HuR drives lung fibroblast differentiation but not metabolic reprogramming in response to TGF-β and hypoxia
Bibliographic record
Abstract
BACKGROUND: Pulmonary fibrosis is thought to be driven by recurrent alveolar epithelial injury which leads to the differentiation of fibroblasts into α-smooth muscle actin (α-SMA)-expressing myofibroblasts and subsequent deposition of extracellular matrix (ECM). Transforming growth factor beta-1 (TGF-β1) plays a key role in fibroblast differentiation, which we have recently shown involves human antigen R (HuR). HuR is an RNA binding protein that also increases the translation of hypoxia inducible factor (HIF-1α) mRNA, a transcription factor critical for inducing a metabolic shift from oxidative phosphorylation towards glycolysis. This metabolic shift may cause fibroblast differentiation. We hypothesized that under hypoxic conditions, HuR controls myofibroblast differentiation and glycolytic reprogramming in human lung fibroblasts (HLFs). METHODS: H- NMR). Separate experiments were conducted to evaluate the effect of HuR on metabolic reprogramming using siRNA-mediated knock-down. RESULTS: Hypoxia alone had no significant effect on fibroblast differentiation or metabolic reprogramming. While hypoxia- together with TGFβ1- increased mRNA levels of differentiation and glycolysis genes, such as ACTA2, LDHA, and HK2, protein levels of α-SMA and collagen 1 were significantly reduced. Hypoxia induced cytoplasmic translocation of HuR. Knockdown of HuR reduced features of fibroblast differentiation in response to TGF-β1 with and without hypoxia, including α-SMA and the ECM marker collagen I, but had no effect on lactate secretion. CONCLUSIONS: Hypoxia reduced myofibroblasts differentiation and lactate secretion in conjunction with TGF-β. HuR is an important protein in the regulation of myofibroblast differentiation but does not control glycolysis in HLFs in response to hypoxia. More research is needed to understand the functional implications of HuR in IPF pathogenesis.
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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 | 0.000 |
| 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".