Application of Label-free Quantitative Peptidomics for the Identification of Urinary Biomarkers of Kidney Chronic Allograft Dysfunction

The advent of quantitative proteomics opens new opportunities in biomedical and clinical research. Although quantitative proteomics methods based on stable isotope labeling are in general preferred for biomolecular research, biomarker discovery is a case example of a biomedical problem that may be better addressed by using label-free MS techniques. As a proof of concept of this paradigm, we report the use of label-free quantitative LC-MS to profile the urinary peptidome of kidney chronic allograft dysfunction (CAD). The aim was to identify predictive biomarkers that could be used to personalize immunosuppressive therapies for kidney transplant patients. We detected (by LC-M/MS) and quantified (by LC-MS) 6000 polypeptide ions in undigested urine specimens across 39 CAD patients and 32 control individuals. Although unsupervised hierarchical clustering differentiated between the groups when including all the identified peptides, specific peptides derived from uromodulin and kininogen were found to be significantly more abundant in control than in CAD patients and correctly identified the two groups. These peptides are therefore potential biomarkers that might be used for the diagnosis of CAD. In addition, ions at m/z 645.59 and m/z 642.61 were able to differentiate between patients with different forms of CAD with specificities and sensitivities of 90% in a training set and, significantly, of ∼70% in an independent validation set of samples. Interestingly low expression of uromodulin at m/z 638.03 coupled with high expression of m/z 642.61 diagnosed CAD in virtually all cases. Multiple reaction monitoring experiments further validated the results, illustrating the power of our label-free quantitative LC-MS approach for obtaining quantitative profiles of urinary polypeptides in a rapid, comprehensive, and precise fashion and for biomarker discovery.A major goal of clinical proteomics is to identify biomarkers that can aid in the diagnosis and prognosis of different conditions. In their ideal form, these biomarkers will not only assist the clinician in the diagnosis of a disease, but they will also give directions as to which therapy may be more appropriate for each patient, thus contributing to the development of personalized medicine. In this regard, urine represents an ideal, but yet largely unexplored, source of biomarkers because of the presence of large numbers of small peptides in this biological fluid and because it can be obtained non-invasively.However, although proteomics techniques are instrumental for increasing our understanding of molecular cell biology (1) the impact of proteomics in clinical practice has not yet reached initial expectations perhaps because of technological limitations (2, 3). Using hyphenated methods such as novel LC-MS techniques for quantitative proteomics (4, 5) may prove advantageous for the identification and validation of biomarkers (3, 6). This is because LC-MS allows the detection of proteomes with greater depth, dynamic range, and enhanced accuracy of quantization than when using one-dimensional profiling techniques that record all ions in a single mass spectrum, such as MALDI-TOF MS or SELDI-TOF MS (7). On-line LC-ESI-MS is quantitative in nature because the initial LC separation step contributes to reducing the amount of analytes that are simultaneously ionized, thus reducing the possibility of ion suppression, and because ion formation by electrospray ionization is proportional to analyte concentration (8, 9). Initial reports that used LC-MS for the analysis of the urinary proteome provided proof of principle of the use of this technique for the analysis of urinary polypeptides (10–12), and recently, using new generation LC-MS/MS instrumentation, more than 1500 proteins have been detected in urine (13). Nevertheless despite these advances in our understanding of the qualitative composition of the urinary proteome, precise and comprehensive quantification of urinary polypeptides to discover potential biomarkers remains a challenge.The ideal, and more widely used, strategies to derive quantitative information from LC-MS experiments are based on differential stable isotope labeling of proteins or peptides, which are then mixed and quantified relative to each other in single multidimensional LC-LC-MS experiments (14). This technique, however, is not ideal for biomarker discovery because of problems associated with protein derivatization in a clinical setting, because of its limited throughput, and because, although not impossible, isotope labeling techniques make it difficult to compare a large number of specimens; at present labeling reagents can be used for simultaneous comparison of up to eight protein samples (15).Novel analytical strategies for quantitative proteomics that do not require isotope labeling have been reported (4, 5, 16). These techniques can quantify polypeptides with precisions and accuracies comparable to those based on isotope labeling (17). In addition, such label-free quantitative LC-MS approaches can compare an unlimited number of samples, and it is therefore ideal for biomarker discovery as experimental designs normally involve comparing a large number of specimens to statistically validate the results. Thus, label-free quantitative LC-MS would clearly assist in analyzing the full potential of urine clinical samples as a source of disease biomarkers. The aim of the study presented herein was to prove this concept taking chronic allograft dysfunction (CAD)¹ as a paradigm.During the last years, the incidence and prevalence of end stage renal disease has increased worldwide (18). Successful renal transplantation improves the patients'' quality of life and increases survival as compared with long term dialysis treatment (19). However, despite these improvements, a substantial portion of grafts develop progressive dysfunction and fail within a decade even with the use of appropriate doses of immunosuppressive drugs to prevent acute rejection (20). CAD is responsible for more than 50% of graft losses and remains a central clinical challenge. Although patients can return to dialysis after transplant failure, loss of a functioning graft is associated with a 3-fold increase in the risk of death, a substantial decrease in quality of life for those who survive, and a 4-fold increase in healthcare costs (21).CAD is mediated by a combination of immune, ischemic, and inflammatory stimuli, and multiple pathways and mediators lead to cumulative structural damage to all compartments of the transplanted kidney. Sclerosing changes associated with tubulointerstitial injury are mediated by the processes of active fibrogenesis, resulting in epithelial loss and the phenotype of tubular atrophy and chronic interstitial fibrosis (22). Available diagnostic methods include clinical presentation, biochemical parameters, and biopsies. Currently the only non-invasive biomarker of CAD is serum creatinine and glomerular filtration rate (GFR), but neither is particularly sensitive or specific and may not reflect early alterations (20, 22). At present, biopsy allograft is regarded as the gold standard for the diagnosis of CAD allowing its early detection; however, this is a costly procedure that is associated with clinical complications (23).Clinicians are hence faced with a dilemma. On the one hand, protocol biopsies may detect rejection at an earlier subclinical stage and allow prompt initiation of treatment, which may translate into improved long term graft survival (24). On the other hand, this also implies that patients with preserved graft function, i.e. without CAD, undergo this invasive procedure unnecessarily. Therefore, identification of non-invasive biomarkers for the early diagnosis of CAD would be invaluable for alleviating the major health and economic burden that this condition causes to western countries (25).The aim of the present study was to evaluate whether the urinary peptidome, as analyzed by a novel analytical strategy based on label-free quantification of urinary polypeptides by LC-MS, would differentiate between patients with CAD, those showing stable renal transplant (SRT), and a group of living donors. To our knowledge, this represents the first study reporting urine polypeptide signatures and individual biomarkers that group patients according to their underlying renal phenotype and hence represent potential candidates for non-invasive diagnosis of CAD.