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Record W4406143859 · doi:10.7554/elife.104530.1.sa2

Reviewer #2 (Public review): Dissociation of the nuclear basket triggers chromosome loss in aging yeast

2025· peer-review· en· W4406143859 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchEidgenössische Technische Hochschule ZürichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsDissociation (chemistry)YeastGeneticsChemistryBiologyComputational biology

Abstract

fetched live from OpenAlex

In many organisms, aging is a clear risk factor for increased rates of chromosome mis-segregation, the main source of aneuploidy. Here, we report that old yeast mother cells lose chromosomes by partitioning them asymmetrically to their daughter cells together with the pre-existing (old) Sindle Pole Body (centrosome equivalent in yeast). Strikingly, remodelling of the NPC and the displacement of its nuclear basket triggered these asymmetric segregation events. Concomitantly, basket displacement also caused unspliced pre-mRNAs to leak to the cytoplasm. We show that removing the introns of three genes involved in chromosome segregation was sufficient to fully suppress chromosome loss in old cells. Furthermore, promoting pre-mRNA leakage in young cells also caused asymmetric chromosome partition and loss through the same three introns. Therefore, we propose that basket displacement from NPCs and its ensuing effects on pre-mRNA quality control are a key trigger of aging phenotypes such as aneuploidy.

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)0.021
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.122
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0050.004
Research integrity0.0160.006
Insufficient payload (model declined to judge)0.0690.035

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreCommentary

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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