Interleukin-1α inhibits transforming growth factor-β1 and β2-induced extracellular matrix production, remodeling and signaling in human lung fibroblasts: Master regulator in lung mucosal repair
Bibliographic record
Abstract
BACKGROUND: Lung fibroblasts play a central role in maintaining lung homeostasis and facilitating repair through the synthesis and organization of the extracellular matrix (ECM). This study investigated the cross-talk between interleukin-1 alpha (IL-1α) and transforming growth factor-β (TGF-β) signaling, two key regulators in tissue repair and fibrosis, in the context of lung fibroblast repair in the healthy lung. RESULTS: Stimulation of lung fibroblasts with TGF-β1 and TGF-β2 induced collagen-I and fibronectin protein expression (p < 0.05), a response inhibited with co-treatment with IL-1α (p < 0.05). Additionally, TGF-β1 and TGF-β2 induced myofibroblast differentiation, and collagen-I gel contraction, which were both suppressed by IL-1α (p < 0.05). In contrast, interleukin (IL)-6, IL-8 and thymic stromal lymphopoietin induced by IL-1α, were unaffected by TGF-β1 or TGF-β2. Mechanistically, IL-1α administration led to the suppression of TGF-β1 and TGF-β2 signaling, through downregulation of mRNA and protein for TGF-β receptor II and the downstream adaptor protein TRAF6, but not through miR-146a that is known to be induced by IL-1α. DISCUSSION: IL-1α acts as a master regulator, modulating TGF-β1 and TGF-β2-induced ECM production, remodeling, and myofibroblast differentiation in human lung fibroblasts, playing a vital role in balancing tissue repair versus fibrosis. Further research is required to understand the dysregulated cross-talk between IL-1α and TGF-β signaling in chronic lung diseases and the exploration of therapeutic opportunities. METHODS: Primary human lung fibroblasts (PHLF) were treated with media control, or 1 ng/ml IL-1α with or without 50 ng/ml TGF-β1 or TGF-β2 for 1, 6 and 72 h. Cell lysates were assessed for the expression of ECM proteins and signaling molecules by western blot, miRNA by qPCR, mRNA by RNA sequencing and cell supernatants for cytokine production by ELISA. PHLFs were also seeded in non-tethered collagen-I gels to measure contraction, and myofibroblast differentiation using confocal microscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".