Polyomavirus BK Nephropathy-Associated Transcriptomic Signatures: A Critical Reevaluation
Bibliographic record
Abstract
BACKGROUND: Recent work using DNA microarrays has suggested that genes related to DNA replication, RNA polymerase assembly, and pathogen recognition receptors can serve as surrogate tissue biomarkers for polyomavirus BK nephropathy (BKPyVN). METHODS: We have examined this premise by looking for differential regulation of these genes using a different technology platform (RNA-seq) and an independent set 25 biopsies covering a wide spectrum of diagnoses. RESULTS: RNA-seq could discriminate T cell-mediated rejection from other common lesions seen in formalin fixed biopsy material. However, overlapping RNA-seq signatures were found among all disease processes investigated. Specifically, genes previously reported as being specific for the diagnosis of BKPyVN were found to be significantly upregulated in T cell-mediated rejection, inflamed areas of fibrosis/tubular atrophy, as well as acute tubular injury. CONCLUSIONS: In conclusion, the search for virus specific molecular signatures is confounded by substantial overlap in pathogenetic mechanisms between BKPyVN and nonviral forms of allograft injury. Clinical heterogeneity, overlapping exposures, and different morphologic patterns and stage of disease are a source of substantial variability in "Omics" experiments. These variables should be better controlled in future biomarker studies on BKPyVN, T cell-mediated rejection, and other forms of allograft injury, before widespread implementation of these tests in the transplant clinic.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".