265 research outputs found

    Polymorphisms in the interleukin-10 gene cluster are possibly involved in the increased risk for major depressive disorder

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Innate immune inflammatory response is suggested to have a role in the pathogenesis of major depressive disorder (MDD). Interleukin (IL)-10 family cytokines IL-10, IL-19, IL-20, and IL-24 are all implicated in the inflammatory processes and polymorphisms in respective genes have been associated with various immunopathological conditions. This study was carried out to investigate whether single-nucleotide polymorphisms (SNPs) in these genes are also associated with MDD.</p> <p>Methods</p> <p>Case-control association study was performed with seven SNPs from the <it>IL10 </it>gene cluster. 153 patients with MDD and 277 healthy control individuals were recruited.</p> <p>Results</p> <p>None of the selected SNPs were individually associated with MDD. The linkage disequilibrium (LD) analysis indicated the existence of two recombination sites in the <it>IL10 </it>gene cluster, thus confirming the formerly established LD pattern of this genomic region. This also created two haplotype blocks, both consisting of three SNPs. Additionally, the haplotype analysis detected a significantly higher frequency of block 2 (<it>IL20 </it>and <it>IL24 </it>genes) haplotype TGC in the patients group compared to healthy control individuals (P = 0.0097).</p> <p>Conclusion</p> <p>Our study established increased risk for MDD related to the <it>IL20 </it>and <it>IL24 </it>haplotype and suggests that cytokines may contribute to the pathogenesis of MDD. Since none of the block 2 SNPs were individually associated with MDD, it is possible that other polymorphisms linked to them contribute to the disease susceptibility. Future studies are needed to confirm the results and to find the possible functional explanation.</p

    Predicting Housekeeping Genes Based on Fourier Analysis

    Get PDF
    Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs

    Compressed representation of a partially defined integer function over multiple arguments

    Get PDF
    In OLAP (OnLine Analitical Processing) data are analysed in an n-dimensional cube. The cube may be represented as a partially defined function over n arguments. Considering that often the function is not defined everywhere, we ask: is there a known way of representing the function or the points in which it is defined, in a more compact manner than the trivial one

    Hypomethylation of Intragenic LINE-1 Represses Transcription in Cancer Cells through AGO2

    Get PDF
    In human cancers, the methylation of long interspersed nuclear element -1 (LINE-1 or L1) retrotransposons is reduced. This occurs within the context of genome wide hypomethylation, and although it is common, its role is poorly understood. L1s are widely distributed both inside and outside of genes, intragenic and intergenic, respectively. Interestingly, the insertion of active full-length L1 sequences into host gene introns disrupts gene expression. Here, we evaluated if intragenic L1 hypomethylation influences their host gene expression in cancer. First, we extracted data from L1base (http://l1base.molgen.mpg.de), a database containing putatively active L1 insertions, and compared intragenic and intergenic L1 characters. We found that intragenic L1 sequences have been conserved across evolutionary time with respect to transcriptional activity and CpG dinucleotide sites for mammalian DNA methylation. Then, we compared regulated mRNA levels of cells from two different experiments available from Gene Expression Omnibus (GEO), a database repository of high throughput gene expression data, (http://www.ncbi.nlm.nih.gov/geo) by chi-square. The odds ratio of down-regulated genes between demethylated normal bronchial epithelium and lung cancer was high (p<1Eβˆ’27; ORβ€Š=β€Š3.14; 95% CIβ€Š=β€Š2.54–3.88), suggesting cancer genome wide hypomethylation down-regulating gene expression. Comprehensive analysis between L1 locations and gene expression showed that expression of genes containing L1s had a significantly higher likelihood to be repressed in cancer and hypomethylated normal cells. In contrast, many mRNAs derived from genes containing L1s are elevated in Argonaute 2 (AGO2 or EIF2C2)-depleted cells. Hypomethylated L1s increase L1 mRNA levels. Finally, we found that AGO2 targets intronic L1 pre-mRNA complexes and represses cancer genes. These findings represent one of the mechanisms of cancer genome wide hypomethylation altering gene expression. Hypomethylated intragenic L1s are a nuclear siRNA mediated cis-regulatory element that can repress genes. This epigenetic regulation of retrotransposons likely influences many aspects of genomic biology

    Long interspersed nuclear element-1 hypomethylation in cancer: biology and clinical applications

    Get PDF
    Epigenetic changes in long interspersed nuclear element-1s (LINE-1s or L1s) occur early during the process of carcinogenesis. A lower methylation level (hypomethylation) of LINE-1 is common in most cancers, and the methylation level is further decreased in more advanced cancers. Consequently, several previous studies have suggested the use of LINE-1 hypomethylation levels in cancer screening, risk assessment, tumor staging, and prognostic prediction. Epigenomic changes are complex, and global hypomethylation influences LINE-1s in a generalized fashion. However, the methylation levels of some loci are dependent on their locations. The consequences of LINE-1 hypomethylation are genomic instability and alteration of gene expression. There are several mechanisms that promote both of these consequences in cis. Therefore, the methylation levels of different sets of LINE-1s may represent certain phenotypes. Furthermore, the methylation levels of specific sets of LINE-1s may indicate carcinogenesis-dependent hypomethylation. LINE-1 methylation pattern analysis can classify LINE-1s into one of three classes based on the number of methylated CpG dinucleotides. These classes include hypermethylation, partial methylation, and hypomethylation. The number of partial and hypermethylated loci, but not hypomethylated LINE-1s, is different among normal cell types. Consequently, the number of hypomethylated loci is a more promising marker than methylation level in the detection of cancer DNA. Further genome-wide studies to measure the methylation level of each LINE-1 locus may improve PCR-based methylation analysis to allow for a more specific and sensitive detection of cancer DNA or for an analysis of certain cancer phenotypes
    • …
    corecore