Cytokine generation, promoter activation, and oxidant-independent NF-κB activation in a transfectable human neutrophilic cellular model
Bibliographic record
Abstract
BACKGROUND: Human neutrophils are key players of innate immunity, and influence inflammatory and immune reactions through the production of numerous cytokines and chemokines. Despite major advances in our understanding of this important functional response of neutrophils, the short lifespan of these cells and their resistance to transfection have always been an obstacle to the detailed dissection of signaling pathways and effector responses that is often possible in other cell types. RESULTS: Here, we report that granulocytic differentiation of human PLB-985 cells with DMSO yields cells that are neutrophil-like with respect to surface markers, acquisition of responsiveness to physiological neutrophil stimuli (fMLP, LPS), cytokine expression and production profile, and transcription factor activation profile (NF-kappaB, C/EBP, AP-1, STAT). We also show that granulocytic PLB-985 cells can be reliably tranfected by nucleofection in a rapid and efficient manner. Indeed, we overexpressed several proteins and luciferase constructs into these cells. In particular, overexpression of a dominant negative IkappaB-alpha confirmed the central role of NF-kappaB in the production of cytokines by granulocytes. Moreover, the use of PLB-985 granulocytes in which the NADPH oxidase is inactive due to the targeted disruption of a key component (gp91phox) revealed that NF-kappaB activation and kappaB-dependent responses are independent of endogenous reactive oxygen intermediates in these cells. Antioxidant studies performed in primary human neutrophils support this conclusion. CONCLUSION: Our results unveil a new facet of the NF-kappaB system of human granulocytes, and pave the way for deciphering signal transduction pathways and promoter activation in these cells.
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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) | 0.001 | 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) | 0.001 | 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".