Toll-like Receptor-4 (TLR4) Down-regulates MicroRNA-107, Increasing Macrophage Adhesion via Cyclin-dependent Kinase 6
Bibliographic record
Abstract
Toll-like receptors (TLRs) modulate the expression of multiple microRNAs (miRNAs). Here, we report the down-regulation of miR-107 by TLR4 in multiple cell types. The miR-107 sequence occurs in an intron within the sequence encoding the gene for pantothenate kinase 1α (PanK1α), which is regulated by the transcription factor peroxisome proliferator-activating receptor α (PPAR-α). PanK1α is also decreased in response to lipopolysaccharide (LPS). The effect on both miR-107 and PanK1α is consistent with a decrease in PPAR-α expression. We have found that the putative miR-107 target cyclin-dependent kinase 6 (CDK6) expression is increased by TLR4 as a result of the decrease in miR-107. This effect is required for increased adhesion of macrophages in response to LPS, and CDK6-deficient mice are resistant to the lethal effect of LPS. We have therefore identified a mechanism for LPS signaling which involves a decrease in miR-107 leading to an increase in CDK6.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".