Anti-inflammatory effects of α-MSH through p-CREB expression in sarcoidosis like granuloma model
Bibliographic record
Abstract
Lung inflammation due to sarcoidosis is characterized by a complex cascade of immunopathologic events, including leukocyte recruitment and granuloma formation. α-melanocyte stimulating hormone (α-MSH) is a melanocortin signaling peptide with anti-inflammatory properties. We aimed to evaluate the effects of α-MSH in a novel in vitro sarcoidosis model. An in vitro sarcoidosis-like granuloma model was developed by challenging peripheral blood mononuclear cells (PBMCs) derived from patients with confirmed treatment-naïve sarcoidosis with microparticles generated from Mycobacterium abscessus cell walls. Unchallenged PBMCsand developed granulomas were treated daily with 10 μM α-MSH or saline as control. Cytokine concentrations in supernatants of culture and in cell extracts were measured using Illumina multiplex Elisa and western blot, respectively. Gene expression was analyzed using RNA-Seq and RT-PCR. Protein secretion and gene expression of IL-7, IL-7R, IFN-γ, MC1R, NF-κB, phosphorylated NF-κB (p-NF-κB), MARCO, and p-CREB were measured with western blot and RNAseq. A significant increase in IL-7, IL-7R, and IFN-γ protein expression was found in developed granulomas comparing to microparticle unchallenged PBMCs. IL-7, IL-7R, and IFN-γ protein expression was significantly reduced in developed granulomas after exposure to α-MSH compared with saline treated granulomas. Compared with microparticle unchallenged PBMCs, total NF-κB and p-NF-κB were significantly increased in developed granulomas, while expression of p-CREB was not changed. Treatment with α-MSH promoted a significantly higher concentration of p-CREB in granulomas. The anti-inflammatory effects of α-MSH were blocked by specific p-CREB inhibition. α-MSH has anti-inflammatory properties in this in vitro granuloma model, which is an effect mediated by induction of phosphorylation of CREB.
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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) | 0.001 | 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".