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Record W4392829359 · doi:10.1093/nar/gkae173

Defining the contribution of microRNA-specific Argonautes with slicer capability in animals

2024· article· en· W4392829359 on OpenAlexafffund
Anisha Pal, Vaishnav Vasudevan, François Houle, Michael Lantin, Katherine A Maniates, Miguel Quévillon Huberdeau, Allison L. Abbott, Martin J. Simard

Bibliographic record

VenueNucleic Acids Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité Laval
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthCanadian Institutes of Health ResearchUniversité Laval
KeywordsBiologymicroRNAComputational biologyBioinformaticsGeneticsGene

Abstract

fetched live from OpenAlex

microRNAs regulate gene expression through interaction with an Argonaute protein. 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 slicer residues in the canonical microRNA pathway is still unclear in animals. To address this, we created Caenorhabditis elegans strains with mutated slicer residues in the endogenous ALG-1 and ALG-2, the only two slicing Argonautes essential for the miRNA pathway in this animal model. We observe that the mutation in ALG-1 and ALG-2 catalytic residues 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 slicer residues of ALG-1 and ALG-2 contribute differentially to regulate the level of specific subsets of miRNAs in young adults. We also demonstrate that altering the catalytic tetrad of those miRNA-specific Argonautes does not result in any defect in the production of canonical miRNAs. Together, these data support that the slicer residues of miRNA-specific Argonautes contribute to maintaining 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.314
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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