Protein-protein Interaction Network Analysis Research Articles

Transcriptome Analysis Reveals Sertoli Cells Adapting Through Redox and Metabolic Pathways Under Heat Stress in Goats

Background/Objectives: Climate change-induced temperature elevations pose significant challenges to livestock reproduction, particularly affecting testicular function in small ruminants. This study investigates the acute heat-stress response in goat Sertoli cells (SCs), aiming to elucidate the molecular mechanisms underlying heat-induced damage to male reproductive tissues. Methods: SCs were isolated from testes of 4-month-old black goats and exposed to heat stress (44 °C for 2.5 h). We employed transcriptome sequencing, CCK-8 assay, electron microscopy, ROS measurement, autophagy detection, Western blot analysis, and lactate concentration measurement. Bioinformatics analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction network analyses were performed on the transcriptome data. Results: Heat stress significantly reduced SC viability, induced oxidative stress and autophagy, and altered gene expression profiles. We identified 1231 significantly differentially expressed genes, with significant enrichment in membrane-related processes and metabolic pathways. Metabolism-related genes, including PKLR, ACOT11, and LPCT12, were significantly downregulated. Protein–protein interaction network analysis revealed ten hub genes potentially crucial in the heat-stress response: HSP90AA1, HSPA5, BAG3, IGF1, HSPH1, IL1A, CCL2, CXCL10, ALB, and CALML4. Conclusions: This study provides comprehensive insights into the molecular mechanisms underlying goat SC response to heat stress. The identified genes and pathways, particularly those related to metabolism and stress response, offer potential targets for developing strategies to mitigate heat-stress effects on livestock reproduction. These findings contribute to our understanding of climate change impacts on animal husbandry and may inform the development of heat-stress resistant livestock lines.

Immune-related gene characterization and biological mechanisms in major depressive disorder revealed based on transcriptomics and network pharmacology

BackgroundMajor depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples.MethodsGene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653. Our approach included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis to identify hub genes associated with MDD. Subsequently, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape plugin CluGO, and Gene Set Enrichment Analysis (GSEA) were utilized to identify immune-related genes. The final selection of immune-related hub genes was determined through the least absolute shrinkage and selection operator (Lasso) regression analysis and PPI analysis. Immune cell infiltration in MDD patients was analyzed using CIBERSORT, and correlation analysis was performed between key immune cells and genes. The diagnostic accuracy of the identified hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, we conducted a study involving 10 MDD patients and 10 healthy controls (HCs) meeting specific criteria to assess the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). The Herbal Ingredient Target Database (HIT) was employed to screen for herbal components that target these genes, potentially identifying novel therapeutic agents.ResultsA total of 159 down-regulated and 51 up-regulated genes were identified for further analysis. WGCNA revealed 12 co-expression modules, with modules “darked”, “darkurquoise” and “light yellow” showing significant positive associations with MDD. Functional enrichment pathway analysis indicated that these differential genes were associated with immune functions. Integration of differential and immune-related gene analysis identified 21 common genes. The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. GSEA analysis suggested that these genes may be involved in biological processes such as protein export, RNA degradation, and fc gamma r mediated cytotoxis. Pathway enrichment analysis identified three highly enriched immune-related pathways associated with the 4 hub genes. ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. Immune infiltration analysis revealed significant correlations between immune cells, including Mast cells resting, T cells CD8, NK cells resting, and Neutrophils, which were significantly correlated with the hub genes expression. HIT identified one herb target related to IL7R and 14 targets related to TLR2.ConclusionsThe study identified four immune-related hub genes (TLR2, RETN, HP, and IL7R) in MDD that may impact the diagnosis and treatment of the disorder. By leveraging the GEO database, our findings contribute to the understanding of the relationship between MDD and immunity, presenting potential therapeutic targets.

The therapeutic effects of Paeoniae Radix Rubra on chronic hepatitis through network pharmacology and molecular docking.

Chronic hepatitis (CH) refers to liver inflammation lasting at least 6 months caused by various factors, significantly impacting patients' daily lives. Paeoniae Radix Rubra (CS) is a classic blood-activating and stasis-dissolving herb known for its protective effects on the liver. This research seeks to investigate the underlying mechanisms by which CS treat CH, employing network pharmacology and molecular docking. The active constituents of CS for CH treatment were identified through the TCMSP database. Targets associated with CH were gathered from GeneCards, the Therapeutic Target Database, and OMIM databases. The intersecting genes between these targets and the components of CS were considered potential therapeutic targets. Protein-protein interaction analysis was performed with the use of the STRING database and Cytoscape software, leading to the identification of core targets. These core targets underwent KEGG and GO enrichment analysis, and the top 10 pathways were chosen for building a drug-compound-target-pathway-disease' network. Finally, molecular docking was utilized to evaluate the binding affinities between the compounds and the core targets. From the TCMSP database, 29 compounds were screened, and 101 potential intersection targets of CS for treating CH were identified. The protein-protein interaction network analysis revealed that the core targets included EGFR, HSP90AA1, SRC, TNF, ALB, ESR1, CASP3, PTGS2, ERBB2, and FGF2. Pathway analysis indicated that CS's treatment of CH is mainly associated with the pathway in cancer. Molecular docking results indicated that Paeoniflorin and Baicalin exhibited strong binding affinity with EGFR and HSP90AA1. This research uncovers the possible mechanisms of CS in CH treatment, offering new avenues for future studies.

Identification of Oxidative Stress-Related Genes in Hyperlipidemia Based on Bioinformatic Analysis.

Oxidative stress(OS) is thought to mediate the processes of glycolipid disorders of a number of metabolic diseases and recent data suggest that OS may be involved in the pathophysiology of hyperlipidemia. The gene expression profiles of hyperlipidemia samples were downloaded from the Gene Expression Omnibus(GEO) database. Oxidative stress-related genes (ORGs) was the intersection of all valid data of discovery dataset and the ORGs in Genecards. The Differentially expressed genes(DEGs) between hyperlipidemia and control samples were obtained via "limma" R package, and differentially expressed oxidative stress-related genes (DEORGs) associated with hyperlipidemia were screened via OS gene sets. Gene Ontology (GO) and Kyoto encyclopaedia of Genes and Genomes (KEGG) enrichment analyses were performed to study the biological function of DEORGs, and protein-protein interaction(PPI) network analysis was conducted to screen hub genes. Then we constructed microRNA(miRNA), transcription factor (TF) and drug component targets network to explain the regulatory mechanism of ORGs in hyperlipidemia. After screening and evaluating we took GSE1010 as the discovery dataset and the GSE13985 as the validation set. There were 395 ORGs and 14 DEORGs retained from the hyperlipidemia. GO and KEGG results showed that DEORGs were mostly related to OS and lipid metabolism. Then, we used miRNA, TF, and drug component targets network to reveal the regulatory mechanism of hub genes. Finally, we verified expression of DEGs and hub gene in validation set. Our study has further confirmed the relationships between OS and hyperlipidemia, providing oxidative stress-related hub genes with possible function analysis and pathways summarized. These molecules might play a crucial role in the progression of hyperlipidemia and serve as potential biomarkers and therapeutic targets, giving us additional insight into the genes and the mechanism linking the OS system and metabolic disorders. We have not only proved hyperlipidemia is associated with OS but also gave foundation and reference for future researches.

Integrative bioinformatics analysis reveals novel insights into osteoarthritis pathogenesis and diagnostic biomarkers

BackgroundOsteoarthritis (OA) is a prevalent joint disorder characterized by degeneration and inflammation. Understanding its molecular mechanisms is crucial for diagnosis and treatment.MethodsWe employed bioinformatics analyses to study OA using gene expression data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted. Enrichment analyses were performed to elucidate the biological significance of identified genes. Additionally, signature genes were identified using LASSO regression analysis, and a diagnostic nomogram was developed. qRT-PCR was conducted to confirm the expression levels of signature genes.ResultsWe identified 200 differentially expressed genes (DEGs) and a lightgreen module strongly correlated with OA. Within this module, 97 core genes were identified. Fifteen core lipopolysaccharide-related genes (LRGs) were found, enriched in immune and inflammatory pathways. Three hub genes (CCL3, ZFP36, and CCN1) emerged as potential biomarkers for OA diagnosis, with a nomogram showing high predictive accuracy, and validated by using clinical samples. Gene set enrichment analysis (GSEA) revealed distinct signaling pathways associated with the signature genes. Immunological analysis indicated altered immune profiles in OA, with the signature genes influencing immune cell infiltration and immune response pathways.ConclusionOur study provides insights into OA pathogenesis and identifies potential diagnostic biomarkers. The developed nomogram shows promise for accurate OA diagnosis. Furthermore, the signature genes play crucial roles in modulating the immune microenvironment in OA, suggesting their therapeutic potential.Clinical trial numberNot applicable.

Identification of common core genes and pathways in childhood sepsis and cancer by bioinformatics analysis

IntroductionSepsis and cancer are both leading causes of death worldwide, and they share several pathophysiological characteristics. Some studies have suggested a possible association between sepsis and cancer; however, few have investigated the core genes involved in both diseases.MethodsCore genes common to both sepsis and cancer were identified using pediatric sepsis datasets (GEO: GSE26378, GSE4607, GSE8121 and GSE13904) and cancer databases (TCGA: BRCA, COADREAD, ESCA, KIRC, LIHC, LUAD, STAD). Gene Ontology (GO) and Reactome enrichment analyses, along with a protein–protein interaction (PPI) network analysis, were performed. Pharmacophore screening was applied to predict the targets of oxymatrine and ulinastatin, and potential target genes shared by both cancer and sepsis were identified. Survival analysis was performed. The association between the target genes and tumor size and number of positive lymph nodes was investigated by Pearson correlation analysis. The association between the target genes and tumor stage was investigated by Fisher’s exact test. Molecular docking analysis was performed to evaluate the affinity of the candidate drugs for their targets.ResultsA total of 641 common genes were identified. GO enrichment analysis showed that common genes were enriched in neutrophil degranulation, inflammatory response and innate immune response. Reactome enrichment analysis showed that common genes were enriched in neutrophil degranulation, interleukin-4 and interleukin-13 signaling, transcriptional regulation of granulopoiesis and interleukin-10 signaling. The PPI network showed that the top 10 core genes were TLR4, IL1B, IL10, ITGAM, TLR2, PTPRC, CDK1, FOS, MMP9 and ITGB2. The survival analysis showed that the high expression of BCAT1, CSAD, G6PD, GM2A, MMP9, PYGL and TOP2A was associated with poorer prognosis in several cancers. Molecular docking showed that oxymatrine and ulinastatin can bind to protein targets with highly stable binding.ConclusionsWe identified genes with common effects on both childhood sepsis and cancer, which provides new insights into the association between sepsis and cancer. In addition, two drugs with potential clinical application value were identified. Further studies are required to validate the role of these common core genes in sepsis and cancer and to evaluate the potential utility of these drugs.

Identification and validation of cigarette smoking-related genes in predicting prostate cancer development through bioinformatic analysis and experiments

The morbidity and mortality rates of prostate cancer (PCa) are high among elderly men worldwide. Several factors, such as heredity, obesity, and environment are associated with the occurrence of PCa. Cigarette smoking, which is also an important factor in the development of PCa, can lead to genetic alterations and consequently promote PCa development. However, the smoking-induced genetic alterations in PCa are unclear. This study aimed to identify the potential smoking-related genes associated with PCa development. The smoking-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus (GEO) which included lots of PCa datasets. DEGs were subjected to protein–protein interaction (PPI) network analysis to identify the hub genes. The pathways in which these hub genes were enriched were identified. The Cancer Genome Atlas (TCGA) dataset was used to examine the expression of smoking-related genes in PCa samples and estimate their value in predicting tumor progression and prognosis. In total, 110 smoking-related DEGs were got from GSE68135 dataset which included microarray data of PCa patients with smoking or not and 14 smoking-related key genes associated with PCa were identified from PPI network. The expression of the following seven key genes was altered in TCGA PCa patients: EWSR1, SRSF6, COL6A3, FBLN1, DCN, CYP2J2, and PLA2G2A. EWSR1, SRSF6, FBLN1, and CYP2J2 also influenced PCa progression. Additionally, EWSR1 influenced disease-free survival. In the logistic regression model, CYP2J2, which exhibited the highest risk scores, was identified as the risk gene for PCa. We also found one of the smoking-related genes: EWSR1 was truly upregulated in clinical PCa patients and influenced PCa cells invasion and proliferation. This study identified the function of smoking-related genes involved in the progression of PCa.

Network Pharmacology and In Vivo Experimental Verification of the Mechanism of the Qing'e Pill for Treating Intervertebral Disc Degeneration.

The Qing'e Pill (QEP) is widely used to alleviate low back pain and sciatica caused by Intervertebral Disc Degeneration (IDD). However, its active components, key targets, and molecular mechanisms are not fully understood. The aim of this study is to elucidate the molecular mechanisms through which the QEP improves IDD using database mining techniques. Active components and candidate targets of the QEP were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine. IDD-related targets were obtained from the GeneCards database, and liver- and kidney-specific genes were retrieved from the BioGPS database. The intersection of these candidate targets was analyzed to identify potential targets for the QEP in IDD. A protein-protein interaction network analysis was performed using STRING and Cytoscape 3.7.2 software. Core targets were further analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking was used to assess the binding affinity of active components to candidate targets, and animal experiments were conducted for validation. We identified 65 potentially active components of the QEP that corresponded to 1,093 candidate targets, 2,108 IDD-related targets, and 1,113 liver- and kidney-specific genes. Key components included quercetin, berberine, isorhamnetin, and emodin. The primary candidate targets were Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3. The GO and KEGG analyses revealed the involvement of these targets in Wnt signaling, TNF signaling, Wnt receptor activation, Frizzled binding, and Wnt-protein interactions. Molecular docking showed strong binding between these components and their targets. Animal experiments demonstrated that the QEP treatment significantly reduced the expression of Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3 at high, medium, and low doses compared with the model group. The QEP alleviated IDD by modulating the Wnt/MAPK/MMP signaling pathways and reducing the release and activation of key factors.

Prognostic and diagnostic value of SPINK mRNAs expression in head and neck squamous cell carcinoma based on genome-wide analysis

Aim: Head and neck squamous cell carcinoma (HNSC) is a major contributor to the global cancer burden. The serine protease inhibitor Kazal-type (SPINK) gene family has been linked to various cancers. This study explores the prognostic value of SPINK genes in predicting overall survival (OS) in HNSC patients. Methods: We analyzed RNA sequencing and clinical data from 504 cancer and 44 non-cancer samples from the TCGA database. Differential expression and functional enrichment analyses gene ontology and Kyoto encyclopedia of genes and genomes (GO and KEGG) were performed using clusterProfiler. Protein-protein interaction (PPI) networks were built with STRING and visualized. Immune infiltration was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA). Survival analysis utilized Kaplan-Meier curves and Cox regression models. Results: Our results showed that SPINK5, SPINK7, SPINK8, SPINK9, and SPINK14 were significantly overexpressed in normal tissues compared to carcinoma tissues, whereas SPINK1, SPINK4, and SPINK6 showed higher expression in carcinoma tissues. Correlation analysis revealed significant relationships among SPINK family members. GO and KEGG analyses highlighted their involvement in processes such as negative regulation of peptidase activity and serine-type endopeptidase inhibitor activity. PPI network analysis indicated close interactions between several SPINK proteins and other relevant proteins. Immune infiltration analysis showed that NK cells and Th2 cells were negatively correlated with SPINK genes, while mast cells and neutrophils were positively correlated. Survival analysis revealed that high mRNA expression levels of SPINK1, SPINK5, and SPINK6 were significantly associated with OS in HNSC patients. Receiver operating characteristic (ROC) curve analysis indicated that these genes have diagnostic value. We developed a nomogram model that combines tumor stage and SPINK gene expression providing a predictive tool for patient prognosis. Conclusions: This study elucidates the multifaceted roles of the SPINK gene family in HNSC. These findings offer valuable insights into their potential as diagnostic biomarkers and therapeutic targets.

Quantitative Proteomic Analysis of APP/PS1 Transgenic Mice.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the central nervous system (CNS), with its etiology still shrouded in uncertainty. The interplay of extracellular amyloid-β (Aβ) deposition, intracellular neurofibrillary tangles (NFTs) composed of tau protein, cholinergic neuronal impairment, and other pathogenic factors is implicated in the progression of AD. The current study endeavors to delineate the proteomic landscape alterations in the hippocampus of an AD murine model, utilizing proteomic analysis to identify key physiological and pathological shifts induced by the disease. This endeavor aims to shed light on the underlying pathogenic mechanisms, which could facilitate early diagnosis and pave the way for novel therapeutic interventions for AD. To dissect the proteomic perturbations induced by Aβ and Presenilin-1 (PS1) in the AD pathogenesis, we undertook a label-free quantitative (LFQ) proteomic analysis focusing on the hippocampal proteome of the APP/PS1 transgenic mouse model. Employing a multi-faceted approach that included differential protein functional enrichment, cluster analysis, and protein-protein interaction (PPI) network analysis, we conducted a comprehensive comparative proteomic study between APP/PS1 transgenic mice and their wild-type C57BL/6 counterparts. Mass spectrometry identified a total of 4817 proteins in the samples, with 2762 proteins being quantifiable. Comparative analysis revealed 396 proteins with differential expression between the APP/PS1 and control groups. Notably, 35 proteins exhibited consistent temporal regulation trends in the hippocampus, with concomitant alterations in biological pathways and PPI networks. This study presents a comparative proteomic profile of transgenic (APP/PS1) and wild-type mice, highlighting the proteomic divergences. Furthermore, it charts the trajectory of proteomic changes in the AD mouse model across the developmental stages from 2 to 12 months, providing insights into the physiological and pathological implications of the disease-associated genetic mutations.

Deciphering the pharmacological mechanism of Radix astragali for allergic rhinitis through network pharmacology and experimental validation

Radix Astragali (RA) has been recognized for its therapeutic potential in allergic rhinitis (AR), yet its potential pharmacological mechanisms remain elusive. This study systematically investigated the physicochemical properties and biological activities of RA’s phytochemicals, aiming to elucidate their targets and mechanisms in AR treatment. We identified 775 potential targets of RA’s key phytochemicals and intersected these with 29,544 AR-related disease targets, pinpointing 747 shared therapeutic targets. A protein-protein interaction network analysis categorized these targets into five subclusters, with TNF, NFKB1, IKBKB, NFKBIA, and CHUK emerging as central nodes. Enrichment analysis revealed their roles in inflammatory and immune responses, particularly through the NF-κB, TNF, IL-17, Toll-like receptor, and NOD-like receptor signaling pathways. Molecular docking and dynamics simulations confirmed the strong binding affinity and stability of RA’s phytochemicals to these targets. In vivo, RA intervention effectively reversed the expression of key inflammatory markers in an IL-13-induced nasal mucosa inflammation model. Our findings suggest that RA’s multitargeted approach involves the modulation of critical inflammatory pathways, highlighting its therapeutic potential.

Revealing key proteins in comparison of tumor and para-tumor tissues in stage I esophageal squamous cell carcinoma: A combined gene expression clustering and protein interaction network analysis

Esophageal Squamous Cell Carcinoma (ESCC) which is diagnosed in advanced stages and metastasis to the vital organs, remained the sixth fatal malignancy among other types of cancer-related death. Considering the lack of specific clinical symptoms in the early development of the disease, finding suitable biomarkers and tissue-specific alterations for effective screening of ESCC is targeted in this project. Gene expression profiles of tumor tissue and para-tumors of stage I ESCC patients were retrieved from the Gene Expression Omnibus (GEO) database. The significant tissue-specific differentially expressed genes (DEGs) were identified and studied based on fold change distribution and the functional role of genes in the action map. A total number of 11,483 significant DEGs discriminated the tumor tissue from para-tumor tissue. Clustering analysis of significant DEGs led to identifying 220 DEGs as the final significant genes. Protein interaction network and action map analysis of the final gene list showed matrix metallopeptidase-9 (MMP9) as the critical gene related to the development of ESCC diseases in stage I. Significant expression change of MMP9 in esophageal carcinoma was validated in UALCAN (The University of Alabama at Birmingham Cancer Data Analysis Portal). This study highlighted the pivotal role of MMP9 in combination with SPP1, MMP13, and IL18 as a possible biomarkers panel in studies of tumor invasion and prognosis for stage I ESCC disease.

Exploration of the key active ingredients and mechanisms of Sparganii Rhizoma-Curcumae Rhizoma compatible formulation against human colorectal cancer through network pharmacology and in vitro experiments

IntroductionSparganii Rhizoma-Curcumae Rhizoma (SR-CR) formulation may offer an effective treatment for human colorectal cancer (CRC). However, the active ingredients and underlying mechanisms of this herbal formulation remain unclear. MethodsThis study integrates network pharmacology, molecular docking and experimental verification to identify key active ingredients and elucidate the mechanisms of SR-CR in CRC treatment. ResultsTwenty-three active components of SR-CR were identified using the TCMSP, UniProt and Gene Cards databases. These components were associated with 178 targets, of which 118 are directly related to CRC. Protein-protein interaction (PPI) network analysis identified protein kinase B (AKT) 1, epidermal growth factor receptor (EGFR), SRC, Bcl2 and CASP3 as core anti-CRC targets. Gene Ontology (GO) and KEGG pathway analyses were performed on these 178 intersecting targets, and the results showed that these targets are involved in EGFR tyrosine kinase inhibitor resistance and AGE-RAGE signaling pathway in diabetic complications. At the same time, five compounds, including bisdemethoxycurcumin, formononetin, β-sitosterol, stigmasterol and hederagenin, were selected based on the target number of effective components and tested for cytotoxicity against human CRC cell lines HT-29, HCT-8 and HCT-116 in vitro. The results showed that bisdemethoxycurcumin exhibited significant anti-proliferative activity against HCT-116 cells. Molecular docking results indicated that bisdemethoxycurcumin effectively binds with AKT, EGFR, Bcl-2 and CASP3. Further pharmacological experiments demonstrated that bisdemethoxycurcumin inhibits HCT-116 cell proliferation and migration via inhibiting EGFR-AKT pathway, and induces apoptosis by up-regulating Bcl-2 expression. DiscussionBased on our study, inhibiting the EGFR-AKT pathway and upregulating Bcl2 may be one of the mechanisms by which SR-CR inhibits the growth of CRC. The main active ingredients of SR-CR include bisdemethoxycurcumin, formononetin, β-sitosterol, stigmasterol and hederagenin, which may exert anti-colon cancer activity by targeting the regulation of AKT, EGFR, Bcl-2 and CASP3. However, we need more in vitro and in vivo evidence to confirm this conclusion. This study lays the foundation for further research on the pharmacological mechanism of SR-CR in the treatment of CRC.

Network Pharmacology Studies on the Molecular Mechanism of Hashimoto's Thyroiditis Treated with Shutiao Qiji Decoction.

In recent years, the number of patients with Hashimoto's thyroiditis has been increasing, and traditional Chinese medicine ingredients and combinations have been applied to treat Hashimoto's thyroiditis to increase efficacy and reduce side effects during the treatment process. Shutiao Qiji Decoction is one of the Chinese traditional medicine prescriptions, which is commonly used to treat cancer, tumor, etc. It is also used for thyroid-related diseases in the clinic. Hashimoto's thyroiditis is an autoimmune disease. In this study, the mechanism of Shutiao Qiji Decoction in treating Hashimoto's thyroiditis was studied through network pharmacology and molecular docking verification. Each Chinese medicine ingredient of Shutiao Qiji Decoction was retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The related genes of HT were searched from the UniProt and GeneCards databases. Meanwhile, we used Cytoscape to construct the protein-protein interaction (PPI) visual network analysis, and used the search tool to search the database of Interacting Genes (STRING) to build a PPI network. These key proteins were enriched and analyzed by molecular docking validation, Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Hashimoto's thyroiditis disease model was established in SD rats, and SQD was administered by gavage after the successful establishment of the model. After 6 weeks of continuous administration of the drug by gavage, tissue samples were collected and the thyroid and spleen tissues were visualized by HE staining to verify the therapeutic effect. The results showed that there were 287 TCM active ingredients, 1920 HT-related disease targets, and 176 drug and disease targets in SQD. Through PPI analysis, GP analysis, and KEGG analysis of the common targets of drugs and diseases, we found their pathways of action to be mainly cancer action pathway, PI3K-AKT signaling pathway, and T-cell action pathway. The active ingredients of the drugs in SQD, malvidin, stigmasterol, porin-5-en-3bta-ol, and chrysanthemum stigmasterol, were docked with the related target proteins, MAPK, GSK3β, TSHR, and NOTCH molecules. The best binding energies obtained from docking were mairin with TSHR, stigmasterol with TSHR, poriferast-5-en-3beta-ol with MAPK, and chryseriol with GSK3β, with binding energies of -6.84 kcal/mol, -6.53 kcal/mol, -5.03 kcal/mol, and -5.05 kcal/mol, respectively. HE staining sections of rat thyroid and spleen tissues showed that SQD had a therapeutic effect on Hashimoto's thyroiditis and restored its immune function. It is verified by molecular docking results that Shutiao Qiji Decoction has a potential therapeutic effect on Hashimoto's thyroiditis in the MAPK/TSHR/NOTCH signal pathway, and that the main components, mairin, stigmasterol, poriferast-5-en-3beta-ol, and chryseriol play a role in it. SQD has been shown to have a good therapeutic effect on Hashimoto's thyroiditis.

Effect of COL11A1 on oral squamous cell carcinoma

BackgroundOral squamous cell carcinoma (OSCC) is the most common malignant tumor of the oral cavity, which is mainly a series of atypical hyperplasia of oral epithelial cells, and the overall prognosis remains poor. MethodsGSE37991 and GSE38517 were downloaded from gene expression omnibus (GEO) to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs was performed, weighted gene co-expression network analysis (WGCNA) was used. Protein-protein interaction (PPI) network was constructed. Core gene expression was visualized using a heatmap. Comparative toxicogenomics database (CTD) and miRNA analyses identified related diseases and regulatory miRNAs. Western blot (WB) was conducted to examine expression of COL11A1 and TGF-SMAD signaling components in OSCC samples. Results5163 DEGs were identified. DEGs were enriched in metabolic processes and signaling pathways, including TGF-β/SMAD and PI3K-Akt. WGCNA identified 11 core modules. PPI network analysis revealed five core genes: COL11A1, AURKA, MELK, CCNA2, and BUB1. Heatmap analysis showed that COL11A1 is highly expressed in OSCC. CTD analysis indicated that COL11A1 is associated with OSCC. miRNA prediction identified potential regulatory factors. Western blot analysis demonstrated that COL11A1 is overexpressed in OSCC and is associated with TGF-SMAD signaling, inflammation, and cell cycle progression. ConclusionCOL11A1 is highly expressed in OSCC and may serve as a target gene interacting with the TGF-SMAD signaling pathway.

Bailing capsule alleviates autoimmune thyroiditis regulating peroxisome proliferator-activated receptor signaling pathway: a multi-omics analysis.

To investigate the efficacy and potential mechanism of Bailing capsule (, BL) anti-autoimmune thyroiditis (AIT). Based on the AIT rat model, the effect of BL in alleviating AIT was evaluated by detecting serum thyroid index free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), thyroglobulin antibody (TGAb), thyroid peroxidase antibody (TPOAb), and inflammatory factors Interferon-gamma (IFN-γ), Interleukin-4, -10, and -12 (IL-4, IL-10, and IL-12) as well as thyroid tissue Hematoxylin-eosin (HE) staining and ultrastructure observation. The mechanism of BL was explored by combining transcriptome and proteome analysis, and further verified by Western blot (WB). BL effectively reduced serum FT3, FT4, TGAb, and TPOAb levels in AIT rats, restored TSH balance, inhibited the release of pro-inflammatory cytokines IFN-γ and IL-12, promoted the production of anti-inflammatory cytokines IL-4 and IL-10, and significantly reduced IFN-γ/IL-4 and IL-12/IL-10, improved thyroid follicular structure, and protected thyroid tissue from injury. Kyoto Encyclopedia of Genes and Genomes and protein interaction network analysis showed that BL affected the expression of fatty acid-binding protein 4, acyl-CoA synthetase long-chain family member 1, and acyl-CoA dehydrogenase long chain to regulate the peroxisome proliferator-activated receptor signaling pathway, thereby inhibiting the fatty acid metabolism and the inflammatory state of AIT rats. BL could effectively reduce thyroid inflammation in AIT model rats. The possible BL mechanism was to regulate the peroxisome proliferator-activated receptor signaling pathway and inhibit fatty acid metabolism. This study suggested that BL has the potential to be used in clinical treatment of AIT.

Screening and identification of potential target of 1′-acetoxychavicol acetate (ACA) in acquired lapatinib-resistant breast cancer

1′-Acetoxychavicol acetate (ACA) eliminates breast cancer cells via the HER2/MAPK/ERK1/2 and PI3K/AKT pathways, and it also directly influences endocrine resistance by both enhancing pro-apoptotic signals and suppressing pro-survival molecules. This study utilized bioinformatics to assess ACA target genes for lapatinib-resistant breast cancer. We identified differentially expressed genes (DEGs) using GSE16179 microarray data. DEGs from ACA-treated and lapatinib-resistant cells were analyses using Panther DB, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and protein-protein interaction (PPI) network analysis. Genomic mutations, expression levels, prognostic significance, and ROC analysis were examined in selected genes. We used AutoDock Vina to conduct ACA molecular docking with potential target genes. In the PPI network analysis, BCL2, CXCR2, and CDC42 were the three highest-scoring genes. Genetic modification analysis identified PLAU and SSTR3 as the genes most frequently altered in breast cancer samples. The RTK-Ras pathway is likely to be affected by changes in BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET genes. Patients with breast cancer who had lower levels of BCL2, SSTR3, PLAU, ICAM1, IGF1R, and MET had worse overall survival compared to other groups. ACA exhibited moderate binding affinity to BCL2, SSTR3, PLAU, ICAM1, IGF1R, and MET. Overall, ACA might counteract breast cancer resistance to lapatinib by targeting BCL2, SSTR3, PLAU, ICAM1, IGF1R, and MET. Further in vitro studies involving gene silencing could provide more detailed insights into the mechanism by which ACA combats lapatinib resistance.

Unraveling a growth-promoting potential for plants: Genome-wide identification and expression state of the TCP gene family in Juglans mandshurica

The TCP gene family stands as a pivotal factor in regulating plant growth, development, and resilience to abiotic stress. Despite extensive research and identification of TCP genes across diverse plant species, the intricate functionality and evolutionary expansion of TCP genes within Juglans mandshurica remain relatively uncharted territories, necessitating thorough investigation. In this work, 35 JmTCP genes belonging to two distinct subfamilies were identified in the genome of J. mandshurica, which were unevenly distributed on 13 chromosomes. To gain insights into structural characteristics, a comprehensive analysis encompassing gene structure was conducted, motif composition, and conserved domain examination. Furthermore, cis-acting element and protein interaction network analyses were leveraged to elucidate potential biological functions associated with these genes. Transcriptome data unveiled that JmTCP12 and JmTCP29 are essential for the development of walnut exocarps, embryos, male and female flowers. Furthermore, the results of the genetic transformation experiments demonstrated that JmTCP31 possesses the capability to significantly promote leaf development and expedite the flowering cycle. Collectively, this study offers novel insights into the molecular mechanisms underlying the TCP gene family's contributions to growth and development in J. mandshurica, enriching understanding of this essential gene family in plants and shedding light on potential applications in agricultural biotechnology.

Network pharmacology and molecular docking study on the potential mechanism of Tongqiao–Biyan granule in treatment of allergic rhinitis

AbstractPurposeAllergic rhinitis (AR) is a pervasive global health problem, imposing a major economic burden and causing disability worldwide. Tongqiao–Biyan granule (TBG) has gained popularity in China for the treatment of AR. This study aimed to elucidate the molecular mechanism underlying TBG's efficacy in treating AR.MethodsThe Traditional Chinese Medicine pharmacological database was utilized to identify the main ingredients in TBG and their corresponding target genes. AR‐related target genes were identified using the GeneCards, OMIM, and DrugBank databases. The Gene Ontology (GO) network, Kyoto Encyclopedia of Genes and Genomes (KEGG), and target protein–protein interaction (PPI) network were employed to analyze the molecular mechanisms. The top five hub genes were selected for molecular docking with the top five ranked compounds to confirm their interaction in TBG when used in the treatment of AR.ResultsA total of 7 active ingredients from TBG, 784 predicted targets of TBG, and 945 AR targets were obtained. Analysis via the GO and KEGG databases revealed that TBG can act on AR by modulating inflammatory responses and promoting cell migration. PPI network analysis and molecular docking results suggested that phellopterin, quercetin, luteolin, denudatin B, and cleomiscosin A in TBG may alleviate the AR symptoms by interacting with key proteins such as TNF, AKT1, STAT3, VEGFA, and EGFR.ConclusionsTBG modulates numerous targets across diverse signaling pathways in the AR therapy. Our results furnish a theoretical foundation for further exploring TBG's pharmacological mechanism in AR treatment.

Exploring bacterial key genes and therapeutic agents for breast cancer among the Ghanaian female population: Insights from In Silico analyses

Exploring bacterial key genes and therapeutic agents for breast cancer among the Ghanaian female population: Insights from In Silico analyses