Jens Lamerz

Identification of Novel Biomarker Candidates by Differential Peptidomics Analysis of Cerebrospinal Fluid in Alzheimers Disease

The objective of this work was the application of peptidomics technologies for the detection and identification of reliable and robust biomarkers for Alzheimers disease (AD) contributing to facilitate and further improve the diagnosis of AD. Using a new method for the comprehensive and comparative profiling of peptides, the differential peptide display (DPD), 312 cerebrospinal fluid (CSF) samples from AD patients, cognitively unimpaired subjects and from patients suffering from other primary dementia disorders were analysed as four independent analytical sets. By combination with a cross validation procedure, candidates were selected from a total of more than 6,000 different peptide signals based on their discriminating power. Twelve candidates were identified using mass-spectrometric techniques as fragments of the possibly neuroprotective neuroendocrine protein VGF and another one as the complement factor C3 descendent C3f. The combination of peptide profiling and cross validation resulted in the detection of novel potential biomarkers with remarkable robustness and a close relation to AD pathophysiology.

In vivo profiling of DPP4 inhibitors reveals alterations in collagen metabolism and accumulation of an amyloid peptide in rat plasma

Dipeptidyl peptidase 4 (DPP4) inhibitors represent a novel class of oral anti-hyperglycemic agents. The complete pharmacological profile of these protease inhibitors remains unclear. In order to gain deeper insight into the in vivo effects caused by DPP4 inhibition, two different DPP4 inhibitors (vildagliptin and AB192) were analyzed using differential peptide display. Wistar rats were treated with the DPP4 inhibitors (0.3mgkg(-1); 1mgkg(-1) or 3mgkg(-1) body weight) and DPP4 activity was measured before and at the end of the experiment. One hour after compound administration, blood plasma samples were collected to generate peptide displays and to subsequently identify differentially regulated peptides. A dose-dependent decrease in blood plasma DPP4 activity was measured for both inhibitors. DPP4 inhibition influenced collagen metabolism leading to depletion of collagen derived peptides (e.g. collagen alpha 1 (III) 521-554) and accumulation of related N-terminally extended collagen derived peptides (e.g. collagen alpha 1 (III) 519-554). Furthermore, the intact amyloid rat BRI (1-23) peptide was detected in plasma following in vivo DPP4 inhibition. DPP4 catalyzed cleavage kinetics of the BRI peptide were determined in vitro. The k(cat) and K(m) for cleavage by DPP4 were 5.2s(-1) and 14microM, respectively, resulting in a specificity constant k(cat)/K(m) of 0.36 x 10(6)s(-1)M(-1). Our results demonstrate that differential peptide analysis can be applied to monitor action of DPP4 inhibition in blood plasma. For the first time effects on basal collagen metabolism following DPP4 inhibition in vivo were demonstrated and the BRI amyloid peptide was identified as a novel DPP4 substrate.

Datamining Methodology for LC-MALDI-MS Based Peptide Profiling

This report will provide a brief overview of the application of data mining in proteomic peptide profiling used for medical biomarker research. Mass spectrometry based profiling of peptides and proteins is frequently used to distinguish disease from non-disease groups and to monitor and predict drug effects. It has the promising potential to enter clinical laboratories as a general purpose diagnostic tool. Data mining methodologies support biomedical science to manage the vast data sets obtained from these instrumentations. Here we will review the typical workflow of peptide profiling, together with typical data mining methodology. Mass spectrometric experiments in peptidomics raise numerous questions in the fields of signal processing, statistics, experimental design and discriminant analysis.

Peptide Sequence Prediction Supported by Correlation-Associated Networks in Human Cerebrospinal Fluid

During the course of biosynthesis, processing and degradation of a peptide, many structurally related intermediate peptide products are generated. Human body fluids and tissues contain several thousand peptides that can be profiled by reversed-phase chromatography and subsequent MALDI-ToF-mass spectrometry. Correlation-Associated Peptide Networks (CAN) efficiently detect structural and biological relations of peptides, based on statistical analysis of peptide concentrations. We combined CAN with recognition of probable cleavage sites for peptidases and proteases in cerebrospinal fluid, resulting in a model able to predict the sequence of unknown peptides with high accuracy. On the basis of this approach, identification of peptide coordinates can be prioritized, and a rapid overview of the peptide content of a novel sample source can be obtained.