A MiR-142-3p/EGR2 Feedback Circuitry In Human CSF-1 Driven Differentiation of Monocytes Into Macrophages
Bibliographic record
Abstract
Abstract Abstract 2366 Colony-stimulating factor-1 (CSF-1 or M-CSF) triggers the differentiation of human peripheral blood monocytes into macrophages through and integrated cytokine/transcription factors circuitry. Using microarray profiling to explore the role of microRNAs (miRNAs) in this molecular circuitry, we identified the down-regulation of miR-142-3p in human macrophages obtained from CSF-1-treated monocytes. We show that miR-142-3p is a repressor of the transcription factor EGR2 (Early Growth Response 2) through direct 3'UTR interactions. Interestingly, EGR2 binds the promoter of the pre-miR-142-3p gene to negatively regulate its expression, identifying a self-regulatory feedback loop. Enforced expression of miR-142-3p in primary human monocytes as well as decreased expression of miR-142-3p observed in monocytes from patients with a chronic myelomonocytic leukemia further assess the link between miR-142-3p and EGR2 expression in these cells. A chemical inhibition of the Src kinase family prevents the regulation loop induced by CSF-1. Thus, our study uncovers an EGR2/miR-142-3p circuitry which regulates CSF-1 driven differentiation of human monocytes into macrophages. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 |
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 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".