PPI Network Analysis Research Articles

Bioinformatics analysis of genes related to ferroptosis in hepatic ischemia-reperfusion injury.

Background: Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death worldwide in 2020, and it ranks fifth in global incidence. Liver resection or liver transplantation are the two most prominent surgical procedures for treating primary liver cancer. Both inevitably result in HIRI, causing severe complications for patients and affecting their prognosis and quality of survival. Ferroptosis, a newly discovered mode of cell death, is closely related to HIRI. We used bioinformatics analysis to explore the relationship between the two further. Methods: The GEO database dataset GSE112713 and the FerrDB database data were selected to use bioinformatic analysis methods (difference analysis, FRGs identification, GO analysis, KEGG analysis, PPI network construction and analysis, Hub gene screening with GO analysis and KEGG analysis, intergenic interaction prediction, drug-gene interaction prediction, miRNA prediction) for both for correlation analysis. The GEO database dataset GSE15480 was selected for preliminary validation of the screened Hub genes. Results: We analysed the dataset GSE112713 for differential gene expression before and after hepatic ischemia-reperfusion and identified by FRGs, yielding 11 genes. These 11 genes were subjected to GO, and KEGG analyses, and PPI networks were constructed and analysed. We also screened these 11 genes again to obtain 5 Hub genes and performed GO analysis, KEGG analysis, intergenic interaction prediction, drug-gene interaction prediction, and miRNA prediction on these 5 Hub genes. Finally, we obtained preliminary validation of all these 5 Hub genes by dataset GSE15480. Conclusion: There is a close relationship between HIRI and ferroptosis, and inhibition of ferroptosis can potentially be a new approach to mitigate HIRI treatment in the future.

Laminarin ameliorates alcohol‐induced liver damage and its molecular mechanism in mice

Alcoholic liver disease (ALD) has become a health issue globally. Laminarin, a low molecular weight marine-derived β-glucan, has been identified with multiple biological activities. In this study, the ameliorative effect on ALD of laminarin isolated from brown algae was investigated. Phenotypic, pathological alterations and biochemical characteristics indicated that laminarin administration (100 mg/kg/day) significantly alleviated liver injury and improved liver function in the alcohol-induced mice. Gene chip results indicated that laminarin treatment caused 52 up-regulated and 13 down-regulated genes in the hepatic tissues of alcohol-induced damage mice, and most of these genes are associated with regulation of oxidative stress (such as CYP450/glutathione-dependent antioxidation), Wnt signaling pathway, retinol metabolism, and cAMP pathway based on GO and KEGG analysis. PPI network analysis indicated that the downstream target genes lied in the hub of the net. Our experiments also confirmed the changed expressions of some target genes. Taken together, these results suggest that laminarin can ameliorate alcohol-induced damage in mice and its molecular mechanism lies in modulating anti-oxidation pathway, WNT pathway, and cAMP pathway, which regulate the expressions of downstream target genes and alleviate alcohol-induced damage. Our study provides new clue to prevent alcohol-induced damage and will be benefit to develop functional foods. PRACTICAL APPLICATIONS: This study verified the positive effect on alcoholic liver disease (ALD) of laminarin, a water-soluble brown algae-derived β-glucan, linked by β-(1,3) glycosidic bonds with β-(1,6) branches. Laminarin significantly alleviated liver injury and improved liver function of ALD mice. Moreover, transcriptomics and bioinformatics analysis further revealed the gene expression patterns, hub targets, and signalings including CYP450/glutathione, Wnt, retinol metabolism, cAMP pathways regulated by laminarin. This research is the first evidence for hepatoprotective effect of laminarin against ALD and its molecular mechanism, which will be advantage to develop functional foods or adjuvant therapy of ALD.

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria.

A lot of studies have revealed that chronic urticaria (CU) is closely linked with COVID-19. However, there is a lack of further study at the gene level. This research is aimed to investigate the molecular mechanism of COVID-19-related CU via bioinformatic ways. The RNA expression profile datasets of CU (GSE72540) and COVID-19 (GSE164805) were used for the training data and GSE57178 for the verification data. After recognizing the shared differently expressed genes (DEGs) of COVID-19 and CU, genes enrichment, WGCNA, PPI network, and immune infiltration analyses were performed. In addition, machine learning LASSO regression was employed to identify key genes from hub genes. Finally, the networks, gene-TF-miRNA-lncRNA, and drug-gene, of key genes were constructed, and RNA expression analysis was utilized for verification. We recognized 322 shared DEGs, and the functional analyses displayed that they mainly participated in immunomodulation of COVID-19-related CU. 9 hub genes (CD86, FCGR3A, AIF1, CD163, CCL4, TNF, CYBB, MMP9, and CCL3) were explored through the WGCNA and PPI network. Moreover, FCGR3A, TNF, and CCL3 were further identified as key genes via LASSO regression analysis, and the ROC curves confirmed the dependability of their diagnostic value. Furthermore, our results showed that the key genes were significantly associated with the primary infiltration cells of CU and COVID-19, such as mast cells and macrophages M0. In addition, the key gene-TF-miRNA-lncRNA network was constructed, which contained 46 regulation axes. And most lncRNAs of the network were proved to be a significant expression in CU. Finally, the key gene-drug interaction network, including 84 possible therapeutical medicines, was developed, and their protein-protein docking might make this prediction more feasible. To sum up, FCGR3A, TNF, and CCL3 might be potential biomarkers for COVID-19-related CU, and the common pathways and related molecules we explored in this study might provide new ideas for further mechanistic research.

Comprehensive Analysis of N6-Methyladenosine (m6A) RNA Methylation Regulators and Tumour Microenvironment Cell Infiltration Involving Prognosis and Immunotherapy in Gastroesophageal Adenocarcinomas.

Gastroesophageal adenocarcinoma (GEA) is a high deadly and heterogeneous cancer. RNA N6-methyladenosine (m6A) modification plays a non-negligible role in shaping individual tumour microenvironment (TME) characterizations. However, the landscape and relationship of m6A modification patterns and TME cell infiltration features remain unknown in GEA. In this study, we examined the TME of GEA using assessments of the RNA-sequencing data focusing on the distinct m6A modification patterns from the public databases. Intrinsic patterns of m6A modification were evaluated for associations with clinicopathological characteristics, underlying biological pathways, tumour immune cell infiltration, oncological outcomes, and treatment responses. The expression of key m6A regulators and module genes was validated by qRT-PCR analysis. We identified two distinct m6A modification patterns of GEA (cluster 1/2 subgroup), and correlated two subgroups with TME cell-infiltrating characteristics. Cluster 2 subgroup correlates with a poorer prognosis, downregulated PD-1 expression, higher risk scores, and distinct immune cell infiltration. In addition, PPI and WGCNA network analysis were integrated to identify key module genes closely related to immune infiltration of GEA to find immunotherapy markers. COL4A1 and COL5A2 in the brown module were significantly correlated to the prognosis, PD-1/L1 and CTLA-4 expression of GEA patients. Finally, a prognostic risk score was constructed using m6A regulator-associated signatures that represented an independent prognosis factor for GEA. Interestingly, COL5A2 expression was linked to the response to anti-PD-1 immunotherapy, m6A regulator expression, and risk score. Our work identified m6A RNA methylation regulators as an important class of players in the malignant progression of GEA and were associated with the complexity of the TME. COL5A2 may be the potential biomarker which contributes to predicting the response to anti-PD-1 immunotherapy and patients' prognosis.

Identifying susceptibility genes for essential hypertension by transcriptome-wide association study

Hypertension is a leading risk factor of cardiovascular disease and mortality in the population worldwide. Recently, hundreds of genomic loci were reported for hypertension by GWAS, however, the most SNPs are located in intergenic regions of genome, where a functional cause is difficult to determine. In the current study, a TWAS of hypertension was conducted using 452,264 individuals including 84,640 patients. KEGG and GO enrichment analyses were performed for the hypertension-related genes identified via TWAS. PPI network analysis based on the STRING database was also performed to detect TWAS-identified genes in hypertension. We have identified 18,420 genes from the GWAS summary data, and of those 1010 non-overlapping genes expression were significantly associated with hypertension after FDR correction (PFDR <0.05) in four tissues (left heart ventricle, aorta, whole blood, and peripheral blood). The KEGG and GO terms were mostly related to autoimmune mechanisms, and the autoimmune-related pathways have also been enriched using GO analysis for PPI genes. We further performed Mendelian randomization analysis, and the results supported a significant association between autoimmunity and hypertension. Moreover, 15 novel hypertension-susceptible genes were identified in all tissues, and five of the genes (RBM6, HLA-DRB5, UHRF1BP1, LYZ, and TMEM116) were associated with autoimmune system, which provide further evidence supporting an autoimmune mechanism in hypertension. In summary, our study supports that an autoimmune mechanism plays an important role in the development of hypertension, and these findings will provide new biological insights that will assist in deciphering the molecular etiology of hypertension.

Exploring the mechanism of Alisma orientale for the treatment of pregnancy induced hypertension and potential hepato-nephrotoxicity by using network pharmacology, network toxicology, molecular docking and molecular dynamics simulation.

Background: Pregnancy-induced Hypertension (PIH) is a disease that causes serious maternal and fetal morbidity and mortality. Alisma Orientale (AO) has a long history of use as traditional Chinese medicine therapy for PIH. This study explores its potential mechanism and biosafety based on network pharmacology, network toxicology, molecular docking and molecular dynamics simulation. Methods: Compounds of AO were screened in TCMSP, TCM-ID, TCM@Taiwan, BATMAN, TOXNET and CTD database; PharmMapper and SwissTargetPrediction, GeneCards, DisGeNET and OMIM databases were used to predict the targets of AO anti-PIH. The protein-protein interaction analysis and the KEGG/GO enrichment analysis were applied by STRING and Metascape databases, respectively. Then, we constructed the "herb-compound-target-pathway-disease" map in Cytoscape software to show the core regulatory network. Finally, molecular docking and molecular dynamics simulation were applied to analyze binding affinity and reliability. The same procedure was conducted for network toxicology to illustrate the mechanisms of AO hepatotoxicity and nephrotoxicity. Results: 29 compounds with 78 potential targets associated with the therapeutic effect of AO on PIH, 10 compounds with 117 and 111 targets associated with AO induced hepatotoxicity and nephrotoxicity were obtained, respectively. The PPI network analysis showed that core therapeutic targets were IGF, MAPK1, AKT1 and EGFR, while PPARG and TNF were toxicity-related targets. Besides, GO/KEGG enrichment analysis showed that AO might modulate the PI3K-AKT and MAPK pathways in treating PIH and mainly interfere with the lipid and atherosclerosis pathways to induce liver and kidney injury. The "herb-compound-target-pathway-disease" network showed that triterpenoids were the main therapeutic compounds, such as Alisol B 23-Acetate and Alisol C, while emodin was the main toxic compounds. The results of molecular docking and molecular dynamics simulation also showed good binding affinity between core compounds and targets. Conclusion: This research illustrated the mechanism underlying the therapeutic effects of AO against PIH and AO induced hepato-nephrotoxicity. However, further experimental verification is warranted for optimal use of AO during clinical practice.

Identification of six hub genes and two key pathways in two rat renal fibrosis models based on bioinformatics and RNA-seq transcriptome analyses.

Renal fibrosis (RF) is the common pathological manifestation and central treatment target of multiple chronic kidney diseases with high morbidity and mortality. Currently, the molecular mechanisms underlying RF remain poorly understood, and exploration of RF-related hub targets and pathways is urgently needed. In this study, two classical RF rat models (adenine and UUO) were established and evaluated by HE, Masson and immunohistochemical staining. To clear molecular mechanisms of RF, differentially expressed genes (DEGs) were identified using RNA-Seq analysis, hub targets and pathways were screened by bioinformatics (functional enrichment analyses, PPI network, and co-expression analysis), the screening results were verified by qRT-PCR, and potential drugs of RF were predicted by network pharmacology and molecular docking. The results illustrated that renal structures were severely damaged and fibrotic in adenine- and UUO-induced models, as evidenced by collagen deposition, enhanced expressions of biomarkers (TGF-β1 and α-SMA), reduction of E-cadherin biomarker, and severe renal function changes (significantly decreased UTP, CREA, Ccr, and ALB levels and increased UUN and BUN levels), etc. 1189 and 1253 RF-related DEGs were screened in the adenine and UUO models, respectively. Two key pathways (AGE-RAGE and NOD-like receptor) and their hub targets (Tgfb1, Col1a1, Nlrc4, Casp4, Trpm2, and Il18) were identified by PPI networks, co-expressed relationships, and qRT-PCR verification. Furthermore, various reported herbal ingredients (curcumin, resveratrol, honokiol, etc.) were considered as important drug candidates due to the strong binding affinity with these hub targets. Overall, this study mainly identified two key RF-related pathways (AGE-RAGE and NOD-like receptor), screened hub targets (Tgfb1, Col1a1, Nlrc4, Casp4, Trpm2, and Il18) that involved inflammation, ECM formation, myofibroblasts generation, and pyroptosis, etc., and provided referable drug candidates (curcumin, resveratrol, honokiol, etc.) in basic research and clinical treatment of RF.

CXCL12, a potential modulator of tumor immune microenvironment (TIME) of bladder cancer: From a comprehensive analysis of TCGA database.

Tumor immune microenvironment (TIME) plays a significant role in the initiation and progression of bladder urothelial carcinoma (BLCA). However, there are only a few researches regarding the association between immune-related genes and tumor-infiltrating immune cells (TICs) in TIME of BLCA. We calculated the proportion of immune/stromal component and TICs of 414 BLCA samples and 19 normal samples downloaded from TCGA database with the help of ESTIMATE and CIBERSORT algorithms. Differentially expressed genes (DEGs) were obtained from the comparison between Stromal and Immune Score and further analyzed by GO and KEGG enrichment analysis, as well as PPI network and COX regression analysis. CXCL12 was overlapping among the above analyses. Single gene analysis of CXCL12 was carried out through difference analysis, paired analysis and GSEA. The association between CXCL12 and TICs was assessed by difference analysis and correlation analysis. Immune and stromal component in TIME of BLCA were associated with patients' clinicopathological characteristics. 284 DEGs were primarily enriched in immune-associated activities, among which CXCL12 was the most significant gene sharing the leading nodes in PPI network and being closely related with patients' survival. Single gene analysis and immunohistochemistry revealed that CXCL12 was down-regulated in BLCA samples and significantly related with the clinicopathological characteristics of patients. Further analysis suggested that CXCL12 was involved in the immune-associated activities probably through its close cross-talk with TICs. CXCL12 down-regulation could be a potential biomarker to predict the unbalanced immune status of TIME of BLCA, which might provide an extra insight for the immunotherapy of BLCA.

APPINetwork: an R package for building and computational analysis of protein-protein interaction networks.

Protein-protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein-protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.

Potential Anti-Tumor Activity of Nardoguaianone L Isolated from Nardostachys jatamansi DC. in SW1990 Cells.

Natural products (NPs) were a rich source of diverse bioactive molecules. Most anti-tumor agents were built on natural scaffolds. Nardostachys jatamansi DC. was an important plant used to process the traditional Chinese herbal medicines "gansong". Pancreatic cancer was the fourth most common cause of cancer-related death in the world. Hence, there was an urgent need to develop novel agents for the treatment of pancreatic cancer. In this paper, nardoguaianone L (G-6) is isolated from N. jatamansi, which inhibited SW1990 cells colony formation and cell migration, and induced cell apoptosis. Furthermore, we analyzed the differential expression proteins after treatment with G-6 in SW1990 cells by using iTRAQ/TMT-based quantitative proteomics technology, and the results showed that G-6 regulated 143 proteins' differential expression by GO annotation, including biological process, cellular component, and molecular function. Meanwhile, KEGG enrichment found that with Human T-cell leukemia virus, one infection was the most highly enhanced pathway. Furthermore, the MET/PTEN/TGF-β pathway was identified as a significant pathway that had important biological functions, including cell migration and motility by PPI network analysis in SW1990 cells. Taken together, our study found that G-6 is a potential anti-pancreatic cancer agent with regulation of MET/PTEN/TGF-β pathway.

Network pharmacology and UPLC‐MS/MS‐based study of active ingredients in Jiu Wei decoction

Jiu Wei decoction (JWD) is a traditional Chinese medicine (TCM) applied to control rheumatoid arthritis (RA). However, the active ingredients of JWD are still unclear. In this study, bioinformatics and ultra-high-performance liquid chromatography coupled with mass spectrometry (UPLC-MS/MS) were used to uncover the material basis and mechanism of JWD against RA. Ingredients and potential targets of herbs in JWD were collected from online databases. Then, these targets and RA related targets were overlapped, and the overlapped targets were subjected to further PPI (protein-protein interaction, PPI) analysis. Drug-component-target network and molecular docking were used to predict the potential active compounds. UPLC-MS/MS method was applied to detect and quantify these compounds in JWD alcohol extract. A total of 90 constituents and 520 targets were collected from JWD and 95 potential targets were overlapped by comparing these targets with RA-related targets. PPI network analysis indicated that TNF-α, IL-6, AKT1 and VEGFA were the main targets of JWD to RA, and the pharmacological mechanism of these targets might be related to anti-inflammation, pro-apoptosis, and inhibition of angiogenesis. Drug-component-target network and molecular docking results showed that quercetin, wogonin, baicalein, ferulic acid, kaempferol and berberine might be the potential compounds of JWD to RA. Through UPLC-MS/MS analysis, four compounds including quercetin, wogonin, baicalein, and berberine were detected and quantified. Moreover, among the four ingredients, quercetin, wogonin, berberine and baicalein accounted for 15.85%, 25.84%, 15.01%, and 43.3%, respectively. Collectively, our study proved that bioinformatic analysis combined with UPLC-MS/MS is a promising method to explore the material basis of anti-RA in JWD and the method is also applicable to other TCMs. The effect of the active compounds still needs further experimental verification in the disease cell model.

Identification of Cofilin-1 as a novel biomarker of atopic dermatitis using iTRAQ quantitative proteomics.

Atopic dermatitis (AD) is a chronic relapsing inflammatory skin condition; however, little is known about the pathogenesis and serum biomarker of this disease. Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomic assay was adopted to identify and quantify the differentially expressed proteins (DEPs) in the serum of AD patients. Bioinformatic analysis, including GO, Reactome, GSEA, PPI, and ssGSEA analysis, were used to identified the enriched pathways, hub proteins and immune cells. The expression level and distribution of hub proteins were confirmed by ELISA and IHC. Sixty-six DEPs were identified with iTRAQ proteomic assay by analyzing serum from AD patients and normal subjects. GO and Reactome analysis shown the alternated pathway were mainly involved in immunity, oxidative stress, and actin cytoskeleton. The GSEA and PPI network analysis among the DEPs were carried out and identified Cofilin-1 and profilin-1 as the core components of this network. Additionally, the disruption of Th1/Th2/Th17 cell balance and the significantly reducing of Treg, MDSC, and γδT cells was also found in AD patients using the ssGSEA analysis. Further ELISA and IHC assay validated the significantly elevated expression of Cofilin-1 in AD patients. Our results suggested that Cofilin-1 may serve as a novel biomarker for AD diagnosis.

In silico investigations identified Butyl Xanalterate to competently target CK2α (CSNK2A1) for therapy of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is an incurable malignancy of B-cells. In this study, bioinformatics analyses were conducted to identify possible pathogenic roles of CK2α, which is a protein encoded by CSNK2A1, in the progression and aggressiveness of CLL. Furthermore, various computational tools were used to search for a competent inhibitor of CK2α from fungal metabolites that could be proposed for CLL therapy. In CLL patients, high-expression of CSNK2A1 was associated with early need for therapy (n = 130, p < 0.0001) and short overall survival (OS; n = 107, p = 0.005). Consistently, bioinformatics analyses showed CSNK2A1 to associate with/play roles in CLL proliferation and survival-dependent pathways. Furthermore, PPI network analysis identified interaction partners of CK2α (PPI enrichment p value = 1 × 10–16) that associated with early need for therapy (n = 130, p < 0.003) and have been known to heavily impact on the progression of CLL. These findings constructed a rational for targeting CK2α for CLL therapy. Consequently, computational analyses reported 35 fungal metabolites out of 5820 (filtered from 19,967 metabolites) to have lower binding energy (ΔG: − 10.9 to − 11.7 kcal/mol) and better binding affinity (Kd: 9.77 × 107 M−1 to 3.77 × 108 M−1) compared with the native ligand (ΔG: − 10.8, Kd: 8.3 × 107 M−−1). Furthermore, molecular dynamics simulation study established that Butyl Xanalterate-CK2α complex continuously remained stable throughout the simulation time (100 ns). Moreover, Butyl Xanalterate interacted with most of the catalytic residues, where complex was stabilized by more than 65% hydrogen bond interactions, and a significant hydrophobic interaction with residue Phe113. Here, high-expression of CSNK2A1 was implicated in the progression and poor prognosis of CLL, making it a potential therapeutic target in the disease. Butyl Xanalterate showed stable and strong interactions with CK2α, thus we propose it as a competitive inhibitor of CK2α for CLL therapy.

Screening of immunopeptides from Zhiciweipi (Corium Erinacei Praeparaum) and their immunity to RAW264.7 macrophages

Zhiciweipi (Corium Erinacei Praeparaum) is one of the traditional Chinese animal medicines used to treat hematochezia and hemorrhoids. At present, there is a lack of the workflow for ingredient identification of Zhiciweipi (Corium Erinacei Praeparaum) and the studies regarding immunocompetent ingredient screening and evaluation. This study aims to screen immunopeptides in Zhiciweipi (Corium Erinacei Praeparaum) and investigate their immunity to RAW264.7 macrophages. In this study, the workflow for protein polypeptide identification and analysis of Zhiciweipi (Corium Erinacei Praeparaum) was established based on LC-MS/MS. According to Gene ontology_Biological process (GO_BP) functional and signal pathway enrichment analysis results, the identified proteins are related with vascular development, wound healing, IL2 STAT5 signal, and so on. A total of 20 immune regulatory genes were identified from the Venn diagram of protein and immune regulatory genes in Zhiciweipi (Corium Erinacei Praeparaum). The PPI network analysis demonstrated that ALB protein ranks the top in term of significance. According to analysis on physical properties and structural and safety prediction of 61 polypeptide fragments related with serum albumin in Zhiciweipi (Corium Erinacei Praeparaum) which are corresponding to ALB protein, RHPYFYAPELL and RHPYFYAPELLYF (RHP-LYF) are the most optimal. The molecular docking showed that RHPYFYAPELL and RHP-LYF can make effective bonding with IL-2 and STAT5. The molecular dynamic experiment reflected that RHP-LYF has the strongest affinity with ALB. In the present study, CCK8 method was applied to test RHP-LYF treatment of RAW264.7 cells in the mice macrophage system at different concentrations. Results showed that such treatment may not inhibit cell growth when the RHP-LYF concentration is lower than 200 ng/mL. The polypeptide fluorescence marking experiment proved that polypeptides can enter into cells and can be detected in the whole cell. Effects of RHP-LYF on IL-2, TNFα, IFNγ and IL10 levels in RAW264.7 cells and mice serum were detected by ELISA kit. Results showed that polypeptide not only can inhibit secretion of inflammatory factors in vivo and in vitro, but also promote secretion of anti-inflammatory factors, and further test effects of RAW264.7 cells on expressions of p-STAT5 and STAT5 through Western Blot. Results showed that polypeptide treatment may inhibit level of p-STAT5 and has dosage-dependent effect. Polypeptides from Zhiciweipi (Corium Erinacei Praeparaum) are safe and have immunization to RAW264.7 macrophage. All of these results can provide a meaningful method to drug quality assessment, immune bioactive peptide screening and evaluation of Zhiciweipi (Corium Erinacei Praeparaum) and other animals rich of peptides.

Identification and verification of the prognostic value of CUL7 in colon adenocarcinoma.

CUL7, a gene composed of 26 exons associated with cullin 7 protein, is also an E3 ligase that is closely related to cell senescence, apoptosis, and cell transformation and also plays an important role in human cancer. However, there is no systematic pan-cancer analysis has been performed to explore its role in prognosis and immune prediction. In this study, the expression of CUL7 in colon adenocarcinoma (COAD) was investigated to determine its prognosis value. First, based on the Cancer Genome Atlas (TCGA), Genotypic-Tissue Expression Project(GTEx), Cancer Cell Line Encyclopedias(CCLE), and TISIDB database, the potential role of CUL7 in different tumors was explored. Subsequently, the expression of CUL7 in COAD was explored and verified by Immunohistochemistry (IHC). Furthermore, the mutation frequency of CUL7 in COAD was analyzed, and the prognostic value of CUL7 in COAD was discussed. In addition, the nomogram was constructed, and its prognostic value was verified by follow-up data from Jiangmen Central Hospital. Finally, PPI network analysis explored the potential biological function of CUL7 in COAD. The results show that CUL7 is upregulated in most tumors, which is significantly associated with poor survival. At the same time, CUL7 is correlated with the clinical stage and immune landscape of various tumors. In colorectal cancer, CUL7 was overexpressed in tumor tissues by IHC with a mutation frequency of about 4%. CUL7 is an independent prognostic factor for colorectal cancer. The nomogram constructed has effective predictive performance, and external databases proved the prognostic value of CUL7. In addition, PPI network analysis showed that CUL7 was closely related to FBXW8, and further pathway enrichment analysis showed that CUL7 was mainly involved in ubiquitin-mediated proteolysis. Therefore, our study provides a comprehensive understanding of the potential role of CUL7 in different tumors, and CUL7 might be a prognostic marker for COAD.

Integrated Single-Cell and RNA Sequencing Analysis Identifies Key Immune Cell and Dendritic Cells Associated Genes Participated in Myocarditis

Myocarditis is a complex disease characterized by myocardial inflammatory cell infiltration. The purpose of our study was to investigate the gene and single-cell signature to explore the involvement of immune cells in myocarditis. Gene expressions merged from GSE35182 and GSE35182 datasets were subjected to differential expression gene (DEG) analysis and PPI network construction. The correlation analysis of DEGs with immune cell infiltration was performed. Single-cell RNA sequencing (scRNA-seq) was downloaded from GSE174458. A total of 58 DEGs were identified, including 51 DEGs upregulated and 7 DEGs downregulated in the myocarditis group compared with the control group. GO and KEGG enrichment analyses revealed that myocarditis triggered DEGs mainly involved in immune-related processes and pathways. PPI network analysis identified 20 central hub genes. Occurrence of myocarditis induced significant enrichment of conventional dendritic cell 2 (cDC2), plasmacytoid DC, and plasma cell in myocardial tissue. Mmp12, Gpnmb, and Atp6v0d2 expressions were positively correlated with cDC abundance, of which only Mmp1 and Gpnmb were shared with hub gene list. A total of 20972 cells in scRNA-seq yielded 26 cell clusters and annotated 9 cell types, including fibroblasts, neutrophils, stromal cells, monocytes, basophils, B cells, natural killer T cells, innate lymphoid cells, and T cells, and only proportion of natural killer T cells and monocytes were higher in the myocarditis than in control. Monocytes annotated 3 subclusters including DC, macrophage, and monocytes. Hub genes of Ctss, Mpeg1, Cybb, H2-Ab1, Ly86, CD74, and Lgals3 were highly expressed in monocytes cluster. Among DC-correlated DEGs, Mmp12 was mainly expressed in monocyte cluster, and Gpnmb was mainly expressed in fibroblast cluster, whereas Atp6v0d2 expression has a weaker signal and weaker cell preference. In conclusion, DC infiltration and its associated pivotal genes may be responsible for progression of myocarditis. Our study expands and provides novel information on the immune cell engagement of myocarditis.

Mining of Potential Biomarkers and Pathway in Valvular Atrial Fibrillation (VAF) via Systematic Screening of Gene Coexpression Network.

Purpose We apply the bioinformatics method to excavate the potential genes and therapeutic targets associated with valvular atrial fibrillation (VAF). Methods The downloaded gene expression files from the gene expression omnibus (GEO) included patients with primary severe mitral regurgitation complicated with sinus or atrial fibrillation rhythm. Subsequently, the differential gene expression in left and right atrium was analyzed by R software. Additionally, weighted correlation network analysis (WGCNA), principal component analysis (PCA), and linear model for microarray data (LIMMA) algorithm were used to determine hub genes. Then, Metascape database, DAVID database, and STRING database were used to annotate and visualize the gene ontology (GO) analysis, KEGG pathway enrichment analysis, and PPI network analysis of differentially expressed genes (DEGs). Finally, the TFs and miRNAs were predicted by using online tools, such as PASTAA and miRDB. Results 20,484 differentially expressed genes related to atrial fibrillation were obtained through the analysis of left and right atrial tissue samples of GSE115574 gene chip, and 1,009 were with statistical significance, including 45 upregulated genes and 964 downregulated genes. And the hub genes implicated in AF of NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B were screened. Finally, the main regulators of targeted candidate biomarkers and microRNAs, EIF5A2, HIF1A, ZIC2, ELF1, and STAT2, were found in this study. Conclusion These hub genes, NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B, are important for the development of VAF, and their enrichment pathways and TFs elucidate the involved molecular mechanisms and assist in the validation of drug targets.

Network Pharmacology and Molecular Docking Analysis on Targets and Mechanisms of Berberine in Atypical Antipsychotic-Induced Metabolic Syndrome

Berberine (BBR), an isoquinoline alkaloid, possesses multiply pharmacological effects as a potential therapeutic drug for antipsychotic-induced metabolic syndrome (MetS). However, the underlying therapeutic mechanisms have not been fully elucidated. In this study, we aim to investigate the possible mechanisms by identifying the key targets and biological pathways through network pharmacology and molecular docking analysis. A total of 23 overlapping targets in the intersection set among BBR, atypical antipsychotic drugs (AADs), and MetS were determined. PPI network analysis showed that 22 out of the 23 overlapping targets closely interacted with the others. The following pathway enrichment analysis and molecular docking indicated a central role of peroxisome proliferator-activated receptor-γ (PPARG) as the key target of BBR against AAD-induced MetS by acting on the PPAR signaling pathway, lipid and atherosclerosis, and AMP activated protein kinase (AMPK) signaling pathway. In addition, cytochrome P-450 2D6 (CYP2D6) could be considered as another target of BBR in ameliorating antipsychotic-induced metabolic side effects. Collectively, this study investigated the central targets and biological pathways of BBR against AAD-induced MetS from a systematic perspective, and thus brings novel insights into further understanding of the protective effects of BBR.

Identification of Significant Genes and Pathways for the Chronic and Subacute Cutaneous Lupus Erythematosus via Bioinformatics Analysis.

Background Chronic cutaneous lupus erythematosus (CCLE) and subacute cutaneous lupus erythematosus (SCLE) are both common variants of cutaneous lupus erythematosus (CLE) that mainly involve the skin and mucous membrane. Oral mucosal involvement is frequently observed in patients of CLE. Despite that they have different clinicopathological features, whether there is a significant difference in pathogenesis between them remains unclear. Herein, we investigated specific genes and pathways of SCLE and CCLE via bioinformatics analysis. Methods Microarray expression datasets of GSE109248 and GSE112943 were both retrieved from the GEO database. Differentially expressed genes (DEGs) between CCLE or SCLE skin tissues and health controls were selected by GEO2R. Common DEGs were picked out via the Venn diagram software. Then, functional enrichment and PPI network analysis were conducted, and the top 10 key genes were identified via Cytohubba. Results Totally, 176 DEGs of SCLE and 287 DEGs of CCLE were identified. The GO enrichment and KEGG analysis of DEGs of SCLE is significantly enriched in the response to virus, defense response to virus, response to IFN-gamma, cellular response to IFN-γ, type I IFN signaling pathway, chemokine activity, chemokine receptor binding, NOD-like receptor signaling pathway, etc. The GO enrichment and KEGG analysis of DEGs of CCLE is significantly enriched in the response to virus, regulation of multiorganism process, negative regulation of viral process, regulation of lymphocyte activation, chemokine receptor binding, CCR chemokine receptor binding, NOD-like receptor signaling pathway, etc. The top 10 hub genes of SCLE and CCLE, respectively, include STAT1, CXCL10, IRF7, ISG15, and RSAD2 and CXCL10, IRF7, IFIT3, CTLA4, and ISG15. Conclusion Our finding suggests that SCLE and CCLE have the similar potential key genes and pathways and majority of them belong to IFN signatures and IFN signaling pathway. Besides, the NOD-like receptor signaling pathway might also have an essential role in the pathogenesis of SCLE and CCLE. Together, the identified genes and signaling pathways have enhanced our understanding of the mechanism underlying the occurrence and development of both SCLE and CCLE.

Identification of hub genes for early detection of bone metastasis in breast cancer.

BackgroundGlobally, among all women, the most frequently detected and diagnosed and the most lethal type of cancer is breast cancer (BC). In particular, bone is one of the most frequent distant metastases 24in breast cancer patients and bone metastasis arises in approximately 80% of advanced patients. Thus, we need to identify and validate early detection markers that can differentiate metastasis from non-metastasis breast cancers.MethodsGSE55715, GSE103357, and GSE146661 gene expression profiling data were downloaded from the GEO database. There was 14 breast cancer with bone metastasis samples and 8 breast cancer tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). The volcano plots, Venn diagrams, and annular heatmap were generated by using the ggplot2 package. By using the cluster Profiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, K-M survival and ROC curves were generated to validate hub gene expression.ResultsBy GO enrichment analysis, 143 DEGs were enriched in the following GO terms: extracellular structure organization, extracellular matrix organization, leukocyte migration class II protein complex, collagen tridermic protein complex, extracellular matrix structural constituent, growth factor binding, and platelet-derived growth factor binding. In the KEGG pathway enrichment analysis, DEGs were enriched in Staphylococcus aureus infection, Complement and coagulation cascades, and Asthma. By PPI network analysis, we selected the top 10 genes, including SLCO2B1, STAB1, SERPING1, HLA-DOA, AIF1, GIMAP4, C1orf162, HLA-DMB, ADAP2, and HAVCR2. By using TCGA and THPA databases, we validated 2 genes, SERPING1 and GIMAP4, that were related to the early detection of bone metastasis in BC.Conclusions2 abnormally expressed hub genes could play a pivotal role in the breast cancer with bone metastasis by affecting bone homeostasis imbalance in the bone microenvironment.