Translational Proteomics

Project 1: Proteomic signatures in prediabetes and type 2 diabetes, the KORA-F4 study

We developed a fast, easy and efficient depletion of high abundant proteins from plasma samples, and previously discovered protein signatures in a mouse model of type 2 diabetes (T2D). Results of this proof-of-principle study were translated to the human context where we first analyzed 500 randomly selected samples from the KORA cohort. In close collaboration with Cornelia Huth and PD Barbara Thorand (EPI2), we aimed to identify proteomic signatures associated with prediabetes and T2D. In our cross sectional study, we discovered novel and independent associations of prediabetes and related traits with MASP1, and some evidence for associations with THBS1, GPLD1, and APOA4, suggesting a role of these proteins in the pathophysiology of type 2 diabetes.

Fig. 1 From von Toerne and Huth et al., Diabetologia, 2016: Odds ratios (OR) with 95% confidence intervals for the association ‘prediabetes vs normoglycaemia’ per one standard deviation increase in SRM-MS measured proteins (n=376). *p<0.05, **p<0.01, ***p<0.001 (uncorrected significance levels), †p<0.05 (Bonferroni-corrected significance levels)

To test the ability of the measured candidates to predict future prediabetes or T2D, additional 750 samples of the KORA-F4 study were measured and combined with the first data set. Data of the KORA-F4 follow-up study (FF4) were available since end of 2016 and the predictive power of candidate proteins is currently being evaluated.

If successful, replication of results in an independent study is planned for 2018/2019.

A patent application ‘biomarkers of cardiometabolic diseases’ comprising the here investigated biomarkers is pending at the European Patent Office. Results are published in von Toerne et al., 2016 (PMID: 27344311).

This project was supported by intramural funding for Translational & Clinical Projects of the Helmholtz Zentrum München (PI: Dr. Stefanie Hauck)



Project 2: Peptide serum markers in islet autoantibody-positive and early onset T1D children

The development of type 1 diabetes (T1D) includes an asymptomatic period of autoimmunity identified by the presence of islet autoantibodies. The development of islet autoantibodies is most prominent around 1 to 2 years of age, but the incidence of clinical diabetes appears to be relatively constant in multiple islet autoantibody-positive children and adolescents. Biomarkers that predict the rate of progression may improve staging the pre-symptomatic disease period of type 1 diabetes. In close collaboration with the institutes of Prof. Anette Ziegler (IDF) and Prof. Fabian Theis (ICB), we applied proteomics to cohorts (BABYDIAB/BABYDIET) of children followed from birth who developed islet autoimmunity and clinical diabetes. We aimed to search for signatures that associate with islet autoimmunity and which could help predict progression rate to clinical diabetes in multiple autoantibody-positive children. We successfully identified several peptides that were significantly different between islet autoantibody-positive and autoantibody-negative children. A double cross-validation statistical approach identified two peptides (from apolipoprotein M and apolipoprotein C-IV) able to discriminate autoantibody-positive from autoantibody-negative children. Serum levels of hepatocyte growth factor activator, complement factor H, and ceruloplasmin, along with age, significantly improved prediction of progression time to type 1 diabetes compared to age alone. For more details read von Toerne et al., 2016 (PMID: 27815605).

Fig. 3 From von Toerne and Laimighofer et al., Diabetologia, 2016: Results of the progression time analysis are displayed as Kaplan–Meier curves of the high, medium and low risk score groups (consisting of age, hepatocyte growth factor activator, complement factor H, ceruloplasmin for the time from seroconversion to type 1 diabetes). The black dashed line indicates the 5-year interval. Blue, grey, and red lines indicate the low, medium, and high risk groups, respectively. The dashed lines indicate the confidence intervals. Low risk and high risk survival curves were significantly different (p=1.6 x 10-05). The numbers of children remaining at risk at a given time are shown below the time axis.

This project was funded by the “Juvenile Diabetes Research Foundation” (JDRF) by grant 17-2012-617 (PI Dr.Stefanie Hauck).

The pathophysiology of autoimmunity in T1D is still poorly understood. An accurate proteomic profile of the T helper cell population has the potential to increase our current understanding of T1D pathogenesis. In collaboration with Prof. Anette Ziegler (IDF), our PhD student Marlen Lepper performed in-depth proteomic profiling of peripheral CD4+ T-cells in a pediatric cohort (TEENDIAB, DiMelli) to identify cellular signatures associated with early disease onset.

This study was supported by grants from JDRF (SRA-2014-161) and the German Center for Diabetes Research (DZD).

Where we are going to:

Applying novel mass spectrometric approaches, targeted measurements are transferred into unbiased “all-ion fragmentation” approaches to combine novel candidate identification with excellent reproducibility and possibly absolute quantification of selected candidates. Currently, we are investigating plasma, serum, and urine proteomes. New sample preparation protocols are developed to forego depletion of high abundant proteins. Suitability of sampling devices currently used in biobanks are evaluated and compared to novel sampling devices. To optimize compatibility with biobank materials, we focus our novel protocols on feasibility with large sample numbers, reproducibility, and cost-effectiveness. Further, we are exploring novel “omics” technologies such as “peptidomics” in body fluids to deeper understand the native peptide atlas in several pathophysiological conditions