Differential cytokine regulation by NF-κB and AP-1 in Jurkat T-cells
Bibliographic record
Abstract
BACKGROUND: Activator protein (AP)-1 and nuclear factor (NF)-kappaB largely control T-cell activation, following binding of foreign antigens to the T-cell receptor leading to cytokine secretion. Elevated levels of pro-inflammatory cytokines and chemokines such as TNF, IL-6 and CXCL8 are associated with several human diseases including cystic fibrosis, pulmonary fibrosis and AIDS. The aim of this study was to investigate the role of the transcription factors, AP-1 and NF-kappaB, in IL-6 and CXCL8 regulation in Jurkat T-cells. RESULTS: Phorbol myristate acetate (PMA) exposure resulted in an up-regulation of AP-1 and down-regulation of NF-kappaB activity, however, exposure to heat killed (HK) Escherichia. coli MG1655 resulted in a dose-dependent increase in NF-kappaB activity without affecting AP-1. The cytokine profile revealed an up-regulation of the chemokine CXCL8 and the pro-inflammatory cytokines TNF, IL-2 and IL-6 following treatment with both PMA and HK E. coli, while the levels of the anti-inflammatory cytokine IL-10 were not affected by PMA but were significantly down-regulated by HK E. coli. AP-1 activation was significantly increased 2 h after PMA exposure and continued to increase thereafter. In contrast, NF-kappaB responded to PMA exposure by a rapid up-regulation followed by a subsequent down-regulation. Increased intracellular Ca2+ concentrations countered the down-regulation of NF-kappaB by PMA, while similar treatment with calcium ionophore resulted in a reduced NF-kappaB activity following induction with HK E. coli. In order to further study NF-kappaB activation, we considered two up-stream signalling proteins, PKC and Bcl10. Phosphorylated-PKC levels increased in response to PMA and HK E. coli, while Bcl10 levels significantly decreased following PMA treatment. Using an NF-kappaB activation inhibitor, we observed complete inhibition of IL-6 expression while CXCL8 levels only decreased by 40% at the highest concentration. Treatment of Jurkat T-cells with PMA in the presence of JNK-inhibitor suppressed both CXCL8 and IL-6 while PKC-inhibitor primarily decreased CXCL8 expression. CONCLUSION: The present study shows that NF-kappaB regulated IL-6 but not CXCL8. This complex regulation of CXCL8 suggests that there is a need to further evaluate the signalling pathways in order to develop new treatment for diseases with elevated CXCL8 levels, such as AIDS and autoimmune diseases.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".