Diurnal profiling of neuroendocrine genes in murine heart, and shift in proopiomelanocortin gene expression with pressure-overload cardiac hypertrophy
Bibliographic record
Abstract
Neuroendocrine peptides express biologic activity relevant to the cardiovascular system, including regulating heart rate and blood pressure, though little is known about the mechanisms involved. Here, we investigated neuroendocrine gene expression underlying diurnal physiology of the heart. We first used microarray and RT-PCR analysis and demonstrate the simultaneous expression of neuroendocrine genes in normal murine heart, including POMC, GnRH, neuropeptide Y, leptin receptor, GH-releasing hormone, cocaine- and amphetamine-regulated transcript, proglucagon, and galanin. We examined diurnal gene expression profiles, with cosinar bioinformatics to evaluate statistically significant rhythms. The POMC gene exhibits a day/night, circadian or diurnal, pattern of expression in heart, and we postulated that this may be important to cardiac growth and renewal. POMC diurnal gene rhythmicity is altered in pressure-overload cardiac hypertrophy, when compared with control heart, and levels increased at the dark-to-light transition times. These findings are also consistent with the proposal that neuropeptides mediate adverse remodeling processes, such as occur in pathologic hypertrophy. To investigate cellular responses, we screened three cell lines representing fibroblasts, cardiac myocytes, and vascular smooth muscle cells (NIH3T3, heart line 1, and mouse vascular smooth muscle cell line 1 (Movas-1) respectively). POMC mRNA expression is the most notable in Movas-1 cells and, furthermore, exhibits rhythmicity with culture synchronization. Taken together, these results highlight the diverse neuroendocrine mRNA expression profiles in cardiovasculature, and provide a novel model vascular culture system to research the role these neuropeptides play in organ health, integrity, and disease.
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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 | 0.000 |
| 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".