Immunoglobulin A Nephropathy


Figure 2: 2D Gel Electrophoresis Map and SELDI-TOF-MS Profile of Human Urine


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a b c f e h g d


The authors and their colleagues are contributing to the creation of standardized procedures for urinary proteome analysis. Figure 2 shows the 2D gel electrophoresis (2DE) human urinary map and the surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) human urine profile of healthy controls standardized in our research laboratory.29,31


Urine Proteomics in the Investigation of Immunoglobulin A Nephropathy


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A. 2D gel electrophoresis (2DE) urinary map of a healthy individual isoelectric point (pI) is indicated on the x-axis, while molecular mass (m) is indicated on the y-axis. ImageMaster™ 2D Platinum analysis of silver-stained analytical 2DE gels detected 475±117 (mean ± standard deviation [SD]; protein spots (coefficient of variation [CV] = 24%).31


Identified proteins are


indicated by letters as follows: a = transferrin; b = kininogen; c = alpha-1-antitrypsin; d = immunoglobulin heavy chain; e = alpha-1-microglobulin; f = prostate-specific antigen; g = immunoglobulin light chain; h = transthyretin. B. Surface-enhanced laser desorption/ ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein profiles of a human urine sample analyzed by four different ProteinChips® (CM10, H50, IMAC-Cu, Q10) illustrate the considerable difference in the proteomic profile of the same subject depending on the ProteinChip used.29


factors concerning urine proteomics and to establish a network for urine and kidney proteomics, first at an international level (Human Urine and Kidney Proteome Project [HKUPP], www.hkupp.kir.jp) and then at a European level (European Kidney and Urine Proteomics [EuroKUP], www.eurokup.org).30


The main objective of these interactions was to


facilitate translational research in kidney diseases by achieving the following specific aims:


• •


• standardization in the clinical setting;


bioinformatics issues pertinent to urine and kidney proteomics analysis; and


protocol optimization and standardization of methods in kidney tissue and urine proteomic analysis (including study design, sample collection, processing and storage, selection of the appropriate proteomic technology and data analysis and normalization).


58 Q10 IMAC-Cu


Profiling technologies allow a high-throughput analysis of hundreds of biologic samples and for this reason they are more appropriate for clinical purposes. Among them, capillary electrophoresis coupled to mass spectrometry (CE/MS) and SELDI-TOF-MS are the most-used strategies for the screening of biologic samples in both kidney and systemic diseases and are demonstrating great potential for clinical applications (see Figure 3). However, the multifactorial nature and complexity of renal diseases requires the use of unbiased methodologies in developing constrained data sets and defining the specific ‘fingerprints’ of kidney injuries. The hundreds or thousands of candidate biomarker proteins identified by these high-throughput approaches need to be properly managed to allow the recognition of reliable, disease-specific biomarkers. This might be achieved by means of powerful bioinformatics tools, capable of identifying a robust, multiparametric panel of biomarkers, which are both sensitive and specific enough for differential diagnoses of renal diseases. It is now evident that the success of any proteomic study is reliant upon a strict collaboration with bioinformatics and statisticians, who have the responsibility of distinguishing from a wide range of candidate biomarkers those that are really associated with the studied disease and those that reflect physiologic diversity among biologic samples.


Several complex statistical methods are currently being applied to the analysis of urinary proteomes in the identification of disease-specific biomarkers. Data analysis usually dissects a small subset of data that are significantly different between control and case samples. As a general approach, an algorithm for analysis in a ‘training set’ should be developed and should include a number of the most differentially expressed proteins for validation against a ‘validation set’—a diagnostic


US NEPHROLOGY 6,000 8,000 10,000 CM10 pl 3-10 H50


To date, a non-invasive marker for the detection of IgAN is unavailable. Discovering specific proteins or peptides in the urine of patients with IgAN—with the potential to provide sensitive and specific information on the disease process and to help predict response to treatment at an early stage—could allow clinicians to make a diagnosis and prognosis without invasive renal biopsy. However, the objectives of proteomic applications in the investigation of IgAN are not only to discover novel biomarkers and therapeutic targets for a better clinical outcome, but also to understand the pathophysiology of IgAN better. Proteomic technologies commonly used in these studies belong either to the ‘classic approach’ to urine proteomic analysis (such as 2D polyacrylamide gel electrophoresis [2D PAGE] and 2D difference in gel electrophoresis [2D DIGE] combined with MS) or to the ‘alternative approach’, based on technologies capable of examining the proteome profile with the aim of identifying a pattern of protein expression (rather than a specific protein) to differentiate types or groups of urine samples (e.g. healthy versus diseased, or a specific disease versus other diseases).22


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