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Record W1983557489 · doi:10.4137/jcd.s9073

RBM10 Modulates Apoptosis and Influences TNF-α Gene Expression

2012· article· en· W1983557489 on OpenAlexafffund
Ke Wang, Mackensey L. Bacon, J Tessier, Nina D. Rintala‐Maki, Vanessa R. Tang, Leslie C. Sutherland

Bibliographic record

VenueJournal of Cell Death · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsNOSM UniversityLaurentian UniversityUniversity of OttawaNortheast Cancer CentreHealth Sciences North
FundersNatural Sciences and Engineering Research Council of CanadaXiamen UniversityCancer Care Ontario
KeywordsBiologyApoptosisGene knockdownCell biologyTumor necrosis factor alphaGene expressionTranscription factorSignal transductionGeneEndocrinologyGenetics

Abstract

fetched live from OpenAlex

Recent evidence suggests that protein encoded by the RNA Binding Motif 10 (RBM10) gene has the ability to modulate apoptosis. The objective of this study was to test this hypothesis by manipulating RBM10 expression levels and examining the downstream consequences. The results showed that transient overexpression of RBM10 correlated with significantly elevated levels of tumour necrosis factor alpha (TNF-α) mRNA and soluble TNF-α (sTNF-α) protein, and increased apoptosis (phosphatidyl serine exposure on the outer cell membrane and nuclear condensation). Stable RNA interference-mediated RBM10 knockdown clones were less susceptible to TNF-α-mediated apoptosis, and had decreased sTNF-α protein levels. Elevated levels of TNF-α associated with RBM10 overexpression resulted from increased TNF-α transcription, not TNF-α mRNA stabilization. These results suggest that RBM10 has the ability to modulate apoptosis, and that it does so via a mechanism involving alterations to TNFR super family-mediated signaling. These data provide the first direct evidence that human RBM10 can function as an apoptosis modulator and cytokine expression regulator.

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 score0.020
Threshold uncertainty score0.202

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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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

Citations47
Published2012
Admission routes2
Has abstractyes

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