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Delirium is an acute change in attention and cognition occurring in ~65% of severe SARS-CoV-2 cases. It is also common following surgery and an indicator of brain vulnerability and risk for the development of dementia. In this work we analyzed the underlying role of metabolism in delirium-susceptibility in the postoperative setting using metabolomic profiling of cerebrospinal fluid and blood taken from the same patients prior to planned orthopaedic surgery. Distance correlation analysis and Random Forest (RF) feature selection were used to determine changes in metabolic networks. We found significant concentration 2 differences in several amino acids, acylcarnitines and polyamines linking delirium-prone patients to known factors in Alzheimer’s disease such as monoamine oxidase B (MAOB) protein. Subsequent computational structural comparison between MAOB and angiotensin converting enzyme 2 as well as protein-protein docking analysis showed that there potentially is strong binding of SARS-CoV-2 spike protein to MAOB. The possibility that SARS-CoV-2 influences MAOB activity leading to the observed neurological and platelet-based complications of SARS-CoV-2 infection requires further investigation.

Cell cultures are one of the most useful tools in life science research and biotechnology, irreplaceable for applications as diverse as testing of drugs or toxins, development of gene and cell therapies, investigation of the function of biomolecules, production of biologics or vaccine particles. Metabolomics and lipidomics with their closest link to cell phenotype and as indicators of functional effects and changes in biological systems provide a highly valuable window into many applications of cell cultures. These omics tools can be used for the real time monitoring of cell culture growth and provide detailed, nonbiased analysis of changes within cells or in secreted media or extracellular vesicles. Metabolomics can be used to optimize cell growth for enhanced production in bioreactor applications or for monitoring cell health, for example, in cell therapy development. In this chapter, we outline some examples and methods used in metabolomics and lipidomics of cell cultures including analysis of cells, media, and extracellular vesicles. Experimental method presentation is followed by introduction to analysis and modeling tools including statistical, mechanistic, and machine learning methods with particular relevance in the applications of metabolomics and lipidomics in cell cultures.

Kidney cancer is one of the top ten cancer diagnosed worldwide and its incidence has increased the last 20 years. Clear Cell Renal Cell Carcinoma (ccRCC) are characterized by mutations that inactivate the von Hippel-Lindau (VHL) tumor suppressor gene and evidence indicated alterations in metabolic pathways, particularly in glutamine metabolism. We previously identified a small molecule, STF-62247, which target VHL-deficient renal tumors by affecting late-stages of autophagy and lysosomal signaling. In this study, we investigated ccRCC metabolism in VHL-deficient and proficient cells exposed to the small molecule. Metabolomics profiling using 1H NMR demonstrated that STF-62247 increases levels of glucose, pyruvate, glycerol 3-phosphate while glutamate, asparagine, and glutathione significantly decreased. Diminution of glutamate and glutamine was further investigated using mass spectrometry, western blot analyses, enzymatic activities, and viability assays. We found that expression of SLC1A5 increases in VHL-deficient cells treated with STF-62247, possibly to stimulate glutamine uptake intracellularly to counteract the diminution of this amino acid. However, exogenous addition of glutamine was not able to rescue cell viability induced by the small molecule. Instead, our results showed that VHL-deficient cells utilize glutamine to produce fatty acid in response to STF-62247. Surprisingly, this occurs through oxidative phosphorylation in STF-treated cells while control cells use reductive carboxylation to sustain lipogenesis. We also demonstrated that STF-62247 stimulated expression of stearoyl-CoA desaturase (SCD1) and peripilin2 (PLIN2) to generate accumulation of lipid droplets in VHL-deficient cells. Moreover, the carnitine palmitoyltransferase 1A (CPT1A), which control the entry of fatty acid into mitochondria for β-oxidation, also increased in response to STF-62247. CPT1A overexpression in ccRCC is known to limit tumor growth. Together, our results demonstrated that STF-62247 modulates cellular metabolism of glutamine, an amino acid involved in the autophagy-lysosome process, to support lipogenesis, which could be implicated in the signaling driving to cell death.

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

The development of effective therapies as well as early, molecular diagnosis of Alzheimer’s disease is impeded by the lack of understanding of the underlying pathological mechanisms. Metabolomics studies of body fluids as well as brain tissues have shown major changes in metabolic profiles of Alzheimer’s patients. However, with analysis performed at the late stages of the disease it is not possible to distinguish causes and consequence. The mouse model APP/PS1 expresses a mutant amyloid precursor protein resulting in early Amyloid β (Aβ) accumulation as well as many resulting physiological changes including changes in metabolic profile and metabolism. Analysis of metabolic profile of cerebrospinal fluid (CSF) and blood of APP/PS1 mouse model can provide information about metabolic changes in these body fluids caused by Aβ accumulation. Using our novel method for analysis of correlation and mathematical ranking of significant correlations between metabolites in CSF and blood, we have explored changes in metabolite correlation and connectedness in APP/PS1 and wild type mice. Metabolites concentration and correlation changes in CSF, blood and across the blood brain barrier determined in this work are affected by the production of amyloid plaque. Metabolite changes observed in the APP/PS1 mouse model are the response to the mutation causing plaque formation, not the cause for the plaque suggesting that they are less relevant in the context of early treatment and prevention then the metabolic changes observed only in humans.

Glioblastoma (GBM) is one of the most aggressive cancers of the central nervous system. Despite current advances in non-invasive imaging and the advent of novel therapeutic modalities, patient survival remains very low. There is a critical need for the development of effective biomarkers for GBM diagnosis and therapeutic monitoring. Extracellular vesicles (EVs) produced by GBM tumors have been shown to play an important role in cellular communication and modulation of the tumor microenvironment. As GBM-derived EVs contain specific &ldquo, molecular signatures&rdquo, of their parental cells and are able to transmigrate across the blood&ndash, brain barrier into biofluids such as the blood and cerebrospinal fluid (CSF), they are considered as a valuable source of potential diagnostic biomarkers. Given the relatively harsh extracellular environment of blood and CSF, EVs have to endure and adapt to different conditions. The ability of EVs to adjust and function depends on their lipid bilayer, metabolic content and enzymes and transport proteins. The knowledge of EVs metabolic characteristics and adaptability is essential for their utilization as diagnostic and therapeutic tools. The main aim of this study was to determine the metabolome of small EVs or exosomes derived from different GBM cells and compare to the metabolic profile of their parental cells using NMR spectroscopy. In addition, a possible flux of metabolic processes in GBM-derived EVs was simulated using constraint-based modeling from published proteomics information. Our results showed a clear difference between the metabolic profiles of GBM cells, EVs and media. Machine learning analysis of EV metabolomics, as well as flux simulation, supports the notion of active metabolism within EVs, including enzymatic reactions and the transfer of metabolites through the EV membrane. These results are discussed in the context of novel GBM diagnostics and therapeutic monitoring.

Introduction: Although age is a major risk factor for Alzheimer’s disease (AD), it is not an inevitable consequence of aging nor is it exclusively an old-age disease. Several other major risk factors for AD are strongly associated with metabolism and include lack of exercise, obesity, diabetes, high blood pressure and cholesterol, over-consumption of alcohol and depression in addition to low educational level, social isolation, and cognitive inactivity. Approaches for Alzheimer prevention and treatment through manipulation of metabolism and utilization of active metabolites have great potential either as a primary or secondary treatment avenue or as a preventative strategy in high-risk individuals. Areas covered: This review outlines the current knowledge concerning the relationship between AD and metabolism and the novel treatments attempting to correct changes in AD patients determined through metabolomics or lipidomic analyses. Expert opinion: Metabolites are one of the main driving factors and indicators of AD and can offer many possible avenues for prevention and treatment. However, with the highly interconnected effects of metabolites and metabolism, as well as the many different routes for metabolism dysfunction, successful treatment would have to include the correction of metabolic errors as well as errors in transport and metabolite processing in order to affect and revert AD progression.

The gene Lr34res is one of the most long-lasting sources of quantitative fungal resistance in wheat. It is shown to be effective against leaf, stem, and stripe rusts, as well as powdery mildew and spot blotch. Recent biochemical characterizations of the encoded ABC transporter have outlined a number of allocrites, including phospholipids and abscisic acid, consistent with the established general promiscuity of ABC transporters, but ultimately leaving its mechanism of rust resistance unclear. Working with flag leaves of Triticum aestivum L. variety 'Thatcher' (Tc) and a near-isogenic line of 'Thatcher' into which the Lr34res allele was introgressed (Tc+Lr34res; RL6058), a comparative semi-targeted metabolomics analysis of flavonoid-rich extracts revealed virtually identical profiles with the exception of one metabolite accumulating in Tc+Lr34res, which was not present at comparable levels in Tc. Structural characterization of the purified metabolite revealed a phenylpropanoid diglyceride structure, 1-O-p-coumaroyl-3-O-feruloylglycerol (CFG). Additional profiling of CFG across a collection of near-isogenic lines and representative Lr34 haplotypes highlighted a broad association between the presence of Lr34res and elevated accumulations of CFG. Depletion of CFG upon infection, juxtaposed to its relatively lower anti-fungal activity, suggests CFG may serve as a storage form of the more potent anti-microbial hydroxycinnamic acids that are accessed during defense responses. Altogether these findings suggest a role for the encoded LR34res ABC transporter in modifying the accumulation of CFG, leading to increased accumulation of anti-fungal metabolites, essentially priming the wheat plant for defense.

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.

High throughput analysis of metabolites combined with systems biology is opening new opportunities in medical research and clinical applications. Investigation of metabolic phenotype and metabolic requirements of a disease opens new avenues for treatments. Analysis of metabolic changes in patients and in disease models following treatments provides new methods for investigation of therapeutic response. Combined examination of disease metabolic characteristics and treatment response provides markers for personalization of therapies. Metabolomics finds itself at an increasingly important junction in all of these applications. In this review, we will explore the advantages of combining metabolomics with systems biology to optimize the drug discovery process via in-depth analysis of biological systems and novel metabolism- targeted therapeutics. Assessment of efficacy and specificity of drugs that directly or indirectly target metabolism through analyses of metabolic changes will also be presented. Finally, we will highlight examples of metabolic biomarkers explored as promising theranostics and theranostic imaging tools aimed at therapy optimization and combined therapy and diagnostics. The focus of this review will be directed towards cancer, however, the overarching ideas as well as experimental and analysis approaches are easily transferable to other applications. In cancer research, metabolism is once again raising interest due to a better appreciation of the significance of altered metabolic phenotype in cancer development and progression. Investigation of metabolic changes in cancer following drug applications and identification of clinically relevant metabolic markers for particular cancer subtypes are becoming increasingly appreciated in treatment development and will be explored in this review.

Introduction: The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. Areas covered: This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Expert opinion: Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

Metabolic shift is one of the major hallmarks of cancer development. Estrogen receptor (ER) activity has a profound effect on breast cancer cell growth through a number of metabolic changes driven by its effect on transcription of several enzymes, including carbonic anhydrases, Stearoyl-CoA desaturase-1, and oncogenes including HER2. Thus, estrogen receptor activators can be expected to lead to the modulation of cell metabolism in estrogen receptor positive cells. In this work we have investigated the effect of 17β-estradiol, an ER activator, and ferulic acid, a carbonic anhydrase inhibitor, as well as ER activator, in the absence and in the presence of the carbonic anhydrase inhibitor acetazolamide on the metabolism of MCF7 cells and MCF7 cells, stably transfected to express HER2 (MCF7HER2). Metabolic profiles were studied using 1D and 2D metabolomic Nuclear Magnetic Resonance (NMR) experiments, combined with the identification and quantification of metabolites, and the annotation of the results in the context of biochemical pathways. Overall changes in hydrophilic metabolites were largest following treatment of MCF7 and MC7HER2 cells with 17β-estradiol. However, the carbonic anhydrase inhibitor acetazolamide had the largest effect on the profile of lipophilic metabolites.

Metabolic changes in spikelets of wheat varieties FL62R1, Stettler, Muchmore and Sumai3 following Fusarium graminearum infection were explored using NMR analysis. Extensive 1D and 2D 1H NMR measurements provided information for detailed metabolite assignment and quantification leading to possible metabolic markers discriminating resistance level in wheat subtypes. In addition, metabolic changes that are observed in all studied varieties as well as wheat variety specific changes have been determined and discussed. A new method for metabolite quantification from NMR data that automatically aligns spectra of standards and samples prior to quantification using multivariate linear regression optimization of spectra of assigned metabolites to samples' 1D spectra is described and utilized. Fusarium infection-induced metabolic changes in different wheat varieties are discussed in the context of metabolic network and resistance.

The metabolism of tumors is remarkably different from the metabolism of corresponding normal cells and tissues. Metabolic alterations are initiated by oncogenes and are required for malignant transformation, allowing cancer cells to resist some cell death signals while producing energy and fulfilling their biosynthetic needs with limiting resources. The distinct metabolic phenotype of cancers provides an interesting avenue for treatment, potentially with minimal side effects. As many cancers show similar metabolic characteristics, drugs targeting the cancer metabolic phenotype are, perhaps optimistically, expected to be ‘magic bullet’ treatments. Over the last few years there have been a number of potential drugs developed to specifically target cancer metabolism. Several of these drugs are currently in clinical and preclinical trials. This review outlines examples of drugs developed for different targets of significance to cancer metabolism, with a focus on small molecule leads, chemical biology and clinical results for these drugs.

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.

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 (1)H 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.

Glioblastoma multiforme (GBM) is the most common form of malignant glioma. Current therapeutic approach to treat this malignancy involves a combination of surgery, radiotherapy and chemotherapy with temozolomide. Numerous mechanisms contributing to inherent and acquired resistance to this chemotherapeutic agent have been identified and can lead to treatment failure. This study undertook a metabolomics-based approach to characterize the metabolic profiles observed in temozolomide-sensitive and temozolomide-resistant GBM cell lines as well as in a small sub-set of primary GBM tumors. This approach was also utilized to explore the metabolic changes modulated upon cell treatment with temozolomide and lomeguatrib, an MGMT inhibitor with temozolomide-sensitizing potential. Metabolites previously explored for their potential role in chemoresistance including glucose, citrate and isocitrate demonstrated elevated levels in temozolomide-resistant GBM cells. In addition, a signature of metabolites comprising alanine, choline, creatine and phosphorylcholine was identified as up-regulated in sensitive GBM cell line across different treatments. These results present the metabolic profiles associated with temozolomide response in selected GBM models and propose interesting leads that could be leveraged for the development of therapeutic or diagnostic tools to impact temozolomide response in GBMs.

Von Hippel-Lindau (VHL) is an onco-suppressor involved in oxygen and energy-dependent promotion of protein ubiquitination and proteosomal degradation. Loss of function mutations of VHL (VHL-cells) result in organ specific cancers with the best studied example in renal cell carcinomas. VHL has a well-established role in deactivation of hypoxia-inducible factor (HIF-1) and in regulation of PI3K/AKT/mTOR activity. Cell culture metabolomics analysis was utilized to determined effect of VHL and HIF-1α or HIF-2α on metabolism of renal cell carcinomas (RCC). RCC cells were stably transfected with VHL or shRNA designed to silence HIF-1α or HIF-2α genes. Obtained metabolic data was analysed qualitatively, searching for overall effects on metabolism as well as quantitatively, using methods developed in our group in order to determine specific metabolic changes. Analysis of the effect of VHL and HIF silencing on cellular metabolic footprints and fingerprints provided information about the metabolic pathways affected by VHL through HIF function as well as independently of HIF. Through correlation network analysis as well as statistical analysis of significant metabolic changes we have determined effects of VHL and HIF on energy production, amino acid metabolism, choline metabolism as well as cell regulation and signaling. VHL was shown to influence cellular metabolism through its effect on HIF proteins as well as by affecting activity of other factors.

Mammalian cell cultures are increasingly used for the production of complex biopharmaceuticals including viral vectors and vaccines. HEK293 is the predominant cell line used for the transient expression of recombinant proteins and a well-established system for the production of viral vectors. Understanding metabolic requirements for high productivity in HEK293 cells remains an important area of investigation. Many authors have presented approaches for increased productivity through optimization of cellular metabolism from two distinct perspectives. One is a non-targeted approach, which is directed to improving feeding strategies by addition of exhausted or critical substrates and eventually removal of toxic metabolites. Alternatively, a targeted approach has attempted to identify specific targets for optimization through better understanding of the cellular metabolism under different operating conditions. This review will present both approaches and their successes with regards to improvement of viral production in HEK293 cells outlining the key relations between HEK293 cell metabolism and viral vector productivity. Also, we will summarize the current knowledge on HEK293 metabolism indicating remaining issues to address and problems to resolve to maximize the productivity of viral vectors in HEK293 cells.

Metabolomics is defined as a global quantitative assessment of metabolites within a biological system. Metabolic profiling of cell cultures has many potential applications as well as advantages to currently utilized methods for cell-line testing. Metabolite concentrations represent sensitive markers of genomic changes and responses of cells to external stimuli. Effects of drugs or toxins on cell cultures can be observed through the changes in metabolite concentrations. When cell cultures used for production of various biomolecules metabolomics can aid in optimization of cell growth. Metabolomics can also be used as a method for routine monitoring of extracellular metabolic changes in real time measurements. Nuclear magnetic resonance spectroscopy and mass spectrometry are major analytical platforms used for metabolomics measurements. These methods provide detailed, non-biased and highly complementary chemical analyses of metabolic changes within cells (fingerprint) and in excreted metabolites (footprint). This chapter provides review of current applications of metabolomics in cell cultures with an overview of experimental and data analysis methodologies., Series: Cell Engineering

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.

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.

Glioblastoma multiforme (GBM) is the most common adult primary tumor of the central nervous system. The current standard of care for glioblastoma patients involves a combination of surgery, radiotherapy and chemotherapy with the alkylating agent temozolomide. Several mechanisms underlying the inherent and acquired temozolomide resistance have been identified and contribute to treatment failure. Early identification of temozolomide-resistant GBM patients and improvement of the therapeutic strategies available to treat this malignancy are of uttermost importance. This review initially looks at the molecular pathways underlying GBM formation and development with a particular emphasis placed on recent therapeutic advances made in the field. Our focus will next be directed toward the molecular mechanisms modulating temozolomide resistance in GBM patients and the strategies envisioned to circumvent this resistance. Finally, we highlight the diagnostic and prognostic value of metabolomics in cancers and assess its potential usefulness in improving the current standard of care for GBM patients.

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. (1)H 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.

Metabolomics or metababonomics is one of the major high throughput analysis methods that endeavors holistic measurement of metabolic profiles of biological systems. Data analysis approaches in metabolomics can broadly be divided into qualitative – analysis of spectral data and quantitative – analysis of individual metabolite concentrations. In this work, the author will demonstrate the benefits and limitations of different unsupervised analysis tools currently utilized in qualitative and quantitative metabolomics data analysis. Following a detailed literature review outlining different applications of unsupervised methods in metabolomics, the author shows examples of an application of the major previously utilized unsupervised analysis methods. The testing of these methods was performed using qualitative as well as corresponding quantitative metabolite data derived to represent a large set of 2,000 objects. Spectra of mixtures were obtained from different combinations of experimental NMR measurements of 13 prevalent metabolites at five different groups of concentrations representing different phenotypes. The analysis shows advantages and disadvantages of standard tools when applied specifically to metabolomics., Series: Advances in Bioinformatics and Biomedical Engineering

Background One-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. Results We 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 MetaboHunter's metabolite identification methods. Conclusions MetaboHunter 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. Availability http://www.nrcbioinformatics.ca/metabohunter/

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.

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

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.