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Record W3022579465 · doi:10.1016/j.celrep.2020.107585

MicroRNA-9 Fine-Tunes Dendritic Cell Function by Suppressing Negative Regulators in a Cell-Type-Specific Manner

2020· article· en· W3022579465 on OpenAlexafffund
Brendan Cordeiro, Peter Jeon, Giselle M. Boukhaled, Mario Corrado, Orsolya Lapohos, Dominic G. Roy, Kelsey S. Williams, Russell G. Jones, Samantha Gruenheid, Selena M. Sagan, Connie M. Krawczyk

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsCell biologyDownregulation and upregulationTranscription factormicroRNADendritic cellRepressorBiologyRegulation of gene expressionImmune systemFunction (biology)CellGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

Dendritic cells, cells of the innate immune system, are found in a steady state poised to respond to activating stimuli. Once stimulated, they rapidly undergo dynamic changes in gene expression to adopt an activated phenotype capable of stimulating immune responses. We find that the microRNA miR-9 is upregulated in both bone marrow-derived DCs and conventional DC1s but not in conventional DC2s following stimulation. miR-9 expression in BMDCs and conventional DC1s promotes enhanced DC activation and function, including the ability to stimulate T cell activation and control tumor growth. We find that miR-9 regulated the expression of several negative regulators of transcription, including the transcriptional repressor Polycomb group factor 6 (Pcgf6). These findings demonstrate that miR-9 facilitates the transition of DCs from steady state to mature state by regulating the expression of several negative regulators of DC function in a cell-type-specific manner.

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 categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

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 head0.209
Teacher spread0.200 · 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.

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

Citations16
Published2020
Admission routes2
Has abstractyes

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