Adrian S. 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.

Solid-Phase Synthesis of N-Substituted Glycine Oligomers (α‑Peptoids) and Derivatives

Peptoids (N-substituted polyglycines and extended peptoids with variant backbone amino-acid monomer units) are oligomeric synthetic polymers that are becoming a valuable molecular tool in the biosciences. Of particular interest are their applications to the exploration of peptoid secondary structures and drug design. Major advantages of peptoids as research and pharmaceutical tools include the ease and economy of synthesis, highly variable backbone and side-chain chemistry possibilities. At the same time, peptoids have been demonstrated as highly active in biological systems while resistant to proteolytic decay. This review with 227 references considers the solid-phase synthetic aspects of peptoid preparation and utilization up to 2010 from the instigation, by R. N. Zuckermann et al., of peptoid chemistry in 1992.

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/

Development and Experimental Validation of a 20K Atlantic Cod (Gadus morhua) Oligonucleotide Microarray Based on a Collection of over 150,000 ESTs

The collapse of Atlantic cod (Gadus morhua) wild populations strongly impacted the Atlantic cod fishery and led to the development of cod aquaculture. In order to improve aquaculture and broodstock quality, we need to gain knowledge of genes and pathways involved in Atlantic cod responses to pathogens and other stressors. The Atlantic Cod Genomics and Broodstock Development Project has generated over 150,000 expressed sequence tags from 42 cDNA libraries representing various tissues, developmental stages, and stimuli. We used this resource to develop an Atlantic cod oligonucleotide microarray containing 20,000 unique probes. Selection of sequences from the full range of cDNA libraries enables application of the microarray for a broad spectrum of Atlantic cod functional genomics studies. We included sequences that were highly abundant in suppression subtractive hybridization (SSH) libraries, which were enriched for transcripts responsive to pathogens or other stressors. These sequences represent genes that potentially play an important role in stress and/or immune responses, making the microarray particularly useful for studies of Atlantic cod gene expression responses to immune stimuli and other stressors. To demonstrate its value, we used the microarray to analyze the Atlantic cod spleen response to stimulation with formalin-killed, atypical Aeromonas salmonicida, resulting in a gene expression profile that indicates a strong innate immune response. These results were further validated by quantitative PCR analysis and comparison to results from previous analysis of an SSH library. This study shows that the Atlantic cod 20K oligonucleotide microarray is a valuable new tool for Atlantic cod functional genomics research.

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.

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

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.

Small Head-to-Tail Macrocyclic α-Peptoids

A convenient and efficient methodology for the head-to-tail macrocyclization of small 3-mer, 4-mer, and 5-mer α-peptoid acids (9-, 12-, and 15-atom N-substituted glycine oligomers) is described. The cyclic trimer has a ccc amide sequence in the crystal structure, whereas the tetramer has ctct and the pentamer has ttccc stereochemistry. NMR analysis reveals rigid structures in solution. These synthetic macrocycles may prove useful in medicinal and materials applications.

1H NMR metabolomics combined with gene expression analysis for the determination of major metabolic differences between subtypes of breast cell lines

1H NMR analysis was performed on metabolic extracts from a selection of six breast cell lines, including normal-immortalized, invasive ductal carcinomas and adenocarcinomas. Metabolites with significant concentration differences between normal and cancerous cells as well as ER+ and ER− (estrogen receptor) cells were determined and their relation to the differentially expressed genes was explored. Major differences have been shown for many amino acids and this was linked to expression level changes of related genes. Observed changes in choline concentration were connected to expression level changes of the SCL44A1 transporter gene.

Metabolic Effects of Known and Novel HDAC and SIRT Inhibitors in Glioblastomas Independently or Combined with Temozolomide

Inhibition of protein deacetylation enzymes, alone or in combination with standard chemotherapies, is an exciting addition to cancer therapy. We have investigated the effect of deacetylase inhibition on the metabolism of glioblastoma cells. 1H NMR metabolomics analysis was used to determine the major metabolic changes following treatment of two distinct glioblastoma cell lines, U373 and LN229, with five different histone deacetylase (HDAC) inhibitors, as well as one inhibitor of NAD+-dependent protein deacetylases (SIRT). The addition of the standard glioblastoma chemotherapy agent, temozolomide, to the HDAC and SIRT treatments led to a reduction in cell survival, suggesting a possibility for combined treatment. This study shows that distinct glioblastoma cell lines, with different metabolic profiles and gene expression, experience dissimilar changes following treatment with protein deacetylase inhibitors. The observed effects of inhibitors on mitochondrial metabolism, glycolysis and fatty acid synthesis suggest possible roles of protein deacetylases in metabolism regulation. Metabolic markers of the effectiveness of anti-protein deacetylase treatments have been explored. In addition to known deacetylation inhibitors, three novel inhibitors have been introduced and tested. Finally, 1H NMR analysis of cellular metabolism is shown to be a fast, inexpensive method for testing drug effects.