NMR-based metabonomics: a useful platform of oncology research

Cancer threatens human health, thus research focusing on oncology has great significance. Metabonomics is the global quantitative assessment of the dynamic metabolic response of a biological system to some exogenous or genetic pathophysiological perturbation. The metabolites are detected in tissues or fluids by various analytical methods, such as nuclear magnetic resonance (NMR) and mass spectroscopy. Metabonomics, as a tool, can provide a link between the laboratory and clinic. NMR-based metabonomics offers a useful tool to understand tumour biochemistry and may also has some potentials for tumour diagnosis and prognosis, even when some other disease processes are present. Here, we review NMR-based metabonomics principles and their applications in oncology research.     

MetaboNexus: an interactive platform for integrated metabolomics analysis

MetaboNexus is an interactive metabolomics data analysis platform that integrates pre-processing of raw peak data with in-depth statistical analysis and metabolite identity search. It is designed to work as a desktop application hence uploading large files to web servers is not required. This could speed up the data analysis process because server queries or queues are avoided, while ensuring security of confidential clinical data on a local computer. With MetaboNexus, users can progressively start from data pre-processing, multi- and univariate analysis to metabolite identity search of significant molecular features, thereby seamlessly integrating critical steps for metabolite biomarker discovery. Data exploration can be first performed using principal components analysis, while prediction and variable importance can be calculated using partial least squares-discriminant analysis and Random Forest. After identifying putative features from multi- and univariate analyses (e.g. t test, ANOVA, Mann–Whitney U test and Kruskal–Wallis test), users can seamlessly determine the molecular identity of these putative features. To assist users in data interpretation, MetaboNexus also automatically generates graphical outputs, such as score plots, diagnostic plots, boxplots, receiver operating characteristic plots and heatmaps. The metabolite search function will match the mass spectrometric peak data to three major metabolite repositories, namely HMDB, MassBank and METLIN, using a comprehensive range of molecular adducts. Biological pathways can also be searched within MetaboNexus. MetaboNexus is available with installation guide and tutorial at http://www.sph.nus.edu.sg/index.php/research-services/research-centres/ceohr/metabonexus, and is meant for the Windows Operating System, XP and onwards (preferably on 64-bit). In summary, MetaboNexus is a desktop-based platform that seamlessly integrates the entire data analytical workflow and further provides the putative identities of mass spectrometric data peaks by matching them to databases.     

WebArray: an online platform for microarray data analysis

Background  

Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments.     

SNP Chart: an integrated platform for visualization and interpretation of microarray genotyping data

SNP Chart is a Java application for the visualization and interpretation of microarray genotyping data primarily derived from arrayed primer extension-based chemistries. Spot intensity output files from microarray analysis tools are imported into SNP Chart, together with a multi-channel TIFF image of the original array experiment and a list of the actual single nucleotide polymorphisms (SNPs) being tested. Data from different and/or replicate probes that interrogate the same SNP, but that are scattered across the array grid, can be reassembled into a single chart format, specific for the SNP. This allows a quick and very effective 'visualization'/'quality control' of the data from multiple probes for the same SNP that can be easily interpreted and manually scored as a genotype. AVAILABILITY: http://www.snpchart.ca.     

Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB

Background  

The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods.     

Androgen receptor activation results in metabolite signatures of an aggressive prostate cancer phenotype: an NMR-based metabonomics study

Metabolomic studies have proven to provide a unique perspective of the cellular dysfunction developing as a result of prostate cancer (PCa) onset and progression, facilitated primarily by mass spectrometry (MS) and nuclear magnetic resonance (NMR) techniques. PCa develops as an androgen-dependent disease with the expression of the androgen receptor (AR), where patient treatment typically involves androgen ablation therapy. In response, it is theorized that PCa transforms to an androgen-hypersensitive or androgen-independent state, where treatment options are severely reduced. Under the hypothesis that AR stimulation increases the aggressivity of pre-existing PCa, NMR spectroscopy was utilized in the delineation of the metabonomic response of an androgen-dependent PCa cell line (LnCAP) as a result of AR activation. Metabolite profiles were determined after 12, 24, and 48?h of exposure to methyltrienolone (R1881), an AR agonist. Principal components analysis revealed the relative myo-inositol and phosphocholine levels were severely altered after androgen treatment. Furthermore, univariate analysis revealed multiple metabolic perturbations in response to R1881 exposure, including amino acid, choline, the phosphocholine/glycerophosphocholine ratio, and UDP-coupled sugar metabolism, which are consistent with reported measurements between normal and PCa samples. These results suggest that androgen-sensitive PCa may transform to an aggressive phenotype upon AR activation.     

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine

In this study, 1H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution 1H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.     

NMR-based metabonomics for detection of Helicobacter pylori infection in gerbils: which is more descriptive

Background:  Helicobacter pylori , the human pathogenic gram-negative microaerophilic bacterium, causes chronic gastric infection in more than half of the human population regardless of race. The infection of microbe is not yet controllable to pose a substantial public health impact and a growing social burden. The management of H. pylori infection primarily necessitates accurate and timely diagnosis at case level, on-demand supervision of pathologic progression, and reliable evaluation of the impact of pharmacologic interventions on the patients' population.
Methods:  The characterization of H. pylori infection on gerbils model was performed by metabolic profiling, employing ¹H NMR spectroscopy compounding multivariate pattern recognition strategies. In the same manner, urine samples were individually collected from 10 gerbils infected with H. pylori GS13, and from 10 uninfected control animals equally accessible to feed and water.
Results:  The resultant metabolic profiles indicate that H. pylori infection disturbs carbohydrate metabolism to elevate the levels of α- and β-glucose, and cis -aconitate (a TCA cycle intermediate). In addition to the energy metabolism alteration, the colonization of H. pylori in gerbil stomach generates a remarkable deviation of amino acid metabolism as indicated by depletion of taurine and arginine, and elevation of proline and glutamine in the animal urine. Moreover, the H. pylori infection modifies the gut microbiota as highlighted by a range of microbial-related metabolites such as indoxyl sulfate and hippurate.
Conclusions:  These findings demonstrate that the ¹H NMR-based urine metabolic profiling is a promising technique capable of providing an accurate, noninvasive, and rapid diagnosis of H. pylori infection.     

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.     

NMR-based metabonomics of bovine blood: an investigation into the effects of long term storage on plasma samples

Freezers in research institutions often contain a plethora of samples left over from studies performed years or even decades ago. Along with samples stored in biobanks, these could prove to be treasure troves for metabonomic research. Although the influence of sample handling and short to medium term storage on conventionally determined blood parameters has been reported, little is known about the effects of long term storage (years to decades) on plasma samples. The aim of this study was to investigate the influence of long term storage on the metabolite profile and to assess the value of archived samples for metabonomic studies. Heparinised plasma of 22 heifers that had been stored at ?20 °C for between 2 and 15 years was analysed using NMR spectroscopy and statistical analysis techniques. Lactate (principal component 1) explained 79.6 % of variance between all spectra, but was not correlated with storage time. The highest correlation with storage time (R ² = 0.474) was found for betaine, with other metabolites (acetoacetate, histidines, glycerol, lipids and glucose) also showing moderate correlation (R ² values between 0.217 and 0.437). Our results indicate that samples stored for extended periods of time can potentially be used in metabonomics studies, if precautions are taken during data analysis.     

ISEApeaks: an Excel platform for GeneScan and Immunoscope data retrieval, management and analysis

ISEApeaks retrieves and handles DNA sequencer raw data (peak area and nucleotidic length). This Macintosh package efficiently interfaces GeneScan and Immunoscope softwares to Excel by extracting data from scattered files, organizing data and gathering it into a unique peak database for all samples.     

CDtool-an integrated software package for circular dichroism spectroscopic data processing, analysis, and archiving

CDtool is a software package written to facilitate circular dichroism (CD) spectroscopic studies on both conventional lab-based instruments and synchrotron beamlines. It takes format-independent input data from any type of CD instrument, enables a wide range of standard and advanced processing methods, and, in a single user-friendly graphics-based package, takes raw data through the entire processing procedure and, importantly, uses data-mining techniques to retain in the final output all the information associated with the processing. It permits the facile comparison of data obtained from different instruments without the need for reformatting and displays it in graphical formats suitable for publication. It also includes the ability to automatically archive the processed data. This latter feature may be especially useful in light of recent funding institution directives with regard to data sharing and archiving and requirements for "good practice" and "traceability" within the pharmaceutical industry. In addition, CDtool includes a means of interfacing with protein data bank coordinate files and calculating secondary structures from them using alternate definitions and algorithms. This feature, along with a function that permits the facile production of new reference databases, enables the creation of specialized databases for secondary structural analyses of specific types of proteins. Thus the CDtool software not only enables rapid data processing and analyses but also includes many enhanced features not available in other CD data processing/analysis packages.