Facing the Challenges of Neuropeptide Gene Knockouts: Why Do They Not Inhibit Reproduction in Adult Teleost Fish?
Bibliographic record
Abstract
Genetic manipulation of teleost endocrine systems started with transgenic overexpression of pituitary growth hormone. Such strategies enhance growth and reduce fertility, but the fish still breed. Genome editing using transcription activator-like effector nuclease in zebrafish and medaka has established the role of follicle stimulating hormone for gonadal development and luteinizing hormone for ovulation. Attempts to genetically manipulate the hypophysiotropic neuropeptidergic systems have been less successful. Overexpression of a gonadotropin-releasing hormone (gnrh) antisense in common carp delays puberty but does not block reproduction. Knockout of Gnrh in zebrafish does not impact either sex, while in medaka this blocks ovulation in females without affecting males. Spawning success is not reduced by knockout of the kisspeptins and receptors, agouti-related protein, agouti signaling peptide or spexin. Hypotheses for the lack of effect of these genome edits are presented. Over evolutionary time, teleosts have lost the median eminence typical of mammals. There is consequently direct innervation of gonadotrophs, with the possibility of independent regulation by >20 neurohormones. Removal of a few may have minimal impact. Neuropeptide knockout could leave co-expressed stimulators of gonadotropins functionally intact. Genetic compensation in response to loss of protein function may maintain sufficient reproduction. The species differences in hypothalamo-hypophysial anatomy could be an example of compensation over the evolutionary timescale as teleosts diversified and adapted to new ecological niches. The key neuropeptidergic systems controlling teleost reproduction remain to be uncovered. Classical neurotransmitters are also regulators of luteinizing hormone release, but have yet to be targeted by genome editing. Their essentiality for reproduction should also be explored.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".