Extracellular matrix deposition drives disease progression and reduces rapamycin response in lymphangioleiomyomatosis
Bibliographic record
Abstract
Rationale Lymphangioleiomyomatosis (LAM) is a rare cystic lung disease driven by nodules containing TSC2 −/− “LAM cells” and recruited LAM-associated fibroblasts. Although rapamycin reduces lung function loss, some patients continue to decline, meaning additional therapies are needed. Objectives To investigate how the LAM nodule environment affects LAM cell proliferation and the response to rapamycin. Methods Proteins altered in advanced LAM were identified using shotgun proteomics and immunohistochemistry in tissue from closely phenotyped patients. Genes associated with rapamycin insensitivity on LAM derived extracellular matrix were identified by RNA sequencing and validated using pharmacological inhibitors. Results More advanced disease was associated a greater decline in forced expiratory volume in 1 s when treated with rapamycin (p=0.005). In advanced LAM, using proteomics analysis, an upregulation of protein clusters comprising extracellular matrix, glucose metabolism and the actin cytoskeleton was identified. RNA sequencing and immunohistochemistry confirmed expression of collagens I and VI in LAM-associated fibroblasts and LAM nodules, and increased markers of collagen turnover in patient serum (p=0.0048). Growth of LAM patient-derived cells in vitro was faster on LAM associated fibroblast-derived extracellular matrix (p<0.0001) and incompletely suppressed by rapamycin. RNA sequencing identified upregulation of pathways driving cell cycle control, transcription and metabolism by extracellular matrix. Tractable, pro-proliferative, upregulated genes included CDK7 , GAS6, PLAUR and PLAU. Inhibitors of these pathways reduced LAM cell proliferation and enhanced the antiproliferative effect of rapamycin. Conclusions Extracellular matrix deposition upregulates the expression of genes which may blunt the response to rapamycin, but offer additional therapeutic opportunities for patients with established LAM.
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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".