Impact of Time Delay in Processing Blood Sample on Next Generation Sequencing for Transcriptome Analysis

No abstract available.

Association between Mitofusin 2 Gene Polymorphisms and Late-Onset Alzheimer's Disease in the Korean Population

OBJECTIVE: Mitochondrial dysfunction is a prominent and early feature of Alzheimer's disease (AD). The morphologic changes observed in the AD brain could be caused by a failure of mitochondrial fusion mechanisms. The aim of this study was to investigate whether genetic polymorphisms of two genes involved in mitochondrial fusion mechanisms, optic atrophy 1 (OPA1) and mitofusin 2 (MFN2), were associated with AD in the Korean population by analyzing genotypes and allele frequencies. METHODS: One coding single nucleotide polymorphism (SNP) in the MFN2, rs1042837, and two coding SNPs in the OPA1, rs7624750 and rs9851685, were compared between 165 patients with AD (83 men and 82 women, mean age 72.3±4.41) and 186 healthy control subjects (82 men and 104 women, mean age 76.5±5.98). RESULTS: Among these three SNPs, rs1042837 showed statistically significant differences in allele frequency, and genotype frequency in the co-dominant 1 model and in the dominant model. CONCLUSION: These results suggest that the rs1042837 polymorphism in MFN2 may be involved in the pathogenesis of AD.

Calibrating Thresholds to Improve the Detection Accuracy of Putative Transcription Factor Binding Sites

To understand the mechanism of transcriptional regulation, it is essential to detect promoters and regulatory elements. Various kinds of methods have been introduced to improve the prediction accuracy of regulatory elements. Since there are few experimentally validated regulatory elements, previous studies have used criteria based solely on the level of scores over background sequences. However, selecting the detection criteria for different prediction methods is not feasible. Here, we studied the calibration of thresholds to improve regulatory element prediction. We predicted a regulatory element using MATCH, which is a powerful tool for transcription factor binding site (TFBS) detection. To increase the prediction accuracy, we used a regulatory potential (RP) score measuring the similarity of patterns in alignments to those in known regulatory regions. Next, we calibrated the thresholds to find relevant scores, increasing the true positives while decreasing possible false positives. By applying various thresholds, we compared predicted regulatory elements with validated regulatory elements from the Open Regulatory Annotation (ORegAnno) database. The predicted regulators by the selected threshold were validated through enrichment analysis of muscle-specific gene sets from the Tissue-Specific Transcripts and Genes (T-STAG) database. We found 14 known muscle-specific regulators with a less than a 5% false discovery rate (FDR) in a single TFBS analysis, as well as known transcription factor combinations in our combinatorial TFBS analysis.

FCAnalyzer: A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms

Numerous studies have reported that genes with similar expression patterns are co-regulated. From gene expression data, we have assumed that genes having similar expression pattern would share similar transcription factor binding sites (TFBSs). These function as the binding regions for transcription factors (TFs) and thereby regulate gene expression. In this context, various analysis tools have been developed. However, they have shortcomings in the combined analysis of expression patterns and significant TFBSs and in the functional analysis of target genes of significantly overrepresented putative regulators. In this study, we present a web-based A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms (FCAnalyzer). This system integrates microarray clustering data with similar expression patterns, and TFBS data in each cluster. FCAnalyzer is designed to perform two independent clustering procedures. The first process clusters gene expression profiles using the K-means clustering method, and the second process clusters predicted TFBSs in the upstream region of previously clustered genes using the hierarchical biclustering method for simultaneous grouping of genes and samples. This system offers retrieved information for predicted TFBSs in each cluster using Match(TM) in the TRANSFAC database. We used gene ontology term analysis for functional annotation of genes in the same cluster. We also provide the user with a combinatorial TFBS analysis of TFBS pairs. The enrichment of TFBS analysis and GO term analysis is statistically by the calculation of P values based on Fisher's exact test, hypergeometric distribution and Bonferroni correction. FCAnalyzer is a web-based, user-friendly functional clustering analysis system that facilitates the transcriptional regulatory analysis of co-expressed genes. This system presents the analyses of clustered genes, significant TFBSs, significantly enriched TFBS combinations, their target genes and TFBS-TF pairs.

Glucose transporter 1 (GLUT1) expression is associated with intestinal type of gastric carcinoma

Increased expression of glucose transporter1 (GLUT1) has been reported in many human cancers. We hypothesized that the degree of GLUT1 might provide a useful biological information in gastric adenocarcinoma. RT-PCR and immunostaining were used to analyze GLUT1 expression in gastric cancer. RT-PCR showed GLUT1 expression was not largely detected in normal gastric tissue but was detected in cancerous gastric tissue of counterpart. By immunohistochemistry, GLUT1 protein was absent in normal gastric epithelium and intestinal metaplasia. 11 of 65 patients with gastric adenocarcinoma had specific GLUT1 immunostaining in a plasma membrane pattern with varied intensities. GLUT1 protein did not show any significant correlation with tumor stage and nodal metastasis (p+AD4-0.05 by Mann-Whitney test). However, the positive immunostaining for GLUT1 is associated with intestinal differentiation (p+AD0-0.003). Our results suggest that GLUT1 protein is associated with intestinal type of gastric cancer.

Glucose transporter 1 (GLUT1) expression is associated with intestinal type of gastric carcinoma

Increased expression of glucose transporter1 (GLUT1) has been reported in many human cancers. We hypothesized that the degree of GLUT1 might provide a useful biological information in gastric adenocarcinoma. RT-PCR and immunostaining were used to analyze GLUT1 expression in gastric cancer. RT-PCR showed GLUT1 expression was not largely detected in normal gastric tissue but was detected in cancerous gastric tissue of counterpart. By immunohistochemistry, GLUT1 protein was absent in normal gastric epithelium and intestinal metaplasia. 11 of 65 patients with gastric adenocarcinoma had specific GLUT1 immunostaining in a plasma membrane pattern with varied intensities. GLUT1 protein did not show any significant correlation with tumor stage and nodal metastasis (p+AD4-0.05 by Mann-Whitney test). However, the positive immunostaining for GLUT1 is associated with intestinal differentiation (p+AD0-0.003). Our results suggest that GLUT1 protein is associated with intestinal type of gastric cancer.