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MTSS1 is epigenetically regulated in glioma cells and inhibits glioma cell motility.

ABSTRACT: Epigenetic silencing by DNA methylation in brain tumors has been reported for many genes, however, their function on pathogenesis needs to be evaluated. We investigated the MTSS1 gene, identified as hypermethylated by differential methylation hybridization (DMH). Fifty-nine glioma tissue samples and seven glioma cell lines were examined for hypermethylation of the MTSS1 promotor, MTSS1 expression levels and gene dosage. GBM cell lines were treated with demethylating agents and interrogated for functional consequences of MTSS1 expression after transient transfection. Hypermethylation was significantly associated with IDH1/2 mutation. Comparative SNP analysis indicates higher incidence of loss of heterozygosity of MTSS1 in anaplastic astrocytomas and secondary glioblastomas as well as hypermethylation of the remaining allele. Reversal of promoter hypermethylation results in an increased MTSS1 expression. Cell motility was significantly inhibited by MTSS1 overexpression without influencing cell growth or apoptosis. Immunofluorescence analysis of MTSS1 in human astrocytes indicates co-localization with actin filaments. MTSS1 is down-regulated by DNA methylation in glioblastoma cell lines and is part of the G-CIMP phenotype in primary glioma tissues. Our data on normal astrocytes suggest a function of MTSS1 at focal contact structures with an impact on migratory capacity but no influence on apoptosis or cellular proliferation.

SUBMITTER: Luxen D

PROVIDER: S-EPMC5167248 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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MTSS1 is epigenetically regulated in glioma cells and inhibits glioma cell motility.

Luxen Daniel D Gielen Gerrit H GH Waha Anke A Isselstein Lukas L Müller Tim T Koch Philipp P Hammes Jennifer J Becker Albert A Simon Matthias M Wurst Peter P Endl Elmar E Pietsch Torsten T Gessi Marco M Waha Andreas A

Translational oncology 20161215 1

Epigenetic silencing by DNA methylation in brain tumors has been reported for many genes, however, their function on pathogenesis needs to be evaluated. We investigated the MTSS1 gene, identified as hypermethylated by differential methylation hybridization (DMH). Fifty-nine glioma tissue samples and seven glioma cell lines were examined for hypermethylation of the MTSS1 promotor, MTSS1 expression levels and gene dosage. GBM cell lines were treated with demethylating agents and interrogated for f ...[more]

PMID: 27988423

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Project description:BackgroundMethylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis.ResultsOur two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically significant negative correlation between methylation profiles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identified 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes.ConclusionsOur results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

Dataset Information

MTSS1 is epigenetically regulated in glioma cells and inhibits glioma cell motility.

Publications

MTSS1 is epigenetically regulated in glioma cells and inhibits glioma cell motility.

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