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ClinicalDegradomics

Background: Recent scientific advances, including sequencing of the genome and new approaches to modeling complex biological systems may ultimately lead to improved understanding of the causes of diseases thereby yielding new diagnostic tools and therapies. The dynamic nature of the circulatory system and its constituents reflects diverse physiological or pathological states, and the ease with which the blood can be sampled makes it a logical choice for biomarker applications [Hanash:2008]. Analysis of the low-molecular-weight range of the circulatory proteome that has been termed the plasma peptidome, shows promise as a source of new biomarkers. It is hypothesized the peptidome could be a rich source of disease-specific diagnostic information because it is a "recording" of the cellular and extracellular proteolytic events that take place at the level of the disease-tissue microenvironment [Hortin:2006], [Petricoin:2006] which was also confirmed in one of our recent studies [Kase:2008] . However, despite an intensive search during the past decade, only a very small number of identified disease biomarkers on the molecular basis of peptides in body fluids have proven clinically useful tools for the prognosis of response to therapy, relapse, and survival and for defining the rate of progression and monitoring of treatment. One of the reasons for this little success according the identification of peptides as biomarkers is that not the peptides but the proteases are the real biomarkers, as was shown by a comprehensive study of Villanueva et al..

Aim: Following the hypothesis of Villanueva et al. it is the aim of the study to investigate the humoral proteolytic activities in the human organism. Since the number of genes coding extracellular proteases exceeds 270, the complexity of the reaction products resulting from these proteases is huge thus requiring an automated analysis to identify the action of individual proteases by analysing the kinetics of the proteolytic degradations. This involves algorithms for signal processing, sound statistical analysis and robust software tools.

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Literature

[KlugeEtal09]
Boguslaw Kluge, Anna Gambin, and Wojciech Niemiro. Modeling exopeptidase activity from LC-MS data. Journal of computational biology : a journal of computational molecular cell biology, 16(2):395--406, February 2009. [ DOI | http ]
Abstract: Recent studies demonstrate that the peptides in the serum of cancer patients that are generated (ex vivo) as a result of tumor protease activity can be used for the detection and classification of cancer. In this paper, we propose the first formal approach to modeling exopeptidase activity from liquid chromatography-mass spectrometry (LC-MS) samples. We design a statistical model of peptidome degradation and a Metropolis-Hastings algorithm for Bayesian inference of model parameters. The model is successfully validated on a real LC-MS dataset. Our findings support the hypotheses about disease-specific exopeptidase activity, which can lead to new diagnostic approach in clinical proteomics.

[YiEtal08]
J Yi, Z Liu, D Craft, P O'Mullan, G Ju, and C A Gelfand. Intrinsic Peptidase Activity Causes a Sequential Multi-Step Reaction (SMSR) in Digestion of Human Plasma Peptides. J Proteome Res, November 2008. [ DOI | http ]
Abstract: Human plasma and serum samples, including protein and peptide biomarkers, are subjected to preanalytical variations and instability caused by intrinsic proteases. In this study, we directly investigated the stability of peptide biomarkers by spiking an isotopically labeled peptide into human plasma and serum samples and then monitoring its time-dependent change. Fibrinogen peptide A (FPA) was used as a model substrate, and its degradation in a conventional serum and plasma either with citrate, heparin, or EDTA as the anticoagulant, or EDTA plus protease inhibitors (inhibited plasma), was measured using time-course MALDI-TOF MS analysis. The FPA and other peptides tested in this study vary in these samples. However, the peptides are most stable in the inhibited plasma followed by, in general order, EDTA plasma, citrate plasma, heparin plasma and serum, demonstrating the benefit of plasma versus serum, and protease inhibitors for biomarker stabilization. Kinetic analysis indicates that intrinsic peptidases cause an observed first-order Sequential Multiple-Step Reaction (SMSR) in digestion of the peptide. Modeling analysis of the SMSR demonstrates that step reactions differ in their kinetic rate constants, suggesting a significant contribution of the truncated end residue on the substrate specificity of the intrinsic peptidase(s). Our observations further show that synthetic peptides introduced into plasma as internal controls can also be degraded, and thus, their (in)stability as a preanalytical variable should not be overlooked.

[VillanuevaEtal06]
Josep Villanueva, David R Shaffer, John Philip, Carlos A Chaparro, Hediye Erdjument-Bromage, Adam B Olshen, Martin Fleisher, Hans Lilja, Edi Brogi, Jeff Boyd, Marta Sanchez-Carbayo, Eric C Holland, Carlos Cordon-Cardo, Howard I Scher, and Paul Tempst. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest, 116(1):271--284, 2006. [ DOI | http ]
Abstract: Recent studies have established distinctive serum polypeptide patterns through mass spectrometry (MS) that reportedly correlate with clinically relevant outcomes. Wider acceptance of these signatures as valid biomarkers for disease may follow sequence characterization of the components and elucidation of the mechanisms by which they are generated. Using a highly optimized peptide extraction and matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS-based approach, we now show that a limited subset of serum peptides (a signature) provides accurate class discrimination between patients with 3 types of solid tumors and controls without cancer. Targeted sequence identification of 61 signature peptides revealed that they fall into several tight clusters and that most are generated by exopeptidase activities that confer cancer type-specific differences superimposed on the proteolytic events of the ex vivo coagulation and complement degradation pathways. This small but robust set of marker peptides then enabled highly accurate class prediction for an external validation set of prostate cancer samples. In sum, this study provides a direct link between peptide marker profiles of disease and differential protease activity, and the patterns we describe may have clinical utility as surrogate markers for detection and classification of cancer. Our findings also have important implications for future peptide biomarker discovery efforts.

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Topic revision: r10 - 12 May 2011, StephanAiche
 
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