To apply modern mass spectrometry based technology to identify possible CSF peptide markers of glioblastoma multiforme (GBM).Mass spectrometry based peptidomics technology enables a systematic and comprehensive screening of cerebrospinal fluid (CSF) with regard to its peptide composition. Differential Peptide Display (DPD) allows the identification of single marker peptides for a target disease. Using both, we analyzed CSF samples of 11 patients harbouring a glioblastoma multiforme in comparison to 13 normal controls.Four CSF peptides which significantly distinguished GBM from controls in all applied statistic tests could be identified out of more than 2,000 detected CSF peptides. They were specific C-terminal fragments of alpha-1-antichymotrypsin, osteopontin, and transthyretin as well as a N-terminal residue of albumin. All molecules are constituents of normal CSF, but none has previously been reported to be significantly elevated in CSF of GBM patients.The study showed that peptidomics technology is able to identify possible biomarkers of neoplastic CNS disease. It remains to be determined if the identified elevated CSF peptides are specific for GBM. With regard to GBM, however, the more important role of CSF peptide biomarkers than aiding initial diagnosis might be early recognition of disease recurrence or monitoring of efficacy of adjuvant therapy protocols.

Peptidomic analysis using Differential Peptide Display (DPD) of human peripheral blood mononuclear cells (PBMC) mock-infected or persistently infected by Chlamydia trachomatis (CT) revealed 10 peptides, expressed upon CT infection. Analysis of these 10 candidates by tandem mass spectrometry enabled the determination of seven candidates as fragments from the precursors (I) ferritin heavy chain subunit, (II) HLA class II histocompatibility antigen, (III) vimentin, (IV) indoleamine 2,3-dioxygenase, (V and VI) pre-B cell enhancing factor (PBEF), and (VII) Interleukin-8 (CXCL8). The identified candidates proved the presence of anti-bacterial and immunologically active monocytic proteins after CT infection.

The search for diagnostic markers for Alzheimer's disease (AD) aims at a further improvement of the differential diagnosis of AD especially at the early presymptomatic state of the disease. Since peptides are degradation products and processed forms of larger proteins, they are possible products of neurodegenerative processes. The comparison of the comprehensive CSF peptidomes of AD patients and controls should identify peptides correlated with AD pathogenesis. The comprehensive analysis of the peptide profile of cerebrospinal fluid from Alzheimer and control patients allows to compare the peptide patterns with respect to AD–specific processes. The main objective was the detection and identification of reliable and robust biomarkers specific for AD. Cerebrospinal fluid samples from patients with AD (n=140), with other forms of dementia (vascular, Lewy–body, frontotemporal dementia, Parkinson's disease, n=70) and from neurologic patients without cognitive impairment (n=102) were analysed using our differential peptide display® (DPD) approach. The diagnosis of AD was made in five hospital–based expert centres in Germany and Sweden in accordance with ICD–10, DSM–IV or NINCDS–ADRDA criteria. The samples were analysed using chromatographic separation of peptides and small proteins combined with mass–spectrometric analysis, allowing the simultaneous examination of several thousands of different compounds. The samples were analysed in four independent analytical sets and data were evaluated by a cross validation procedure. Non–parametric statistics and ROC curves (receiver operator characteristic) which represent sensitivity as well as specificity of a marker candidate were used to discriminate between the AD and the two control groups and to define possible marker candidates. The comprehensive peptide profiling of a large number of well–characterised CSF samples for AD–specific biomarker candidates resulted in the detection of 30 signals from the mass–spectrometric read out which discriminate between the AD and the control groups. Twelve candidates were identified as fragments of the possibly neuroprotective neuroendocrine protein VGF and another one as the complement factor C3–derived C3f. The combination of peptide profiling from CSF with a cross validation procedure yielded novel potential biomarkers with remarkable robustness and a close relation to AD pathophysiology.

Over the last few years the interest in diagnostic markers for specific diseases has increased continuously. It is expected that they not only improve a patient's medical treatment but also contribute to accelerating the process of drug development. This demand for new biomarkers is caused by a lack of specific and sensitive diagnosis in many diseases. Moreover, diseases usually occur in different types or stages which may need different diagnostic and therapeutic measures. Their differentiation has to be considered in clinical studies as well. Therefore, it is important to translate a macroscopic pathological or physiological finding into a microscopic view of molecular processes and vice versa, though it is a difficult and tedious task. Peptides play a central role in many physiological processes and are of importance in several areas of drug research. Exploration of endogenous peptides in biologically relevant sources may directly lead to new drug substances, serve as key information on a new target and can as well result in relevant biomarker candidates. A comprehensive analysis of peptides and small proteins of a biological system corresponding to the respective genomic information (peptidomics®methods) was a missing link in proteomics. A new peptidomic technology platform addressing peptides was recently presented, developed by adaptation of the striving proteomic technologies. Here, concepts of using peptidomics technologies for biomarker discovery are presented and illustrated with examples. It is discussed how the biological hypothesis and sample quality determine the result of the study. A detailed study design, appropriate choice and application of technology as well as thorough data interpretation can lead to significant results which have to be interpreted in the context of the underlying disease. The identified biomarker candidates will be characterised in validation studies before use. This approach for discovery of peptide biomarkes has potential for improving clinical studies.

The medical demand for useful biomarkers is large and still increasing. This is especially true for cancer, because for this disease adequate diagnostic markers with high specificity and sensitivity are still lacking. Despite advances in imaging technologies for early detection of cancer, peptidomic multiplex techniques evolved in recent years will provide new opportunities for detection of low molecular weight (LMW) proteome biomarker (peptides) by mass spectrometry. Improvements in peptidomics research were made based on separation of peptides and/or proteins by their physico-chemical properties in combination with mass spectrometric detection, respectively identification, and sophisticated bioinformatic tools for data analysis. To evaluate the potential of serological tumor marker detection by differential peptide display (DPD) we analyzed plasma samples from a tumor graft model. After subcutaneous injection of HCT-116 cells in immunodeficient mice and their growth to a palpable tumor, plasma samples were analyzed by DPD. The comparison of obtained mass spectrometric data allows discovery of tumor specific peptides which fit well into the biological context of cancer pathogenesis and show a strong correlation to tumor growth. The identified peptides indicate events associated with hyper-proliferation and dedifferentiation of cells from an epithelial origin, which are typical characteristics of human carcinomas. We conclude that these findings are a "proof of principle" to detect differentially expressed, tumor-related peptides in plasma of tumor-bearing mice.

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.

The general awareness of the importance of peptides in physiology and pathophysiology has increased strongly over the last few years. With worldwide progress in the analysis of whole genomes, the knowledge base in gene sequence and expression data useful for protein and peptide analysis has drastically increased. The medical need for relevant biomarkers is enormous. This is particularly true for the many types of cancer, but other diseases such as Type 2 diabetes also lack useful and adequate diagnostic markers with high specificity and sensitivity. Despite advances in imaging technologies for early detection of diseases, proteomic and peptidomic multiplex techniques have evolved in recent years. This review focuses on the application of peptidomics technologies to peptides in health and disease. Peptidomics technologies provide new opportunities for the detection of low-molecular-weight proteome biomarkers (peptides) by mass spectrometry. Improvements in peptidomics research are based on separation of peptides and/or proteins by their physicochemical properties in combination with mass spectrometric detection, identification and sophisticated bioinformatics tools for data analysis. Therefore, peptidomics technologies offer an opportunity to discover novel biomarkers for diagnosis and management of disease (e.g., prognosis, treatment decision and monitoring response to therapy).

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.

Peptides and proteins define the actual phenotype of an organism and thus reflect the current physiology as well as any pathogenic processes. Proteomics is regarded as a central technology in describing the transition from genotype to phenotype and, in more practical terms, from health to disease by describing the underlying mechanisms at a molecular level that is specific to time, quantity, and localization. This chapter focuses on a certain aspect of the proteome—the smaller native endogenous peptides. The research area and technologies addressing this field of endogenous peptides in general are referred to as “peptidomics.” Specifically, the field of clinical peptidomics, by analyzing blood samples from patients within clinical trials, is evolving rapidly. The prerequisites for this type of research are addressed in the chapter. It may be considered as a general principle that peptide precursors sometimes carry several distinct biological activities that are silent after protein biosynthesis. Only at certain times and in specific compartments are they activated by different proteolytic processes. This mechanistic way for the regulated release of biologically active compounds from inactive precursor molecules is probably a very safe way to assure the presence of the molecules at the desired concentration and conformation at the precise location and time when they are needed. In addition to the several modes of intracellular processing, extracellular processing of proteins is a frequently used way to tailor proteins and peptides. In a relevant number of instances, peptidases can liberate biologically active peptides.

Profiling of peptides and small proteins from either human body fluids or tissues by chromatography and subsequent mass spectrometry reveals several thousand individual peptide signals per sample. Any peptide is an intermediate in the course of biosynthesis, post-translational modification (PTM), proteolytic processing and degradation. Changes in the concentration of one peptide often affects the concentration of the other, hence a challenge consists in the development of suitable tools to turn this large amount of data into biologically relevant information. Comprehensive statistical analysis of the peptide profiling data allows associating peptides, which are closely related in terms of peptide biochemistry. Here, the bioinformatic concept of peptide networks, correlation-associated peptide networks (CANs), is introduced. Peptides with statistical similarity of their concentrations are grouped in form of networks, and these networks are interpreted in terms of peptide biochemistry. The spectrum of functional relationships found in cerebrospinal fluid CAN covers PTM and proteolytic degradation of peptides, clearance processing in the complement cascade, common secretion of peptides by neuroendocrine cells as well as ubiquitin-mediated degradation. Our results indicate that CAN is a powerful bioinformatic tool for the systematic analysis and interpretation of large peptidomics and proteomics data and helps to discover novel bioactive and diagnostic peptides.

Proteomics and peptidomics are different and supplemental to genomics, since in contrast to the basically constant genome - the proteome and peptidome are dynamic, constantly changing, and complex networks. Proteomics is traditionally linked to 2D-gel electrophoresis techniques. Concerning peptidomics, three different approaches are currently available, all using mass spectrometry as a key element. The use of proteomics or peptidomics in traumatic brain injury (TBI) research is demanding. From the technical point of view there are high-level requirements concerning the preanalytical phase, specific machinery, sophisticated software and skilled manpower/intellectual input. There are currently no bedside techniques and most methods are suitable for experimental TBI research in specialized laboratories. In screening experiments of CSF following controlled cortical impact in rats we identified several peptides, which, although previously known, were so far not reported in the TBI context or in CSF. Peptidomics and proteomics, as highly complex screening technologies, thus seem to carry a large potential to lead TBI science. Newly "discovered" peptide targets have to be validated with different methodology to establish a real diagnostic or therapeutic value.

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.

Peptides, such as many hormones, cytokines and growth factors play a central role in biological processes. Furthermore, as degradation products and processed forms of larger proteins they are part of the protein turnover. Thus, they can reflect disease-related changes in an organism's homeostasis in several ways. Since two-dimensional gel electrophoresis is restricted to analysis and display of proteins with relative molecular masses above 5000, we developed Differential Peptide Display (DPD), a new technology for analysis and visualization of peptides. Here we describe its application to cerebrospinal fluid of three subjects without a disease of the central nervous system (CNS) undergoing routine myelography and of two patients suffering from a primary CNS lymphoma. Peptides with a relative molecular mass below 20000 were extracted and analysed by a combination of chromatography and mass spectrometry. The peptide pattern of a sample was depicted as a multi-dimensional peptide mass fingerprint with each peptide's position being characterized by its molecular mass and chromatographic behaviour. Such a fingerprint of a CNS sample consists of more than 6000 different signals. Data analysis of peptide patterns from patients with CNS lymphoma compared to controls revealed obvious differences regarding the peptide content of the samples. By analysing peptides within a mass range of 750-20000, DPD extends 2D gel electrophoresis, thus offering the chance to investigate CNS diseases on the level of peptides. This represents a new approach for diagnosis and possible therapy.