Comparing gene expression profiles of Kashin-Beck and Keshan diseases occurring within the same endemic areas of China

In this study, differentially expressed genes in peripheral blood from patients with Kashin-Beck disease and Keshan disease were compared to further investigate the etiology and pathogenesis of both diseases, which occur in a common endemic area of China. Twenty Kashin-Beck disease patients and 12 healthy controls, and 16 Keshan disease patients and 16 healthy controls, were grouped into four pairs. Patients and controls were selected from common endemic areas for the two diseases. Total RNA was isolated from peripheral blood mononuclear cells from all patients and controls, and gene expression profiles analyzed by oligonucleotide microarrays. Sixteen genes differentially expressed in both Kashin-Beck disease and Keshan disease (versus controls) were identified, and comprised nine genes showing synchronous and seven asynchronous expression. The Comparative Toxicogenomics Database shows that expression and biological function of these genes can be affected by multiple environmental factors, including mycotoxin and selenium content, potential environmental risk factors for the two diseases. Thus, these shared differentially expressed genes may contribute to the distinct organ lesions, caused by common environmental risk factors of Kashin-Beck disease and Keshan disease.     

Genome-wide gene expression analysis suggests an important role of hypoxia in the pathogenesis of endemic osteochondropathy Kashin-Beck disease

Kashin-Beck Disease (KBD) is an endemic osteochondropathy, the pathogenesis of which remains unclear now. In this study, we compared gene expression profiles of articular cartilage derived respectively from KBD patients and normal controls. Total RNA were isolated, amplified, labeled and hybridized to Agilent human 1A 22 k whole genome microarray chip. qRT-PCR was conducted to validate our microarray data. We detected 57 up-regulated genes (ratios ≥2.0) and 24 down-regulated genes (ratios ≤0.5) in KBD cartilage. To further identify the key genes involved in the pathogenesis of KBD, Bayesian analysis of variance for microarrays (BAM) software was applied and identified 12 potential key genes with an average ratio 6.64, involved in apoptosis, metabolism, cytokine & growth factor and cytoskeleton & cell movement. Gene Set Enrichment Analysis (GSEA) software was used to identify differently expressed gene ontology categories and pathways. GSEA found that a set of apoptosis, hypoxia and mitochondrial function related gene ontology categories and pathways were significantly up-regulated in KBD compared to normal controls. Based on the results of this study, we suggest that chronic hypoxia-induced mitochondrial damage and apoptosis might play an important role in the pathogenesis of KBD. Our efforts may help to understand the pathogenesis of KBD as well as other osteoarthrosis with similar articular cartilage lesions.     

Systems genetics approach reveals candidate genes for parasite resistance from quantitative trait loci studies in agricultural species

A systems genetics approach combining pathway analysis of quantitative trait loci (QTL) and gene expression information has provided strong evidence for common pathways associated with genetic resistance to internal parasites. Gene data, collected from published QTL regions in sheep, cattle, mice, rats and humans, and microarray data from sheep, were converted to human Entrez Gene IDs and compared to the KEGG pathway database. Selection of pathways from QTL data was based on a selection index that ensured that the selected pathways were in all species and the majority of the projects overall and within species. Pathways with either up- and down-regulated genes, primarily up-regulated genes or primarily down-regulated genes, were selected from gene expression data. After comparing the data sets independently, the pathways from each data set were compared and the common set of pathways and genes was identified. Comparisons within data sets identified 21 pathways from QTL data and 66 pathways from gene expression data. Both selected sets were enriched with pathways involved in immune functions, disease and cell responses to signals. The analysis identified 14 pathways that were common between QTL and gene expression data, and four directly associated with IFNγ or MHCII, with 31 common genes, including three MHCII genes. In conclusion, a systems genetics approach combining data from multiple QTL and gene expression projects led to the discovery of common pathways associated with genetic resistance to internal parasites. This systems genetics approach may prove significant for the discovery of candidate genes for many other multifactorial, economically important traits.     

Identifying causal genes and dysregulated pathways in complex diseases

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.     

环氧化物水解酶2在肝细胞癌中的表达水平与功能作用

本研究通过公共数据和实验数据,全面分析环氧化物水解酶2(epoxide hydrolase 2, EPHX2)在肝细胞癌中的表达情况、功能作用以及预后意义。利用GEO和MitoCarta数据集,筛选肝细胞癌中呈差异表达的线粒体相关基因;利用TCGA数据库分析EPHX2及其相关基因在肝细胞癌中的表达水平;运行R包绘制Kaplan-Meier生存曲线和功能富集分析;基于STRING和GSEA构建蛋白质互作网络和基因集富集分析;荧光定量PCR和GEO数据集验证EPHX2在肝细胞癌中的表达水平。本研究共筛选得到15个在肝细胞癌中呈差异表达的线粒体相关基因。EPHX2在肝细胞癌组织中的表达水平显著降低(P<0.01)。EPHX2表达水平与肝癌患者性别、分期和级别有关,而与年龄、T分期等因素无关。与EPHX2低表达组肝癌患者相比,EPHX2高表达组肝癌患者预后较好。功能富集结果显示,EPHX2与补体途径、脂肪酸降解等信号通路有关。蛋白质互作网络结果显示,EPHX2与HAO1、AGXT、ACOX1、GSTκ1、SCP-2、CAT、CYP2C8,CYP2C9,CYP2B6,和CYP2J2等密切相关。GSEA结果显示,EPHX2低表达组与肝癌细胞增殖、肝癌复发等基因集正相关。荧光定量PCR和GEO数据集验证结果显示,EPHX2在肝细胞癌组织和肝癌细胞株中呈显著低表达。EPHX2在肝细胞癌中呈显著低表达,提示其可能在肝细胞癌发生发展过程中发挥抑癌基因作用,但具体作用机制还需进一步验证。     

Identification of differential gene expression profiles in placentas from preeclamptic pregnancies versus normal pregnancies by DNA microarrays

The purpose of this study was to perform a comprehensive analysis of gene expression profiles in placentas from preeclamptic pregnancies versus normal placentas. Placental tissues were obtained immediately after delivery from women with normal pregnancies (n=6) and patients with preeclampsia (n=6). The gene expression profile was assessed by oligonucleotide-based DNA microarrays and validated by quantitative real-time RT-PCR. Functional relationships and canonical pathways/networks of differentially-expressed genes were evaluated by GeneSpring? GX 11.0 software, and ingenuity pathways analysis (IPA). A total of 939 genes were identified that differed significantly in expression: 483 genes were upregulated and 456 genes were downregulated in preeclamptic placentas compared with normal placentas (fold change ≥ 2 and p<0.05 by unpaired t-test corrected with Bonferroni multiple testing). The IPA revealed that the primary molecular functions of these genes are involved in cellular function and maintenance, cellular development, cell signaling, and lipid metabolism. Pathway analysis provided evidence that a number of biological pathways, including Notch, Wnt, NF-κB, and transforming growth factor-β (TGF-β) signaling pathways, were aberrantly regulated in preeclampsia. In conclusion, our microarray analysis represents a comprehensive list of placental gene expression profiles and various dysregulated signaling pathways that are altered in preeclampsia. These observations may provide the basis for developing novel predictive, diagnostic, and prognostic biomarkers of preeclampsia to improve reproductive outcomes and reduce the risk for subsequent cardiovascular disease.     

Integration of expression profiles and genetic mapping data to identify candidate genes in intracranial aneurysm.

Intracranial aneurysm (IA) is a complex genetic disease for which, to date, 10 loci have been identified by linkage. Identification of the risk-conferring genes in the loci has proven difficult, since the regions often contain several hundreds of genes. An approach to prioritize positional candidate genes for further studies is to use gene expression data from diseased and nondiseased tissue. Genes that are not expressed, either in diseased or nondiseased tissue, are ranked as unlikely to contribute to the disease. We demonstrate an approach for integrating expression and genetic mapping data to identify likely pathways involved in the pathogenesis of a disease. We used expression profiles for IAs and nonaneurysmal intracranial arteries (IVs) together with the 10 reported linkage intervals for IA. Expressed genes were analyzed for membership in Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways. The 10 IA loci harbor 1,858 candidate genes, of which 1,561 (84%) were represented on the microarrays. We identified 810 positional candidate genes for IA that were expressed in IVs or IAs. Pathway information was available for 294 of these genes and involved 32 KEGG biological function pathways represented on at least 2 loci. A likelihood-based score was calculated to rank pathways for involvement in the pathogenesis of IA. Adherens junction, MAPK, and Notch signaling pathways ranked high. Integration of gene expression profiles with genetic mapping data for IA provides an approach to identify candidate genes that are more likely to function in the pathology of IA.     

Bioinformatics analysis of the gene expression profile in Bladder carcinoma

Bladder carcinoma, which has the ninth highest incidence among malignant tumors in the world, is a complex, multifactorial disease. The malignant transformation of bladder cells results from DNA mutations and alterations in gene expression levels. In this work, we used a bioinformatics approach to investigate the molecular mechanisms of bladder carcinoma. Biochips downloaded from the Gene Expression Omnibus (GEO) were used to analyze the gene expression profile in urinary bladder cells from individuals with carcinoma. The gene expression profile of normal genomes was used as a control. The analysis of gene expression revealed important alterations in genes involved in biological processes and metabolic pathways. We also identified some small molecules capable of reversing the altered gene expression in bladder carcinoma; these molecules could provide a basis for future therapies for the treatment of this disease.     

A computational approach to measuring coherence of gene expression in pathways

This study uses a computational approach to analyze coherence of expression of genes in pathways. Microarray data were analyzed with respect to coherent gene expression in a group of genes defined as a pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Our hypothesis is that genes in the same pathway are more likely to be coordinately regulated than a randomly selected gene set. A correlation coefficient for each pair of genes in a pathway was estimated based on gene expression in normal or tumor samples, and statistically significant correlation coefficients were identified. The coherence indicator was defined as the ratio of the number of gene pairs in the pathway whose correlation coefficients are significant, divided by the total number of gene pairs in the pathway. We defined all genes that appeared in the KEGG pathways as a reference gene set. Our analysis indicated that the mean coherence indicator of pathways is significantly larger than the mean coherence indicator of random gene sets drawn from the reference gene set. Thus, the result supports our hypothesis. The significance of each individual pathway of n genes was evaluated by comparing its coherence indicator with coherence indicators of 1000 random permutation sets of n genes chosen from the reference gene set. We analyzed three data sets: two Affymetrix microarrays and one cDNA microarray. For each of the three data sets, statistically significant pathways were identified among all KEGG pathways. Seven of 96 pathways had a significant coherence indicator in normal tissue and 14 of 96 pathways had a significant coherence indicator in tumor tissue in all three data sets. The increase in the number of pathways with significant coherence indicators may reflect the fact that tumor cells have a higher rate of metabolism than normal cells. Five pathways involved in oxidative phosphorylation, ATP synthesis, protein synthesis, or RNA synthesis were coherent in both normal and tumor tissue, demonstrating that these are essential genes, a high level of expression of which is required regardless of cell type.     

Match/X,A Gene Expression Pattern Recognition Algorithm Used to Identify Genes Which May be Related to CDC2 Function and Cell Cycle Regulation

Large-scale microarray gene expression studies can provide insight into complex genetic networks and biological pathways. A comprehensive gene expression database was constructed using Affymetrix GeneChip microarrays and RNA isolated from more than 6,400 distinct normal and diseased human tissues. These individual patient samples were grouped into over 700 sample sets based on common tissue and disease morphologies, and each set contained averaged expression data for over 45,000 gene probe sets representing more than 33,000 known human genes. Sample sets were compared to each other in more than 750 normal vs. disease pairwise comparisons. Relative up or down-regulation patterns of genes across these pairwise comparisons provided unique expression fingerprints that could be compared and matched to a gene of interest using the Match/X algorithm. This algorithm uses the kappa statistic to compute correlations between genes and calculate a distance score between a gene of interest and all other genes in the database. Using cdc2 as a query gene, we identified several hundred genes that had similar expression patterns and highly correlated distance scores. Most of these genes were known components of the cell cycle involved in G2/M progression, spindle function or chromosome arrangement. Some of the identified genes had unknown biological functions but may be related to cdc2 mediated mechanism based on their closely correlated distance scores. This algorithm may provide novel insights into unknown gene function based on correlation to expression profiles of known genes and can identify elements of cellular pathways and gene interactions in a high throughput fashion.     

Identifying consensus disease pathways in Parkinson's disease using an integrative systems biology approach

Parkinson's disease (PD) has had six genome-wide association studies (GWAS) conducted as well as several gene expression studies. However, only variants in MAPT and SNCA have been consistently replicated. To improve the utility of these approaches, we applied pathway analyses integrating both GWAS and gene expression. The top 5000 SNPs (p<0.01) from a joint analysis of three existing PD GWAS were identified and each assigned to a gene. For gene expression, rather than the traditional comparison of one anatomical region between sets of patients and controls, we identified differentially expressed genes between adjacent Braak regions in each individual and adjusted using average control expression profiles. Over-represented pathways were calculated using a hyper-geometric statistical comparison. An integrated, systems meta-analysis of the over-represented pathways combined the expression and GWAS results using a Fisher's combined probability test. Four of the top seven pathways from each approach were identical. The top three pathways in the meta-analysis, with their corrected p-values, were axonal guidance (p = 2.8E-07), focal adhesion (p = 7.7E-06) and calcium signaling (p = 2.9E-05). These results support that a systems biology (pathway) approach will provide additional insight into the genetic etiology of PD and that these pathways have both biological and statistical support to be important in PD.