LPS-induced release of IL-6 from glia modulates production of IL-1β in a JAK2-dependent manner
Bibliographic record
Abstract
BACKGROUND: Compelling evidence has implicated neuroinflammation in the pathogenesis of a number of neurodegenerative conditions. Chronic activation of both astrocytes and microglia leads to excessive secretion of proinflammatory molecules such as TNF α, IL-6 and IL-1 β with potentially deleterious consequences for neuronal viability. Many signaling pathways involving the mitogen-activated protein kinases (MAPKs), nuclear factor κ B (NF κ B) complex and the Janus kinases (JAKs)/signal transducers and activators of transcription (STAT)-1 have been implicated in the secretion of proinflammatory cytokines from glia. We sought to identify signaling kinases responsible for cytokine production and to delineate the complex interactions which govern time-related responses to lipopolysaccharide (LPS). METHODS: We examined the time-related changes in certain signaling events and the release of proinflammatory cytokines from LPS-stimulated co-cultures of astrocytes and microglia isolated from neonatal rats. RESULTS: TNF α was detected in the supernatant approximately 1 to 2 hours after LPS treatment while IL-1 β and IL-6 were detected after 2 to 3 and 4 to 6 hours, respectively. Interestingly, activation of NF κ B signaling preceded release of all cytokines while phosphorylation of STAT1 was evident only after 2 hours, indicating that activation of JAK/STAT may be important in the up-regulation of IL-6 production. Additionally, incubation of glia with TNF α induced both phosphorylation of JAK2 and STAT1 and the interaction of JAK2 with the TNF α receptor (TNFR1). Co-treatment of glia with LPS and recombinant IL-6 protein attenuated the LPS-induced release of both TNF α and IL-1 β while potentiating the effect of LPS on suppressor of cytokine signaling (SOCS)3 expression and IL-10 release. CONCLUSIONS: These data indicate that TNF α may regulate IL-6 production through activation of JAK/STAT signaling and that the subsequent production of IL-6 may impact on the release of TNF α, IL-1 β and IL-10.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".