Neuroendocrine Regulation of Reproduction in Fish
Bibliographic record
Abstract
Fish is an essential source of food, and it is important to improve fish yield in aquaculture to enhance food production and sustainability. Several lines of research are in progress to increase reproductive capacity in cultured fishes. Hormones play a critical role in fish reproductive success and endocrine approaches are being successfully tested in stimulating fish reproduction. In this context, my thesis research aimed to further characterize three relatively new orphan ligands and their role in the regulation of reproductive hormones and oocyte maturation in fish. Phoenixin-20 (PNX-20) is a newly identified peptide with endocrine-like functions in mammals. Nesfatin-1 is an 82 amino acid multifunctional peptide with hormone-like actions in vertebrates. Nesfatin-1-like peptide (NLP) is 77 amino acids long, resembles nesfatin-1, and share similar biological actions. Based on the available information from mammals and fish, I hypothesized that PNX is stimulatory, while both nesfatin-1 and NLP are inhibitory to reproductive hormones and oocyte maturation in teleosts. I tested this hypothesis using goldfish and zebrafish, two well-characterized models in neuroendocrinology. qPCR-based gene expression results indicated a widespread distribution of PNX in central and peripheral tissues of zebrafish. Immunohistochemical localization of PNX and its putative receptor (SREB3) in the gonads suggested a role of PNX in zebrafish reproduction. In vivo administration of PNX-20 upregulated reproductive regulatory hormonal transcripts in the hypothalamus and gonads of zebrafish. In vitro incubation of PNX-20 upregulated all vitellogenin transcripts in zebrafish liver cells. In addition, incubation of ovarian follicles with PNX-20 promoted oocyte maturation in zebrafish. Together, my research outcomes supported my hypothesis that PNX-20 promotes reproductive functions in zebrafish, by positively influencing hormones, vitellogenesis and oocytes. For both nesfatin-1 and NLP studies, I used male and female goldfish as a research model. Single intraperitoneal (IP) injection of synthetic gfnesfatin-1 and gfNLP (50 ng/g body weight) suppressed several reproductive regulatory hormonal mRNAs in both sexes. These include the gonadotropin-releasing hormones (hypothalamus), kisspeptin system (multiple tissues), gonadotropin β subunits (pituitary), gonadotropin receptors and genes involved in sex steroidogenic synthetic pathway (gonads). In addition, both peptides upregulated the expression of several reproductive suppressors in goldfish, including the gonadotropin inhibitory hormone and anti-Müllerian hormone. Both nesfatin-1 and NLP decreased serum testosterone and estradiol levels in male and female goldfish at 60 minutes post-injection. Incubation of ovarian follicles with NLP suppressed oocyte maturation in zebrafish. Overall, my results suggested that both nesfatin-1 and NLP negatively influence aspects of reproductive biology in fish by suppressing the expression of critical hormonal mRNAs, gonadal steroids, and oocyte maturation. The results supported my hypotheses about nesfatin-1 and NLP. While limitations exist, my research identified PNX-20 as a positive regulator, and both nesfatin-1 and NLP as negative regulators of reproductive biology of fish. This is the major contribution of my thesis research. Future research to extend these findings to male fish and commercially cultured fish, and to understand the mechanism of action of these hormones is essential.
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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) | 0.002 | 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".