The future of NMR metabolomics in cancer therapy: towards personalizing treatment and developing targeted drugs?

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Metabolomic Analysis to Elucidate Mechanisms of Sunitinib Resistance in Renal Cell Carcinoma

Metabolomics analysis possibly identifies new therapeutic targets in treatment resistance by measuring changes in metabolites accompanying cancer progression. We previously conducted a global metabolomics (G-Met) study of renal cell carcinoma (RCC) and identified metabolites that may be involved in sunitinib resistance in RCC. Here, we aimed to elucidate possible mechanisms of sunitinib resistance in RCC through intracellular metabolites. We established sunitinib-resistant and control RCC cell lines from tumor tissues of RCC cell (786-O)-injected mice. We also quantified characteristic metabolites identified in our G-Met study to compare intracellular metabolism between the two cell lines using liquid chromatography-mass spectrometry. The established sunitinib-resistant RCC cell line demonstrated significantly desuppressed protein kinase B (Akt) and mesenchymal-to-epithelial transition (MET) phosphorylation compared with the control RCC cell line under sunitinib exposure. Among identified metabolites, glutamine, glutamic acid, and α-KG (involved in glutamine uptake into the tricarboxylic acid (TCA) cycle for energy metabolism); fructose 6-phosphate, D-sedoheptulose 7-phosphate, and glucose 1-phosphate (involved in increased glycolysis and its intermediate metabolites); and glutathione and myoinositol (antioxidant effects) were significantly increased in the sunitinib-resistant RCC cell line. Particularly, glutamine transporter (SLC1A5) expression was significantly increased in sunitinib-resistant RCC cells compared with control cells. In this study, we demonstrated energy metabolism with glutamine uptake and glycolysis upregulation, as well as antioxidant activity, was also associated with sunitinib resistance in RCC cells.

Novel Antiproliferative Biphenyl Nicotinamide: NMR Metabolomic Study of its Effect on the MCF-7 Cell in Comparison with Cisplatin and Vinblastine

A 1H-NMR-based metabolomic study was performed on MCF-7 cell lines treated with a novel nicotinamide derivative (DT-8) in comparison with two drugs characterized by a well-established mechanism of action, namely the DNA-metalating drug cisplatin (cis-diamminedichloridoplatinum(II), CDDP) and the antimitotic drug vinblastine (vinblastine, VIN). The effects of the three compounds, each one at the concentration corresponding to the IC50 value, were investigated, with respect to the controls (K), by the 1H-NMR of cells lysates and multivariate analysis (MVA) of the spectroscopic data. Relevant differences were found in the metabolic profiles of the different treatments with respect to the controls. A large overlap of the metabolic profiles in DT-8 vs. K and VIN vs. K suggests a similar biological response and mechanism of action, significantly diverse with respect to CDDP. On the other hand, DT8 seems to act by disorganizing the mitotic spindle and ultimately blocking the cell division, through a mechanism implying methionine depletion and/or S-adenosylmethionine (SAM) limitation.

An Integrative In Silico Mathematical Modelling Study of The Anti-Cancer Effect of Clove Extract ( Syzygium aromaticum ) Combined with In Vitro Metabolomics Study Using 1HNMR Spectroscopy

BACKGROUND

Clove oil is known for its medicinal properties. The mechanism of anti-cancer properties of Syzygium aromaticum were investigated by mathematical modelling on the genome scale with metabolomics using 1H Nuclear Magnetic Resonance spectroscopy on Raji cells.

OBJECTIVES

An integrative analysis correlated the metabolites identified by 1HNMR and genes with the detected pathways.

MATERIALS AND METHODS

Raji cells treated with clove oil were collected and sent for 1HNMR spectroscopy and the spectra analyzed by MATLAB and Human Metabolome Database for metabolite identification. Pathway and topology analysis was implemented using the genes and metabolites in the integrative analysis of Metaboanalyst software.

RESULTS

50% inhibitory concentration of clove oil was 50 µg/ml and the model anticipated 74 genes with differentiating metabolites being some amino acids, cholesterol and fucose.

CONCLUSION

The integrative study predicted that the anti cancer mechanism of clove oil involves novel enzymes, as likely drug targets, 24-dehydrocholesterol reductase and 7-dehydrocholesterol reductase in cholesterol biosynthesis, dehydrofolate reductase in one carbon metabolism and serine palmitoyl-transferase long chain in sphingolipid biosynthesis.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Standardization of complex biologically derived spectrochemical datasets

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO₂ levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines

Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of 1 H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in-depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases.

Response of Cisplatin Resistant Skov-3 Cells to [Pt(O,O'-Acac)(γ-Acac)(DMS)] Treatment Revealed by a Metabolomic ¹H-NMR Study

The novel [Pt(O,O′-acac)(γ-acac)(DMS)], Ptac2S, Pt(II) complex has recently gained increasing attention as a potential anticancer agent for its pharmacological activity shown in different tumor cell lines, studied both in vitro and in vivo. The mechanism of action of Ptac2S, operating on non-genomic targets, is known to be very different from that of cis-[PtCl₂(NH₃)₂], cisplatin, targeting nucleic acids. In this work, we evaluated the cytotoxicity of Ptac2S on the cisplatin resistant Epithelial Ovarian Carcinoma (EOC), SKOV-3 cells, by the MTT assay. A ¹H-NMR metabolomic approach coupled with multivariate statistical analysis was used for the first time for Ptac2S to figure out the biological mechanisms of action of the complex. The metabolic variations of intracellular metabolites and the composition of the corresponding extracellular culture media were compared to those of cisplatin (cells were treated at the IC₅₀ doses of both drugs). The reported comparative metabolomic analysis revealed a very different metabolic profile between Ptac2S and cisplatin treated samples, thus confirming the different mechanism of action of Ptac2S also in the Epithelial Ovarian Carcinoma (EOC), SKOV-3 cells line. In particular, higher levels of pyruvate were observed in Ptac2S treated, with respect to cisplatin treated, cells (in both aqueous and culture media). In addition, a very different lipid expression resulted after the exposure to the two drugs (Ptac2S and cisplatin). These results suggest a possible explanation for the Ptac2S ability to circumvent cisplatin resistance in SKOV-3 cells.

Impact of chemotherapy on metabolic reprogramming: Characterization of the metabolic profile of breast cancer MDA-MB-231 cells using 1H HR-MAS NMR spectroscopy

Doxorubicin, cisplatin, and tamoxifen are part of many chemotherapeutic regimens. However, studies investigating the effect of chemotherapy on the metabolism of breast cancer cells are still limited. We used ¹H high-resolution magic angle spinning (HR-MAS) NMR spectroscopy to study the metabolic profile of human breast cancer MDA-MB-231 cells either untreated (control) or treated with tamoxifen, cisplatin, and doxorubicin. ¹H HR-MAS NMR single pulse spectra evidenced signals from all mobile cell compounds, including fatty acids (membranes), water-soluble proteins, and metabolites. NMR spectra showed that phosphocholine (i.e., a biomarker of breast cancer malignant transformation) signals were stronger in control than in treated cells, but significantly decreased upon treatment with tamoxifen/cisplatin. NMR spectra acquired with Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence were interpreted only qualitatively because signal areas were attenuated according to their transverse relaxation times (T₂). The CPMG method was used to identify soluble metabolites such as organic acids, amino acids, choline and derivatives, taurine, guanidine acetate, tyrosine, and phenylalanine. The fatty acid variations observed by single pulse as well as the lactate, acetate, glycine, and phosphocholine variations observed through CPMG ¹H HR-MAS NMR have potential to characterize both responder and non-responder tumors in a molecular level. Additionally, we emphasized that comparable tumors (i.e., with the same origin, in this case breast cancer) may respond totally differently to chemotherapy. Our observations reinforce the theory that alterations in cellular metabolism may contribute to the development of a malignant phenotype and cell resistance.

Applications of Metabolomics in Cancer Studies

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

Comparative study on microsampling techniques in metabolic fingerprinting studies applying gas chromatography–MS analysis

Aim: Sample collection and preparation are important steps in the metabolomics workflow. Any improvement should be aimed toward making them simpler, faster and more reproducible. This paper describes the evaluation of different types of whole blood microsampling techniques applied in a metabolic fingerprinting study of breast cancer patients. Results: A total of 139, 124 and 128 metabolites were identified in protein precipitation, dried matrix on paper discs and Mitra® volumetric absorptive microsampling, respectively in 80% of the sample sets, where the quality control samples had a relative standard deviation of <30%. Ten metabolites in breast cancer samples were detected as being altered significantly (p < 0.05). Conclusion: Our results suggest that whole blood microsampling techniques do not obtain statistically different results in comparison with the metabolomics applied standard reference method of protein precipitation, in terms of the number of detected compounds, the reproducibility and modeling of differences between the groups.

Improved Quantum Chemical NMR Chemical Shift Prediction of Metabolites in Aqueous Solution toward the Validation of Unknowns

A quantum-chemistry based protocol, termed MOSS-DFT, is presented for the prediction of ¹³C and ¹H NMR chemical shifts of a wide range of organic molecules in aqueous solution, including metabolites. Molecular motif-specific linear scaling parameters are reported for five different density functional theory (DFT) methods (B97-2/pcS-1, B97-2/pcS-2, B97-2/pcS-3, B3LYP/pcS-2, and BLYP/pcS-2), which were applied to a large set of 176 metabolite molecules. The chemical shift root-mean-square deviations (RMSD) for the best method, B97-2/pcS-3, are 1.93 and 0.154 ppm for ¹³C and ¹H chemical shifts, respectively. Excellent results have been obtained for chemical shifts of methyl and aromatic ¹³C and ¹H that are not directly bonded to a heteroatom (O, N, S, or P) with RMSD values of 1.15/0.079 and 1.31/0.118 ppm, respectively. This study not only demonstrates how NMR chemical shift in aqueous environment can be improved over the commonly used global linear scaling approach, but also allows for motif-specific error estimates, which are useful for an improved chemical shift-based verification of metabolite candidates of metabolomics samples containing unknown components.

Metabolomic Profiling of the Synergistic Effects of Melittin in Combination with Cisplatin on Ovarian Cancer Cells

Melittin, the main peptide present in bee venom, has been proposed as having potential for anticancer therapy; the addition of melittin to cisplatin, a first line treatment for ovarian cancer, may increase the therapeutic response in cancer treatment via synergy, resulting in improved tolerability, reduced relapse, and decreased drug resistance. Thus, this study was designed to compare the metabolomic effects of melittin in combination with cisplatin in cisplatin-sensitive (A2780) and resistant (A2780CR) ovarian cancer cells. Liquid chromatography (LC) coupled with mass spectrometry (MS) was applied to identify metabolic changes in A2780 (combination treatment 5 μg/mL melittin + 2 μg/mL cisplatin) and A2780CR (combination treatment 2 μg/mL melittin + 10 μg/mL cisplatin) cells. Principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) multivariate data analysis models were produced using SIMCA-P software. All models displayed good separation between experimental groups and high-quality goodness of fit (R²) and goodness of prediction (Q²), respectively. The combination treatment induced significant changes in both cell lines involving reduction in the levels of metabolites in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, purine and pyrimidine metabolism, and the arginine/proline pathway. The combination of melittin with cisplatin that targets these pathways had a synergistic effect. The melittin-cisplatin combination had a stronger effect on the A2780 cell line in comparison with the A2780CR cell line. The metabolic effects of melittin and cisplatin in combination were very different from those of each agent alone.

Effects of Doxorubicin, Cisplatin, and Tamoxifen on the Metabolic Profile of Human Breast Cancer MCF-7 Cells As Determined by 1H High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance

Doxorubicin (Doxo), cisplatin (Cis), and tamoxifen (Tamo) are part of many chemotherapeutic regimens. However, there have been limited studies of the way metabolism in breast cancer is affected by chemotherapy. We studied, through ¹H high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy, the metabolic profile of human breast cancer MCF-7 control (Con) cells as well as MCF-7 cells treated with Tamo, Cis, and Doxo. ¹H HR-MAS NMR single-pulse spectra evidenced signals from the cell compounds, including fatty acids (membranes), water-soluble proteins, and metabolites. The spectra showed that phosphocholine (i.e., biomarker of breast cancer malignant transformation) signals were stronger in Con than in treated cells. Betaine (i.e., the major osmolyte in cells) was observed at similar concentrations in MCF-7 control and treated cells but was absent in nontumor MCF-10A cells. The NMR spectra acquired with the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence were used only in qualitative analyses because the signal areas were attenuated according to their transverse relaxation time (T₂). The CPMG method was used to identify soluble metabolites such as organic acids, amino acids, choline and its derivatives, taurine, and guanidino acetate. ¹H HR-MAS NMR spectroscopy efficiently demonstrated the effects of Tamo, Cis, and Doxo on the metabolic profile of MCF-7 cells. The fatty acid, phosphocholine, and choline variations observed by single-pulse HR-MAS NMR have the potential to characterize both responder and nonresponder tumors at a molecular level.

Urine and Serum Metabolomics Analyses May Distinguish between Stages of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes. Preoperative fasting urine and serum samples were collected from patients with clinical renal masses and assessed with ¹H NMR and GCMS (gas chromatography-mass spectrometry) based metabolomics and multivariate statistical analysis. Alterations in levels of glycolytic and tricarboxylic acid (TCA) cycle intermediates were detected in RCC relative to benign masses. Orthogonal Partial Least Square Discriminant Analysis plots discriminated between benign vs. pT1 (R2 = 0.46, Q2 = 0.28; AUC = 0.83), benign vs. pT3 (R2 = 0.58, Q2 = 0.37; AUC = 0.87) for ¹H NMR-analyzed serum and between benign vs. pT1 (R2 = 0.50, Q2 = 0.37; AUC = 0.83), benign vs. pT3 (R2 = 0.72, Q2 = 0.68, AUC = 0.98) for urine samples. Separation was observed between benign vs. pT3 (R2 = 0.63, Q2 = 0.48; AUC = 0.93), pT1 vs. pT3 (R2 = 0.70, Q2 = 0.54) for GCMS-analyzed serum and between benign vs. pT3 (R2Y = 0.87; Q2 = 0.70; AUC = 0.98) for urine samples. This pilot study suggests that urine and serum metabolomics may be useful in differentiating benign renal tumors from RCC and for staging RCC.

Solution state nuclear magnetic resonance spectroscopy for biological metabolism and pathway intermediate analysis

Using nuclear magnetic resonance (NMR) spectroscopy in the study of metabolism has been immensely popular in medical- and health-related research but has yet to be widely applied to more fundamental biological problems. This review provides some NMR background relevant to metabolism, describes why ¹H NMR spectra are complex as well as introducing relevant terminology and definitions. The applications and practical considerations of NMR metabolic profiling and ¹³C NMR-based flux analyses are discussed together with the elegant 'enzyme trap' approach for identifying novel metabolic pathway intermediates. The importance of sample preparation and data analysis are also described and explained with reference to data precision and multivariate analysis to introduce researchers unfamiliar with NMR and metabolism to consider this technique for their research interests. Finally, a brief glance into the future suggests NMR-based metabolism has room to expand in the 21st century through new isotope labels, and NMR technologies and methodologies.

Metabolic Study of Breast MCF-7 Tumor Spheroids after Gamma Irradiation by (1)H NMR Spectroscopy and Microimaging

Multicellular tumor spheroids are an important model system to investigate the response of tumor cells to radio- and chemotherapy. They share more properties with the original tumor than cells cultured as 2D monolayers do, which helps distinguish the intrinsic properties of monolayer cells from those induced during cell aggregation in 3D spheroids. The paper investigates some metabolic aspects of small tumor spheroids of breast cancer and their originating MCF-7 cells, grown as monolayer, by means of high-resolution (HR) (1)H NMR spectroscopy and MR microimaging before and after gamma irradiation. The spectra of spheroids were characterized by higher intensity of mobile lipids, mostly neutral lipids, and glutamine (Gln) signals with respect to their monolayer cells counterpart, mainly owing to the lower oxygen supply in spheroids. Morphological changes of small spheroids after gamma-ray irradiation, such as loss of their regular shape, were observed by MR microimaging. Lipid signal intensity increased after irradiation, as evidenced in both MR localized spectra of the single spheroid and in HR NMR spectra of spheroid suspensions. Furthermore, the intense Gln signal from spectra of irradiated spheroids remained unchanged, while the low Gln signal observed in monolayer cells increased after irradiation. Similar results were observed in cells grown in hypoxic conditions. The different behavior of Gln in 2D monolayers and in 3D spheroids supports the hypothesis that a lower oxygen supply induces both an upregulation of Gln synthetase and a downregulation of glutaminases with the consequent increase in Gln content, as already observed under hypoxic conditions. The data herein indicate that (1)H NMR spectroscopy can be a useful tool for monitoring cell response to different constraints. The use of spheroid suspensions seems to be a feasible alternative to localized spectroscopy since similar effects were found after radiation treatment.

Integrative functional genomic analysis unveils the differing dysregulated metabolic processes across hepatocellular carcinoma stages

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease and the development of targeted therapeutics is still at an early stage. The 'omics' based genome-wide profiling comprising the transcriptome, miRNome and proteome are highly useful in identifying the deregulated molecular processes involved in hepatocarcinogenesis. One of the end products and processes of the central dogma being the metabolites and metabolic processes mediate the cellular functions. In recent years, metabolomics based investigations have revealed the major deregulated metabolic processes involved in carcinogenesis. However, the integrative analysis of the holistic metabolic processes with genomics is at an early stage. Since the gene-sets are highly useful in assessing the biological processes and pathways, we made an attempt to infer the deregulated cellular metabolic processes involved in HCC by employing metabolism associated gene-set enrichment analysis. Further, the metabolic process enrichment scores were integrated with the transcriptome profiles of HCC. Integrative analysis shows three distinct metabolic deregulations: i) hepatocyte function related molecular processes involving lipid/fatty acid/bile acid synthesis, ii) inflammatory processes with cytokine, sphingolipid & chondriotin sulphate metabolism and iii) enriched nucleotide metabolic process involving purine/pyrimidine & glucose mediated catabolic process, in hepatocarcinogenesis. The three distinct metabolic processes were found to occur both in tumor and liver cancer cell line profiles. Unsupervised hierarchical clustering of the metabolic processes along with clinical sample information has identified two major clusters based on AFP (alpha-fetoprotein) and metastasis. The study reveals the three major regulatory processes involved in HCC stages.

Emerging new strategies for successful metabolite identification in metabolomics

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.

Noninvasive and repetitive measurement of cellular metabolite from human osteosarcoma cells (MG-63) using 3.0 tesla proton (1 H) MR spectroscopy

PURPOSE

This study suggests a noninvasive and repetitive measurement method using ¹ H magnetic resonance spectroscopy to monitor changes in cellular metabolites within a single sample.

METHODS

Longitudinal acquisition of cellular metabolites from three-dimensional cultured human osteosarcoma (MG-63) cells was conducted using 3.0 Tesla ¹ H MRS for 2 weeks at three time points: days 1, 7, and 14. During the MR spectroscopy (MRS) scan, cell specimen temperatures were kept constant at 37°C by a lab-developed magnetic resonance compatible thermostatic device. A DNA assay and live/dead staining of the cell specimens were carried out at each time point to verify the MRS measurements.

RESULTS

Cell viability in the proposed device did not significantly differ from that of cells in a conventional incubator (P = 0.946). Cell proliferation and choline concentration increased during the first week, but remained constant during the second week. Lactate did not change during the first week, but increased during the second week. Likewise, cell viability remained constant until day 7, then decreased.

CONCLUSION

The proposed MRS technique results in a survivable environment for longitudinal studies of cells and provides a new way to measure metabolomic changes over time in single specimens of cells. Magn Reson Med 76:1912-1918, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Nanoparticle-Assisted Removal of Protein in Human Serum for Metabolomics Studies

Among human body fluids, serum plays a key role for diagnostic tests and, increasingly, for metabolomics analysis. However, the high protein content of serum poses significant challenges for nuclear magnetic resonance (NMR)-based metabolomics studies because it can strongly interfere with metabolite signal detection and quantitation. Although several methods for protein removal have been proposed, including ultrafiltration and organic-solvent-induced protein precipitation, there is currently no standard operating procedure for the elimination of protein from human serum samples. Here, we introduce novel procedures for the removal of protein from serum by the addition of nanoparticles. It is demonstrated how serum protein can be efficiently, cost-effectively, and environmentally friendly removed at physiological pH (pH 7.4) through attractive interactions with silica nanoparticles. It is further shown how serum can be processed with nanoparticles prior to ultrafiltration or organic-solvent-induced protein precipitation for optimal protein removal. After examination of all of the procedures, the combination of nanoparticle treatment and ultrafiltration is found to have a minimal effect on the metabolite content, leading to remarkably clean homo- and heteronuclear NMR spectra of the serum metabolome that compare favorably to other methods for protein removal.

The Breast Cancer to Bone (B2B) Metastases Research Program: a multi-disciplinary investigation of bone metastases from breast cancer

Background

Bone is the most common site of breast cancer distant metastasis, affecting 50–70 % of patients who develop metastatic disease. Despite decades of informative research, the effective prevention, prediction and treatment of these lesions remains elusive. The Breast Cancer to Bone (B2B) Metastases Research Program consists of a prospective cohort of incident breast cancer patients and four sub-projects that are investigating priority areas in breast cancer bone metastases. These include the impact of lifestyle factors and inflammation on risk of bone metastases, the gene expression features of the primary tumour, the potential role for metabolomics in early detection of bone metastatic disease and the signalling pathways that drive the metastatic lesions in the bone.

Methods/Design

The B2B Research Program is enrolling a prospective cohort of 600 newly diagnosed, incident, stage I-IIIc breast cancer survivors in Alberta, Canada over a five year period. At baseline, pre-treatment/surgery blood samples are collected and detailed epidemiologic data is collected by in-person interview and self-administered questionnaires. Additional self-administered questionnaires and blood samples are completed at specified follow-up intervals (24, 48 and 72 months). Vital status is obtained prior to each follow-up through record linkages with the Alberta Cancer Registry. Recurrences are identified through medical chart abstractions. Each of the four projects applies specific methods and analyses to assess the impact of serum vitamin D and cytokine concentrations, tumour transcript and protein expression, serum metabolomic profiles and in vitro cell signalling on breast cancer bone metastases.

Discussion

The B2B Research Program will address key issues in breast cancer bone metastases including the association between lifestyle factors (particularly a comprehensive assessment of vitamin D status) inflammation and bone metastases, the significance or primary tumour gene expression in tissue tropism, the potential of metabolomic profiles for risk assessment and early detection and the signalling pathways controlling the metastatic tumour microenvironment. There is substantial synergy between the four projects and it is hoped that this integrated program of research will advance our understanding of key aspects of bone metastases from breast cancer to improve the prevention, prediction, detection, and treatment of these lesions.