PDGFRα-positive fibroblasts display a proinflammatory phenotype in infection-driven exacerbation of experimental lung fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a severe diffuse parenchymal lung disease characterized by high mortality rates and poor prognosis. Patients with IPF can develop an acute worsening of the disease, named acute exacerbation (AE-IPF), which can be triggered by bacterial infections. Here we examined the proinflammatory contribution of PDGFRα-positive fibroblasts to S. pneumoniae (Spn) induced exacerbation of AdTGF-β1 induced lung fibrosis in mice. We found that numbers of PDGFRα- positive lung fibroblasts did not change substantially upon Spn driven worsening of established fibrosis. However, transcriptomic profiling along with single-cell RNA sequencing and proteomic profiling of flow-sorted fibroblasts showed a strong plasticity of fibroblast subsets developing a proinflammatory phenotype within 24 h after bacterial challenge. Remarkably, defined fibroblast subsets from AdTGF-β1+Spn treated mice exhibited strongly increased mRNA and protein levels of leukocyte-subset recruiting chemokines including CCL2, CXCL2, CXCL10, CXCL12 and CXCL13, but also other inflammation-related genes like Serum Amyloid A, Lipocalin 2, Haptoglobin, Tetherin, Mucin 16, Mesothelin, Osteopontin and Gremlin 1. At the same time, we found strongly increased TLR2 but not TLR4 mRNA levels in sorted fibroblasts of mice with Spn-driven fibrosis exacerbation. Collectively, the data suggest that in addition to their prominent role as ECM producing cells, subsets of fibroblasts appear to directly contribute to progression of lung fibrosis through the development of proinflammatory secretory phenotype.
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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) | 0.001 | 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 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".