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Record W2930954504 · doi:10.1038/s41418-019-0325-6

MiR-35 buffers apoptosis thresholds in the C. elegans germline by antagonizing both MAPK and core apoptosis pathways

2019· article· en· W2930954504 on OpenAlexafffund
Anh Tran, Eric M. Chapman, Mathieu N. Flamand, Bin Yu, Samuel J. Krempel, Thomas F. Duchaîne, Matthew Eroglu, W. Brent Derry

Bibliographic record

VenueCell Death and Differentiation · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcGill UniversityUniversity of TorontoSickKids FoundationNational Research Council CanadaHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenGarron Family Cancer CentreNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsCell biologyGermlineApoptosisMAPK/ERK pathwayProgrammed cell deathBiologymicroRNADNA damageSignal transductionGeneticsGeneDNA

Abstract

fetched live from OpenAlex

Apoptosis is a genetically programmed cell death process with profound roles in development and disease. MicroRNAs modulate the expression of many proteins and are often deregulated in human diseases, such as cancer. C. elegans germ cells undergo apoptosis in response to genotoxic stress by the combined activities of the core apoptosis and MAPK pathways, but how their signalling thresholds are buffered is an open question. Here we show mir-35-42 miRNA family play a dual role in antagonizing both NDK-1, a positive regulator of MAPK signalling, and the BH3-only pro-apoptotic protein EGL-1 to regulate the magnitude of DNA damage-induced apoptosis in the C. elegans germline. We show that while miR-35 represses EGL-1 by promoting transcript degradation, repression of NDK-1 may be through sequestration of the transcript to inhibit translation. Importantly, dramatic increase in NDK-1 expression was observed in cells about to die. In the absence of miR-35, increased NDK-1 activity enhanced MAPK signalling that lead to significant increases in germ cell death. Our findings demonstrate that NDK-1 acts upstream of (or in parallel to) EGL-1, and that miR-35 targets both egl-1 and ndk-1 to fine-tune cell killing in response to genotoxic stress.

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 score0.006
Threshold uncertainty score0.649

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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

Citations43
Published2019
Admission routes2
Has abstractyes

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