TNF‐α expression in neutrophils and its regulation by glycogen synthase kinase‐3: A potentiating role for lithium
Bibliographic record
Abstract
Glycogen synthase kinase 3 (GSK‐3) is associated with several cellular systems, including immune response. Lithium, a widely used pharmacological treatment for bipolar disorder, is a GSK‐3 inhibitor. GSK‐3α is the predominant isoform in human neutrophils. In this study, we examined the effect of GSK‐3 inhibition on the production of TNF‐α by neutrophils. In the murine air pouch model of inflammation, lithium chloride (LiCl) amplified TNF‐α release. In lipopolysaccharide‐stimulated human neutrophils, GSK‐3 inhibitors mimicked the effect of LiCl, each potentiating TNF‐α release after 4 h, in a concentration‐dependent fashion, by up to a 3‐fold increase (ED 50 of 1 mM for lithium). LiCl had no significant effect on cell viability. A positive association was revealed between GSK‐3 inhibition and prolonged activation of the p38/MNK1/eIF4E pathway of mRNA translation. Using lysine and arginine labeled with stable heavy isotopes followed by quantitative mass spectrometry, we determined that GSK‐3 inhibition markedly increases (by more than 3‐fold) de novo TNF‐α protein synthesis. Our findings shed light on a novel mechanism of control of TNF‐α expression in neutrophils with GSK‐3 regulating mRNA translation and raise the possibility that lithium could be having a hitherto unforeseen effect on inflammatory diseases.—Giambelluca, M. S., Bertheau‐Mailhot, G., Laflamme, C., Rollet‐Labelle, E., Servant, M. J., Pouliot, M. TNF‐α expression in neutrophils and its regulation by glycogen synthase kinase‐3: a potentiating role for lithium. FASEB J. 28, 3679–3690 (2014). www.fasebj.org
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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".