It takes two (Las1 HEPN Endoribonuclease Motifs) to cut the RNA right
Bibliographic record
Abstract
Abstract Las1 is an essential endoribonuclease that is well-conserved across eukaryotes and a newly established member of the HEPN (higher eukaryotes and prokaryotes nucleotide-binding) nuclease family. HEPN nucleases participate in diverse RNA cleavage pathways and share a short HEPN nuclease motif important for RNA cleavage. While most HEPN nucleases participate in stress activated RNA cleavage pathways, Las1 plays a fundamental role in processing the pre-ribosomal RNA. Underscoring the significance of Las1 function, mutations to the LAS1L gene have been associated with neurological dysfunction. Two juxtaposed Las1 HEPN nuclease motifs create its composite nuclease active site, however the roles of the individual HEPN residues are poorly defined. Here we show through a combination of in vivo and in vitro studies that both Las1 HEPN nuclease motifs are required for nuclease activity and fidelity. Through in-depth sequence analysis and systematic mutagenesis, we define the consensus Las1 HEPN nuclease motif and uncover its canonical and specialized elements. Using reconstituted Las1 HEPN-HEPN’chimeras, we define the molecular requirements for RNA cleavage. Intriguingly, both copies of the Las1 HEPN motif are necessary for nuclease specificity revealing that both HEPN motifs participate in coordinating the RNA within the active site. Taken together, our work reveals critical information about HEPN nuclease function and establishes that HEPN nucleases can be re-wired to cleave alternative RNA substrates.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".