Identification of germinal disk region derived genes potentially involved in hen fertility

Despite the regular decrease in fertility observed in hens, especially in “meat” lines, little is known about genes affecting fertility. We used the Affymetrix microarray to search for oocyte genes whose expression would vary in relation to fertility rate in both “laying” and “meat” line hens. We focused on oocyte genes because several of them have been found to be involved in fertility in other species. Based on microarray analysis, 54 and 84 genes were differentially expressed between germinal disc regions (GDR) of F1 maturation stage oocytes from hens exhibiting either high (100%) or low (from 22% to 80%) fertility rate from laying and meat lines respectively. Most of these differentially expressed genes were distributed between “laying” and “meat” lines indicating that mechanisms involved in the decrease in fertility rates in these two cases were independent. Real time RT‐PCR performed on the same samples which were used for microarray confirmed in several cases differences in gene expression levels detected by microarray. Moreover the correlations between gene expression levels and fertility rates were evaluated for the 10 most interesting genes at different stages of follicular maturation and early embryo development on individual GDR samples from hens exhibiting different fertility rates. In total, we identified five genes whose expression levels correlated with fertility rate in accordance with findings of microarray analysis and real time RT‐PCR: VWC2, CR407412, TAPA, FGL2, and TRAP6. The biological significance of these genes sheds light on potential mechanisms influencing fertility and could provide candidates for fertility markers in the hen. Mol. Reprod. Dev. 76: 1043–1055, 2009. © 2009 Wiley‐Liss, Inc.     

Identification of potential mechanism and hub genes for neuropathic pain by expression-based genome-wide association study

Neuropathic pain (NP) is a common pathological pain state with limited effective treatments. This study was designed to identify potential mechanisms and candidate genes using gene expression–based genome-wide association study (eGWAS). All NP-related microarray experiments were obtained from Gene Expression Omnibus and ArrayExpress. Significantly dysregulated genes were identified between experimental and untreated groups, and the number of microarray experiments in which each gene was dysregulated was calculated. Significantly dysregulated genes were ranked according to P values of the chi-square test. Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database, we performed functional and pathway enrichment analysis. Protein-protein interaction (PPI) network and module analysis was performed using Cytoscape software. A total of 115 candidate genes were identified from 19 independent microarray experiments by eGWAS based on the Bonferroni threshold ( P < 2.97 × 10 ⁻⁶). Immune and inflammatory responses, and complement and coagulation cascades, were respectively the most enriched biological process and pathways for candidate genes. The hub genes with highest connectivity in PPI network and two modules Ccl2 and Jun, and Ctss application of the eGWAS methodology can identify mechanisms and candidate genes associated with NP. Our results support the validity and prevalence of inflammatory and immune mechanisms across different NP models, and Ccl2, Jun, and Ctss may be the hub genes for NP.     

Expression profiles of microRNAs in oxidized low-density lipoprotein-stimulated RAW 264.7 cells

Macrophage-derived foam cells were one of the hallmarks of atherosclerosis, and microRNAs played an important role in the formation of foam cells. In order to explore the roles of miRNA in the formation of foam cells, we investigated miRNA expression profiles in foam cells through high-throughput sequencing technology. A total of 84 miRNAs were differentially expressed between RAW 264.7 macrophages and foam cells induced by ox-LDL. Thirty miRNAs were upregulated and 54 miRNAs were downregulated. GO terms and KEGG pathways analysis revealed that the target genes of most of DE miRNAs were mainly enriched in “cell differentiation,” “endocytosis,” “MAPK signaling pathway,” and “FoxO signaling pathway.” The target genes of some DE miRNAs were enriched in “Insulin signaling pathway,” “Hippo signaling pathway,” “TNF signaling pathway,” “NF-kappa B signaling pathway,” and “cell death.” Using bioinformatics analyses and dual-luciferase reporter assays, we found that miR-28a-5p and miR-30c-1-3p directly inhibited LRAD3 and LOX-1 mRNA expression through targeting the 3’UTR of LRAD3 and LOX-1 mRNA, respectively. Our study indicates that miRNAs are extensively involved in the formation of foam cells, and provides a valuable resource for further study the role of miRNAs in atherosclerosis.     

Gene coexpression networks analysis of sickle stroke risk

Stroke is one of the most destructive complications of sickle cell disease (SCD), and SCD is also the most common cause of childhood stroke. Sickle cell stroke is complex and has a genetic endothelial basis. Here, we further investigated this genetic basis using weighted gene coexpression network analysis. This systems biology approach revealed the correlation between coexpressed gene modules and sickle stroke risk. The pink module was significantly correlated with stroke risk and genes in this module were mainly related to GO:0044877 (protein-containing complex binding). In addition hub genes were identified through protein-protein interaction enrichment analysis, including CXCR7, VCAM1, CD44, BMP2, SMAD3, BCL2L1, ITPR2, ITPR3, etc. These hub genes were significantly enriched for three Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including “gastric acid secretion,” “pathways in cancer,” and “TGF- β signaling pathway.” Altogether, our results based on this innovative method provided some novel understanding of the pathology of sickle cell stroke. Hub genes identified in this study could be potential targets for screening and prevention of stroke risk in SCD children.     

The piRNA response to BmNPV infection in the silkworm fat body and midgut

Bombyx mori nucleopolyhedrovirus (BmNPV) is a DNA virus that causes huge losses to the silkworm industry but the piRNA responses during BmNPV infection in the silkworm remain uninvestigated. Here, silkworm piRNA profiles of uninfected and BmNPV-infected fat body and midgut were determined by high-through sequencing in the early stages of BmNPV infection. A total of 2675 and 3396 genome-derived piRNAs were identified from fat body and midgut, respectively. These genome-derived piRNAs mainly originated from unannotated instead of transposon regions in the silkworm genome. In total, 572 piRNAs were associated with 280 putative target genes in fat body and 805 piRNAs with 380 target genes in midgut. Compared to uninfected tissues, 322 and 129 piRNAs were significantly upregulated in BmNPV-infected fat body and midgut, respectively. In addition, 276 and 117 piRNAs were significantly downregulated. Moreover, differentially expressed (DE) piRNAs during BmNPV infection differed significantly between fat body and midgut. Putative DE piRNA–targeted genes were associated with “response to stimulus” and “environmental information processing” in fat body after infection with BmNPV, which may indicate an active piRNA response to BmNPV infection in fat body. This study may lay the foundation for future research of the potential roles of the piRNA pathway and specific piRNAs in BmNPV pathogenesis.     

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system.     

Key modules and hub genes identified by coexpression network analysis for revealing novel biomarkers for larynx squamous cell carcinoma

Larynx squamous cell carcinoma (LSCC) is the second most aggressive head and neck squamous cell carcinoma. Numerous genes have been identified to be aberrantly expressed during the development of LSCC. However, currently, researchers focus more on the individual molecule and downstream genes, leaving the coexpression among genes and key upstream disease driver genes unexploited. In this study, we applied weighted gene coexpression analysis (WGCNA) to decipher potential hub genes driving the development of LSCC. After downloading of LSCC microarray profile from gene expression omnibus, different expression analysis was performed, which was used to conduct functional enrichment analysis. Then, we applied WGCNA to highlight the hub genes which were relevant to the carcinogenesis and progression. A total of 2858 differentially expressed genes were identified in LSCC samples compared with adjacent non-neoplastic tissues. WGCNA revealed three LSCC set-specific modules having significant Kyoto Encyclopedia of Genes and Genomes enrichment effect, including pink, cyan, and black module. Nine hub genes were identified to be crucial in LSCC onset and progression, which may assist clinical decisions and serve as potential targets for LSCC treatment.     

Repeatedly identified differentially expressed proteins (RIDEPs) from antibody microarray proteomic analysis

Antibody microarrays are powerful new tools in the field of comparative proteomics. The success of the biomarker discovery pipeline relies on the quality of data generated in the discovery phase and careful selection of proteins for the verification phase. Recent meta-analyses found a number of repeatedly identified differentially expressed proteins (RIDEPs) from mass spectrometry-based proteomics research in a range of species. We aimed to assess RIDEPs based on antibody microarray data-sets. A total of 13 independent experiments encompassing a range of oncology-related research on human tissue, cells or cell lines from 5 distinct sample groups were performed utilising a commercial 725 antibody microarray platform (Panorama XPRESS Profiler725; Sigma-Aldrich). Analysis of all microarray slides was carried out by the same individual to reduce inter-observer variability. Fold changes of ≥1.8 were considered significant. A total of 13 RIDEPs were seen, each appearing in at least 4/13 (30%) antibody microarray analyses from at least 2 out of 5 experimental sample groups. The phenomenon of RIDEPs may exist in antibody microarray proteomics and we report a preliminary list of 13 RIDEPs from the XPRESS Profiler725 platform. This information will be useful when interpreting experimental data and considering which DEPs should be prioritised for verification.     

Identifying differentially expressed genes in cancer patients using a non-parameter Ising model

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.     

乳腺癌转移相关基因与功能识别的可重复性

基于基因表达谱识别乳腺癌转移相关差异表达基因及其功能时,由于基因表达在个体间的变异相对较高而样本量相对较少,由不同研究识别的差异表达基因的可重复性较低。本文基于两套乳腺癌转移基因表达谱,评价两组差异表达基因及其所富集的功能的可重复性。结果显示:在两套表达谱中识别的差异表达基因的表达改变方向高度一致并具有显著的表达相关性;基于两组差异表达基因识别的转移相关功能在两套表达谱中高度可重复,主要涉及细胞分裂、细胞周期、DNA复制、染色体分离、磷酸肌醇介导信号转导和DNA损伤刺激应答等。     

Identification of gene coexpression modules,hub genes,and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway “natural killer cell-mediated cytotoxicity” were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the “natural killer cell–mediated cytotoxicity” pathway.     

Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses

Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. The present study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process. A total of 33 rats were divided randomly into control (n=3), mild contusion (n=15), and severe contusion (n=15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n=3 per subgroup). A total of 2844 and 2298 differentially expressed genes (DEGs) were identified using microarray analyses in the mild and severe contusions, respectively. From the analysis of the 1620 coexpressed genes in mildly and severely contused muscle, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. The functional analyses of genes in the functional modules and temporal clusters were performed, and the hub genes in each module–cluster pair were identified. Interestingly, we found that genes down-regulated at 24-48 h were largely associated with metabolic processes, especially of the oxidative phosphorylation (OXPHOS), which has been rarely reported. These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.     

Investigation of key microRNAs associated with hepatocellular carcinoma using small RNA-seq data

To identify key microRNAs (miRNAs) associated with hepatocellular carcinoma (HCC) using small RNA-seq data. Small RNA-seq data for two HCC samples and two normal samples were downloaded from NCBI Gene Expression Omnibus. MiRNAs were identified through database search. Differentially expressed miRNAs were screened out with t test and their target genes were retrieved. Functional enrichment analysis was performed to uncover their biological functions. Regulatory networks and core metabolic networks were also constructed to present the global patterns. In addition, new miRNAs and their target genes were predicted. A total of 59 differentially expressed miRNAs were obtained, 12 up-regulated and 47 down-regulated. A total of 3,306 target genes were retrieved for eight miRNAs. Pathway enrichment analysis for the target genes showed that “pathways in cancer” and “MAPK signaling pathway” were significantly over-represented. Functional enrichment analysis found that “biological regulation” and “macromolecule modification” were significantly related to the target genes. Two regulatory networks were constructed for up- and down-regulated differentially expressed miRNAs with information from Ingenuity Pathway Analysis database. Two metabolic networks were also established based upon “pathways in cancer” and “MAPK signaling pathway”, consisting of miRNAs, target genes, compounds and others genes. Moreover, a number of new miRNAs and relevant target genes were predicted. Our study discloses a number of miRNAs as well as genes which may be involved in the development of HCC and these findings are beneficial in guiding future researches.     

Identification of virulence factors in vibrio vulnificus by comparative transcriptomic analyses between clinical and environmental isolates using cDNA microarray

We compared the gene expression among four clinical and five environmental V. vulnificus isolates, using a cDNA microarray containing 131 genes possibly associated with pathogenicity, transport, signal transduction, and gene regulations in the pathogen. cDNAs from total RNAs of these isolates were hybridized into the cDNA microarray using the cDNA of the wild-type strain MO6/24-O as a reference. We focused on selecting differentially expressed (DE) genes between clinical and environmental isolates using a modified t-statistic. We could detect two statistically significant DE genes between virulent isolates and less-virulent isolates with a marginal statistical significance (pvalue of 0.008). These were genes putatively encoding pilin and adenlyate cylase. Real time-PCR confirmed that these two selected genes transcribed in significantly higher levels in virulent isolates than in less-virulent isolates. Mutants with lesions in the gene encoding pilin showed significantly higher LD50 values than that of wild type.     

肾肿瘤相关基因的共表达网络构建与分析

作为泌尿系统常见的肿瘤之一,肾肿瘤发病率在逐年上升。针对Affymetrix hgu133b的基因芯片数据进行差异表达基因筛选,应用加权基因共表达网络分析算法构建肾肿瘤差异表达基因的共表达网络。分析肾部正常组织和肿瘤组织差异表达基因之间的关联模式;选取与肿瘤发生关联程度最高的模块,筛选枢纽基因。最后,针对枢纽基因进行基因本体富集分析。细胞衰老是抑制肿瘤发生的主要机制之一,分析结果显示枢纽基因PLA2R1和TBX3与细胞衰老有关,可能对肾肿瘤的形成具有重要影响。该结果与基因PLA2R1通过促进细胞衰老抑制肾部肿瘤发生的研究结论一致。     

DNA methylation-based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients

Currently, there are few studies on patients with nonsmoking lung adenocarcinoma, and the pathogenesis is still unclear. The role of DNA methylation in the pathogenesis of cancer is gradually being recognized. The purpose of this study was to determine the abnormal methylation genes and pathways involved in nonsmoking lung adenocarcinoma patients. Gene expression microarray data (GSE10072, GSE43458) and gene methylation microarray data (GSE62948) were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes were obtained through GEO2R. Next, we analyzed the function and enrichment of the selected genes using Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) networks were constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized in Cytoscape. Finally, we performed module analysis of the PPI network using Molecular Complex Detection. And we obtained 10 hub genes by Cytoscape Centiscape. We analyzed the independent prognostic value of each hub gene in nonsmoking nonsmall cell lung cancer patients through Kaplan-Meier plotter. Seven hub genes (CXCL12, CDH1, CASP3, CREB1, COL1A1, ERBB2, and ENO2) were closely related to the overall survival time. This study provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of nonsmoking lung adenocarcinoma patients. Hub genes with prognostic value could be selected as effective biomarkers for timely diagnosis and prognostic of nonsmoking lung adenocarcinoma patients.