MétaCan
Menu
Back to cohort
Record W2083676791 · doi:10.1534/genetics.108.091553

Proteasomal Regulation of the Proliferation <i>vs</i>. Meiotic Entry Decision in the <i>Caenorhabditis elegans</i> Germ Line

2008· article· en· W2083676791 on OpenAlexafffund
Lindsay D MacDonald, Aaron J. Knox, Dave Hansen

Bibliographic record

VenueGenetics · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Calgary
FundersNational Center for Research ResourcesNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthFondation pour la Recherche Médicale
KeywordsCaenorhabditis elegansBiologyGermlineMeiosisGeneticsGermCell biologyGene

Abstract

fetched live from OpenAlex

Reproductive fitness in many animals relies upon a tight balance between the number of cells that proliferate in the germ line and the number of cells that enter meiosis and differentiate as gametes. In the Caenorhabditis elegans germ line, the GLP-1/Notch signaling pathway controls this balance between proliferation and meiotic entry. Here we describe the identification of the proteasome as an additional regulator of this balance. We show that a decrease in proteasome activity, through either genetic mutation or RNAi to core components of the proteasome, shifts this balance toward excess germ-line proliferation. We further demonstrate that there are likely two or more proteasome targets that contribute to excess germ-line proliferation when proteasome activity is reduced. One of these targets is likely a component or regulator of the Notch-signaling pathway, while the other functions on one of the two major redundant genetic pathways downstream of GLP-1/Notch signaling. We propose a model in which the proteasome degrades proteins that are necessary for proliferation as cells switch from proliferation to meiotic entry.

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 score0.152
Threshold uncertainty score0.499

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 head0.215
Teacher spread0.206 · 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

Citations36
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueGeneticsSame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207