A multi-omics approach identifies that human antigen R (HuR) controls multiple cellular pathways in idiopathic pulmonary fibrosis (IPF) pathogenesis
Bibliographic record
Abstract
Rationale: Idiopathic pulmonary fibrosis (IPF), a chronic and irreversible disease, is characterized by permanent scarring of the lung that results in respiratory failure. A key driver of this scarring is the accumulation of myofibroblasts, induced by transforming growth factor β (TGF-β) producing excessive amounts of extracellular matrix (ECM) proteins. Recently, we showed human antigen R (HuR) drives the differentiation of normal lung fibroblasts (NLFs) into myofibroblasts in response to TGF-β. We hypothesized, dysregulated function of HuR in IPF-LFs drives disease pathogenesis. Methods: NLFs and IPF-LFs were obtained from the University of Michigan Interstitial Lung Disease Biorepository. Fibroblasts were treated with TGF-β (5 ng/ml) following HuR knockdown with siRNA. Then, RNA-sequencing (RNA-Seq) as well as mass spectrometry (MS)-based proteomics were performed to comprehensively evaluate the changes in HuR-regulated RNA and proteins, respectively, under myofibroblast differentiating conditions. Results: RNA-Sequencing and proteomics analysis revealed that in both NLFs and IPF-LFs, HuR controlled numerous genes and proteins involved in pathways associated with ECM organization, and response to wounding pathways implicated in IPF. HuR controls the expression of proteins associated with IPF pathogenesis, including those involved in the ECM (COL1A1), cell survival (IFGBP2), and protein translation (EIF4) in response to TGF-β. Conclusion: This study provides novel information on the critical function of HuR in lung fibroblast differentiation establishing HuR as a driver of multiple pathways in IPF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".