Defining the contribution of microRNA-specific slicing Argonautes in animals
Bibliographic record
Abstract
ABSTRACT microRNAs regulate gene expression through interaction with an Argonaute protein family member. While some members of this protein family retain an enzymatic activity capable of cleaving RNA molecules complementary to Argonaute-bound small RNAs, the role of the slicing activity in the canonical microRNA pathway is still unclear in animals. To address the importance of slicing Argonautes in animals, we created Caenorhabditis elegans strains, carrying catalytically dead endogenous ALG-1 and ALG-2, the only two slicing Argonautes essential for the miRNA pathway in this animal model. We observe that the loss of ALG-1 and ALG-2 slicing activity affects overall animal fitness and causes phenotypes, reminiscent of miRNA defects, only when grown and maintained at restrictive temperature. Furthermore, the analysis of global miRNA expression shows that the catalytic activity of ALG-1 and ALG-2 differentially regulate the level of specific subsets of miRNAs in young adults. We also demonstrate that altering the slicing activity of those miRNA-specific Argonautes does not result in any defect in the production of canonical miRNAs. Together, these data support that the slicing activity of miRNA- specific Argonautes function to maintain the levels of a set of miRNAs for optimal viability and fitness in animals particularly exposed to specific growing conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".