K. S. Famulski

Understanding the Causes of Kidney Transplant Failure: The Dominant Role of Antibody-Mediated Rejection and Nonadherence

We prospectively studied kidney transplants that progressed to failure after a biopsy for clinical indications, aiming to assign a cause to every failure. We followed 315 allograft recipients who underwent indication biopsies at 6 days to 32 years posttransplant. Sixty kidneys progressed to failure in the follow‐up period (median 31.4 months). Failure was rare after T‐cell–mediated rejection and acute kidney injury and common after antibody‐mediated rejection or glomerulonephritis. We developed rules for using biopsy diagnoses, HLA antibody and clinical data to explain each failure. Excluding four with missing information, 56 failures were attributed to four causes: rejection 36 (64%), glomerulonephritis 10 (18%), polyoma virus nephropathy 4 (7%) and intercurrent events 6 (11%). Every rejection loss had evidence of antibody‐mediated rejection by the time of failure. Among rejection losses, 17 of 36 (47%) had been independently identified as nonadherent by attending clinicians. Nonadherence was more frequent in patients who progressed to failure (32%) versus those who survived (3%). Pure T‐cell–mediated rejection, acute kidney injury, drug toxicity and unexplained progressive fibrosis were not causes of loss. This prospective cohort indicates that many actual failures after indication biopsies manifest phenotypic features of antibody‐mediated or mixed rejection and also underscores the major role of nonadherence.

Improving gene set analysis of microarray data by SAM-GS

BackgroundGene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).ResultsUsing a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53.ConclusionWe conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.

Molecular Diagnosis of Antibody‐Mediated Rejection in Human Kidney Transplants

Antibody‐mediated rejection is the major cause of kidney transplant failure, but the histology‐based diagnostic system misses most cases due to its requirement for C4d positivity. We hypothesized that gene expression data could be used to test biopsies for the presence of antibody‐mediated rejection. To develop a molecular test, we prospectively assigned diagnoses, including C4d‐negative antibody‐mediated rejection, to 403 indication biopsies from 315 patients, based on histology (microcirculation lesions) and donor‐specific HLA antibody. We then used microarray data to develop classifiers that assigned antibody‐mediated rejection scores to each biopsy. The transcripts distinguishing antibody‐mediated rejection from other conditions were mostly expressed in endothelial cells or NK cells, or were IFNG‐inducible. The scores correlated with the presence of microcirculation lesions and donor‐specific antibody. Of 45 biopsies with scores >0.5, 39 had been diagnosed as antibody‐mediated rejection on the basis of histology and donor‐specific antibody. High scores were also associated with unanimity among pathologists that antibody‐mediated rejection was present. The molecular score also strongly predicted future graft loss in Cox regression analysis. We conclude that microarray assessment of gene expression can assign a probability of ABMR to transplant biopsies without knowledge of HLA antibody status, histology, or C4d staining, and predicts future failure.

Scoring Total Inflammation Is Superior to the Current Banff Inflammation Score in Predicting Outcome and the Degree of Molecular Disturbance in Renal Allografts

Emerging molecular analysis can be used as an objective and independent assessment of histopathological scoring systems. We compared the existing Banff i‐score to the total inflammation (total i‐) score for assessing the molecular phenotype in 129 renal allograft biopsies for cause. The total i‐score showed stronger correlations with microarray‐based gene sets representing major biological processes during allograft rejection. Receiver operating characteristic curves showed that total‐i was superior (areas under the curves 0.85 vs. 0.73 for Banff i‐score, p = 0.012) at assessing an abnormal cytotoxic T‐cell burden, because it identified molecular disturbances in biopsies with advanced scarring. The total‐i score was also a better predictor of graft survival than the Banff i‐score and essentially all current diagnostic Banff categories. The exception was antibody‐mediated rejection which is able to predict graft loss with greater specificity (96%) but at low sensitivity (38%) due to the fact that it only applies to cases with this diagnosis. The total i‐score is able to achieve moderate sensitivities (60–80%) with losses in specificity (60–80%) across the whole population. Thus, the total i‐score is superior to the current Banff i‐score and most diagnostic Banff categories in predicting outcome and assessing the molecular phenotype of renal allografts.

Calcium binding proteins in the sarcoplasmic/endoplasmic reticulum of muscle and nonmuscle cells

In this paper we review some of the large quantities of information currently available concerning the identification, structure and function of Ca2+-binding proteins of endoplasmic and sarcoplasmic reticulum membranes. The review places particular emphasis on identification and discussion of Ca2+ ‘storage’ proteins in these membranes. We believe that the evidence reviewed here supports the contention that the Ca2+-binding capacity of both calsequestrin and calreticulin favor their contribution as the major Ca2+-binding proteins of muscle and nonmuscle cells, respectively. Other Ca2+-binding proteins discovered in both endoplasmic reticulum and sarcoplasmic reticulum membranes probably contribute to the overall Ca2+ storage capacity of these membrane organelles, and they also play other important functional role such as posttranslational modification of newly synthesized proteins, a cytoskeletal (structural) function, or movement of Ca2+ within the lumen of the sarcoplasmic/endoplasmic reticulum towards the storage sites.

Molecular Diagnosis of T Cell-Mediated Rejection in Human Kidney Transplant Biopsies

Histologic diagnosis of T cell‐mediated rejection is flawed by subjective assessments, nonspecific lesions and arbitrary rules. This study developed a molecular test for T cell‐mediated rejection. We used microarray results from 403 kidney transplant biopsies to derive a classifier assigning T cell‐mediated rejection scores to all biopsies, and compared these with histologic assessments. The score correlated with histologic lesions of T cell‐mediated rejection (infiltrate, tubulitis). The accuracy of the classifier for the histology diagnoses was 89%. Very high and low molecular scores corresponded with unanimity among three pathologists on the presence or absence of T cell‐mediated rejection, respectively. The molecular score had low sensitivity (50%) and positive predictive value (62%) for the histology diagnoses. However, histology showed similar disagreement between pathologists—only 45–56% sensitivity of one pathologist with diagnoses of T cell‐mediated rejection by another. Discrepancies between molecular scores and histology were mostly when histology was ambiguous (“borderline”) or unreliable, e.g. in cases with scarring or inflammation induced by tissue injury. Vasculitis (isolated v‐lesion TCMR) was particularly discrepant, with most cases exhibiting low TCMR scores. We propose new rules to integrate molecular tests and histology into a precision diagnostic system that can reduce errors, ambiguity and interpathologist disagreement.

Changes in the Transcriptome in Allograft Rejection: IFN-γ-Induced Transcripts in Mouse Kidney Allografts

We used Affymetrix Microarrays to define interferon‐γ (IFN‐γ)‐dependent, rejection‐induced transcripts (GRITs) in mouse kidney allografts. The algorithm included inducibility by recombinant IFN‐γ in kidneys of three normal mouse strains, increase in kidney allografts in three strain combinations and less induction in IFN‐γ‐deficient allografts. We identified 40 transcripts, which were highly IFN‐γ inducible (e.g. Cxcl9, ubiquitin D, MHC), and 168 less sensitive to IFN‐γ in normal kidney. In allografts, expression of GRITs was intense and consistent at all time points (day 3 through 42). These transcripts were partially dependent on donor IFN‐γ receptors (IFN‐γrs): receptor‐deficient allografts manifested up to 76% less expression, but some transcripts were highly dependent (ubiquitin D) and others relatively independent (Cxcl9). Kidneys of hosts rejecting allografts showed expression similar to that observed with IFN‐γ injections. Many GRITs showed transient IFN‐γ‐dependent increase in isografts, peaking at day 4–5. GRITs were increased in heart allografts, indicating them as generalized feature of alloresponse. Thus, expression of rejection‐induced transcripts is robust and consistent in allografts, reflecting the IFN‐γ produced by the alloresponse locally and systemically, acting via host and donor IFN‐γr, as well as local IFN‐γ production induced by post‐operative stress.

The Transcriptome of the Implant Biopsy Identifies Donor Kidneys at Increased Risk of Delayed Graft Function

Improved assessment of donor organ quality at time of transplantation would help in management of potentially usable organs. The transcriptome might correlate with risk of delayed graft function (DGF) better than conventional risk factors. Microarray results of 87 consecutive implantation biopsies taken postreperfusion in 42 deceased (DD) and 45 living (LD) donor kidneys were compared to clinical and histopathology‐based scores. Unsupervised analysis separated the 87 kidneys into three groups: LD, DD1 and DD2. Kidneys in DD2 had a greater incidence of DGF (38.1 vs. 9.5%, p < 0.05) than those in DD1. Clinical and histopathological risk scores did not discriminate DD1 from DD2. A total of 1051 transcripts were differentially expressed between DD1 and DD2, but no transcripts separated DGF from immediate graft function (adjusted p < 0.01). Principal components analysis revealed a continuum from LD to DD1 to DD2, i.e. from best to poorest functioning kidneys. Within DD kidneys, the odds ratio for DGF was significantly increased with a transcriptome‐based score and recipient age (p < 0.03) but not with clinical or histopathologic scores. The transcriptome reflects kidney quality and susceptibility to DGF better than available clinical and histopathological scoring systems.

An integrated view of molecular changes, histopathology and outcomes in kidney transplants.

Data‐driven approaches to deteriorating kidney transplants, incorporating histologic, molecular and HLA antibody findings, have created a new understanding of transplant pathology and why transplants fail. Transplant dysfunction is best understood in terms of three elements: diseases, the active injury–repair response and the cumulative burden of injury. Progression to failure is mainly attributable to antibody‐mediated rejection, nonadherence and glomerular disease. Antibody‐mediated rejection usually develops late due to de novo HLA antibodies, particularly anti‐class II, and is often C4d negative. Pure treated T cell‐mediated rejection does not predispose to graft loss because it responds well, even with endothelialitis, but it may indicate nonadherence. The cumulative burden of injury results in atrophy‐fibrosis (nephron loss), arterial fibrous intimal thickening and arteriolar hyalinosis, but these are not progressive without ongoing disease/injury, and do not explain progression. Calcineurin inhibitor toxicity has been overestimated because burden‐of‐injury lesions invite this default diagnosis when diseases such as antibody‐mediated rejection are missed. Disease/injury triggers a stereotyped active injury–repair response, including de‐differentiation, cell cycling and apoptosis. The active injury–repair response is the strongest correlate of organ function and future progression to failure, but should always prompt a search for the initiating injury or disease.

The molecular phenotype of kidney transplants.

Microarray studies of kidney transplant biopsies provide an opportunity to define the molecular phenotype. To facilitate this process, we used experimental systems to annotate transcripts as members of pathogenesis‐based transcript sets (PBTs) representing biological processes in injured or diseased tissue. Applying this annotation to microarray results revealed that changes in single molecules and PBTs reflected a large‐scale coordinate disturbance, stereotyped across various diseases and injuries, without absolute specificity of individual molecules or PBTs for rejection. Nevertheless, expression of molecules and PBTs was quantitatively specific: IFNG effects for rejection; T cell and macrophage transcripts for T cell‐mediated rejection; endothelial and NK transcripts for antibody‐mediated rejection. Various diseases and injuries induced the same injury–repair response, undetectable by histopathology, involving epithelium, stroma and endothelium, with increased expression of developmental, cell cycle and apoptosis genes and decreased expression of differentiated epithelial features. Transcripts reflecting this injury–repair response were the best correlates of functional disturbance and risk of future graft loss. Late biopsies with atrophy‐fibrosis, reflecting their cumulative burden of injury, displayed more transcripts for B cells, plasma cells and mast cells. Thus the molecular phenotype is best described in terms of three elements: specific diseases, including rejection; the injury–repair response and the cumulative burden of injury.