Splicing Isoforms Associated with TGFβ-Induced Myofibroblast Activation
Bibliographic record
Abstract
ABSTRACT Myofibroblast differentiation is a key process in developmental biology and involved in numerous physiopathology. The gene expression program orchestrating fibroblast to myofibroblast differentiation, as well as its recapitulation by TGFβ stimulation in vitro , is relatively well characterized. Intriguingly, it is known that the splicing isoform EDA+FN1 is a marker and driver of myofibroblast differentiation, but the alternative splicing landscape of myofibroblast is unknown. Here, we performed a high-throughput transcriptomic approach by RNA-Seq in a primary skin fibroblast line and uncover more than 250 splicing isoforms associated with TGFβ-induced myofibroblasts using two different bioinformatic pipelines. This splicing profile highlights a distinct layer of regulation when compared to the global gene expression profile of myofibroblasts. A 5 alternative splicing event (ASE) signature [ ACTN1- 19A/19B; COL5A1 -64A/64B; COL6A3 exon 4; FLNA exon 30 and TPM1 -6a/6b] was further validated by ddPCR and AS-PCR and retrieved in publicly available RNA-Seq datasets describing other TGFβ-stimulated lung and skin fibroblasts. Surprisingly, TGFβ does not induce an EDA+FN1 splicing shift, although it stimulates global fibronectin expression. Thus, we conclude that the 5 ASEs signature may be used as putative universal myofibroblast markers and be of functional significance to myofibroblast formation and biology.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".