Profiling neuroendocrine gene expression changes following fadrozole-induced estrogen decline in the female goldfish
Bibliographic record
Abstract
Teleost fish represent unique models to study the role of neuroestrogens because of the extremely high activity of brain aromatase (AroB; the product of cyp19a1b). Aromatase respectively converts androstenedione and testosterone to estrone and 17beta-estradiol (E2). Specific inhibition of aromatase activity by fadrozole has been shown to impair estrogen production and influence neuroendocrine and reproductive functions in fish, amphibians, and rodents. However, very few studies have identified the global transcriptomic response to fadrozole-induced decline of estrogens in a physiological context. In our study, sexually mature prespawning female goldfish were exposed to fadrozole (50 mcirog/l) in March and April when goldfish have the highest AroB activity and maximal gonadal size. Fadrozole treatment significantly decreased serum E2 levels (4.7 times lower; P = 0.027) and depressed AroB mRNA expression threefold in both the telencephalon (P = 0.021) and the hypothalamus (P = 0.006). Microarray expression profiling of the telencephalon identified 98 differentially expressed genes after fadrozole treatment (q value <0.05). Some of these genes have shown previously to be estrogen responsive in either fish or other species, including rat, mouse, and human. Gene ontology analysis together with functional annotations revealed several regulatory themes for physiological estrogen action in fish brain that include the regulation of calcium signaling pathway and autoregulation of estrogen receptor action. Real-time PCR verified microarray data for decreased (activin-betaA) or increased (calmodulin, ornithine decarboxylase 1) mRNA expression. These data have implications for our understanding of estrogen actions in the adult vertebrate brain.
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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".