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.

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.

Peptidomics biomarker discovery in mouse models of obesity and type 2 diabetes.

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.

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.