Urinary proteome profiling using 2D-DIGE and LC-MS/MS

DIGE Analysis Software and Protein Identification Approaches

Two-dimensional difference gel electrophoresis (2D-DIGE) is a high-resolution protein separation technique, with the excellent dynamic range obtained by fluorescent tag labeling of protein samples. Scanned images of 2D-DIGE gels show thousands of protein spots, each spot representing a single or a group of protein isoforms. By using commercially available software, each protein spot is defined by an outline, which is digitized and correlated with the quantity of proteins present in each spot. Software packages include DeCyder, SameSpots, and Dymension 3. In addition, proteins of interest can be excised from post-stained gels and identified with conventional mass spectrometric techniques. High-throughput mass spectrometry is performed using sophisticated instrumentation, including matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF), MALDI-TOF/TOF, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Tandem MS (MALDI-TOF/TOF or LC-MS/MS) analyzes fragmented peptides, resulting in amino acid sequence information, which is especially useful when protein spots are low abundant or where a mixture of proteins is present.

DIGE Analysis Software and Protein Identification Approaches

DIGE is a high-resolution two-dimensional gel electrophoresis method, with excellent dynamic range obtained by fluorescent tag labeling of protein samples. Scanned images of DIGE gels show thousands of protein spots, each spot representing a single or a group of protein isoforms. By using commercially available software, each protein spot is defined by an outline, which is digitized and correlated with the quantity of proteins present in each spot. Software packages include DeCyder, SameSpots, and Dymension 3. In addition, proteins of interest can be excised from post-stained gels and identified with conventional mass spectrometry techniques. High-throughput mass spectrometry is performed using sophisticated instrumentation including matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF), MALDI-TOF/TOF, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Tandem MS (MALDI-TOF/TOF or LC-MS/MS), analyzes fragmented peptides, resulting in amino acid sequence information, especially useful when protein spots are low abundant or where a mixture of proteins is present.

Potential urine proteomics biomarkers for primary nephrotic syndrome

BACKGROUND

Nephrotic syndrome (NS) is a nonspecific kidney disorder, commonly caused by minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous nephropathy (MN). Here we analyzed urinary protein profiles, aiming to discover disease-specific biomarkers of these three common diseases in NS.

METHODS

Sixteen urine samples were collected from patients with biopsy-proven NS and healthy controls. After removal of high-abundance proteins, the urinary protein profile was analyzed by LC-MS/MS to generate a discovery set. For validation, ELISA was used to analyze the selected proteins in 61 urine samples.

RESULTS

The discovery set included 228 urine proteins, of which 22 proteins were differently expressed in MCD, MN, and FSGS. Among these, C9, CD14, and SERPINA1 were validated by ELISA. All three proteins were elevated in MCD, MN, and FSGS groups compared with in IgA nephropathy and healthy controls. When a regression model was applied, receiver operating characteristic analysis clearly discriminated MCD from the other causative diseases in NS.

CONCLUSIONS

We developed a disease-specific protein panel that discriminated between three main causes of NS. Through this pilot study, we suggest that urine proteomics could be a non-invasive and clinically available tool to discriminate MCD from MN and FSGS.

Proteomics for breast cancer urine biomarkers

Although the survival of breast cancer (BC) patients has increased over the last two decades due to improved screening programs and postoperative adjuvant systemic therapies, many patients die from metastatic relapse. Current biomarkers used in the clinic are not useful for the early detection of BC, or monitoring its progression, and have limited value in predicting response to treatment. The development of proteomic techniques has sparked new searches for novel protein markers for many diseases including BC. Proteomic techniques allow for a high-throughput analysis of samples with the visualization and quantification of thousands of potential protein and peptide markers. Human urine is one of the most interesting and useful biofluids for routine testing and provides an excellent resource for the discovery of novel biomarkers, with the advantage over tissue biopsy samples due to the ease and less invasive nature of collection. In this review, we summarize the results from studies where urine was used as a source for BC biomarker research and discuss urine sample preparation, its advantage, challenges, and limitation. We focus on the gel-based proteomic approaches as well as the recent development of quantitative techniques in BC urine biomarker detection. Finally, the future use of modern proteomic techniques in BC biomarker identification will be discussed.

An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis

Aim

Early diagnosis of prostate cancer (PCa), which is a clinically heterogeneous-multifocal disease, is essential to improve the prognosis of patients. However, published PCa diagnostic markers share little overlap and are poorly validated using independent data. Therefore, we here developed an integrative proteomics and interaction network-based classifier by combining the differential protein expression with topological features of human protein interaction networks to enhance the ability of PCa diagnosis.

Methods and Results

By two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MS using PCa and adjacent benign tissues of prostate, a total of 60 proteins with the differential expression in PCa tissues were identified as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and three hub proteins (PTEN, SFPQ and HDAC1) were chosen. After that, a PCa diagnostic classifier was constructed by support vector machine (SVM) modeling based on the microarray gene expression data of the genes which encode the hub proteins mentioned above. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.96∼90.18%) and area under ROC curve (approximating 1.0). Furthermore, the clinical significance of PTEN, SFPQ and HDAC1 proteins in PCa was validated by both ELISA and immunohistochemistry analyses. More interestingly, PTEN protein was identified as an independent prognostic marker for biochemical recurrence-free survival in PCa patients according to the multivariate analysis by Cox Regression.

Conclusions

Our data indicated that the integrative proteomics and interaction network-based classifier which combines the differential protein expression and topological features of human protein interaction network may be a powerful tool for the diagnosis of PCa. We also identified PTEN protein as a novel prognostic marker for biochemical recurrence-free survival in PCa patients.