Protein Bioinformatics In The Investigation Of Diabetic Nephropathy

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Bioinformatics can be applied to obtain additional protein information including:

1. protein identities using MS or MS/MS data;

2. protein characterization and structure prediction;

3. homology and sequence alignment;

4. motifs and domains;

5. protein interactions and networks;

6. potential PTMs;

7. predicted transmembrane regions;

8. subcellular localization; and

9. miscellaneous (51).

There are numerous on-line analytical tools that are freely accessible in the websites for bioinformatic analysis (http://www.expasy.org/tools/). The data obtained from bioinformatic analysis are very helpful to predict the correlation between "protein expression" and "protein function" and make functional studies more focused.

An obvious example of the usefulness of bioinformatics in the investigation of DN has been demonstrated in a recent proteomic study, which has identified an unnamed protein product (gi| 12841975; BAB25424) as a highly regulated protein in the kidney of the OVE26 diabetic mice (51,73,74). The MS-based protein identification frequently provides the identities that are designated as "unknown protein," "unnamed protein," "putative protein," "hypothetical protein," "unnamed gene product," and so on. All of these terms refer to the proteins that have been submitted to the database without detailed characterizations or those, whose sequences have been predicted from DNA sequences but their other information is limited or unknown. When this occurs, the investigators frequently ignore the proteins and the data may be of limited usefulness.

Bioinformatic analysis is of substantial assistance in characterizing hypothetical proteins identified by mass spectrometric analysis. It can "unmask" those unknown proteins, which may turn out to be common or well-known proteins. Using data mining, the unnamed protein (gil12841975; BAB25424) that is upregulated in diabetic mouse kidney has been finally unmasked (51). The data indicate that this unknown protein is, indeed, phosphatidylethanolamine-binding protein. Additionally, motif scanning shows that this unknown protein contains several kinase motifs, especially protein kinase C that plays an important role in the pathogenesis of diabetic nephropathy. Therefore, this protein phosphatidylethanolamine-binding protein should have a potential functional role in protein kinase C-dependent pathogenic mechanisms of diabetic nephropathy.

The same group of the investigators, who have applied the classical proteomics approach to DN (51,73-75,95), have also performed bioinformatic analysis of the altered proteins identified from the OVE26 diabetic mice, integrating with literature search to make a correlation between the altered proteins and the pathogenesis and pathophysiology of DN. A model of renal protein trafficking that occurs in DN has been proposed (Fig. 7). The pathogenic mechanisms that are involved in this model are apoptosis, vasculopathy, glomerulopathy, and fibrogenesis. However, it should be noted that this model represents only the phenomena occur in an animal model of type 1 diabetes and has not yet been examined in humans. Moreover, the data do not cover all of the known proteins that have previously been shown to be regulated during diabetes.

Fig. 7. The model of renal protein trafficking in type 1 diabetic nephropathy. This model has been created by extensive bioinformatic analysis and literature search of the data obtained from a pro-teomic analysis of the kidney in the OVE26 model of type 1 diabetes (73). All of the altered proteins that have been identified in this model play important roles in apoptosis, vasculopathy, glomerulopathy, and fibrogenesis. Abbreviations used: +, activation or stimulation; -, inhibition; TM, tropomyosin; RBP, retinol-binding protein; ClqBP, Complement lq-binding protein. (Modified from ref. 131 with permission.)

Fig. 7. The model of renal protein trafficking in type 1 diabetic nephropathy. This model has been created by extensive bioinformatic analysis and literature search of the data obtained from a pro-teomic analysis of the kidney in the OVE26 model of type 1 diabetes (73). All of the altered proteins that have been identified in this model play important roles in apoptosis, vasculopathy, glomerulopathy, and fibrogenesis. Abbreviations used: +, activation or stimulation; -, inhibition; TM, tropomyosin; RBP, retinol-binding protein; ClqBP, Complement lq-binding protein. (Modified from ref. 131 with permission.)

This model will be more valuable when a systematic functional study is designed to address all of these multiplexed mechanisms globally, not just a particular pathway.

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Diabetes 2

Diabetes 2

Diabetes is a disease that affects the way your body uses food. Normally, your body converts sugars, starches and other foods into a form of sugar called glucose. Your body uses glucose for fuel. The cells receive the glucose through the bloodstream. They then use insulin a hormone made by the pancreas to absorb the glucose, convert it into energy, and either use it or store it for later use. Learn more...

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