MicroRNA-9 fine tunes dendritic cell function by suppressing negative regulators
Bibliographic record
Abstract
Abstract MicroRNAs (miRNA) are emerging as important regulators of immune function due to their fast action and ability to regulate programs of gene expression. Dendritic cell (DC) responses to stimuli involves a rapid transition from steady-state an activated state, leading to numerous phenotypic changes underpinned by changes in gene expression. We have found that microRNA-9 (miR-9) expression is rapidly increased upon LPS stimulation. Pathway analysis on predicted miR-9 targets show a significant enrichment of negative regulators of gene expression. We investigated whether miR-9 promotes DC activation through targeting negative regulators. We found that DCs overexpressing miR-9 showed an increased activation phenotype whereas DCs sequestering miR-9 showed blunted activation. Co-culture of miR-9 sequestering DCs with T cells led to decreased T-cell activation, whereas miR-9 overexpressing DCs promoted T cell activation. Mice immunized with miR-9 overexpressing DCs following injection with B16-OVA melanoma cells displayed decreased tumour volume compared to controls. This work demonstrates that miR-9 promotes the activation and function of DCs, adding to the growing evidence that miRNAs are involved in the regulation of immune responses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".