Beneficial Effects of Metformin and/or Salicylate on Palmitate- or TNFα-Induced Neuroinflammatory Marker and Neuropeptide Gene Regulation in Immortalized NPY/AgRP Neurons
Bibliographic record
Abstract
Neuropeptide Y (NPY)/Agouti-related peptide (AgRP)-expressing neurons in the hypothalamus induce feeding and decrease energy expenditure. With consumption of a diet high in fat, there is an increase in circulating saturated free fatty acids, including palmitate, leading to the development of neuroinflammation and secretion of cytokines, such as TNFα, and in turn activation of the canonical IKKβ/NFκB cascade. We describe a model of palmitate- and TNFα-induced neuroinflammation in a functionally characterized, immortalized NPY/AgRP-expressing cell model, mHypoE-46, to study whether the anti-diabetic metformin alone or in combination with the anti-inflammatory agent salicylate can ameliorate these detrimental effects. Treatment with palmitate increased mRNA expression of feeding peptides Npy and Agrp, and inflammatory cytokines Tnfa and Il-6, whereas treatment with TNFα increased mRNA expression of Npy, Nfkb, Ikba, Tnfa, and Il-6. The effects of metformin and/or sodium salicylate on these genes were assessed. Metformin increased phosphorylation of AMPK and S6K, while sodium salicylate increased phospho-AMPK and decreased phospho-S6K, but neither had any effect on phospho-ERK, -JNK or -p38 in the mHypoE-46 NPY/AgRP neurons. Furthermore, we utilized a pre-treatment and/or co-treatment paradigm to model potential clinical regimens. We determined co-treatment with metformin or sodium salicylate alone was successful in alleviating changes observed in feeding peptide mRNA regulation, whereas a preventative pre-treatment with metformin and sodium salicylate together was able to alleviate palmitate- and TNFα-induced induction of NPY and/or AgRP mRNA levels. These results highlight important differences in reactive versus preventative treatments on palmitate- and TNFα-induced neuroinflammation in NPY/AgRP 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) | 0.001 | 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".