Analyzing time-series microarray data reveals key genes in spinal cord injury

Although many scholars have utilized high-throughput microarrays to delineate gene expression patterns after spinal cord injury (SCI), no study has evaluated gene changes in raphe magnus (RM) and somatomotor cortex (SMTC), two areas in brain primarily affected by SCI. In present study, we aimed to analyze the differentially expressed genes (DEGs) of RM and SMTC between SCI model and sham injured control at 4, 24 h, 7, 14, 28 days, and 3 months using microarray dataset GSE2270 downloaded from gene expression omnibus and unpaired significance analysis of microarray method. Protein–protein interaction (PPI) network was constructed for DEGs at crucial time points and significant biological functions were enriched using DAVID. The results indicated that more DEGs were identified at 14 days in RM and at 4 h/3 months in SMTC after SCI. In the PPI network for DEGs at 14 days in RM, interleukin 6, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), FBJ murine osteosarcoma viral oncogene homolog (FOS), tumor necrosis factor, and nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) were the top 5 hub genes; In the PPI network for DEGs at 3 months in SMTC, the top 5 hub genes were ubiquitin B, Ras‐related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1), FOS, Janus kinase 2 and vascular endothelial growth factor A. Hedgehog and Wnt signaling pathways were the top 2 significant pathways in RM. These hub DEGs and pathways may be underlying therapeutic targets for SCI.     

Analysis of the potential pathways and target genes in spinal cord injury using bioinformatics methods

Spinal cord injury (SCI) remains to be the most devastating type of trauma for patients because of long lasting disability and limited response to the acute drug administration and efforts at rehabilitation. With the purpose to identify potential targets for SCI treatment and to gain more insights into the mechanisms of SCI, the microarray data of GSE2270, including 119 raphe magnus (RM) samples and 125 sensorimotor cortex (SMTC) samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened in RM group and SMTC group compared with their corresponding controls, respectively. A protein–protein interaction (PPI) network was constructed based on the common DEGs identified in both RM group and SMTC group. Gene ontology (GO) and pathway enrichment analyses of the overlapping DEGs were performed. Furthermore, the common DEGs enriched in each pathway were analyzed to identify significant regulatory elements. Totally, 173 overlapping DEGs (130 up-regulated and 43 down-regulated) were identified in both RM and SMTC samples. These overlapping DEGs were enriched in different GO terms. Pathway enrichment analysis revealed that DEGs were mainly related to inflammation and immunity. CD68 molecule (CD68) was a hub protein in the PPI network. Moreover, the regulatory network showed that ras-related C3 botulinum toxin substrate 2 (RAC2), CD44 molecule (CD44), and actin related protein 2/3 complex (ARPC1B) were hub genes. RAC2, CD44, and ARPC1B may be significantly involved in the pathogenesis of SCI by participating significant pathways such as extracellular matrix-receptor signaling pathway and Toll-like receptor signaling pathway.     

基于转录组的肝豆状核变性调控网络的构建和分析

本研究旨在利用生物信息学方法构建经铜诱导的ATP7B基因敲除HepG2细胞系的转录调控网络。探讨关键转录因子在肝豆状核变性发生、发展中的潜在作用机制。收集公共基因表达数据库（gene expression omnibus,GEO）中包含野生型、ATP7B基因敲除型、铜诱导的野生型和铜诱导的ATP7B基因敲除型HepG2细胞系数据。筛选由铜诱导产生的差异表达基因（differentially expressed genes,DEGs）后进行基因本体论（gene ontology,GO）、京都基因和基因组百科全书（Kyoto encyclopedia of genes and genomes,KEGG）富集分析。基于蛋白相互作用网络,识别疾病关键基因和功能模块,并对关键功能模块中的基因进行富集分析。最后,构建转录调控网络,筛选核心转录因子。共筛选出1 034个差异表达基因,其中上调525个,下调509个。上、下调关键功能模块分别包括了3 785个和3 931个基因。关键功能模块中的基因主要定位于细胞-基质连接、染色体、剪接复合体、核糖体等区域,共同参与了mRNA加工、组蛋白修饰、RNA剪切、DNA代谢调节、蛋白磷酸化等生物学过程,且与转录共调控活性、DNA转录因子结合、泛素样蛋白连接酶结合等分子功能相关。KEGG分析表明功能模块中的基因显著富集的通路包括乙型肝炎、有丝分裂原活化蛋白激酶（mitogen-activated protein kinase,MAPK）信号通路、细胞衰老和凋亡、神经营养信号通路和神经变性途径等。肝豆状核变性转录调控网络包括11个差异表达转录因子和96个差异表达基因,其中U2AF1、NFRKB、FUS、MAX、SRSF1、CEBPA和RXRA为核心差异表达转录因子。该研究为肝豆状核变性转录调控相关分子的生物学功能研究提供了重要的参考依据。     

Uncovering the pathogenesis and identifying novel targets of pancreatic cancer using bioinformatics approach

Pancreatic cancer is a uniformly lethal disease that can be difficult to diagnose at its early stage. Thus, our present study aimed to explore the underlying mechanism and identify new targets for this disease. The data GSE16515, including 36 tumor and 16 normal samples were available from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened out using Robust Multichip Averaging and LIMMA package. Moreover, gene ontology and pathway enrichment analyses were performed to DEGs. Followed with protein–protein interaction (PPI) network construction by STRING and Cytoscape, module analysis was conducted using ClusterONE. Finally, based on PubMed, text mining about these DEGs was carried out. Total 274 up-regulated and 93 down-regulated genes were identified as the common DEGs and these genes were discovered significantly enriched in cell adhesion and extracellular region terms, as well as ECM-receptor interaction pathway. In addition, five modules were screened out from the up-regulated PPI network with none in down-regulated network. Finally, the up-regulated genes, including MIA, MET and CEACAMS, and down-regulated genes, such as FGF, INS and LAPP, had the most references in text mining analysis. Our findings demonstrate that the up- and down-regulated genes play important roles in pancreatic cancer development and might be new targets for the therapy.     

Comprehensive analysis of microRNA-messenger RNA regulatory network in gemcitabine-resistant bladder cancer cells

Chemotherapy is still a standard treatment of unresectable bladder cancer or distant metastases. The chemotherapy resistance always occurs after a period of treatment indicating poor prognosis. The current study aimed to explore the molecular mechanism of chemoresistance in bladder cancer cells. The gene expression profiles of GSE77883, including three untreated T24 cells samples and three gemcitabine-resistant T24 cells samples, was downloaded from Gene Expression Omnibus database. The screening of differentially expressed genes (DEGs), gene function analysis, and interaction prediction between microRNAs (miRNAs) and DEGs were performed by R software. The protein-protein interaction (PPI) and miRNA-DEGs networks were constructed and visualized by Cytoscape software. Then, the small molecules, with potential synergistic or antagonistic effects to gemcitabine resistance, were identified using the Connectivity Map database. Finally, gemcitabine-resistant T24 cell line was established and key genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR). In total, 536 upregulated and 513 downregulated genes were screened and mainly enriched in oxidative stress response and signaling pathways related to extracellular matrix–receptor interaction and focal adhesion. PPI network showed interleukin 6, tumor necrosis factor, kinesin family member 11, and BUB1 mitotic checkpoint serine/threonine kinase B were key genes. The miRNA-DEGs regulatory networks included 18 miRNAs and 185 DEGs, including miR-182-5p, miR-590-3p, miR-320a and serum- and glucocorticoid-regulated kinase 1 (SGK1). Then, the related key genes and miRNAs were confirmed by qRT-PCR. Furthermore, 81 small molecules with antagonistic or synergistic effect to GEM were screened. We have investigated the molecular mechanisms driving GEM-resistance in bladder cancer cells that would contribute to the development of chemotherapy for advanced bladder cancer.     

Integrated microRNA–mRNA analysis of coronary artery disease

Although patients with coronary artery disease (CAD) have a high mortality rate, the pathogenesis of CAD is still poorly understood. The purpose of this study was to explore the underlying molecular mechanisms and potential target molecules for CAD. The platelet miRNA (GSE28858) and blood mRNA (GSE42148) expression profiles of patients with CAD and healthy controls were downloaded from Gene Expression Omnibus. Differentially expressed miRNAs and genes (DEGs) were identified by significant analysis of microarray algorithm after data preprocessing. Furthermore, the miRNA-target gene regulatory network was constructed based on miRecords database. The spearman correlation coefficients (ρ) between miRNAs and their target genes were calculated. Six up- (miR-340, miR-545, miR-451, miR454-5p, miR-624 and miR-585) and four down-regulated (miR-199a, miR-17-3p, miR-154 and miR-339) miRNAs were screened. Total 295 target genes of miR-545, miR-451, miR-585 and miR-154 were predicted. Among these 295 target genes, 7 genes were DEGs. Further analysis showed miR-545-TFEC and miR-585-SPOCK1 were highly positively correlated (ρ = 0.808091264; ρ = 0.874680776) in CAD samples. Therefore, differentially expressed miRNAs might participate in the pathogenesis of CAD by regulating their target genes.     

流体剪切力对内皮细胞miR-21和miR-199a表达的影响

为探讨流体剪切力对内皮细胞micorRNAs表达的影响。采用旋转锥形圆盘剪切力系统对内皮细胞分别加载低(4dyn/cm2)、中(10 dyn/cm2)和高(15 dyn/cm2)3种不同梯度的剪切力作用24h。对照组未加载剪切力。采用高通量筛选芯片检测microRNAs表达变化,qRT-PCR验证,并进行生物信息学分析。与对照组比较,低剪切力组表达差异的microRNAs有33个(FC1.5或0.5倍,P0.05),其中28个上调,5个下调;中剪切力组表达差异的microRNAs有8个(FC1.5或0.5倍,P0.05),其中6个上调,2个下调;高剪切力组表达差异的microRNAs有31个(FC1.5或0.5倍,P0.05),其中25个上调,6个下调。miR-21在高剪切力组中上调最显著(FC=0.026),在低剪切力组中显著下调(FC=3.531)。miR-199a在低剪切力组中上调最显著(FC=0.075),在高剪切力组中显著下调(FC=3.031)。表达差异的microRNA的靶基因主要与内皮细胞的力学信号转导、细胞跨膜迁移、钙离子信号通路、细胞内吞作用等相关。流体剪切力可诱导内皮细胞miR-21和miR-199a表达发生改变。     

Transcriptomic analysis of differentially expressed genes in flower-buds of genetic male sterile and wild type cucumber by RNA sequencing

Cucumber (Cucumis sativus L.) pollen development involves a diverse range of gene interactions between sporophytic and gametophytic tissues. Previous studies in our laboratory showed that male sterility was controlled by a single recessive nuclear gene, and occurred in pollen mother cell meiophase. To fully explore the global gene expression and identify genes related to male sterility, a RNA-seq analysis was adopted in this study. Young male flower-buds (1–2 mm in length) from genetic male sterility (GMS) mutant and homozygous fertile cucumber (WT) were collected for two sequencing libraries. Total 545 differentially expressed genes (DEGs), including 142 up-regulated DEGs and 403 down-regulated DEGs, were detected in two libraries (Fold Change ≥ 2, FDR < 0.01). These genes were involved in a variety of metabolic pathways, like ethylene-activated signaling pathway, sporopollenin biosynthetic pathway, cell cycle and DNA damage repair pathway. qRT-PCR analysis was performed and showed that the correlation between RNA-Seq and qRT-PCR was 0.876. These findings contribute to a better understanding of the mechanism that leads to GMS in cucumber.     

COPD 差异表达基因的生物信息学分析及在LUSC样本中的表达

为确定慢性阻塞性肺病(COPD)的分子标记物及COPD与肺鳞状细胞癌(LUSC)共存的差异表达基因,探寻COPD合并肺癌的预测因子,发现新的治疗靶点。本研究采用生物信息学方法,从GEO数据库中筛选3套基因芯片数据集,挖掘COPD患者小气道上皮细胞(SAEC)的差异表达基因(DEG)以及潜在的生物标记物,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)富集分析预测DEGs的功能及参与的代谢途径。继而对DEGs构建PPI网络,使用Cytoscape软件筛选子模块和Hub基因,并将Hub基因通过TCGA数据库分析其在LUSC中的差异表达情况及差异基因间的相关性。结果共获得52个上调基因和24个下调基因,代谢通路主要集中在细胞色素P450对外源物质的代谢、化学致癌、花生四烯酸代谢及甲状腺激素合成四条途径上,通过Cytoscape软件从PPI网络中筛选得到2个功能模块和10个Hub基因,进一步验证发现其中5个基因在TCGA数据库中的LUSC样本中同样差异表达。由此推测SPP1、ALDH3A1、SPRR3、KRT6A和SPRR1B 可能为COPD 分子标记物及COPD与LUSC共存的DEGs,从而为研究COPD和LUSC的发病机制及二者潜在关系奠定良好的基础。     

Bioinformatics Analysis of Microarray Data to Reveal Novel Genes Related to Cold-Resistance of Maize

Low temperature has become a major abiotic stress factor that can reduce maize yield and cause a number of economic loss. This study was designed to identify key genes and pathways associated with coldresistance of maize. The gene expression profile GSE46704, including 4 control temperature treated plants and 4 low temperature treated plants, was downloaded from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) were identified by limma package. Then, protein-protein interaction (PPI) network and module selection were constructed using Cytoscape. Moreover, the DEGs were re-matched based on the Zea mays L. gene ID and symbol data from PlantRegMap. Finally, the re-matched DEGs were performed functional and pathway enrichment analyses by the DAVID online tool. A total of 750 DEGs were screened (including 387 up-regulated and 363 down-regulated genes) In the PPI network, GRMZM2G070837_P01 and GRMZM2G114578_P01 had higher degrees. Besides, carbohydrate metabolic process, starch and sucrose metabolism and biosynthesis of secondary metabolites were significantly enriched in functional and pathway enrichment analysis. GRMZM2G070837_P01 and GRMZM2G114578_P01 might play a critical role in cold-resistance of maize. Meanwhile, carbohydrate metabolic process, starch and sucrose metabolism and biosynthesis of secondary metabolites might function in cold-resistance of maize.     

Comprehensive gene expression analysis reveals multiple signal pathways associated with prostate cancer

Prostate cancer (PC) depends on androgenic signaling for growth and survival. To data, the exact molecular mechanism of hormone controlling proliferation and tumorigenesis in the PC remains unclear. Therefore, in this study, we explored the differentially expressed genes (DEGs) and identified featured genes related to hormone stimulus from PC. Two sets of gene expression data, including PC and normal control sample, were downloaded from Gene Expression Omnibus (GEO) database. The t-test was used to identify DEGs between PC and controls. Gene ontology (GO) functional annotation was applied to analyze the function of DEGs and screen hormone-related DEGs. Then these hormone-related DEGs were further analyzed in constructed cancer network and Human Protein Reference Database to screen important signaling pathways they participated in. A total of 912 DEGs were obtained which included 326 up-regulated genes and 586 down-regulated genes. GO functional enrichment analysis identified 50 hormone-related DEGs associated with PC. After pathway and PPI network analysis, we found these hormone-related DEGs participated in several important signaling pathways including TGF-β (TGFB2, TGFB3 and TGFBR2), MAPK (TGFB2, TGFB3 and TGFBR2), insulin (PIK3R3, SHC1 and EIF4EBP1), and p53 signaling pathways (CCND2 and CDKN1A). In addition, a total of five hormone-related DEGs (SHC1, CAV1, RXRA, CDKN1A and SRF) were located in the center of PPI network and 12 hormone-related DEGs formed six protein modules. These important signal pathways and hormone-related DEGs may provide potential therapeutic targets for PC.     

Effects of thymoquinone on liver miRNAs and oxidative stress in Ehrlich acid mouse solid tumor model

We investigated the effects of thymoquinone (TQ) on the expression of liver microRNAs (miRNAs), liver histopathology and oxidative stress in Ehrlich acid solid tumor model induced mice. We used 24 male BALB/c mice divided randomly into three groups. Control (C) group mice were injected intraperitoneally (i.p.) with 0.5 ml saline for four weeks. Tumor (T) group mice were injected i.p. with 0.5 ml saline for four weeks, then Ehrlich acid tumor cells were injected subcutaneously into the neck to induce solid tumor formation. TQ (T + Tq) group mice injected i.p. with 10 mg/kg TQ for four weeks, then Ehrlich acid tumor cells were injected subcutaneously into the neck of the mice in this group to induce solid tumor formation. At the end of the study, liver from all groups were removed for histopathological and miRNAs analysis, and oxidative stress measurement. We found that the expression of miR-206b-3p was up-regulated and the oxidative stress and necrosis increased in the liver tissue of mice with Ehrlich acid solid tumor. TQ application decreased the oxidative stress, prevented necrosis, increased regeneration and down-regulated the expression of miR-206b-3p in the liver tissue.     

microRNAs参与韧带成纤维细胞成骨分化相关分子表达的调节

韧带成纤维细胞成骨分化与强直性脊柱炎等多种异位骨化相关性疾病有关,但其机制尚不清楚.本研究目的在于观察韧带成纤维细胞在成骨分化过程中microRNA和mRNA的表达谱变化,以期为揭示成纤维细胞成骨分化机制提供研究基础.原代培养韧带成纤维细胞、地塞米松、抗坏血酸和β 磷酸甘油体外诱导其成骨分化, RT-PCR检测成骨标志物骨钙素和Runx2的表达.采用表达谱基因芯片分析诱导0、7、14 d后韧带成纤维细胞的microRNAs和mRNAs表达,并用实时定量PCR（RT-qPCR）和Western印迹方法验证生物信息学分析结果.结果显示,与未诱导前相比,成骨分化第7 d有66个microRNAs和640个mRNA表达上调,94个microRNAs和744个mRNA表达下调;成骨分化第14 d有58个microRNAs和781个mRNA表达上调,96个microRNAs和603个mRNA表达下调.实时定量PCR和Western印迹验证结果显示,miR-29b在成骨分化过程中表达上调,TGFβ3表达水平降低,与芯片结果一致,miR-29b通过抑制TGFβ3蛋白的翻译来促进Runx2的表达,从而促进韧带成纤维细胞向成骨细胞分化.韧带成纤维细胞成骨分化过程中,microRNA调控基因及mRNA差异表达基因除涉及BMPs、Wnt和Ihh等信号通路外,在成纤维细胞成骨分化过程中可能还存在其它的新机制.     

Bioinformatics identification of the methylerythritol phosphate pathway associated genes in <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis> with <Emphasis Type="Italic">ceh1</Emphasis> mutant

The methylerythritol phosphate (MEP) pathway for the production of isoprenoids is recently discovered. The current study aimed to identify MEP pathway disorder-related molecular mechanisms and potential genes in Arabidopsis thaliana. Microarray data (GSE61675) obtained from ceh1 mutant plants and corresponding parental lines were retrieved from Gene Expression Omnibus (GEO) database and were applied for differentially expressed genes (DEGs) screening. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed. Protein-protein interaction (PPI) network was then constructed and displayed by Cytoscape software. Total 762 DEGs including 620 up-regulated and 142 down-regulated genes were screened. In addition, a great many of DEGs were mainly involved in biosynthesis and metabolism-related pathways, such as stilbenoid, diarylheptanoid, and gingerol biosynthesis, and biosynthesis of terpenoids and steroids. Moreover, a PPI network contained 90 down-regulated genes and 497 up-regulated genes were obtained. Up-regulated DEGs including glutaredoxin (GRX480, cytochrome BC1 synthase (BCS1, syntaxin of plants 121 (SYP121) and A. thaliana MAP kinase 11 (ATMPK11) with higher degree in this network were hub nodes. Pathways including stilbenoid, diarylheptanoid, and gingerol biosynthesis obtained in our study were consistent with previous studies. Importantly, GRX480, BCS1 and ATMPK11 could have close interactions with the MEP pathway and may play important roles in the biosynthesis of isoprenoids.