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Gametogenesis: Exploring an Endogenous Rejuvenation Program to Understand Cellular Aging and Quality Control

2022· review· en· W4287834768 on OpenAlexfundno aff
Tina L. Sing, Gloria A. Brar, Elçin Ünal

Bibliographic record

VenueAnnual Review of Genetics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaFondation pour la Recherche MédicaleNational Institute on AgingNational Institutes of HealthGlenn Foundation for Medical Research
KeywordsGametogenesisBiologyCell biologyGeneticsContext (archaeology)MeiosisGene

Abstract

fetched live from OpenAlex

Gametogenesis is a conserved developmental program whereby a diploid progenitor cell differentiates into haploid gametes, the precursors for sexually reproducing organisms. In addition to ploidy reduction and extensive organelle remodeling, gametogenesis naturally rejuvenates the ensuing gametes, leading to resetting of life span. Excitingly, ectopic expression of the gametogenesis-specific transcription factor Ndt80 is sufficient to extend life span in mitotically dividing budding yeast, suggesting that meiotic rejuvenation pathways can be repurposed outside of their natural context. In this review, we highlight recent studies of gametogenesis that provide emerging insight into natural quality control, organelle remodeling, and rejuvenation strategies that exist within a cell. These include selective inheritance, programmed degradation, and de novo synthesis, all of which are governed by the meiotic gene expression program entailing many forms of noncanonical gene regulation. Finally, we highlight critical questions that remain in the field and provide perspective on the implications of gametogenesis research on human health span.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.164
GPT teacher head0.366
Teacher spread0.202 · 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
GenreReview

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

Citations16
Published2022
Admission routes1
Has abstractyes

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