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

Extracellular vesicles in glioblastoma: Biomarkers and therapeutic tools

Glioblastoma (GBM) is the most aggressive tumor among the gliomas and intracranial tumors and to date prognosis for GBM patients remains poor, with a median survival typically measured in months to a few years depending on various factors. Although standardized therapies are routinely employed, it is clear that these strategies are unable to cope with heterogeneity and invasiveness of GBM. Furthermore, diagnosis and monitoring of responses to therapies are directly dependent on tissue biopsies or magnetic resonance imaging (MRI) techniques. From this point of view, liquid biopsies are arising as key sources of a variety of biomarkers with the advantage of being easily accessible and monitorable. In this context, extracellular vesicles (EVs), physiologically shed into body fluids by virtually all cells, are gaining increasing interest both as natural carriers of biomarkers and as specific signatures even for GBM. What makes these vesicles particularly attractive is they are also emerging as therapeutical vehicles to treat GBM given their native ability to cross the blood-brain barrier (BBB). Here, we reviewed recent advances on the use of EVs as biomarker for liquid biopsy and nanocarriers for targeted delivery of anticancer drugs in glioblastoma.

Metabolic remodeling in astrocytes: Paving the path to brain tumor development

The brain is a highly metabolic organ, composed of multiple cell classes, that controls crucial functions of the body. Although neurons have traditionally been the main protagonist, astrocytes have gained significant attention over the last decade. In this regard, astrocytes are a type of glial cells that have recently emerged as critical regulators of central nervous system (CNS) function and play a significant role in maintaining brain energy metabolism. However, in certain scenarios, astrocyte behavior can go awry, which poses a significant threat to brain integrity and function. This is definitively the case for mutations that turn normal astrocytes and astrocytic precursors into gliomas, an aggressive type of brain tumor. In addition, healthy astrocytes can interact with tumor cells, becoming part of the tumor microenvironment and influencing disease progression. In this review, we discuss the recent evidence suggesting that disturbed metabolism in astrocytes can contribute to the development and progression of fatal human diseases such as cancer. Emphasis is placed on detailing the molecular bases and metabolic pathways of this disease and highlighting unique metabolic vulnerabilities that can potentially be exploited to develop successful therapeutic opportunities.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.

Advancements and Technical Considerations for Extracellular Vesicle Isolation and Biomarker Identification in Glioblastoma

Extracellular vesicles (EVs) are membrane-bound particles released by all cells. Previous research has found that these microscopic vesicles contribute to intercellular signaling and communication. EVs carry a variety of cargo, including nucleic acids, proteins, metabolites, and lipids. The composition of EVs varies based on cell of origin. Therefore, EVs can serve as an important biomarker in the diagnosis and treatment of various cancers. EVs derived from glioblastoma (GBM) cells carry biomarkers, which could serve as the basis for a potential diagnostic strategy known as liquid biopsy. Multiple EV isolation techniques exist, including ultrafiltration, size exclusion chromatography, flow field-flow fractionation, sequential filtration, differential ultracentrifugation, and density-gradient ultracentrifugation. Recent and ongoing work aims to identify cellular markers to distinguish GBM-derived EVs from those released by noncancerous cells. Strategies include proteomic analysis of GBM EVs, identification of GBM-specific metabolites, and use of Food and Drug Administration-approved 5-aminolevulinic acid-an oral agent that causes fluorescence of GBM cells-to recognize GBM EVs in a patient's blood. In addition, accurately and precisely monitoring changes in EV cargo concentrations could help differentiate between pseudoprogression and GBM recurrence, thus preventing unnecessary surgical interventions.

Tissue and plasma free amino acid detection by LC-MS/MS method in high grade glioma patients

PURPOSE

The changes in serum amino acid profiles are evaluated in different types of cancers and screening tests were developed for estimating the risk of cancer by rapid analysis of plasma free amino acid (PFAA) levels. There is scarce evidence about the metabolomics analysis of PFAA in malignant gliomas. The aim of the present study was to identify the most promising diagnostic amino acid biomarkers that could be objectively measured for high-grade glioma and to compare their level with the tissue counterpart.

METHODS

In this prospective study, we collected serum samples from 22 patients with the pathological diagnosis of high-grade diffuse glioma according to WHO 2016 classification and 22 healthy subjects, and brain tissue from 22 controls. Plasma and tissue amino acid concentrations were analyzed applying liquid chromatography-tandem mass spectrometry (LC-MS/MS) method.

RESULTS

Serum alanine, alpha-aminobutyric acid (AABA), lysine (Lys) and cysteine concentrations were significantly higher in high-grade glioma patients despite low levels of alanine and Lys in the tumor tissue. Aspartic acid, histidine and taurine were significantly decreased in both serum and tumors of glioma patients. A positive correlation was detected between tumor volumes and serum levels of latter three amino acids.

CONCLUSION

This study demonstrated potential amino acids which may have diagnostic value for high-grade glioma patients by utilizing LC-MS/MS method. Our results are preliminary to compare serum and tissue levels of amino acids in patients with malignant gliomas. The data presented here may provide feature ideas about the metabolic pathways in the pathogenesis of gliomas.

Regulative Roles of Metabolic Plasticity Caused by Mitochondrial Oxidative Phosphorylation and Glycolysis on the Initiation and Progression of Tumorigenesis

One important feature of tumour development is the regulatory role of metabolic plasticity in maintaining the balance of mitochondrial oxidative phosphorylation and glycolysis in cancer cells. In recent years, the transition and/or function of metabolic phenotypes between mitochondrial oxidative phosphorylation and glycolysis in tumour cells have been extensively studied. In this review, we aimed to elucidate the characteristics of metabolic plasticity (emphasizing their effects, such as immune escape, angiogenesis migration, invasiveness, heterogeneity, adhesion, and phenotypic properties of cancers, among others) on tumour progression, including the initiation and progression phases. Thus, this article provides an overall understanding of the influence of abnormal metabolic remodeling on malignant proliferation and pathophysiological changes in carcinoma.

Bioenergetic Profiling in Glioblastoma Multiforme Patients with Different Clinical Outcomes

The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.

Metabolites as extracellular vesicle cargo in health, cancer, pleural effusion, and cardiovascular diseases: An emerging field of study to diagnostic and therapeutic purposes

Extracellular vesicles (EVs) are highly diverse nanoscale membrane-bound structures released from different cell types into the extracellular environment. They play essential functions in cell signaling by transporting their cargo, such as proteins, RNA, DNA, lipids, metabolites, and small molecules, to recipient cells. It has recently been shown that EVs might modulate carcinogenesis by delivering cargo to recipient cells. Furthermore, recent discoveries revealed that changes in plasma-derived EV levels and cargo in subjects with metabolic diseases were documented by many researchers, suggesting that EVs might be a promising source of disease biomarkers. One of the cargos of EVs that has recently attracted the most attention is metabolites. The metabolome of these vesicles introduces a plethora of disease indicators; hence, examining the metabolomics of EVs detected in human biofluids would be an effective approach. On the other hand, metabolites have various roles in biological systems, including the production of energies, synthesizing macromolecules, and serving as signaling molecules and hormones. Metabolome rewiring in cancer and stromal cells is a characteristic of malignancy, but the current understanding of how this affects the metabolite composition and activity of tumor-derived EVs remains in its infancy. Since new findings and studies in the field of exosome biology and metabolism are constantly being published, it is likely that diagnostic and treatment techniques, including the use of exosome metabolites, will be launched in the coming years. Recent years have seen increased interest in the EV metabolome as a possible source for biomarker development. However, our understanding of the role of these molecules in health and disease is still immature. In this work, we have provided the latest findings regarding the role of metabolites as EV cargoes in the pathophysiology of diseases, including cancer, pleural effusion (PE), and cardiovascular disease (CVD). We also discussed the significance of metabolites as EV cargoes of microbiota and their role in host-microbe interaction. In addition, the latest findings on metabolites in the form of EV cargoes as biomarkers for disease diagnosis and treatment are presented in this study.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Comprehensive metabolomics study on the pathogenesis of anaplastic astrocytoma via UPLC-Q/TOF-MS

Anaplastic astrocytoma (AA) is a malignant carcinoma whose pathogenesis remains to be fully elucidated. System biology techniques have been widely used to clarify the mechanism of diseases from a systematic perspective. The present study aimed to explore the pathogenesis and novel potential biomarkers for the diagnosis of AA according to metabolic differences. Patients with AA (n = 12) and healthy controls (n = 15) were recruited. Serum was assayed with untargeted ultraperformance liquid chromatography-quadrupole/time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) metabolomic techniques. The data were further evaluated using multivariate analysis and bioinformatic methods based on the KEGG database to determine the distinct metabolites and perturbed pathways. Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) identified the significance of the distinct metabolic pattern between patients with AA and healthy controls (P < .001) in both ESI modes. Permutation testing confirmed the validity of the OPLS-DA model (permutation = 200, Q2 < 0.5). In total, 24 differentiated metabolites and 5 metabolic pathways, including sphingolipid, glycerophospholipid, caffeine, linoleic acid, and porphyrin metabolism, were identified based on the OPLS-DA model. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide were recognized as potential biomarkers with excellent sensitivity and specificity (area under the curve > 98%). These findings indicate that the perturbed metabolic pattern related to immune regulation and cellular signal transduction is associated with the pathogenesis of AA. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide could be used as biomarkers of AA in future clinical practice. This study provides a therapeutic basis for further studies on the mechanism and precise clinical diagnosis of AA.

NMR-Based Metabolomics Analysis Predicts Response to Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most fatal type of breast cancer (BC). Due to the lack of relevant targeted drug therapy, in addition to surgery, chemotherapy is still the most common treatment option for TNBC. TNBC is heterogeneous, and different patients have an unusual sensitivity to chemotherapy. Only part of the patients will benefit from chemotherapy, so neoadjuvant chemotherapy (NAC) is controversial in the treatment of TNBC. Here, we performed an NMR spectroscopy–based metabolomics study to analyze the relationship between the patients’ metabolic phenotypes and chemotherapy sensitivity in the serum samples. Metabolic phenotypes from patients with pathological partial response, pathological complete response, and pathological stable disease (pPR, pCR, and pSD) could be distinguished. Furthermore, we conducted metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with three groups of TNBC patients. We evaluated the discriminative ability of metabolites related to significantly disturbed metabolic pathways by using the multi-receiver–operating characteristic (ROC) curve analysis. Three significantly disturbed metabolic pathways of glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism could be used as potential predictive models to distinguish three types of TNBC patients. These results indicate that a metabolic phenotype could be used to predict whether a patient is suitable for NAC. Metabolomics research could provide data in support of metabolic phenotypes for personalized treatment of TNBC.

Metabolomics and computational analysis of the role of monoamine oxidase activity in delirium and SARS-COV-2 infection

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 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.

Glioblastoma Metabolomics-In Vitro Studies

In 2016, the WHO introduced new guidelines for the diagnosis of brain gliomas based on new genomic markers. The addition of these new markers to the pre-existing diagnostic methods provided a new level of precision for the diagnosis of glioma and the prediction of treatment effectiveness. Yet, despite this new classification tool, glioblastoma (GBM), a grade IV glioma, continues to have one of the highest mortality rates among central nervous system tumors. Metabolomics is a particularly promising tool for the analysis of GBM tumors and potential methods of treating them, as it is the only "omics" approach that is capable of providing a metabolic signature of a tumor's phenotype. With careful experimental design, cell cultures can be a useful matrix in GBM metabolomics, as they ensure stable conditions and, under proper conditions, are capable of capturing different tumor phenotypes. This paper reviews in vitro metabolomic profiling studies of high-grade gliomas, with a particular focus on sample-preparation techniques, crucial metabolites identified, cell culture conditions, in vitro-in vivo extrapolation, and pharmacometabolomics. Ultimately, this review aims to elucidate potential future directions for in vitro GBM metabolomics.

NMR analysis of the correlation of metabolic changes in blood and cerebrospinal fluid in Alzheimer model male and female mice

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.

Plasma amino acids indicate glioblastoma with ATRX loss

Glioblastoma (GB) is the most common primary brain tumour in adults. The lack of molecular biomarker, non-specific symptoms and fast growth rate often result in a significant delay in diagnosis. Despite multimodal treatment, the prognosis remains poor. Here, we verified the hypothesis that amino acids (AA) regulating the critical metabolic pathways necessary for maintenance, growth, reproduction, and immunity of an organism, may constitute a favourable target in GB biomarker research. We measured the plasma amino acids levels in 18 GB patients and 15 controls and performed the quantitative and qualitative metabolomic analysis of free AA applying high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). We present both the raw data and the results of our statistical analysis. The majority of AA were lowered in the study group in comparison to the control group. Five of these (arginine, glutamic acid, glutamine, glycine, and histidine) differed significantly (all p < 10^-5 and AUC > 0.9). Plasma levels of leucine and phenylalanine decreased in the case of GB with lost alpha-thalassemia/mental retardation X-linked (ATRX) expression on immunohistochemistry (p = 0.003 and 0.045, respectively). We demonstrated for the first time that certain plasma-free AA levels of GB patients were significantly different from those in healthy volunteers. Target profiling of plasma-free AA, identified utilizing LC-QTOF-MS, may present prognostic value by indicating GB patients with lost ATRX expression. The on-going quest for glioma biomarkers still aims to determine the detailed metabolic profile and evaluate its impact on therapy and prognosis.

Role of Glutathione in Cancer: From Mechanisms to Therapies

Glutathione (GSH) is the most abundant non-protein thiol present at millimolar concentrations in mammalian tissues. As an important intracellular antioxidant, it acts as a regulator of cellular redox state protecting cells from damage caused by lipid peroxides, reactive oxygen and nitrogen species, and xenobiotics. Recent studies have highlighted the importance of GSH in key signal transduction reactions as a controller of cell differentiation, proliferation, apoptosis, ferroptosis and immune function. Molecular changes in the GSH antioxidant system and disturbances in GSH homeostasis have been implicated in tumor initiation, progression, and treatment response. Hence, GSH has both protective and pathogenic roles. Although in healthy cells it is crucial for the removal and detoxification of carcinogens, elevated GSH levels in tumor cells are associated with tumor progression and increased resistance to chemotherapeutic drugs. Recently, several novel therapies have been developed to target the GSH antioxidant system in tumors as a means for increased response and decreased drug resistance. In this comprehensive review we explore mechanisms of GSH functionalities and different therapeutic approaches that either target GSH directly, indirectly or use GSH-based prodrugs. Consideration is also given to the computational methods used to describe GSH related processes for in silico testing of treatment effects.

Evaluating line-broadening factors on a reference spectrum as a bucketing method for NMR based metabolomics

Metabolomics based nuclear magnetic resonance (NMR) is widely used in disease mechanism analysis and drug discovery. One of the most important factors in NMR based metabolomics study is the accuracy of spectra bucketing which plays a critical role in data interpretation. Though various methods have been developed for automatic bucketing, the most popular approach is still the traditional rectangular bucketing method which is mainly due to the requirement of user expertise for the automatic bucketing methods. In this study, we developed a new automatic bucketing method that not only efficiently increases peak bucketing accuracy but also allows the bucketing process to be conveniently visualized and adjusted by the end-users. This method applied the line broadening (lb) factor to the average spectrum for a study set which serves as the reference spectrum, and the peak width of the reference spectrum was then set as the peak bucketing pattern. The approach to pick the bucket boundaries is simple but powerful after the line broadening factor was applied. The line broadening factors from 0 to 2 lb were tested using mouse fecal samples and the 1 lb method showed similar peak patterns and data interpretation results compared with a careful manual bucketing pattern. Besides this, the new method generated bucketing patterns could be easily visualized using the Amix software and revised by general users without excessive data science and NMR instrumentation expertise. In summary, our study showed a powerful and convenient tool in NMR peak auto bucketing with flexible visualization and adjustment ability for metabolomics studies.

Boron neutron capture therapy: Current status and future perspectives

The development of new accelerators has given a new impetus to the development of new drugs and treatment technologies using boron neutron capture therapy (BNCT). We analyzed the current status and future directions of BNCT for cancer treatment, as well as the main issues related to its introduction. This review highlights the principles of BNCT and the key milestones in its development: new boron delivery drugs and different types of charged particle accelerators are described; several important aspects of BNCT implementation are discussed. BCNT could be used alone or in combination with chemotherapy and radiotherapy, and it is evaluated in light of the outlined issues. For the speedy implementation of BCNT in medical practice, it is necessary to develop more selective boron delivery agents and to generate an epithermal neutron beam with definite characteristics. Pharmacological companies and research laboratories should have access to accelerators for large-scale screening of new, more specific boron delivery agents.

A multi-omic analysis of birthweight in newborn cord blood reveals new underlying mechanisms related to cholesterol metabolism

BACKGROUND

Birthweight reflects in utero exposures and later health evolution. Despite existing studies employing high-dimensional molecular measurements, the understanding of underlying mechanisms of birthweight remains limited.

METHODS

To investigate the systems biology of birthweight, we cross-sectionally integrated the methylome, the transcriptome, the metabolome and a set of inflammatory proteins measured in cord blood samples, collected from four birth-cohorts (n = 489). We focused on two sets of 68 metabolites and 903 CpGs previously related to birthweight and investigated the correlation structures existing between these two sets and all other omic features via bipartite Pearson correlations.

RESULTS

This dataset revealed that the set of metabolome and methylome signatures of birthweight have seven signals in common, including three metabolites [PC(34:2), plasmalogen PC(36:4)/PC(O-36:5), and a compound with m/z of 781.0545], two CpGs (on the DHCR24 and SC4MOL gene), and two proteins (periostin and CCL22). CCL22, a macrophage-derived chemokine has not been previously identified in relation to birthweight. Since the results of the omics integration indicated the central role of cholesterol metabolism, we explored the association of cholesterol levels in cord blood with birthweight in the ENVIRONAGE cohort (n = 1097), finding that higher birthweight was associated with increased high-density lipoprotein cholesterol and that high-density lipoprotein cholesterol was lower in small versus large for gestational age newborns.

CONCLUSIONS

Our data suggests that an integration of different omic-layers in addition to single omics studies is a useful approach to generate new hypotheses regarding biological mechanisms. CCL22 and cholesterol metabolism in cord blood play a mechanistic role in birthweight.

DNA CpG methylation in sequential glioblastoma specimens

PURPOSE

Glioblastoma is the most aggressive form of brain tumors. A better understanding of the molecular mechanisms leading to its evolution is essential for the development of treatments more effective than the available modalities. Here, we aim to identify molecular drivers of glioblastoma development and recurrence by analyzing DNA CpG methylation patterns in sequential samples.

METHODS

DNA was isolated from 22 pairs of primary and recurrent formalin-fixed, paraffin-embedded glioblastoma specimens, and subjected to reduced representation bisulfite sequencing. Bioinformatic analyses were conducted to identify differentially methylated sites and pathways, and biostatistics was used to test correlations among clinical and pathological parameters.

RESULTS

Differentially methylated pathways likely involved in primary tumor development included those of neuronal differentiation, myelination, metabolic processes, synapse organization and endothelial cell proliferation, while pathways differentially active during glioblastoma recurrence involved those associated with cell processes and differentiation, immune response, Wnt regulation and catecholamine secretion and transport.

CONCLUSION

DNA CpG methylation analyses in sequential clinical specimens revealed hypomethylation in certain pathways such as neuronal tissue development and angiogenesis likely involved in early tumor development and growth, while suggested altered regulation in catecholamine secretion and transport, Wnt expression and immune response contributing to glioblastoma recurrence. These pathways merit further investigations and may represent novel therapeutic targets.

Investigation of Inter- and Intratumoral Heterogeneity of Glioblastoma Using TOF-SIMS

Glioblastoma (GBM) is one of the most aggressive human cancers with a median survival of less than two years. A distinguishing pathological feature of GBM is a high degree of inter- and intratumoral heterogeneity. Intertumoral heterogeneity of GBM has been extensively investigated on genomic, methylomic, transcriptomic, proteomic and metabolomics levels, however only a few studies describe intratumoral heterogeneity because of the lack of methods allowing to analyze GBM samples with high spatial resolution. Here, we applied TOF-SIMS (Time-of-flight secondary ion mass spectrometry) for the analysis of single cells and clinical samples such as paraffin and frozen tumor sections obtained from 57 patients. We developed a technique that allows us to simultaneously detect the distribution of proteins and metabolites in glioma tissue with 800 nm spatial resolution. Our results demonstrate that according to TOF-SIMS data glioma samples can be subdivided into clinically relevant groups and distinguished from the normal brain tissue. In addition, TOF-SIMS was able to elucidate differences between morphologically distinct regions of GBM within the same tumor. By staining GBM sections with gold-conjugated antibodies against Caveolin-1 we could visualize border between zones of necrotic and cellular tumor and subdivide glioma samples into groups characterized by different survival of the patients. Finally, we demonstrated that GBM contains cells that are characterized by high levels of Caveolin-1 protein and cholesterol. This population may partly represent a glioma stem cells. Collectively, our results show that the technique described here allows to analyze glioma tissues with a spatial resolution beyond reach of most of other omics approaches and the obtained data may be used to predict clinical behavior of the tumor.

Metabolic Variations between Low-Grade and High-Grade Gliomas-Profiling by 1H NMR Spectroscopy

Altered cellular metabolism is one of the crucial hallmarks of glioma that deserves exploration, as the metabolites act as direct indicators of protein function and genetic variations. The current study focused on the metabolomic profiling of patients from whom glioma specimens were obtained for the identification of specific metabolites that could distinguish the low grade and high grade. In the current study, ¹H NMR spectroscopy was carried out and the data were analyzed by partial least-squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). Pathway analysis was done to associate characteristic metabolites with the grades of sample using MetaboAnalyst 4.0 software based on the KEGG metabolic pathways database. Distinctive metabolic profiles among low- and high-grade gliomas with top 15 characteristic metabolites that could discriminate these grades were identified on the basis of their VIP scores from the OPLS-DA model. The major altered metabolic pathways include choline, taurine and hypotaurine, glutamate/glutamine, glutathione, and phenyl alanine/tyrosine, which were found to be consistent with the particular grade of a sample. Our study clearly demonstrated a characteristic metabolic profile of individual grades of glioma, suggesting that an altered metabolism is consistent with the specific grades of glioma appreciation and could lead to the development novel treatment strategies.

Radiation Induced Metabolic Alterations Associate With Tumor Aggressiveness and Poor Outcome in Glioblastoma

Glioblastoma (GBM) is uniformly fatal with a 1-year median survival, despite best available treatment, including radiotherapy (RT). Impacts of prior RT on tumor recurrence are poorly understood but may increase tumor aggressiveness. Metabolic changes have been investigated in radiation-induced brain injury; however, the tumor-promoting effect following prior radiation is lacking. Since RT is vital to GBM management, we quantified tumor-promoting effects of prior RT on patient-derived intracranial GBM xenografts and characterized metabolic alterations associated with the protumorigenic microenvironment. Human xenografts (GBM143) were implanted into nude mice 24 hrs following 20 Gy cranial radiation vs. sham animals. Tumors in pre-radiated mice were more proliferative and more infiltrative, yielding faster mortality (p < 0.0001). Histologic evaluation of tumor associated macrophage/microglia (TAMs) revealed cells with a more fully activated ameboid morphology in pre-radiated animals. Microdialyzates from radiated brain at the margin of tumor infiltration contralateral to the site of implantation were analyzed by unsupervised liquid chromatography-mass spectrometry (LC-MS). In pre-radiated animals, metabolites known to be associated with tumor progression (i.e., modified nucleotides and polyols) were identified. Whole-tissue metabolomic analysis of pre-radiated brain microenvironment for metabolic alterations in a separate cohort of nude mice using ¹H-NMR revealed a significant decrease in levels of antioxidants (glutathione (GSH) and ascorbate (ASC)), NAD⁺, Tricarboxylic acid cycle (TCA) intermediates, and rise in energy carriers (ATP, GTP). GSH and ASC showed highest Variable Importance on Projection prediction (VIPpred) (1.65) in Orthogonal Partial least square Discriminant Analysis (OPLS-DA); Ascorbate catabolism was identified by GC-MS. To assess longevity of radiation effects, we compared survival with implantation occurring 2 months vs. 24 hrs following radiation, finding worse survival in animals implanted at 2 months. These radiation-induced alterations are consistent with a chronic disease-like microenvironment characterized by reduced levels of antioxidants and NAD⁺, and elevated extracellular ATP and GTP serving as chemoattractants, promoting cell motility and vesicular secretion with decreased levels of GSH and ASC exacerbating oxidative stress. Taken together, these data suggest IR induces tumor-permissive changes in the microenvironment with metabolomic alterations that may facilitate tumor aggressiveness with important implications for recurrent glioblastoma. Harnessing these metabolomic insights may provide opportunities to attenuate RT-associated aggressiveness of recurrent GBM.

The effect of polysaccharides from Cibotium barometz on enhancing temozolomide-induced glutathione exhausted in human glioblastoma U87 cells, as revealed by 1H NMR metabolomics analysis

Glioblastoma (GBM) is the most malignant central nervous system tumor, with poor prognosis. Temozolomide (TMZ) has been used as a first-line drug for the treatment of GBM for over a decade, but its treatment benefits are limited by acquired resistance. Polysaccharides from Cibotium barometz (CBPs) are polysaccharides purified from the root of Cibotium barometz (L.) J. Sm., possessing sensitizing activity. The purpose of this study was to investigate the anti-cancer effect of CBP from different processing methods on U87 cells using a ¹H NMR-based metabolic approach, complemented with qRT-PCR and flow cytometry, to identify potential markers and discover the targets to explore the underlying mechanism. Cibotium barometz is usually processed under sand heating in clinical applications. Polysaccharides from both the processed (PCBP) and raw (RCBP) C. barometz were prepared, and the effect on enhancing the sensitivity to TMZ was investigated in vitro. CBP can significantly increase the toxicity of TMZ to the U87 cell line, promote apoptosis, enhance cell cycle changes, and arrest cells in S phase, and RCBP demonstrated better activity. Multivariate statistical analyses, such as principal component analysis (PCA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA), were used to identify metabolic biomarkers, and 12 metabolites in the cell extract samples were clearly identified as altered after RCBP exposure. NMR-based cell metabolomics provided a holistic method for the identification of CBP's apoptosis-enhancing mechanisms and the exploration of its potential applications in preclinical and clinical studies.

Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome

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 "molecular signatures" of their parental cells and are able to transmigrate across the blood-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.

Targeting Glutamine Addiction in Gliomas

The most common malignant brain tumors are those of astrocytic origin, gliomas, with the most aggressive glioblastoma (WHO grade IV) among them. Despite efforts, medicine has not made progress in terms of the prognosis and life expectancy of glioma patients. Behind the malignant phenotype of gliomas lies multiple genetic mutations leading to reprogramming of their metabolism, which gives those highly proliferating cells an advantage over healthy ones. The so-called glutamine addiction is a metabolic adaptation that supplements oxidative glycolysis in order to secure neoplastic cells with nutrients and energy in unfavorable conditions of hypoxia. The present review aims at presenting the research and clinical attempts targeting the different metabolic pathways involved in glutamine metabolism in gliomas. A brief description of the biochemistry of glutamine transport, synthesis, and glutaminolysis, etc. will forego a detailed comparison of the therapeutic strategies undertaken to inhibit glutamine utilization by gliomas.

Assessment of Overall Survival in Glioma Patients as Predicted by Metabolomic Criteria

Objective: We assess the efficacy of the metabolomic profile from glioma biopsies in providing estimates of postsurgical Overall Survival in glioma patients.

Methods: Tumor biopsies from 46 patients bearing gliomas, obtained neurosurgically in the period 1992–1998, were analyzed by high resolution ¹H magnetic resonance spectroscopy (HR- ¹H MRS), following retrospectively individual postsurgical Overall Survival up to 720 weeks.

Results: The Overall Survival profile could be resolved in three groups; Short (shorter than 52 weeks, n = 19), Intermediate (between 53 and 364 weeks, n = 19) or Long (longer than 365 weeks, n = 8), respectively. Classical histopathological analysis assigned WHO grades II–IV to every biopsy but notably, some patients with low grade glioma depicted unexpectedly Short Overall Survival, while some patients with high grade glioma, presented unpredictably Long Overall Survival. To explore the reasons underlying these different responses, we analyzed HR-¹H MRS spectra from acid extracts of the same biopsies, to characterize the metabolite patterns associated to OS predictions. Poor prognosis was found in biopsies with higher contents of alanine, acetate, glutamate, total choline, phosphorylcholine, and glycine, while more favorable prognosis was achieved in biopsies with larger contents of total creatine, glycerol-phosphorylcholine, and myo-inositol. We then implemented a multivariate analysis to identify hierarchically the influence of metabolomic biomarkers on OS predictions, using a Classification Regression Tree (CRT) approach. The CRT based in metabolomic biomarkers grew up to three branches and split into eight nodes, predicting correctly the outcome of 94.7% of the patients in the Short Overall Survival group, 78.9% of the patients in the Intermediate Overall Survival group, and 75% of the patients in the Long Overall Survival group, respectively.

Conclusion: Present results indicate that metabolic profiling by HR-¹H MRS improves the Overall Survival predictions derived exclusively from classical histopathological gradings, thus favoring more precise therapeutic decisions.

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.

Glycerophosphatidylcholine PC(36:1) absence and 3'-phosphoadenylate (pAp) accumulation are hallmarks of the human glioma metabolome

Glioma is the most prevalent malignant brain tumor. A comprehensive analysis of the glioma metabolome is still lacking. This study aims to explore new special metabolites in glioma tissues. A non-targeted human glioma metabolomics was performed by UPLC-Q-TOF/MS. The gene expressions of 18 enzymes associated with 3’-phosphoadenylate (pAp) metabolism was examined by qRT-PCR. Those enzymes cover the primary metabolic pathway of pAp. We identified 15 new metabolites (13 lipids and 2 nucleotides) that were significantly different between the glioma and control tissues. Glycerophosphatidylcholine [PC(36:1)] content was high and pAp content was significantly low in the control brain (p  < 0.01). In glioma tissues, PC(36:1) was not detected and pAp content was significantly increased. The gene expressions of 3′-nucleotidases (Inositol monophosphatase (IMPAD-1) and 3′(2′),5′-bisphosphate nucleotidase 1(BPNT-1)) were dramatically down-regulated. Meanwhile, the gene expression of 8 sulfotransferases (SULT), 2 phosphoadenosine phosphosulfate synthases (PAPSS-1 and PAPSS-2) and L-aminoadipate-semialdehyde dehydrogenase-phosphopante-theinyl transferase (AASDHPPT) were up-regulated. PC(36:1) absence and pAp accumulation are the most noticeable metabolic aberration in glioma. The dramatic down-regulation of IMPAD-1 and BPNT-1 are the primary cause for pAp dramatic accumulation. Our findings suggest that differential metabolites discovered in glioma could be used as potentially novel therapeutic targets or diagnostic biomarkers and that abnormal metabolism of lipids and nucleotides play roles in the pathogenesis of glioma.

Role of Endogenous Metabolite Alterations in Neuropsychiatric Disease

The potential role in neuropsychiatric disease risk arising from alterations and derangements of endogenous small-molecule metabolites remains understudied. Alterations of endogenous metabolite concentrations can arise in multiple ways. Marked derangements of single endogenous small-molecule metabolites are found in a large group of rare genetic human diseases termed "inborn errors of metabolism", many of which are associated with prominent neuropsychiatric symptomology. Whether such metabolites act neuroactively to directly lead to distinct neural dysfunction has been frequently hypothesized but rarely demonstrated unequivocally. Here we discuss this disease concept in the context of our recent findings demonstrating that neural dysfunction arising from accumulation of the schizophrenia-associated metabolite l-proline is due to its structural mimicry of the neurotransmitter GABA that leads to alterations in GABA-ergic short-term synaptic plasticity. For cases in which a similar direct action upon neurotransmitter binding sites is suspected, we lay out a systematic approach that can be extended to assessing the potential disruptive action of such candidate disease metabolites. To address the potentially important and broader role in neuropsychiatric disease, we also consider whether the more subtle yet more ubiquitous variations in endogenous metabolites arising from natural allelic variation may likewise contribute to disease risk but in a more complex and nuanced manner.

900MHz 1H-/13C-NMR analysis of 2-hydroxyglutarate and other brain metabolites in human brain tumor tissue extracts

PURPOSE

To perform in vitro high-resolution 900 MHz magnetic resonance spectroscopy (NMR) analysis of human brain tumor tissue extracts and analyze for the oncometabolite 2-hydroxyglutarate (2HG) and other brain metabolites, not only for 1H but also for 13C with indirect detection by heteronuclear single quantum correlation (HSQC).

MATERIAL AND METHODS

Four surgically removed human brain tumor tissue samples were used for extraction and preparation of NMR samples. These tissue samples were extracted with 4% perchloric acid and chloroform, freeze-dried, then dissolved into 0.28 mL of deuterium oxide (D2O, 99.9 atom % deuterium) containing 0.025 wt % sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4 (TSP). All samples were adjusted to pH range of 6.9-7.1 before finally transferred to 5 mm Shigemi™ NMR microtube. NMR experiments were performed on Bruker DRX 900 MHz spectrometer with 1H/13C/15N Cryo-probe™ with Z-gradient, without further temperature control for the samples. All chemical shift values were presented relative to TSP at 0.00 ppm for both 1H and 13C. 1H 1D, 1H-13C HSQC, 1H-1H correlation spectroscopy (COSY) and 1H-13C heteronuclear multiple bond correlation (HMBC) spectra were acquired and analyzed.

RESULTS

2-hydroxyglutarate, an oncometabolite associated with gliomas with IDH mutations, was successfully detected and assigned by both 1H-13C HSQC and 1H-1H COSY experiments as well as 1H 1D experiments in two of the tissue samples. In particular, to our knowledge this work shows the first example of detecting 900 MHz 13C-NMR spectral lines of 2-hydroxyglutarate in human brain tumor tissue samples. In addition to the oncometabolite 2-hydroxyglutarate, at least 42 more metabolites were identified from our series of NMR experiment.

CONCLUSION

The detection of 2-hydroxyglutarate and other metabolites can be facilitated by homonuclear and heteronuclear two-dimensional 900 MHz NMR spectroscopy even in case of real tumor tissue sample extracts without physical separation of metabolites.

The integrative metabolomic-transcriptomic landscape of glioblastome multiforme

The purpose of this study was to map the landscape of metabolic-transcriptional alterations in glioblastoma multiforme. Omic-datasets were acquired by metabolic profiling (1D-NMR spectroscopy n=33 Patient) and transcriptomic profiling (n=48 Patients). Both datasets were analyzed by integrative network modeling. The computed model concluded in four different metabolic-transcriptomic signatures containing: oligodendrocytic differentiation, cell-cycle functions, immune response and hypoxia. These clusters were found being distinguished by individual metabolism and distinct transcriptional programs. The study highlighted the association between metabolism and hallmarks of oncogenic signaling such as cell-cycle alterations, immune escape mechanism and other cancer pathway alterations. In conclusion, this study showed the strong influence of metabolic alterations in the wide scope of oncogenic transcriptional alterations.

Using NMR in saliva to identify possible biomarkers of glioblastoma and chronic periodontitis

Nowadays there is increasing interest in identifying–and using–metabolites that can be employed as biomarkers for diagnosing, treating and monitoring diseases. Saliva and NMR have been widely used for this purpose as they are fast and inexpensive methods. This case-control study aimed to find biomarkers that could be related to glioblastoma (GBL) and periodontal disease (PD) and studied a possible association between GBL and periodontal status. The participants numbered 130, of whom 10 were diagnosed with GBL and were assigned to the cases group, while the remaining 120 did not present any pathology and were assigned to the control group. On one hand, significantly increased (p < 0.05) metabolites were found in GBL group: leucine, valine, isoleucine, propionate, alanine, acetate, ethanolamine and sucrose. Moreover, a good tendency to separation between the two groups was observed on the scatterplot of the NMR. On the other hand, the distribution of the groups attending to the periodontal status was very similar and we didn´t find any association between GBL and periodontal status (Chi-Square 0.1968, p = 0.91). Subsequently, the sample as a whole (130 individuals) was divided into three groups by periodontal status in order to identify biomarkers for PD. Group 1 was composed of periodontally healthy individuals, group 2 had gingivitis or early periodontitis and group 3 had moderate to advanced periodontitis. On comparing periodontal status, a significant increase (p < 0.05) in certain metabolites was observed. These findings along with previous reports suggest that these could be used as biomarkers of a PD: caproate, isocaproate+butyrate, isovalerate, isopropanol+methanol, 4 aminobutyrate, choline, sucrose, sucrose-glucose-lysine, lactate-proline, lactate and proline. The scatter plot showed a good tendency to wards separation between group 1 and 3.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Substituted Caffeic and Ferulic Acid Phenethyl Esters: Synthesis, Leukotrienes Biosynthesis Inhibition, and Cytotoxic Activity

Glioblastoma multiforme (GBM) is an aggressive brain tumor that correlates with short patient survival and for which therapeutic options are limited. Polyphenolic compounds, including caffeic acid phenethyl ester (CAPE, 1a), have been investigated for their anticancer properties in several types of cancer. To further explore these properties in brain cancer cells, a series of caffeic and ferulic acid esters bearing additional oxygens moieties (OH or OCH₃) were designed and synthesized. (CAPE, 1a), but not ferulic acid phenethyl ester (FAPE, 1b), displayed substantial cytotoxicity against two glioma cell lines. Some but not all selected compounds derived from both (CAPE, 1a) and (FAPE, 1b) also displayed cytotoxicity. All CAPE-derived compounds were able to significantly inhibit 5-lipoxygenase (5-LO), however FAPE-derived compounds were largely ineffective 5-LO inhibitors. Molecular docking revealed new hydrogen bonds and π-π interactions between the enzyme and some of the investigated compounds. Overall, this work highlights the relevance of exploring polyphenolic compounds in cancer models and provides additional leads in the development of novel therapeutic strategies in gliomas.

Metabolic Reprogramming in Glioma

Many cancers have long been thought to primarily metabolize glucose for energy production—a phenomenon known as the Warburg Effect, after the classic studies of Otto Warburg in the early twentieth century. Yet cancer cells also utilize other substrates, such as amino acids and fatty acids, to produce raw materials for cellular maintenance and energetic currency to accomplish cellular tasks. The contribution of these substrates is increasingly appreciated in the context of glioma, the most common form of malignant brain tumor. Multiple catabolic pathways are used for energy production within glioma cells, and are linked in many ways to anabolic pathways supporting cellular function. For example: glycolysis both supports energy production and provides carbon skeletons for the synthesis of nucleic acids; meanwhile fatty acids are used both as energetic substrates and as raw materials for lipid membranes. Furthermore, bio-energetic pathways are connected to pro-oncogenic signaling within glioma cells. For example: AMPK signaling links catabolism with cell cycle progression; mTOR signaling contributes to metabolic flexibility and cancer cell survival; the electron transport chain produces ATP and reactive oxygen species (ROS) which act as signaling molecules; Hypoxia Inducible Factors (HIFs) mediate interactions with cells and vasculature within the tumor environment. Mutations in the tumor suppressor p53, and the tricarboxylic acid cycle enzymes Isocitrate Dehydrogenase 1 and 2 have been implicated in oncogenic signaling as well as establishing metabolic phenotypes in genetically-defined subsets of malignant glioma. These pathways critically contribute to tumor biology. The aim of this review is two-fold. Firstly, we present the current state of knowledge regarding the metabolic strategies employed by malignant glioma cells, including aerobic glycolysis; the pentose phosphate pathway; one-carbon metabolism; the tricarboxylic acid cycle, which is central to amino acid metabolism; oxidative phosphorylation; and fatty acid metabolism, which significantly contributes to energy production in glioma cells. Secondly, we highlight processes (including the Randle Effect, AMPK signaling, mTOR activation, etc.) which are understood to link bio-energetic pathways with oncogenic signals, thereby allowing the glioma cell to achieve a pro-malignant state.

LC-MS-based metabolomics reveals metabolic signatures related to glioma stem-like cell self-renewal and differentiation

A metabolomic study of three glioma cell lines with different stemness were conducted. The specific metabolite signatures associated with SLC self-renewal and differentiation were characterized.

Recent applications of NMR in food and dietary studies

Over the last decade, a wide variety of new foods have been introduced into the global marketplace, many with health benefits that exceed those of traditional foods. Simultaneously, a wide range of analytical technologies has evolved that allow greater capability for the determination of food composition. Nuclear magnetic resonance (NMR), traditionally a research tool used for structural elucidation, is now being used frequently for metabolomics and chemical fingerprinting. Its stability and inherent ease of quantification have been exploited extensively to identify and quantify bioactive components in foods and dietary supplements. In addition, NMR fingerprints have been used to differentiate cultivars, evaluate sensory properties of food and investigate the influence of growing conditions on food crops. Here we review the latest applications of NMR in food analysis. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Molecular and cellular effects of a novel hydroxamate-based HDAC inhibitor - belinostat - in glioblastoma cell lines: a preliminary report

Histone deacetylase (HDAC) inhibitors are now intensively investigated as potential cytostatic agents in many malignancies. Here, we provide novel information concerning the influence of belinostat (Bel), a hydroxamate-based pan-HDAC inhibitor, on glioblastoma LN-229 and LN-18 cells. We found that LN-229 cells stimulated with 2 μmol/L of Bel for 48 h resulted in 70 % apoptosis, while equivalent treatment of LN-18 cells resulted in only 28 % apoptosis. In LN-229 cells this effect was followed by up-regulation of pro-apoptotic genes including Puma, Bim, Chop and p21. In treated LN-18 cells only p21 was markedly overexpressed. Simultaneously, LN-229 cells treated with 2 μmol/L of Bel for 48 h exhibited down-regulation of molecular chaperones GRP78 and GRP94 at the protein level. In contrast, in LN-18 cells Western blot analysis did not show any marked changes in GRP78 nor GRP94 expression. Despite noticeable overexpression of p21, there were no signs of evident G1 nor G2/M cell cycle arrest, however, the reduction in number of the S phase cells was observed in both cell lines. These results collectively suggest that Bel can be considered as potential anti-glioblastoma agent. To our knowledge this is the first report presenting the effects of belinostat treatment in glioblastoma cell lines.

Metabolic Effect of Estrogen Receptor Agonists on Breast Cancer Cells in the Presence or Absence of Carbonic Anhydrase Inhibitors

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 Biomarker Panels of Response to Fusarium Head Blight Infection in Different Wheat Varieties

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.

Effects of culture media on metabolic profiling of the human gastric cancer cell line SGC7901

Cell culture metabolomics has demonstrated significant advantages in cancer research. However, its applications have been impeded by some influencing factors such as culture media, which could significantly affect cellular metabolic profiles and lead to inaccuracy and unreliability of comparative metabolomic analysis of cells. To evaluate the effects of different culture media on cellular metabolic profiling, we performed NMR-based metabolomic analysis of the human gastric cancer cell line SGC7901 cultured in both RPMI1640 and DMEM. We found that SGC7901 cultured in the two media exhibited distinct metabolic profiles with obviously different levels of discrepant metabolites, even though they showed almost the same cellular morphology and proliferation rates. When SGC7901 originally cultured in RPMI1640 was gradually acclimated in DMEM, both the metabolic profiles and most of the discrepant metabolite levels gradually converged toward those of the cells originally cultured in DMEM without significantly altered cell proliferation rates. However, several metabolite levels did not show the converging trends. Our results indicate that the effects of culture media on metabolic profiling must be carefully taken into account for comparative metabolomic analysis of cell lines. This work may be of benefit to the development of cell culture metabolomics.

(1)H NMR metabolomics analysis of renal cell carcinoma cells: Effect of VHL inactivation on metabolism

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.

Glutamine Metabolism in Gliomas

By histological, morphological criteria, and malignancy, brain tumors are classified by WHO into grades I (most benign) to IV (highly malignant), and gliomas are the most frequently occurring class throughout the grades. Similar to peripheral tumors, the growth of glia-derived tumor cells largely depends on glutamine (Gln), which is vividly taken up by the cells, using mostly ASCT2 and SN1 as Gln carriers. Tumor growth-promoting effects of Gln are associated with its phosphate-activated glutaminase (GA) (specifically KGA)-mediated degradation to glutamate (Glu) and/or with its entry to the energy- and intermediate metabolite-generating pathways related to the tricarboxylic acid cycle. However, a subclass of liver-type GA are absent in glioma cells, a circumstance which allows phenotype manipulations upon their transfection to the cells. Gln-derived Glu plays a major role in promoting tumor proliferation and invasion. Glu is relatively inefficiently recycled to Gln and readily leaves the cells by exchange with the extracellular pool of the glutathione (GSH) precursor Cys mediated by xc- transporter. This results in (a) cell invasion-fostering interaction of Glu with ionotropic Glu receptors in the surrounding tissue, (b) intracellular accumulation of GSH which increases tumor resistance to radio- and chemotherapy.

Alterations in cellular metabolome after pharmacological inhibition of Notch in glioblastoma cells

Notch signaling can promote tumorigenesis in the nervous system and plays important roles in stem-like cancer cells. However, little is known about how Notch inhibition might alter tumor metabolism, particularly in lesions arising in the brain. The gamma-secretase inhibitor MRK003 was used to treat glioblastoma neurospheres, and they were subdivided into sensitive and insensitive groups in terms of canonical Notch target response. Global metabolomes were then examined using proton magnetic resonance spectroscopy, and changes in intracellular concentration of various metabolites identified which correlate with Notch inhibition. Reductions in glutamate were verified by oxidation-based colorimetric assays. Interestingly, the alkylating chemotherapeutic agent temozolomide, the mTOR-inhibitor MLN0128, and the WNT inhibitor LGK974 did not reduce glutamate levels, suggesting that changes to this metabolite might reflect specific downstream effects of Notch blockade in gliomas rather than general sequelae of tumor growth inhibition. Global and targeted expression analyses revealed that multiple genes important in glutamate homeostasis, including glutaminase, are dysregulated after Notch inhibition. Treatment with an allosteric inhibitor of glutaminase, compound 968, could slow glioblastoma growth, and Notch inhibition may act at least in part by regulating glutaminase and glutamate.

NMR metabolomics of renal cancer: an overview

This paper reviews the use of NMR metabolomics for the metabolic characterization of renal cancer. The existing challenges in the clinical management of this disease are first presented, followed by a brief introduction to the metabolomics approach, in the context of cancer research. A subsequent review of the literature on NMR metabolic studies of renal cancer reveals that the subject has been clearly underdeveloped, compared with other types of cancer, particularly regarding cultured cells and tissue analysis. NMR analysis of biofluids has focused on blood (plasma or serum) metabolomics, comprising no account of studies on human urine, in spite of its noninvasiveness and physiological proximity to the affected organs. Finally, some areas of potential future development are identified.