PREDICTORS OF TRANSPLANT VASCULOPATHY AND GLOMERULOPATHY
Roslyn B. Mannon, Director of Research, Alabama Transplant Center; Professor, Department of Medicine, Division of Nephrology; Department of Surgery, Division of Transplantation, University of Alabama at Birmingham, Birmingham, AL, USA
1. To understand the methodology utilizing low density real time PCR arrays.
2. To recognize the potential factors mediating transplant glomerulopathy.
3. To identify novel strategies to diagnose and monitor for late kidney allograft injury.
Long-term kidney allograft survival continues to improve modestly, despite dramatic improvements in acute rejection rates and short term patient and graft survivals (1). Identification of biomarkers of allograft failure and the development of tools for their interpretation is of critical interest, both in providing disease detection in a more sensitive and specific fashion, and in allowing sufficient lead time for intervention. Additionally, such markers may allow for risk assessment and medical-regimen tailoring that is personalized to provide optimum outcomes.
Transplant glomerulopathy (TG) is a disease of the kidney allograft initiated by endothelial injury. Morphologically, there is widening of the subendothelial space with accumulation of debris, mesangial interpositioning and matrix deposition in the glomerular capillary wall, and capillary wall double-contouring in the absence of immune complex deposition (2). The etiology of TG is under considerable scrutiny. Prior studies implicated an antibody mediated response (3-5), but this has not been consistently demonstrated (6-7). Accompanying this lesion may be evidence of chronic injury, including interstitial fibrosis and tubular atrophy (IF/TA), the hallmarks of chronic allograft nephropathy (8). Clinical presentation often occurs a year or more after transplantation, although in the context of protocol kidney biopsies, light microscopic changes may be seen earlier, with associated proteinuria, hypertension, and a progressive decline in function culminating in graft loss (9). Importantly, there is no specific effective therapeutic strategy beyond augmentation of immunosuppression. Thus, identifying pathogenic mediators not only for therapeutic purposes but also for early identification may lead to improved outcomes.
We have assessed the potential of a novel diagnostic method utilizing custom low density gene expression arrays and machine learning algorithms in an effort to determine the transcriptional features associated with TG and to begin to identify biomarkers that may be indicative of TG. Initial data analysis using conventional statistical methods confirms the pro-inflammatory state of this lesion. Incorporation of these data utilizing machine-learning software, however, has derived statistically significant yet substantially novel associations between individual transcripts. Moreover,
the resulting model provides insight into the probable pathogenesis of TG and a set of potential biomarkers to test and characterize recipients at risk for disease. These results highlight the hypothesis-generating potential of this method by elucidating potential pathways for investigation and the decision-supportive utility of defined, quantitative classification models of disease versus health states.
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