Dynamic Interplay between the Transcriptome and Methylome in Response to Oxidative and Alkylating Stress
Bibliographic record
Abstract
In recent years, it has been shown that free radicals not only react directly with DNA but also regulate epigenetic processes such as DNA methylation, which may be relevant within the context of, for example, tumorigenesis. However, how these free radicals impact the epigenome remains unclear. We therefore investigated whether methyl and hydroxyl radicals, formed by tert-butyl hydroperoxide (TBH), change temporal DNA methylation patterns and how this interferes with genome-wide gene expression. At three time points, TBH-induced radicals in HepG2 cells were identified by electron spin resonance spectroscopy. Total 5-methylcytosine (5mC) levels were determined by liquid chromatography and tandem mass spectrometry and genome-wide changes in 5mC and gene expression by microarrays. Induced methylome changes rather represent an adaptive response to the oxidative stress-related reactions observed in the transcriptome. More specifically, we found that methyl radicals did not induce DNA methylation directly. An initial oxidative and alkylating stress-related response of the transcriptome during the early phase of TBH treatment was followed by an epigenetic response associated with cell survival signaling. Also, we identified genes of which the expression seems directly regulated by DNA methylation. This work suggests an important role of the methylome in counter-regulating primary oxidative and alkylating stress responses in the transcriptome to restore normal cell function. Altogether, the methylome may play an important role in counter-regulating primary oxidative and alkylating stress responses in the transcriptome presumably to restore normal cell function.
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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 | 0.000 |
| 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".