Abstract 1596: Effects of folylpolyglutamyl synthase and γ-glutamyl hydrolase modulation on DNA methylation
Bibliographic record
Abstract
Abstract Background: Folate mediates the transfer of one-carbon units for the provision of S-adenosylmethionine, the primary methyl group donor for most biological methylation reactions including DNA methylation, which is catalyzed by DNA methyltransferase (DNMT). Both 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) that facilitate intracellular retention of folate by polyglutamylation and by γ -glutamyl hydrolase (GGH) that catalyzes the hydrolysis of polyglutamylated folate into monoglutamates, thereby facilitating export of folate out of the cell. We investigated whether GGH and FPGS modulation would affect genomic DNA methylation and DNMT activity. Methods: We generated an in vitro model of GGH overexpression and inhibition in human HCT116 colon and MDA-MB-435 breast cancer cells by stably transfecting the cells with the sense GGH cDNA or GGH-targeted siRNA, respectively. An in vitro model of FPGS overexpression and inhibition in HCT116 cells was generated by stably transfecting the cells with the sense or antisense FPGS cDNA, respectively. Similarly, MDA-MB-435 cells were transfected with the sense FPGS cDNA or FPGS-targeted siRNA, respectively, to generate an in vitro model of FPGS overexpression and inhibition. Genomic DNA methylation and DNMT activity were determined. Results: Functionally significant GGH/FPGS overexpression and inhibition were confirmed by GGH and FPGS protein expression, GGH and FPGS activity, and total and long-chain polyglutamylated intracellular folate concentrations. In both cell lines, GGH overexpression was associated with significantly lower genomic DNA methylation and DNMT activity than controls, while GGH inhibition was associated with significantly higher genomic DNA methylation and DNMT activity than controls (P<0.05). FPGS overexpression was associated with lower genomic DNA methylation and DNMT activity than controls in HCT116 cells while it was associated with higher genomic DNA methylation and DNMT activity than controls in MDA-MB-435 cells (P<0.05). FPGS inhibition was associated with higher genomic DNA methylation and lower DNMT activity compared with controls in HCT116 cells (P<0.05) whereas it did not affect genomic DNA methylation in MDA-MB-435 cells. Conclusions: Our data indicate that GGH and FPGS modulation can affect genomic DNA methylation and DNMT activity of colon and breast cancer cells. Studies are underway to determine whether these altered DNA methylation and DNMT activity influence chemosensitivity of these cancer cells to 5-fluorouracil and antifolates. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1596.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".