The Molecular Phenotype of Endocapillary Proliferation: Novel Therapeutic Targets for IgA Nephropathy
Bibliographic record
Abstract
IgA nephropathy (IgAN) is a clinically and pathologically heterogeneous disease. Endocapillary proliferation is associated with higher risk of progressive disease, and clinical studies suggest that corticosteroids mitigate this risk. However, corticosteroids are associated with protean cellular effects and significant toxicity. Furthermore the precise mechanism by which they modulate kidney injury in IgAN is not well delineated. To better understand molecular pathways involved in the development of endocapillary proliferation and to identify novel specific therapeutic targets, we evaluated the glomerular transcriptome of microdissected kidney biopsies from 22 patients with IgAN. Endocapillary proliferation was defined according to the Oxford scoring system independently by 3 nephropathologists. We analyzed mRNA expression using microarrays and identified transcripts differentially expressed in patients with endocapillary proliferation compared to IgAN without endocapillary lesions. Next, we employed both transcription factor analysis and in silico drug screening and confirmed that the endocapillary proliferation transcriptome is significantly enriched with pathways that can be impacted by corticosteroids. With this approach we also identified novel therapeutic targets and bioactive small molecules that may be considered for therapeutic trials for the treatment of IgAN, including resveratrol and hydroquinine. In summary, we have defined the distinct molecular profile of a pathologic phenotype associated with progressive renal insufficiency in IgAN. Exploration of the pathways associated with endocapillary proliferation confirms a molecular basis for the clinical effectiveness of corticosteroids in this subgroup of IgAN, and elucidates new therapeutic strategies for IgAN.
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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) | 0.001 | 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 | 0.001 |
| 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".