The transmembrane glycoprotein Gpnmb is required for the immune and fibrotic responses during zebrafish heart regeneration
Bibliographic record
Abstract
Abstract Myocardial infarction occurs when coronary supply of oxygen and nutrients to part of the heart is interrupted. In contrast to adult mammals, adult zebrafish have a unique ability to regenerate their heart after cardiac injury. Several processes are involved in this regenerative response including inflammation, coronary endothelial cell proliferation and revascularization, endocardial expansion, cardiomyocyte repopulation, and transient scar formation. To identify potential regulators of zebrafish cardiac regeneration, we profiled the transcriptome of regenerating coronary endothelial cells at 7 days post cryoinjury (dpci) and observed the significant upregulation of dozens of genes including gpnmb . Gpnmb (glycoprotein non-metastatic melanoma protein B) is a transmembrane glycoprotein implicated in inflammation resolution and tissue regeneration. Transcriptomic profiling data of cryoinjured zebrafish hearts reveal that gpnmb is mostly expressed by macrophages. To investigate gpnmb function during zebrafish cardiac regeneration, we generated a full locus deletion (FLD) allele. We find that after cardiac cryoinjury, animals lacking gpnmb exhibit neutrophil retention and decreased macrophage recruitment as well as reduced myofibroblast numbers. Moreover, loss of gpnmb impairs coronary endothelial cell regeneration and cardiomyocyte dedifferentiation. Transcriptomic analyses of cryoinjured gpnmb mutant hearts identified enhanced collagen gene expression and the activation of extracellular matrix (ECM) related pathways. Furthermore, gpnmb mutant hearts exhibit larger fibrotic scars revealing additional defects in cardiac regeneration. Altogether, these data indicate that gpnmb expressing macrophages modulate inflammation and ECM deposition after cardiac cryoinjury in zebrafish and further highlight the importance of this subset of immune cells to support a regenerative response.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".