Abstract 1620: Effects of γ-glutamyl hydrolase and folylpolyglutamyl synthase modulation on gene-specific promoter CpG island methylation
Bibliographic record
Abstract
Abstract Background: Folate mediates the transfer of one-carbon units for most biological methylation reactions including DNA methylation, which is catalyzed by DNA methyltransferase (DNMT). Genomic DNA hypomethylation and gene-specific promoter CpG island hypermethylation are important epigenetic mechanisms of carcinogenesis. DNA methylation and DNMT also are potential therapeutic targets and may modify the effect of specific chemotherapeutic agents. Intracellular folate homeostasis is maintained by folylpolyglutamate synthase (FPGS) and γ-glutamyl hydrolase (GGH) for folate-dependent one-carbon transfer reactions and antifolate-induced cytotoxic effects. We have previously shown that GGH/FPGS modulation significantly affects global DNA methylation and DNMT activity in human HCT116 colon and MDA-MB-435 breast cancer cells in a predictably manner. We investigated whether GGH/FPGS modulation would also affect gene-specific promoter CpG island methylation. Methods: We generated an in vitro model of GGH overexpression/inhibition in HCT116 and MDA-MB-435 cells by transfecting the cells with the sense GGH cDNA or GGH-targeted siRNA, respectively. An in vitro model of FPGS overexpression/inhibition in HCT116 was generated by transfecting the cells with the sense or antisense FPGS cDNA, respectively. An in vitro model of FPGS overexpression/inhibition in MDA-MB-435 cells was generated by transfecting cells with the sense FPGS cDNA or FPGS-targeted siRNA, respectively. Illumina Infinium DNA methylation assay was used to interrogate 27,578 individual CpG loci in 14,495 different gene promoters. Results: MDA-MB-435 cells showed more CpG methylation alterations in response to GGH and FPGS modulation than HCT116 cells. Sixty-one hyper- and 54 hypomethylated genes were common between HCT116 and MDA-MB-435 cell lines in GGH overexpression while 117 hyper- and 129 hypomethylated genes were common between these cell lines in GGH inhibition. In both cell lines, 64 hyper- and 52 hypomethylated genes were common in FPGS overexpression whereas 31 hyper- and 38 hypomethylated genes were common in FPGS inhibition. Preliminary analysis demonstrated that genes associated with efflux and influx of (anti)folate (ABCC1, ABCC2, ABCC3, ABCG2, SLC19A1), de novo purine synthesis, glutathione transferase (GSTM1, GSTP1, GSTT1), growth regulation (IGF2, IGFBP3), mismatch repair (MLH1), tumor suppressor (PTEN) and transcription (RUNX3) were hyper- or hypomethylated in both cell lines. Conclusions: Our data indicate that GGH/FPGS modulation can affect gene-specific promoter CpG island methylation as well as genomic DNA methylation, which might influence chemosensitivity of colon and breast cancer cells to 5FU and antifolates. Studies are underway to investigate the functional ramifications of the altered CpG methylation in response to GGH/FPGS modulation in these cell lines. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1620. doi:10.1158/1538-7445.AM2011-1620
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".