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Record W4413445644 · doi:10.1101/gad.352912.125

A kinetic ruler controls mRNA poly(A) tail length

2025· article· en· W4413445644 on OpenAlexfundno aff
Emilie Gabs, Emil Aalto-Setälä, Aada Välisaari, Anssi M. Malinen, Torben Heick Jensen, Stephen H. McLaughlin, Lori A. Passmore, Matti Turtola

Bibliographic record

VenueGenes & Development · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
FundersMagnus Ehrnroothin SäätiöAcademy of FinlandMedical Research Council CanadaWellcome Trust
KeywordsBiologyMessenger RNARulerCell biologyMolecular biologyGeneticsGenePhysics

Abstract

fetched live from OpenAlex

Poly(A) tails of newly synthesized mRNAs have uniform lengths, arising through cooperation between the cleavage and polyadenylation complex (CPAC) and poly(A) binding proteins (PABPs). In the budding yeast Saccharomyces cerevisiae , the responsible PABP is the evolutionarily conserved CCCH zinc finger protein Nab2 that facilitates the biogenesis of ∼60 adenosine mRNA poly(A) tails. Here, we address the molecular basis for such length control. Reconstituting polyadenylation reactions during the formation of Nab2:poly(A) RNA ribonucleoprotein particles in vitro, we found that Nab2 dimerization directs polyadenylation termination. The Nab2 dimer is stable only on poly(A) tails that are >25 adenosines, explaining how Nab2 avoids prematurely terminating poly(A) synthesis. However, the mature tail length is not determined by the footprint of Nab2 on the RNA but rather by the kinetic competition between CPAC-mediated tail elongation and Nab2 RNA binding. Variations in Nab2 RNA binding rate can shift poly(A) tail lengths, but in cells such variations are buffered by autoregulation of Nab2 protein concentration. As a result, poly(A) tail length control operates through a “kinetic ruler” mechanism, whereby the concentration of Nab2 quantifies RNA length.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.472
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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