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Record W3093697902 · doi:10.1016/j.isci.2020.101726

Severe Impairment of TNF Post-transcriptional Regulation Leads to Embryonic Death

2020· article· en· W3093697902 on OpenAlexfundno aff
Elise Clayer, Destiny Dalseno, Andrew J. Kueh, Derek Lacey, Minhsuang Tsai, Elyas H. Arvell, Verena C. Wimmer, Philippe Bouillet

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
FundersStanford Institute for Research in the Social SciencesAustralian GovernmentCampbell Family Institute for Breast Cancer ResearchNational Health and Medical Research CouncilArthritis AustraliaLeukemia and Lymphoma Society
KeywordsPost-transcriptional regulationTranscriptional regulationTumor necrosis factor alphaDownregulation and upregulationMessenger RNAUntranslated regionCell biologyBiologyTranslational efficiencyInflammationEmbryonic stem cellRegulation of gene expressionCytokineTranslational regulationIn vivoGene expressionTranslation (biology)ImmunologyGeneticsGene

Abstract

fetched live from OpenAlex

Post-transcriptional regulation mechanisms control mRNA stability or translational efficiency via ribosomes, and recent evidence indicates that it is a major determinant of the accurate levels of cytokine mRNAs. Transcriptional regulation of Tnf has been well studied and found to be important for the rapid induction of Tnf mRNA and regulation of the acute phase of inflammation, whereas study of its post-transcriptional regulation has been largely limited to the role of the AU-rich element (ARE), and to a lesser extent, to that of the constitutive decay element (CDE). We have identified another regulatory element (NRE) in the 3 0 UTR of Tnf and demonstrate that ARE, CDE, and NRE cooperate in vivo to efficiently downregulate Tnf expression and prevent autoimmune inflammatory diseases. We also show that excessive TNF may lead to embryonic death.

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 score0.283
Threshold uncertainty score0.172

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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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