Author response: Aven recognition of RNA G-quadruplexes regulates translation of the mixed lineage leukemia protooncogenes
Bibliographic record
Abstract
To make a protein, the DNA sequence that encodes it is first copied to make a molecule of messenger RNA (or mRNA for short). The mRNA is then used as a set of instructions to assemble a protein in a process called translation. Both DNA and RNA molecules can fold into particular shapes. One such structure is known as a G-quartet and involves the DNA or RNA folding back on itself to form a highly stable planar structure. Stacks of G-quartets can form structures known as G-quadruplexes, but little is known about the G-quadruplexes that form in mRNA molecules. Leukemia affects cells in the bone marrow and causes blood cells to develop abnormally. A protein called Aven is often found in increased amounts in certain types of leukemic cells, but it was not clear how Aven affects how leukemia develops. Thandapani, Song et al. have now found that in leukemic cells, Aven binds to G-quadruplexes found in two mRNA molecules that encode proteins that are linked to leukemia. This binding increases the translation of these mRNAs, with translation occurring most efficiently when a particular enzyme called a helicase—which remodels RNA—also bound to Aven. Reducing the amount of Aven in cells caused fewer of the leukemic proteins to be produced, which also reduced the growth and multiplcation of leukemic cells. These findings raise the possibility that drugs that disrupt how Aven works could form part of treatments for leukemia. The next challenge will be to identify the signaling pathways that communicate with Aven and to define all the G-quadruplex mRNAs that are regulated by Aven.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.190 | 0.048 |
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".