Analysis of urinary proteomic patterns for diabetic nephropathy by ProteinChip

Insulin and the kidneys: a contemporary view on the molecular basis

Insulin is a hormone that is composed of 51 amino acids and structurally organized as a hexamer comprising three heterodimers. Insulin is the central hormone involved in the control of glucose and lipid metabolism, aiding in processes such as body homeostasis and cell growth. Insulin is synthesized as a large preprohormone and has a leader sequence or signal peptide that appears to be responsible for transport to the endoplasmic reticulum membranes. The interaction of insulin with the kidneys is a dynamic and multicenter process, as it acts in multiple sites throughout the nephron. Insulin acts on a range of tissues, from the glomerulus to the renal tubule, by modulating different functions such as glomerular filtration, gluconeogenesis, natriuresis, glucose uptake, regulation of ion transport, and the prevention of apoptosis. On the other hand, there is sufficient evidence showing the insulin receptor's involvement in renal functions and its responsibility for the regulation of glucose homeostasis, which enables us to understand its contribution to the insulin resistance phenomenon and its association with the progression of diabetic kidney disease.

Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease

Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.

Urinary Proteomics for Diagnosis and Monitoring of Diabetic Nephropathy

The last decade has seen a surge in publications describing novel biomarkers for early detection of diabetic nephropathy (DN), but as yet none have outperformed albuminuria in well-designed prospective studies. This is partially attributable to our incomplete understanding of the many complex interrelated mechanisms underlying DN development, a heterogeneous process unlikely to be captured by a single biomarker. Proteomics offers the advantage of simultaneously analysing the entire protein content of a biological sample, and the technique has gained attention as a potential tool for a more accurate diagnosis of disease at an earlier stage as well as a means by which to unravel the pathogenesis of complex diseases such as DN using an untargeted approach. This review will discuss the potential of proteomics as both a clinical and research tool, evaluating exploratory work in animal models as well as diagnostic potential in human subjects.

Urinary fetuin-A is a novel marker for diabetic nephropathy in type 2 diabetes identified by lectin microarray

We analyzed the urine samples of patients with type 2 diabetes at various stages of diabetic nephropathy by lectin microarray to identify a biomarker to predict the progression of diabetic nephropathy. Japanese patients with type 2 diabetes at various stages of nephropathy were enrolled and we performed lectin microarray analyses (n = 17) and measured urinary excretion of fetuin-A (n = 85). The increased signals of urine samples were observed in Siaα2-6Gal/GalNAc-binding lectins (SNA, SSA, TJA-I) during the progression of diabetic nephropathy. We next isolated sialylated glycoproteins by using SSA-lectin affinity chromatography and identified fetuin-A by liquid chromatography–tandem mass spectrometer. Urinary excretion of fetuin-A significantly increased during the progression of albuminuria (A1, 0.40±0.43; A2, 0.60±0.53; A3 1.57±1.13 ng/gCr; p = 7.29×10⁻⁸) and of GFR stages (G1, 0.39±0.39; G2, 0.49±0.45; G3, 1.25±1.18; G4, 1.34±0.80 ng/gCr; p = 3.89×10⁻⁴). Multivariate logistic regression analysis was employed to assess fetuin-A as a risk for diabetic nephropathy with microalbuminuria or GFR<60 mL/min. Fetuin-A is demonstrated as a risk factor for both microalbuminuria and reduction of GFR in diabetic nephropathy with the odds ratio of 4.721 (1.881–11.844) and 3.739 (1.785–7.841), respectively. Collectively, the glycan profiling analysis is useful method to identify the urine biomarkers and fetuin-A is a candidate to predict the progression of diabetic nephropathy.

Proteome-base biomarkers in diabetes mellitus: progress on biofluids' protein profiling using mass spectrometry

The worldwide number of individuals suffering from diabetes mellitus (DM) has been projected to rise from 171 million in 2000 to 366 million in 2030. Identification of specific biomarkers for prediction and monitoring of DM is needed not only for the adequate screening diagnosis but also to assist the design of interventions to prevent or delay progression of this pathology and its attendant complications. Proteomic methods based on MS hold special promise for the identification of novel biomarkers that might form the foundation for new clinical tests, but to date, their contribution has been somehow unfruitful. Indeed, from more than 300 proteins found differently modulated in body fluids from diabetic patients, approximately 50 were validated with other approaches like ELISA or Western blotting and the clinical trials are being initiated to employ biofluids' proteomics (specifically urinary proteomics) in clinical decision. This review provides an overview of MS-based applications in the identification of potential biomarkers for DM, emphasizing the methodological challenges involved.

Serum amyloid A and C-reactive protein levels may predict microalbuminuria and macroalbuminuria in newly diagnosed type 1 diabetic patients

BACKGROUND

In this study we evaluated the association of baseline levels of six different candidate proteins for the development of microalbuminuria and macroalbuminuria in type 1 diabetic patients, who were followed for approximately 30 years. Two of the proteins are markers of inflammation: serum amyloid A (SAA) and C-reactive protein (CRP), three are involved in lipid metabolism: apolipoprotein A1, apolipoprotein E and adiponectin and the last protein, fibronectin, is related to structural changes.

METHODS

A nested case control study population of 60 patients from an inception cohort of type 1 diabetic patients where 20 developed microalbuminuria followed by macroalbuminuria and 40 stayed normoalbuminuric during approximately 30 years of follow-up time was used to evaluate baseline levels of the six candidate biomarkers. The proteins were quantified by multiplexed immunoassays.

RESULTS

Log SAA levels were borderline predictor of microalbuminuria, HR 2.31 (1-5.4) p=0.053 in a univariate Cox regression model and predicted the development of macroalbuminuria HR 2.432 (1-6) p=0.049, also univariate. When adjusting for covariates, log SAA predicted the development of microalbuminuria with an HR 4.131 (1.1-15) p=0.03. Log CRP predicted the development of microalbuminuria, HR 2.928 (1.4-6.1) p=0.004, and macroalbuminuria, HR 2.785 (1.3-5.8) p=0.007 in univariate models. When adjusting for covariates, log CRP predicted the development of microalbuminuria with an HR 5.882 (1.7-20.9) p=0.006 and macroalbuminuria with an HR 3.233 (1.1-9.8) p=0.038. Apolipoprotein A1, apolipoprotein E, fibronectin and adiponectin were not associated with development of elevated albumin excretion rate.

CONCLUSIONS

SAA and CRP baseline levels predicted development of micro- and macroalbuminuria during 30 years of follow up, supporting the theory that inflammation is involved in the progression of diabetic nephropathy. Further studies are needed to fully establish the two proteins' potential as additional biomarkers for the development of diabetic nephropathy.

Proteome analysis of laser microdissected glomeruli from formalin-fixed paraffin-embedded kidneys of autopsies of diabetic patients: nephronectin is associated with the development of diabetic glomerulosclerosis

BACKGROUND

To date, little proteomic information has been available from the glomeruli of diabetic patients, possibly due to the clinical limitations of renal biopsy in diabetic patients and insufficient quantities of such specimens for proteome analysis. The purpose of the present study was to identify altered protein expression profiles in diabetic glomeruli using formalin-fixed paraffin-embedded (FFPE) kidney tissues from diabetic patients.

METHODS

Glomeruli were laser microdissected from FFPE autopsy kidney tissues from 10 patients with diabetic nephropathy and 10 non-diabetic control patients and underwent proteome analysis using QSTAR Elite liquid chromatography with tandem mass spectrometry and iTRAQ technology. Immunohistochemical analysis was performed on 93 autopsy samples from diabetic patients with and without nephropathy (n = 45 and n = 48, respectively).

RESULTS

Thirty-one renal and urological disease-related proteins displayed a differential abundance in glomerular samples from patients with diabetic nephropathy compared with non-diabetic control patients. Among them, we found that nephronectin, which functions in the assembly of extracellular matrix, showed clearly positive immunoreactivity in diabetic glomeruli. The numerical fraction of nephronectin-positive glomerular cross sections was increased significantly in diabetic patients with nephropathy compared to those without nephropathy (32.1 ± 31.5 versus 4.14 ± 5.65%, P < 0.0001). Furthermore, there was a significant positive correlation between this numerical fraction of nephronectin-positive glomerular cross sections and the glomerular sclerosis index (ρ = 0.881, P < 0.0001, n = 93).

CONCLUSION

The present study demonstrated, for the first time, that nephronectin may be associated with the development of diabetic glomerulosclerosis and that proteome analysis with FFPE kidney tissues from diabetic patients with nephropathy is useful in understanding diabetic nephropathy.

Analysis of urinary proteomic patterns for type 2 diabetic nephropathy by ProteinChip

OBJECTIVE

To detect urinary proteomic profiling of patients with type 2 diabetes by using ProteinChip array technology, for searching new potential biomarkers in early diagnosis of type 2 diabetic nephropathy (T2DN).

METHODS

A total of 95 urine samples from type 2 diabetic patients with normoalbuminuria (DM, n=30), microalbuminuria (DNl, n=25) and macroalbuminuria (DN2, n=20), and healthy controls (n=20) were analyzed by SELDI-TOF-MS (the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry) technology combined with bioinformatics tools.

RESULTS

Over 300 proteins or peptides from 1 to 80 kDa were obtained using ProteinChip. About 40 of them with the m/z values from 2008.78 to 79176.55 Da were significantly differentiated between type 2 diabetic patients and control subjects. Four proteins of mass 2797.03, 4545.77, 4984.03 and 9083.71 Da were selected as the potential biomarkers for T2DN with sensitivity of 88% and specificity of 96.7%.

CONCLUSION

ProteinChip technology can help to discover new biomarkers and provide a novel non-invasive tool to early diagnosis of T2DN.

Quantitative iTRAQ-Based Proteomic Identification of Candidate Biomarkers for Diabetic Nephropathy in Plasma of Type 1 Diabetic Patients

Introduction

As part of a clinical proteomics programme focused on diabetes and its complications, it was our goal to investigate the proteome of plasma in order to find improved candidate biomarkers to predict diabetic nephropathy.

Methods

Proteins derived from plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric were enriched with hexapeptide library beads and subsequently pooled within three groups. Proteins from the three groups were compared by online liquid chromatography and tandem mass spectrometry in three identical repetitions using isobaric mass tags (iTRAQ). The results were further analysed with ingenuity pathway analysis. Levels of apolipoprotein A1, A2, B, C3, E and J were analysed and validated by a multiplex immunoassay in 20 type 1 diabetic patients with macroalbuminuria and 10 with normoalbuminuria.

Results

A total of 112 proteins were identified in at least two out of three replicates. The global protein ratios were further evaluated by ingenuity pathway analysis, resulting in the recognition of apolipoprotein A2, B, C3, D and E as key nodes in the top-rated network. The multiplex immunoassay confirmed the overall protein expression patterns observed by the iTRAQ analysis.

Conclusion

The candidate biomarkers discovered in this cross-sectional cohort may turn out to be progression biomarkers and might have several clinical applications in the treatment and monitoring of diabetic nephropathy; however, they need to be confirmed in a longitudinal cohort.

Electronic supplementary material

The online version of this article (doi:10.1007/s12014-010-9053-0) contains supplementary material, which is available to authorized users.

Urinary proteomics as a novel tool for biomarker discovery in kidney diseases

Urine has become one of the most attractive biofluids in clinical proteomics, for its procurement is easy and noninvasive and it contains sufficient proteins and peptides. Urinary proteomics has thus rapidly developed and has been extensively applied to biomarker discovery in clinical diseases, especially kidney diseases. In this review, we discuss two important aspects of urinary proteomics in detail, namely, sample preparation and proteomic technologies. In addition, data mining in urinary proteomics is also briefly introduced. At last, we present several successful examples on the application of urinary proteomics for biomarker discovery in kidney diseases, including diabetic nephropathy, IgA nephropathy, lupus nephritis, renal Fanconi syndrome, acute kidney injury, and renal allograft rejection.

Plasma proteome analysis of patients with type 1 diabetes with diabetic nephropathy

Background

As part of a clinical proteomics program focused on diabetes and its complications we are looking for new and better protein biomarkers for diabetic nephropathy. The search for new and better biomarkers for diabetic nephropathy has, with a few exceptions, previously focused on either hypothesis-driven studies or urinary based investigations. To date only two studies have investigated the proteome of blood in search for new biomarkers, and these studies were conducted in sera from patients with type 2 diabetes. This is the first reported in depth proteomic study where plasma from type 1 diabetic patients was investigated with the goal of finding improved candidate biomarkers to predict diabetic nephropathy. In order to reach lower concentration proteins in plasma a pre-fractionation step, either hexapeptide bead-based libraries or anion exchange chromatography, was performed prior to surface enhanced laser desorption/ionization time-of-flight mass spectrometry analysis.

Results

Proteomic analysis of plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric, gave rise to 290 peaks clusters of which 16 were selected as the most promising biomarker candidates based on statistical performance, including independent component analysis. Four of the peaks that were discovered have been identified as transthyretin, apolipoprotein A1, apolipoprotein C1 and cystatin C. Several yet unidentified proteins discovered by this novel approach appear to have more potential as biomarkers for diabetic nephropathy.

Conclusion

These results demonstrate the capacity of proteomic analysis of plasma, by confirming the presence of known biomarkers as well as revealing new biomarkers for diabetic nephropathy in plasma in type 1 diabetic patients.