Mining the human urine proteome for monitoring renal transplant injury Tara K. Sigdel, Yuqian Gao, Jintang He, Anyou Wang, Carrie D. Nicora, Thomas L. Fillmore, Tujin Shi, Bobbie-Jo Webb-Robertson, Richard D. Smith, Wei-Jun Qian, Oscar Salvatierra, David G. Camp, Minnie M. Sarwal  Kidney International  Volume 89, Issue 6, Pages 1244-1252 (June 2016) DOI: 10.1016/j.kint.2015.12.049 Copyright © 2016 International Society of Nephrology Terms and Conditions

Figure 1 Principal component analyses of proteomic data on 4 transplant phenotypes. Two different approaches were employed to identify potential biomarker urine proteins for kidney transplant injury. The first approach used quantitative proteomics using pooled samples that identified proteins specific to acute rejection (AR), BK virus nephritis (BKVN), and chronic allograft nephropathy (CAN) from stable graft (STA). (a) A principal component analysis plot demonstrates overall protein expression in-between injury phenotypes (AR, CAN, and BKVN) and no-injury phenotypes (STA) for all 958 proteins identified. The second approach used a shotgun proteomics (label-free) method analyzing individual samples from transplant injury phenotypes. (b) Principal component analysis plot demonstrates overall protein expression in-between injury phenotypes (AR, CAN, and BKVN) and no-injury phenotypes (STA) for all 1574 proteins identified. iTRAQ, isobaric Tags for Relative and Absolute Quantitation; LC-MS, liquid chromatography–mass spectrometry. Kidney International 2016 89, 1244-1252DOI: (10.1016/j.kint.2015.12.049) Copyright © 2016 International Society of Nephrology Terms and Conditions

Figure 2 Injury mechanisms through gene expression and urine protein analysis. Gene enrichment analysis for cellular and biological processes demonstrated enrichment for proteins involved in the immune response in (a) acute rejection (AR), and both (b) solute/ion transporters and (c) extracellular matrix reorganization in chronic allograft nephropathy (CAN). The P value was calculated by Fisher exact. Next, we analyzed proteins mapping to immune cell infiltrate–specific transcriptional profiles from matching kidney biopsies. There was an overlap of similar patterns of immune cell infiltrates in both acute and chronic rejection, with the strongest enrichment for B cell, dendritic cell, and granulocyte activation in both (d) AR and (e) CAN urine samples. ns, not significant; STA, stable graft. Kidney International 2016 89, 1244-1252DOI: (10.1016/j.kint.2015.12.049) Copyright © 2016 International Society of Nephrology Terms and Conditions

Figure 3 Identification of injury-specific peptide panels. (a) Selection of 109 peptides for selected reaction monitoring validation was based on their specificity to distinguish injury phenotypes from no-injury phenotypes samples. (b) The validation phase provided injury-specific panels of 11 peptides for acute rejection (AR), 12 peptides for chronic allograft nephropathy (CAN), and 12 peptides for BK virus nephritis (BKVN). (c) Cross-validation of selected reaction monitoring data on 35 peptides by testing the ability of peptide panels in differentiating injury specificity by the sensitivity and specificity of injury designation in terms of area under the curve (AUC) for (1) AR, (2) BKVN, and (3) CAN. There were 26 surveillance biopsies from 46 CAN biopsies. Receiver operating characteristic analysis resulted in almost the same AUCs for surveillance (AUC = 99.4%) and for cause (AUC = 99.5%) compared with overall AUC of 99.5%. Kidney International 2016 89, 1244-1252DOI: (10.1016/j.kint.2015.12.049) Copyright © 2016 International Society of Nephrology Terms and Conditions

Figure 4 Study summary. (a) Sample selection flowchart. (b) Using state-of-the-art mass spectrometric methods, the study implemented a carefully designed biomarker discovery and validation strategy to identify transplant injury–specific biomarkers for kidney transplantation. iTRAQ, isobaric Tags for Relative and Absolute Quantitation; LC-MS, liquid chromatography–mass spectrometry; QC, quality control; SRM, selected reaction monitoring. Kidney International 2016 89, 1244-1252DOI: (10.1016/j.kint.2015.12.049) Copyright © 2016 International Society of Nephrology Terms and Conditions

Figure S1 Selection process for SRM validation of biomarker peptides. A thorough screening process was executed in the selection of 100 proteins for selected reaction monitoring (SRM) validation, which evaluated proteins identified from both isobaric Tags for Relative and Absolute Quantitation (iTRAQ)-based and label-free-based liquid chromatography–mass spectrometry (LC-MS) methods. Kidney International 2016 89, 1244-1252DOI: (10.1016/j.kint.2015.12.049) Copyright © 2016 International Society of Nephrology Terms and Conditions

Figure S2 Batch effect in removal in LC-SRM data. Batch effect was observed in 2 batches (batch 1—labeled as red dots, and batch 2—labeled as blue dots) of liquid chromatography–selected reaction monitoring (LC-SRM) data (left panel). This effect was removed by batch removal as described in the methods section (right panel). Kidney International 2016 89, 1244-1252DOI: (10.1016/j.kint.2015.12.049) Copyright © 2016 International Society of Nephrology Terms and Conditions