Looking into the crystal chip: can microarrays predict graft function?

Ischemic injury of the renal allograft is a critical early perioperative insult that augments the risk of acute tubular necrosis and delayed graft function (DGF). In addition to ischemia, reperfusion injury, endothelial dysfunction, and renovascular resistance alterations also contribute to early tubular injury and ultimately tubular necrosis (1). Finally, leukocyte infiltration and increased immunogenicity of the injured tissue set the stage for a secondary greater risk for acute rejection, further predisposing these kidneys to progressive atrophic and fibrotic changes in the tubulointerstitial compartment (2), which is a major cause for late graft loss (3). Known triggers of this early renal allograft injury, such as the use of brain-dead donors over living donors, expanded criteria donors, non-heart beating donors, and prolonged cold ischemia times, result in a greater risk of DGF and worse long-term graft survival (1). DGF is thus an important clinical and pathophysiological entity, and any means to better understand its trigger mechanisms and mitigate its occurrence are very relevant. Given the current organ shortage, many of the underlying risk factors for DGF are difficult to always control. Strategies to prevent ischemia–reperfusion injury have focused on improving the preservation solutions, the use of pulsatile perfusion machines instead of cold storage, the additional efforts to avoid hypovolemia of the recipient, and local or systemic administration of vasodilatory agents. Understanding and harnessing molecular targets of early DGF injury could potentially modulate more specific and ubiquitously functional pathways, ideally in the form of pretreatment of the donor organ after recovery but before transplantation. With the introduction of genomic microarrays in the mid-1990s, together with the availability of other highthroughput tools such as proteomics and metabolomics, paradigms of performing science have shifted from a hypothesis-driven to a hypothesis-generating approach, allowing scientists and clinicians to broaden the scope of research by identifying the involvement of previously unknown and potentially targetable molecular mechanisms in complex disease processes. Genomic studies on in vitro model systems of the effect of hypoxia on human proximal tubular epithelial cell cultures (4) and animal models of ischemia–reperfusion injury (5– 8) offer valuable molecular insights into renal injury but cannot distinguish the direct effects of ischemia from the effects of reperfusion. The article by Mas et al. in this issue of Transplantation (9) joins and extends other human studies that have examined gene expression patterns of donor kidneys immediately before reperfusion (10). Earlier studies have investigated differences in gene expression between living and deceased donors (11, 12), the effects of donor age on gene expression patterns (13), genes associated with delayed graft function (11), or the association between baseline gene expression profiles with late renal allograft function (14). The study by Mas et al. (9) compares prereperfusion deceased donor biopsy gene expression profiles from 21 donor kidneys with immediate graft function with the profiles observed in 12 biopsies of deceased donor kidneys with DGF (defined as the requirement for dialysis support in the first posttransplant week). It is well known that the intrinsic qualities of the engrafted kidney are a major determinant of DGF, subsequent graft function, and graft survival; thus, a genome scanning approach could highlight unknown pathogenetic mechanisms contributing to DGF or, conversely, protective pathways in the donor kidney with excellent graft function. Sample size, sample selection, independent blinded validation sets of samples, and adequate controls for clinical variables between groups are critical in microarray studies of complex human diseases. An inherent problem with the use of human samples for research is the paucity of wellcontrolled phenotypes and their variables. By producing enormous amounts of data on low sample numbers, microarray studies are extremely prone to experimental bias. The analysis of gene expression differences between pumped (n 14) and unpumped (n 19) kidneys would be difficult to support without blinded validation. In addition, the low yield of significant genes in this study (n 69 probes with a q score 1 Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. 2 Department of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium. 3 Address correspondence to Minnie Sarwal, M.D., M.R. C.P., D.C.H. , Ph.D., Stanford University, G306, 300 Pasteur Drive, Stanford, CA 94304. E-mail: msarwal@stanford.edu Received 22 October 2007. Accepted 29 November 2007. Copyright © 2008 by Lippincott Williams & Wilkins ISSN 0041-1337/08/8504-499 DOI: 10.1097/TP.0b013e31816548c8