Integrated aqueous humor ceRNA and miRNA–TF–mRNA network analysis reveals potential molecular mechanisms governing primary open-angle glaucoma pathogenesis

To conduct an integrated bioinformatics analysis of extant aqueous humor (AH) gene expression datasets in order to identify key genes and the regulatory mechanism governing primary open?angle glaucoma (POAG) progression. Methods: Two datasets (GSE101727 and GSE105269) were downloaded from the Gene Expression Omnibus, and the messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) were identified between controls and POAG patients. Differentially expressed (DE) mRNAs and DElncRNAs were then subjected to pathway enrichment analyses, after which a protein–protein interaction (PPI) network was generated. This network was then expanded to establish lncRNA–miRNA–mRNA and miRNA–transcription factor (TF)–mRNA networks. Results: The GSE101727 dataset was used to identify 2746 DElncRNAs and 2208 DEmRNAs, while the GSE105269 dataset was used to identify 45 DEmiRNAs. We ultimately constructed a competing endogenous RNA (ceRNA) network incorporating 47 lncRNAs, six miRNAs, and 17 mRNAs. The proteins encoded by these 17 hub mRNAs were found to be significantly enriched for activities that may be linked to POAG pathogenesis. In addition, we generated a miRNA–TF–mRNA regulatory network containing two miRNAs (miR?135a?5p and miR?139?5p), five TFs (TGIF2, TCF3, FOS, and so on), and five mRNAs (SHISA7, ST6GAL2, TXNIP, and so on). Conclusion: The SHISA7, ST6GAL2, TXNIP, FOS, and DCBLD2 genes may be viable therapeutic targets for the prevention or treatment of POAG and are regulated by the TFs (TGIF2, HNF1A, TCF3, and FOS)

Expression analysis of transcription factors in sugarcane during cold stress / Análise de expressão de fatores de transcrição em cana-de-açúcar sob estresse por frio

Abstract Transcription factors (TF) are a wide class of genes in plants, and these can regulate the expression of other genes in response to various environmental stresses (biotic and abiotic). In the current study, transcription factor activity in sugarcane was examined during cold stress. Initially, RNA transcript reads of two sugarcane cultivars (ROC22 and GT08-1108) under cold stress were downloaded from SRA NCBI database. The reads were aligned into a reference genome and the differential expression analyses were performed with the R/Bioconductor edgeR package. Based on our analyses in the ROC22 cultivar, 963 TF genes were significantly upregulated under cold stress among a total of 5649 upregulated genes, while 293 TF genes were downregulated among a total of 3,289 downregulated genes. In the GT08-1108 cultivar, 974 TF genes were identified among 5,649 upregulated genes and 283 TF genes were found among 3,289 downregulated genes. Most transcription factors were annotated with GO categories related to protein binding, transcription factor binding, DNA-sequence-specific binding, transcription factor complex, transcription factor activity in RNA polymerase II, the activity of nucleic acid binding transcription factor, transcription corepressor activity, sequence-specific regulatory region, the activity of transcription factor of RNA polymerase II, transcription factor cofactor activity, transcription factor activity from plastid promoter, transcription factor activity from RNA polymerase I promoter, polymerase II and RNA polymerase III. The findings of above results will help to identify differentially expressed transcription factors during cold stress. It also provides a comprehensive analysis of the regulation of the transcription activity of many genes. Therefore, this study provides the molecular basis for improving cold tolerance in sugarcane and other economically important grasses.

Resumo Fatores de transcrição (FT) são uma ampla classe de genes em plantas e podem regular a expressão de outros genes em resposta a vários estresses ambientais (estresses bióticos e abióticos). No presente estudo, a atividade do fator de transcrição na cana-de-açúcar foi examinada durante o estresse pelo frio. Inicialmente, as leituras de transcrição de RNA de duas cultivares de cana-de-açúcar (ROC22 e GT08-1108) sob estresse frio foram baixadas do banco de dados SRA NCBI. As leituras foram alinhadas em um genoma de referência e as análises de expressão diferencial foram realizadas com o pacote R / Bioconductor edgeR. Com base em nossas análises no cultivar ROC22, 963 genes TF foram significativamente regulados positivamente sob estresse pelo frio entre um total de 5.649 genes regulados positivamente, enquanto 293 genes TF foram regulados negativamente entre um total de 3.289 genes regulados negativamente. No cultivar GT08-1108, 974 genes TF foram identificados entre 5.649 genes regulados positivamente e 283 genes TF foram encontrados entre 3.289 genes regulados negativamente. Os fatores de transcrição, em sua maioria, foram anotados com categorias GO relacionadas à ligação de proteína, ligação de fator de transcrição, ligação específica de sequência de DNA, complexo de fator de transcrição, atividade de fator de transcrição em RNA polimerase II, atividade de fator de transcrição de ligação de ácido nucleico, atividade de corepressor de transcrição, sequência específica da região reguladora, atividade do fator de transcrição da RNA polimerase II, atividade do cofator do fator de transcrição, atividade do fator de transcrição do promotor do plastídio, atividade do fator de transcrição do promotor da RNA polimerase I, polimerase II e RNA polimerase III. As descobertas dos resultados acima ajudarão a identificar fatores de transcrição expressos diferencialmente durante o estresse pelo frio. Ele também fornece uma análise abrangente da regulação da atividade de transcrição de muitos genes. Portanto, este estudo fornece base molecular para melhorar a tolerância ao frio em cana-de-açúcar e outras gramíneas economicamente importantes.

Search for potential target genes in dexamethasone-induced open-angle glaucoma by bioinformatics / 中华实验眼科杂志

Objective:To predict potential target genes in dexamethasone-induced open-angle glaucoma via bioinformatics technology.Methods:The GEO datasets GSE16643, GSE37474, and GSE124114 were used to analyze the differentially expressed genes by GEO2R.Gene Set Enrichment Analysis (GSEA) was performed on the differentially expressed genes between GSE37474 and GSE124114.Intersection of the three datasets were displayed by Venn diagram.The annotation and enrichment analysis of the intersection genes were performed through Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and then were compared with normal tissue in GTEx Portal database.The corresponding protein interaction network was obtained by STRING.Finally, the candidate genes were searched for their transcription factors in UCSC and JASPAR.Primary human trabecular cells were divided into dexamethasone group and control group, treated with 2 ml 500 nmol/L dexamethasone and the same amount of ethanol, respectively.The expression of BDKRB1 and TAGLN in trabecular cells was detected by Western blot.Results:Differential genes between GSE37474 and GSE124114 datasets enriched in complement and coagulation cascade by GSEA.There were 89 intersecting genes of the three datasets.These genes mainly regulated the formation of extracellular matrix by GO analysis.The gene with the highest enrichment score and collagen-containing extracellular matrix was found to be associated with fibroblasts in GTEx Portal database.ACTA2, MYL9, TAGLN, and LMOD1 were closely related in STRING protein-protein interaction network.Transcription factor SP1 in UCSC and JASPAR according to related genes, BDKRB1, NID1, MFGE8 and TAGLN.The relative expression levels of BDKRB1 and TAGLN proteins were 1.32±0.14 and 0.44±0.09 in dexamethasone group, respectively, which were significantly higher than 1.00±0.00 and 0.20±0.10 in the control group, respectively ( t=-3.61, 2.89; both at P<0.05). Conclusions:Bioinformatics analysis showed that transcription factor SP1 may play a role in human trabecular meshwork cells to myofibroblasts transition after dexamethasone treatment.

Identification of potential hub genes involved in alcohol use disorder via bioinformatics analysis / 四川精神卫生

BackgroundAlcohol use disorder （AUD） is a type of chronic relapsing brain disorder. Genetic factors play an important role in the pathogenesis of AUD. And screening for molecular markers of AUD is of great significance for further elucidating the pathogenesis of the disease， discovering novel therapeutic targets and preventing relapse. ObjectiveTo explore relevant hub genes and potential signal pathways associated with the development of AUD through bioinformatics analysis， and to provide a new direction for the prevention and treatment of AUD. MethodsThe GSE161986 dataset was acquired from the Gene Expression Omnibus （GEO） database. The limma package in R was utilized to identify differentially expressed genes （DEGs）. Gene set enrichment analysis （GSEA） was carried out using the Database for Annotation， Visualization and Integrated Discovery （DAVID）. A protein–protein interaction （PPI） network of DEGs was assessed using the STRING database and visualized by Cytoscape software. Finally， hub genes were validated in GSE44456 dataset. ResultsA total of 114 DEGs were identified. GSEA revealed that these genes were mainly involved in the regulation of signal transduction， protein binding， membrane trafficking and MAPK signaling pathway. PPI network and validation study indicated that GAD1， TIMP1 and CD44 were potential hub genes involved in AUD. ConclusionAberrant expression of GAD1 and TIMP1 as well as MAPK signaling pathway may play a key role in the pathogenesis of AUD， and may serve as potential molecular targets for the diagnosis and treatment of AUD. ［Funded by "Flying Project" of Shanghai Mental Health Center （number， 2022-FX-01）］

Comparative analysis of differentially expressed genes for biosynthesis of active ingredients in fruits of different cultivars of Lycium barbarum L. based on transcriptome sequencing / 生物工程学报

To explore the differentially expressed genes (DEGs) related to biosynthesis of active ingredients in wolfberry fruits of different varieties of Lycium barbarum L. and reveal the molecular mechanism of the differences of active ingredients, we utilized Illumina NovaSeq 6000 high-throughput sequencing technology to conduct transcriptome sequencing on the fruits of 'Ningqi No.1' and 'Ningqi No.7' during the green fruit stage, color turning stage and maturity stage. Subsequently, we compared the profiles of related gene expression in the fruits of the two varieties at different development stages. The results showed that a total of 811 818 178 clean reads were obtained, resulting in 121.76 Gb of valid data. There were 2 827, 2 552 and 2 311 DEGs obtained during the green fruit stage, color turning stage and maturity stage of 'Ningqi No. 1' and 'Ningqi No. 7', respectively, among which 2 153, 2 050 and 1 825 genes were annotated in six databases, including gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) and clusters of orthologous groups of proteins (KOG). In GO database, 1 307, 865 and 624 DEGs of green fruit stage, color turning stage and maturity stage were found to be enriched in biological processes, cell components and molecular functions, respectively. In the KEGG database, the DEGs at three developmental stages were mainly concentrated in metabolic pathways, biosynthesis of secondary metabolites and plant-pathogen interaction. In KOG database, 1 775, 1 751 and 1 541 DEGs were annotated at three developmental stages, respectively. Searching the annotated genes against the PubMed database revealed 18, 26 and 24 DEGs related to the synthesis of active ingredients were mined at the green fruit stage, color turning stage and maturity stage, respectively. These genes are involved in carotenoid, flavonoid, terpenoid, alkaloid, vitamin metabolic pathways, etc. Seven DEGs were verified by RT-qPCR, which showed consistent results with transcriptome sequencing. This study provides preliminary evidences for the differences in the content of active ingredients in different Lycium barbarum L. varieties from the transcriptional level. These evidences may facilitate further exploring the key genes for active ingredients biosynthesis in Lycium barbarum L. and analyzing their expression regulation mechanism.

Mechanism of Huangjing Qianshi Decoction in treatment of prediabetic mice based on transcriptome sequencing / 中国中药杂志

Based on transcriptome sequencing technology, the mouse model of prediabetes treated with Huangjing Qianshi Decoction was sequenced to explore the possible mechanism of treating prediabetes. First of all, transcriptome sequencing was performed on the normal BKS-DB mouse group, the prediabetic model group, and the Huangjing Qianshi Decoction treatment group(treatment group) to obtain differentially expressed genes in the skeletal muscle samples of mice. The serum biochemical indexes were detected in each group to screen out the core genes of Huangjing Qianshi Decoction in prediabetes. Gene Ontology(GO) database and Kyoto Encyclopedia of Genes and Genomes(KEGG) database were used to conduct signaling pathway enrichment analysis of differentially expressed genes, and real-time quantitative polymerase chain reaction(RT-qPCR) was used to verify them. The results showed that the levels of fasting blood glucose(FBG), fasting insulin(FINS), insulin resistance index(HOMA-IR), total cholesterol(TC), triglycerides(TG), and low-density lipoprotein cholesterol(LDL-C) in the mouse model were significantly decreased after treatment with Huangjing Qianshi Decoction. In the results of differential gene screening, there were 1 666 differentially expressed genes in the model group as compared with the normal group, and there were 971 differentially expressed genes in the treatment group as compared with the model group. Among them, interleukin-6(IL-6) and NR3C2 genes, which were closely related to the regulation of insulin resis-tance function, were significantly up-regulated between the model group and the normal group, and vascular endothelial growth factor A(VEGFA) genes were significantly down-regulated between the model group and the normal group. However, the expression results of IL-6, NR3C2, and VEGFA genes were adverse between the treatment group and the model group. GO functional enrichment analysis found that the biological process annotation mainly focused on cell synthesis, cycle, and metabolism; cell component annotation mainly focused on organelles and internal components; and molecular function annotation mainly focused on binding molecular functions. KEGG pathway enrichment analysis found that it involved the protein tyrosine kinase 6(PTK6) pathway, CD28-dependent phosphoinositide 3-kinase/protein kinase B(PI3K/AKT) pathway, p53 pathway, etc. Therefore, Huangjing Qianshi Decoction can improve the state of prediabetes, and the mechanism may be related to cell cycle and apoptosis, PI3K/AKT pathway, p53 pathway, and other biological pathways regulated by IL-6, NR3C2, and VEGFA.

Integrated bioinformatics approach reveals methylation-regulated differentially expressed genes in obesity

ABSTRACT Objective: To identify DNA methylation and gene expression profiles involved in obesity by implementing an integrated bioinformatics approach. Materials and methods: Gene expression (GSE94752, GSE55200, and GSE48964) and DNA methylation (GSE67024 and GSE111632) datasets were obtained from the GEO database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in subcutaneous adipose tissue of patients with obesity were identified using GEO2R. Methylation-regulated DEGs (MeDEGs) were identified by overlapping DEGs and DMGs. The protein-protein interaction (PPI) network was constructed with the STRING database and analyzed using Cytoscape. Functional modules and hub-bottleneck genes were identified by using MCODE and CytoHubba plugins. Functional enrichment analyses were performed based on Gene Ontology terms and KEGG pathways. To prioritize and identify candidate genes for obesity, MeDEGs were compared with obesity-related genes available at the DisGeNET database. Results: A total of 54 MeDEGs were identified after overlapping the lists of significant 274 DEGs and 11,556 DMGs. Of these, 25 were hypermethylated-low expression genes and 29 were hypomethylated-high expression genes. The PPI network showed three hub-bottleneck genes (PTGS2, TNFAIP3, and FBXL20) and one functional module. The 54 MeDEGs were mainly involved in the regulation of fibroblast growth factor production, the molecular function of arachidonic acid, and ubiquitin-protein transferase activity. Data collected from DisGeNET showed that 11 of the 54 MeDEGs were involved in obesity. Conclusion: This study identifies new MeDEGs involved in obesity and assessed their related pathways and functions. These results data may provide a deeper understanding of methylation-mediated regulatory mechanisms of obesity.

Expression analysis of transcription factors in sugarcane during cold stress / Análise de expressão de fatores de transcrição em cana-de-açúcar sob estresse por frio

Transcription factors (TF) are a wide class of genes in plants, and these can regulate the expression of other genes in response to various environmental stresses (biotic and abiotic). In the current study, transcription factor activity in sugarcane was examined during cold stress. Initially, RNA transcript reads of two sugarcane cultivars (ROC22 and GT08-1108) under cold stress were downloaded from SRA NCBI database. The reads were aligned into a reference genome and the differential expression analyses were performed with the R/Bioconductor edgeR package. Based on our analyses in the ROC22 cultivar, 963 TF genes were significantly upregulated under cold stress among a total of 5649 upregulated genes, while 293 TF genes were downregulated among a total of 3,289 downregulated genes. In the GT08-1108 cultivar, 974 TF genes were identified among 5,649 upregulated genes and 283 TF genes were found among 3,289 downregulated genes. Most transcription factors were annotated with GO categories related to protein binding, transcription factor binding, DNA-sequence-specific binding, transcription factor complex, transcription factor activity in RNA polymerase II, the activity of nucleic acid binding transcription factor, transcription corepressor activity, sequence-specific regulatory region, the activity of transcription factor of RNA polymerase II, transcription factor cofactor activity, transcription factor activity from plastid promoter, transcription factor activity from RNA polymerase I promoter, polymerase II and RNA polymerase III. The findings of above results will help to identify differentially expressed transcription factors during cold stress. It also provides a comprehensive analysis of the regulation of the transcription activity of many genes. Therefore, this study provides the molecular basis for improving cold tolerance in sugarcane and other economically important grasses.

Fatores de transcrição (FT) são uma ampla classe de genes em plantas e podem regular a expressão de outros genes em resposta a vários estresses ambientais (estresses bióticos e abióticos). No presente estudo, a atividade do fator de transcrição na cana-de-açúcar foi examinada durante o estresse pelo frio. Inicialmente, as leituras de transcrição de RNA de duas cultivares de cana-de-açúcar (ROC22 e GT08-1108) sob estresse frio foram baixadas do banco de dados SRA NCBI. As leituras foram alinhadas em um genoma de referência e as análises de expressão diferencial foram realizadas com o pacote R / Bioconductor edgeR. Com base em nossas análises no cultivar ROC22, 963 genes TF foram significativamente regulados positivamente sob estresse pelo frio entre um total de 5.649 genes regulados positivamente, enquanto 293 genes TF foram regulados negativamente entre um total de 3.289 genes regulados negativamente. No cultivar GT08-1108, 974 genes TF foram identificados entre 5.649 genes regulados positivamente e 283 genes TF foram encontrados entre 3.289 genes regulados negativamente. Os fatores de transcrição, em sua maioria, foram anotados com categorias GO relacionadas à ligação de proteína, ligação de fator de transcrição, ligação específica de sequência de DNA, complexo de fator de transcrição, atividade de fator de transcrição em RNA polimerase II, atividade de fator de transcrição de ligação de ácido nucleico, atividade de corepressor de transcrição, sequência específica da região reguladora, atividade do fator de transcrição da RNA polimerase II, atividade do cofator do fator de transcrição, atividade do fator de transcrição do promotor do plastídio, atividade do fator de transcrição do promotor da RNA polimerase I, polimerase II e RNA polimerase III. As descobertas dos resultados acima ajudarão a identificar fatores de transcrição expressos diferencialmente durante o estresse pelo frio. Ele também fornece uma análise abrangente da regulação da atividade [...].

Expression analysis of transcription factors in sugarcane during cold stress

Abstract Transcription factors (TF) are a wide class of genes in plants, and these can regulate the expression of other genes in response to various environmental stresses (biotic and abiotic). In the current study, transcription factor activity in sugarcane was examined during cold stress. Initially, RNA transcript reads of two sugarcane cultivars (ROC22 and GT08-1108) under cold stress were downloaded from SRA NCBI database. The reads were aligned into a reference genome and the differential expression analyses were performed with the R/Bioconductor edgeR package. Based on our analyses in the ROC22 cultivar, 963 TF genes were significantly upregulated under cold stress among a total of 5649 upregulated genes, while 293 TF genes were downregulated among a total of 3,289 downregulated genes. In the GT08-1108 cultivar, 974 TF genes were identified among 5,649 upregulated genes and 283 TF genes were found among 3,289 downregulated genes. Most transcription factors were annotated with GO categories related to protein binding, transcription factor binding, DNA-sequence-specific binding, transcription factor complex, transcription factor activity in RNA polymerase II, the activity of nucleic acid binding transcription factor, transcription corepressor activity, sequence-specific regulatory region, the activity of transcription factor of RNA polymerase II, transcription factor cofactor activity, transcription factor activity from plastid promoter, transcription factor activity from RNA polymerase I promoter, polymerase II and RNA polymerase III. The findings of above results will help to identify differentially expressed transcription factors during cold stress. It also provides a comprehensive analysis of the regulation of the transcription activity of many genes. Therefore, this study provides the molecular basis for improving cold tolerance in sugarcane and other economically important grasses.

Resumo Fatores de transcrição (FT) são uma ampla classe de genes em plantas e podem regular a expressão de outros genes em resposta a vários estresses ambientais (estresses bióticos e abióticos). No presente estudo, a atividade do fator de transcrição na cana-de-açúcar foi examinada durante o estresse pelo frio. Inicialmente, as leituras de transcrição de RNA de duas cultivares de cana-de-açúcar (ROC22 e GT08-1108) sob estresse frio foram baixadas do banco de dados SRA NCBI. As leituras foram alinhadas em um genoma de referência e as análises de expressão diferencial foram realizadas com o pacote R / Bioconductor edgeR. Com base em nossas análises no cultivar ROC22, 963 genes TF foram significativamente regulados positivamente sob estresse pelo frio entre um total de 5.649 genes regulados positivamente, enquanto 293 genes TF foram regulados negativamente entre um total de 3.289 genes regulados negativamente. No cultivar GT08-1108, 974 genes TF foram identificados entre 5.649 genes regulados positivamente e 283 genes TF foram encontrados entre 3.289 genes regulados negativamente. Os fatores de transcrição, em sua maioria, foram anotados com categorias GO relacionadas à ligação de proteína, ligação de fator de transcrição, ligação específica de sequência de DNA, complexo de fator de transcrição, atividade de fator de transcrição em RNA polimerase II, atividade de fator de transcrição de ligação de ácido nucleico, atividade de corepressor de transcrição, sequência específica da região reguladora, atividade do fator de transcrição da RNA polimerase II, atividade do cofator do fator de transcrição, atividade do fator de transcrição do promotor do plastídio, atividade do fator de transcrição do promotor da RNA polimerase I, polimerase II e RNA polimerase III. As descobertas dos resultados acima ajudarão a identificar fatores de transcrição expressos diferencialmente durante o estresse pelo frio. Ele também fornece uma análise abrangente da regulação da atividade de transcrição de muitos genes. Portanto, este estudo fornece base molecular para melhorar a tolerância ao frio em cana-de-açúcar e outras gramíneas economicamente importantes.

Mechanism of Huashi Baidu Prescription in Treating Influenza Viral Pneumonia Based on Transcriptome / 中国实验方剂学杂志

ObjectiveTo clarify the therapeutic effect of Huashi Baidu prescription on pneumonia in mice caused by influenza A （H1N1） virus and explore its mechanism based on the transcriptome. MethodA mouse influenza viral pneumonia model was built by intranasal infection with influenza A virus， and mice were continuously administered the drug for five days， so as to investigate the general condition， lung index， viral load， pathological morphology of lung tissue， survival time， and prolongation rate of survival time of mice and clarify the therapeutic effect of Huashi Baidu prescription on influenza viral pneumonia. Transcriptome technology was used to detect the differentially expressed genes in the lung tissue of mice in the model group and the normal group， as well as the Huashi Baidu prescription group and the model group， and the potential core target of the Huashi Baidu prescription for the treatment of influenza viral pneumonia was screened. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to verify the effect of Huashi Baidu prescription on the mRNA expression level of core target genes. ResultCompared with the normal group， the lung index and viral load in the lung tissue of the model group were significantly increased （P<0.05， P<0.01）. Compared with the model group， the high-dose group of Huashi Baidu prescription significantly prolonged the survival time of mice infected with influenza A virus （P<0.05） and significantly reduced the lung index value of mice （P<0.05） and the viral load of lung tissue. The high-dose， medium-dose， and low-dose groups of Huashi Baidu prescription could significantly reduce lung tissue inflammation， blood stasis， swelling， and other pathological changes in mice （P<0.05， P<0.01）. Transcriptome analysis of lung tissue showed that core genes were mainly enriched in the nuclear transcription factor-κB （NF-κB） signaling pathway， interleukin-17 （IL-17） signaling pathway， cytokine-cytokine receptor interaction， and other pathways after the intervention of Huashi Baidu prescription. TRAF6， NFKBIA， CCL2， CCL7， and CXCL2 were the top five node genes with combined score values. Real-time PCR validation showed that Huashi Baidu prescription significantly downregulated the mRNA expression of key genes TRAF6 and NFKBIA in the NF-κB signaling pathway， as well as chemokines CCL2， CCL7， and CXCL2 （P<0.05， P<0.01）. ConclusionHuashi Baidu prescription has a therapeutic effect on influenza viral pneumonia， possibly by inhibiting the expression of key nodes TRAF6 and NFKBIA in the NF-κB signaling pathway and that of chemokines CCL2， CCL7， and CXCL2， reducing the recruitment of inflammatory cells and viral load， and exerting anti-influenza viral pneumonia effects.

Effect of Flavanomarein from Coreopsis tinctoria Ethanol Extract in Small Intestinal Organoids in Insulin-resistant Mice Based on Transcriptome Sequencing / 中国实验方剂学杂志

ObjectiveTo investigate the effects of flavanomarein on the transcriptome of small intestinal organoids in insulin-resistant mice. MethodFirstly， small intestinal organoids of C57BL/6J and db/db mice were established. Ki-67 and E-cadherin expression was determined by immunofluorescence. Small intestinal organoids were divided into the following three groups： C57BL/6J mouse small intestinal organoids as the normal control group， db/db mouse small intestinal organoids as the model group （IR group）， and db/db mouse small intestinal organoids treated with flavanomarein as the administration group （FM group）. Western blot was used to detect the expression of glucagon-like peptide-1（GLP-1） protein on the small intestinal organoids of the three groups. Finally， transcriptome sequencing was performed on samples from the three groups. ResultOn the 6th day of small intestine organoids culture， a cyclic structure was formed around the lumen， and a small intestine organoids culture model was preliminarily established. Immunofluorescence detection showed that ki-67 and E-cadherin were expressed in small intestinal organoids. Western blot results showed that the expression of GLP-1 protein was increased by flavanomarein. In the results of differential expressed gene （DEG） screening， there were 1 862 DEGs in the IR group as compared with the normal control group， and 2 282 DEGs in the FM group as compared with the IR group. Through protein-protein interaction（PPI） network analysis of the DEGs of the two groups， 10 Hub genes， including Nr1i3， Cyp2c44， Ugt2b1， Gsta1， Gstm2， Ptgs1， Gstm4， Cyp2c38， Cyp4a32， and Gpx3， were obtained. These genes were highly expressed in the normal control group， and their expression was reduced in the IR group. After the intervention of flavanomarein， the expression of the above genes was reversed. ConclusionFlavanomarein may play its role in improving insulin resistance by reversing the expression levels of 10 Hub genes， including Nr1i3， Cyp2c44， Ugt2b1， Gsta1， Gstm2， Ptgs1， Gstm4， Cyp2c38， Cyp4a32， and Gpx3.

Identification of hub genes in airway epithelial cells of asthma patients by WGCNA and PPI network

Bronchial asthma is a common chronic disease of airway inflammation, high mucus secretion and airway hyper responsiveness. The pathogenetic mechanisms of asthma remain unclear. In this study, we aimed at identifying genes playing an import role in disease-related pathways in airway epithelial cells of asthma patients. Microarray data GSE41861 of asthma airway epithelial cells was used to screen differentially expressed genes (DEGs) through GEO2R analysis. The weighted gene co-expression network analysis (WGCNA) was performed to identify gene co-expression network modules in bronchial asthma. The DAVID database was then used to perform functional and pathway enrichment analysis of these DEGs. In addition, we have conducted protein-protein interaction (PPI) network of DEGs by STRING, and eventually found key genes and significant modules. A total of 315 DEGs (111 up-regulated and 204 down-regulated) were identified between severe asthma and healthy individual, which were mainly involved in pathways of cilium assembly, cilium morphogenesis, axon guidance, positive regulation of fat cell differentiation, and positive regulation of cell substrate adhesion. A total of 60 genes in the black module and green module were considered to be correlated with the severity of asthma. Combining PPI network, several key genes were identified, such as BP2RY14, PTGS1, SLC18A2, SIGLEC6, RGS13, CPA3, and HPGDS. Our findings revealed several genes that may be involved in the process of development of bronchial asthma and potentially be candidate targets for diagnosis or therapy of bronchial asthma.

Prediction of protein-protein interaction networks and druggable genes associated with parkinson’s disease-Supplementary data

Parkinson’s disease (PD) affects about 2-3% of the global population over 65 years of age and hence, it is the second most common neurodegenerative disorder in the world. This study explored the key genes and miRNA involved in PD. Microarray dataset (accession number GSE19587) comprising of two regions of medulla: dorsal motor nucleus of vagus (DMNV) and inferior olivary nucleus (ION) was downloaded from Gene Expression Omnibus (GEO) database. A total of 697 DEGs from ION (605 up-regulated genes and 92 down-regulated genes) and 663 DEGs from DMNV (638 up-regulated genes and 25 down-regulated genes) were screened. These DEGs were found to be enriched in 46 (DMNV) and 24 (ION) pathways common in DAVID and Comparative Toxicogenomics Database. In PPI network analysis, IGF1 and CD44 were identified as hub genes in DMNV whereas for ION, the hub genes identified were CSF2 and CD44. In TF-miRNA-target gene networks, an aggregate of 11 transcription factors and 46 miRNA were observed to influence the target genes. In drug-gene interaction studies, CYP3A5 and ESR1 had higher connective degrees and hence, they might be novel druggable targets for Parkinson’s disease.

Prediction of protein-protein interaction networks and druggable genes associated with parkinson’s disease

Parkinson’s disease (PD) affects about 2-3% of the global population over 65 years of age and hence, it is the second most common neurodegenerative disorder in the world. This study explored the key genes and miRNA involved in PD. Microarray dataset (accession number GSE19587) comprising of two regions of medulla: dorsal motor nucleus of vagus (DMNV) and inferior olivary nucleus (ION) was downloaded from Gene Expression Omnibus (GEO) database. A total of 697 DEGs from ION (605 up-regulated genes and 92 down-regulated genes) and 663 DEGs from DMNV (638 up-regulated genes and 25 down-regulated genes) were screened. These DEGs were found to be enriched in 46 (DMNV) and 24 (ION) pathways common in DAVID and Comparative Toxicogenomics Database. In PPI network analysis, IGF1 and CD44 were identified as hub genes in DMNV whereas for ION, the hub genes identified were CSF2 and CD44. In TF-miRNA-target gene networks, an aggregate of 11 transcription factors and 46 miRNA were observed to influence the target genes. In drug-gene interaction studies, CYP3A5 and ESR1 had higher connective degrees and hence, they might be novel druggable targets for Parkinson’s disease.

Molecular mechanism of fluoride toxicity to ameloblasts based on bioinformatics method / 中华地方病学杂志

Objective:To explore the molecular mechanism of fluoride toxicity to ameloblasts.Methods:Mouse ameloblast cell line (LS8 cells) was taken and divided into control group [0.0 mmol/L sodium fluoride (NaF)] and fluoride exposed group (1.6 mmol/L NaF) according to the final concentration of NaF. Transcriptome sequencing was performed to screen differentially expressed genes (DEGs), and gene ontology (GO) analysis and gene set enrichment analysis (GSEA) were performed on DEGs. The STRING database was used to construct the protein-protein interaction (PPI) network of DEGs, and Cytoscape 3.8.0 software was used to visualize the PPI network to screen key modules and key genes. At the same time, real-time fluorescence quantitative PCR was used to detect the mRNA expression level of key genes, and the key genes were verified by gene expression database (GEO database).Results:Compared with the control group, there were 709 DEGs in the fluoride exposed group, including 223 up-regulated genes and 486 down-regulated genes. The GO analysis of DEGs mainly involved molecular functions such as receptor-ligand activity, cell adhesion molecule binding, structural components of extracellular matrix, cellular components such as collagen of extracellular matrix, receptor complex, membrane raft, biological processes such as external packaging structure organization, extracellular structure organization, and extracellular matrix organization. The GSEA of the whole gene set found that the interleukin-17 (IL-17) signaling pathway, ribosome biogenesis in eukaryotes, and the nuclear factor kappa-B (NF-κB) signaling pathway were activated, while fatty acid degradation, pyruvate metabolism and fatty acid metabolism were inhibited. After constructing PPI network, three key modules and four key genes [typeⅠcollagen α1 (Col1a1), typeⅠcollagen α2 (Col1a2), typeⅤcollagen α1 (Col5a1) and fibrinogen 1 (Fbn1)] were obtained. Compared with the control group, the mRNA expression levels of Col1a1, Col1a2, Col5a1 and Fbn1 in LS8 cells of the fluoride exposed group were significantly decreased ( P < 0.05), which was consistent with the change trend of gene expression in the GEO database. Conclusion:Key genes such as Col1a1, Col1a2, Col5a1, Fbn1, and signaling pathways such as IL-17 and NF-κB, which are screened by bioinformatics method, may be closely related to the toxic effects of fluoride on ameloblasts.

Identification of Down syndrome fetal encephalopathy related genes and signaling pathways via bioinformatics analysis / 中华实用儿科临床杂志

Objective:To identify Down syndrome (DS) fetal encephalopathy related genes and signaling pathways via bioinformatics analysis, and to explore their potential mechanisms underlying the occurrence and development of DS neuropathology.Methods:Retrospective study.In December 2021, dataset GSE59630 was downloaded from the gene expression omnibus (GEO), and differentially expressed genes (DEGs) between DS and normal fetal brain tissue were identified by R software.Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and gene set enrichment analysis (GSEA) were performed on the genes identified.The protein-protein interaction (PPI) network was constructed based on search tool for the retrieval of interacting genes online database and Cytoscape software, and key modules and hub DEGs were identified.Real-time quantitative polymerase chain reaction technique was used to verify the expression of hub genes related to neurodegeneration in brain tissue of 3 pairs of DS and normal fetuses at the gestational age of 22-33 weeks.Results:A total of 225 DEGs were screened out from DS and normal fetal brain tissue, including 18 up-regulated genes and 207 down-regulated genes.GO functional enrichment analysis showed that DEGs were mainly enriched in neurogenesis, neuronal apoptosis, transcriptional regulation, mitochondrial energy metabolism, etc.KEGG pathway enrichment analysis revealed that DEGs were associated with a variety of neurodegenerative diseases.GSEA suggested that apoptosis and inflammatory responses play a vital part in the occurrence of DS neuropathology.Ten hub genes were identified by the PPI network established, and they were mainly related to histone acetylation and transcriptional regulation.According to the tissue verification result, the variations of RAB8A, TBP and TAF6 expression conformed to the microarray data. Conclusions:The key genes and signaling pathways identified by transcriptome analysis of fetal brain tissue facilitate the comprehensive understanding of the molecular mechanism of DS neuropathology.This study provides a novel insight into the clinical diagnosis and treatment of neurodevelopmental abnormalities and mental retardation in DS.

Differentially expressed genes in chronic periodontitis and the correlation with disease severity / 国际生物医学工程杂志

Objective:To study the differentially expressed genes in chronic periodontitis (CP) and to explore the correlation with disease severity.Methods:Gene expression profile data associated with CP were screened in the Gene Expression Omnibus (GEO) database and analyzed with GEO2R online software to create volcano maps. Kyoto Encyclopedia of Genes and Genomes (KEEG) and Gene Ontology (GO) analyses were performed on the screened CP-associated differentially expressed genes to predict their possible functions and signaling pathways. The protein-protein interaction database (STRING) was used to analyze the interaction relationships between the encoded proteins of the screened CP-related differentially expressed genes. Cytohubba software was used to identify key genes in the signaling pathway. One120 CP patients and 40 healthy controls were selected. The screened CP-related genes were validated by the real-time polymerase chain reaction (q-PCR) method.Results:A total of 1 151 CP differentially expressed genes that met the requirements were screened. These genes were mainly enriched in the GO pathway for positive regulation of granulocyte differentiation, helper T-cell differentiation, leukocyte aggregation, regulation of acute inflammatory response, chemokine-mediated and endoplasmic reticulum unfolded protein response, as well as in the KEGG pathway for NFB pathway, chemokine pathway, cytokine receptor interaction, leukocyte transendothelial migration pathway, B-cell receptor pathway, Toll-like receptor pathway, etc. The protein-protein interaction network was constructed using the screened CP-related differentially expressed genes, which included 78 nodes and 496 links, with a mean aggregation coefficient and mean connectivity of 0.69 and 12.7, respectively. Cytohubba analysis showed that Sell was a key gene in the signaling pathway, and its relative expression levels in the gingival fluids of the three CP groups with different degrees(1.14±0.46, 0.86±0.41, 0.52±0.46) was significantly lower than that of the control group (1.50±0.65) (all P<0.05). The area under the ROC curve (AUC) of subjects diagnosed with CP using Sell expression levels in gingival fluid was 0.79 (95% CI: 0.71 to 0.86). The AUC values were greater than 0.65 at 95% CI when Sell was used as a biological marker to evaluate the severity of CP. Conclusions:CP-related differentially expressed genes are mainly enriched in the number of pathways associated with the inflammatory response of periodontitis. The expression levels of Sell genes were significantly reduced in the gingival sulcus fluid of CP patients and correlated with the severity of the disease. The Sell genes are expected to be a biomarker for CP grading.

Screening of Molecular Markers of Cisplatin Resistance in Lung Adenocarcinoma and Functional Verification Based on TCGA Database / 肿瘤防治研究

Objective To explore the related genes that play a key regulatory role in cisplatin resistance in lung adenocarcinoma. Methods Bioinformatics methods were used to download the differentially-expressed genes between cisplatin sensitive group and drug resistant group in patients with lung adenocarcinoma in TCGA database and GDSC database. GO function analysis and KEGG pathway enrichment analysis were carried out to analyze the differentially-expressed genes. The protein-protein interaction network was constructed and hierarchical cluster analysis was carried out to screen the key genes. The key genes were verified at the cell level by real-time fluorescence quantitative PCR and ELISA. Then the expression of the selected key gene in A549/DDP cells was silenced by siRNA and its sensitivity to cisplatin was detected. Results We screened out 178 differentially-expressed genes. After cluster analysis, CXCL9, CXCL10, NKX2-1 and SFTPA1 were regarded as the key genes of cisplatin resistance in lung adenocarcinoma. CXCL10 was temporarily selected for subsequent verification and function experiment. The mRNA expression of CXCL10 in A549/DDP cells was significantly higher than that in A549 cells (P < 0.001), and the expression of CXCL10 protein in the supernatant of A549/DDP cells was higher than that in A549 cells, which were consistent with the prediction of bioinformatics. The sensitivity of A549/DDP cells to DDP increased after silencing CXCL10 expression. Conclusions CXCL10 is a key gene to regulate cisplatin resistance in lung adenocarcinoma. Downregulating the expression of CXCL10 can become a potential target for reversing cisplatin resistance in lung adenocarcinoma.

Gene Expression Profiles at Different Time Points after Acute Myocardial Infarction in Mice / 法医学杂志

OBJECTIVES@#To explore the mRNA differential expressions and the sequential change pattern in acute myocardial infarction (AMI) mice.@*METHODS@#The AMI mice relevant dataset GSE4648 was downloaded from Gene Expression Omnibus (GEO). In the dataset, 6 left ventricular myocardial tissue samples were selected at 0.25, 1, 4, 12, 24 and 48 h after operation in AMI group and sham control group, and 6 left ventricular myocardial tissue samples were selected in blank control group, a total of 78 samples were analyzed. Differentially expressed genes (DEGs) were analyzed by R/Bioconductor package limma, functional pathway enrichment analysis was performed by clusterProfiler, protein-protein interaction (PPI) network was constructed by STRING database and Cytoscape software, the key genes were identified by Degree topological algorithm, cluster sequential changes on DEGs were analyzed by Mfuzz.@*RESULTS@#A total of 1 320 DEGs were associated with the development of AMI. Functional enrichment results included cellular catabolic process, regulation of inflammatory response, development of muscle system and vasculature system, cell adhesion and signaling pathways mainly enriched in mitogen-activated protein kinase (MAPK) signaling pathway. The key genes of AMI included MYL7, TSC22D2, HSPA1A, BTG2, NR4A1, RYR2 were up-regulated or down-regulated at 0.25-48 h after the occurrence of AMI.@*CONCLUSIONS@#The functional signaling pathway of DEGs and the sequential expression of key genes in AMI may provide a reference for the forensic identification of AMI.

Bioinformatics analysis of gene expression changes induced by Dexamethasone in human trabecular meshwork / 国际眼科杂志(Guoji Yanke Zazhi)

@#AIM:To profile gene expression changes induced by dexamethasone in human trabecular meshwork using bioinformatics analysis, and to elucidate the possible mechanisms involved in the pathogenesis of glucocorticoid-induced glaucoma(GIG). <p>METHODS:The gene expression dataset GSE37474 was obtained from the Gene Expression Omnibus(GEO). GEO2R was utilized to identify differentially expressed genes(DEGs)in trabecular meshwork between the dexamethasone group and the control group. Gene Ontology(GO)function annotation and the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment were constructed using the DAVID database. STRING database and Cytoscape software were used to construct protein-protein interaction(PPI). The hub genes were screened by CytoHubba plug-in. Finally, the mRNA expression of key hub genes was verified by RT-PCR. <p>RESULTS:Compared to normal trabecular meshwork, dexamethasone-treated trabecular meshwork had a total of 252 DEGs, with 141 genes up-regulated and 111 genes down-regulated. GO function annotation showed that DEGs were mostly located in the extracellular matrix, where they engaged in the biological processes of positive regulation of inflammation and extracellular matrix remodeling. KEGG pathway enrichment showed that DEGs were largely involved in vascular smooth muscle contraction, arachidonic acid metabolism, ether lipid metabolism and choline metabolism. The PPI network yielded seven hub genes, three of which were up-regulated(<i>EDN1</i>, <i>FOS</i>, and <i>LPL</i>)and four of which were down-regulated(<i>CCL2</i>, <i>IGF1</i>, <i>PTGS2</i>, <i>CCL5</i>). In RT-PCR, the mRNA expression levels of the seven hub genes matched those in the gene expression profile. <p>CONCLUSION:Dexamethasone can cause dramatic changes in the gene expression profile in trabecular meshwork. The enriched pathways of DEGs and certain hub genes play an important role in the remodeling of the extracellular matrix and the regulation of aqueous humor outflow, providing a full knowledge of the molecular mechanism of GIG.