Ram (http://nkdep.nih.gov/NKDEP). Progressors were defined as Acadesine chemical S28463 msds information grafts with a continued decrease in eGFR from transplant (with eGFR <40mL/min/1.73m2 at 24 months post-KT) and histological evidence of IF/TA (TA [ct 1] and IF [ci 1] involving more than 25 of the cortical area) (6). Patients with continuos eGFR 60mL/min/1.73m2 from transplant and normal histology were classified as nonprogressors (25) (biopsy collection mean time 23.6?.5 month's post-KT). Consequently, enrolled patients were classified as either progressors (P, n=30) or nonprogressors (NP, n=31) to CAD. RNA isolation and Microarray Data Analysis Pre-Processing Total RNA was isolated and quality was checked as previously described (18). One-Cycle Target Labeling kit or the 3' IVT Express kit from Affymetrix (Santa Clara, CA) was used following the recommended protocol. Samples were then hybridized to Affymetrix GeneChip Human Genome U133A v2.0 arrays and scanned with a GeneChip Scanner 3000 (GEO accession number (GSE53605). Microarray Expression Analysis RMAexpress was used to normalize probeset data by quantile normalization and summarized with median polish summarization using the Robust Multiarray Average method (21, 22). Quality assessment was performed as previously described (23). A probe set level t-test comparing groups was performed and an adjusted p-value of 0.01 was used as threshold to identify differentially expressed genes. Statistical significance for multivariate analysis was assessed by estimating the q-values for probe set specific false discovery rates (FDR) using the Bioconductor qvalue package (24). Genes with a FDR <5 were considered significant (25, 27). Interaction Networks, Functional Analysis, and Upstream regulators Lists of mRNAs differentially expressed between each condition (with FDR < 5 ), were uploaded in the IPA tool (Ingenuity?Systems, www.ingenuity.com) and analyzed based on the IPA library of canonical pathways (content date 2013-11-08). The significance of the association between each list and a canonical pathway was measured by Fisher's exact test. As a result, a P-value was obtained, determining the probability that the association between the genes in our data set and a canonical pathway can be explained by chance alone. The biological functions that are expected to be increased or decreased according to the gene expression changes in our dataset were identified using the IPA regulation z-score algorithm. A positive or negative z-score value indicates that a function is predicted to be increased or decreased in the study conditions. In order to enhance the stringency of ourAm J Transplant. Author manuscript; available in PMC 2015 May 01.Maluf et al.Pageanalysis, we considered only functions with a z-score 2 or -2. Results were also confirmed through the use of ToppGene (http://toppgene.cchmc.org/).Author Manuscript Author Manuscript Author Manuscript Author ManuscriptResultsPatients and samples A total of 243 allograft biopsies from 182 DD KT recipients were included. From these patients, 18 KT patients underwent CNI-based therapy and 18 underwent CNI sparing protocol (used as normal control). Included samples with CNIT were collected from patients at different times post-KT (range= 6-48 months). The study design is described in the Figure 1. Clinical and demographic characteristics of patients (validation and training sets) are shown in the Table 1. Patient's characteristics are shown in the Table 2. Statistically significant pa.Ram (http://nkdep.nih.gov/NKDEP). Progressors were defined as grafts with a continued decrease in eGFR from transplant (with eGFR <40mL/min/1.73m2 at 24 months post-KT) and histological evidence of IF/TA (TA [ct 1] and IF [ci 1] involving more than 25 of the cortical area) (6). Patients with continuos eGFR 60mL/min/1.73m2 from transplant and normal histology were classified as nonprogressors (25) (biopsy collection mean time 23.6?.5 month's post-KT). Consequently, enrolled patients were classified as either progressors (P, n=30) or nonprogressors (NP, n=31) to CAD. RNA isolation and Microarray Data Analysis Pre-Processing Total RNA was isolated and quality was checked as previously described (18). One-Cycle Target Labeling kit or the 3' IVT Express kit from Affymetrix (Santa Clara, CA) was used following the recommended protocol. Samples were then hybridized to Affymetrix GeneChip Human Genome U133A v2.0 arrays and scanned with a GeneChip Scanner 3000 (GEO accession number (GSE53605). Microarray Expression Analysis RMAexpress was used to normalize probeset data by quantile normalization and summarized with median polish summarization using the Robust Multiarray Average method (21, 22). Quality assessment was performed as previously described (23). A probe set level t-test comparing groups was performed and an adjusted p-value of 0.01 was used as threshold to identify differentially expressed genes. Statistical significance for multivariate analysis was assessed by estimating the q-values for probe set specific false discovery rates (FDR) using the Bioconductor qvalue package (24). Genes with a FDR <5 were considered significant (25, 27). Interaction Networks, Functional Analysis, and Upstream regulators Lists of mRNAs differentially expressed between each condition (with FDR < 5 ), were uploaded in the IPA tool (Ingenuity?Systems, www.ingenuity.com) and analyzed based on the IPA library of canonical pathways (content date 2013-11-08). The significance of the association between each list and a canonical pathway was measured by Fisher's exact test. As a result, a P-value was obtained, determining the probability that the association between the genes in our data set and a canonical pathway can be explained by chance alone. The biological functions that are expected to be increased or decreased according to the gene expression changes in our dataset were identified using the IPA regulation z-score algorithm. A positive or negative z-score value indicates that a function is predicted to be increased or decreased in the study conditions. In order to enhance the stringency of ourAm J Transplant. Author manuscript; available in PMC 2015 May 01.Maluf et al.Pageanalysis, we considered only functions with a z-score 2 or -2. Results were also confirmed through the use of ToppGene (http://toppgene.cchmc.org/).Author Manuscript Author Manuscript Author Manuscript Author ManuscriptResultsPatients and samples A total of 243 allograft biopsies from 182 DD KT recipients were included. From these patients, 18 KT patients underwent CNI-based therapy and 18 underwent CNI sparing protocol (used as normal control). Included samples with CNIT were collected from patients at different times post-KT (range= 6-48 months). The study design is described in the Figure 1. Clinical and demographic characteristics of patients (validation and training sets) are shown in the Table 1. Patient's characteristics are shown in the Table 2. Statistically significant pa.