Cardiomyocyte-expressed TGFβ signals to fibroblasts to program early heart maturation and adult myocyte identity
Bibliographic record
Abstract
Transforming growth factor β (TGFβ) is a secreted growth factor that is sequestered to the extracellular matrix (ECM) as a latent complex. In adult disease TGFβ release in the heart transforms fibroblasts into a differentiated state that synthesizes more ECM. However, it is not known how TGFβ functions in the early developing heart to impact resident fibroblasts. Here, we observe that deletion of the Tgfb1, Tgfb2, and Tgfb3 genes (TGFβ ligands) from cardiomyocytes in the early developing heart results in cardiac dysfunction by 6 weeks of age with altered fibroblast activity and altered ECM content. Early postnatal hearts from Tgfb1/2/3 cardiomyocyte-deleted mice are dysmorphic and cardiac fibroblasts have incorrect activity and produce inappropriate ECM with reduced stiffness. Gene expression profiling of hearts from myocyte-specific Tgfb1/2/3 deleted mice reveal defects in both cardiomyocyte and fibroblast maturation with ectopic expression of multiple skeletal muscle-specific genes beginning at embryonic day 17.5 and progressing with age. However, cardiomyocyte-specific deletion of TGFβ receptors I/II encoding genes (Tgfbr1/2) or Smad2/3 encoding genes (Smad2/3) do not recapitulate this phenotype suggesting that TGFβ directly programs early heart fibroblast development that in turn specifies cardiomyocyte maturation. Importantly, Col1a2-/-;Col6a2-/- mice with defective cardiac ECM stiffness, mice lacking cardiomyocyte Itgb1 with reduced ECM load sensing, and Tcf21-/- embryos at E17.5 lacking cardiac fibroblasts each fail to generate the same pathologic ECM program with ectopic cardiomyocyte differentiation observed with Tgfb1/2/3 myocyte-specific deletion. These and additional results indicate that TGFβ generated by cardiomyocytes in the embryonic heart mediates fibroblast differentiation that co-evolves the ECM environment that in turn programs cardiomyocyte maturation to establish their identity.
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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) | 0.002 | 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".