Palmitic acid triggers inflammatory responses in N42 cultured hypothalamic cells partially via ceramide synthesis but not via TLR4
Bibliographic record
Abstract
A high-fat diet induces hypothalamic inflammation in rodents which, in turn, contributes to the development of obesity by eliciting both insulin and leptin resistance. However, the mechanism by which long-chain saturated fatty acids trigger inflammation is still contentious. To elucidate this mechanism, the effect of fatty acids on the expression of the pro-inflammatory cytokines IL-6 and TNFα was investigated in the mHypoE-N42 hypothalamic cell line (N42). N42 cells were treated with lauric acid (LA) and palmitic acid (PA). PA challenge was carried out in the presence of either a TLR4 inhibitor, a ceramide synthesis inhibitor (L-cycloserine), oleic acid (OA) or eicosapentaenoic acid (EPA). Intracellular ceramide accumulation was quantified using LC-ESI-MS/MS. PA but not LA upregulated IL-6 and TNFα. L-cycloserine, OA and EPA all counteracted PA-induced intracellular ceramide accumulation leading to a downregulation of IL-6 and TNFα. However, a TLR4 inhibitor failed to inhibit PA-induced upregulation of pro-inflammatory cytokines.In conclusion, PA induced the expression of IL-6 and TNFα in N42 neuronal cells independently of TLR4 but, partially, via ceramide synthesis with OA and EPA being anti-inflammatory by decreasing PA-induced intracellular ceramide build-up. Thus, ceramide accumulation represents one on the mechanisms by which PA induces inflammation in neurons.
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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 | 0.001 |
| 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".