Bibliographic record
Abstract
Regions of low oxygen (hypoxia) occur in most solid tumours and correlate with poor patient prognosis due to their resistance to chemo- and radiotherapy and to their increased metastatic potential. Severe levels of hypoxia induce DNA replication stress characterised by an increased number of stalled replication forks and significantly reduced replication rates, which occurs in the absence of DNA damage. Ribonucleotide Reductase (RNR) is the only enzyme capable of de novo synthesis of dNTPs – the building blocks of DNA synthesis and repair. However, oxygen is essential for mammalian RNRs (RRM1/RRM2 and RRM1/RRM2B), leading us to question the source of dNTPs in hypoxia. Here we show that the RRM2B subunit of RNR is significantly induced in response to hypoxia in a universal manner. Interestingly, the hypoxic induction of RRM2B occurs both at transcriptional and translational levels and likely through two distinct mechanisms, one of which is p53-dependent. Most importantly, we demonstrate that RRM1/RRM2B enzyme is capable of retaining activity in hypoxia and therefore is favoured over RRM1/RRM2 in order to preserve on-going replication. We found two distinct mechanisms by which RRM2B maintains hypoxic activity and we identified specific RRM2B-residues (Y164 and K37/K151) responsible for this function. The importance of RRM2B in the response to tumour hypoxia is further illustrated by increased expression in the hypoxic regions of glioblastoma biopsies, roles in tumour growth and radioresistance, as well as prevention of DNA damage and apoptosis. In this study we present multi-disciplinary evidence, demonstrating the molecular rationale for the ability of RRM1/RRM2B to function in hypoxia. We propose that RRM2B has been evolutionary conserved so as to act as the hypoxic specific RNR subunit in order to be able to react promptly when this physiologically relevant stress occurs. Our data provide new insight into RNR biology, highlighting RRM2B as an important, hypoxic-specific, anti-cancer therapeutic target.
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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 | 0.001 |
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".