SET8 participates in lipopolysaccharide-mediated BV2 cell inflammation via modulation of TICAM-2 expression
Bibliographic record
Abstract
Microglial inflammation, involved in the occurrence and development of sepsis-associated encephalopathy, exhibits upregulation of proinflammatory cytokine and proinflammatory enzyme expression, leading to inflammation-induced neuronal cell apoptosis. TIR domain containing adaptor molecule-2 (TICAM-2) participates in lipopolysaccharide (LPS) mediated BV2 cell inflammation. SET8 plays a crucial role in a variety of cellular signal pathways. In this study, we hypothesize that SET8 participates in LPS-mediated microglial inflammation via modulation of TICAM-2 expression. Our data indicated that LPS induced BV2 inflammation via upregulation of TICAM-2 expression. Moreover, LPS treatment inhibited SET8 expression, while it increased activating transcription factor 2 (ATF2) expression. The effects of sh-SET8 and ATF2 overexpression were similar to that of LPS treatments. Inhibition of TICAM-2 expression counteracted sh-SET8-mediated and ATF2 overexpression mediated BV2 cell inflammation. Further, SET8 was found to interact with ATF2. A mechanistic study found that H4K20me1, a downstream target of SET8, and ATF2 enriched at the TICAM-2 promoter region. Luciferase reporter assays indicated that sh-SET8 increased TICAM-2 promoter activity but augmented the effect of ATF2 overexpression on TICAM-2 promoter activity as well. Co-transfection of sh-SET8 with ATF2 overexpression more dramatically increased TICAM-2 expression in BV2 cells. The present study indicated that SET8 interacted with ATF2 to modulate TICAM-2 expression, which participated in LPS-mediated BV2 cell inflammation.
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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 | 0.001 |
| 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) | 0.002 | 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".