Differences among brain tumor stem cell types and fetal neural stem cells in focal regions of histone modifications and DNA methylation, broad regions of modifications, and bivalent promoters
Bibliographic record
Abstract
BACKGROUND: Aberrational epigenetic marks are believed to play a major role in establishing the abnormal features of cancer cells. Rational use and development of drugs aimed at epigenetic processes requires an understanding of the range, extent, and roles of epigenetic reprogramming in cancer cells. Using ChIP-chip and MeDIP-chip approaches, we localized well-established and prevalent epigenetic marks (H3K27me3, H3K4me3, H3K9me3, DNA methylation) on a genome scale in several lines of putative glioma stem cells (brain tumor stem cells, BTSCs) and, for comparison, normal human fetal neural stem cells (fNSCs). RESULTS: We determined a substantial "core" set of promoters possessing each mark in every surveyed BTSC cell type, which largely overlapped the corresponding fNSC sets. However, there was substantial diversity among cell types in mark localization. We observed large differences among cell types in total number of H3K9me3+ positive promoters and peaks and in broad modifications (defined as >50 kb peak length) for H3K27me3 and, to a lesser extent, H3K9me3. We verified that a change in a broad modification affected gene expression of CACNG7. We detected large numbers of bivalent promoters, but most bivalent promoters did not display direct overlap of contrasting epigenetic marks, but rather occupied nearby regions of the proximal promoter. There were significant differences in the sets of promoters bearing bivalent marks in the different cell types and few consistent differences between fNSCs and BTSCs. CONCLUSIONS: Overall, our "core set" data establishes sets of potential therapeutic targets, but the diversity in sets of sites and broad modifications among cell types underscores the need to carefully consider BTSC subtype variation in epigenetic therapy. Our results point toward substantial differences among cell types in the activity of the production/maintenance systems for H3K9me3 and for broad regions of modification (H3K27me3 or H3K9me3). Finally, the unexpected diversity in bivalent promoter sets among these multipotent cells indicates that bivalent promoters may play complex roles in the overall biology of these cells. These results provide key information for forming the basis for future rational drug therapy aimed at epigenetic processes in these cells.
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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 | 0.001 | 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) | 0.001 | 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".