Hnologies, CA, USA) according to standard protocols. Data was analyzed using

Hnologies, CA, USA) according to standard protocols. Data was analyzed using SDS 2.3 software (Applied Biosystems, Life Technologies, Foster City, CA). Mouse allograft and recipient spleen gene expression was assessed using the high throughput Fluidigm BioMark instrument (BioMark; Fluidigm, San Francisco, CA) as described in detail in SM. In brief, cDNA was amplified for 14 target genes using Applied Silmitasertib Biosystems primers and probes. Preamplified cDNA was loaded into a Dynamic 96.96 chip (Fluidigm) for a 40 cycle QPCR. Expression of 18S served as endogenous control, and data was analyzed in the Biomark RT-PCR analysis software V.2.0. Assay IDs are listed in SM.Data AnalysisMicroarray data analysis. Affymetrix HG U133 plus2.0 gene chip CEL files from 66 pre-transplant donor samples (D0, n = 33) and post-transplant Banff graded renal allograft biopsy samples (STA, n = 16; BL, n = 4; 23727046 ARIA, n = 7; ARIB, n = 6) were uploaded into dChip 2006 software [22] for processing and normalization. Perfect match only for background correction was performed and arrays were checked for single, array and probe outliers before quantile normalization and computing of model based expression values [23]. Only genes with expression values present on each array were used for analyses. Raw data are stored in gene expression omnibus (GEO) under GSE34437, sample IDs used for microarray analyses are listed in Table S1. To expand the number of patients and to validate the findings in our data-set an additional microarray data-set (Affymetrix HG U133 plus2.0) was downloaded from GEO (GSE9493) consisting of whole genome expression profiles from 21 STA, 4 BL and 10 AR renal allograft biopsies. Data had been preprocessed and normalized (RMA, Quantile normalization, log2 transformation) as described [16,24]. Computational analyses of databases for rejection pathway analysis. Significance analysis of microarrays (SAM,[25]) for two-class and for quantitative gene-set analysis (GSA) [26] was performed to detect rejection specific gene-sets in patient biopsies with AR. Enrichment in AR compared to STA was identified by two-class GSA. To identify pathways with increasing enrichment from D0.STA.BL.ARIA.ARIAB quantitative GSA was applied. GSA uses maxmean statistics and applies a restandardization of genes and sample permutations to estimate false discovery rate (FDR) which means that a gene-set must be unusual both as compared to gene-sets of the same size sampled at random from the set of genes represented by the gene-set, and as compared to itself, when the outcome labels are permuted [27,28]. Here, the FDR was calculated in 1000 permutations, and significance level was set at an FDR of 0.5. Correlation between gene expression values and phenotype was based on T-test for 2class GSA and on regression for quantitative GSA. A total ofDrug CPI-455 web Repositioning Fenofibrate for TransplantationTable 2. GSA identifies increasing enrichment of IL-17 gene-sets in human renal allograft acute rejection across independent Patient Data-Sets.Analysis Discovery Data-Set (n = 66) AR vs. STAGene-Setsp-valueFDRIL17-Pathway Th17 gene set0.007 0 0.0.3 0.2 0.D0. STA. BL.ARIA. ARIB Verification Data-Set (n = 35) AR vs. STAIL17-PathwayIL17-Pathway Th17 gene set0.011 0.026 0.0.33 0.39 0.STA. BL.ARIL17-PathwayThere were total 140 gene-sets significantly enriched in AR in both Data-Sets (FDR = 0.5), the IL-17 pathway and Th17 gene-sets are listed above. Other significant genesets that reached the threshold of F.Hnologies, CA, USA) according to standard protocols. Data was analyzed using SDS 2.3 software (Applied Biosystems, Life Technologies, Foster City, CA). Mouse allograft and recipient spleen gene expression was assessed using the high throughput Fluidigm BioMark instrument (BioMark; Fluidigm, San Francisco, CA) as described in detail in SM. In brief, cDNA was amplified for 14 target genes using Applied Biosystems primers and probes. Preamplified cDNA was loaded into a Dynamic 96.96 chip (Fluidigm) for a 40 cycle QPCR. Expression of 18S served as endogenous control, and data was analyzed in the Biomark RT-PCR analysis software V.2.0. Assay IDs are listed in SM.Data AnalysisMicroarray data analysis. Affymetrix HG U133 plus2.0 gene chip CEL files from 66 pre-transplant donor samples (D0, n = 33) and post-transplant Banff graded renal allograft biopsy samples (STA, n = 16; BL, n = 4; 23727046 ARIA, n = 7; ARIB, n = 6) were uploaded into dChip 2006 software [22] for processing and normalization. Perfect match only for background correction was performed and arrays were checked for single, array and probe outliers before quantile normalization and computing of model based expression values [23]. Only genes with expression values present on each array were used for analyses. Raw data are stored in gene expression omnibus (GEO) under GSE34437, sample IDs used for microarray analyses are listed in Table S1. To expand the number of patients and to validate the findings in our data-set an additional microarray data-set (Affymetrix HG U133 plus2.0) was downloaded from GEO (GSE9493) consisting of whole genome expression profiles from 21 STA, 4 BL and 10 AR renal allograft biopsies. Data had been preprocessed and normalized (RMA, Quantile normalization, log2 transformation) as described [16,24]. Computational analyses of databases for rejection pathway analysis. Significance analysis of microarrays (SAM,[25]) for two-class and for quantitative gene-set analysis (GSA) [26] was performed to detect rejection specific gene-sets in patient biopsies with AR. Enrichment in AR compared to STA was identified by two-class GSA. To identify pathways with increasing enrichment from D0.STA.BL.ARIA.ARIAB quantitative GSA was applied. GSA uses maxmean statistics and applies a restandardization of genes and sample permutations to estimate false discovery rate (FDR) which means that a gene-set must be unusual both as compared to gene-sets of the same size sampled at random from the set of genes represented by the gene-set, and as compared to itself, when the outcome labels are permuted [27,28]. Here, the FDR was calculated in 1000 permutations, and significance level was set at an FDR of 0.5. Correlation between gene expression values and phenotype was based on T-test for 2class GSA and on regression for quantitative GSA. A total ofDrug Repositioning Fenofibrate for TransplantationTable 2. GSA identifies increasing enrichment of IL-17 gene-sets in human renal allograft acute rejection across independent Patient Data-Sets.Analysis Discovery Data-Set (n = 66) AR vs. STAGene-Setsp-valueFDRIL17-Pathway Th17 gene set0.007 0 0.0.3 0.2 0.D0. STA. BL.ARIA. ARIB Verification Data-Set (n = 35) AR vs. STAIL17-PathwayIL17-Pathway Th17 gene set0.011 0.026 0.0.33 0.39 0.STA. BL.ARIL17-PathwayThere were total 140 gene-sets significantly enriched in AR in both Data-Sets (FDR = 0.5), the IL-17 pathway and Th17 gene-sets are listed above. Other significant genesets that reached the threshold of F.

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