Zebrafish models of inflammation in hematopoietic development and disease
Bibliographic record
Abstract
Zebrafish offer an excellent tool for studying the vertebrate hematopoietic system thanks to a highly conserved and rapidly developing hematopoietic program, genetic amenability, optical transparency, and experimental accessibility. Zebrafish studies have contributed to our understanding of hematopoiesis, a complex process regulated by signaling cues, inflammation being crucial among them. Hematopoietic stem cells (HSCs) are multipotent cells producing all the functional blood cells, including immune cells. HSCs respond to inflammation during infection and malignancy by proliferating and producing the blood cells in demand for a specific scenario. We first focus on how inflammation plays a crucial part in steady-state HSC development and describe the critical role of the inflammasome complex in regulating HSC expansion and balanced lineage production. Next, we review zebrafish studies of inflammatory innate immune mechanisms focusing on interferon signaling and the downstream JAK-STAT pathway. We also highlight insights gained from zebrafish models harbouring genetic perturbations in the role of inflammation in hematopoietic disorders such as bone marrow failure, myelodysplastic syndrome, and myeloid leukemia. Indeed, inflammation has been recently identified as a potential driver of clonal hematopoiesis and leukemogenesis, where cells acquire somatic mutations that provide a proliferative advantage in the presence of inflammation. Important insights in this area come from mutant zebrafish studies showing that hematopoietic differentiation can be compromised by epigenetic dysregulation and the aberrant induction of signaling pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".