Microarray and Bioinformatics Analysis of Gene Expression in Experimental Membranous Nephropathy
Bibliographic record
Abstract
BACKGROUND: Passive Heymann nephritis (PHN), the best characterized animal model of experimental membranous nephropathy, is characterized by subepithelial immune deposits, podocyte foot processes effacement and massive proteinuria beginning 4 days following disease induction. Although single genes involved in PHN have been studied, no whole genome-wide expression analysis of kidney tissue has been performed. METHODS: Microarray analysis was performed to identify gene expression changes in PHN rat kidneys during the onset of proteinuria. RESULTS: Our results showed that 234 transcripts were differentially expressed in diseased animals compared to controls. Genes exclusively upregulated in diseased animals were mainly required for cell structure and motility, immunity and defense, cell cycle, and developmental processes. The single most increased gene was transgelin (Tagln) showing a 70-fold upregulation in animals with PHN. Protein-protein interaction analysis revealed the following four processes of major relevance in disease manifestation: (i) DNA damage and repair; (ii) changes in the extracellular matrix; (iii) deregulation of cytokines and growth factors, as well as (iv) rearrangements of the cytoskeleton. CONCLUSION: We show for the first time the complex interplay between multiple different genes in experimental membranous nephropathy, supporting a role for genomic approaches to better understanding and defining specific disease processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 0.001 |
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".