Base-pairing interactions between substrate RNA and H/ACA guide RNA modulate the kinetics of pseudouridylation, but not the affinity of substrate binding by H/ACA small nucleolar ribonucleoproteins
Bibliographic record
Abstract
H/ACA small nucleolar ribonucleoproteins (snoRNPs) pseudouridylate RNA in eukaryotes and archaea. They target many RNAs site-specifically through base-pairing interactions between H/ACA guide and substrate RNA. Besides ribosomal RNA (rRNA) and small nuclear RNA (snRNA), H/ACA snoRNPs are thought to also modify messenger RNA (mRNA) with potential impacts on gene expression. However, the base pairing between known target RNAs and H/ACA guide RNAs varies widely in nature, and therefore the rules governing substrate RNA selection are still not fully understood. To provide quantitative insight into substrate RNA recognition, we systematically altered the sequence of a substrate RNA target by the Saccharomyces cerevisiae H/ACA guide RNA snR34. Time courses measuring pseudouridine formation revealed a gradual decrease in the initial velocity of pseudouridylation upon reducing the number of base pairs between substrate and guide RNA. Changing or inserting nucleotides close to the target uridine severely impairs pseudouridine formation. Interestingly, filter binding experiments show that all substrate RNA variants bind to H/ACA snoRNPs with nanomolar affinity. Next, we showed that binding of inactive, near-cognate RNAs to H/ACA snoRNPs does not inhibit their activity for cognate RNAs, presumably because near-cognate RNAs dissociate rapidly. We discuss that the modulation of initial velocities by the base-pairing strength might affect the order and efficiency of pseudouridylation in rRNA during ribosome biogenesis. Moreover, the binding of H/ACA snoRNPs to near-cognate RNAs may be a mechanism to search for cognate target sites. Together, our data provide critical information to aid in the prediction of productive H/ACA guide—substrate RNA pairs.
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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".