Unraveling the role of IL-17 signaling pathway in breast cancer-related depression: insights from in vivo/in vitro models and transcriptomic analysis.

Objective This study is aimed to reveal the possible mechanisms of artemisinin in the treatment of ulcerative colitis (UC) through bioinformatics analysis and experimental verification in UC model rats. Methods Firstly, we searched two microarray data of the Gene Expression Omnibus (GEO) database to explore the differentially expressed genes (DEGs) between UC samples and normal samples. Then, we selected DEGs for gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The acute UC model of rats was established by using 3.5% dextran sulfate sodium (DSS) for 10 days to verify the core pathway. Finally, we evaluated the therapeutic effect of artemisinin at the molecular level and used metabonomics to study the endogenous metabolites in the rat serum. Results We screened in the GEO database and selected two eligible microarray datasets, GSE36807 and GSE9452. We performed GO function and KEGG pathway enrichment analyses of DEGs and found that these DEGs were mainly enriched in the inflammatory response, immune response, and IL-17 and NF-κB signaling pathways. Finally, we verified the IL-17 signaling pathway and key cytokines, and ELISA and immunohistochemical results showed that artemisinin could downregulate the expression of proinflammatory cytokines such as IL-1β and IL-17 in the IL-17 signaling pathway and upregulate the expression of the anti-inflammatory cytokine PPAR-γ. Metabolomics analysis showed that 33 differential metabolites were identified in the artemisinin group (AG) compared to the model group (MG). Differential metabolites were mainly involved in alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies. Conclusion In this study, we found that artemisinin can significantly inhibit the inflammatory response in UC rats and regulate metabolites and related metabolic pathways. This study provides a foundation for further research on the mechanism of artemisinin in the treatment of UC.

Ischemic post-conditioning (I-post C) is a recognized therapeutic strategy for lung ischemia/reperfusion injury (LIRI). However, the specific mechanisms underlying the lung protection conferred by I-post C remain unclear. This study aimed to investigate the protective mechanisms and potential molecular regulatory networks of I-post C on lung tissue. Transcriptome analysis was performed on rat lung tissues obtained from Sham, ischemia-reperfusion (IR), and I-post C groups using RNA-seq to identify differentially expressed genes (DEGs). Subsequently, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted to elucidate significantly enriched pathways in the IR and I-post C groups. Additionally, protein-protein interaction (PPI) network analysis was carried out to examine associations among the DEGs. Pathological changes in lung tissues were assessed using hematoxylin-eosin (H&E) staining. The expression levels of CXCL1 and CXCL6 in the IR and I-post C groups were evaluated through immunofluorescence and Western blotting. Our results showed that I-post C significantly attenuated both pulmonary edema and inflammatory cell infiltration. Transcriptome analysis identified 38 DEGs in the I-post C group compared to the IR group, comprising 21 upregulated and 17 downregulated genes. Among these, seven inflammation-related DEGs exhibited co-expression patterns with the Sham and IR groups, with notable downregulation of Cxcl1 and Cxcl6. GO analysis primarily linked these DEGs to neutrophil activation, chemotaxis, cytokine activity, and CCR chemokine receptor binding. KEGG analysis revealed enriched pathways, including the IL-17, TNF, and NF-κB signaling pathways. GSEA indicated downregulation of neutrophil chemotaxis and the IL-17 signaling pathway, correlating with reduced expression of Cxcl1 and Cxcl6. Validation of Cxcl1 and Cxcl6 mRNA expression via immunofluorescence and Western blotting supported the RNA-seq findings. Furthermore, a PPI network was constructed to elucidate interactions among the 29 DEGs. Through RNA-Seq analysis, we concluded that I-post C may reduce inflammation and suppress the IL-17 signaling pathway, thereby protecting against lung damage caused by LIRI, potentially involving neutrophil extracellular traps.

We aimed to investigate immune-related candidate genes for predicting the severity of acute pancreatitis (AP). RNA sequencing profile GSE194331 was downloaded, and differentially expressed genes (DEGs) were investigated. Meanwhile, the infiltration of immune cells in AP were assessed using CIBERSORT. Genes related with the infiltration of immune cells were investigated using weighted gene co-expression network analysis (WGCNA). Furthermore, immune subtypes, micro-environment, and DEGs between immune subtypes were explored. Immune-related genes, protein-protein interaction (PPI) network, and functional enrichment analysis were further performed. Overall, 2533 DEGs between AP and healthy controls were obtained. After trend cluster analysis, 411 upregulated and 604 downregulated genes were identified. Genes involved in two modules were significantly positively related to neutrophils and negatively associated with T cells CD4 memory resting, with correlation coefficient more than 0.7. Then, 39 common immune-related genes were obtained, and 56 GO BP were enriched these genes, including inflammatory response, immune response, and innate immune response; 10 KEGG pathways were enriched, including cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, and IL-17 signaling pathway. Genes, including S100A12, MMP9, IL18, S100A8, HCK, S100A9, RETN, OSM, FGR, CAMP, were selected as genes with top 10 degree in PPI, and the expression levels of these genes increased gradually in subjects of healthy, mild, moderately severe, and severe AP. Our findings indicate a central role of immune-related genes in predicting the severity of AP, and the hub genes involved in PPI represent logical candidates for further study.

Objective To analyze the differentially expressed genes (DEGs) in rats with endogenous acute respiratory distress syndrome (ARDS) lung injury and explore the pathogenesis and early diagnostic molecular markers using whole transcriptomic data. Methods Twelve 8-week-old male Sprague Dawley rats were selected and randomly and equally divided into ARDS lung injury group and normal control group. RNA was extracted from the left lung tissues of both the groups and sequenced using the paired-end sequencing mode of the Illumina Hiseq sequencing platform. The DEGs of miRNA, cirRNA, lncRNA, and mRNA were screened using DESeq2 software, and the ceRNA regulatory network was constructed using Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using the mRNA DEGs. STRING and Cytoscape software were used to construct the protein interaction network and identify the 15 key genes, which were verified using quantitative real-time polymerase chain reaction (qRT-PCR). Results Based on different screening conditions, and compared with the control group, the ARDS lung injury group showed 836 mRNA DEGs (386 upregulated and 450 downregulated), 110 lncRNA DEGs (53 upregulated and 57 downregulated), 19 circRNA DEGs (3 upregulated and 16 downregulated), and 6 miRNA DEGs (5 upregulated and 1 downregulated gene). GO showed that the DEGs of mRNA were mainly involved in biological processes, such as defense response to lipopolysaccharide and other organisms, leukocyte chemotaxis, neutrophil chemotaxis, and cytokine-mediated signaling. KEGG enrichment analysis showed that the DEGs played their biological roles mainly by participating in IL-17, TNF, and chemokine signaling pathways. The PPI analysis showed a total of 281 node proteins and 634 interaction edges. The top 15 key genes, which were screened, included Cxcl10, Mx1, Irf7, Isg15, Ifit3, Ifit2, Rsad2, Ifi47, Oasl, Dhx58, Usp18, Cmpk2, Herc6, Ifit1, and Gbp4. The ceRNA network analysis showed 69 nodes and 73 correlation pairs, where the key gene nodes were miR-21-3p, Camk2g, and Stx2. Conclusions The chemotaxis, migration, and degranulation of inflammatory cells, cytokine immune response, autophagy, and apoptosis have significant biological functions in the occurrence and development of endogenous acute lung injury during ARDS. Thus, the camk2g/miR-21-3p/lncRNA/circRNA network, CXCL10/CXCR3, and IL-17 signaling pathways might provide novel insights and targets for further studying the lung injury mechanism and clinical treatment.

PM2.5 activates IL-17 signaling pathway in human nasal mucosa-derived fibroblasts

11,12-Diacetyl-carnosol (DACA), a derivative of carnosol, exhibits significant anti-inflammatory and antioxidant properties. However, its antidepressant effects and underlying mechanisms remain unclear. High mobility group box1 protein (HMGB1)-mediated inflammatory responses and associated neurofunctional impairments play a crucial role in the pathogenesis of depression. This study aimed to investigate whether DACA exerts anti-inflammatory and antidepressant effects and whether its mechanisms involve the HMGB1/NF-κB/NLRP3 signaling pathway. (1) A depression model was established in mice through 6 weeks of chronic unpredictable mild stress (CUMS). From the 4th week of stimulation, the treatment group received DACA for 3 weeks. (2) BV2 cells were stimulated with LPS+ATP, and the treatment group was cultured in DACA medium for 24 h. (3) Supernatants from BV2 cells were used to culture primary neurons. To confirm the critical role of HMGB1 in DACA's antidepressant effects, CUMS-stressed mice were treated with glycyrrhizin (GZA) or the DACA+GZA combination. Depressive-like behaviors were evaluated using the sucrose preference test (SPT), open field test (OFT), tail suspension test (TST), forced swim test (FST), and Morris water maze (MWM). Hippocampal microglial cell and primary neuron morphology were assessed by immunofluorescence, and dendritic spine density in hippocampal neurons was examined using Golgi staining. IL-6 and TNF-α concentrations in mouse serum and BV2 supernatant were measured by ELISA. Western blotting was used to detect protein expressions of HMGB1, NF-κB p65, p-NF-κB p65, NLRP3, and IL-1β in the hippocampus and BV2 cells. CUMS-exposed mice showed decreased sucrose preference, increased immobility in TST and FST, prolonged escape latency in MWM, and reduced crossings. Microglial activation and upregulation of HMGB1, NF-κB p65, p-NF-κB p65, NLRP3, and IL-1β were observed in both CUMS-stressed mice and LPS+ATP-induced BV2 cells, with reduced dendritic spine density in the hippocampus. DACA significantly reversed these phenomena. The effects of DACA were comparable to those of GZA treatment, and no changes were observed with the DACA+GZA combination. The HMGB1/NF-κB/NLRP3 signaling pathway is involved in DACA's therapeutic effects on depression.

BackgroundHypopharyngeal cancer accounts for 2% in head and neck cancers and has a poor prognosis. Cisplatin is a widely used chemotherapeutic drug in kinds of carcinomas, concluding hypopharyngeal cancer. However, the resistance of cisplatin appeared in recent years. Cisplatin-resistance has been partly explored before, but rarely in hypopharyngeal cancer.MethodsWe cultured the hypopharyngeal cancer cell (FaDu) and induced its cisplatin-resistant cell (FaDu/DDP4). Then we tested the differentially expressed genes (DEGs) between FaDu and FaDu/DDP4. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted on the DEGs, and we drew the ceRNA networks of DEGs. Finally, we chose eight miRNAs and six mRNAs for qRT-PCR to verify our microarray.ResultsWe induced cisplatin-resistant FaDu/DDP4 and proved its chemoresistance. The resistance index (RI) of FaDu/DDP4 was 2.828. DEGs contain 2,388 lncRNAs, 1,932 circRNAs, 745 mRNAs and 202 miRNAs. These 745 mRNAs were classified into three domains and 47 secondary GO terms. In KEGG pathway enrichment, the “TNF signaling pathway”, “IL-17 signaling pathway” and “JAK-STAT signaling pathway” were potentially significant signaling pathways. Then, 52 lncRNAs, 148 circRNAs, 155 mRNAs and 18 miRNAs were selected to draw the network. We noticed several potential targets (as miR-197-5p, miR-6808-5p, APOE, MMP1, S100A9 and CYP24A1). At last, the eight miRNAs and six mRNAs that are critical RNAs in ceRNA network were verified by qRT-PCR.ConclusionThe microarray helped to find DEGs in cisplatin-resistant hypopharyngeal cancer. TNF, IL-17 and JAK-STAT signaling pathways might be more significant for cisplatin-resistance. MiR-197-5p, miR-6808-5p, APOE, MMP1, S100A9 and CYP24A1 might be potential genes inducing resistance.

HMGB1 promotes LPS-induced M1 polarization and apoptosis in microglia by mediating the expression of immune and inflammation-related genes.

The IL17 signaling pathway: A potential signaling pathway mediating gill hyperplasia and inflammation under ammonia nitrogen stress was identified by multi-omics analysis

Diabetic foot ulcer (DFU) is a serious clinical problem with high amputation and mortality rates, yet there is a lack of desirable therapy. While the extracellular matrix (ECM) contributes significantly to wound healing, ECM-related biomarker for DFU is still unknown. The study was designed to identify ECM-related biomarker in DFU using bioinformatics and machine learning and validate it in STZ-induced mice models. GSE80178 and GSE134431 microarray datasets were fetched from the GEO database, and differentially expressed genes (DEGs) analysis was performed, respectively. By analyzing DEGs and ECM genes, we identified ECM-related DEGs, and functional enrichment analysis was conducted. Subsequently, three machine learning algorithms (LASSO, RF and SVM-RFE) were applied to filter ECM-related DEGs to identify key ECM-related biomarkers. Next, we conducted immune infiltration analysis, GSEA, and correlation analysis to explore the hub gene underlying mechanism. A lncRNA-miRNA-mRNA and drug regulatory network were constructed. Finally, we validated the key ECM-related biomarker in STZ-induced mice models. One hundred and forty-five common DEGs in adult DFU between the two datasets were identified. Taking the intersection of 145 common DEGs and 964 ECM genes, we identified 13 ECM-related DEGs. Thirteen ECM-related DEGs were mainly enriched in pathways associatedwith tissue remodeling, inflammation and defense against infectious agents. Ultimately, CTSH was identified as the key ECM-related biomarker. CTSH was associated with difference immune cells during the occurrence and development of DFU, and it influenced hedgehog, IL-17 and TNF signaling pathway. Additionally, CTSH expression is correlated with many ECM- and immune-related genes. A lncRNA-miRNA-mRNA and drug regulatory network were constructed with 10 lncRNAs, 2 miRNAs, CTSH and 1 drug. Finally, CTSH was validated as a key biomarker for DFU in animal models. Our study found that CTSH can be used for both diagnostic and prognostic purposes and might be a potential therapeutic target.

Integrative network pharmacology and transcriptomics analysis reveal the mechanism of Tanreqing in the treatment of acne vulgaris

Objective The present investigation is aimed at identifying key immune-related genes linked with SLE and their roles using integrative analysis of publically available gene expression datasets. Methods Four gene expression datasets pertaining to SLE, 2 from whole blood and 2 experimental PMBC, were sourced from GEO. Shared differentially expressed genes (DEG) were determined as SLE-related genes. Immune cell infiltration analysis was performed using CIBERSORT, and case samples were subjected to k-means cluster analysis using high-abundance immune cells. Key immune-related SLE genes were identified using correlation analysis with high-abundance immune cells and subjected to functional enrichment analysis for enriched Gene Ontology Biological Processes and KEGG pathways. A PPI network of genes interacting with the key immune-related SLE genes was constructed. LASSO regression analysis was performed to identify the most significant key immune-related SLE genes, and correlation with clinicopathological features was examined. Results 309 SLE-related genes were identified and found functionally enriched in several pathways related to regulation of viral defenses and T cell functions. k-means cluster analysis identified 2 sample clusters which significantly differed in monocytes, dendritic cell resting, and neutrophil abundance. 65 immune-related SLE genes were identified, functionally enriched in immune response-related signaling, antigen receptor-mediated signaling, and T cell receptor signaling, along with Th17, Th1, and Th2 cell differentiation, IL-17, NF-kappa B, and VEGF signaling pathways. LASSO regression identified 9 key immune-related genes: DUSP7, DYSF, KCNA3, P2RY10, S100A12, SLC38A1, TLR2, TSR2, and TXN. Imputed neutrophil percentage was consistent with their expression pattern, whereas anti-Ro showed the inverse pattern as gene expression. Conclusions Comprehensive bioinformatics analyses revealed 9 key immune-related genes and their associated functions highly pertinent to SLE pathogenesis, subtypes, and identified valuable candidates for experimental research.

AimsTo explore molecular mechanisms that link peri-implantitis and type 2 diabetes mellitus (T2DM) by bioinformatic analysis of publicly available experimental transcriptomic data.Materials and methodsGene expression data from peri-implantitis were downloaded from the Gene Expression Omnibus database, integrated and differentially expressed genes (DEGs) in peri-implantitis were identified. Next, experimentally validated and computationally predicted genes related to T2DM were downloaded from the DisGeNET database. Protein–protein interaction network (PPI) pairs of DEGs related to peri-implantitis and T2DM related genes were constructed, “hub” genes and overlapping DEG were determined. Functional enrichment analysis was used to identify significant shared biological processes and signaling pathways. The PPI networks were subjected to cluster and specific class analysis for identifying “leader” genes. Module network analysis of the merged PPI network identified common or cross-talk genes connecting the two networks.ResultsA total of 92 DEGs overlapped between peri-implantitis and T2DM datasets. Three hub genes (IL-6, NFKB1, and PIK3CG) had the highest degree in PPI networks of both peri-implantitis and T2DM. Three leader genes (PSMD10, SOS1, WASF3), eight cross-talk genes (PSMD10, PSMD6, EIF2S1, GSTP1, DNAJC3, SEC61A1, MAPT, and NME1), and one signaling pathway (IL-17 signaling) emerged as peri-implantitis and T2DM linkage mechanisms.ConclusionsExploration of available transcriptomic datasets revealed IL-6, NFKB1, and PIK3CG expression along with the IL-17 signaling pathway as top candidate molecular linkage mechanisms between peri-implantitis and T2DM.

Protective effect of Lizhong Pill on nonsteroidal anti-inflammatory drug-induced gastric mucosal injury in rats: Possible involvement of TNF and IL-17 signaling pathways

Identification of novel targets for multiple myeloma through integrative approach with Monte Carlo cross-validation analysis