Proteomic study is an effective approach in the disease research, because proteomic aberrations should exist behind any type of diseases and it is proteome that directly regulate the disease phenotypes. Two-dimensional difference gel electrophoresis (2D-DIGE) is a high performance proteomic technology. Although it is based on two-dimensional gel electrophoresis (2D-PAGE), owing to its multiplex detection system, it solves many drawbacks of classical 2D-PAGE. 2D-DIGE allows obtain the quantitative protein expression profiles across many clinical specimens in a reproducible and high-throughout manner. 2D-DIGE with high sensitive fluorescent dyes enables the proteomic study on the laser microdissected tissues, and thus accurate proteomics can be achieved using 2D-DIGE. Mass spectrometry can identify the proteins corresponding to any spots observed by 2D-DIGE and we can utilize the gene and literature database to interpret the proteomic data. Bioinformatic approach can determine the proteomic signatures responsible for the important clinico-pathological features and identify a small number of key proteins, which will be candidates for disease markers and therapeutic targets. Combination between 2D-DIGE, mass spectrometry and bioinformatics approach will be a powerful tool for disease proteomics. The efforts to understand the overall feature of proteome by bioinformatics approach to 2D-DIGE data, together with the integrated information of the individual proteins identified by 2D-DIGE, will give us novel molecular backgrounds of the diseases. The proteome database should facilitate the use of the wealth of abundant information. The data integration between genome, transcriptome and proteome is also important approach to understand the background of proteomic aberrations in diseases.
Fluorescence 2-D difference gel electrophoresis (DIGE) uses spectrally resolvable dyes to label protein samples prior to 2-D electrophoresis. By using different fluorescent dyes to separately label protein samples multiple samples can be co-separated and visualized on a single 2-D gel. Differences between samples are resolved using image analysis software such as DeCyder 2D. This fluorescent multiplexing approach is compatible with mass spectrometry and overcomes many of the disadvantages of traditional 2-D analyses. A broad dynamic range provides more accurate quantitative data than traditional 2-D silver staining techniques while rapid image overlay simplifies image analysis and improves comparative accuracy.
A two-dimensional gel electrophoresis (2-DE) method that uses an agarose isoelectric focusing (IEF) gel in the first dimension (agarose 2-DE) offers advantages over the more widely used immobilized pH gradient 2-DE for separating high-molecular-mass proteins (100-500kDa) and for having a higher loading capacity (1.5mg in total). We have applied the Fluorescent 2-D differential gel electrophoresis (2-D DIGE) to our agarose 2-DE system. This allowed us to see clear differences in the 2-DE patterns from liver extracts of a diabetic model Otsuka Long-Evans Tokushima Fatty (OLETF) and its control Long-Evans Tokushima Otsuka (LETO) rats including changes in the amounts of several proteins larger than 100kDa. The combined method would increase its power to detect changes in disease proteomics.
Recent advances on analytical technology of proteomics offer exciting opportunities to find novel and multiple biomarkers. Among proteomic procedures for differential analysis developed until today, two-dimensional differential gel electrophoresis (2D-DIGE) is one of the most useful techniques on analyzing the proteins containing their modified forms. Using this 2D-DIGE, we carried out a search for the disease-associated proteins linking to the potential diagnostic biomarkers for a highly malignant type in human ovarian cancer, clear cell adenocarcinoma (CCA). In this study, we first performed a differential analysis using human ovarian cultured cell lines OVISE and OVTOKO as CCA cell lines in comparison with MCAS as a control cell line. Then the proteins characteristically expressed in CCA cell lines were identified by mass spectrometry. As a result of this analysis, some of the identified proteins were previously observed as alternative expression levels in ovarian and/or other cancers in clinical samples. In a subsequent preliminary differential study using surgical specimens from patients with CCA, it was demonstrated that some of the identified proteins were expressed differentially in actual tissues as well as in the CCA culture cells. The results from this investigation show the effectiveness of a proteomic approach to identify expressed proteins that are characteristic for particular cancers, which may eventually serve as diagnostic markers or therapeutic targets in CCA.
Recent advances in 2-D differential gel electrophoresis (2-D DIGE) have made it possible to detect and quantitate the critical changes involved in disease pathogenesis. We have identified novel proteins with altered expression in primary esophageal cancer using the powerful method of agarose 2-D DIGE. Excised tissues from 12 patients of primary esophageal cancer were obtained. Proteins with altered expression between cancer and adjacent non-cancer tissues were analyzed by agarose 2-D DIGE and identified by mass spectrometry. 2-D DIGE has many advantages on proteomic analysis for clinical specimens. 33 proteins with altered expression in tumors were identified. Among them, a 195kDa protein, periplakin, was significantly downregulated in esophageal cancer, which was confirmed by immunoblotting. Immunohistochemistry showed that periplakin was mainly localized at cell-cell boundaries in normal epithelium and dysplastic lesions, while it disappeared from cell boundaries, shifted to cytoplasm, in early cancers and less expressed in advanced cancers. These results suggest that periplakin could be a useful marker for detection of early esophageal cancer and evaluation of tumor invasion, metastasis and progression.
Two-dimensional differential in-gel electrophoresis technology (2D DIGE) technique is highly useful for differential analysis of protein spots in two-dimensional differential gels. Utilizing this technique, we attempted to search for diabetes-related drug targets and biomarkers. In human hepatoma cell line, HepG2, we analyzed secretome in the presence or absence of a Liver X receptor agonist, TO-901317, and identified one of the up-regulated proteins in response to LXR activation as apolipoprotein E. We also evaluated nuclear proteome of cultured cells overexpressing insulin receptor substrate proteins, in which insulin-stimulated cell cycle progression is differentially regulated, and the gel pattern indicated that insulin-induced phosphorylation of a nuclear protein may be impaired in cells overexpressing cell cycle-suppressive insulin receptor substrate-3. In addition, to search for urinary markers of diabetic nephropathy using 2D DIGE, we analyzed urine samples in which most abundant proteins were removed by immunoaffinity depletion. These findings indicate that the 2D DIGE-based approach is useful for the discovery of disease-specific drug targets and diagnostic biomarkers.
Human granulocytic anaplasomsis and human monocytic ehrlichiosis are tick-borne emerging infectious diseases which are caused by Anaplasma phagocytophilum and Ehrlichia chaffeensis. These bacteria are obligatory intracellular parasites with tropism for hematopoietic cells. Here, we introduced analysis of protein expression profiles of HL-60 host cells in the response to infection with those bacteria by proteomic approach using fluorescence-prelabeled two dimensional difference gel electrophoresis to clarify the molecular mechanisms of the intracellular parasitism. Three experiments were designed for this purpose: differential analysis of infection at late (i) or early (iii) stages, and time-course analysis postinfection (ii). The detailed technical approaches, advantage of this procedure, and technical improvements desired were described and discussed.
We have attempted to separate a minimal amount of proteins of glomeluli isolated from biopsy tissues of human kidney on large format 2-DE gels by exploiting extremely sensitive fluorescent dyes (Cy3- and Cy-5 saturation dye). Only 2.5μg glomerular proteins corresponding to 2-3 glomeruli, gave highly resolved 2-DE profiles, suggesting the applicability of 2-DE to analyze glomerulus proteome in biopsy specimens. We propose a platform for highly quantitative analysis of differential protein expression in the glomerulus in health and disease with the saturation dye as a detection tool.
Recent advances of innovative technologies and valuable data bases for proteome analysis enable us to identify extremely miner but important cellular proteins that related to the pathogenesis, with high throughput and quantitative manner. We established the differential analysis method with the combination of proteomic (2D-DIGE and cICAT/iTRAQ) and transcriptomic (DNA array) techniques using same brain tumor samples that show no pathological differences but significant variations to the chemotherapies, and tried to extract and identify the specific cellular signal cascade related to the chemotherapy sensitivities. For the basic information of human brain proteins, proteome database of brain tissues/cells were constructed with their 2D images and linked to the other brain database. After all of the differential analysis between chemotherapy sensitive and insensitive tumors using above technologies, the significant proteins identified were assembled, integrated, and subjected to the cellular signal network analysis. In the case of anaplastic origodendroglioma/astrosytoma: AOG, a series of proteins that changes their expressions and post-translational modification statuses, in relation to the tumor stages or chemotherapy sensitivities, were quantitatively identified. As glioma specific proteins, 738 proteins were identified in total, and 201 were related to chemotherapy sensitivities in the group of anaplastic gliomas. Using these protein data sets, the activated cellular signals in the chemotherapy insensitive glioma were extracted by the network analysis. They include several unknown signal cascades in gliomas, such as signals related to the specific cell cycle, apoptosis, and cell adhesions. These newly established differential analysis and data mining methods will be useful not only for finding of diagnostics for gliomas, but also for those of other brain diseases.