Function of Latent TGFβ Binding Protein 4 and Fibulin 5 in Elastogenesis and Lung Development
Bibliographic record
Abstract
Mice deficient in Latent TGFβ Binding Protein 4 (Ltbp4) display a defect in lung septation and elastogenesis. The lung septation defect is normalized by genetically decreasing TGFβ2 levels. However, the elastic fiber assembly is not improved in Tgfb2(-/-) ;Ltbp4S(-/-) compared to Ltbp4S(-/-) lungs. We found that decreased levels of TGFβ1 or TGFβ3 did not improve lung septation indicating that the TGFβ isoform elevated in Ltbp4S(-/-) lungs is TGFβ2. Expression of a form of Ltbp4 that could not bind latent TGFβ did not affect lung phenotype indicating that normal lung development does not require the formation of LTBP4-latent TGFβ complexes. Therefore, the change in TGFβ-level in the lungs is not directly related to Ltbp4 deficiency but probably is a consequence of changes in the extracellular matrix. Interestingly, combination of the Ltbp4S(-/-) mutation with a fibulin-5 null mutant in Fbln5(-/-) ;Ltbp4S(-/-) mice improves the lung septation compared to Ltbp4S(-/-) lungs. Large globular elastin aggregates characteristic for Ltbp4S(-/-) lungs do not form in Fbln5(-/-) ;Ltbp4S(-/-) lungs and EM studies showed that elastic fibers in Fbln5(-/-) ;Ltbp4S(-/-) lungs resemble those found in Fbln5(-/-) mice. These results are consistent with a role for TGFβ2 in lung septation and for Ltbp4 in regulating fibulin-5 dependent elastic fiber assembly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".