Application of holistic liquid chromatography-high resolution mass spectrometry based urinary metabolomics for prostate cancer detection and biomarker discovery

Human exhibit wide variations in their metabolic profiles because of differences in genetic factors, diet and lifestyle. Therefore in order to detect metabolic differences between individuals robust analytical methods are required. A protocol was produced based on the use of Liquid Chromatography- High Resolution Mass Spectrometry (LC-HRMS) in combination with orthogonal Hydrophilic Interaction (HILIC) and Reversed Phase (RP) liquid chromatography methods for the analysis of the urinary metabolome, which was then evaluated as a diagnostic tool for prostate cancer (a common but highly heterogeneous condition). The LC-HRMS method was found to be robust and exhibited excellent repeatability for retention times (0.9. In addition, using the receiver operator characteristics (ROC) test, the area under curve (AUC) for the combination of the four best characterised biomarker compounds was 0.896. The four biomarker compounds were also found to differ significantly (

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud

Advances in Quantitative Hepcidin Measurements by Time-of-Flight Mass Spectrometry

Assays for the detection of the iron regulatory hormone hepcidin in plasma or urine have not yet been widely available, whereas quantitative comparisons between hepcidin levels in these different matrices were thus far even impossible due to technical restrictions. To circumvent these limitations, we here describe several advances in time-of flight mass spectrometry (TOF MS), the most important of which concerned spiking of a synthetic hepcidin analogue as internal standard into serum and urine samples. This serves both as a control for experimental variation, such as recovery and matrix-dependent ionization and ion suppression, and at the same time allows value assignment to the measured hepcidin peak intensities. The assay improvements were clinically evaluated using samples from various patients groups and its relevance was further underscored by the significant correlation of serum hepcidin levels with serum iron indices in healthy individuals. Most importantly, this approach allowed kinetic studies as illustrated by the paired analyses of serum and urine samples, showing that more than 97% of the freely filtered serum hepcidin can be reabsorbed in the kidney. Thus, the here reported advances in TOF MS-based hepcidin measurements represent critical steps in the accurate quantification of hepcidin in various body fluids and pave the way for clinical studies on the kinetic behavior of hepcidin in both healthy and diseased states

Mass Spectrometry-Based (GeLC-MS/MS) Comparative Proteomic Analysis of Endoscopically (ePFT) Collected Pancreatic and Gastroduodenal Fluids

Objectives: The secretin-stimulated endoscopic pancreatic function test (ePFT) allows for the safe collection of gastroduodenal and pancreatic fluid from the duodenum. We test the hypothesis that these endoscopically collected fluids have different proteomes. As such, we aim to show that the ePFT method can be used to collect fluid enriched in pancreatic proteins to test for pancreatic function. Methods: Gastroduodenal and pancreatic fluid were collected sequentially from chronic pancreatitis patients undergoing an ePFT. Proteins from each fluid type were extracted using previously published optimized methods and subjected to GeLC-MS/MS analysis for protein identification and bioinformatics analysis. Results: Mass spectrometry analysis identified proteins that were exclusive in either gastroduodenal (46) or pancreatic fluid (234). Subsequent quantitative analysis revealed proteins that were differentially abundant with statistical significance. As expected, proteolytic enzymes and protease inhibitors were among the differentially detected proteins. The proteases pepsinogens and gastrin were enriched in gastroduodenal fluid, while common pancreatic enzymes (e.g., aminopeptidase N, chymotrypsin C, elastase-3A, trypsin, and carboxypeptidase A1, and elastase 2B) were found in greater abundance in pancreatic fluid. Similarly for protease inhibitors, members of the cystatin family were exclusive to gastroduodenal fluid, while serpins A11, B4, and D1 were exclusive to pancreatic fluid. Conclusions: We have shown that ePFT collection coupled with mass spectrometry can be used to identify differentially detected proteins in gastroduodenal and pancreatic fluids. The data obtained using GeLC-MS/MS techniques provide further evidence supporting the feasibility of using ePFT-collected fluid to study specific diseases of the upper gastrointestinal tract, such as chronic pancreatitis

Estimation of Relevant Variables on High-Dimensional Biological Patterns Using Iterated Weighted Kernel Functions

BACKGROUND The analysis of complex proteomic and genomic profiles involves the identification of significant markers within a set of hundreds or even thousands of variables that represent a high-dimensional problem space. The occurrence of noise, redundancy or combinatorial interactions in the profile makes the selection of relevant variables harder. METHODOLOGY/PRINCIPAL FINDINGS Here we propose a method to select variables based on estimated relevance to hidden patterns. Our method combines a weighted-kernel discriminant with an iterative stochastic probability estimation algorithm to discover the relevance distribution over the set of variables. We verified the ability of our method to select predefined relevant variables in synthetic proteome-like data and then assessed its performance on biological high-dimensional problems. Experiments were run on serum proteomic datasets of infectious diseases. The resulting variable subsets achieved classification accuracies of 99% on Human African Trypanosomiasis, 91% on Tuberculosis, and 91% on Malaria serum proteomic profiles with fewer than 20% of variables selected. Our method scaled-up to dimensionalities of much higher orders of magnitude as shown with gene expression microarray datasets in which we obtained classification accuracies close to 90% with fewer than 1% of the total number of variables. CONCLUSIONS Our method consistently found relevant variables attaining high classification accuracies across synthetic and biological datasets. Notably, it yielded very compact subsets compared to the original number of variables, which should simplify downstream biological experimentation

Identification of novel biomarker candidates by proteomic analysis of cerebrospinal fluid from patients with moyamoya disease using SELDI-TOF-MS

<p>Abstract</p> <p>Background</p> <p>Moyamoya disease (MMD) is an uncommon cerebrovascular condition with unknown etiology characterized by slowly progressive stenosis or occlusion of the bilateral internal carotid arteries associated with an abnormal vascular network. MMD is a major cause of stroke, specifically in the younger population. Diagnosis is based on only radiological features as no other clinical data are available. The purpose of this study was to identify novel biomarker candidate proteins differentially expressed in the cerebrospinal fluid (CSF) of patients with MMD using proteomic analysis.</p> <p>Methods</p> <p>For detection of biomarkers, CSF samples were obtained from 20 patients with MMD and 12 control patients. Mass spectral data were generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with an anion exchange chip in three different buffer conditions. After expression difference mapping was undertaken using the obtained protein profiles, a comparative analysis was performed.</p> <p>Results</p> <p>A statistically significant number of proteins (34) were recognized as single biomarker candidate proteins which were differentially detected in the CSF of patients with MMD, compared to the control patients (p < 0.05). All peak intensity profiles of the biomarker candidates underwent classification and regression tree (CART) analysis to produce prediction models. Two important biomarkers could successfully classify the patients with MMD and control patients.</p> <p>Conclusions</p> <p>In this study, several novel biomarker candidate proteins differentially expressed in the CSF of patients with MMD were identified by a recently developed proteomic approach. This is a pilot study of CSF proteomics for MMD using SELDI technology. These biomarker candidates have the potential to shed light on the underlying pathogenesis of MMD.</p

Differentiation of Gram-Negative Bacterial Aerosol Exposure Using Detected Markers in Bronchial-Alveolar Lavage Fluid

The identification of biosignatures of aerosol exposure to pathogens has the potential to provide useful diagnostic information. In particular, markers of exposure to different types of respiratory pathogens may yield diverse sets of markers that can be used to differentiate exposure. We examine a mouse model of aerosol exposure to known Gram negative bacterial pathogens, Francisella tularensis novicida and Pseudomonas aeruginosa. Mice were subjected to either a pathogen or control exposure and bronchial alveolar lavage fluid (BALF) was collected at four and twenty four hours post exposure. Small protein and peptide markers within the BALF were detected by matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) and analyzed using both exploratory and predictive data analysis methods; principle component analysis and degree of association. The markers detected were successfully used to accurately identify the four hour exposed samples from the control samples. This report demonstrates the potential for small protein and peptide marker profiles to identify aerosol exposure in a short post-exposure time frame