Thomas F. Mueller

Transplant Glomerulopathy, Late Antibody-Mediated Rejection and the ABCD Tetrad in Kidney Allograft Biopsies for Cause

To define the relative frequency of phenotypes of transplant glomerulopathy, we retrospectively reviewed the findings in 1036 biopsies for clinical indications from 1320 renal transplant patients followed in our clinics between 1997 and 2005. Transplant glomerulopathy, defined by double contours of glomerular basement membranes (D), was diagnosed in 53 biopsies (5.1%) from 41 patients (3.1%) at a median of 5.5 years post‐transplant (range 3.8–381 months). In cases with D, we studied the frequency of circulating anti‐HLA alloantibody (A), peritubular capillary basement membrane multilayering (B) and peritubular capillary C4d deposition (C). B was present in 48 (91%) of D biopsies. C4d staining by indirect immunofluorescence was detected in 18 of 50 D biopsies studied (36%). By Flow PRA® Screening or ELISA, A was detected in 33 (70%) in 47 D cases with available sera, of which 28/33 or 85% were donor‐specific. Class II (13/33) or class I and II (17/33) were more common than class I (3/33) antibodies. Thus 73% of transplant glomerulopathy has evidence of alloantibody‐mediated injury (A and/or C), with ABCD and ABD being the common phenotypes in biopsies for cause. The remaining 27%, mostly BD, may be a different disease or a stage in which A and C are undetectable.

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.

Microarray Analysis of Rejection in Human Kidney Transplants Using Pathogenesis-Based Transcript Sets

Microarrays offer potential for objective diagnosis and insights into pathogenesis of allograft rejection. We used mouse transplants to annotate pathogenesis‐based transcript sets (PBTs) that reflect major biologic events in allograft rejection—cytotoxic T‐cell infiltration, interferon‐γ effects and parenchymal deterioration. We examined the relationship between PBT expression, histopathologic lesions and clinical diagnoses in 143 consecutive human kidney transplant biopsies for cause. PBTs correlated strongly with one another, indicating that transcriptome disturbances in renal transplants have a stereotyped internal structure. This disturbance was continuous, not dichotomous, across rejection and nonrejection. PBTs correlated with histopathologic lesions and were the highest in biopsies with clinically apparent rejection episodes. Surprisingly, antibody‐mediated rejection had changes similar to T‐cell mediated rejection. Biopsies lacking PBT disturbances did not have rejection. PBTs suggested that some current Banff histopathology criteria are unreliable, particularly at the cut‐off between borderline and rejection. Results were validated in 51 additional biopsies. Thus many transcriptome changes previously described in rejection are features of a large‐scale disturbance characteristic of rejection but occurring at lower levels in many forms of injury. PBTs represent a quantitative measure of the inflammatory disturbances in organ transplants, and a new window on the mechanisms of these changes.

FOXP3 Expression in Human Kidney Transplant Biopsies Is Associated with Rejection and Time Post Transplant but Not with Favorable Outcomes

Expression of the transcription factor forkhead box P3 (FOXP3) in transplant biopsies is of interest due to its role in a population of regulatory T cells. We analyzed FOXP3 mRNA expression using RT‐PCR in 83 renal transplant biopsies for cause in relationship to histopathology, clinical findings and expression of pathogenesis‐based transcript sets assessed by microarrays. FOXP3 mRNA was higher in rejection (T‐cell and antibody‐mediated) than nonrejection. Surprisingly, some native kidney controls also expressed FOXP3 mRNA. Immunostaining for FOXP3 was consistent with RT‐PCR, showing interstitial FOXP3+ lymphocytes, even in some native kidney controls. FOXP3 expression correlated with interstitial inflammation, tubulitis, interstitial fibrosis, tubular atrophy, C4d positivity, longer time posttransplant, younger donors, class II panel reactive antibody >20% and transcript sets reflecting inflammation and injury, but unlike these features was time dependent. In multivariate analysis, higher FOXP3 mRNA was independently associated with rejection, T‐cell‐associated transcripts, younger donor age and longer time posttransplant. FOXP3 expression did not correlate with favorable graft outcomes, even when the analysis was restricted to biopsies with rejection. Thus FOXP3 mRNA expression is a time‐dependent feature of inflammatory infiltrates in renal tissue. We hypothesize that time‐dependent entry of FOXP3‐positive cells represents a mechanism for stabilizing inflammatory sites.

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.

Gene-set analysis and reduction

Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between self-contained versus competitive methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of core members that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.

Molecular Correlates of Scarring in Kidney Transplants: The Emergence of Mast Cell Transcripts

In the Banff consensus, infiltrates in areas of scarring are ignored. This study aimed to characterize the molecular correlates and clinical significance of scarring and inflammation in scarred areas.

Molecular Correlates of Renal Function in Kidney Transplant Biopsies

The molecular changes in the parenchyma that reflect disturbances in the function of kidney transplants are unknown. We studied the relationships among histopathology, gene expression, and renal function in 146 human kidney transplant biopsies performed for clinical indications. Impaired function (estimated GFR) correlated with tubular atrophy and fibrosis but not with inflammation or rejection. Functional deterioration before biopsy correlated with inflammation and tubulitis and was greater in cases of rejection. Microarray analysis revealed a correlation between impaired renal function and altered expression of sets of transcripts consistent with tissue injury but not with those consistent with cytotoxic T cell infiltration or IFN-gamma effects. Multivariate analysis of clinical variables, histologic lesions, and transcript sets confirmed that expression of injury-related transcript sets independently correlated with renal function. Analysis of individual genes confirmed that the transcripts with the greatest positive or negative correlations with renal function were those suggestive of response to injury and parenchymal dedifferentiation not inflammation. We defined new sets of genes based on individual transcripts that correlated with renal function, and these highly correlated with the previously developed injury sets and with atrophy and fibrosis. Thus, in biopsies performed for clinical reasons, functional disturbances are reflected in transcriptome changes representing tissue injury and dedifferentiation but not the inflammatory burden.

Expression of B Cell and Immunoglobulin Transcripts Is a Feature of Inflammation in Late Allografts

To assess the significance of B‐cell and plasma cell infiltrates in renal allografts, we compared expression of B‐cell‐associated transcripts (BATs) and immunoglobulin transcripts (IGTs) to histopathology and function in 177 renal allograft biopsies for clinical indications. BAT and IGT expression correlated with immunostaining for B cells and plasma cells and with expression of B‐cell and plasma cell transcription factors. BATs and IGTs were increased in both T‐cell‐mediated and antibody‐mediated rejection. BAT and IGT scores were strongly related to time posttransplant: biopsies <5 months expressed less BATs and did not express increased IGTs. In contrast, T‐cell‐associated transcripts were independent of time posttransplant. In biopsies ≥5 months, BAT and IGT scores correlated with interstitial inflammation, tubular atrophy and interstitial fibrosis. By regression tree analysis, the only variables independently correlated with BATs and IGTs were time and inflammation. Expression of BATs and IGTs correlated with renal function, but this relationship was due to differences in early versus late biopsies: BATs and IGTs were not related to function or future function after correcting for time.