Rapid generation of a sdhb loss-of-function zebrafish model for secreting pheochromocytomas and paragangliomas
Bibliographic record
Abstract
Genotype plays a central role in the comprehensive management of pheochromocytomas and paragangliomas, highlighting the critical need for specific in vivo genetic models. Yet, animal models fall short of fully recapitulating the biological complexity of these tumours. We generated first-generation loss-of-function zebrafish models for sdhb, a canonical PPGL-associated gene, using CRISPR/Cas9. Sdhb-CRISPants exhibit increased heart rates, reduced swimming activity and premature death. In whole fish extracts, normetanephrine (NM), metanephrine (MN), and dopamine (DA) levels were about three times higher in sdhb CRISPants than in control larvae. In the bathing medium, NM and MN were also significantly elevated, along with 3-MT. Complementary metabolic and transcriptomic profiling revealed that sdhb CRISPants exhibit a clear signature of Complex II dysfunction and upregulation of genes involved in the hypoxia response, angiogenesis, stress response, and glycolysis. Our work validates the relevance of CRISPant zebrafish models to study the pathogenicity of PPGL-causing genetic variants in vivo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".