Miroslava Cuperlovic-Culf

Cell culture metabolomics: applications and future directions.

Metabolomics represents a global quantitative assessment of metabolites within a biological system. The metabolic analysis of cell cultures has many potential applications and advantages to currently used methods for cell line testing. Metabolite concentrations represent sensitive markers of both genomic and phenotypic changes. Consequently, the development of robust metabolomic platforms will greatly facilitate various applications of cell cultures - including, for example, the understanding of the in vitro and in vivo actions of drugs - and aid in their rapid incorporation into novel therapeutic settings. In addition, metabolomic analysis of cell lines provides information, either independently or in conjunction with other omics measurements, essential for system level analysis and modeling of biological systems. This review outlines some of the applications of metabolomics in cell culture analysis and some of the issues that need to be addressed to make this approach more relevant.

MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

BackgroundOne-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.ResultsWe introduce a web server application, called MetaboHunter, which can be used for automatic assignment of 1H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunters metabolite identification methods.ConclusionsMetaboHunter is a freely accessible, easy to use and user friendly 1H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.Availabilityhttp://www.nrcbioinformatics.ca/metabohunter/

Fuzzy J-Means and VNS methods for clustering genes from microarray data

MOTIVATION In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. RESULTS Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. AVAILABILITY The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca

1H NMR metabolomics analysis of glioblastoma subtypes: correlation between metabolomics and gene expression characteristics

Background: Unpredictable clinical behavior of glioblastoma multiforme suggests distinct molecular subtypes. Results: Metabolic profiles of different glioblastoma lines indicate distinct subtypes correlated with gene expression differences. Conclusion: A subset of metabolites can be used to distinguish between four subtypes of glioblastomas. Significance: Metabolic profiling of cancers provides a way for subtype determination with possible diagnostic and prognostic applications. Glioblastoma multiforme (GBM) is the most common form of malignant glioma, characterized by unpredictable clinical behaviors that suggest distinct molecular subtypes. With the tumor metabolic phenotype being one of the hallmarks of cancer, we have set upon to investigate whether GBMs show differences in their metabolic profiles. 1H NMR analysis was performed on metabolite extracts from a selection of nine glioblastoma cell lines. Analysis was performed directly on spectral data and on relative concentrations of metabolites obtained from spectra using a multivariate regression method developed in this work. Both qualitative and quantitative sample clustering have shown that cell lines can be divided into four groups for which the most significantly different metabolites have been determined. Analysis shows that some of the major cancer metabolic markers (such as choline, lactate, and glutamine) have significantly dissimilar concentrations in different GBM groups. The obtained lists of metabolic markers for subgroups were correlated with gene expression data for the same cell lines. Metabolic analysis generally agrees with gene expression measurements, and in several cases, we have shown in detail how the metabolic results can be correlated with the analysis of gene expression. Combined gene expression and metabolomics analysis have shown differential expression of transporters of metabolic markers in these cells as well as some of the major metabolic pathways leading to accumulation of metabolites. Obtained lists of marker metabolites can be leveraged for subtype determination in glioblastomas.

Determination of tumour marker genes from gene expression data.

Cancer classification has traditionally been based on the morphological study of tumours. However, tumours with similar histological appearances can exhibit different responses to therapy, indicating differences in tumour characteristics on the molecular level. Thus, development of a novel, reliable and precise method for classification of tumours is essential for more successful diagnosis and treatment. The high-throughput gene expression data obtained using microarray technology are currently being investigated for diagnostic applications. However, these large datasets introduce a range of challenges, making data analysis a major part of every experiment for any application, including cancer classification and diagnosis. One of the major concerns in the application of microarrays to tumour diagnostics is the fact that the expression levels of many genes are not measurably affected by carcinogenic changes in the cells. Thus, a crucial step in the application of microarrays to cancer diagnostics is the selection of diagnostic marker genes from the gene expression profiles. These molecular markers give valuable additional information for tumour diagnosis, prognosis and therapy development.

NMR metabolic analysis of samples using fuzzy K-means clustering

The global analysis of metabolites can be used to define the phenotypes of cells, tissues or organisms. Classifying groups of samples based on their metabolic profile is one of the main topics of metabolomics research. Crisp clustering methods assign each feature to one cluster, thereby omitting information about the multiplicity of sample subtypes. Here, we present the application of fuzzy K‐means clustering method for the classification of samples based on metabolomics 1D 1H NMR fingerprints. The sample classification was performed on NMR spectra of cancer cell line extracts and of urine samples of type 2 diabetes patients and animal models. The cell line dataset included NMR spectra of lipophilic cell extracts for two normal and three cancer cell lines with cancer cell lines including two invasive and one non‐invasive cancers. The second dataset included previously published NMR spectra of urine samples of human type 2 diabetics and healthy controls, mouse wild type and diabetes model and rat obese and lean phenotypes. The fuzzy K‐means clustering method allowed more accurate sample classification in both datasets relative to the other tested methods including principal component analysis (PCA), hierarchical clustering (HCL) and K‐means clustering. In the cell line samples, fuzzy clustering provided a clear separation of individual cell lines, groups of cancer and normal cell lines as well as non‐invasive and invasive tumour cell lines. In the diabetes dataset, clear separation of healthy controls and diabetics in all three models was possible only by using the fuzzy clustering method. Copyright

Human Pax-5 C-terminal isoforms possess distinct transactivation properties and are differentially modulated in normal and malignant B cells.

The transcription factor Pax-5 occupies a central role in B cell differentiation and has been implicated in the development of B cell lymphoma. The transcriptional activation function of Pax-5 requires an intact N-terminal DNA-binding domain and is strongly influenced by the C-terminal transactivation domain. We report the identification and characterization of five human Pax-5 isoforms, which occur through the alternative splicing of exons that encode for the C-terminal transactivation domain. These isoforms arise from the inclusion or exclusion of exon 7, exon 8, and/or exon 9. Three of the Pax-5 isoforms generate novel protein sequences rich in proline, serine, and threonine amino acids that are the hallmarks of transactivation domains. The Pax-5 isoforms are expressed in peripheral blood mononuclear cells, cancerous and non-cancerous B cell lines, as well as in primary B cell lymphoma tissue. Electrophoretic mobility shift assays demonstrate that the isoforms possess specific DNA binding activity and recognize the PAX-5 consensus binding sites. In reporter assays using the CD19 promoter, the transactivation properties of the various isoforms were significantly influenced by the changes in the C-terminal protein sequence. Finally, we demonstrate, for the first time, that human Pax-5 isoform expression is modulated by specific signaling pathways in B lymphocytes.

Synthesis and structure-activity relationship of 1- and 2-substituted-1,2,3-triazole letrozole-based analogues as aromatase inhibitors.

A series of bis- and mono-benzonitrile or phenyl analogues of letrozole 1, bearing (1,2,3 and 1,2,5)-triazole or imidazole, were synthesized and screened for their anti-aromatase activities. The unsubstituted 1,2,3-triazole 10a derivative displayed inhibitory activity comparable with that of the aromatase inhibitor, letrozole 1. Compound 10a, bearing a 1,2,3-triazole, is also 10000-times more tightly binding than the corresponding analogue 25 bearing a 1,2,5-triazole, which confirms the importance of a nitrogen atom at position 3 or 4 of the 5-membered ring needed for high activity. The effect on human epithelial adrenocortical carcinoma cell line (H295R) proliferation was also evaluated. The compound 10j (IC(50) = 4.64 μM), a letrozole 1 analogue bearing para-cyanophenoxymethylene-1,2,3-triazole decreased proliferation rates of H295R cells by 76 and 99% in 24 and 72 h respectively. Computer calculations, using quantum ab initio structures, suggest a possible correlation between anti-aromatase activity and the distance between the nitrogen in position 3 or 4 of triazole nitrogen and the cyano group nitrogen.

1H NMR metabolomics analysis of the effect of dichloroacetate and allopurinol on breast cancers

Metabolomics analysis was used to determine the effect of two well known, non-proprietary metabolic modulators, dichloroacetate and allopurinol on breast cancer cell lines. Dichloroacetate, a pyruvate dehydrogenase kinase inhibitor and allopurinol, a xanthine oxidase/dehydrogenase inhibitor, have been previously explored as chemotherapeutics showing potential in some cancer subtypes while at the same time leading to unexpected increase in proliferation in others. In this work, metabolic effects of these drugs, applied singly and in combination, were explored in three different breast cell lines including cancer cells, MDA-MB-231 and MCF-7 and normal control cell line, MCF-10A. The metabolic changes induced by these drugs were monitored by (1)H NMR metabolic profiling. Analyses were performed on complete spectral data as well as quantified metabolic data in intracellular fractions and extracellular media leading to the determination of the most significantly affected metabolites. The effect of dichloroacetate and allopurinol is the most apparent in the metabolic profile of extracellular media. In MCF-7 cells, dichloroacetate treatment is dominant with only a minor observed influence of allopurinol in combined treatment. In MDA-MB-231 cells, both allopurinol and DCA lead to a metabolic shift with the allopurinol change dominating the effect of combined treatment. Results show the power of metabolomics as a tool for fast molecular profiling of drug effects in cells. In summary, treatments of breast cancer cells with DCA and allopurinol result in larger changes in metabolites found in extracellular medium than intracellular pools.

NMR Metabolomics Analysis of the Effects of 5-Lipoxygenase Inhibitors on Metabolism in Glioblastomas

Changes across metabolic networks are emerging as an integral part of cancer development and progression. Increasing comprehension of the importance of metabolic processes as well as metabolites in cancer is stimulating exploration of novel, targeted treatment options. Arachidonic acid (AA) is a major component of phospholipids. Through the cascade catalyzed by cyclooxygenases and lipoxygenases, AA is also a precursor to cellular signaling molecules as well as molecules associated with a variety of diseases including cancer. 5-Lipoxygenase catalyzes the transformation of AA into leukotrienes (LT), important mediators of inflammation. High-throughput analysis of metabolic profiles was used to investigate the response of glioblastoma cell lines to treatment with 5-lipoxygenase inhibitors. Metabolic profiling of cells following drug treatment provides valuable information about the response and metabolic alterations induced by the drug action and give an indication of both on-target and off-target effects of drugs. Four different 5-lipoxygenase inhibitors and antioxidants were tested including zileuton, caffeic acid, and its analogues caffeic acid phenethyl ester and caffeic acid cyclohexethyl ester. A NMR approach identified metabolic signatures resulting from application of these compounds to glioblastoma cell lines, and metabolic data were used to develop a better understanding of the mode of action of these inhibitors.