheral neuropathy based on bioinformatics and network pharmacology.

Background: Xuebifang (XBF), a potent Chinese herbal formula, has been employed in managing diabetic peripheral neuropathy (DPN). Nevertheless, the precise mechanism of its action remains enigmatic. Purpose: The primary objective of this investigation is to employ a bioinformatics-driven approach combined with network pharmacology to comprehensively explore the therapeutic mechanism of XBF in the context of DPN. Study design and Methods: The active chemicals and their respective targets of XBF were sourced from the TCMSP and BATMAN databases. Differentially expressed genes (DEGs) related to DPN were obtained from the GEO database. The targets associated with DPN were compiled from the OMIM, GeneCards, and DrugBank databases. The analysis of GO, KEGG pathway enrichment, as well as immuno-infiltration analysis, was conducted using the R language. The investigation focused on the distribution of therapeutic targets of XBF within human organs or cells. Subsequently, molecular docking was employed to evaluate the interactions between potential targets and active compounds of XBF concerning the treatment of DPN. Results: The study successfully identified a total of 122 active compounds and 272 targets associated with XBF. 5 core targets of XBF for DPN were discovered by building PPI network. According to GO and KEGG pathway enrichment analysis, the mechanisms of XBF for DPN could be related to inflammation, immune regulation, and pivotal signalling pathways such as the TNF, TLR, CLR, and NOD-like receptor signalling pathways. These findings were further supported by immune infiltration analysis and localization of immune organs and cells. Moreover, the molecular docking simulations demonstrated a strong binding affinity between the active chemicals and the carefully selected targets. Conclusion: In summary, this study proposes a novel treatment model for XBF in DPN, and it also offers a new perspective for exploring the principles of traditional Chinese medicine (TCM) in the clinical management of DPN.

Evaluating the efficacy and mechanisms of Hua-Zhuo-Ning-Fu-Decoction on psoriasis using integrated bioinformatics analysis and metabolomics.

ETHNOPHARMACOLOGICAL RELEVANCE: Hua Zhuo Ning Fu Decoction (HZD) is an empirical prescription from traditional Chinese medicine that shows excellent clinical results for psoriasis patients. Uncertainty lingered over HZD's potential anti-psoriasis mechanisms. AIM OF THE STUDY: The study's objective is to investigate the pharmacological processes and therapeutic effects of HZD on psoriasis. MATERIALS AND METHODS: In the initial phase of the study, an investigation was conducted to assess the effects of HZD on psoriasis-afflicted mice using an imiquimod (IMQ)-induced murine model. The experimental mice were randomly allocated to different groups, including the IMQ-induced model group, the control group, the HZD therapy groups with varying dosage levels (low, medium, and high), and Dexamethasone (DEX, the positive control medicine) group. Bioinformatics analysis and molecular docking were subsequently employed to identify the primary components and molecular targets associated with the therapeutic action of HZD in the context of psoriasis. Additionally, to find the impacts on metabolite regulation, plasma metabolomics based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was used. It's interesting to note that the combined mechanisms from metabolomics were examined in tandem with the targets. In vivo tests were the last step in validating the potential mechanism. Throughout the trial, the following data were recorded: body weight, psoriasis area and severity index (PASI). The molecular targets connected to HZD's anti-psoriasis activities were revealed using histological examination, western blot (WB), and ELISA investigation. RESULTS: In mice induced with IMQ, HZD shown good anti-psoriasis effects in terms of PASI score and epidermal acanthosis. 95 HZD targets and 77 bioactive chemicals connected to psoriasis were found by bioinformatics research; of these, 7 key targets (EPHX2, PLA2G2A, TBXAS1, MAOA, ALDH1A3, ADH1A, and ADH1B) were linked to the mechanisms of HZD, the combination degree of which was finally expressed by the score of docking. In addition, HZD regulated nine metabolites. In line with this, HZD modified three metabolic pathways. Additionally, a combined examination of 7 key targets and 9 metabolites suggested that the metabolism of arachidonic acid might be the key metabolic route, which was identified by ELISA analysis. The in vivo investigation shown that HZD could control cytokines associated to inflammation (IL-10, TGF-ß, IL-17A, and IL-23), as well as important antioxidant system markers (ROS, GSH, and MDA). Moreover, HZD controlled iron levels and the expression of ferroptosis-related proteins (ACSL4 and GPX4), suggesting that ferroptosis played a crucial role in this process. CONCLUSIONS: Our findings demonstrated the whole mechanism and anti-psoriasis effectiveness of HZD, which will promote its clinical application and aid in the investigation of new bioactive components of HZD against psoriasis.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare.

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

Recent advances in the culture-independent discovery of natural products using metagenomic approaches.

Natural products derived from bacterial sources have long been pivotal in the discovery of drug leads. However, the cultivation of only about 1% of bacteria in laboratory settings has left a significant portion of biosynthetic diversity hidden within the genomes of uncultured bacteria. Advances in sequencing technologies now enable the exploration of genetic material from these metagenomes through culture-independent methods. This approach involves extracting genetic sequences from environmental DNA and applying a hybrid methodology that combines functional screening, sequence tag-based homology screening, and bioinformatic-assisted chemical synthesis. Through this process, numerous valuable natural products have been identified and synthesized from previously uncharted metagenomic territories. This paper provides an overview of the recent advancements in the utilization of culture-independent techniques for the discovery of novel biosynthetic gene clusters and bioactive small molecules within metagenomic libraries.

Exploring the anti-ferroptosis mechanism of Kai-Xin-San against Alzheimer's disease through integrating network pharmacology, bioinformatics, and experimental validation strategy in vivo and in vitro.

ETHNOPHARMACOLOGICAL RELEVANCE: Kai Xin San (KXS), first proposed by Sun Simiao during the Tang Dynasty, has been utilized to treat dementia by tonifying qi and dispersing phlegm. AIM OF THE STUDY: This study aimed to elucidate the mechanism by which KXS exerts its therapeutic effects on Alzheimer's disease (AD) by targeting ferroptosis, using a combination of network pharmacology, bioinformatics, and experimental validation strategies. MATERIALS AND METHODS: The active target sites and the further potential mechanisms of KXS in protecting against AD were investigated through molecular docking, molecular dynamics simulation, and network pharmacology, and combined with the validation of animal experiments. RESULTS: Computational and experimental findings provide the first indication that KXS significantly improves learning and memory defects and inhibits neuronal ferroptosis by repairing mitochondria damage and upregulating the protein expression of ferroptosis suppressor protein 1 (FSP1) in vivo APP/PS1 mice AD model. According to bioinformatics analysis, the mechanism by which KXS inhibits ferroptosis may involve SIRT1. KXS notably upregulated the mRNA and protein expression of SIRT1 in both vivo APP/PS1 mice and in vitro APP-overexpressed HT22 cells. Additionally, KXS inhibited ferroptosis induced by APP-overexpression in HT22 cells through activating the SIRT1-FSP1 signal pathway. CONCLUSIONS: Collectively, our findings suggest that KXS may inhibit neuronal ferroptosis through activating the SIRT1/FSP1 signaling pathway. This study reveals the scientific basis and underlying modern theory of replenishing qi and eliminating phlegm, which involves the inhibition of ferroptosis. Moreover, it highlights the potential application of SIRT1 or FSP1 activators in the treatment of AD and other ferroptosis-related diseases.