Probabilistic (Bayesian) Modeling of Gene Expression in Transplant Glomerulopathy
Abstract
Transplant glomerulopathy (TG) is associated with rapid decline in glomerular filtration rate and poor outcome. We utilized low-density arrays with a novel probabilistic analysis to characterize relationships between gene transcripts and the development of TG in allograft recipients. Retrospective review identified TG in 10.8% of 963 core biopsies from 166 patients; patients with stable function (SF) were studied for comparison. The biopsies were analyzed for expression of 87 genes related to immune function and fibrosis using real-time PCR, and a Bayesian model was generated and validated to predict histopathology based on gene expression. A total of 57 individual genes were increased in TG compared with SF biopsies (p<0.05). The Bayesian analysis identified critical relationships between ICAM-1, IL- 10, CCL3, CD86, VCAM-1, MMP-9, MMP-7, and LAMC2 and allograft pathology. Moreover, Bayesian models predicted TG when derived from either immune function {AUC (95% CI) of 0.875 (0.675-0.999) p=0.004} or fibrosis {AUC (95% CI) of 0.859 (0.754-0.963), p<0.001} gene networks. Critical pathways in the Bayesian models were also analyzed using the Fisher exact test and had p-values < 0.005. This study demonstrates that evaluating quantitative gene expression profiles with Bayesian modeling can identify significant transcriptional associations that have the potential to support the diagnostic capability of allograft histology. This integrated approach has broad implications in the field of transplant diagnostics.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2010
- Accession Number
- ADA534766
Entities
People
- David B. Leeser
- David E. Kleiner
- Douglas K. Tadaki
- Eric A. Elster
- Jason S. Hawksworth
- John S. Eberhardt
- Michael Ring
- Oriena Cheng
- Roslyn B Mannon
- Trevor S. Brown
Organizations
- Naval Medical Research Center