Faculty Opinions recommendation of A crucial RNA-binding lysine residue in the Nab3 RRM domain undergoes SET1 and SET3-responsive methylation.
Bibliographic record
Abstract
The Nrd1-Nab3-Sen1 (NNS) complex integrates molecular cues to direct termination of noncoding transcription in budding yeast.NNS is positively regulated by histone methylation as well as through Nrd1 binding to the initiating form of RNA PolII.These cues collaborate with Nrd1 and Nab3 binding to target RNA sequences in nascent transcripts through their RRM RNA recognition motifs.In this study, we identify nine lysine residues distributed amongst Nrd1, Nab3 and Sen1 that are methylated, suggesting novel molecular inputs for NNS regulation.We identify monomethylation of one these residues (Nab3-K363me1) as being partly dependent on the H3K4 methyltransferase, Set1, a known regulator of NNS function.Moreover, the accumulation of Nab3-K363me1 is essentially abolished in strains lacking SET3, a SET domain containing protein that is positively regulated by H3K4 methylation.Nab3-K363 resides within its RRM and physically contacts target RNA.Mutation of Nab3-K363 to arginine (Nab3-K363R) decreases RNA binding of the Nab3 RRM in vitro and causes transcription termination defects and slow growth.These findings identify SET3 as a potential contextual regulator of Nab3 function through its role in methylation of Nab3-K363.Consistent with this hypothesis, we report that SET3 exhibits genetic activation of NAB3 that is observed in a sensitized context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.461 | 0.210 |
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".