SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-METHODS Microarray processing: Total RNA was reverse-transcribed using the One-Cycle Target Labeling kit or the 3’ IVT Express kit from Affymetrix (Santa Clara, CA) following the manufacturer’s protocol. Samples were then hybridized to Affymetrix GeneChip Human Genome U133A v2.0 arrays, scanned with a GeneChip Scanner 3000 and the CEL files processed using the GeneChip Operating software, version 5.0. Interaction networks and functional analysis: Gene ontology and gene interaction analyses were carried out using ToppGene [1] and Ingenuity Pathways Analysis (IPA, Ingenuity® Systems, Redwood City, CA). Gene lists containing Entrez GeneIDs (ToppGene) or Affymetrix IDs (IPA) were used as inputs. A Bonferroni correction was used in the Toppgene analyses while the default settings were used for IPA. Biological functions and pathways with a p-value <0.05 were considered significant in the analyses. Cytoscape: Cytoscape [2] was used to create a single gene interaction network from the differentially expressed genes. The MiMI [3] plug-in was used to individually query six protein- protein interaction databases - BIND, IntAct, DIP, MINT, CCSB, MDC [4-9] - and create separate interaction networks. The GeneMania [10] plug-in was also used as a second source of protein- protein interaction information. The individually generated networks were then merged, duplicate interactions were removed and only first neighbors shared by two or more query genes were retained. Biological processes were then identified from genes within the network and mapped using the BiNGO [11] plug-in. To reduce the complexity of the figure only biological processes with p-values <0.001 after a Bonferroni correction were mapped. Validation of microarray results: Real-time (RT) quantitative-PCR (qPCR) reactions were used for quantifying the expression of CCL5 (chemokine (C-C motif) ligand 5, Hs99999048_m1), 1 SUPPLEMENTAL DIGITAL CONTENT (SDC) ITGB2 (integrin, beta 2 (complement component 3 receptor 3 and 4 subunit), Hs00164957_m1), CXCR4 (chemokine (C-X-C motif) receptor 4, Hs00976734_m1), and EGF (epidermal growth factor, Hs00153181_m1) using pre-developed TaqMan® gene expression assays (Life Technologies, Grand Island, NY). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH, ID: 4310884E) expression was used as the endogenous control for normalization. Threshold cycle (Ct) values were used to calculate relative expression using the ∆∆Ct method. 2 SUPPLEMENTAL DIGITAL CONTENT (SDC) REFERENCES for SDC-METHODS [1] Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305-11. [2] Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431-432. [3] Gao J, Ade AS, Tarcea VG, Weymouth TE, Mirel BR, Jagadish HV, States DJ (2009) Integrating and annotating the interactome using the MiMI plugin for cytoscape. Bioinformatics 25:137-138. [4] Isserlin R, El-Badrawi RA, Bader GD (2011) The Biomolecular Interaction Network Database in PSI-MI 2.5. Database (Oxford) 2011:baq037. [5] Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J et al. (2010) The IntAct molecular interaction database in 2010. Nucleic Acids Res 38:D525-31. [6] Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The Database of Interacting Proteins: 2004 update. Nucleic Acids Res 32:D449-51. [7] Ceol A, Chatr Aryamontri A, Licata L, Peluso D, Briganti L, Perfetto L, Castagnoli L, Cesareni G (2010) MINT, the molecular interaction database: 2009 update. Nucleic Acids Res 38:D532-9. [8] Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N et al. (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437:1173-1178. [9] Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S et al. (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell 122:957-968. [10] Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL, Morris Q, Bader GD (2010) GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics 26:2927-2928. [11] Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448- 3449. 3 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-FIGURE 1. AB Variable by Variable Spearman ρ Prob>|ρ| eGFR 3mo eGFR 1mo 0.8035 <0.001 eGFR 6mo eGFR 1mo 0.7876 <0.001 eGFR 9mo eGFR 1mo 0.7436 <0.001 eGFR 12mo eGFR 1mo 0.7076 <0.001 eGFR 15mo eGFR 1mo 0.6827 <0.001 eGFR 18mo eGFR 1mo 0.7254 <0.001 eGFR 21mo eGFR 1mo 0.6976 <0.001 eGFR 24mo eGFR 1mo 0.7717 <0.001 SDC-Figure 1. Correlation of eGFR at different time points. A. Scatterplot matrix comparing the eGFR values at each time point after segregation of patients based on their 1-month eGFR. B. Table insert with Spearman’s rank correlations between eGFR values at each time point. 4 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-FIGURE 2. 80 Age) 60 Donor ‐ 40 Age 20 (Recipient 0 ‐20 ‐100 1020304050607080 Difference ‐20 ‐40 Mean ((Recipient Age + Donor Age)/2) Mean Mean Group (Recipient Age ‐ Donor Age) ((Recipient Age + Donor Age)/2) GFR‐high 11.8 43.3 GFR‐lo 11.0 49.5 p‐value (across groups) 0.8348 0.0028 SDC-Figure 2. Age comparison between the two sample groups. Scatterplot of the Mean (Recipient Age + Donor Age)/2) vs. the Difference (Recipient Age – Donor Age) between recipient and donor age by group. GFR-low (red) subjects tended to be composed of older donor-recipient pairs compared to the GFR-high (green) subjects. There was a statistical significant difference between the two groups when comparing means (table insert). 5 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-FIGURE 3. p-value SDC-Figure 3. Network of biological processes identified. Biological processes identified from up-regulated differentially expressed genes within the gene interaction network in Figure 2. The size of the category indicates the number of genes indentified within that category; the coloring represents the p-value according to the scale (inset). To reduce the complexity of the analysis only biological processes with p-values <0.001 after a Bonferroni correction were mapped. 6 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-FIGURE 4. SDC-Figure 4. Network analysis by Ingenuity Pathway. Merging of the top 2 scoring networks identified by IPA from the differentially expressed genes between GFR-high and GFR- low. Solid lines represent direct interaction; dashed lines represent indirect interactions. The coloring indicates whether the gene was up-regulated (red) or down-regulated (green). 7 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-FIGURE 5. SDC-Figure 5. Results from the Random Forest algorithm. Relative importance based on permutation procedure of the probesets and clinical values identified by the random forest algorithm as important predictors of graft function post-transplantation. 8 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-FIGURE 6. 15 10 5 0 -5 -10 Fold Change Detectedby Microarray -15 PI K3 IFTA SDC-Figure 6. Fold changes of selected genes detected in PI and K3 biopsies. Graphical representation of the fold changes detected by microarray of the 42 genes found to be differentially expressed between GFR-high and GFR-low patient groups at pre-implantation (PI, blue diamonds), 3-months post-transplant (K3, red diamonds) and between NFA and CAD with IF/TA (IFTA, green diamonds) samples. 9 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-Table 1. DGF diagnoses. Diagnosed DGF causes listed by subject group. Oliguria was a common diagnosis within the GFR-low group while hyperkalemia was more often diagnosed within the GFR-high group. Sample ID DGF Cause eGFR (1mo) Group 51 Fluid Overload 95.5 GFR-high 123-K Fluid Overload, Uremia 80.7 GFR-high 57 Hyperkalemia 78.7 GFR-high 178-K Hyperkalemia 76.6 GFR-high 125-K Hyperkalemia 75.3 GFR-high 89-K Hyperkalemia 75.0 GFR-high 71-K Oliguria 69.8 GFR-high 83-K Hyperkalemia 66.4 GFR-high 122-K Hyperkalemia, Oliguria 63.9 GFR-high 54 Hyperkalemia 61.6 GFR-high 102 Oliguria 55.1 GFR-high 96 Hyperkalemia 54.9 GFR-high 45 Hyperkalemia 50.7 GFR-high 34 Fluid Overload 50.0 GFR-high 95 Hyperkalemia/Oliguria 40.4 GFR-low 85 Oliguria 39.3 GFR-low 105-K Oliguria 38.4 GFR-low 64-K Hyperkalemia 38.3 GFR-low 50 Uremia 37.3 GFR-low 137-K Anuria 35.6 GFR-low 3-NK-9 Oliguria 35.4 GFR-low 79-K Hyperkalemia 32.2 GFR-low 104 Oliguria 30.5 GFR-low 103 Oliguria 29.3 GFR-low 144-K Oliguria 23.7 GFR-low 80-K Oliguria 23.2 GFR-low 20 Oliguria 22.8 GFR-low 66 Anuria 19.6 GFR-low 110 Hyperkalemia 16.9 GFR-low 106 Hyperkalemia/Oliguria 15.6 GFR-low 98 Oliguria 11.6 GFR-low 99 Hyperkalemia/Oliguria 10.7 GFR-low 97 Hyperkalemia/Oliguria 9.6 GFR-low 1 Hyperkalemia, orthopnea 8.3 GFR-low 10 SUPPLEMENTAL DIGITAL CONTENT (SDC) SDC-Table 2. Differentially expressed genes. Complete list of differentially expressed genes (n=197) identified by comparing GFR-low (patients with eGFR ≤45 mL/min/1.73m2) and GFR- high (patients with eGFR >45 mL/min/1.73m2). Affymetrix ID Gene Symbol Entrez ID p-value FDR 1405_i_at CCL5 6352 1.15E-06 0.0207 206336_at CXCL6 6372 8.48E-06 0.0357 203535_at S100A9 6280 1.17E-05 0.0357 204655_at CCL5 6352 4.94E-06 0.0357 210915_x_at TRBC1 28639 6.08E-06 0.0357 202803_s_at