NPAS4 Depletion in POMC Neurons Protects From Obesity and Alters the Feeding-regulated Transcriptome in Male Mice
Bibliographic record
Abstract
Immediate early genes (IEGs), such as neuronal PAS domain protein 4 (Npas4), are induced as part of the response to environmental stimuli. In the arcuate nucleus (ARC), proopiomelanocortin (POMC) neurons are critical in detecting peripheral signals to regulate food intake. To date, Npas4 has not been studied in the context of regulating food intake, and its sites of action in the ARC are unknown. We found that Npas4 was induced in POMC neurons by refeeding, oral glucose, and a high-fat diet (HFD). In order to explore the role of NPAS4 in POMC neurons, a conditional knockout approach was used. Male mice with Npas4 knockout in POMC neurons showed significantly reduced body weight starting at 10 weeks of HFD, which was due to decreased food intake. Single-cell RNA sequencing on ARC cells demonstrated that POMC neurons of knockout mice showed an enhanced refeeding-induced transcriptional response, dysregulated IEG expression in response to refeeding, and reduced expression of genes encoding gamma-aminobutyric acid (GABA)-A receptor subunits. Cell-to-cell communication analysis revealed that POMC neurons of knockout mice lost inhibitory GABAergic signaling inputs and gained excitatory glutamatergic signaling inputs. Taken together, these results suggest that Npas4 tempers the activity of POMC neurons and loss of Npas4 causes impairments in nutrient intake sensing. Mechanistically, this results from reduced expression of inhibitory GABA-A receptors and an overall increase in the feeding-induced POMC neuron transcriptional response. In conclusion, we report a role for the transcription factor Npas4 in POMC neurons of the ARC and demonstrate its importance 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".