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. 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".