Prerequisites for peptidomic analysis of blood samples: II. Analysis of human plasma after oral glucose challenge -- a proof of concept

Mass spectrometric plasma analysis for biomarker discovery has become an exploratory focus in proteomic research: the challenges of analyzing plasma samples by mass spectrometry have become apparent not only since the human proteome organization (HUPO) has put much emphasis on the human plasma proteome. This work demonstrates fundamental proteomic research to reveal sensitivity and quantification capabilities of our Peptidomics technologies by detecting distinct changes in plasma peptide composition in samples after challenging healthy volunteers with orally administered glucose. Differential Peptide Display (DPD) is a technique for peptidomics studies to compare peptides from distinct biological samples. Mass spectrometry (MS) is used as a qualitative and quantitative analysis tool without previous trypsin digestion or labeling of the samples. Circulating peptides (< 15 kDa) were extracted from 1.3 mL plasma samples and the extracts separated by liquid chromatography into 96 fractions. Each fraction was subjected to MALDI MS, and mass spectra of all fractions were combined resulting in a 2D-display of > 2,000 peptides from each sample. Endogenous peptides that responded to oral glucose challenge were detected by DPD of pre-and post-challenge plasma samples from 16 healthy volunteers and subsequently identified by nESI-qTOF MS. Two of the 15 MS peaks that were significantly modulated by glucose challenge were subsequently identified as insulin and C-peptide. These results were validated by using immunoassays for insulin and C-peptide. This paper serves as a proof of principle for proteomic biomarker discovery down to the pM concentration range by using small amounts of human plasma.     

Photoproduction of positive pions from hydrogen with PHOENICS at ELSA

The differential cross section of the reaction γp→π⁺n has been measured with the PHOENICS detector at ELSA in Bonn. For the first time this cross section has been determined simultaneously over a large range of photon energies (E_γ = 220−900 MeV) and pion angles (Θ^c.m._pi = 35°−135°) with a tagged photon facility. The experimental set-up allowed a considerable kinematic overdetermination of the investigated reaction. Accordingly, the background contributions have been suppressed to below 1%. The measured differential cross section is in good agreement with existing data. The comparison with different model calculations is presented.     

Identification of novel biomarker candidates by differential peptidomics analysis of cerebrospinal fluid in Alzheimer's disease

The objective of this work was the application of peptidomics technologies for the detection and identification of reliable and robust biomarkers for Alzheimer's 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.     

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.     

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.     

Oxygen content of superconducting Ba2YCu3O6.5+x

Single-phase non-stoichiometric Ba₂YCu₃O_6.5+x with –0.248x0.300 can be obtained by annealing prereacted samples at 0.01–1 bar oxygen partial pressure. Samples withx=–0.248 are semiconducting, samples at 0.239x0.300 are metallic withT _c increasing from 92.2 to 94.0 K for annealing in 0.02–1 bar O₂.     

Peptidomics: the comprehensive analysis of peptides in complex biological mixtures

Progress in the sequencing of genomes has resulted in an increasing demand for a functional analysis of gene products in order to understand the underlying physiology. Proteomics has established itself as a highly valuable technology for producing functionally related data in an unparalleled fashion, but is methodologically restricted to the analysis of proteins with higher molecular masses (>10 kDa). The development of a technology which covers peptides with low molecular weight and small proteins (0.5 to 15 kDa) was necessary, since peptides, amongst them families of hormones, cytokines and growth factors, play a central role in many biological processes. To summarise the technologies used for this approach the term "peptidomics" is introduced. In this article, we present the rationale and first results of a novel, universal peptide display approach for the analysis and visualisation of peptides and small proteins from biological samples. Special attention is given to samples derived from extracellular fluids such as blood plasma and cerebrospinal fluid. Additionally, a high throughput identification procedure for the analysis of peptides in their native and processed molecular form is outlined.     

Towards characterization of the human urinary peptidome

Biomarker discovery in human urine has become an evolving and potentially valuable topic in relation to renal function and diseases of the urinary tract. In order to deliver on the promises and to facilitate the development of validated biomarkers or biomarker panels, protein and peptide profiling techniques need high sample throughput, speed of analysis, and reproducibility of results. Here, we outline the performance characteristics of the liquid chromatography/MALDI-TOF-MS based differential peptide display (DPD(1)) approach for separating, detecting, abundance profiling and identification of native peptides derived from human urine. The typical complexity of peptides in human urine (resolution of the technique with respect to detectable number of peptides), the reproducibility (coefficient of variation for abundance profiles of all peptides detected in biological samples) and dynamic range of the technique as well as the lower limit of detection were characterized. A substantial number of peptides present in normal human urine were identified and compared to findings in four published proteome studies. In an explorative approach, pathological urines from patients suffering from post-renal-filtration diseases were qualitatively compared to normal urine. In conclusion, the peptidomics technology as shown here has a great potential for high throughput and high resolution urine peptide profiling analyses. It is a promising tool to study not only renal physiology and pathophysiology and to determine new biomarkers of renal diseases; it also has the potential to study remotely localized or systemic aberrations within human biology.     

The future of post-genomic biology at the proteomic level: an outlook

Drug discovery and early-stage drugs and biomarkers development is a continuous adaptation and maturation process. The cycle of changes based on new findings is coupled with shifts in research priorities and make this part of pharmaceutical research a challenging endeavour. Over the last years, the emphasis on genomics has shifted to proteomics, the science of understanding how proteins translate gene information into function, and metabonomics, the science of small metabolites that are further apart from genomic projects. Proteomics describes the analysis of the protein complement of a biological sample with respect to temporal and spatial resolution. This technology is based on separation of complex protein mixtures by 2D gel-electrophoresis, in gel digest and mass spectrometric analysis of the protein fragments. Proteomics has been recently flanked by peptidomics, a new research direction aimed at the comprehensive analysis of small (1-20 kDa) polypeptides, thus covering the gap between proteomics and metabonomics. The refinement of peptidomics is based on an essential paradigm related to modularity and diversity. Peptides are a paramount example of how one single gene can release multiple functionalities. We can expect fast progress in understanding protein and peptide networks from a systems biology approach ending in the discovery of new peptide targets. However, the way from a complex sample to potential diagnostic and therapeutic targets will depend on technological developments and from the ability to discriminate true disease-related signals from false positive and negative signals, and the way from target discovery to target validation will not be short.