A transgenic zebrafish model expressing <i><scp>KIT</scp></i>‐<scp>D</scp>816<scp>V</scp> recapitulates features of aggressive systemic mastocytosis
Bibliographic record
Abstract
Systemic mastocytosis (SM) is a rare myeloproliferative disease without curative therapy. Despite clinical variability, the majority of patients harbour a KIT-D816V mutation, but efforts to inhibit mutant KIT with tyrosine kinase inhibitors have been unsatisfactory, indicating a need for new preclinical approaches to identify alternative targets and novel therapies in this disease. Murine models to date have been limited and do not fully recapitulate the most aggressive forms of SM. We describe the generation of a transgenic zebrafish model expressing the human KIT-D816V mutation. Adult fish demonstrate a myeloproliferative disease phenotype, including features of aggressive SM in haematopoeitic tissues and high expression levels of endopeptidases, consistent with SM patients. Transgenic embryos demonstrate a cell-cycle phenotype with corresponding expression changes in genes associated with DNA maintenance and repair, such as reduced dnmt1. In addition, epcam was consistently downregulated in both transgenic adults and embryos. Decreased embryonic epcam expression was associated with reduced neuromast numbers, providing a robust in vivo phenotypic readout for chemical screening in KIT-D816V-induced disease. This study represents the first zebrafish model of a mast cell disease with an aggressive adult phenotype and embryonic markers that could be exploited to screen for novel agents in SM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".