Proteomic-based detection of a protein cluster dysregulated during cardiovascular development identifies biomarkers of congenital heart defects.

Background: Cardiovascular development is vital for embryonic survival and growth. Early gestation embryo loss or malformation has been linked to yolk sac vasculopathy and congenital heart defects (CHDs). However, the molecular pathways that underlie these structural defects in humans remain largely unknown hindering the development of molecular-based diagnostic tools and novel therapies.

Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes.

The risk determinants of type 1 diabetes, initiators of autoimmune response, mechanisms regulating progress toward beta cell failure, and factors determining time of presentation of clinical diabetes are poorly understood. We investigated changes in the serum metabolome prospectively in children who later progressed to type 1 diabetes. Serum metabolite profiles were compared between sample series drawn from 56 children who progressed to type 1 diabetes and 73 controls who remained nondiabetic and permanently autoantibody negative.

Quantitative proteomics reveals GIMAP family proteins 1 and 4 to be differentially regulated during human T helper cell differentiation.

T helper (Th) cells differentiate into functionally distinct effector cell subsets of which Th1 and Th2 cells are best characterized. Besides T cell receptor signaling, IL-12-induced STAT4 and T-bet- and IL-4-induced STAT6 and GATA3 signaling pathways are the major players regulating the Th1 and Th2 differentiation process, respectively. However, there are likely to be other yet unknown factors or pathways involved.

Data processing for mass spectrometry-based metabolomics.

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results.

Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis.

Background: Lipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available.

Normalization method for metabolomics data using optimal selection of multiple internal standards.

Background: Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.

A systems biology strategy reveals biological pathways and plasma biomarker candidates for potentially toxic statin-induced changes in muscle.

Background: Aggressive lipid lowering with high doses of statins increases the risk of statin-induced myopathy. However, the cellular mechanisms leading to muscle damage are not known and sensitive biomarkers are needed to identify patients at risk of developing statin-induced serious side effects.

Methodology: We performed bioinformatics analysis of whole genome expression profiling of muscle specimens and UPLC/MS based lipidomics analyses of plasma samples obtained in an earlier randomized trial from patients either on high dose simvastatin (80 mg), atorvastatin (40 mg), or placebo.

Improving identification of differentially expressed genes by integrative analysis of Affymetrix and Illumina arrays.

Together with the widely used Affymetrix microarrays, the recently introduced Illumina platform has become a cost-effective alternative for genome-wide studies. To efficiently use data from both array platforms, there is a pressing need for methods that allow systematic integration of multiple datasets, especially when the number of samples is small. To address these needs, we introduce a meta-analytic procedure for combining Affymetrix and Illumina data in the context of detecting differentially expressed genes between the platforms.

Gene-to-metabolite networks for terpenoid indole alkaloid biosynthesis in Catharanthus roseus cells.

Rational engineering of complicated metabolic networks involved in the production of biologically active plant compounds has been greatly impeded by our poor understanding of the regulatory and metabolic pathways underlying the biosynthesis of these compounds. Whereas comprehensive genome-wide functional genomics approaches can be successfully applied to analyze a select number of model plants, these holistic approaches are not yet available for the study of nonmodel plants that include most, if not all, medicinal plants. We report here a comprehensive profiling analysis of the Madagascar periwinkle (Catharanthus roseus), a source of the anticancer drugs vinblastine and vincristine.

MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.

Summary: New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis.

Availability: MZmine is available under GNU Public license from http://mzmine.

Processing methods for differential analysis of LC/MS profile data.

Background: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e.

A comparative evaluation of software for the analysis of liquid chromatography-tandem mass spectrometry data from isotope coded affinity tag experiments.

The options available for processing quantitative data from isotope coded affinity tag (ICAT) experiments have mostly been confined to software specific to the instrument of acquisition. However, recent developments with data format conversion have subsequently increased such processing opportunities. In the present study, data sets from ICAT experiments, analysed with liquid chromatography/tandem mass spectrometry (MS/MS), using an Applied Biosystems QSTAR Pulsar quadrupole-TOF mass spectrometer, were processed in triplicate using separate mass spectrometry software packages.