Natalie P Thorne | Search PubMed

PubMed2006

MMASS: an optimized array-based method for assessing CpG island methylation.

We describe an optimized microarray method for identifying genome-wide CpG island methylation called microarray-based methylation assessment of single samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimized combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared with a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison with previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation.

Cell Line, Tumor↗

PubMed2006

Metabolic consequences of p300 gene deletion in human colon cancer cells.

Metabolite profiling using (1)H nuclear magnetic resonance (NMR) spectroscopy was used to investigate the metabolic changes associated with deletion of the gene for the transcriptional coactivator p300 in the human colon carcinoma cell line HCT116. Multivariate statistical methods were used to distinguish between metabolite patterns that were dependent on cell growth conditions and those that were specifically associated with loss of p300 function. In the absence of serum, wild-type cells showed slower growth, which was accompanied by a marked decrease in phosphocholine concentration, which was not observed in otherwise isogenic cell lines lacking p300. In the presence of serum, several metabolites were identified as being significantly different between the two cell types, including glutamate and glutamine, a nicotinamide-related compound and glycerophosphocholine (GPC). However, in the absence of serum, these metabolites, with the exception of GPC, were not significantly different, leading us to conclude that most of these changes were context dependent. Transcript profiling, using DNA microarrays, showed changes in the levels of transcripts for several enzymes involved in choline metabolism, which might explain the change in GPC concentration. Localized in vivo (1)H NMR measurements on the tumors formed following s.c. implantation of these cells into mice showed an increase in the intensity of the peak from choline-containing compounds in the p300(-) tumors. These data show that NMR-based metabolite profiling has sufficient sensitivity to identify the metabolic consequences of p300 gene deletion in tumor cells in vitro and in vivo.

Animals↗