Unravelling 2-oxoglutarate turnover and substrate oxidation dynamics in 5-methylcytosine-oxidising TET enzymes
Bibliographic record
Abstract
Abstract Fe(II)- and 2-oxoglutarate (2OG)-dependent dioxygenases use 2OG and O2 cofactors to catalyse substrate oxidation and yield oxidised product, succinate, and CO2. Simultaneous detection of substrate and cofactors is difficult, contributing to a poor understanding of the dynamics between substrate oxidation and 2OG decarboxylation activities. Here, we profile 5-methylcytosine (5mC)-oxidising Ten-Eleven Translocation (TET) enzymes using MS and 1H NMR spectroscopy methods and reveal a high degree of substrate oxidation-independent 2OG turnover under a range of conditions. 2OG decarboxylase activity is substantial (>20% 2OG turned over after 1 h) in the absence of substrate, while, under substrate-saturating conditions, half of total 2OG consumption is uncoupled from substrate oxidation. 2OG kinetics are affected by substrate and non-substrate DNA oligomers, and the sequence-agnostic effects are observed in amoeboflagellate Naegleria gruberi NgTet1 and human TET2. TET inhibitors also alter uncoupled 2OG kinetics, highlighting the potential effect of 2OG dioxygenase inhibitors on the intracellular balance of 2OG/succinate.
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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".