Akt-Mediated Proinflammatory Response of Mononuclear Phagocytes Infected with <i>Burkholderia cenocepacia</i> Occurs by a Novel GSK3β-Dependent, IκB Kinase-Independent Mechanism
Bibliographic record
Abstract
The environmental bacterium Burkholderia cenocepacia causes opportunistic lung infections in immunocompromised individuals, particularly in patients with cystic fibrosis. Infections in these patients are associated with exacerbated inflammation leading to rapid decay of lung function, and in some cases resulting in cepacia syndrome, which is characterized by a fatal acute necrotizing pneumonia and sepsis. B. cenocepacia can survive intracellularly in macrophages by altering the maturation of the phagosome, but very little is known on macrophage responses to the intracellular infection. In this study, we have examined the role of the PI3K/Akt signaling pathway in B. cenocepacia-infected monocytes and macrophages. We show that PI3K/Akt activity was required for NF-κB activity and the secretion of proinflammatory cytokines during infection with B. cenocepacia. In contrast to previous observations in epithelial cells infected with other Gram-negative bacteria, Akt did not enhance IκB kinase or NF-κB p65 phosphorylation, but rather inhibited GSK3β, a negative regulator of NF-κB transcriptional activity. This novel mechanism of modulation of NF-κB activity may provide a unique therapeutic target for controlling excessive inflammation upon B. cenocepacia infection.
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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".