Proteomics and diabetic nephropathy

Transcriptome analysis of human diabetic kidney disease

OBJECTIVE

Diabetic kidney disease (DKD) is the single leading cause of kidney failure in the U.S., for which a cure has not yet been found. The aim of our study was to provide an unbiased catalog of gene-expression changes in human diabetic kidney biopsy samples.

RESEARCH DESIGN AND METHODS

Affymetrix expression arrays were used to identify differentially regulated transcripts in 44 microdissected human kidney samples. DKD samples were significant for their racial diversity and decreased glomerular filtration rate (~25-35 mL/min). Stringent statistical analysis, using the Benjamini-Hochberg corrected two-tailed t test, was used to identify differentially expressed transcripts in control and diseased glomeruli and tubuli. Two different web-based algorithms were used to define differentially regulated pathways.

RESULTS

We identified 1,700 differentially expressed probesets in DKD glomeruli and 1,831 in diabetic tubuli, and 330 probesets were commonly differentially expressed in both compartments. Pathway analysis highlighted the regulation of Ras homolog gene family member A, Cdc42, integrin, integrin-linked kinase, and vascular endothelial growth factor signaling in DKD glomeruli. The tubulointerstitial compartment showed strong enrichment for inflammation-related pathways. The canonical complement signaling pathway was determined to be statistically differentially regulated in both DKD glomeruli and tubuli and was associated with increased glomerulosclerosis even in a different set of DKD samples.

CONCLUSIONS

Our studies have cataloged gene-expression regulation and identified multiple novel genes and pathways that may play a role in the pathogenesis of DKD or could serve as biomarkers.

Identification of diabetic nephropathy-selective proteins in human plasma by multi-lectin affinity chromatography and LC-MS/MS

PURPOSE

Diabetic nephropathy (DN) is a serious complication of diabetes mellitus. Microalbuminuria has been established as a risk factor for the development of diabetic renal disease. Recently, microalbuminuria has been reported to have limitations in determining disease risk and predicting DN. Therefore, identification of more specific biomarkers for prediction of DN is needed.

EXPERIMENTAL DESIGN

When kidney damage is initiated, glycoprotein leakage into the blood may occur, thus altering the glycoproteome profile of the blood. Here, we have used a combined approach of glycoprotein enrichment of plasma with a proteomic analysis to discover potential DN biomarkers. We isolated glycoproteins from plasma provided by six type 2 diabetes control (DC) and six type 2 DN patients using multi-lectin affinity chromatography. Captured glycoproteins were resolved by 1-D PAGE and tryptic digests of isolated proteins were analyzed by LC-MS/MS.

RESULTS

From the comparative and semi-quantitative proteome analysis, we identified 13 up- and 14 down-regulated glycoproteins in DN plasma. Among the up-regulated glycoproteins, the levels of lumican, vasorin and retinol binding protein-4 were verified by Western blot analysis of individual plasma samples.

CONCLUSION AND CLINICAL RELEVANCE

Collectively, our findings show that biomarker discovery has considerable potential for predicting diabetic nephropathy in diabetic patients.

Different expression of fibrinopeptide A and related fragments in serum of type 1 diabetic patients with nephropathy

Type 1 diabetes (insulin-dependent diabetes mellitus, IDDM) is an autoimmune disease affecting about 0.12% of the world's population. Diabetic nephropathy (DN) is a major long-term complication of both types of diabetes and retains a high human, social and economic cost. Thus, the identification of markers for the early detection of DN represents a relevant target of diabetic research. The present work is a pilot study focused on proteomic analysis of serum of controls (n=9), IDDM patients (n=10) and DN patients (n=4) by the ClinProt profiling technology based on mass spectrometry. This approach allowed to identify a pattern of peptides able to differentiate the studied populations with sensitivity and specificity close to 100%. Variance of the results allowed to estimate the sample size needed to keep the expected False Discovery Rate low. Moreover, three peptides differentially expressed in the serum of patients as compared to controls were identified by LC-ESI MS/MS as the whole fibrinopeptide A peptide and two of its fragments, respectively. The two fragments were under-expressed in diabetic patients, while Fibrinopeptide A was over-expressed, suggesting that anomalous turnover of Fibrinopeptide A could be involved in the pathogenesis of DN.

New ways to identify novel bacterial antigens for vaccine development

This article provides an overview of developments in approaches to identify novel bacterial components for use in recombinant subunit vaccines. In particular it describes the processes involved in "reverse vaccinology", and some associated complementary technologies such as proteomics that can be used in the identification of new and potentially useful vaccine antigens. Results obtained from the application of these new methods are forming a basis for a new generation of vaccines for use in the control of bacterial infections of humans and animals.

Characterization of Diabetic Nephropathy by Urinary Proteomic Analysis: Identification of a Processed Ubiquitin Form as a Differentially Excreted Protein in Diabetic Nephropathy Patients

Background: Identification of markers for prediction of the clinical course of diabetic nephropathy remains a major challenge in disease management. We established a proteomics approach for identification of diabetic nephropathy-related biomarkers in urine.

Methods: We used SELDI-TOF mass spectrometry and SAX2 protein arrays to compare protein profiles from urine of 4 defined patient groups. Samples from patients with type 2 diabetes (DM; n = 45) without nephropathy and without microalbuminuria (DM-WNP), patients with DM with macro- or microalbuminuria (DM-NP; n = 38), patients with proteinuria due to nondiabetic renal disease (n = 34), and healthy controls (n = 45) were analyzed. Anionic exchange, reversed-phase fractionation, gel electrophoresis, and mass spectrometry were used to isolate and identify proteins with high discriminatory power.

Results: A protein with m/z 6188 (P <0.0000004) was strongly released in the urine of healthy controls, patients with proteinuria due to nondiabetic disease, and DM-WNP in contrast to DM-NP patients. An m/z 14 766 protein (P <0.00008) was selectively excreted in the urine of DM-NP patients, whereas the protein with m/z 11 774 (P <0.000004) was significantly excreted by patients with proteinuria and DM-NP. The m/z 11 774 and m/z 14 766 mass peaks were identified as β2-microglobulin and UbA52, a ubiquitin ribosomal fusion protein, respectively. The protein with m/z 6188 was identified as a processed form of ubiquitin.

Conclusion: The release of high amounts of UbA52 in urine of DM-NP patients could serve as a diagnostic marker, whereas the lack of the short form of ubiquitin raises interesting questions about the pathophysiology.

Proteomics for diabetes research: an update and future perspectives

Diabetes is a common disease worldwide and can cause several complications, leading to systemic derangements and end-organ damage. Despite blood sugar control and adequate therapy with currently available drugs, diabetic complications remain a serious issue in clinical practice, indicating that our knowledge of diabetes and its complications is only at the tip of the iceberg. Better understanding of its pathogenesis and pathophysiology is crucial to achieve better therapeutic outcomes and to prevent its complications. This review provides an overview of proteomics and introduces proteomic technologies commonly used for diabetes research. Recent proteomic studies for the investigation of diabetes and its complications are summarized. Finally, the future perspectives for the field of proteomics in diabetes research are discussed.