In the mouse model with a type 2 diabetes phenotype, the proteins apolipoprotein E (apoe), mannose-binding lectin 2 (Mbl2) and parotid secretory protein (Psp) show clearly elevated concentrations in plasma. In order to detect disease-typical protein signatures, the research groups led by Dr. Stefanie Hauck, Professor Marius Ueffing (PROT) and Dr. Susanne Neschen (IEG) used a targeted form of mass spectrometry to analyze the plasma of obese mice, which develop type 2 diabetes in pathophysiological stages similar to humans. The specific method used was selected reaction monitoring (SRM), which enables a rapid quantification of the proteins. SRM was recently designated “method of the year 2012” by the renowned journal Nature. The expression of 13 proteins was measured, and the three above-mentioned (Apoe, Mbl2, Psp) proteins correlated with type 2 diabetes or with precursors thereof.
Thus, these proteins may provide new biomarkers to aid in the diagnosis of type 2 diabetes. To verify this, the protein signatures shall be investigated in cohort studies with large numbers of patients. The expression and the significance of the respective protein signature can be determined on the basis of such a large collection of human samples. “This would enable us to gain ‘proteotypes’ – descriptive data down to the protein level for various diseases – in addition to the so-called genotypes, phenotypes, and metabotypes, that is on the basis of genes, external appearance and metabolic activity,” said Dr. Stefanie Hauck of PROT. These findings could be a further contribution to understanding the pathogenesis of type 2 diabetes and its precursor stages.
Research activities at Helmholtz Zentrum München focus on the major widespread diseases in Germany, such as type 2 diabetes. The objective is to develop new strategies for diagnosis, treatment and prevention.
Further information
Original publication:
von Toerne, C. et al. (2013), Apoe, Mbl2 and PSp plasma protein levels correlate with diabetic phenotype in NZO mice – an optimized rapid workflow for SRM-based quantification, Journal of Proteome Research, doi: 10.1021/pr3009836
Link to journal publication: http://pubs.acs.org/doi/abs/10.1021/pr3009836