Adipose Tissue Expression of PACAP, VIP, and Their Receptors in Response to Cold Stress
Bibliographic record
Abstract
Obesity arises from disrupted energy balance and is caused by chronically higher energy intake compared to expenditure via basal metabolic rate, exercise, and thermogenesis. The brown adipose tissue (BAT), the primary thermogenic organ, has received considerable attention as a potential therapeutic target due to its ability to burn lipids in the production of heat. Pituitary adenylate cyclase-activating polypeptide (PACAP) has been identified as a key regulator of the physiological stress response both centrally and peripherally. While PACAP has been shown to increase thermogenesis by acting at the hypothalamus to increase sympathetic output to BAT, a peripheral role for PACAP-activated thermogenesis has not been studied. We identified PACAP receptor (PAC1, VPAC1/2) expression for the first time in murine BAT and confirmed their expression in white adipose tissues. PAC1 receptor expression was significantly altered in all three adipose tissues studied in response to 3.5-week cold acclimation, with expression patterns differing by depot type. In primary cell culture, VPAC1 was increased in differentiated compared to non-differentiated brown adipocytes, and the same trend was observed for the PACAP-specific receptor PAC1 in gonadal white fat primary cultures. The primary PAC1R mRNA splice variant in interscapular BAT was determined as isoform 2 by RNA-Seq. These results show that PACAP receptors are present in adipose tissues and may have important functional roles in adipocyte differentiation, lipid metabolism, or adipose sensitization to sympathetic signaling in response to thermogenic stimuli.
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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 | 0.001 | 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".