Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Proteomics and metabolomics approach in adult and pediatric glioma diagnostics

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.

Complementary Nuclear Magnetic Resonance-Based Metabolomics Approaches for Glioma Biomarker Identification in a Drosophila melanogaster Model

Human malignant gliomas are the most common type of primary brain tumor. Composed of glial cells and their precursors, they are aggressive and highly invasive, leading to a poor prognosis. Due to the difficulty of surgically removing tumors and their resistance to treatments, novel therapeutic approaches are needed to improve patient life expectancy and comfort. Drosophila melanogaster is a compelling genetic model to better understanding human neurological diseases owing to its high conservation in signaling pathways and cellular content of the brain. Here, glioma has been induced in Drosophila by co-activating the epidermal growth factor receptor and the phosphatidyl-inositol-3 kinase signaling pathways. Complementary nuclear magnetic resonance (NMR) techniques were used to obtain metabolic profiles in the third instar larvae brains. Fresh organs were directly studied by ¹H high resolution-magic angle spinning (HR-MAS) NMR, and brain extracts were analyzed by solution-state ¹H-NMR. Statistical analyses revealed differential metabolic signatures, impacted metabolic pathways, and glioma biomarkers. Each method was efficient to determine biomarkers. The highlighted metabolites including glucose, myo-inositol, sarcosine, glycine, alanine, and pyruvate for solution-state NMR and proline, myo-inositol, acetate, and glucose for HR-MAS show very good performances in discriminating samples according to their nature with data mining based on receiver operating characteristic curves. Combining results allows for a more complete view of induced disturbances and opens the possibility of deciphering the biochemical mechanisms of these tumors. The identified biomarkers provide a means to rebalance specific pathways through targeted metabolic therapy and to study the effects of pharmacological treatments using Drosophila as a model organism.

GSH and GABA decreases in IDH1-mutated low-grade gliomas detected by HERMES spectral editing at 3 T in vivo

Isocitrate dehydrogenase 1 (IDH1) mutational status is an important prognostic biomarker in gliomas. γ-aminobutyric acid (GABA) and reduced glutathione (GSH) play an important role in energy production, which is related to tumor progression. Hadamard Encoding and Reconstruction of Mega-Edited Spectroscopy (HERMES) is able to detect GABA and GSH in healthy controls. This study aims to examine GABA and GSH alterations in IDH1-mutated low-grade gliomas using HERMES. We prospectively enrolled 14 suspected low-grade gliomas and 6 healthy control patients in this study, all cases underwent a 3 T MRI scan, including T1-weighted imaging and HERMES acquisition with a volume of interest 3 × 3 × 3 cm3. HERMES detects a "GABA+" signal that includes contributions from macromolecules and homocarnosine. GABA+ and GSH in tumor foci (group 1), contralateral cerebral regions (group 2) and healthy controls (group 3) were quantified using Gannet. The fitting errors and SNR of HERMES for GABA+ and GSH were analyzed; FWHM of the unsuppressed water signal was also recorded. The Wilcoxon signed-rank test was performed to test for differences between contralateral GABA+ and GSH levels, and differences in GABA+, GSH and fitting errors/SNR between the three groups were analyzed using analysis of variance (ANOVA). Eleven IDH1-mutant low-grade gliomas (5 Female and 6 Male, age 33-69) and 6 healthy subjects (2 Female and 4 Male, age 35-60) were finally enrolled this study. The mean water linewidth across all subjects was 9.67 ± 2.28 Hz. The Wilcoxon signed-rank test revealed that GABA+ and GSH were decreased significantly in glioma foci compared with contralateral regions, whereas no differences were seen between the left and right regions in healthy controls. ANOVA showed that GABA+ and GSH levels in tumor were lower than contralaterally and in healthy controls, while no differences were observed between the contralateral healthy tissue and healthy controls. No differences of fitting errors or SNR were found between tumors, contralateral regions or healthy controls. Our results suggest that HERMES is a reliable tool to simultaneously measure GABA and GSH alterations in low-grade gliomas with IDH1 mutations.

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.

Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information

Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (p(tumor) = 2.46 × 10(-8), p(serum) = 1.3 × 10(-5)) and oligodendroglioma grade II from oligodendroglioma grade III (p(tumor) = 0.01, p(serum) = 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma.

1H NMR detects different metabolic profiles in glioblastoma stem-like cells

The metabolic profiles of glioblastoma stem-like cells (GSCs) growing in neurospheres were examined by (1)H NMR spectroscopy. Spectra of two GSC lines, labelled 1 and 83, from tumours close to the subventricular zone of the temporal lobe were studied in detail and compared with those of neural stem/progenitor cells from the adult olfactory bulb (OB-NPCs) and of the T98G glioblastoma cell line. In both GSCs, signals from myoinositol (Myo-I), UDP-hexosamines (UDP-Hex) and glycine indicated an astrocyte/glioma metabolism. For line 1, the presence of signals from N-acetyl aspartate, GABA and creatine pointed to a neuronal fingerprint. These metabolites were almost absent from line 83 spectra, whereas lipid signals, absent from normal neural lineages, were intense in line 83 spectra and remained low in those of line 1, irrespective of apoptotic fate. Spectra of OB-NPC cells displayed strong similarities with those from line 1, with low lipid signals and clearly detectable neuronal signals. In contrast, the spectral profile of line 83 was more similar to that of T98G, displaying high lipids and nearly complete absence of the neuronal markers. A mixed neural-astrocyte metabolic phenotype with a strong neuronal fingerprint was therefore found in line 1, while an astrocytic/glioma-like metabolism prevailed in line 83. We found a signal assigned to the amide proton of N-acetyl galactosamine in GSC lines and in OB-NPC spectra, whereas it was absent from those of T98G cells. This signal may be related to a stem-cell-specific protein glycosylation pattern and is therefore suggested as a marker of cell multipotency. Other GSC lines from patients with different clinical outcomes were then examined. Unsupervised analysis of spectral data from 13 lines yielded two clusters, with six lines resembling spectral features of line 1 and seven resembling those of line 83, suggesting that distinct metabolic phenotypes may be present in GSC lines.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.