Development of Novel Tools for Dissection of Central Versus Peripheral Dopamine D2-Like Receptor Signaling in Dysglycemia
Bibliographic record
Abstract
Dopamine (DA) D2-like receptors in both the central nervous system (CNS) and the periphery are key modulators of metabolism. Moreover, disruption of D2-like receptor signaling is implicated in dysglycemia. Yet, the respective metabolic contributions of CNS versus peripheral D2-like receptors, including D2 (D2R) and D3 (D3R) receptors, remain poorly understood. To address this, we developed new pharmacological tools, D2-like receptor agonists with diminished and delayed blood-brain barrier capability, to selectively manipulate D2R/D3R signaling in the periphery. We designated bromocriptine methiodide (BrMeI), a quaternary methiodide analog of D2R/D3R agonist and diabetes drug bromocriptine, as our lead compound based on preservation of D2R/D3R binding and functional efficacy. We then used BrMeI and unmodified bromocriptine to dissect relative contributions of CNS versus peripheral D2R/D3R signaling in treating dysglycemia. Systemic administration of bromocriptine, with unrestricted access to CNS and peripheral targets, significantly improved both insulin sensitivity and glucose tolerance in obese, dysglycemic mice in vivo. In contrast, metabolic improvements were attenuated when access to bromocriptine was restricted either to the CNS through intracerebroventricular administration or delayed access to the CNS via BrMeI. Our findings demonstrate that the coordinated actions of both CNS and peripheral D2-like receptors are required for correcting dysglycemia. Ultimately, the development of a first-generation of drugs designed to selectively target the periphery provides a blueprint for dissecting mechanisms of central versus peripheral DA signaling and paves the way for novel strategies to treat dysglycemia.
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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) | 0.001 | 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".