Which actin genes are necessary for zebrafish heart development and function?
Bibliographic record
Abstract
Abstract Heart failure is the number one cause of mortality in the world, contributed to by cardiovascular disease. Many diseases of the heart muscle are caused by mutations in genes encoding contractile proteins, including cardiac actin mutations. Zebrafish are an advantageous system for modeling cardiac disease since embryos can develop without a functional heart. However, genome duplication in the teleost lineage creates a unique obstacle by increasing the number of genes involved in heart development. Four actin genes are expressed in the zebrafish heart: acta1b; actc1c; and duplicates of actc1a on chromosome 19 and 20. Here, we characterize the actin genes involved in early zebrafish heart development using in situ hybridization and CRISPR targeting to determine which gene is best to model changes seen in human patients with heart disease. The actc1a and acta1b genes are predominant during embryonic heart development, resulting in severe cardiac phenotypes when targeted with CRISPRs. Targeting these two cardiac genes with CRISPRs simultaneously results in a more severe phenotype than their individual counterparts, with the results suggesting compensation for lost actin genes by other actin paralogues. Given the duplication of the actc1a gene, we recommend acta1b as the best gene for targeted cardiac actin research.
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) | 0.000 | 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) | 0.003 | 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".