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Molecular & Cellular Proteomics 4:441-457, 2005.
© 2005 by The American Society for Biochemistry and Molecular Biology, Inc.
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From the Steno Diabetes Center, DK-2820 Gentofte, Denmark; ¶ Institute for Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense, Denmark
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ABSTRACT |
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Based upon etiology, diabetes can be divided into four main groups (1) as described below.
T1D—Absolute insulin deficiency due to an autoimmune-associated destruction specifically of the insulin-producing ß-cells in the islets of Langerhans (Fig. 1).
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Other Specific Types of Diabetes—For example, specific genetic defects of ß-cell function, genetic defects in insulin action, diseases of the exocrine pancreas, endocrinopathies, drug or chemically induced, infections, uncommon forms of immune-mediated diabetes, and other genetic syndromes sometimes associated with diabetes.
Gestational Diabetes—Carbohydrate intolerance resulting in hyperglycemia of variable serverity with onset or first recognition during pregnancy (includes gestational impaired glucose tolerance and gestational diabetes).
The metabolic state between normal glucose homeostasis and diabetes encompasses a state termed "impaired glucose regulation" (impaired glucose tolerance (IGT) and impaired fasting glycaemia (IFG)). Individuals with IGT or IFG may be normo-glycaemic in daily life with normal or near-normal glycated hemoglobin levels. IGT and IFG are not independent clinical entities, but are rather risk categories for future diabetes and/or cardiovascular disease (1, 4, 5).
Over the last two decades, the incidence of diabetes has increased in an epidemic-like fashion worldwide. The increasing number of patients relates to T2D in particular, and the total number of diabetics, is estimated to increase from 151 million in 2000, 180 million in 2003, to 
220 million in 2010 (6, 7). T1D is estimated to increase from 4.4 million in 2000 to 5.4 million in 2010 (7). The increasing number of diabetics is not only a reflection of an increased population, but reflects a true increase in the incidence of both T1D (8–10) and T2D (6, 7, 9, 11).
The overall age-adjusted incidence of T1D displays enormous differences world-wide with a variation in incidences of more than 350-fold. The highest incidence is seen among Caucasoid populations, in Sardinia (36.8/100,000 per year) and Finland (36.5/100,000 per year), and the lowest is seen in the Zunyi province of China (0.1/100,000 per year) (8).
The etiology for the most diabetic cases (both T1D and T2D) is believed to be the result of interactions between environmental and a genetic components in genetically predisposed individuals. In a few percent of the cases the etiology and pathogenesis are known, e.g. maturity onset diabetes of the young (12), Wolfram Syndrome (13), and Walcott-Rallison syndrome (14), which are caused by alterations in single genes.
In this review, we will deal with the pathogenesis of the autoimmune T1D studied through proteomics and describe the active involvement of the ß-cell itself in ß-cell destruction.
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TYPE 1 DIABETES |
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90%. T1D is a complex genetic trait, with multiple genetic loci contributing to susceptibility, but also with environmental factors playing a role in determining risk. There is a significant familial clustering of T1D with an average prevalence risk in siblings of 6% compared with 0.4% in the general population (8, 16). At least one locus that contributes strongly to this familial clustering resides within the major histocompability (MHC) region on chromosome 6p21.3. Other established T1D risk genes include the insulin gene region on chromosome 11p15 and the cytotoxic T-lymphocyte-associated 4 gene (CTLA4) region on chromosome 2q33 (17).
Most attention to environmental factors has been paid to virus infections and early exposure to cow’s milk protein as triggers of autoimmunity in individuals genetically susceptible to T1D (18–20), although no consensus can be reached by evaluating all published studies.
Another epigenetic factor is the influence of the intrauterine environment on ß-cell development and later development of diabetes. Fetal undernutrition may dispose to T2D and coronary heart disease later in life in both humans and rodents (21–24). Low-protein (LP) diet given to pregnant rats during gestation induces permanent changes in the offspring e.g. altered islet cell proliferation, islet size, pancreatic insulin, apoptotic rate, and increased sensitivity to IL-1 ß and nitric oxide (NO) (25–27). In the offspring of T1D mothers, it has been suggested that exposure to a diabetic environment in utero is associated with increased prevalence of IGT and defective insulin secretory response in adulthood (28).
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MODEL FOR THE PATHOGENESIS OF T1D |
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CYTOKINES IN T1D AND RATIONALE FOR STUDYING IL-1 ß-EXPOSED RAT ISLETS |
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(TNF-), and interferon gamma (IFN-
) that induce several signal transduction pathways and alterations in gene transcription and protein synthesis (31–34). In addition, increased expression of IL-1, TNF-, and IFN-/ are observed in both animal models and in humans at the onset of diabetes (35–46). IL-1 ß is selectively cytotoxic to isolated rat ß-cells (47–49), and the effects are potentiated by TNF- and IFN- (44, 50–53). The effects of IL-1 ß can be inhibited both in vitro and in vivo by IL-1 receptor antagonists (33, 54). One of IL-1 ß’s effects on the ß-cell is enhanced transcription of inducible NO-synthase (iNOS), which is especially toxic to ß-cells (34, 44, 55–64). Inhibitors of iNOS protect ß-cells in vitro and in vivo from the cytotoxic effects of cytokines (58, 65, 66).
Thus, IL-1 ß alone or in combination with TNF- and IFN- is cytotoxic to rat, mouse, and human islets in vitro through the production of NO in ß-cells. NO is, however, necessary but not sufficient for the destruction of ß-cells. Inhibition of NO only partially protects against IL-1-induced ß-cell destruction, indicating that additional mechanisms must be involved (34, 44, 67).
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CELL LINES AND ANIMAL MODELS FOR T1D |
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It is important to have in mind that phenomena studied in the inbred NOD mice and BB rats represent observations in one individual due to genetic homogeneity, whereas in humans, T1D cases are genetically heterogeneous. This implies that observations made in spontaneous animal models inbred for dozens of generations might probably only represent specific subgroups of T1D patients and should be interpreted with some caution (72).
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PROTEOMICS IN T1D RESEARCH |
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Today, no published proteomic data based on human material directly studying ß-cell destruction and development of T1D are available. One study has used SELDI coupled with TOF-MS to identify autoantibodies directed against glial fibrillary acidic protein from the peri-islet Schwann cells, enveloping the islets of Langerhans, in both NOD mice and in diabetic patients (77). The authors results suggested that autoimmunity against the pancreatic nervous tissue could be involved in T1D pathogenesis. Another study has described the global protein expression in whole human pancreata and created a reference two-dimensional gel electrophoresis (2-DGE) map with 302 identified proteins (78). Because the proteins identified in that study were from both endocrine and exocrine tissue, it is difficult to use the data for T1D pathogenesis research. Another published protein database is "The Mouse SWISS-2D PAGE Database," which is a descriptive analysis of the protein expression in normal mouse liver, liver nuclei, muscle, white and brown adipose tissue, and pancreatic islets accessible at the ExPASy molecular biology server (79). These protein databases may serve as reference 2-DGE maps. However, proteins that change expression cannot be identified on 2-DGE databases alone, but have to be further identified using MS because co-localization of proteins on 2-DGE is quite common.
The first studies using 2-DGE for protein separation in relation to islets, ß-cell lines, and T1D were done in the 1980s describing changes in the protein expression pattern in islets from mice with virus-induced hyperglycemia (80). Other researchers investigated the expression of specific enzymes such as hexokinase, glucokinase, and other proteins in ß-cells combining 2-DGE and specific antibodies for identification (81–83). In 1987, Nepom et al. used 2-DGE and immunoprecipitation to analyze human leukocyte antigen (HLA) molecules from T1D patients and demonstrated that hybrid HLA molecules are associated with heterozygosity (84). Others have used 2-DGE to describe insulin secretory granule biogenesis (85), exocytosis (86), and the effect of glucose on ß-cells (87, 88). 2-DGE has furthermore been used to characterize autoantigenes and epitopes (89, 90).
More recent proteomic studies of mouse islets compared islet protein expression with proteins involved in Alzheimer’s disease. Several of the proteins identified in the islets are known to be related to Alzheimer’s disease suggesting common pathways of T2D and Alzheimer’s disease (91).
The studies described do not address or only superficially address issues of the pathogenesis of T1D. Proteome analyses in investigating the pathogenesis of T1D have been the focus of our research group.
We have in our research focused on pancreatic islets of Langerhans as a whole organ, because most of the cells in an islet are ß-cells, and that islet cells do work together. Our published studies have yielded a complex and detailed picture of protein expression changes associated with maturation into the ß-cell phenotype, cytokine-mediated ß-cell destruction, and islet destruction in vitro and in vivo during diabetes development. The picture that emerges from these analyses is complicated and far from complete. Still many questions remain to be answered. However, we are studying the ailing ß-cell through a new window and the challenge is now to fully understand what we are seeing.
The purpose is to identify and describe the changes in protein expression specific for ß-cells or of proteins induced by IL-1 ß exposure of Wistar Furth (WF) and BB-DP islets in vitro and in vivo during development of diabetes, respectively (Fig. 2). Furthermore, the aim is to test the hypothesis that the ß-cell is not a passive bystander of its own destruction. In addition, we have demonstrated that an environmental factor (intrauterine protein restriction) may influence islet protein expression in ways unfavorable for long-term ß-cell survival and that when a pre- ß-cell becomes a ß-cell it changes its cellular defense mechanisms.
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ß-CELL DIFFERENTIATION |
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-cells. During differentiation of the - and ß-cells, their sensitivity to cytokines and toxins diverges in favoring increased ß-cell sensitivity (93). Studies of a glucagon-producing rat cell line (NHI-glu) that changes phenotype to an insulin-producing cell line (NHI-ins) after in vivo passage as a subcutaneous tumor in rats (93, 94) suggest that ß-cell sensitivity to cytokines is acquired and reflected in the protein expression pattern (95). By comparative 2-DG analysis of the NHI-glu versus NHI-ins cells, 135 protein spots out of 2.239 detectable were significantly differently expressed as a result of maturation into the cytokine-sensitive NHI-ins phenotype. Of these, 74 were down-regulated, 44 were up-regulated, 16 were suppressed, and 1 was expressed de novo in the insulin-producing cells compared with glucagon-producing cells. From 93 of the 135 protein spots, 97 different proteins were identified by MALDI-MS and revealed a complex pattern of alterations affecting many different cellular functions such as protein synthesis, cellular defense, and apoptosis. More than 30% of the identified proteins were present in more than one spot, indicating that posttranslational modifications are important for the change in phenotype. The data suggest that during differentiation the ß-cell phenotype is altered in its ability to protect itself against hydrogen peroxide and other organic hydroperoxides through down-regulation of GST (95). Several of the identified proteins have also been identified in islets exposed to IL-1 ß (96, 97). Taken together, the study has identified proteins and probably modified proteins involved in ß-cell maturation, insulin gene expression, and acquired ß-cell IL-1 ß sensitivity (95).
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PROTEOME ANALYSIS OF IN VITRO IL-1 ß-EXPOSED RAT ISLETS |
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In comparison, islets from nondiabetes developing WF rats exposed to 150 pg/ml IL-1 ß for 24 h showed 105 out of 1,500 protein expression changes (96, 99). Most of the proteins changed in the two studies were identical or affecting the same pathways. Fewer proteins are changed in the BB-DP islets under the same experimental conditions, suggesting that fewer changes are needed in the BB-DP islets to induce instability and destruction. The involvement of a limited number of genes/proteins in response to cytokine exposure is further supported by microarray analyses of sorted rat ß-cells and rat insulinoma (RIN) cells, where a large number of mRNA transcripts change expression dependent upon cytokine and exposure time (100, 101). Many of the same genes and pathways were affected in all four studies, although different methods were used, and expression analyses of mRNA and protein are not directly comparable (102, 103).
Taken together, these studies indicate that cytokine exposure of islets and ß-cells induces changes in the expression of both mRNAs and proteins involved in a variety of different functions comprising both primary and secondary changes. No single gene or protein has been held responsible for the observed effects. These studies do not allow any discrimination between primary or secondary changes, nor do they describe the importance of the observed changes. Furthermore, the findings should be interpreted with some caution because they reflect the specific experimental conditions, e.g. islet isolation procedure, concentration of IL-1 ß, exposure time, labeling interval, and the general culture conditions as well as the limitations in the methods used.
In the described studies 1,900 protein spots were found emphasizing the importance of the methods used, because under optimal experimental conditions in 2-DGE studies of mouse tissue more than 10,000 protein spots can be visualized (104). However, the proteins identified in the islet studies are all newly synthesized during the labeling period because we used a metabolic labeling method with sulfur-35 (35S) methionine incorporation. However, even with these limitations, the complexity of the effects of IL-1 ß on islets and ß-cell lines substantiates the theory that development of T1D is the result of a collective, dynamic instability, rather than the result of a single factor (105).
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IN VIVO ANALYSIS OF IL-1 ß-EXPOSED RAT ISLETS |
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Taken together, this suggests that IL-1 ß is present in islets early in the prediabetic period and may participate in the initiation of diabetes in the BB-DP rat through the induction of changes in protein expression levels. These changes in protein expression may alter the stability of the ß-cell to a state of instability and potential destruction. The role of each specific protein in the disease process is difficult to determine, and no single protein seems to be responsible for the development of diabetes, but rather the cumulative number of changes seems to interfere with the stability of the ß-cell, thereby pushing it toward destruction and development of diabetes.
When using spontaneous diabetes-developing animal models, such as the BB rat, it is important to keep in mind that during ß-cell destruction in vivo a plethora of cytokines, including IL-1 ß, and other factors are involved (108) and that IL-1 ß exposure of islets in vitro is a simplified model for ß-cell destruction. The BB-DP transplantation model reflects the spontaneous diabetes development with all its known and unknown influencing factors and their interactions in the BB-DP rat (46, 107). Using other cytokines or combinations of cytokines and exposure time in vitro might possibly give a different protein expression profile, as seen for example in mRNA expression studies of sorted ß-cells and ß-cell lines (100, 101), although many changes involve the same genes/proteins and pathways.
Following the expression of proteins over time might also reflect the dynamic changes in the ß-cell mass changes dependent on age, function, and demand (109, 110) and, in the transplantation model, changes in the re-organization and re-vascularization taking place in the transplanted islets (111). Because the ß-cell mass changes during life (112), a number of proteins are expected to change their expression level throughout the study period. Proteins changing expression over time in rats not developing diabetes, in casu BB-DR/WF transplants, may in part originate from the normal processes of aging and ß-cell turnover. Hence, such proteins may be of minor interest for the disease process. Furthermore, this study does not allow us to consider any small and statistically nonsignificant changes in protein expression levels and to decide whether many minor changes together are what produce the diabetic phenotype, ß-cell destruction, and diabetes.
However, in this study we have identified and followed IL-1 ß-induced changes in protein expression in vitro in syngeneically transplanted islets and have related these changes to the development of diabetes in the BB-DP rat. Furthermore, we have produced a rather detailed picture of protein expression during development of diabetes in the BB-DP rat and produced evidence to support that IL-1 ß-induced in vitro protein expression changes in islets also occur during autoimmune ß-cell destruction.
In BB-DP rats escaping diabetes (day 174 after transplantation) and in age-matched BB-DR rats, only a few proteins (5 proteins) were expressed differently, the lowest number for all the comparisons between BB-DP and BB-DR/WF transplants. This suggested that very few differences are indicative of stability. These protein spots contained galectin-3 (gal-3), the progesterone receptor membrane component 1 (25-Dx), adenosine phosphoribosyltransferase/uridine monophosphate-cytidine momophosphate (UMP-CMP) kinase, and two yet unidentified proteins. Four of these are changed in expression as by IL-1 ß in vitro. Gal-3, an inhibitor of apoptosis (113–115), is less expressed in BB-DR transplants compared with BB-DP transplants escaping diabetes, which might suggest a lower need for inhibition of apoptosis in BB-DR transplants.
The transplant also contains other cell types than ß-cells, e.g. -cells, infiltrating mononuclear cells, endothelial cells, and kidney tissue. Non-islet tissues are sought identified and removed under a microscope immediately after retrieval from the rat. All these factors mentioned are supposed to be equal in age-matched groups, except for the mononuclear cell infiltration, which is primarily seen in the BB-DP transplants at onset of diabetes. Nevertheless, proteins that are changed in expression despite their origin are believed to play a role in changing the stability of the ß-cell and are as such of potential interest.
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INTRAUTERINE PROTEIN RESTRICTION |
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We have tested the hypothesis that the effects in utero of a LP diet on ß-cell development and function are caused by intrauterine changes in the programming of ß-cell gene expression and that this is reflected in the protein expression pattern (121). This study demonstrated that changes in the protein expression pattern occurred in fetal rat islets after protein restriction during gestation. Overall, the specific protein expression changes were compatible with the functional findings described above, and the study suggested that the changes in the islet protein expression pattern of expression were acquired (121).
The proteins identified comprise proteins from the following pathways and functional groups: 1) energy transduction, redox potentials; 2) glycolysis and Krebs cycle; 3) RNA and DNA metabolism; 4) protein synthesis and metabolism; 5) protein folding and chaperoning; 6) cell cycle, differentiation, signal transduction, and transcription; 7) cellular structure; 8) cellular defense, and 9) miscellaneous functions. The study showed that in Wistar rats, the intrauterine LP milieu may program islet gene expression in ways unfavorable for the future of the progeny (121). Lasting consequences, e.g. lower ß-cell mass, lower plasma insulin levels, and lower insulin secretion after a glucose challenge and increased susceptibility to cytokines, are present also in adulthood (27). Theoretically, this may be of importance for our understanding of the development of both T1D and T2D and suggests that environmental factors such as the diet may change the susceptibility to cytokines and thereby change the stability of the ß-cell toward a pathological phenotype.
Whether the lasting consequences induced by the LP diet, e.g. lower ß-cell mass, lower plasma insulin levels, and lower insulin secretion and increased susceptibility to cytokines present in both fetal islets and in adulthood (27, 119, 122), can be directly related to changes in the fetal islet protein expression pattern previously described is likely but not definitely proven. The control (C) and the LP islets are comprised of about 90% ß-cells and have proliferated and differentiated during 7 days in culture during which they were withdrawn from the maternal milieu. However, the C and the LP islets are kept under the same environmental influence during the in vitro culture period. Therefore, the difference in the phenotype of endocrine cells observed after 7 days following withdrawal from the abnormal metabolic milieu is of great interest, because it suggests maternal programming of gene expression. Similar dietary influence on phenotype and programming of islet function is seen in rat pups offered a high-carbohydrate milk formula during the suckling period. These animals develop chronic hyperinsulinemia in the post-weaning period and obesity in adulthood, despite a normal rat chow diet. This hyperinsulinemia phenotype is transferred to the next generation despite normal diet both during pregnancy and after pregnancy (123).
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DISTURBANCES IN THE STABILITY-DYNAMIC INSTABILITY |
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TRANSCRIPTOMICS VERSUS PROTEOMICS IN T1D RESEARCH |
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To date, a number of studies using gene array technology in the study of ß-cell physiology and pathogenesis of T1D have been published (100, 101, 131–138).
Proteins are the product of a highly specialized and regulated process encoded in the genetic information. The expression of a gene can be examined at the level of mRNA or at the corresponding protein. However, the relationship between the mRNA of a specific gene and the corresponding protein products is not necessarily linear. Proteins with high abundance have a generally better correlation to the corresponding mRNA than low-abundance proteins (102, 103, 139–145). Furthermore, proteins are often modified post-translationally or present in one or more isoforms in higher species. This is not necessarily encoded for in the mRNA sequence and the complexity of protein profiles therefore exceeds that of corresponding mRNA profiles (146). This is illustrated in the comparison of proteome and transcriptome data obtained from the same ß-cell line during maturation where only two proteins and transcripts were in common (137). In the proteome study, 37% of the proteins were posttranslationally modified. Further discrepancies between proteome and transcriptome studies can be explained by e.g. differences in detection sensitivity of the selected method, sample preparation, specific experimental conditions, translational regulation, alternative splicing of mRNA, posttranslational modifications, time difference in mRNA, and protein production and degradation. This further emphasizes the importance of cautiousness when comparing transcriptome and proteome studies studying the same phenomenon such as cytokine-exposed islets, ß-cells, or ß-cell lines.
Two studies have compared gene and protein expression in islets and ß-cell lines, and both studies were able to suggest a close correlation between basal mRNA and protein expression (133, 138).
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OBSTACLES |
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Another important issue for present and future studies is to extract the important information from expression profiling studies with large numbers of data derived from both transcriptomics and proteomics and to understand the biological sense. Furthermore, the correlation between a gene and its gene products needs to be better elucidated because there is no consensus regarding this in the literature.
Not only studying a single linear biological/chemical reaction but the entire set of expressed genes or proteins in a biological sample, systems biology, requires advanced data mining analysis methods such as cluster analysis. These tools may identify groups or clusters of proteins relevant for the issue studied.
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FUTURE STUDIES |
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T1D is characterized by production of autoantibodies to ß-cell antigens, and today approximately two dozen of ß-cell antigens have been identified although their pathogenic role is unclear. The autoantibodies can be present years before onset of diabetes and can be used as predictors of later T1D development (151, 152). With autoantibodies and potentially also autoantigens present in the blood years prior to onset of diabetes, the ability to study the plasma proteome (153, 154) makes proteomics studies of blood samples from diabetes-disposed individuals an obvious issue for future studies and identification of new biomarkers for T1D. Other new and future tasks will be to study membrane proteins, secreted proteins, posttranslational modifications, and to have access to human material.
Hopefully, this will unveil new patterns of expression changes and bring new clues and ideas into the study of T1D pathogenesis and create new ideas for protection or even cure of T1D.
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EMERGING PROTEOMIC TECHNOLOGIES |
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To date, global proteomic studies of diabetes have been performed by 2-DGE, image analysis, and MS. As outlined in previous sections, such studies have provided insights into some of the cellular processes that are perturbed in this disease.
2-DGE-based proteomic technology has several advantages due to high protein resolving power, including separation of protein variants with altered isoelectric points due to amino acid substitutions, splice variants, or posttranslational modifications such as phosphorylation. In addition, identification of 2-DGE-separated proteins by MS is relatively straightforward, because a large number of peptides are usually matched to the amino acid sequence of the protein, providing high-confidence protein identification. However, standard 2-DGE technology has some disadvantages, which limit the number of detectable proteins in a given sample. The main disadvantage is that membrane proteins are poorly soluble in standard IEF buffers, and they are therefore underrepresented in most 2-DGE-based proteomic studies. More than one-third of the proteins coded by the human genome are estimated to be membrane proteins or membrane-associated proteins with functions spanning from cell-cell communication/attachment to cell signaling, maintenance of cell membrane potential, mediation of the transport of ions<
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ABSTRACT
TYPE 1 DIABETES
