Combinatorial Chemistry & High Throughput Screening - Volume 8, Issue 8, 2005

The problems we face today in public health as a result of the - fortunately - increasing age of people and the requirements of developing countries create an urgent need for new and innovative approaches in medicine and in agronomics. Genomic and functional genomic approaches have a great potential to at least partially solve these problems in the future. Important progress has been made by procedures to decode genomic information of humans, but also of other key organisms. The basic comprehension of genomic information (and its transfer) should now give us the possibility to pursue the next important step in life science eventually leading to a basic understanding of biological information flow; the elucidation of the function of all genes and correlative products encoded in the genome, as well as the discovery of their interactions in a molecular context and the response to environmental factors. As a result of the sequencing projects, we are now able to ask important questions about sequence variation and can start to comprehensively study the function of expressed genes on different levels such as RNA, protein or the cell in a systematic context including underlying networks. In this article we review and comment on current trends in large-scale systematic biological research. A particular emphasis is put on technology developments that can provide means to accomplish the tasks of future lines of functional genomics.

With the sequencing of the genome of over 150 organisms, the field of biology has been revolutionised. Instead of studying one gene or protein at the time, it is now possible to study the effect of physiological or pathological changes on the expressioin of all genes or proteins in the organism. Proteomics aims at the simultaneous analysis of all proteins expressed by a cell, tissue or organism in a specific physiological condition. Because proteins are the effector molecules in all organsims, it is evident that changes in the physiological condition of an organism will be reflected by changes in protein expression and/or processing. Since the formulation of the concept of proteomics in the mid 90's proteomics has relied heavily on 2 dimensional gel electroforesis (2DGE) for the separation and visualization of proteins. 2DGE, however, has a number of inherent drawbacks. 2DGE is costly, fairly insensitive to low copy proteins and cannot be used for the entire proteome. Therefore, over the years, several gel-free proteomics techniques have been developed to either fill the gaps left by 2DGE or to entirely abolish the gel based techniques. This review summarizes the most important gel-free and gel-based proteomics techniques and compares their advantages and drawbacks.

Proteomics, the comprehensive analysis of a protein complement in a cell, tissue or biological fluid at a given time, has been enabled by quantum leaps in mass spectrometric technology, which allowed identification of large, involatile biomolecules. Over the last two decades, this discipline evolved from the sole delivery of protein identities to a platform, which reveals clues to function through e.g. characterisation of protein modifications and interactions as well as through quantitative proteomics, i.e. the global comparison of protein amounts between two defined biological states. Proteomics is an integral part and key player in the family of -omic disciplines as there are genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). Considering the complexity, dynamics and protein concentration range of any given proteome, proteomics is the most challenging -omic discipline and requires the most sophisticated analysis pipeline. Proteomics represents an established technology in the pharmaceutical industry mainly for biomarker and drug target discovery. The potential of proteomics for research in the food industry is increasingly being recognised and the employment of proteomic approaches to nutrition and health issues is now emerging. This review summarizes (i) major technological achievements in mass spectrometry and proteomics, (ii) deliverables of proteomics in the context of nutrition and health, and (iii) applications of proteomics, and - if appropriate - transcriptomics to the research fields of digestive health, obesity and diabetes, immunity and allergy, probiotics, milk, and food preference.

Peptides are a paramount example of how nature diversifies from one single gene to release multiple, regulated functionalities at the desired sites and time. To achieve this, peptides are sequentially generated by a complex network of more than 500 proteases, acting at intracellular sites, upon secretion, in extracellular environments, and, finally, serving (regulated) degradation. This cycle of maturation, activation, and degradation points out that the peptidome is mechanistically linked to the proteome: the distribution between both is regulated by proteases and counter-regulated by protease inhibitors. Given the high diversity of peptides in living systems and their involvement in key regulatory processes, a need for improved peptide discovery, ideally combining peptide sequence identification with peptide profiling, has emerged. Standard proteomic approaches are not suitable for a systematic peptide analysis, since they do not cover the low molecular mass window. The new direction in proteomic research to analyse this "terra incognita" is peptidomics. This novel concept aims at the comprehensive visualization and analysis of small polypeptides, thus covering the mass range between proteomics and metabonomics. The pacemakers for the development of peptidomics technologies are modern mass spectrometry and bioinformatics. They are ideally suited for sensitive and comprehensive peptide analysis, especially in combination with the massive information content of todays genomic and transcriptomic databases. Given the high diversity of native peptides in living systems, clinical chemistry and modern medicine are the prime application areas. The discovery of relevant peptide biomarkers and drug targets will strongly benefit from peptidomics.

A little after the genomic revolution had been celebrated, it seemed as if a competition began to found new -omics disciplines that ultimately all have the same goal, the understanding of biological function. There are many similar definitions for proteomics that can be summarized as follows: proteomics is a largescale study of structure and function of proteins in an organism or cell. Importantly, the proteome is much more variable than the genome through its interactions with the genome and secondary modifications. It differs depending on the tissue and stage in life-cycle. Hence, proteomics is a very diverse discipline that uses a variety of experimental set-ups and targets in order to elucidate function. Its dissociation from other disciplines can only remain artificial. The bioinformatics applied to proteomics are equally varied. In this review we will focus mainly on a few areas of bioinformatics that seem to us as particularly noteworthy or characteristic for proteomics research, for example in 2DE analysis or mass spectrometry. Another important task of bioinformatics is the prediction of functional properties. We will summarize the approaches taken in order to predict protein networks, which are based on the extensive integration of several kinds of -omics data. We will give a short overview of a demanding field in computational biology, the analysis and prediction of protein 3D structures. In order to provide a broader perspective we will close this review with a generalized description of activities and databases in the realm of proteomics.

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.

Proteomics studies aiming at a detailed analysis of proteins, and peptidomics, aiming at the analysis of the low molecular weight proteome (peptidome) offer a promising approach to discover novel biomarkers valuable for different crucial steps in detection, prevention and treatment of disease. Much emphasis has been given to the analysis of blood, since this source would by far offer the largest number of meaningful biomarker applications. Blood is a complex liquid tissue that comprises cells and extra-cellular fluid. The choice of suitable specimen collection is crucial to minimize artificial occurring processes during specimen collection and preparation (e.g. cell lysis, proteolysis). After specimen collection, sample preparation for peptidomics is carried out by physical methods (filtration, gel-chromatography, precipitation) which allow for separation based on molecular size, with and without immunodepletion of major abundant proteins. Differential Peptide Display (DPD) is an offline-coupled combination of Reversed-Phase-HPLC and MALDI mass spectrometry in combination with in-house developed data display and analysis tools. Identifications of peptides are carried out by additional mass spectrometric methods (e.g. online LC-ESI-MS/MS). In the work presented here, insights into semi-quantitative mass spectrometric profiling of plasma peptides by DPD are given. This includes proper specimen selection (plasma vs. serum), sample preparation, especially peptide extraction, the determination of sensitivity (i.e. by establishing detection limits of exogenously spiked peptides), the reproducibility for individual as well as for all peptides (Coefficient of Variation calculations) and quantification (correlation between signal intensity and concentration). Finally, the implications for clinical peptidomics are discussed.

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 postchallenge 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.

Native peptides and proteins are of increasing interest in biomedical research because they hold promise to represent a large number of useful diagnostic and therapeutic biomarkers. Discovery attempts from patient samples have to deal with the complexity of biology from a disease perspective as well as with a high individual variability. High throughput screening of samples is therefore the strategy of choice to detect relevant peptidic biomarkers, and requires a high order of automation particularly in the detection process. In this contribution, a novel technical approach employing a fully automated MALDI-TOF/TOF mass spectrometer is described. This approach combines high throughput biomarker discovery with the identification of corresponding endogenous peptides in one instrument and from the same set of samples. The degree of automation allows the analysis of thousands of chromatographic fractions corresponding to up to one hundred patient samples per day. The applied relative quantification via Differential Peptide Display® is performed in a label-free way and shows a dynamic range of up to four orders of magnitude in the accessible peptide concentrations. The typical limit of detection is in the mid- to low-picomolar range for body fluids such as blood plasma, urine and cerebrospinal fluid. Sequence assignment via MALDI-TOF/TOF mass spectrometry is carried out either in an overview approach, characterizing rapidly the peptide composition e.g. of a novel sample, or in a directed approach, analyzing a list of biomarker candidates deduced from statistically significant abundance differences from the biomarker discovery process.

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 (DPD¹) 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.

Detection and purification of novel bioactive peptides from biological sources is a scientific task that led to a substantial number of important discoveries. One major laborious approach used is the repetitive stepwise separation of the test sample into several fractions followed by the determination of their bioactivity, until purity allows for sequence identification. We tested whether functional peptidomics, a combination of biological read-outs with differential peptide display (DPD) is a suitable strategy to isolate bioactive peptides at lower workload and with improved success. Additionally, we evaluated the use of DPD to monitor the processing status of proinsulin by inhibition of the insulin processing pathway. The rat insulinoma cell line INS-1 stimulated either with 2 mmol/l or 10 mmol/l glucose was used as model to generate differential peptide displays. In parallel, the bioactivity of the supernatants from the INS-1 cells was measured by glucose uptake and lipolysis assays using the adipocyte cell line 3T3-L1. We were able to quickly and elegantly trace the known activity of insulin to increase glucose uptake and inhibit lipolysis. Following re-chromatography of selected fractions, relevant peptides were identified by DPD and bioassays: the rat insulin-1 precursor and two different insulin peptides. We demonstrated in a semi-quantitative fashion that inhibition of proinsulin processing leads to accumulation of the insulin precursor, and reduced secretion of insulin-1. Thus, we conclude that DPD is an attractive support technology in peptide purification strategies aiming to identify bioactive compounds, and is superior to ELISA in discriminating between the processing status of insulin and its precursor.

Type 2 diabetes mellitus (T2DM) is caused by the failure of the pancreatic beta-cell to secrete sufficient insulin to compensate a decreased response of peripheral tissues to insulin action. The pathological events causing beta-cell dysfunctions are only poorly understood and early markers that would predict islet function are missing. In contrast to immunoassays, unbiased proteomic technologies provide the opportunity to screen for novel marker protein and peptides of T2DM. An important subset of the proteome, peptides and peptide hormones secreted by the pancreas are deregulated in T2DM. The mass range of peptides and small proteins (1-20 kDa) is only sufficiently targeted by peptidomics, a combination of liquid chromatographic and mass spectrometric (MS) peptide analysis. Here, we describe the application of isotope label-free quantitative peptidomics to display and quantify relevant changes in the level of pancreatic peptides and peptide hormones in a preclinical model of T2DM, the Lep ob/Lep ob mouse. The amino acid sequence of statistical relevant top candidates was determined by MS/MS fragmentation or Edman degradation. The comparison of lean versus obese mice revealed increased levels of islet-specific peptides that can be divided into the following categories 1) the major islet peptide hormones insulin, amylin and glucagon; 2) proinsulin and C-peptide and 3) novel processing products of secretogranin, glucagon and amylin. Furthermore, we found increased levels of proteins and peptides implicated in zymogen granule maturation (syncollin) and nutritional digestion. In summary, our findings demonstrate that peptidomics is a valid approach to screen for novel peptide biomarkers.

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.

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.

The objective of this work was the application of peptidomics®1 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.

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 gelelectrophoresis, 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.

Reto Crameri studied microbiology and biochemistry at the Swiss Federal Institute of Technology (ETH) Zürich, where he completed his PhD on genetics of industrial microorganisms in 1980. He was a research scientist and head of the antibiotic research group until the end of 1982 at the same institution. Thereafter, he joined Biogen SA in Geneva as senior scientist in molecular biology until end of 1986. After a short period as head of the Swiss Radon Project at the Paul Scherrer Institute (1987-1989), he moved at the Swiss Institute of Allergy and Asthma Research in Davos as head of the Division of Molecular Allergology. In addition he became guest Professor for Molecular Immunology at the University of Salzburg, Austria, in 1996. Dr. Crameri is on the Editorial Board of Allergy, Biochemical Journal, and International Archives of Allergy and Immunology and is or has been a reviewer of grant applications for the österreichiser Nationalfonds (FWF), the Swiss National Science Foundation (SNF), The Wellcome Trust, and The Eli and Edythe L. Broad Foundation, among others. Besides contributions to the fields of allergology and immunology, he recently played an important role as a founder of ImVisioN GmbH & Co. KG, Hannover, Germany, a spin-off company dealing with recombinant vaccines. The research interests of Dr. Crameri include development of phage display and robot-based high throughput screening technology for fast identification of medically important target molecules, studies on the mechanisms of innate immunity, development of diagnostic tools based on molecular biology, elucidation of molecular structures, and mass spectrometric-based profiling of peptides and small proteins from human body fluids and tissues. Some recent publications are cited below. SELECTED PUBLICATIONS Kodzius R.; Rhyner C.; Konthur Z.; Buczek D.; Lehrach H.; Walter G.; Crameri R. Rapid identification of allergen-encoding cDNA clones by phage display and high-density arrays. Combin. Chem. High Throughput Screen. 2003, 6, 147-154. Fossa A.; Alsoe L.; Crameri R.; Funderud S.; Gaudernack G.; Smeland E. B. Serological cloning of cancer/testis antigens expressed in prostate cancer using cDNA phage surface display. Cancer Immunol. Immunother. 2004, 53, 431-438. Andersson A.; Rasool O.; Schmidt M.; Kodzius R.; Flückiger S.; Zagari A.; Crameri R.; Scheynius A. Cloning, expression and characterization of two new IgE-binding proteins from the yeast Malassezia sympodialis with sequence similarities to heat shock proteins and manganese superoxide dismutase. Eur. J. Biochem. 2004, 271, 1885-1894. Akdis M., Verhagen J., Taylor A., Karmaloo F., Karagiannidis C.; Crameri R.; Thunberg S.; Deniz G.; Valenta R.; Fiebig H.; Kegel C.; Disch R.; Schmidt-Weber C.B.; Blaser K.; Akdis C. A. Immune responses in healthy and allergic individuals are characterized by a fine balance between allergen-specific T regulatory 1 and T helper 2 cells. J. Exp. Med. 2004, 199, 1567- 1575. Kussebi F.; Karamloo F.; Rhyner C., Schmid-Grendelmeier P,: Salagianni M.; Manhart C.; Akdis M.; Soldatova L.; Markovic-Housley Z.; Von Beust B.R.; Kündig T.; Kemeny D.M.; Blaser K.; Crameri R.; Akdis C. A. A major allergen gene-fision protein for potential usage in allergen-specific immunotherapy. J. Allergy Clin. Immunol. 2005, 115, 323-329. Schmid-Grendelmeier P.; Flückiger S.; Disch R.; Trautmann A., Wüthrich B.; Blaser K.; Scheynius A.; Crameri R. IgEmeidated and T cell-mediated autoimmunity against manganese superoxide dismutase in atopic dermatitis. J. Allergy Clin. Immunol. 2005, 115, 1068-1075. Lamerz J.; Selle H.; Scapozza L.; Crameri R.; Schulz-Knappe P.; Mohring T.; Kellmann M.; Khameina V.; Zucht H.D. Correlation-associated peptide networks of the human cerebrospinal fluid. Proteomics 2005, 5, 2789-2798. Crameri R. The potential of proteomics and peptidomics for allergy and asthma research. Allergy 2005, 60, 1227-1237. Limacher A.; Kloer D. P.; Flückiger S.; Folkers G.; Crameri R.; Scapozza L. The crystal structure of Aspergillus fumigatus cyclophilin reveals 3D domain swapping of a central element. Structure 2005 (in press).