Taurine: a potential marker of apoptosis in gliomas

New cancer therapies are being developed that trigger tumour apoptosis and an in vivo method of apoptotic detection and early treatment response would be of great value. Magnetic resonance spectroscopy (MRS) can determine the tumour biochemical profile in vivo, and we have investigated whether a specific spectroscopic signature exists for apoptosis in human astrocytomas. High-resolution magic angle spinning (HRMAS) 1H MRS provided detailed 1H spectra of brain tumour biopsies for direct correlation with histopathology. Metabolites, mobile lipids and macromolecules were quantified from presaturation HRMAS 1H spectra acquired from 41 biopsies of grades II (n=8), III (n=3) and IV (n=30) astrocytomas. Subsequently, TUNEL and H&E staining provided quantification of apoptosis, cell density and necrosis. Taurine was found to significantly correlate with apoptotic cell density (TUNEL) in both non-necrotic (R=0.727, P=0.003) and necrotic (R=0.626, P=0.0005) biopsies. However, the ca 2.8 p.p.m. polyunsaturated fatty acid peak, observed in other studies as a marker of apoptosis, correlated only in non-necrotic biopsies (R=0.705, P<0.005). We suggest that the taurine 1H MRS signal in astrocytomas may be a robust apoptotic biomarker that is independent of tumour necrotic status

Use of 1H and 31P HRMAS to evaluate the relationship between quantitative alterations in metabolite concentrations and tissue features in human brain tumour biopsies

[EN] Quantitative multinuclear high-resolution magic angle spinning (HRMAS) was performed in order to determine the tissue pH values of and the absolute metabolite concentrations in 33 samples of human brain tumour tissue. Metabolite concentrations were quantified by 1D 1 H and 31P HRMAS using the electronic reference to in vivo concentrations (ERETIC) synthetic signal. 1 H–1 H homonuclear and 1 H–31P heteronuclear correlation experiments enabled the direct assessment of the 1 H–31P spin systems for signals that suffered from overlapping in the 1D 1 H spectra, and linked the information present in the 1D 1 H and 31P spectra. Afterwards, the main histological features were determined, and high heterogeneity in the tumour content, necrotic content and nonaffected tissue content was observed. The metabolite profiles obtained by HRMAS showed characteristics typical of tumour tissues: rather low levels of energetic molecules and increased concentrations of protective metabolites. Nevertheless, these characteristics were more strongly correlated with the total amount of living tissue than with the tumour cell contents of the samples alone, which could indicate that the sampling conditions make a significant contribution aside from the effect of tumour development in vivo. The use of methylene diphosphonic acid as a chemical shift and concentration reference for the 31P HRMAS spectra of tissues presented important drawbacks due to its interaction with the tissue. Moreover, the pH data obtained from 31P HRMAS enabled us to establish a correlation between the pH and the distance between the N(CH3)3 signals of phosphocholine and choline in 1 H spectra of the tissue in these tumour samples.The authors acknowledge the SCSIE-University of Valencia Microscopy Service for the histological preparations. They also acknowledge Martial Piotto (Bruker BioSpin, France) for providing the ERETIC synthetic signal. Furthermore, they acknowledge financial support from the Spanish Government project SAF2007-6547, the Generalitat Valenciana project GVACOMP2009-303, and the E.U.'s VI Framework Programme via the project "Web accessible MR decision support system for brain tumor diagnosis and prognosis, incorporating in vivo and ex vivo genomic and metabolomic data" (FP6-2002-LSH 503094). CIBER-BBN is an initiative funded by the VI National R&D&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions, and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund.Esteve Moya, V.; Celda, B.; Martínez Bisbal, MC. (2012). Use of 1H and 31P HRMAS to evaluate the relationship between quantitative alterations in metabolite concentrations and tissue features in human brain tumour biopsies. 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Metabolic assessment of a novel chronic myelogenous leukemic cell line and an imatinib resistant subline by 1H NMR spectroscopy

The goal of this study was to examine metabolic differences between a novel chronic myelogenous leukemic (CML) cell line, MyL, and a sub-clone, MyL-R, which displays enhanced resistance to the targeted Bcr-Abl tyrosine kinase inhibitor imatinib. 1H nuclear magnetic resonance (NMR) spectroscopy was carried out on cell extracts and conditioned media from each cell type. Both principal component analysis (PCA) and specific metabolite identification and quantification were used to examine metabolic differences between the cell types. MyL cells showed enhanced glucose removal from the media compared to MyL-R cells with significant differences in production rates of the glycolytic end-products, lactate and alanine. Interestingly, the total intracellular creatine pool (creatine + phosphocreatine) was significantly elevated in MyL-R compared to MyL cells. We further demonstrated that the MyL-R cells converted the creatine to phosphocreatine using non-invasive monitoring of perfused alginate-encapsulated MyL-R and MyL cells by in vivo 31P NMR spectroscopy and subsequent HPLC analysis of extracts. Our data demonstrated a clear difference in the metabolite profiles of drug-resistant and sensitive cells, with the biggest difference being an elevation of creatine metabolites in the imatinib-resistant MyL-R cells

In vivo magnetic resonance spectroscopy: basic methodology and clinical applications

The clinical use of in vivo magnetic resonance spectroscopy (MRS) has been limited for a long time, mainly due to its low sensitivity. However, with the advent of clinical MR systems with higher magnetic field strengths such as 3 Tesla, the development of better coils, and the design of optimized radio-frequency pulses, sensitivity has been considerably improved. Therefore, in vivo MRS has become a technique that is routinely used more and more in the clinic. In this review, the basic methodology of in vivo MRS is described—mainly focused on 1H MRS of the brain—with attention to hardware requirements, patient safety, acquisition methods, data post-processing, and quantification. Furthermore, examples of clinical applications of in vivo brain MRS in two interesting fields are described. First, together with a description of the major resonances present in brain MR spectra, several examples are presented of deviations from the normal spectral pattern associated with inborn errors of metabolism. Second, through examples of MR spectra of brain tumors, it is shown that MRS can play an important role in oncology

1H nuclear magnetic resonance spectroscopy characterisation of metabolic phenotypes in the medulloblastoma of the SMO transgenic mice

BACKGROUND: Human medulloblastomas exhibit diverse molecular pathology. Aberrant hedgehog signalling is found in 20-30% of human medulloblastomas with largely unknown metabolic consequences. METHODS: Transgenic mice over-expressing smoothened (SMO) receptor in granule cell precursors with high incidence of exophytic medulloblastomas were sequentially followed up by magnetic resonance imaging (MRI) and characterised for metabolite phenotypes by ¹H MR spectroscopy (MRS) in vivo and ex vivo using high-resolution magic angle spinning (HR-MAS) ¹H MRS. RESULTS: Medulloblastomas in the SMO mice presented as T₂ hyperintense tumours in MRI. These tumours showed low concentrations of N-acetyl aspartate and high concentrations of choline-containing metabolites (CCMs), glycine, and taurine relative to the cerebellar parenchyma in the wild-type (WT) C57BL/6 mice. In contrast, ¹H MRS metabolite concentrations in normal appearing cerebellum of the SMO mice were not different from those in the WT mice. Macromolecule and lipid ¹H MRS signals in SMO medulloblastomas were not different from those detected in the cerebellum of WT mice. The HR-MAS analysis of SMO medulloblastomas confirmed the in vivo ¹H MRS metabolite profiles, and additionally revealed that phosphocholine was strongly elevated in medulloblastomas accounting for the high in vivo CCM. CONCLUSIONS: These metabolite profiles closely mirror those reported from human medulloblastomas confirming that SMO mice provide a realistic model for investigating metabolic aspects of this disease. Taurine, glycine, and CCM are potential metabolite biomarkers for the SMO medulloblastomas. The MRS data from the medulloblastomas with defined molecular pathology is discussed in the light of metabolite profiles reported from human tumours

Additive QTLs on three chromosomes control flowering time in woodland strawberry (Fragaria vesca L.)

Flowering time is an important trait that affects survival, reproduction and yield in both wild and cultivated plants. Therefore, many studies have focused on the identification of flowering time quantitative trait locus (QTLs) in different crops, and molecular control of this trait has been extensively investigated in model species. Here we report the mapping of QTLs for flowering time and vegetative traits in a large woodland strawberry mapping population that was phenotyped both under field conditions and in a greenhouse after flower induction in the field. The greenhouse experiment revealed additive QTLs in three linkage groups (LG), two on both LG4 and LG7, and one on LG6 that explain about half of the flowering time variance in the population. Three of the QTLs were newly identified in this study, and one co-localized with the previously characterized FvTFL1 gene. An additional strong QTL corresponding to previously mapped PFRU was detected in both field and greenhouse experiments indicating that gene(s) in this locus can control the timing of flowering in different environments in addition to the duration of flowering and axillary bud differentiation to runners and branch crowns. Several putative flowering time genes were identified in these QTL regions that await functional validation. Our results indicate that a few major QTLs may control flowering time and axillary bud differentiation in strawberries. We suggest that the identification of causal genes in the diploid strawberry may enable fine tuning of flowering time and vegetative growth in the closely related octoploid cultivated strawberry.Peer reviewe

Classification of single voxel 1H spectra of brain tumours using LCModel

[EN] This study presents a novel method for the direct classification of H-1 single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS. Copyright (C) 2011 John Wiley & Sons, Ltd.FR was supported by grant C7809/A10342 as part of the Cancer Research-UK and Engineering and Physical Sciences Research Council Cancer Imaging Programme at the Children's Cancer and Leukaemia Group (CCLG), in association with the Medical Research Council and Department of Health (England). EF-G acknowledges funding by the Health Institute Carlos III through the RETICS Combiomed.Raschke, F.; Fuster García, E.; Opstad, KS.; Howe, F. (2012). Classification of single voxel 1H spectra of brain tumours using LCModel. NMR in Biomedicine. 25(2):322-331. doi:10.1002/nbm.1753S322331252García-Gómez, J. M., Luts, J., Julià-Sapé, M., Krooshof, P., Tortajada, S., Robledo, J. V., … Robles, M. (2008). Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magnetic Resonance Materials in Physics, Biology and Medicine, 22(1), 5-18. doi:10.1007/s10334-008-0146-yDevos, A., Lukas, L., Suykens, J. A. K., Vanhamme, L., Tate, A. R., Howe, F. A., … Van Huffel, S. (2004). Classification of brain tumours using short echo time 1H MR spectra. Journal of Magnetic Resonance, 170(1), 164-175. doi:10.1016/j.jmr.2004.06.010Opstad, K. S., Ladroue, C., Bell, B. A., Griffiths, J. R., & Howe, F. A. (2007). Linear discriminant analysis of brain tumour1H MR spectra: a comparison of classification using whole spectra versus metabolite quantification. 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Improved Lanczos Algorithms for Blackbox MRS Data Quantitation. Journal of Magnetic Resonance, 157(2), 292-297. doi:10.1006/jmre.2002.2593Hao, J., Zou, X., Wilson, M. P., Davies, N. P., Sun, Y., Peet, A. C., & Arvanitis, T. N. (2009). A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour1H magnetic resonance spectra. NMR in Biomedicine, 22(8), 809-818. doi:10.1002/nbm.1393Ladroue, C., Howe, F. A., Griffiths, J. R., & Tate, A. R. (2003). Independent component analysis for automated decomposition of in vivo magnetic resonance spectra. Magnetic Resonance in Medicine, 50(4), 697-703. doi:10.1002/mrm.10595Herminghaus, S., Dierks, T., Pilatus, U., Möller-Hartmann, W., Wittsack, J., Marquardt, G., … Zanella, F. E. (2003). Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data. Journal of Neurosurgery, 98(1), 74-81. doi:10.3171/jns.2003.98.1.0074Laudadio, T., Selén, Y., Vanhamme, L., Stoica, P., Van Hecke, P., & Van Huffel, S. (2004). Subspace-based MRS data quantitation of multiplets using prior knowledge. Journal of Magnetic Resonance, 168(1), 53-65. doi:10.1016/j.jmr.2004.01.015Vanhamme, L., van den Boogaart, A., & Van Huffel, S. (1997). Improved Method for Accurate and Efficient Quantification of MRS Data with Use of Prior Knowledge. Journal of Magnetic Resonance, 129(1), 35-43. doi:10.1006/jmre.1997.1244Vanhamme, L., Van Huffel, S., Van Hecke, P., & van Ormondt, D. (1999). Time-Domain Quantification of Series of Biomedical Magnetic Resonance Spectroscopy Signals. Journal of Magnetic Resonance, 140(1), 120-130. doi:10.1006/jmre.1999.1835Provencher, S. W. (1993). Estimation of metabolite concentrations from localizedin vivo proton NMR spectra. Magnetic Resonance in Medicine, 30(6), 672-679. doi:10.1002/mrm.1910300604Pérez-Ruiz, A., Julià-Sapé, M., Mercadal, G., Olier, I., Majós, C., & Arús, C. (2010). The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses. BMC Bioinformatics, 11(1), 581. doi:10.1186/1471-2105-11-581Tate, A. R., Majós, C., Moreno, A., Howe, F. A., Griffiths, J. R., & Arús, C. (2002). Automated classification of short echo time in in vivo1H brain tumor spectra: A multicenter study. Magnetic Resonance in Medicine, 49(1), 29-36. doi:10.1002/mrm.10315Howe, F. A., Barton, S. J., Cudlip, S. A., Stubbs, M., Saunders, D. E., Murphy, M., … Griffiths, J. R. (2003). Metabolic profiles of human brain tumors using quantitative in vivo1H magnetic resonance spectroscopy. Magnetic Resonance in Medicine, 49(2), 223-232. doi:10.1002/mrm.10367Van der Graaf, M., Julià-Sapé, M., Howe, F. A., Ziegler, A., Majós, C., Moreno-Torres, A., … Heerschap, A. (2008). MRS quality assessment in a multicentre study on MRS-based classification of brain tumours. NMR in Biomedicine, 21(2), 148-158. doi:10.1002/nbm.1172Chen, L., Weng, Z., Goh, L., & Garland, M. (2002). An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization. Journal of Magnetic Resonance, 158(1-2), 164-168. doi:10.1016/s1090-7807(02)00069-1Stoyanova, R., & Brown, T. R. (2001). NMR spectral quantitation by principal component analysis. NMR in Biomedicine, 14(4), 271-277. doi:10.1002/nbm.700Shlens J A tutorial on principal component analysis 2009 http://www.snl.salk.edu/~shlens/pca.pdfKennard, R. W., & Stone, L. A. (1969). Computer Aided Design of Experiments. Technometrics, 11(1), 137. doi:10.2307/1266770Daszykowski, M., Walczak, B., & Massart, D. L. (2002). Representative subset selection. Analytica Chimica Acta, 468(1), 91-103. doi:10.1016/s0003-2670(02)00651-7Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., … Kleihues, P. (2007). The 2007 WHO Classification of Tumours of the Central Nervous System. Acta Neuropathologica, 114(2), 97-109. doi:10.1007/s00401-007-0243-4Fuster-Garcia, E., Navarro, C., Vicente, J., Tortajada, S., García-Gómez, J. M., Sáez, C., … Robles, M. (2011). Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra. Magnetic Resonance Materials in Physics, Biology and Medicine, 24(1), 35-42. doi:10.1007/s10334-010-0241-