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Proteomics—A Blessing or a Curse? Application of Proteomics Technology to Transplant Medicine

Kienzl-Wagner, Katrin1; Pratschke, Johann1; Brandacher, Gerald1,2,3

doi: 10.1097/TP.0b013e3182265358
Editorials and Perspectives: Overview

Proteomics has emerged as a powerful tool in clinical biomarker research. In the field of transplantation, proteomics aims not only at developing noninvasive tools for immune monitoring and identifying biomarkers of allograft rejection but also to gain mechanistic insights into the pathophysiology of an alloimmune response and hence defining new therapeutic targets. A basic knowledge of proteomic technology is a prerequisite to appreciate the complex data generated and required for critical evaluation/interpretation of proteomic-driven studies. This review provides an overview of proteomic approaches and its underlying concepts and discusses the advantages, clinical implications, challenges, and limitations of this exciting modality in transplantation.

1 Department of Visceral, Transplant and Thoracic Surgery, Center of Operative Medicine, Innsbruck Medical University, Innsbruck, Austria.

2 Department of Plastic and Reconstructive Surgery, Johns Hopkins University School of Medicine, Baltimore, MD.

The authors declare no funding or conflicts of interest.

3 Address correspondence to: Gerald Brandacher, M.D., Department of Plastic and Reconstructive Surgery, Johns Hopkins University School of Medicine, Ross 749D, 720 Rutland Avenue, Baltimore, MD 21205.


K.K.-W. and G.B. participated in writing of the manuscript; and J.P. contributed to writing of the overview and critically revised the manuscript.

Received 2 March 2011. Revision requested 14 April 2011.

Accepted 24 May 2011.

Proteomic science is considered a current hot topic and has become a hype in particular in the field of oncology and most recently in transplantation. Frequently referred to as the next revolution in basic science, by the same token proteomics has raised considerable skepticism and controversy over its clinical applicability.

Per definition, proteomics is the systematic analysis of all the proteins in any defined biologic compartment. This compartment can be a whole organism, a specific type of tissue or cell, an organelle, or body fluids such as serum or urine (1). The analysis of the full proteome represents a challenging task as proteomes have a large and unknown complexity. This complexity and diversity of the proteome is the result of alternative splicing of primary transcripts, the presence of sequence polymorphisms, and posttranslational modifications (2). Proteomics also complements genomic approaches such as gene chip microarrays because there is no strict linear relationship between the genome and the proteome and often a poor correlation between mRNA abundance in a cell or tissue and the quantity of the corresponding functional protein (3–7).

Nevertheless, with the tremendous progress in proteomic technology, particularly in mass spectrometry (MS) instrumentation, proteomics has emerged as a powerful tool for biomarker research (8,9). Instead of the classical approach where a defined candidate gene or protein guides all analyses to investigate its role in a certain disease, protein profiling studies offer the unique possibility to analyze disease-associated changes at the level of the whole proteome and to unravel proteins or peptides as disease-specific markers that from a hypothesis-driven point of view might never have believed to be related to the corresponding disease.

Thus, in clinical biomarker research, proteomics is expected to discover and identify disease-specific biomarkers (10). An “ideal” validated biomarker should be able to be detected noninvasively, has to be specific for the corresponding disease, and therefore has to unambiguously discriminate it from disease-related changes or other disease entities, and finally it should be predictive to indicate disease before its onset or before manifestation of histopathologic changes. In transplantation, proteomics primarily aims at identifying noninvasive biomarkers of acute allograft rejection and chronic allograft dysfunction (CAD). For both entities, diagnosis is still dependent on graft biopsy as there are no reliable noninvasive parameters yet that enable timely diagnosis and treatment (11,12). A further effort in transplantation proteomics is to develop novel tools for immune monitoring with the potential to tailor immunosuppressive therapy to the individual needs of every patient (13) and to gain further mechanistic insights into the pathophysiology of an alloimmune response and hence define new therapeutic targets. Because proteomic experiments generate an almost overwhelming amount of complex data paired with advanced bioinformatic analyses, a basic knowledge of proteomic technology is a prerequisite to appreciate such data and is of crucial importance for the critical interpretation of proteomic-driven studies.

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There are two main approaches to proteomic biomarker research: (1) pattern proteomics, where the biomarker constitutes a specific proteomic pattern of peaks obtained by MS classifying diseased from normal samples. This method does not rely on the actual identification of the diagnostic peaks (14); and (2) discovery or descriptive proteomics, which aims at identifying the discriminatory peaks and therefore enables mechanistic insights into the underlying pathophysiologic process.

MS has become the method of choice to analyze complex protein samples with regards to protein and peptide characterization, identification, and quantification. MS-based proteomic experiments share several common steps. A typical workflow consists of sample preparation followed by qualitative and quantitative MS measurements and finally MS data analysis (Fig. 1).



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Sample Preparation

One of the most critical issues in any clinical proteomic experiment is the quality of the biologic specimen used. Therefore, sample collection, initial processing, and storage are of paramount importance for obtaining reproducible data. Especially serum proteomics is hampered by the complexity of the serum proteome containing proteins with concentrations varying over 12 orders of magnitude. Therefore, blood samples must be depleted of high-abundance proteins that account for more than 95% of the total protein content to enable detection of the remaining low-abundance proteins. In urinary proteomics, the high salt content and variable and changing physicochemical properties (dilution, pH) and cellular components affect its protein content and the stability of proteins (1,11,15).

Because there is no amplification method for proteins analogous to polymerase chain reaction for nucleic acids, prefractionation is used for protein/peptide enrichment to ensure effective biomarker discovery. In addition, complex protein or peptide mixtures require protein separation before MS analysis. Powerful gel-based separation techniques include conventional 2D-polyacrylamide gel electrophoresis and fluorescence 2D-difference gel electrophoresis (DIGE) (16–18). Alternatively, nongel separation techniques include coupling of high-performance liquid chromatography (HPLC) or capillary electrophoresis (CE) systems with MS (19).

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Mass Spectrometry

Mass spectrometers are used to measure the molecular mass of a polypeptide (single-stage MS) or to determine additional structural features such as the amino acid sequence for protein identification and type of posttranslational modification (tandem MS, MS/MS).

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Ionization Methods

Proteins or peptides need to be volatized and ionized to generate intact peptide ions for MS analysis. There are four ionization methods that are currently encountered in proteomic experiments (20) (see also Table 1): electrospray ionization (ESI), matrix-assisted laser desorption/ionization (MALDI), surface-enhanced laser desorption/ionization (SELDI), and desorption electrospray ionization (DESI).





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Mass Analyzers

There are five basic types of mass analyzers currently used in proteomic research, which differ in how they determine the mass-to-charge ratio (m/z ratio) of peptides (2,9,20,21) (see also Table 1): (1) time-of-flight (TOF) instruments that are the simplest and fastest MS. (2) Quadrupoles (Q), triple quadrupole, are tandem instruments that are widely used because of their ability to detect posttranslational modifications with high sensitivity. (3) Ion trap (IT) instruments allow for high-throughput analyses. (4) Fourier transform ion cyclotron resonance (FT-ICR) instruments achieve high resolution and mass accuracy but have a relatively slow acquisition rate and a limited dynamic range. (5) Orbitrap instruments that achieve high resolution and mass accuracy without the need for a superconducting magnet as in FT-ICR instruments. Common combinations of those MS instruments are TOF-TOF, Q-Q-TOF, IT-TOF, Q-Q-Q, IT-LIT, Q-Q-linear ion trap (LIT), and FT-ICR.

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Ion-Activation Methods

Tandem MS (MS/MS) involves the activation of a known precursor ion formed in the ion source and the mass analysis of its fragmentation products. The method of ion activation is crucial to a proteomic experiment as it defines what types of products result. The most common ion activation method used in present mass spectrometers is collision-induced dissociation. Surface-induced dissociation is an activation procedure analogous to collision- induced dissociation except that a solid surface is used as a collision target instead of an inert gas. Alternate fragmentation techniques include electron capture dissociation and electron transfer dissociation. These techniques are particularly useful in localizing posttranslational modifications. Infrared multiphoton dissociation and blackbody infrared radiative dissociation represent further methods for the dissociation of large ions (22,23) (Table 1).

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Strategies for Proteomic Identification

Even though no single proteomic strategy is currently capable to analyze the entire proteome, the potential of proteomic technology is increasing at a rapid pace with the development of new instrumentation and improved analytical methods. Depending on the objective of a proteomic study, different proteomic approaches need to be applied to ensure adequate readout and interpretation of acquired data (2,21,22).

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MS Analysis of Substantially Purified Proteins

This classical proteomic approach is characterized by 2D gel electrophoresis of digested proteins followed by MS identification of proteins in isolated gel spots. Quantitation is subsequently attained by comparing the signal intensities of identical proteins.

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Shotgun Proteomics—MS Analysis of Complex Peptide Mixtures

In shotgun proteomics, complex protein samples such as complete cell lysates or tissue extracts are digested, and the resulting peptide samples are extensively fractionated and analyzed by automated MS/MS. The strength of the shotgun approach is its conceptual and experimental simplicity, increased proteomic coverage, and accurate quantitation. However, it suffers from limited dynamic range, informatics challenges, a high redundancy, and the enormous complexity of the generated peptide sample.

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Comparative Pattern Analysis

Pattern-only methods focus on the production of MS-derived protein patterns for sample classification using raw m/z values or peaks together with intensity information to define a MS pattern. In identity-based MS pattern analysis, protein samples are proteolyzed, fractionated, and the resulting peptides are analyzed by CE- or LC-MS/MS. The major advantage of this method is that all features detectable by MS can be quantified.

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Data Analysis

Proteomic studies result in enormous amounts of complex data, and interpretation of these data sets still remains a challenge. Analysis of obtained MS-spectra includes three dimensions: identification of peptides, quantitation, and statistical analysis of the results. There are basically two methods of protein identification: peptide mass fingerprinting (5) and true peptide sequencing by MS/MS (24).

For data quantitation, a frequent approach is stable isotope labeling in which a labeled moiety is incorporated biosynthetically or chemically. Currently, four methods are being applied: isotope-coded affinity tags, stable isotope labeling by amino acids in cell culture, isobaric tag for relative and absolute quantitation, and 16O/18O labeling methods (25).

Third, proteome analysis for clinical samples is a multidimensional assay that encompasses the comparative statistical analysis of large numbers of variables. A generic workflow of clinical proteomic data after MS analysis for biomarker discovery involves several steps (22) (Fig. 1).

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Protein Microarrays

Apart from the MS-based proteomic approach, protein microarrays are increasingly being applied for the high-throughput discovery of molecular interactions, profiling of protein expression, and monitoring of protein modifications, therefore representing a valuable and versatile tools in proteomic sciences. Three types of protein microarrays are employed at the moment: functional protein arrays, analytical or capture arrays, and reverse-phase arrays (26).

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Urinary Biomarkers of Renal Allograft Rejection

To date, only few groups have applied proteomic technology in the setting of clinical solid organ transplantation. The majority have focused on urinary proteomics to detect noninvasive biomarkers of renal allograft rejection. The first to report an unbiased proteomic approach for the detection of urinary biomarkers of acute renal allograft rejection were Clarke et al. (27). Using SELDI-TOF-MS, candidate biomarkers of 6.5, 6.7, 6.6, 7.1, and 13.4 kDa demonstrating highly successful diagnostic performance were identified. In their landmark article, Schaub et al. (28) detected a characteristic urine protein profile with distinct peak clusters that were closely associated with biopsy-proven renal allograft rejection and could discriminate rejection from patients with stable transplants, acute tubular necrosis, recurrent glomerulopathy, or urinary tract infection. These discriminatory peak clusters were subsequently identified as nontryptically cleaved forms of β2-microglobulin (29). However, urinary β2-microglobulin rather could be considered a biomarker for any acute tubular injury than a specific diagnostic parameter for acute rejection. Similar to the tubular injury biomarkers α1-microglobulin, retinol-binding protein, and neutrophil-gelatinase-associated lipocalin, intact/cleaved urinary β2-microglobulin is significantly up-regulated in patients with tubular pathologies (i.e., tubulitis Ia/Ib, polyomavirus-associated nephropathy, and moderate to severe interstitial fibrosis and tubular atrophy [IF/TA]) but is unable to distinguish normal tubular histology from subclinical tubulitis (30).

To overcome the apparent discrepancy between the different diagnostic patterns of urinary protein biomarkers detected by the groups of Clarke and Schaub, O'Riordan et al. (31) applied two independent bioinformatic approaches for sample classification and used four different chip surfaces for SELDI-TOF-MS to detect a wide variety of proteins. Depending on the classifier used, patients with acute rejection could be distinguished from stable graft recipients with a sensitivity of 90.5% to 91.3% and a specificity of 77.2% to 83.3%. The most valuable discriminatory peptides were identified as human β–defensin-1 and C-terminal fragment of human α-1-antichymotrypsin (32). By applying CE-MS technology, Wittke et al. (33) described a urinary peptide pattern that in blinded analysis correctly identified acute rejection from urinary tract infection and control samples in six out of nine patients.

By performing urinary peptidomic analysis, Ling et al. (34) and Sigdel et al. (35,36) identified 40 acute rejection specific urine peptides that were mapped to the collagen family and uromodulin. Integrative analysis of this urine peptidome and the biopsy transcriptome from matched renal transplant biopsies revealed coordinated transcriptional changes for the corresponding genes.

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Proteomic Signatures in Plasma During Renal Allograft Rejection

Although exploration of biomarkers within the plasma proteome is extremely challenging, studies to identify proteomic signatures in the plasma are a field of intense investigation. Freue et al. (37) published pilot data from their isobaric tag for relative and absolute quantitation-MALDI-TOF/TOF analysis identifying 18 plasma protein diagnostic markers of acute renal allograft rejection. A small-scaled clinical study by Sui et al. (38) applied magnetic bead-based weak cation exchange chromatography and MADI-TOF/TOF to obtain serum peptide fingerprints with differential peptide peaks to distinguish acute and chronic renal rejection. Furthermore, plasma proteome profiling of acutely rejecting renal allograft recipients and subsequent construction of transcription regulation networks by Wu et al. (39) identified key transcription regulators in acute rejection including nuclear factor-κB, STAT1, and STAT3. The key issue in proteomic profiling of serum/plasma samples is to expand the number of proteins detectable in human serum and to confidently identify plasma proteins at the low nanogram per milliliter level. The estimated dynamic range offered by current LC-MS technologies is approximately 105, in combination with strong cation exchange chromatography fractionation, a dynamic range of 106 to 107 can be achieved. Even though this dynamic range falls three orders of magnitude short for detecting picogram per milliliter protein concentrations, it still offers the potential to discover novel candidates from clinical plasma/serum samples (40).

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Proteomic Profiling of Chronic Renal Allograft Dysfunction

A further effort in renal transplant proteomics aims at identifying biomarkers of CAD, also previously referred to as chronic allograft nephropathy (CAN). Noninvasive specific and sensitive markers of CAD are urgently needed to identify high-risk patients or onset of CAD without any clinical evidence of renal deterioration, thereby initiating appropriate treatment and hence improving long-term allograft survival. Proteomics may also be able to contribute to better and more precisely define chronic pathologic changes specifically attributable to alloimmunity and transplantation and thereby distinguish such mechanisms from the broad range of pathologies that have been conflated under the umbrella of CAD or CAN.

Quintana et al. (41) applied a label-free quantitative LC-MS/MS-based strategy to report on urine polypeptide signatures that differentiate between patients with CAD from healthy controls and stable renal transplants. Peptide ions that best discriminated between controls and CAD were those derived from uromodulin at m/z 638 and kininogen at m/z 1003 resulting in correct discrimination in 84%. Considering the mean intensity of all, the peptides derived from uromodulin yielded 94% sensitivity and 100% specificity for CAD. Classification of CAD into clinical subtypes was achieved by ions at m/z 645 and 642 that correctly identified 100% of samples with pure IF/TA and 90% of patients with chronic-active antibody-mediated rejection. Using MALDI-MS and unsupervised hierarchical cluster analysis by the same group led to correct discrimination between patients with pure IF/TA and those with chronic-active antibody-mediated rejection in 100%. The discriminatory urinary protein signature included peaks in the narrow range of 1539.8 and 1657.4 Da (42). Urinary proteomic characterization by SELDI-TOF-MS distinguished Banff CAN scores 2 and 3 from patients without chronic histological change with 86% sensitivity and 92% specificity (43). One potential biomarker of advanced CAN identified was and subsequently confirmed by ELISA to be significantly increased in patients with CAN grade 2 and 3. In a 2D-DIGE-based proteomic analysis, Bañón-Maneus et al. established the normal urine proteomic map of stable renal transplant patients comprised a panel of 41 different proteins and furthermore identified 11 proteins with increased levels in advanced IF/TA. These proteins are related not only to the immunologic response in the graft but also to structural changes in the kidney promoting the epithelial to mesenchymal transition or fibrosis (44).

In their large-scale proteogenomic profiling analysis of renal allograft biopsies from patients with IF/TA, Nakorchevsky et al. (45) and Nickerson and Heeger (46) identified molecular pathways that are up-regulated during the different stages of IF/TA. Cluster analysis of the proteomic data sets obtained suggest that overexpressed or unique proteins reveal IF/TA as a shifting landscape of biologic functions from housekeeping to increasingly stress-related, immune/inflammatory, and tissue injury functions. Up-regulated pathways during progressive IF/TA include acute-phase response signaling, actin cytoskeleton and chemokine signaling, coagulation and complement systems, regulation of actin-based motility by Rho, integrin signaling, hepatic fibrosis, and PI3K/AKT signaling. Although comparison of gene with protein expression showed low overall correlation, the transcript-to-protein correlations within the frameworks of specific molecular pathways was high. A similar proteogenomic approach using peripheral blood cell profiling led to the successful discovery of several hundred mRNA and proteomic biomarkers defining unique proteogenomic signatures of histologically mild and moderate/severe IF/TA with 80% and 92% class prediction accuracy. Nevertheless, candidate validation based on the 2 independent technologies yielded only 11 protein/transcript matches for the 393 consensus genes for mild CAN and no matches for the 62 consensus genes for moderate/severe CAN (47).

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Proteomics at the Services of Heart Transplantation

Particularly, in the setting of heart transplantation, the search for reliable noninvasive biomarkers to diagnose acute rejection is of even greater importance because routine surveillance endomyocardial biopsies are still associated with significant morbidity (48–50). Borozdenkova et al. applied a conventional gel-based proteomic approach to protocol endomyocardial biopsies and identified 11 proteins displaying a 2- to 5-fold change in response to acute rejection. These potential markers of rejection constituted cardiac specific and stress-related proteins. Importantly, for tropomyosin-1 significant intragraft up-regulation on rejection could be correlated with a significant increase in the corresponding tropomyosin-1 serum levels (51). Along this line, our group recently compared intragraft protein expression of acutely rejecting allografts and syngeneic cardiac grafts in an experimental model of heart transplantation using 2D-DIGE for differential expression analysis. Characterization of selected protein spots revealed 17 proteins that were unambiguously identified, among those the antioxidant enzyme peroxiredoxin 6 and pyruvate kinase isozyme M2 displaying a more than 4- and 1.7-fold increase during acute rejection, respectively (52,53). The findings by Meirovich et al. (54) suggest that circulating brain natriuretic peptide increases during acute cardiac allograft rejection, and cytokine antibody arrays further revealed that regulated-on-activation, normal T-expressed and secreted, neutrophil-activating protein-2, and insulin growth factor-binding protein-1 correlate with brain natriuretic peptide plasma levels during grade 3A rejection.

One of the most serious long-term complications of heart transplantation is cardiac allograft vasculopathy (CAV). Comparing biopsies from long-term survivors of cardiac transplantation without CAV and biopsies from patients who had developed CAV within the first 3 years of their transplant, De Souza et al. (55) and Trott et al. (56) demonstrated that a specific diphosphorylated form of heat shock protein 27 displayed a 20-fold increase in spot intensity in biopsies from patients without CAV and concluded that vascular expression of heat shock protein 27 is associated with freedom from CAV.

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Further Applications in Clinical Transplantation

As outlined earlier, the possible applications of proteomic technology to transplant-related issues are as diverse as the field of transplant medicine itself. Current reports from the different fields of transplantation include proteomic analysis of urine in kidney transplant recipients to differentiate BK virus-associated renal allograft nephropathy (BKVAN) from acute allograft rejection (57), a proteome survey of bronchoalveolar lavage fluid to identify human neutrophil peptides as biomarkers for bronchiolitis obliterans syndrome (58), or even the proteomic composition of aqueous humor from patients with acute corneal rejection (59). In allogeneic hematopoietic stem-cell transplantation, CE-MS allows for the diagnosis of acute graft-versus-host disease from urine based on a specific polypeptide pattern (60,61). Attempts to define biomarkers for tolerance include one experimental study by Pan et al. (62), who identified haptoglobin as a candidate tolerance marker in a rat model of spontaneous tolerance in orthotopic liver transplantation, and the article by Hsu et al. (63), who characterized the serum protein profile of an immunosuppressant-free liver transplant recipient by means of MS/MS. They too suggest a potential role for haptoglobin and for transthyretin and α1- antitrypsin in maintaining drug-free tolerance after cessation of immunosuppression. Table 2 provides an overview of the clinical and experimental studies on proteomics in transplant medicine published so far.





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Proteomics definitely will continue its revolution of basic and clinical sciences at a rapid pace with high-throughput technologies being the most attractive development. To look at what has already been achieved in transplantation proteomics, the large-scale proteomic profiling studies cited herein can certainly be considered the foundation to gain further insights into molecular pathways and networks that seem to play a role in the pathogenesis of an alloimmune response and CAD. Furthermore, several discriminatory proteins for the diagnosis of acute allograft rejection have been identified by various different groups. Yet so far, none of the identified candidate biomarkers has emerged as a reliable clinical diagnostic tool. This seems to be related to the inconsistency in sample handling and processing and the lack of standardization of experimental design leading to the identification of different discriminatory proteins by different groups. Furthermore, robust study design with appropriate statistical power, blinding and validation is of crucial importance to improve reliability of proteomic-driven results. The ideal study design in a proteomic experiment requires a training set of adequate sample size and a validation set. Also, the diagnostic performance of a biomarker has to be evaluated by calculating its accuracy and its predictability. Current MS-based proteomic studies suggest that a single biomarker does not provide sufficient specificity to distinguish between diseased and healthy status; therefore, the concept to combine a panel of candidate markers to form a signature pattern may result in sufficient diagnostic performance (22). However, future prospective studies are needed to answer the question if the biomarkers and diagnostic patterns identified so far will prove to be specific and predictive.

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Proteomics; Transplantation; Biomarker

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