Precision Medicine Approach Identifies Patients with IgA Nephropathy at Risk for Progression Using Endothelin Activation Signatures
Bibliographic record
Abstract
Background: IgA nephropathy (IgAN) is the most common glomerulonephritis globally, with up to 40% of patients at risk of progressing to ESKD. Endothelin (ET) A receptor activation results in mesangial cell (MC) activation, proteinuria, inflammation, and fibrosis, all considered hallmarks of IgAN progression, suggesting the potential for therapeutic benefit of ETA antagonists. The aim of our study was to identify intra-renal transcriptional signatures of ET-activation to stratify patients at high risk of IgAN progression Methods: We used two approaches to establish a transcriptional signature of ET-activation. First, using a targeted approach, an ET-activation network was generated using three publicly available datasets, produced a geneset of 60 transcripts to create an activity score which was assessed in kidney biopsy profiles in patients with IgAN (n= 25) from the European Renal cDNA Bank (ERCB). In addition, an ET-activation signature was also generated experimentally via RNAseq profiling of primary human MCs simulated with ET1 (4nM) +/- the selective ETA antagonist atrasentan (1nM, 25nM, n=3/group). Pairwise differential gene expression and gene set enrichment analysis (GSEA) was performed. Results: The targeted analysis showed that the ET-activity score correlated with increased proteinuria (r=0.42, p=0.05) and decreased eGFR (r=-0.47, p=0.02) in patients with IgAN. The transcript network showed enrichment in endothelial and mesangial cell clusters in renal single cell RNASeq profiles. Differential expression analysis identified the ET gene network was reversed by atrasentan in MCs (25nM, n=780 genes, q£0.05). GSEA in MCs revealed up-regulation of cell proliferation, inflammatory and fibrotic networks, with ET1 treatment, which were blocked by atrasentan. Conclusions: We generated an ET-activation score using a systems biology approach to stratify patients with IgAN. Intra-renal ET-activation signatures were associated with progression, providing additional support for the therapeutic potential of ETA receptor blockade in IgAN patients at high risk of progression. Ongoing work is focused on optimizing the signature, extending findings to additional cohorts and identifying mechanistic biomarkers Funding: Other NIH Support - Federal grant, Commercial Support - Chinook Therapeutics
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".