A cross-omics approach to investigate temporal gene expression regulation by 5-hydroxymethylcytosine via TBH-derived oxidative stress showed involvement of different regulatory kinases
Bibliographic record
Abstract
Regulation of DNA methylation plays a crucial role in biological processes and carcinogenesis. The formation of 5-hydroxymethylcytosine (5hmC) by oxidation of 5-methylcytosine (5mC) has been proposed as an intermediate of active demethylation. However, whether and how active demethylation is regulated by oxidative stress-related processes is not well understood. Here we investigated whether free oxygen radicals are capable of directly forming 5hmC and how this enhanced whole genome gene expression. We applied LC-MS/MS technology for the analysis of 5mC, 5hmC, 5-formylcytosine (5fC) and 5-hydroxymethyluracyl (5hmU) in HepG2 cells exposed to hydroxyl- and methyl radicals, formed by tert-butyl hydroperoxide (TBH) at multiple time points. We observed that TBH is able to induce a significant increase in 5hmC. A detailed evaluation of the hydroxymethylome using a combination of 5hmC-immunoprecipitation and microarrays resulted in the identification of highly dynamic modifications that appear to increase during prolonged oxidant exposure. Analyses of temporal gene expression changes in combination with network analysis revealed different subnetworks containing differentially expressed genes (DEGs) with differentially hydroxyl-methylated regions (DhMRs) in different regulatory kinases enriched with serine-threonine kinases. These serine-threonine kinases compromises MAPK14, RPSK6KA1, RIPK1, and PLK3 and were all previously identified as key-regulators in hepatocarcinogenesis and subject of study for chemotherapeutic interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".