MétaCan
Menu
Back to cohort
Record W2315651413 · doi:10.1242/dev.132738

LEP-2/Makorin regulates LIN-28 stability to promote the juvenile-to-adult transition in <i>Caenorhabditis elegans</i>

2016· article· en· W2315651413 on OpenAlexfundno aff
Raúl Herrera, Karin Kiontke, David Fitch

Bibliographic record

VenueDevelopment · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of HealthNew York University Shanghai
KeywordsBiologyHeterochronyCaenorhabditis elegansJuvenileMutantCaenorhabditisGeneGeneticsMutationVertebrateDevelopmental biologyMoultingCell biologyEvolutionary biologyEcologyLarva

Abstract

fetched live from OpenAlex

The heterochronic genes lin-28, let-7 and lin-41 regulate fundamental developmental transitions in animals, such as stemness versus differentiation and juvenile versus adult states. We identify a new heterochronic gene, lep-2, in Caenorhabditis elegans. Mutations in lep-2 cause a delay in the juvenile-to-adult transition, with adult males retaining pointed, juvenile tail tips, and displaying defective sexual behaviors. In both sexes, lep-2 mutants fail to cease molting or produce an adult cuticle. We find that LEP-2 post-translationally regulates LIN-28 by promoting LIN-28 protein degradation. lep-2 encodes the sole C. elegans ortholog of the Makorin (Mkrn) family of proteins. Like lin-28 and other heterochronic pathway members, vertebrate Mkrns are involved in developmental switches, including the timing of pubertal onset in humans. Based on shared roles, conservation and the interaction between lep-2 and lin-28 shown here, we propose that Mkrns, together with other heterochronic genes, constitute an evolutionarily ancient conserved module regulating switches in development.

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 score0.090
Threshold uncertainty score0.613

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 head0.210
Teacher spread0.201 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

Explore more

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