PTEN regulates vagal-insulin signaling to optimize autonomic output determining peripheral inflammatory and metabolic homeostasis
Bibliographic record
Abstract
The vagus nerve (VN) is a major component of the parasympathetic nervous system that regulates glucose and energy homeostasis. However, the specific molecular signaling pathways within the VN that regulates this homeostasis remain unclear. Here, we show that vagal neuron-specific deletion of phosphatase and tensin homolog (Pten), the endogenous negative regulator of PI3K, led to increased vagal output. Intriguingly, dopaminergic signaling genes were upregulated, correlating with elevated sympathetic nerve density and increased norepinephrine levels in adipose tissue of vagal Pten-deficient mice. These mice were also protected against high-fat diet-induced obesity, insulin resistance, and systemic inflammation. To investigate insulin-specific PI3K signaling within the VN, we generated mice with vagal neuron-specific insulin receptor deletion that resulted in exacerbation of metabolic defects, which was rescued by concomitant Pten deletion. In summary, we show that insulin-PI3K-PTEN axis within vagal neurons is essential in optimizing the autonomic output that determines peripheral inflammatory and metabolic homeostasis. The vagus nerve is known to promote rest and digest, to counter physiologic stresses. Here, authors show that insulin signaling plays a critical role in optimizing autonomic function to maintain metabolic and inflammatory homeostasis.
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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".