Kiss and Tell: Deletion of Kisspeptins and Receptors Reveal Surprising Results
Bibliographic record
Abstract
The well-known expression “Kiss and Tell” usually refers to revealing one's sexual exploits or private matters. In the case of the transcription activator-like effector nucleases gene knockout studies by Tang et al (1), sequential removal of the 2 known kisspeptins and their receptors surprisingly reveals that this system is not required for reproduction. The authors rightly note that this challenges the idea that kisspeptin neurons are central regulators of vertebrate reproduction (eg, Refs. 2, 3 among many). To date, most observations leading to this conclusion are based on studies in mammals, including humans, rats, and mice, and thus may not reflect the diversity of neuroendocrine control mechanisms across the vertebrate groups. On the surface, this lack-of-effect study in zebrafish could be quite discouraging, except in the light of the evolution of neuropeptidergic systems controlling reproductive function. Zebrafish are members of a group of fish called teleosts. Teleosts are wonderfully diverse in their reproductive strategies, often beautiful, economically important, and there are nearly 30 000 species currently known (4). The evolution of these ray-finned fish goes back approximately 200 million years before present, after undergoing the third lineage-specific genome duplication (5). Complete or partial gene duplications in many teleosts have presented real challenges when trying to sort out the evolution and specific functions of reproductive neuropeptides. Many neuropeptides, neurotransmitter synthesis enzymes, and neurohormone receptors duplicated and/or mutated over time, resulting in an array of novel molecular substrates for the evolution of hormone systems. This may be one reason that teleosts are successful, being both widespread in distribution and fecund.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".