Glucocorticoids regulate innate immunity in a model of multiple sclerosis: reciprocal interactions between the A1 adenosine receptor and β‐arrestin‐1 in monocytoid cells
Bibliographic record
Abstract
Desensitization of seven transmembrane receptors (7TMRs), which are modulated by the beta-arrestins, leads to altered G protein activation. The A1 adenosine receptor (A1AR) is an antiinflammatory 7TMR exhibiting reduced expression and activity in both multiple sclerosis (MS) and the murine MS model, experimental autoimmune encephalomyelitis (EAE) in monocytoid cells. Herein, we report that beta-arrestin-1 expression was increased in brains of MS patients relative to non-MS brains, whereas A1AR expression was concomitantly reduced. This inverse relationship between beta-arrestin-1 and A1AR was confirmed in cultured monocytoid cells as beta-arrestin-1 overexpression resulted in a down-regulation of A1AR together with the internalization of the surface receptor. Moreover, a physical interaction between beta-arrestin-1 and A1AR was demonstrated in monocytoid cells. Proinflammatory cytokines regulated the A1AR/beta-arrestin-1 interactions, while A1AR activation also modulated proinflammatory cytokines expression. During EAE, beta-arrestin-1 and A1AR expression in the spinal cord displayed a similar pattern compared to that observed in MS brains. EAE-induced neuroinflammation and neurobehavioral deficits were suppressed by glucocorticoid treatments, accompanied by concurrent reduced beta-arrestin-1 and enhanced A1AR expression. Thus, the interplay between beta-arrestin-1 and A1AR in the central nervous system during neuroinflammation represents a reciprocal regulatory mechanism through which neuroprotective therapeutic strategies for neuroinflammatory diseases might be further developed.
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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".