Human Inflammatory Neutrophils Express Genes Encoding Peptidase Inhibitors: Production of Elafin Mediated by NF-κB and CCAAT/Enhancer-Binding Protein β
Bibliographic record
Abstract
Abstract The concept of plasticity of neutrophils is highlighted by studies showing their ability to transdifferentiate into APCs. In this regard, transdifferentiated neutrophils were found at inflammatory sites of autoimmune arthritis (AIA). Exposure of neutrophils to inflammatory stimuli prolongs their survival, thereby favoring the acquisition of pathophysiologically relevant phenotypes and functions. By using microarrays, quantitative RT-PCR, and ELISAs, we showed that long-lived (LL) neutrophils obtained after 48 h of culture in the presence of GM-CSF, TNF, and IL-4 differentially expressed genes related to apoptosis, MHC class II, immune response, and inflammation. The expression of anti-inflammatory genes mainly of peptidase inhibitor families is upregulated in LL neutrophils. Among these, the PI3 gene encoding elafin was the most highly expressed. The de novo production of elafin by LL neutrophils depended on a synergism between GM-CSF and TNF via the activation and cooperativity of C/EBPβ and NF-κB pathways, respectively. Elafin concentrations were higher in synovial fluids (SF) of patients with AIA than in SF of osteoarthritis. SF neutrophils produced more elafin than blood counterparts. These results are discussed with respect to implications of neutrophils in chronic inflammation and the potential influence of elafin in AIA.
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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".