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. 2010 Mar;1(1):19-31.
doi: 10.1007/s13167-010-0005-6. Epub 2010 Mar 24.

Advanced proteomics procedure as a detection tool for predictive screening in type 2 pre-Diabetes

Jadranka Koehn  1 , Kurt Krapfenbauer
Affiliations

Advanced proteomics procedure as a detection tool for predictive screening in type 2 pre-Diabetes

Jadranka Koehn et al. EPMA J. 2010 Mar.

Abstract

It has been suggested that a more precise selection of predictive biomarkers may prove useful in the early diagnosis of type 2 diabetes (T2D), even when glucose tolerance is normal. This is vital since many T2D cases may be preventable by avoiding those factors that trigger the disease process (primary prevention) or by use of therapy that modulates the disease process before the onset of clinical symptoms (secondary prevention) occurs. The selection of predictive markers must be carefully assessed and depends mainly on three important parameters: sensitivity, specificity and positive predictive value. Unfortunately, biomarkers with ideal specificity and sensitivity are difficult to find. One potential solution is to use the combinatorial power of different biomarkers, each of which alone may not offer satisfactory specificity and sensitivity. Recent technological advances in proteomics and bioinformatics offer a great opportunity for the discovery of different potential predictive markers. In this review, we described a cellular T2D model as an example with the intent of providing specific enrichment and new identification strategies, which might have the potential to improve predictive biomarker identification and to bring accuracy in disease diagnosis and classification, as well as therapeutic monitoring in the early phase of T2D.

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Figures

Fig. 1
Fig. 1
In vitro model of β-cell failure: Concept of gluco/lipotoxicity
Fig. 2
Fig. 2
Characterization and whole protein profiling of INS-1 cells: Model of β-cell failure induced by gluco- and lipotoxicity after 35 h exposure time
Fig. 3
Fig. 3
Flowchart of proteomics approach to identify markers of β-cell failure using mass spectrometry (MudPIT = Multi protein identification technology and MALDI = matrix assisted laser desorption/ionisation mass spectrometry) in combination with in silico analysis
Fig. 4
Fig. 4
Scheme of centrifugal prefractionation of rat INS-1 proteins. Different centrifugal force leads to enrichment of cellular components such as mitochondria, microsomal and cytosolic proteins. The cytosolic fraction was subjected to further fractionation by heparin chromatography followed by separation on a 10% homogenous polyacrylamide gel and protein identification by LC-MS/MS
Fig. 5
Fig. 5
2D-PAGE images of differential protein expression patterns in INS-1 cells incubated under lipo/glucotoxic conditions (10 mM glucose and 0.5 palimate) versus control (5 mM glucose). The selected spot of protein (P06303) was identified by MALDI-TOF as pancreatic polypeptide Y [43]
Fig. 6
Fig. 6
1DE gel analysis of fractions eluted from heparin chromatography. Fraction enriched with cytosolic rat INS-1 proteins was prepared as stated in [43]. Gels were stained with colloidal Coomassie blue and protein bands were identified by MudPIT [43]
Fig. 7
Fig. 7
Primary results: Validation of PPY in human blood / plasma samples in five different healthy donors by ELISA [43]
Fig. 8
Fig. 8
Means of plasma glucose, insulin and PPY concentration during a 100 g OGTT in 4 healthy normal weight individuals. The results indicate that early insulin secretion has a less predictive value for the β-cell function than the determination of PPY levels. The absolute PPY concentration varies much less between healthy individuals and the results show that the secretion rate of PPY from the endocrine pancreas is independent of the plasma glucose and insulin concentration [43]
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