Control of the Inflammatory Macrophage Transcriptional Signature by miR-155
Bibliographic record
Abstract
Inflammatory M1 spectrum macrophages protect from infection but can cause inflammatory disease and tissue damage, whereas alternatively activated/M2 spectrum macrophages reduce inflammation and promote tissue repair. Modulation of macrophage phenotype may be therapeutically beneficial and requires further understanding of the molecular programs that control macrophage differentiation. A potential mechanism by which macrophages differentiate may be through microRNA (miRNA), which bind to messenger RNA and post-transcriptionally modify gene expression, cell phenotype and function. We hypothesized that the inflammation-associated miRNA, miR-155, would be required for typical development of macrophage inflammatory state. miR-155 was rapidly up-regulated over 100-fold in inflammatory M1(LPS + IFN-γ), but not M2(IL-4), macrophages. Inflammatory genes Inos, Il1b and Tnfa and their corresponding protein or enzymatic products were reduced up to 72% in miR-155 knockout mouse M1(LPS + IFN-γ) macrophages, but miR-155 deficiency did not affect expression of the M2-associated gene Arg1 in M2(IL-4) macrophages. Additionally, a miR-155 oligonucleotide inhibitor efficiently suppressed Inos and Tnfa gene expression in wild-type M1(LPS + IFN-γ) macrophages. Comparative transcriptional profiling of unstimulated and M1(LPS + IFN-γ) macrophages derived from wild-type (WT) and miR-155 knockout (KO) mice revealed that half (approximately 650 genes) of the signature we previously identified in WT M1(LPS + IFN-γ) macrophages was dependent on miR-155. Real-Time PCR of independent datasets confirmed that miR-155 contributed to suppression of its validated mRNA targets Inpp5d, Tspan14, Ptprj and Mafb and induction of Inos, Il1b, Tnfa, Il6 and Il12. Overall, these data indicate that miR-155 plays an essential role in driving the inflammatory phenotype of M1(LPS+ IFN-γ) macrophages.
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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".