NPAS4 is an allostatic regulator of POMC neuronal activity during diet-induced obesity
Bibliographic record
Abstract
Abstract Rationale Obesity is characterized by a chronic positive energy balance and altered function of cell types that regulate food intake. These cell types include proopiomelanocortin (POMC) neurons in the arcuate nucleus (ARC) of the hypothalamus that detect peripheral signals and promote a reduction in food intake upon activation. Downstream of neuronal activation, activity- regulated genes such as Neuronal PAS domain protein 4 (Npas4) are induced as part of the response to environmental stimuli. Npas4 is known to have cytoprotective roles in both neurons and pancreatic beta cells. A previous Npas4 knockout study in both mouse pancreatic beta cells and ARC neurons implied a potential role of Npas4 in regulating food intake. However, the specific sites of Npas4 action in the ARC are unknown. We hypothesized that Npas4 in POMC neurons of the ARC has a role in regulating food intake during obesity. Methods We quantified Npas4 expression in POMC neurons of the arcuate nucleus in mice exposed to various positive energy states known to activate POMC neurons using RNAscope fluorescent in situ hybridization. Next, we generated adult male mice with a conditional Npas4 knockout specifically in their ARC POMC neurons (POMC-NPAS4 KO) and metabolically characterized them for 30 weeks on regular chow or 60% high-fat diet (HFD) at room temperature. In addition, we performed single cell RNA sequencing (scRNA-seq) on microdissected ARC tissue and neighbouring regions from fasted or 1hr refed POMC-NPAS4 KO mice and controls at 6 weeks of HFD, in order to identify Npas4-regulated and feeding- regulated transcriptional changes in POMC neurons. Results Npas4 was expressed in POMC neurons, and its expression was induced in response to positive energy states such as refeeding, oral glucose, and acute HFD feeding. HFD-fed POMC- NPAS4 KO males showed significantly reduced body weight starting at 10 weeks of HFD, and weighed 8-10 grams less than controls by 30 weeks. With metabolic cages and manual food intake measurements, we determined this difference was not the result of increased energy expenditure or physical activity, but was due to decreased food intake prior to the observed lack of gain in body weight. Using the ARC single-cell dataset, we found that POMC neurons of KO mice showed an enhanced refeeding-induced transcriptional response, dysregulated immediate early gene expression in response to refeeding, and reduced expression of genes encoding GABA-A receptor subunits. Furthermore, cell-to-cell communication analysis revealed that POMC neurons of KO mice specifically lost inhibitory GABAergic signaling inputs, some of which came from agouti-related protein (AgRP) neurons, and gained excitatory glutamatergic signaling inputs compared to POMC neurons of control littermates. Conclusions Taken together, the results suggest that activity-dependent expression of Npas4 in POMC neurons tempers the activity of these cells upon overnutrition. Loss of Npas4 causes cell- autonomous loss of the capacity to sense nutrient intake. Molecularly, this is driven by reduced expression of inhibitory GABA-A receptors and an overall increase in POMC neuronal activity, leading to decreased food intake and decreased weight gain. In conclusion, for the first time we report a role for the transcription factor Npas4 in POMC neurons of the ARC, and demonstrate it plays an indispensable role in controlling feeding behavior in states of overnutrition.
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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) | 0.002 | 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".