Systems biology of autosomal dominant polycystic kidney disease (ADPKD): computational identification of gene expression pathways and integrated regulatory networks
Bibliographic record
Abstract
To elucidate the molecular pathways that modulate renal cyst growth in ADPKD, we performed global gene profiling on cysts of different size (<1 ml, n = 5; 10-20 ml, n = 5; >50 ml, n = 3) and minimally cystic tissue (MCT, n = 5) from five PKD1 human polycystic kidneys using Affymetrix HG-U133 Plus 2.0 arrays. We used gene set enrichment analysis to identify overrepresented signaling pathways and key transcription factors (TFs) between cysts and MCT. We found down-regulation of kidney epithelial restricted genes (e.g. nephron segment-specific markers and cilia-associated cystic genes such as HNF1B, PKHD1, IFT88 and CYS1) in the renal cysts. On the other hand, PKD1 cysts displayed a rich profile of gene sets associated with renal development, mitogen-mediated proliferation, cell cycle progression, epithelial-mesenchymal transition, hypoxia, aging and immune/inflammatory responses. Notably, our data suggest that up-regulation of Wnt/beta-catenin, pleiotropic growth factor/receptor tyrosine kinase (e.g. IGF/IGF1R, FGF/FGFR, EGF/EGFR, VEGF/VEGFR), G-protein-coupled receptor (e.g. PTGER2) signaling was associated with renal cystic growth. By integrating these pathways with a number of dysregulated networks of TFs (e.g. SRF, MYC, E2F1, CREB1, LEF1, TCF7, HNF1B/ HNF1A and HNF4A), our data suggest that epithelial dedifferentiation accompanied by aberrant activation and cross-talk of specific signaling pathways may be required for PKD1 cyst growth and disease progression. Pharmacological modulation of some of these signaling pathways may provide a potential therapeutic strategy for ADPKD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".