Proteomics and diabetic nephropathy

MALDI-TOF Protein Profiling Reflects Changes in Type 1 Diabetes Patients Depending on the Increased Amount of Adipose Tissue, Poor Control of Diabetes and the Presence of Chronic Complications

Introduction: Protein profiling allows the determination of the presence of proteins marking various stages of the disease, and differentiates between people at risk of various diseases. In type 1 diabetes, protein profiling had been previously used to find blood markers other than islet autoantibodies to indicate the pancreatic beta cell destruction process and to reflect the progression of type 1 diabetes mellitus (T1DM). However, T1DM is an auto-immune disease and its clinical presentation changes in time of its duration. The aim of the study: To find differences in protein profiles in patients with type 1 diabetes according to diabetes control (HbA1c > 7%) and with presence of diabetic complications or obesity. It may help to identify subgroups of patients who may need a better clinical supervision and individualized treatment. Material and methods: A group of 103 patients with auto-immunologically confirmed T1DM, and meeting the following inclusion criteria: Caucasian race, duration of diabetes >5 years, were used in the study. Criteria of exclusion: past or present cancer (treated with chemo-/radiotherapy), diseases of the liver (ALT > 3 × ULN) except for people with simple hepatic steatosis, chronic renal disease (eGFR < 30 mL/1.73 m²/min), and acute inflammation (CRP > 5 mg/dL). The study group was divided in terms of the presence of chronic complications, obesity, or poor metabolic control (HbA1c > 7%). Protein profiling was completed by using the MALDI-TOF MS (matrix-assisted laser desorption/ionization-time of flight mass spectrometry) analyzer. Results: Differentiating proteins were identified in all of the groups. The groups burdened with complications, obesity, and poor metabolic control were characterized by increased levels of fibrinogen, complement C4 and C3. Conclusion: The groups of type 1 diabetes patients burdened with complications, obesity, and poor metabolic control were characterized by increased levels of fibrinogen, complement C4 and C3. Further detailed studies are necessary to determine more subtle changes in the proteomic profile of patients with type 1 diabetes.

Proteomic analysis indicates altered expression of plasma proteins in a rat nephropathy model

BACKGROUND

Minimal-change nephrotic syndrome is an idiopathic disease in which protein leaks through podocytes into the urine. We used proteomic tools to examine differences of plasma protein expression in healthy rats and rats with doxorubicin-induced nephropathy treated with or without prednisone.

METHODS

Healthy three-month-old Sprague-Dawley male rats were randomly chosen for one injection of doxorubicin (5.5 mg/kg) through the caudal vein to induce nephropathy (n = 50) or the same volume of saline (control, n = 20). After 1 week, 25 rats in the nephropathy group received topical prednisone (5.5 mg/kg/day) for 21 days and another 25 rats (untreated nephropathy) and the control rats received topical water. At 4 weeks, protein chips generated from rat plasma samples were analyzed by surface enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS) to obtain mass-to-charge ratios (m/z) of proteins of 2-50 kDa.

RESULTS

Relative to control rats, untreated nephropathic rats had four significantly higher and seven significantly lower m/z peaks. Prednisone treatment significantly normalized the intensities of peaks 9069 and 15005 (which correspond to cortexin-1 and interleukin-17A, respectively, according to Swiss Prot database) by increasing the expression of 9069 but reducing expression of 15005.

CONCLUSION

Significant differences in plasma proteins can be identified by proteomic analysis using SELDI-TOF-MS in a rat model of nephropathy.

The renal transcriptome of db/db mice identifies putative urinary biomarker proteins in patients with type 2 diabetes: a pilot study

We sought to identify novel urinary biomarkers of kidney function in type 2 diabetes. We screened the renal transcriptome of db/db and db/m mice for differentially expressed mRNA transcripts that encode secreted proteins with human orthologs. Whether elevated urine levels of the orthologous proteins correlated with diminished glomerular filtration rate was tested in a cross-sectional study of n = 56 patients with type 2 diabetes. We identified 36 putative biomarker genes in db/db kidneys: 31 upregulated and 5 downregulated. Urinary protein levels of six selected candidates (endothelin-1, lipocalin-2, transforming growth factor-β, growth and differentiation factor-15, interleukin-6, and macrophage chemoattractant protein-1) were elevated in type 2 diabetic patients with subnormal glomerular filtration rate (i.e., <90 ml·min(-1)·1.73 m(-2)), independent of microalbuminuria, age, sex, race, and use of angiotensin-converting enzyme inhibitors and angiotensin receptor antagonists. In contrast, urinary levels of fibroblast growth factor were not increased. A composite variable of urine albumin and any of the six candidate markers was associated with subnormal estimated glomerular filtration rate more closely than albumin alone. In addition, urinary endothelin-1, growth and differentiation factor-15, and interleukin-6 were associated with a marker of proximal tubule damage, N-acetyl-β-d-glucosaminidase activity. These results suggest that gene expression profiling in diabetic mouse kidney can complement existing proteomic-based approaches for renal biomarker discovery in humans.

Systems biology of kidney diseases

Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. The mechanisms underlying the origins and progression of kidney diseases are not fully understood. Multiple factors involved in the pathogenesis of kidney diseases have made the traditional candidate gene approach of limited value toward full understanding of the molecular mechanisms of these diseases. A systems biology approach that integrates computational modeling with large-scale data gathering of the molecular changes could be useful in identifying the multiple interacting genes and their products that drive kidney diseases. Advances in biotechnology now make it possible to gather large data sets to characterize the role of the genome, epigenome, transcriptome, proteome, and metabolome in kidney diseases. When combined with computational analyses, these experimental approaches will provide a comprehensive understanding of the underlying biological processes. Multiscale analysis that connects the molecular interactions and cell biology of different kidney cells to renal physiology and pathology can be utilized to identify modules of biological and clinical importance that are perturbed in disease processes. This integration of experimental approaches and computational modeling is expected to generate new knowledge that can help to identify marker sets to guide the diagnosis, monitor disease progression, and identify new therapeutic targets.

Gel-based and gel-free proteomic technologies

Proteomics refers to the analysis of expression, localization, functions, posttranslational modifications, and interactions of proteins expressed by a genome at a specific condition and at a specific time. Mass spectrometry (MS)-based proteomic methods have emerged as a key technology for unbiased systematic and high-throughput identification and quantification of complex protein mixtures. These methods have the potential to reveal unknown and novel changes in protein interactions and assemblies that regulate cellular and physiological processes. Both gel-based (one-dimensional [1D] gel electrophoresis, two-dimensional [2D] polyacrylamide gel electrophoresis, 2D difference in-gel electrophoresis [DIGE]) and gel-free (liquid chromatography [LC], capillary electrophoresis) approaches have been developed and utilized in a variety of combinations to separate proteins prior to mass spectrometric analysis. Detailed protocols for global proteomic analysis from adipose-derived stem cells (ASCs) using two central strategies, 2D-DIGE-MS and 2D-LC-MS, are presented here.

Dietary whey hydrolysate with exercise alters the plasma protein profile: a comprehensive protein analysis

OBJECTIVE

It has been shown that dietary whey protein accelerates glucose uptake by altering glycoregulatory enzyme activity in skeletal muscle. In the present study, we investigated the effect of dietary whey protein on endurance and glycogen resynthesis and attempted to identify plasma proteins that reflected the physical condition by a comprehensive proteomics approach.

METHODS

Male c57BL/6 mice were divided into four groups: sedentary, sedentary with whey protein hydrolysate, exercise, and exercise with whey protein hydrolysate. The mice in the exercise groups performed treadmill running exercise five times per week for 4 wk. Protein profiling of plasma sample obtained from individuals was performed, as were measurements of endurance performance and the glycogen content of gastrocnemius muscle.

RESULTS

After the training period, the endurance of mice fed the whey diet was improved compared with that of mice fed the control diet. Muscle glycogen content was significantly increased after 4 wk of exercise, and intake of whey protein led to a further increase in glycogen. Apolipoproteins A-II and C-I and β(2)-glycoprotein-1 were found to be altered by training combined with the intake of whey protein, without significant changes induced by exercise or whey protein alone.

CONCLUSION

Results of the present study suggest that these three proteins may be potential biomarkers of improved endurance and glycogen resynthesis and part of the mechanism that mediates the benefits of whey protein.

Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy

OBJECTIVE

Chronic renal insufficiency and/or proteinuria in type 2 diabetes may stem from chronic renal diseases (CKD) other than classic diabetic nephropathy in more than one-third of patients. We interrogated urine proteomic profiles generated by surface-enhanced laser desorption/ionization-time of flight/mass spectrometry with the aim of isolating a set of biomarkers able to reliably identify biopsy-proven diabetic nephropathy and to establish a stringent correlation with the different patterns of renal injury.

RESEARCH DESIGN AND METHODS

Ten micrograms of urine proteins from 190 subjects (20 healthy subjects, 20 normoalbuminuric, and 18 microalbuminuric diabetic patients and 132 patients with biopsy-proven nephropathy: 65 diabetic nephropathy, 10 diabetic with nondiabetic CKD [nd-CKD], and 57 nondiabetic with CKD) were run using a CM10 ProteinChip array and analyzed by supervised learning methods (Classification and Regression Tree analysis).

RESULTS

The classification model correctly identified 75% of patients with normoalbuminuria, 87.5% of those with microalbuminuria, and 87.5% of those with diabetic nephropathy when applied to a blinded testing set. Most importantly, it was able to reliably differentiate diabetic nephropathy from nd-CKD in both diabetic and nondiabetic patients. Among the best predictors of the classification model, we identified and validated two proteins, ubiquitin and β2-microglobulin.

CONCLUSIONS

Our data suggest the presence of a specific urine proteomic signature able to reliably identify type 2 diabetic patients with diabetic glomerulosclerosis.

Proteomic discovery of diabetic nephropathy biomarkers

Diabetes mellitus (DM) is a complex systemic disease with complications that result from both genetic predisposition and dysregulated metabolic pathways. It is highly prevalent, with current estimates stating that there are 17.5 million diagnosed and 6.6 million undiagnosed patients with diabetes in the United States. DM and its complications impose a significant societal and economic burden. The medical costs of common microvascular complications of uncontrolled DM, diabetic nephropathy (DN) and diabetic retinopathy account for 29% and 15%, respectively, of the $116 billion worth expenditures associated with diabetes. A substantial gap exists in our knowledge related to the understanding of these complications. To advance therapy and decrease the societal burden of DM, there is a clear need for biomarkers that can diagnose DN at an early stage and predict its course. Proteomics has evolved into a high-throughput, analytical discipline used to analyze complex biological data sets. These open-ended, hypothesis-generating approaches, when appropriately designed and interpreted, are well suited to the study of the pathogenic mechanisms of diabetic microvascular disease and the identification of biomarkers of DN. In this study, we review the evolving role played by proteomics in expanding our understanding of the diagnosis and pathogenesis of DN.

Advances in urinary proteome analysis and biomarker discovery in pediatric renal disease

Recent progress in proteomic analysis and strategies for the identification of clinically useful biomarkers in biofluids has led to the identification of urine as an excellent non-invasive reservoir for biomarkers of disease. Urinary biomarkers have been identified and validated on independent cohorts in different high-incidence adult renal diseases, including diabetic nephropathy, chronic kidney disease and immunoglobulin A-nephropathy, but also in extrarenal disease, such as coronary artery disease. Unfortunately, this type of research is underrepresented in the pediatric population. Here, we present the rare studies in the pediatric population that identified potential clinically useful urinary biomarkers in ureteropelvic junction (UPJ) obstruction and renal Fanconi syndrome. These studies, although limited in number, clearly show the potential of urinary proteomics, especially in the pediatric population. It is anticipated that the advances made in the adult population, the lessons learned on the use of appropriate statistics and the inclusion of independent blinded validation cohorts in these types of studies will rapidly lead to clinical useful urinary biomarkers for other pediatric (renal) disease in a population where non-invasive analysis is particularly appreciated.

Urinary Peptide patterns in native kidneys and kidney allografts

BACKGROUND

The use of biopsies to determine kidney health after kidney transplantation is an invasive procedure with some risk to the patient. Consequently, a noninvasive test for transplanted kidney health would provide a significant advantage over current clinical practice.

METHODS

Urines from kidney donors before nephrectomy, pretransplant patients with native kidney disease, and posttransplant kidney recipients were examined for protein biomarkers to diagnose or prognose kidney disease. Proteins were extracted by C4 reverse phase affinity and analyzed by matrix assisted laser desorption/ionization time-of-flight mass spectrometry.

RESULTS

Urine from individuals with healthy kidneys showed few components other than two ubiquitous saposin B glycoisoforms. Patients with kidney disease lacked saposin B and showed new components in two patterns: the most common contained beta-2 microglobulin (B2M, m/z=11,732) plus one or more peaks at m/z=10,350, 9480, 4337, and 4180. Pattern 2 lacked beta-2 microglobulin but contained several degradation products of alpha-1 antitrypsin. Other pathologic components included urinary protein 1 (m/z=15,835), transthyretin (m/z=13,880), and a component at m/z=13,350.

CONCLUSIONS

Patients with acute rejection showed profiles that ranged from those of kidney donors to those of advanced kidney disease. The range of patterns may be useful for analysis of transplant patients without complications and persons with developing kidney disease before or after transplant.

The human urinary proteome reveals high similarity between kidney aging and chronic kidney disease

Aging induces morphological changes of the kidney and reduces renal function. We analyzed the low molecular weight urinary proteome of 324 healthy individuals from 2-73 years of age to gain insight on human renal aging. We observed age-related modification of secretion of 325 out of over 5000 urinary peptides. The majority of these changes were associated with renal development before and during puberty, while 49 peptides were related to aging in adults. We therefore focussed the remainder of the study on these 49 peptides. The majority of these 49 peptides were also markers of chronic kidney disease, suggesting high similarity between aging and chronic kidney disease. Blinded evaluation of samples from healthy volunteers and diabetic nephropathy patients confirmed both the correlation of biomarkers with aging and with renal disease. Identification of a number of these aging-related peptides led us to hypothesize that reduced proteolytic activity is involved in human renal aging. Finally, among the 324 supposedly healthy individuals, some had urinary aging-related peptide excretion patterns typical of an individual significantly older than their actual age. In conclusion, these aging-related biomarkers may allow noninvasive detection of renal lesions in healthy persons and show high resemblance between human aging and chronic kidney disease. This similarity has to be taken into account when searching for biomarkers of renal disease.

Agents in development for the treatment of diabetic nephropathy

BACKGROUND

Nephropathy is a major cause of morbidity and mortality in diabetic patients. Current treatments include optimization of glycemic and blood pressure control, but more innovative strategies are needed for the prevention and treatment of diabetic nephropathy.

OBJECTIVES

To review emerging therapies for diabetic nephropathy.

METHODS

This paper discusses the molecular mechanisms of diabetic nephropathy and the potential therapeutic interventions.

RESULTS/CONCLUSION

New therapies, including those targeting the accumulation of advanced glycation end products (AGEs) and reactive oxygen species (ROS) generation, are likely to feature in future treatment regimens. Other approaches that at this stage do not appear to be progressing include the glycosaminoglycan sulodexide and the protein kinase C-beta (PKC-beta) inhibitor, ruboxistaurin.