Applying proteomics to diagnosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one the major complications of diabetes and has become the most common cause of chronic kidney disease worldwide [1,2]. The pathogenesis of DKD is complex and has different mechanisms which contribute to the development and outcomes of the disease. Hyperglycemia plays a central role in the onset and progression of renal damage, since it induces metabolic and hemodynamic changes and contributes to the development of oxidative and inflammatory processes [1,3,4]. Importantly, glomerular hyperfiltration and hyperperfusion have been reported as the first renal changes in diabetes [1,2]. Additionally, tubular damage plays a significant role in the pathogenesis of DKD and may be associated with the onset of albuminuria in patients with diabetes [5]. At the urinary level, microalbuminuria (urine albumin excretion 30–300 mg/24 h) is usually the first sign of diabetic renal damage [6]. However, some diabetic patients have renal histopathological lesions even in the normoalbuminuric range. Furthermore, microalbuminuria does not correlate well with glomerular damage and appears to be a nonspecific indicator of ongoing renal damage [6–8]. Another significant limitation refers to laboratory methodologies commonly used to measure albuminuria since these analytical methods are based on immunoassays, which can detect only the immunoreactive forms of albumin [8]. The histopathological analysis of renal biopsies is recommended for the accurate diagnosis of histological damage in diabetes [6]. However, the analysis of renal biopsy is not routinely performed in most centers. Therefore, there is an urgent need for reliable noninvasive biomarkers for early diagnosis of DKD. Hence, proteomics has emerged as a promising tool for the identification of proteins that present potential use as biomarkers for the diagnosis of diseases. Proteomics analysis consists of the assessment of the proteome, which is the complete inventory of proteins expressed within a biological sample, such as urine, plasma, or serum [9]. Urine has been accepted as the most appropriate sample for proteomic biomarker discovery in DKD [2,6,7,10–12]. The main advantages of using urine are (1) its collection is simple and noninvasive; (2) urine samples have excellent stability and can be stored at −20°C for many years; (3) urinary proteome is quite stable; and (4) preanalytical procedures for the preparation of the urine sample are simpler than for blood samples. However, the urinary proteome may be affected by different processes that occur during the day, which may impair the identification of disease-specific biomarkers in urine [10]. Different proteomic approaches have been performed for the discovery of biomarkers, which include analytical methods such as capillary electrophoresis/mass spectrometry (CE-MS), matrix-assisted laser desorption/ionization time of flight/mass spectrometry (MALDI-TOF-MS), liquid chromatography coupled with tandem mass spectrometry, two-dimensional gel electrophoresis (2-DE), 2-DE coupled to MALDI-TOF-TOF, protein microarrays, and microfluidic technology on a chip [8]. Indeed, recent advances in mass spectrometry and bioinformatics have improved the ability to detect proteins in the urine, some of which have the potential to be used as new biomarkers [9,12]. However, it is important to observe that each of these methodologies has advantages and disadvantages. The desirable characteristics for an ideal analytical technique include the high sensitivity and resolution, as well as the high capacity for separation and identification of proteins [8]. Interestingly, Pandey and Mann had predicted for nearly 20 years ago that proteomics would move away from monitoring protein expression using two-dimensional gels to methods based on mass spectrometry [13]. Currently, many of the proteomics studies carried out in patients with diabetes and DKD for diagnosis and prognosis purposes have been performed with the use of CE-MS as the main analytical methodology [11,14–17]. CE-MS has offered several advantages in comparison with other analytical methods. The main benefits of CE-MS are good reproducibility, high sensitivity, small volume sample required, and the availability of automated systems [8,11,18]. Additionally, other methods such as microarray technology and microfluidic technology on a chip have emerged as promising methodologies for the evaluation of urinary proteome, since both allow the high-throughput profiling of multiple proteins in urine samples by use of automated platforms [8]. Urinary proteomics has identified many proteins and peptides associated with the development of DKD, and some of them have potential to be used as biomarkers, including specific fragments of collagen, beta-2-microglobulin, retinolbinding protein 4, epidermal growth factor, uromodulin, and vanin-1. Despite these findings, there are two panels