Estrous Cycle Modulation of Feeding and Relaxin-3/Rxfp3 mRNA Expression: Implications for Estradiol Action
Bibliographic record
Abstract
INTRODUCTION: Food intake varies during the ovarian hormone/estrous cycle in humans and rodents, an effect mediated mainly by estradiol. A potential mediator of the central anorectic effects of estradiol is the neuropeptide relaxin-3 (RLN3) synthetized in the nucleus incertus (NI) and acting via the relaxin family peptide-3 receptor (RXFP3). METHODS: We investigated the relationship between RLN3/RXFP3 signaling and feeding behavior across the female rat estrous cycle. We used in situ hybridization to investigate expression patterns of Rln3 mRNA in NI and Rxfp3 mRNA in the hypothalamic paraventricular nucleus (PVN), lateral hypothalamic area (LHA), medial preoptic area (MPA), and bed nucleus of the stria terminalis (BNST), across the estrous cycle. We identified expression of estrogen receptors (ERs) in the NI using droplet digital PCR and assessed the electrophysiological responsiveness of NI neurons to estradiol in brain slices. RESULTS: Rln3 mRNA reached the lowest levels in the NI pars compacta during proestrus. Rxfp3 mRNA levels varied across the estrous cycle in a region-specific manner, with changes observed in the perifornical LHA, magnocellular PVN, dorsal BNST, and MPA, but not in the parvocellular PVN or lateral LHA. G protein-coupled estrogen receptor 1 (Gper1) mRNA was the most abundant ER transcript in the NI. Estradiol inhibited 33% of type 1 NI neurons, including RLN3-positive cells. CONCLUSION: These findings demonstrate that the RLN3/RXFP3 system is modulated by the estrous cycle, and although further studies are required to better elucidate the cellular and molecular mechanisms of estradiol signaling, current results implicate the involvement of the RLN3/RXFP3 system in food intake fluctuations observed across the estrous cycle in female rats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".