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Matej Oresic

Publications and source records attributed to Matej Oresic.

16 recordsLinked to original sources
PubMed2006

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. PRINCIPAL FINDINGS: High dose simvastatin treatment resulted in 111 differentially expressed genes (1.5-fold change and p-value<0.05), while expression of only one and five genes was altered in the placebo and atorvastatin groups, respectively. The Gene Set Enrichment Analysis identified several affected pathways (23 gene lists with False Discovery Rate q-value<0.1) in muscle following high dose simvastatin, including eicosanoid synthesis and Phospholipase C pathways. Using lipidomic analysis we identified previously uncharacterized drug-specific changes in the plasma lipid profile despite similar statin-induced changes in plasma LDL-cholesterol. We also found that the plasma lipidomic changes following simvastatin treatment correlate with the muscle expression of the arachidonate 5-lipoxygenase-activating protein. CONCLUSIONS: High dose simvastatin affects multiple metabolic and signaling pathways in skeletal muscle, including the pro-inflammatory pathways. Thus, our results demonstrate that clinically used high statin dosages may lead to unexpected metabolic effects in non-hepatic tissues. The lipidomic profiles may serve as highly sensitive biomarkers of statin-induced metabolic alterations in muscle and may thus allow us to identify patients who should be treated with a lower dose to prevent a possible toxicity.

Atorvastatin↗
PubMed2006

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. Genome-wide transcript profiling by cDNA-amplified fragment-length polymorphism combined with metabolic profiling of elicited C. roseus cell cultures yielded a collection of known and previously undescribed transcript tags and metabolites associated with terpenoid indole alkaloids. Previously undescribed gene-to-gene and gene-to-metabolite networks were drawn up by searching for correlations between the expression profiles of 417 gene tags and the accumulation profiles of 178 metabolite peaks. These networks revealed that the different branches of terpenoid indole alkaloid biosynthesis and various other metabolic pathways are subject to differing hormonal regulation. These networks also served to identify a select number of genes and metabolites likely to be involved in the biosynthesis of terpenoid indole alkaloids. This study provides the basis for a better understanding of periwinkle secondary metabolism and increases the practical potential of metabolic engineering of this important medicinal plant.

Catharanthus↗
PubMed2006

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.sourceforge.net/.

Computer Graphics↗
PubMed2006

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. We first investigate the effect of different expression change estimation procedures within the platforms on the agreement of the most differentially expressed genes. Using the best estimation methods, we then show the benefits of the integrative analysis in producing reproducible results across bootstrap samples. In particular, we demonstrate its biological relevance in identifying small but consistent changes during T helper 2 cell differentiation.

Cell Differentiation↗
PubMed2006

Ablation of PGC-1beta results in defective mitochondrial activity, thermogenesis, hepatic function, and cardiac performance.

The transcriptional coactivator peroxisome proliferator-activated receptor-gamma coactivator-1beta (PGC-1beta) has been implicated in important metabolic processes. A mouse lacking PGC-1beta (PGC1betaKO) was generated and phenotyped using physiological, molecular, and bioinformatic approaches. PGC1betaKO mice are generally viable and metabolically healthy. Using systems biology, we identified a general defect in the expression of genes involved in mitochondrial function and, specifically, the electron transport chain. This defect correlated with reduced mitochondrial volume fraction in soleus muscle and heart, but not brown adipose tissue (BAT). Under ambient temperature conditions, PGC-1beta ablation was partially compensated by up-regulation of PGC-1alpha in BAT and white adipose tissue (WAT) that lead to increased thermogenesis, reduced body weight, and reduced fat mass. Despite their decreased fat mass, PGC1betaKO mice had hypertrophic adipocytes in WAT. The thermogenic role of PGC-1beta was identified in thermoneutral and cold-adapted conditions by inadequate responses to norepinephrine injection. Furthermore, PGC1betaKO hearts showed a blunted chronotropic response to dobutamine stimulation, and isolated soleus muscle fibres from PGC1betaKO mice have impaired mitochondrial function. Lack of PGC-1beta also impaired hepatic lipid metabolism in response to acute high fat dietary loads, resulting in hepatic steatosis and reduced lipoprotein-associated triglyceride and cholesterol content. Altogether, our data suggest that PGC-1beta plays a general role in controlling basal mitochondrial function and also participates in tissue-specific adaptive responses during metabolic stress.

Adipose Tissue, Brown↗
PubMed2006

Metabolomic approaches to phenotype characterization and applications to complex diseases.

Metabolites are the key regulators of systems homeostasis. As such, concentration changes of specific groups of metabolites may reflect systemic responses to environmental, therapeutic or genetic interventions. Thus, the study of metabolites is a powerful tool for the characterization of complex phenotypes as well as for the development of biomarkers for specific physiological responses. Therefore, metabolomics is a valuable platform for studies of complex diseases and the development of new therapies, both in nonclinical disease model characterization and clinical settings.

Animals↗
PubMed2005

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. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data. RESULTS: We present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures. CONCLUSION: The software is freely available under the GNU General Public License and it can be obtained from the project web page at: http://mzmine.sourceforge.net/.

Algorithms↗
PubMed2005

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. The programs Pro ICAT, Spectrum Mill and SEQUEST with XPRESS were employed. Attention was paid towards the extent of common identification and agreement of quantitative results, with additional interest in the flexibility and productivity of these programs. The comparisons were made with data from the analysis of a specifically prepared test mixture, nine proteins at a range of relative concentration ratios from 0.1 to 10 (light to heavy labelled forms), as a known control, and data selected from an ICAT study involving the measurement of cytokine induced protein expression in human lymphoblasts, as an applied example. Dissimilarities were detected in peptide identification that reflected how the associated scoring parameters favoured information from the MS/MS data sets. Accordingly, there were differences in the numbers of peptides and protein identifications, although from these it was apparent that both confirmatory and complementary information was present. In the quantitative results from the three programs, no statistically significant differences were observed.

Algorithms↗
PubMed2005

Pathways to the analysis of microarray data.

The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.

Algorithms↗
PubMed2005

Data integration and visualization system for enabling conceptual biology.

MOTIVATION: Integration of heterogeneous data in life sciences is a growing and recognized challenge. The problem is not only to enable the study of such data within the context of a biological question but also more fundamentally, how to represent the available knowledge and make it accessible for mining. RESULTS: Our integration approach is based on the premise that relationships between biological entities can be represented as a complex network. The context dependency is achieved by a judicious use of distance measures on these networks. The biological entities and the distances between them are mapped for the purpose of visualization into the lower dimensional space using the Sammon's mapping. The system implementation is based on a multi-tier architecture using a native XML database and a software tool for querying and visualizing complex biological networks. The functionality of our system is demonstrated with two examples: (1) A multiple pathway retrieval, in which, given a pathway name, the system finds all the relationships related to the query by checking available metabolic pathway, transcriptional, signaling, protein-protein interaction and ontology annotation resources and (2) A protein neighborhood search, in which given a protein name, the system finds all its connected entities within a specified depth. These two examples show that our system is able to conceptually traverse different databases to produce testable hypotheses and lead towards answers to complex biological questions.

Computational Biology↗
PubMed2005

The link between nutritional status and insulin sensitivity is dependent on the adipocyte-specific peroxisome proliferator-activated receptor-gamma2 isoform.

The nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARgamma) is critically required for adipogenesis. PPARgamma exists as two isoforms, gamma1 and gamma2. PPARgamma2 is the more potent adipogenic isoform in vitro and is normally restricted to adipose tissues, where it is regulated more by nutritional state than PPARgamma1. To elucidate the relevance of the PPARgamma2 in vivo, we generated a mouse model in which the PPARgamma2 isoform was specifically disrupted. Despite similar weight, body composition, food intake, energy expenditure, and adipose tissue morphology, male mice lacking the gamma2 isoform were more insulin resistant than wild-type animals when fed a regular diet. These results indicate that insulin resistance associated with ablation of PPARgamma2 is not the result of lipodystrophy and suggests a specific role for PPARgamma2 in maintaining insulin sensitivity independently of its effects on adipogenesis. Furthermore, PPARgamma2 knockout mice fed a high-fat diet did not become more insulin resistant than those on a normal diet, despite a marked increase in their mean adipocyte cell size. These findings suggest that PPARgamma2 is required for the maintenance of normal insulin sensitivity in mice but also raises the intriguing notion that PPARgamma2 may be necessary for the adverse effects of a high-fat diet on carbohydrate metabolism.

Adipocytes↗
PubMed2004

Integrative biological analysis of the APOE*3-leiden transgenic mouse.

Integrative (or systems biology) is a new approach to analyzing biological entities as integrated systems of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. Here, we demonstrate the application of intregrative biological analysis to a mammalian disease model, the apolipoprotein E3-Leiden (APO*E3) transgenic mouse. Mice selected for the study were fed a normal chow diet and sacrificed at 9 weeks of age-conditions under which they develop only mild type I and II atherosclerotic lesions. Hepatic mRNA expression analysis showed a 25% decrease in APO A1 and a 43% increase in liver fatty acid binding protein expression between transgenic and wild type control mice, while there was no change in PPAR-alpha expression. On-line high performance liquid chromatography-mass spectrometry quantitative profiling of tryptic digests of soluble liver proteins and liver lipids, coupled with principle component analysis, enabled rapid identification of early protein and metabolite markers of disease pathology. These included a 44% increase in L-FABP in transgenic animals compared to controls, as well as an increase in triglycerides and select bioactive lysophosphatidylcholine species. A correlation analysis of identified genes, proteins, and lipids was used to construct an interaction network. Taken together, these results indicate that integrative biology is a powerful tool for rapid identification of early markers and key components of pathophysiologic processes, and constitute the first application of this approach to a mammalian system.

Animals↗
PubMed2004

Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model.

Multitiered quantitative analysis of biological systems is rapidly becoming the desired approach to study hierarchical functional interactions between proteins and metabolites. We describe here a novel systematic approach to analyze organisms with complex metabolic regulatory networks. By using precise analytical methods to measure biochemical constituents and their relative abundance in whole plasma of transgenic ApoE*3-Leiden mice and an isogenic wild-type control group, simultaneous snapshots of metabolic and protein states were obtained. Novel data processing and multivariate analysis tools such as Impurity Resolution Software (IMPRESS) and Windows-based linear fit program (WINLIN) were used to compare protein and metabolic profiles in parallel. Canonical correlations of the resulting data show quantitative relationships between heterogeneous components in the TG animals. These results, obtained solely from whole plasma analysis allowed us, in a rapid manner, to corroborate previous findings as well as find new events pertaining to dominant and peripheral events in lipoprotein metabolism of a genetically modified mammalian organism in relation to ApoE3, a key mediator of lipoprotein metabolism.

Animals↗
PubMed2004

Phenotype characterisation using integrated gene transcript, protein and metabolite profiling.

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels. This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes. These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying--well in advance of pathogenic manifestations of disease--the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

Algorithms↗
PubMed2003

Tracing specific synonymous codon-secondary structure correlations through evolution.

We previously showed that GAU codons are preferred (relative to synonymous GAC codons) for encoding aspartates specifically at the N-termini of alpha-helices in human, but not in E. coli, proteins. To test if this difference reflected a general difference between eucaryotes and procaryotes, we now extended the analysis to include the proteins and coding sequences of mammals, vertebrates, S. cerevisiae, and plants. We found that the GAU-alpha-helix correlation is also strong in non-human mammalian and vertebrate proteins but is much weaker or insignificant in S. cerevisiae and plants. The vertebrate correlations are of sufficient strength to enhance alpha-helix N-terminus prediction. Additional results, including the observation that the correlation is significantly enhanced when proteins that are known to be correctly expressed in recombinant procaryotic systems are excluded, suggest that the correlation is induced at the level of protein translation and folding and not at the nucleic acid level. To the best of our knowledge, it is not explicable by the canonical picture of protein expression and folding, suggesting the existence of a novel evolutionary selection mechanism. One possible explanation is that some alpha-helix N-terminal GAU codons may facilitate correct co-translational folding in vertebrates.

Codon↗