Pyroglutamylated RF-amide Peptide (QRFP) Gene Is Regulated by Metabolic Endotoxemia
Bibliographic record
Abstract
Pyroglutamylated RF-amide peptide (QRFP) is involved in the regulation of food intake, thermogenesis, adipogenesis, and lipolysis. The expression of QRFP in adipose tissue is reduced in diet-induced obesity, a mouse model in which plasma concentrations of endotoxins are slightly elevated. The present study investigated the role of metabolic endotoxemia (ME) on QRFP gene regulation. Our results uncovered the expression of QRFP in murine macrophages and cell lines. This expression has been found to be decreased in mice with ME. Low doses of lipopolysaccharide (LPS) transiently down-regulated QRFP by 59% in RAW264.7 macrophages but not in 3T3-L1 adipocytes. The effect of LPS on QRFP expression in macrophages was dependent on the inhibitor of kB kinase and TIR-domain-containing adapter-inducing interferon (IFN)-β (TRIF) but not myeloid differentiation primary response gene 88. IFN-β was induced by ME in macrophages. IFN-β sustainably reduced QRFP expression in macrophages (64%) and adipocytes (49%). IFN-γ down-regulated QRFP (74%) in macrophages only. Both IFNs inhibited QRFP secretion from macrophages. LPS-stimulated macrophage-conditioned medium reduced QRFP expression in adipocytes, an effect blocked by IFN-β neutralizing antibody. The effect of IFN-β on QRFP expression was dependent on phosphoinositide 3-kinase, p38 MAPK, and histone deacetylases. The effect of IFN-γ was dependent on MAPK/ERK kinase 1/2 and histone deacetylases. Macrophage-conditioned medium containing increased amounts of QRFP preserved adipogenesis in adipocytes. In conclusion, LPS induces IFN-β release from macrophages, which reduces QRFP expression in both macrophages and adipocytes in an autocrine/paracrine-dependent manner, suggesting QRFP as a potential biomarker in ME.
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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".