Oncogenic IDH1 <sup>mut</sup> drives robust loss of histone acetylation and increases chromatin heterogeneity
Bibliographic record
Abstract
Malignant gliomas are heterogeneous tumors, mostly incurable, arising in the central nervous system (CNS) driven by genetic, epigenetic, and metabolic aberrations. Mutations in isocitrate dehydrogenase (IDH1/2 mut ) enzymes are predominantly found in low-grade gliomas and secondary high-grade gliomas, with IDH1 mutations being more prevalent. Mutant-IDH1/2 confers a gain-of-function activity that favors the conversion of a-ketoglutarate (α-KG) to the oncometabolite 2-hydroxyglutarate (2-HG), resulting in an aberrant hypermethylation phenotype. Yet, the complete depiction of the epigenetic alterations in IDH mut cells has not been thoroughly explored. Here, we applied an unbiased approach, leveraging epigenetic-focused cytometry by time-of-flight (CyTOF) analysis, to systematically profile the effect of mutant-IDH1 expression on a broad panel of histone modifications at single-cell resolution. This analysis revealed extensive remodeling of chromatin patterns by mutant-IDH1, with the most prominent being deregulation of histone acetylation marks. The loss of histone acetylation occurs rapidly following mutant-IDH1 induction and affects acetylation patterns over enhancers and intergenic regions. Notably, the changes in acetylation are not predominantly driven by 2-HG, can be rescued by pharmacological inhibition of mutant-IDH1, and reversed by acetate supplementations. Furthermore, cells expressing mutant-IDH1 show higher epigenetic and transcriptional heterogeneity and upregulation of oncogenes such as KRAS and MYC, highlighting its tumorigenic potential. Our study underscores the tight interaction between chromatin and metabolism dysregulation in glioma and highlights epigenetic and oncogenic pathways affected by mutant-IDH1-driven metabolic rewiring.
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 | 0.001 |
| 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".