Dataset Information

DNA methylation in transcriptional repression of two differentially expressed X-linked genes, GPC3 and SYBL1.

ABSTRACT: Methylation of CpG islands is an established transcriptional repressive mechanism and is a feature of silencing in X chromosome inactivation. Housekeeping genes that are subject to X inactivation exhibit differential methylation of their CpG islands such that the inactive alleles are hypermethylated. In this report, we examine two contrasting X-linked genes with CpG islands for regulation by DNA methylation: SYBL1, a housekeeping gene in the Xq pseudoautosomal region, and GPC3, a tissue-specific gene in Xq26 that is implicated in the etiology of the Simpson-Golabi-Behmel overgrowth syndrome. We observed that in vitro methylation of either the SYBL1 or the GPC3 promoter resulted in repression of reporter constructs. In normal contexts, we found that both the Y and inactive X alleles of SYBL1 are repressed and hypermethylated, whereas the active X allele is expressed and unmethylated. Furthermore, the Y and inactive X alleles of SYBL1 were derepressed by treatment with the demethylating agent azadeoxycytidine. GPC3 is also subject to X inactivation, and the active X allele is unmethylated in nonexpressing leukocytes as well as in an expressing cell line, suggesting that methylation is not involved in the tissue-specific repression of this allele. The inactive X allele, however, is hypermethylated in leukocytes, presumably reflecting early X inactivation events that become important for gene dosage in expressing lineages. These and other data suggest that all CpG islands on Xq, including the pseudoautosomal region, are subject to X inactivation-induced methylation. Additionally, methylation of SYBL1 on Yq may derive from a process related to X inactivation that targets large chromatin domains for transcriptional repression.

SUBMITTER: Huber R

PROVIDER: S-EPMC15185 | biostudies-literature | 1999 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DNA methylation in transcriptional repression of two differentially expressed X-linked genes, GPC3 and SYBL1.

Huber R R Hansen R S RS Strazzullo M M Pengue G G Mazzarella R R D'Urso M M Schlessinger D D Pilia G G Gartler S M SM D'Esposito M M

Proceedings of the National Academy of Sciences of the United States of America 19990101 2

Methylation of CpG islands is an established transcriptional repressive mechanism and is a feature of silencing in X chromosome inactivation. Housekeeping genes that are subject to X inactivation exhibit differential methylation of their CpG islands such that the inactive alleles are hypermethylated. In this report, we examine two contrasting X-linked genes with CpG islands for regulation by DNA methylation: SYBL1, a housekeeping gene in the Xq pseudoautosomal region, and GPC3, a tissue-specific ...[more]

PMID: 9892682

Similar Datasets

Project description:ObjectiveTo identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in RA by integrated analysis of DNA methylation and RNA-Seq data.MethodsThe transcription and DNA methylation profiles of 9 RA and 15 OA synovial tissue were generated by RNA-Seq and Illumina 850K DNA methylation BeadChip. Gene set enrichment analysis (GSEA) and Weighted gene co-expression network analysis (WGCNA) were used to analyze methylation-regulated expressed genes by R software. The differentially expressed genes (DEGs), differentially methylated probes (DMPs), differentially methylated genes (DMGs) were analyzed by DESeq and ChAMP R package. The functional correlation of MeDEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction (PPI) network of MeDEGs was constructed by STRING and Reactome FI Cytoscape Plugin. Correlation analysis between methylation level and mRNA expression was conducted with R software.ResultsA total of 17,736 genes, 25,578 methylated genes and 755,852 methylation probes were detected. A total of 16,421 methylation-regulated expressed genes were obtained. The GSEA showed that these genes are associated with activation of immune response, adaptive immune response, Inflammatory response in C5 (ontology gene sets). For KEGG analysis, these genes are associated with rheumatoid arthritis, NF-kappa B signaling pathway, T cell receptor signaling pathway. The WGCNA showed that the turquoise module exhibited the strongest correlation with RA (R = 0.78, P = 1.27 × 10- 05), 660 genes were screened in the turquoise module. A total of 707 MeDEGs were obtained. GO analysis showed that MeDEGs were enriched in signal transduction, cell adhesion for BP, enriched in plasma membrane, integral component of membrane for CC, and enriched in identical protein binding, calcium ion binding for MF. The KEGG pathway analysis showed that the MeDEGs were enriched in calcium signaling pathway, T cell receptor signaling pathway, NF-kappa B signaling pathway, Rheumatoid arthritis. The PPI network containing 706 nodes and 882 edges, and the enrichment p value < 1.0 × 10- 16. With Cytoscape, based on the range of more than 10 genes, a total of 8 modules were screened out. Spearman correlation analysis showed RGS1(cg10718027), RGS1(cg02586212), RGS1(cg10861751) were significantly correlated with RA.ConclusionsRGS1 can be used as novel methylated biomarkers for RA.

Project description:Background: Biochemical recurrence (BCR) after radical prostatectomy indicates poor prognosis in patients with prostate cancer (PCA). DNA methylation (DNAm) is a critical factor in tumorigenesis and has attracted attention as a biomarker for the diagnosis, treatment, and prognosis of PCA. However, the predictive value of DNAm-derived differentially expressed genes (DMGs) in PCA with BCR remains elusive. Methods: We filtered the methylated genes and the differentially expressed genes (DGEs) for more than 1,000 clinical samples from the TCGA cohort using the chAMP and DESeq2 packages of R language, respectively. Next, we integrated the DNAm beta value and gene expression data with the Mithymix package of R language to obtain the DMGs. Then, 1,000 times Cox LASSO regression with 10-fold cross validation was performed to screen signature DMGs and establish a predictive classifier. Univariate and multivariate cox regressive analyses were used to identify the prognostic factors to build a predictive model, and its performance was measured by receiver operating characteristic, calibration curves, and Harrell's concordance index (C-index). Additionally, a GEO dataset was used to validate the prognostic classifier. Results: One hundred DMGs were mined using the chAMP and Methymix packages of R language. Of these, seven DMGs (CCK, CD38, CYP27A1, EID3, HABP2, LRRC4, and LY6G6D) were identified to build the prognostic classifier (Classifier) through LASSO analysis. Moreover, univariate and multivariate Cox regression analysis determined that the Classifier and pathological T stage (pathological_T) were independent predictors of BCR (hazard ratio (HR 2.2), (95% CI 1.4-3.5), p < 0.0012, and (HR 1.8), (95% CI 1.0-3.2), p < 0.046). A nomogram based on the Classifier was constructed, with high prediction accuracy for BCR-free survival in TCGA and GEO datasets. GSEA enrichment analysis showed that the DMGs were mainly enriched in the metabolism pathways. Conclusion: We identified and validated the nomogram of BCR-free survival for PCA patients, which has the potential to guide treatment decisions for patients at differing risks of BCR. Our study deepens the understanding of DMGs in the pathogenesis of PCA.

Project description:BackgroundSkeletal muscle growth and development from embryo to adult consists of a series of carefully regulated changes in gene expression. Understanding these developmental changes in agriculturally important species is essential to the production of high quality meat products. For example, consumer demand for lean, inexpensive meat products has driven the turkey industry to unprecedented production through intensive genetic selection. However, achievements of increased body weight and muscle mass have been countered by an increased incidence of myopathies and meat quality defects. In a previous study, we developed and validated a turkey skeletal muscle-specific microarray as a tool for functional genomics studies. The goals of the current study were to utilize this microarray to elucidate functional pathways of genes responsible for key events in turkey skeletal muscle development and to compare differences in gene expression between two genetic lines of turkeys. To achieve these goals, skeletal muscle samples were collected at three critical stages in muscle development: 18d embryo (hyperplasia), 1d post-hatch (shift from myoblast-mediated growth to satellite cell-modulated growth by hypertrophy), and 16 wk (market age) from two genetic lines: a randombred control line (RBC2) maintained without selection pressure, and a line (F) selected from the RBC2 line for increased 16 wk body weight. Array hybridizations were performed in two experiments: Experiment 1 directly compared the developmental stages within genetic line, while Experiment 2 directly compared the two lines within each developmental stage.ResultsA total of 3474 genes were differentially expressed (false discovery rate; FDR < 0.001) by overall effect of development, while 16 genes were differentially expressed (FDR < 0.10) by overall effect of genetic line. Ingenuity Pathways Analysis was used to group annotated genes into networks, functions, and canonical pathways. The expression of 28 genes involved in extracellular matrix regulation, cell death/apoptosis, and calcium signaling/muscle function, as well as genes with miscellaneous function was confirmed by qPCR.ConclusionsThe current study identified gene pathways and uncovered novel genes important in turkey muscle growth and development. Future experiments will focus further on several of these candidate genes and the expression and mechanism of action of their protein products.

Project description:BACKGROUND: Cotton (Gossypium spp.) is one of the major fibre crops of the world. Although it is classified as salt tolerant crop, cotton growth and productivity are adversely affected by high salinity, especially at germination and seedling stages. Identification of genes and miRNAs responsible for salt tolerance in upland cotton (Gossypium hirsutum L.) would help reveal the molecular mechanisms of salt tolerance. We performed physiological experiments and transcriptome sequencing (mRNA-seq and small RNA-seq) of cotton leaves under salt stress using Illumina sequencing technology. RESULTS: We investigated two distinct salt stress phases--dehydration (4 h) and ionic stress (osmotic restoration; 24 h)--that were identified by physiological changes of 14-day-old seedlings of two cotton genotypes, one salt tolerant and the other salt sensitive, during a 72-h NaCl exposure. A comparative transcriptomics was used to monitor gene and miRNA differential expression at two time points (4 and 24 h) in leaves of the two cotton genotypes under salinity conditions. The expression patterns of differentially co-expressed unigenes were divided into six groups using short time-servies expression miner software. During a 24-h salt exposure, 819 transcription factor unigenes were differentially expressed in both genotypes, with 129 unigenes specifically expressed in the salt-tolerant genotype. Under salt stress, 108 conserved miRNAs from known families were differentially expressed at two time points in the salt-tolerant genotype. We further analyzed the predicted target genes of these miRNAs along with the transcriptome for each time point. Important expressed genes encoding membrane receptors, transporters, and pathways involved in biosynthesis and signal transduction of calcium-dependent protein kinase, mitogen-activated protein kinase, and hormones (abscisic acid and ethylene) were up-regulated. We also analyzed the salt stress response of some key miRNAs and their target genes and found that the expressions of five of nine target genes exhibited significant inverse correlations with their corresponding miRNAs. On the basis of these results, we constructed molecular regulatory pathways and a potential regulatory network for these salt-responsive miRNAs. CONCLUSIONS: Our comprehensive transcriptome analysis has provided new insights into salt-stress response of upland cotton. The results should contribute to the development of genetically modified cotton with salt tolerance.

Project description:Background/Aims: The incidence of gastric cancer (GC) ranks fifth among common tumors and GC is the third leading cause of cancer-related death worldwide. The aim of this study was to develop and validate a nomogram for predicting the overall survival (OS) of patients with GC. Methods: DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. Another cohort (GSE62254) was used for external validation. Results: Thirteen differentially expressed DNAm-driven genes were narrowed down to a six-gene signature (PODN, NPY, MICU3, TUBB6 and RHOJ were hypermethylated, and MYO1A was hypomethylated), which was associated with OS (P < 0.05) after survival and LASSO regression analyses. These differentially expressed genes (DEGs) with altered DNAm statuses were included in the prognostic risk score model. The univariate Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly associated with survival time in GC patients. The multivariate Cox regression analysis also indicated that these variables were significant prognostic factors for GC. A nomogram including these variables was constructed, and its performance in predicting the 1-, 3- and 5-year survival outcomes of GC patients was estimated through time-dependent receiver operating characteristic (ROC) curves. In addition, the clinical benefit of this model was revealed by decision curve analysis (DCA). Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" pathways. Conclusions: The altered status of the DNAm-driven gene signature (PODN, MYO1A, NPY, MICU3, TUBB6 and RHOJ) was significantly associated with the OS of GC patients. A nomogram incorporating risk score, age and number of positive lymph nodes can be conveniently used to facilitate the individualized prediction of OS in patients with GC.

Dataset Information

DNA methylation in transcriptional repression of two differentially expressed X-linked genes, GPC3 and SYBL1.

Publications

DNA methylation in transcriptional repression of two differentially expressed X-linked genes, GPC3 and SYBL1.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets