Kyoto Encyclopedia Of Genes And Genomes Pathway Enrichment Analysis Research Articles

Mechanism of mangiferin in the treatment of oral submucous fibrosis based on Gene Expression Omnibus database chip mining combined with network pharmacology and molecular docking.

This study aims to investigate the primary target and potential mechanism of mangiferin (MF) in treating oral submucous fibrosis (OSF) through Gene Expression Omnibus (GEO) database chip mining, network pharmacology, and molecular docking techniques. Potential therapeutic targets for OSF were identified using GEO chip data. The potential targets of MF were predicted, and disease-related targets for OSF were collected from databases. A Venn diagram was created using the EVenn platform to identify overlapping targets. The protein-protein interaction (PPI) network was constructed using the STRING database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID platform. Cytoscape 3.10.1 software was used to visualize a drug-target-pathway-disease network, while AutoDocktools 1.5.6 software was employed for molecular docking analysis. A total of 356 potential targets for MF and 360 disease-related targets for OSF were obtained from multiple databases. The top 15 key target proteins in the PPI network were selected as significant candidates. GO function and KEGG pathway enrichment analyses revealed that MF treatment primarily involved advanced glycation end products-receptor (AGE-RAGE), epidermal growth factor receptor (EGFR), and other signaling pathways associated with OSF pathogenesis. Molecular docking analysis demonstrated that MF exhibited a strong binding activity toward AKT serine kinase 1 (AKT1), tumor necrosis factor (TNF), and other core targets. These findings suggest that MF may exert its therapeutic effects on OSF through a multitarget approach involving various signaling pathways.

Network pharmacology- and molecular docking-based exploration of the molecular mechanism underlying Jianpi Yiwei Recipe treatment of gastric cancer.

Traditional Chinese medicine (TCM) is widely used as an important complementary and alternative healthcare system for cancer treatment in Asian countries. Network pharmacology, which utilizes various database platforms and computer software to study the interactions between complex drug components in vivo, is particularly useful for studying the pharmacodynamic mechanisms of multi-pathway and multi-target Chinese medicines. To explore the potential targets and function of Jianpi Yiwei Recipe treatment of gastric cancer (GC) through network pharmacology and molecular docking. Data on the components of Jianpi Yiwei Recipe (Radix Astragali, Radix Codonopsis, Agrimonia eupatoria, Atractylodes macrocephala Koidz., Poria cocos, stir-baked rhizoma dioscoreae, Amomum villosum Lour., fried Fructus Aurantii, pericarpium citri reticulatae, Rhizoma Pinelliae Preparata, and Radix Glycyrrhizae Preparata) were collected and screened by using the TCM systems pharmacology database and analysis platform (TCMSP). Then the targets of these compounds were predicted. GC-related targets were screened using the GeneCards database. Venn diagram was used to identify common targets. An active ingredient-core target interaction network and a protein-protein interaction (PPI) network were built. Moreover, we performed gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the core targets and validated them by molecular docking. TCMSP screening revealed 11 active components and 184 targets, whereas GeneCards found 10118 disease-related targets, with 180 shared targets between them. Topology analysis of the PPI network identified 38 targets, including ATK1, TP53, and tumor necrosis factor, as key targets for the treatment of GC by Jianpi Yiwei Recipe. Quercetin, naringenin, luteolin, etc., may be the main active components of Jianpi Yiwei Recipe. GO enrichment analysis identified 2809, 1218, and 553 functions related to biological process, molecular function, and cellular component, respectively. KEGG pathway enrichment analysis revealed 167 related pathways, mainly involved in cancer, endocrine resistance, and AGE-RAGE signaling in diabetic complication. Validation with molecular docking analysis showed docking of key active components with core targets. Jianpi Yiwei Recipe plays a therapeutic role in GC through multiple components, targets, and pathways. These findings form a basis for follow-up exploration of Jianpi Yiwei Recipe in the treatment of GC.

Network pharmacology and molecular dynamics study of the effect of the Astragalus-Coptis drug pair on diabetic kidney disease

BACKGROUND Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. The Astragalus -Coptis drug pair is frequently employed in the management of DKD. However, the precise molecular mechanism underlying its therapeutic effect remains elusive. AIM To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways. METHODS The ingredients of the Astragalus -Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform. The targets were predicted using the SwissTargetPrediction database, while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database. DKD targets were acquired from the GeneCards, Online Mendelian Inheritance in Man database, and DisGeNET databases, with common targets identified through the Venny platform. The protein-protein interaction network and the “disease-active ingredient-target” network of the common targets were constructed utilizing the STRING database and Cytoscape software, followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients. Gene Ontology (GO) function and Kyoto Ency-clopedia of Genes and Genomes (KEGG) pathway enrichments were performed using the DAVID database. The tissue and organ distributions of key targets were evaluated. PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets. Finally, molecular dynamics (MD) simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins. RESULTS A total of 27 active ingredients and 512 potential targets of the Astragalus -Coptis drug pair were identified. There were 273 common targets between DKD and the Astragalus -Coptis drug pair. Through protein-protein interaction network topology analysis, we identified 9 core active ingredients and 10 key targets. GO and KEGG pathway enrichment analyses revealed that Astragalus -Coptis drug pair treatment for DKD involves various biological processes, including protein phosphorylation, negative regulation of apoptosis, inflammatory response, and endoplasmic reticulum unfolded protein response. These pathways are mainly associated with the advanced glycation end products (AGE)-receptor for AGE products signaling pathway in diabetic complications, as well as the Lipid and atherosclerosis. Molecular docking and MD simulations demonstrated high affinity and stability between the core active ingredients and key targets. Notably, the quercetin-AKT serine/threonine kinase 1 (AKT1) and quercetin-tumor necrosis factor (TNF) protein complexes exhibited exceptional stability. CONCLUSION This study demonstrated that DKD treatment with the Astragalus -Coptis drug pair involves multiple ingredients, targets, and signaling pathways. We propose a novel approach for investigating the molecular mechanism underlying the therapeutic effects of the Astragalus -Coptis drug pair on DKD. Furthermore, we suggest that quercetin is the most potent active ingredient and specifically targets AKT1 and TNF, providing a theoretical foundation for further exploration of pharmacologically active ingredients and elucidating their molecular mechanisms in DKD treatment.

Integrated proteomic and metabolomic profiling reveals novel insights on the inflammation and immune response in HFpEF

BackgroundThe precise mechanisms leading to the development of heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. In this study, an integrative approach utilizing untargeted proteomics and metabolomics was employed to delineate the altered proteomic and metabolomic profiles in patients with HFpEF compared to healthy controls.Materials and methodsData were collected from a prospective cohort consisting of 30 HFpEF participants and 30 healthy controls, matched by gender and age. plasma samples were analyzed by multi-omics platforms. The quantification of plasma proteins and metabolites was performed using data-independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), respectively. Additionally, Proteomic and metabolomic results were analyzed separately and integrated using correlation and pathway analysis. This was followed by the execution of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies to elucidate the biological relevance of the observed results.ResultsA total of 46 significantly differentially expressed proteins (DEPs) and 102 differentially expressed metabolites (DEMs) were identified. Then, GO and KEGG pathway enrichment analyses were performed by DEPs and DEMs. Integrated analysis of proteomics and metabolomics has revealed Tuberculosis and African trypanosomiasis pathways that are significantly enriched and the DEPs and DEMs enriched within them, are associated with inflammation and immune response.ConclusionsIntegrated proteomic and metabolomic analyses revealed distinct inflammatory and immune response pathways in HFpEF, highlighting novel therapeutic avenues.

Transcriptome analysis of Kunming mice responses to the bite of Xenopsylla cheopis

BackgroundFlea bites could trigger a series of complex molecular responses in the host. However, our understanding of the responses at the molecular level is still relatively limited. This study quantifies the changes in gene expression in mice after flea bites by RNA sequencing (RNA-seq) from their spleens, revealing the potential biological effects of host response to flea bites.MethodsRNA-seq was used for transcriptome analysis to screen for differentially expressed genes (DEGs) between the control mice group and the flea bite mice group. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on DEGs. Protein–protein interaction (PPI) network analysis on DEGs related to immune processes was performed. Finally, we randomly selected several genes from the screened DEGs to validate the results from the transcriptome data by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR).ResultsA total of 521 DEGs were identified, including 277 upregulated and 244 downregulated. There were 258 GO terms significantly enriched by upregulated DEGs and 419 GO terms significantly enriched by downregulated DEGs. Among the upregulated DEGs, 22 GO terms were associated with immune cells (e.g., B cells and T cells) and immune regulatory processes, while among the downregulated DEGs, 58 GO terms were associated with immune cells and immune regulatory processes. Through PPI analysis, we found that CD40 molecules with significantly downregulated expression levels after flea bites may play an important role in host immune regulation. Through KEGG pathway enrichment analysis, a total of 26 significantly enriched KEGG pathways were identified. The RT-qPCR analysis results indicated that the transcriptome sequencing results were reliable.ConclusionsThrough in-depth analysis of transcriptome changes in mice caused by flea bites, we revealed that flea bites could stimulate a series of biological and immunological responses in mice. These findings not only provided a deeper understanding of the impact of flea bites on the host but also provided a basis for further research on the interaction between ectoparasites and the host. We believe that digging deeper into the significance of these transcriptome changes will help reveal more about the adaptive response of the host to ectoparasites.Graphical

Exploring the Potential Lipid-Lowering and Weight-Reducing Mechanisms of FH06 Fermented Beverages Based on Non-Targeted Metabolomics and Network Pharmacology

Investigating the intricate pathways through which FH06 fermentation broth exerts lipid-lowering and weight-loss effects is pivotal for advancing our comprehension of metabolic regulation and therapeutic interventions. Ultrahigh-performance liquid chromatography quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-QE-MS) detection and the ChEMBL database were used to determine the effective compounds in the FH06 fermentation broth and predict their targets. The TTD database and DisGeNET database were used to query obesity-related targets. The STRING database was used to construct protein interaction information. The Gene Ontology (GO) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to perform biological function annotation (GO) and KEGG pathway enrichment analyses of the targets. Results: A total of 85 effective compounds were screened from the fermentation broth of FH06; these compounds may act on TP53, PPARG, TNF, and other targets through 10 signaling pathways, such as the chemical carcinogenesis-receptor activation and lipid and atherosclerosis pathways, and exert pharmacological effects, such as hypoglycemic effects and weight loss. They also have anti-inflammatory, antioxidant, antitumor, and immunoregulatory effects. These findings reveal the active ingredients of FH06 fermentation broth and its multi-target and multi-channel characteristics in lipid lowering and weight loss. This study has positive implications for the clinical treatment of obesity using FH06, providing a theoretical and scientific basis for further developing of FH06-assisted lipid-lowering products.

Effect of SlSAHH2 on metabolites in over-expressed and wild-type tomato fruit.

Tomato (Solanum lycopersicum) is an annual or perennial herb that occupies an important position in daily agricultural production. It is an essential food crop for humans and its ripening process is regulated by a number of genes. S-adenosyl-l-homocysteine hydrolase (AdoHcyase, EC 3.3.1.1) is widespread in organisms and plays an important role in regulating biological methylation reactions. Previous studies have revealed that transgenic tomato that over-express SlSAHH2 ripen earlier than the wild-type (WT). However, the differences in metabolites and the mechanisms driving how these differences affect the ripening cycle are unclear. To investigate the effects of SlSAHH2 on metabolites in over-expressed tomato and WT tomato. SlSAHH2 over-expressed tomato fruit (OE-5# and OE-6#) and WT tomato fruit at the breaker stage (Br) were selected for non-targeted metabolome analysis. A total of 733 metabolites were identified by mass spectrometry using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Human Metabolome database (HMDB). The metabolites were divided into 12 categories based on the superclass results and a comparison with the HMDB. The differences between the two databases were analyzed by PLS-DA. Based on a variable important in projection value >1 and P < 0.05, 103 differential metabolites were found between tomato variety OE-5# and WT and 63 differential metabolites were found between OE-6# and WT. These included dehydrotomatine, L-serine, and gallic acid amongst others. Many metabolites are associated with fruit ripening and eight common metabolites were found between the OE-5# vs. WT and OE-6# vs. WT comparison groups. The low L-tryptophan expression in OE-5# and OE-6# is consistent with previous reports that its content decreases with fruit ripening. A KEGG pathway enrichment analysis of the significantly different metabolites revealed that in the OE-5# and WT groups, up-regulated metabolites were enriched in 23 metabolic pathways and down-regulated metabolites were enriched in 11 metabolic pathways. In the OE-6# and WT groups, up-regulated metabolites were enriched in 29 pathways and down-regulated metabolites were enriched in six metabolic pathways. In addition, the differential metabolite changes in the L-serine to flavonoid transformation metabolic pathway also provide evidence that there is a phenotypic explanation for the changes in transgenic tomato. The metabolomic mechanism controlling SlSAHH2 promotion of tomato fruit ripening has been further elucidated.

Identification of blood exosomal metabolomic profiling for high-altitude cerebral edema

High-altitude cerebral edema (HACE) is a severe neurological condition that can occur at high altitudes. It is characterized by the accumulation of fluid in the brain, leading to a range of symptoms, including severe headache, confusion, loss of coordination, and even coma and death. Exosomes play a crucial role in intercellular communication, and their contents have been found to change in various diseases. This study analyzed the metabolomic characteristics of blood exosomes from HACE patients compared to those from healthy controls (HCs) with the aim of identifying specific metabolites or metabolic pathways associated with the development of HACE conditions. A total of 21 HACE patients and 21 healthy controls were recruited for this study. Comprehensive metabolomic profiling of the serum exosome samples was conducted using ultraperformance liquid chromatography–tandem mass spectrometry (UPLC‒MS/MS). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to identify the metabolic pathways affected in HACE patients. Twenty-six metabolites, including ( +)-camphoric acid, choline, adenosine, adenosine 5′-monophosphate, deoxyguanosine 5′-monophosphate, guanosine, and hypoxanthine-9-β-D-arabinofuranoside, among others, exhibited significant changes in expression in HACE patients compared to HCs. Additionally, these differentially abundant metabolites were confirmed to be potential biomarkers for HACE. KEGG pathway enrichment analysis revealed several pathways that significantly affect energy metabolism regulation (such as purine metabolism, thermogenesis, and nucleotide metabolism), estrogen-related pathways (the estrogen signaling pathway, GnRH signaling pathway, and GnRH pathway), cyclic nucleotide signaling pathways (the cGMP-PKG signaling pathway and cAMP signaling pathway), and hormone synthesis and secretion pathways (renin secretion, parathyroid hormone synthesis, secretion and action, and aldosterone synthesis and secretion). In patients with HACE, adenosine, guanosine, and hypoxanthine-9-β-D-arabinofuranoside were negatively correlated with height. Deoxyguanosine 5′-monophosphate is negatively correlated with weight and BMI. Additionally, LPE (18:2/0:0) and pregnanetriol were positively correlated with age. This study identified potential biomarkers for HACE and provided valuable insights into the underlying metabolic mechanisms of this disease. These findings may lead to potential targets for early diagnosis and therapeutic intervention in HACE patients.

Transcriptome analysis reveals the molecular mechanisms underlying the enhancement of salt-tolerance in Melia azedarach under salinity stress

Melia azedarach demonstrates strong salt tolerance and thrives in harsh saline soil conditions, but the underlying mechanisms are poorly understood. In this study, we analyzed gene expression under low, medium, and high salinity conditions to gain a deeper understanding of adaptation mechanisms of M. azedarach under salt stress. The GO (gene ontology) analysis unveiled a prominent trend: as salt stress intensified, a greater number of differentially expressed genes (DEGs) became enriched in categories related to metabolic processes, catalytic activities, and membrane components. Through the analysis of the category GO:0009651 (response to salt stress), we identified four key candidate genes (CBL7, SAPK10, EDL3, and AKT1) that play a pivotal role in salt stress responses. Furthermore, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis revealed that DEGs were significantly enriched in the plant hormone signaling pathways and starch and sucrose metabolism under both medium and high salt exposure in comparison to low salt conditions. Notably, genes involved in JAZ and MYC2 in the jasmonic acid (JA) metabolic pathway were markedly upregulated in response to high salt stress. This study offers valuable insights into the molecular mechanisms underlying M. azedarach salt tolerance and identifies potential candidate genes for enhancing salt tolerance in M. azedarach.

Transcriptomics reveals dynamic changes in the “gene profiles” of rat supraspinatus tendon at three different time points after diabetes induction

ObjectiveThere is increasing evidence that type 2 diabetes mellitus (T2DM) is an independent risk factor for the occur of tendinopathy. Therefore, this study is the first to explore the dynamic changes of the “gene profile” of supraspinatus tendon in rats at different time points after T2DM induction through transcriptomics, providing potential molecular markers for exploring the pathogenesis of diabetic tendinopathy.MethodsA total of 40 Sprague-Dawley rats were randomly divided into normal (NG, n = 10) and T2DM groups (T2DM, n = 30) and subdivided into three groups according to the duration of diabetes: T2DM-4w, T2DM-8w, and T2DM-12w groups; the duration was calculated from the time point of T2DM rat model establishment. The three comparison groups were set up in this study, T2DM-4w group vs. NG, T2DM-8w group vs. NG, and T2DM-12w group vs. NG. Differentially expressed genes (DEGs) in 3 comparison groups were screened. The intersection of the three comparison groups’ DEGs was defined as key genes that changed consistently in the supraspinatus tendon after diabetes induction. Cluster analysis, gene ontology (GO) functional annotation analysis and Kyoto encyclopedia of genes and genomes (KEGG) functional annotation and enrichment analysis were performed for DEGs.ResultsT2DM-4w group vs. NG, T2DM-8w group vs. NG, and T2DM-12w group vs. NG detected 519 (251 up-regulated and 268 down-regulated), 459 (342 up-regulated and 117 down-regulated) and 328 (255 up-regulated and 73 down-regulated) DEGs, respectively. 103 key genes of sustained changes in the supraspinatus tendon following induction of diabetes, which are the first identified biomarkers of the supraspinatus tendon as it progresses through the course of diabetes.The GO analysis results showed that the most significant enrichment in biological processes was calcium ion transmembrane import into cytosol (3 DEGs). The most significant enrichment in cellular component was extracellular matrix (9 DEGs). The most significant enrichment in molecular function was glutamate-gated calcium ion channel activity (3 DEGs). The results of KEGG pathway enrichment analysis showed that there were 17 major pathways (p < 0.05) that diabetes affected supratinusculus tendinopathy, including cAMP signaling pathway and Calcium signaling pathway.ConclusionsTranscriptomics reveals dynamic changes in the“gene profiles”of rat supraspinatus tendon at three different time points after diabetes induction. The 103 DEGs identified in this study may provide potential molecular markers for exploring the pathogenesis of diabetic tendinopathy, and the 17 major pathways enriched in KEGG may provide new ideas for exploring the pathogenesis of diabetic tendinopathy.

Identification of osteoporosis ferroptosis-related markers and potential therapeutic compounds based on bioinformatics methods and molecular docking technology

Research background and purposeOsteoporosis (OP) is one of the most common bone diseases worldwide, characterized by low bone mineral density and susceptibility to pathological fractures, especially in postmenopausal women and elderly men. Ferroptosis is one of the newly discovered forms of cell death regulated by genes in recent years. Many studies have shown that ferroptosis is closely related to many diseases. However, there are few studies on ferroptosis in osteoporosis, and the mechanism of ferroptosis in osteoporosis is still unclear. This study aims to identify biomarkers related to osteoporosis ferroptosis from the GEO (Gene Expression Omnibus) database through bioinformatics technology, and to mine potential therapeutic small molecule compounds through molecular docking technology, trying to provide a basis for the diagnosis and treatment of osteoporosis in the future.Materials and methodsWe downloaded the ferroptosis-related gene set from the FerrDb database (http://www.zhounan.org/ferrdb/index.html), downloaded the data sets GSE56815 and GSE7429 from the GEO database, and used the R software “limma” package to screen differentially expressed genes (DEGs) from GSE56815, and intersected with the ferroptosis gene set to obtain ferroptosis-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by the R software “clusterProfiler” package. The random forest model was further screened to obtain essential ferroptosis genes. R software “corrplot” package was used for correlation analysis of essential ferroptosis genes, and the Wilcox test was used for significance analysis. The lncRNA-miRNA-mRNA-TF regulatory network was constructed using Cytoscape software. The least absolute shrinkage and selection operator (LASSO) was used to construct a disease diagnosis model, and a Receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic performance, and then GSE7429 was used to verify the reliability of the diagnosis model. Molecular docking technology was used to screen potential small molecule compounds from the Drugbank database. Finally, a rat osteoporosis model was constructed, and peripheral blood mononuclear cells were extracted for qRT-PCR detection to verify the mRNA expression levels of crucial ferroptosis genes.ResultSix DEGs related to ferroptosis were initially screened out. GO function and KEGG pathway enrichment analysis showed that ferroptosis-related DEGs were mainly enriched in signaling pathways such as maintenance of iron ion homeostasis, copper ion binding function, and ferroptosis. The random forest model identified five key ferroptosis genes, including CP, FLT3, HAMP, HMOX1, and SLC2A3. Gene correlation analysis found a relatively low correlation between these five key ferroptosis genes. The lncRNA-miRNA-mRNA-TF regulatory network shows that BAZ1B and STAT3 may also be potential molecules. The ROC curve of the disease diagnosis model shows that the model has a good diagnostic performance. Molecular docking technology screened out three small molecule compounds, including NADH, Midostaurin, and Nintedanib small molecule compounds. qRT-PCR detection confirmed the differential expression of CP, FLT3, HAMP, HMOX1 and SLC2A3 between OP and normal control group.ConclusionThis study identified five key ferroptosis genes (CP, FLT3, HAMP, HMOX1, and SLC2A3), they were most likely related to OP ferroptosis. In addition, we found that the small molecule compounds of NADH, Midostaurin, and Nintedanib had good docking scores with these five key ferroptosis genes. These findings may provide new clues for the early diagnosis and treatment of osteoporosis in the future.

Exploring Acori Tatarinowii Rhizoma and Polygalae Radix in Alzheimer's: Network pharmacology and molecular docking analysis.

Explore Acori Tatarinowii Rhizoma (ATR) and Polygalae Radix (PR) mechanisms in Alzheimer's disease (AD) treatment through network pharmacology. ATR-PR was investigated in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, Batman, and Traditional Chinese Medicines Integrated Database (TCMID) to gather information on its chemical components and target proteins. Target genes associated with AD were retrieved from the GeneCards and National Center for Biotechnology Information (NCBI) databases. The integration of these datasets with potential targets facilitated the construction of an AD and ATR-PR protein-protein interaction (PPI) network using the STRING database. The resulting network identified the core active ingredients and main targets of ATR-PR in AD treatment. Cluster analysis of the PPI network was performed using Cytoscape 3.7.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Metascape database. Molecular docking simulations revealed potential interactions between the main active ingredients and core targets. Our analysis identified 8 putative components and 455 targets of ATR-PR. We systematically searched for 1306 genes associated with AD, conducted Venn diagram analysis resulting in 156 common targets, and constructed a PPI network with 57 key targets. GO functional analysis highlighted the primary biological processes associated with oxidative stress. KEGG pathway enrichment analysis revealed the involvement of 64 signaling pathways, with the PI3K/Akt signaling pathway playing a key role. Molecular docking analysis indicated a high affinity between the potential targets of ATR-PR and the main compounds of AD. This study sheds light on the complex network of interactions involving ATR-PR in the context of AD. The identified targets, pathways, and interactions provide a foundation for understanding the potential therapeutic mechanisms. The involvement of oxidative stress-related processes and the crucial role of the PI3K/Akt signaling pathway suggest avenues for targeted therapeutic interventions in Alzheimer's disease treatment. Our proposition of the combined use of ATR-PR has emerged as a potential treatment strategy for AD, supported by a network pharmacology approach. This framework provides a robust foundation for future clinical applications and experimental research in the pursuit of effective Alzheimer's disease treatments.

Bioinformatics-based screening and analysis of the key genes involved in the influence of antiangiogenesis on myeloid-derived suppressor cells and their effects on the immune microenvironment.

This study aimed to screen differentially expressed genes (DEGs) involved in the influence of antiangiogenic therapy on myeloid-derived suppressor cell (MDSC) infiltration and investigate their mechanisms of action. Data on DEGs after the action of antiangiogenic drugs in a pan-cancer context were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package in R software. Single-sample gene set enrichment analysis was performed using the gene set variation analysis package to evaluate the levels of immune cells and the activity of immune-related pathways. The relationships of DEGs with the infiltration levels of MDSCs and specific immune cell subpopulations were investigated via gene module analysis. The top 10 key genes were subsequently obtained from PPI network analysis using the cytoHubba plugin of the Cytoscape platform. When the DEGs of the four datasets were intersected, a DEG in the intersection of three datasets and 12 DEGs in the intersection of two datasets were upregulated, and 28 DEGs in the intersection of two datasets were downregulated. GO and KEGG pathway enrichment analyses revealed that the DEGs were associated with multiple important signaling pathways closely related to tumor onset and development, including cell differentiation, cell proliferation, the cell cycle, and immune responses. Most downregulated genes in lung adenocarcinoma (LUAD) were positively correlated with MDSC expression. Only MGP was negatively correlated; the correlation between CACNG6 and MDSC expression was statistically insignificant. In lung squamous cell carcinoma (LUSC), the relationships of PMEPA1, PCDH7, NEURL1B, and CACNG6 with MDSC expression were statistically insignificant; MGP was negatively correlated with MDSC expression. The top 10 key genes with the highest degree scores obtained using the cytoHubba plugin of Cytoscape were AURKB, RRM2, BUB1, NUSAP1, PRC1, TOP2A, NCAPH, CENPA, KIF2C, and CCNA2. Most of these genes were upregulated in LUAD and associated with immune cell infiltration and prognosis in tumors. An analysis of the relationships between DEGs and infiltration by other specific immune cells revealed the presence of consistent patterns in the downregulated genes, which exhibited positive correlations with the levels of Th2 cells, γδ T cells, and CD56dim NK cells, and negative correlations with other infiltrating immune cells. Antiangiogenic therapy may regulate MDSC infiltration through multiple important signaling pathways closely associated with tumor onset and development, such as cell differentiation, cell proliferation, the cell cycle, and immune responses. Antiangiogenic drugs may exert effects by affecting various types of infiltrating cells associated with immune suppression.

Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Mechanism of Gualou Xiebai Banxia Decoction against Myocardial Infarction.

The aim of this study was to investigate targets through which Gualou Xiebai Banxia decoction aids in treating myocardial infarction (MI) using network pharmacology in combination with molecular docking. The principal active ingredients of Gualou Xiebai Banxia decoction were identified from the TCMSP database using the criteria of drug-likeness ≥30% and oral bioavailability ≥0.18. Interactions and pathway enrichment were investigated using protein-protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively. Active component structures were docked with those of potential protein targets using AutoDock molecular docking relative softwares. HIF1A was of particular interest as it was identified by the PPI network, GO and KEGG pathway enrichment analyses. In conclusion, the use of network pharmacology prediction and molecular docking assessments provides further information on the active components and mechanisms of action Gualou Xiebai Banxia decoction.

Identification of EPSTI1 as a new potential biomarker for SLE based on GEO database.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous. The aim of this study is to find the key genes in peripheral blood mononuclear cells (PBMCs) of SLE patients and to provide a new direction for the diagnosis and treatment of lupus. GSE121239, GSE50772, GSE81622, and GSE144390 mRNA expression profiles were obtained from the website of Gene Expression Omnibus (GEO), and differential expressed genes (DEGs) analysis was performed by R. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate signaling pathways for the DEGs. Real-time qPCR (RT-qPCR) was used to verify the key gene EPSTI1 in PBMCs of SLE patients. Finally, the correlation analysis and ROC curve analysis of EPSTI1 for SLE were performed. A total of 12 upregulated DEGs were identified, including MMP8, MX1, IFI44, EPSTI1, OAS1, OAS3, HERC5, IFIT1, RSAD2, USP18, IFI44L, and IFI27. GO and KEGG pathway enrichment analysis showed that those DEGs were mainly concentrated in the response to virus and IFN signaling pathways. Real-time qPCR (RT-qPCR) revealed that EPSTI1 was increased in PBMCs of SLE. EPSTI1 was positively correlated with SLEDAI score in SLE patients. Besides, EPSTI1 was positively correlated with T cell activation- or differentiation-associated genes (BCL6 and RORC). Furthermore, ROC analyses proved EPSTI1 may have diagnostic value for SLE. Together, EPSTI1 was found to be a potential biomarker for SLE, closely related to T cell immune imbalance. Key Points • EPSTI1 expression was significantly increased in PBMCs of SLE patients. • EPSTI1 was positively correlated with disease activity and T cell activation- or differentiation-associated genes in SLE patients. • EPSTI1 might have a good diagnostic value for SLE.

Identification of key autophagy-related genes and pathways in spinal cord injury

Spinal cord injury (SCI) can cause a range of functional impairments, and patients with SCI have limited potential for functional recovery. Previous studies have demonstrated that autophagy plays a role in the pathological process of SCI, but the specific mechanism of autophagy in this context remains unclear. Therefore, we explored the role of autophagy in SCI by identifying key autophagy-related genes and pathways. This study utilized the GSE132242 expression profile dataset, which consists of four control samples and four SCI samples; autophagy-related genes were sourced from GeneCards. R software was used to screen differentially expressed genes (DEGs) in the GSE132242 dataset, which were then intersected with autophagy-related genes to identify autophagy-related DEGs in SCI. Subsequently, the expression levels of these genes were confirmed and analyzed with gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein–protein interaction (PPI) analysis was conducted to identify interaction genes, and the resulting network was visualized with Cytoscape. The MCODE plug-in was used to build gene cluster modules, and the cytoHubba plug-in was applied to screen for hub genes. Finally, the GSE5296 dataset was used to verify the reliability of the hub genes. We screened 129 autophagy-related DEGs, including 126 up-regulated and 3 down-regulated genes. GO and KEGG pathway enrichment analysis showed that these 129 genes were mainly involved in the process of cell apoptosis, angiogenesis, IL-1 production, and inflammatory reactions, the TNF signaling pathway and the p53 signaling pathway. PPI identified 10 hub genes, including CCL2, TGFB1, PTGS2, FN1, HGF, MYC, IGF1, CD44, CXCR4, and SERPINEL1. The GSE5296 dataset revealed that the control group exhibited lower expression levels than the SCI group, although only CD44 and TGFB1 showed significant differences. This study identified 129 autophagy-related genes that might play a role in SCI. CD44 and TGFB1 were identified as potentially important genes in the autophagy process after SCI. These findings provide new targets for future research and offer new perspectives on the pathogenesis of SCI.

The Analgesic Mechanism of Radix Wikstroemia indica “Sweat Soaking Method” Processed Product Based on Metabolomics and Network Pharmacology

Background: Miao medicine Radix Wikstroemia indica (RWI) is derived from the root of W. indica (L.) C. A. Mey. which has an analgesic effect, but its mechanism is not clear. Because of the toxicity of RWI, the research group used the “Sweat soaking method” to process RWI in the early stage to reduce its toxicity. Therefore, this study explored the analgesic effect and mechanism of processed RWI through network pharmacology and metabolomics. Purpose: To explore the analgesic effect and therapeutic mechanism of RWI processed by “Sweat soaking method”. Materials and methods: The torsion experiment was carried out with acetic acid. The metabolomic analysis of serum samples was carried out based on 1H-NMR technology, and the intersection targets of RWI and pain diseases were screened by network pharmacology for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Results: RWI has an analgesic effect and is related to metabolites such as 4-pyridoxic acid, l-glutamic acid, and agmatine. It is involved in arginine and proline metabolism, arginine biosynthesis, and alanine, aspartate, and glutamate metabolism. In network pharmacology, there were 404 common targets between RWI and pain diseases, and eight core targets were screened, including SRC, STAT3, and HSP90AA1. GO functional enrichment analysis found that RWI had effects on molecular processes such as protein phosphorylation and response to xenobiotic stimulus, cell composition such as receptor complex and membrane raft, and molecular functions such as enzyme binding. KEGG pathway enrichment analysis obtained 193 pathways. Arginine proline metabolism and nitrogen metabolism are involved in the same pathway as metabolomic analysis. Conclusion: RWI has an analgesic effect, and its therapeutic mechanism mainly involves arginine and proline metabolism.

Network Pharmacology and Molecular Docking to Explore the Mechanism of Compound Qilian Tablets in Treating Diabetic Retinopathy

Background: Diabetic Retinopathy (DR) is one of the common chronic complications of diabetes mellitus, which has developed into the leading cause of irreversible visual impairment in adults worldwide. The Compound Qilian Tablets (CQLT) were developed in China for the treatment and prevention of DR, but their mechanism of action is still unclear. Objective: In the present study, network pharmacology, molecular docking, and in vivo validation experiments were used to investigate the active components and molecular mechanisms of CQLT against DR. Methods: The active components and targets of CQLT were collected through the TCSMP database, and the targets of DR were obtained from GeneCards, OMIM, and Drugbank databases. We established a protein-protein interaction network using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Metascape database. Molecular docking using AutoDock Vina was performed to investigate the interactions between components of CQLT and core targets. Moreover, we selected ZDF rats to establish a DR model for the experimental studies. Results: 39 active components and 448 targets in CQLT were screened, among which 90 targets were shared with DR. KEGG pathway enrichment analysis identified 181 pathways. The molecular docking results demonstrated that the main active components had strong binding ability to the core targets. The results from animal experiments indicate that the mechanism of CQLT against DR is associated with inhibiting the retinal mTOR/HIF-1α/VEGF signaling pathway, alleviating the inflammatory response, suppressing retinal neovascularization, and protecting the function and morphology of the retina. Conclusion: The present study preliminarily explored the mechanism of CQLT in treating DR and demonstrated that CQLT exerts anti-DR effects through multiple components, multiple targets, and multiple pathways. These findings suggest that CQLT shows promise as a potential therapeutic agent for DR and could contribute to developing novel treatments.

Multiplexing of TMT labeling reveals folate-deficient diet-specific proteome changes in NTDs.

Introduction: In the early stage of embryonic development, the neural tube (NT) cannot be closed properly due to some complex factors, including environmental factors, genetic factors, and the relationship between various factors, leading to the occurrence of neural tube defects (NTDs). Methods: In this study, we induced a mouse model of NTDs by feeding mice with a low-folate diet and intraperitoneally injecting them with 1.5mg/kg methotrexate on E7.5. Fetal mice were achieved at E13.5, and we extracted proteins from brain tissues with trypsin digestion. After enzymatic digestion, peptides were labeled with TMT/iTRAQ and separated in high-performance liquid chromatography (HPLC) for subsequent liquid chromatography tandem mass spectroscopy (LC-MS/MS) analysis. We used gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation to analyze proteomic changes and analyze the functional enrichment of differentially expressed proteins (DEPs) in the NTD mice tissues. Results: A low-folate-induced mouse model was successfully constructed. Folate was used as a sensitizing agent, and the teratogenicity rate of the NTD fetal mice increased to 36.5% when the concentration of methotrexate was at 1.5mg/kg. Mass spectrometry was used to identify 6,614 proteins, and among them, 5,656 proteins were quantified. In the following proteomic analysis, GO classification and KEGG pathway enrichment analysis were conducted, and heatmaps were drawn for differentially expressed proteins (DEPs). The main pathways associated with NTDs, such as the Hedgehog, Wnt, p53, and Hippo signaling pathways and the one-carbon pool mediated by folate, can be identified through a protein-protein interaction (PPI) network. It was also found that the regulation of ribosomal proteins, such as RPL13 and RPL14, which are upregulated in NTDs, has a certain impact on neural tube development. Discussion: Our results revealed proteomic changes in the tissues of low-folate-induced NTD mice. Validation showed that ribosomal proteins play a regulatory role during the development of NTDs and provides new ideas for the pathogenesis and preventive measures of NTDs.

Network Pharmacology and Experimental Validation of Jinwei Decoction for Enhancement of Glucocorticoid Anti-Inflammatory Effect in COPD through miR-155-5p

Background: Jinwei decoction can enhance the anti-inflammatory effect of glucocorticoid (GC) on chronic obstructive pulmonary disease (COPD) by restoring the activity of human histone deacetylase-2 (HDAC2). However the upstream mechanism of Jinwei decoction on HDAC2 expression is not clear. Objective: To explore the target of Jinwei decoction to enhance the anti-inflammatory effect of GC on COPD through microRNA155-5p (miR-155-5p) by network pharmacology and experimental verification. Methods: The TCMSP database was used to screen active ingredients and target genes of Jinwei decoction, and miRWalk2.0 was used to predict downstream target genes of miR-155-5p. COPD-related genes were identified by searching GeneCards, Grugbank and OMIM databases; Venny 2.1 was used to screen intersection genes; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of intersection genes were analyzed by R software. Protein-protein interactions (PPIs) were analyzed by Cytoscape 3.7.2 software to identify core genes. Finally, interactions between main compounds and potential targets were verified by molecular docking. A COPD cell model was established by 5% cigarette smoke extract (CSE)- induced bronchial epithelial cell (BEAS-2B), and the results of network pharmacology were verified by in vitro experiments. Results: Two hundred thirty-one active ingredients, 352 Jinwei decoction drug targets, 5949 miR-155-5p target genes, 8286 COPD target genes, and 127 intersection genes were identified. Twelve core proteins of PPI networks may be involved. GO enrichment analysis showed that regulation of membrane potential， response to steroid hormone, and histone modification were involved; KEGG pathway enrichment analysis concentrated in the PI3K-Akt, mitogen-activated protein kinase (MAPK), HIF-1, and other signaling pathways. The molecular docking results showed that quercetin, luteolin and stigmasterol have higher affinity with PTGS2, HIF1A and AKT1. The results of cell experiments revealed that Jinwei decoction not only enhances the anti- inflammatory effect of GC in the COPD cell model but also reverses the high expression of miR-155-5p、PI3k、Akt, and low expression of HDAC2, thereby inhibiting the inflammatory response of COPD. result: Two hundred thirty-one active ingredients, 352 Jinwei decoction drug targets, 5949 miR-155-5p target genes, 8286 COPD target genes, and 127 intersection gene were identified. Twelve core proteins of PPI networks may be involved. GO enrichment analysis showed that regulation of membrane potential，response to steroid hormone, and histone modification were involved; KEGG pathway enrichment analysis concentrated in the PI3K-Akt, mitogen-activated protein kinase (MAPK), HIF-1, and other signaling pathways. The molecular docking results showed that quercetin, luteolin and stigmasterol have higher affinity with PTGS2, HIF1A and AKT1. The results of cell experiments revealed that Jinwei decoction not only enhances the anti-inflammatory effect of GC in the COPD cell model, but also reversed the high expression of miR-155-5p、PI3k、Akt, and low expression of HDAC2, thereby inhibiting the inflammatory response of COPD. Conclusion: Jinwei decoction can regulate HDAC2 activity and enhance the anti-inflammatory effect of GC on COPD by modulating miR-155-5p. Its mechanism of action may be related to its effect on the PI3K-Akt through miR-155-5p.