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Record W4366487513 · doi:10.1096/fj.202300062r

Kinetochore deterioration concommitant with centromere weakening during aging in mouse oocyte meiosis‐I

2023· article· en· W4366487513 on OpenAlexafffund
Lin Yin, Aleksandar I. Mihajlović, Gongshe Yang, Greg FitzHarris

Bibliographic record

VenueThe FASEB Journal · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health ResearchChina Scholarship Council
KeywordsKinetochoreMeiosisCentromereChromosome segregationMeiosis IIOocyteBiologyGerminal vesicleCell biologyAneuploidyChromosomeSpindle apparatusGeneticsMicrotubuleCell divisionEmbryoCellGene

Abstract

fetched live from OpenAlex

Age-related oocyte aneuploidy occurs as a result of chromosome segregation errors in female meiosis-I and meiosis-II, and is caused by a progressive age-related deterioration of the chromosome segregation machinery. Here, we assess the impact of age upon the kinetochore, the multi-protein structure that forms the link between the chromosome and spindle microtubules. We find that in meiosis-I the outer kinetochore assembles at germinal vesicle breakdown, but that a substantially smaller outer kinetochore is assembled in oocytes from aged mice. We show this correlates with a weaker centromere in aged oocytes and, using nuclear transfer approaches to generate young-aged hybrid oocytes, we show that outer kinetochore assembly always mirrors the status of the centromere, regardless of cytoplasmic age. Finally, we show that weaker kinetochores in aged oocytes are associated with thinner microtubule bundles, that are more likely to be mis-attached. We conclude that progressive loss of the centromere with advancing maternal age underpins a loss of the outer kinetochore in meiosis-I, which likely contributes to chromosome segregation fallibility in oocytes from older females.

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 score0.016
Threshold uncertainty score0.332

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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