Weighted least-squares deconvolution method for discovery of group differences between complex biofluid 1H NMR spectra

Magnetic resonance spectroscopy for the study of cns malignancies

Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.

Quantitative assessment of C-polysaccharide in capsular polysaccharides of Streptococcus pneumoniae by 31PNMR

Capsular polysaccharides of Streptococcus pneumoniae are key components of commercially available anti-pneumococcal vaccines; meanwhile C-polysaccharide is considered an impurity. World Health Organization recommends a strict control over the presence of this biomolecule due to the possibility of introducing an undesired response. An alternative way for assessing this impurity is focused on detect the phosphocholine residues by means of quantitative ¹H-NMR. This could be tricky due to the amounts of this substituent may vary generating two C-polysaccharides forms. In this work we propose an improved quantitative NMR methodology based on ³¹P-NMR for the quantification of C-polysaccharide on capsular polysaccharide preparations. The technique also focuses on phosphocholine but, conversely to above-mentioned methods, allows to discriminate between phosphocholine linked in different positions. The methodology was run on samples of eleven vaccine serotypes, including seven with phosphate groups. From a rational acceptance criterion of 10 wt%, the method allows to quantified from 30 μg of the impurity in 3 mg of total polysaccharide (1 wt%) with a signal/noise ratio of 16:1. Repeatability and intermediate precision evaluation showed a relative standard deviation of 3.33 % and 8.34 % respectively. Additionally, the method provides information about structural identity of phosphate contained in capsular polysaccharides and C-polysaccharide species. This constitutes a new contribution from the NMR that highlights the power of these techniques for assessing imperative parameters in carbohydrate-based vaccines.

MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

BACKGROUND

One-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.

RESULTS

We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of 1H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods.

CONCLUSIONS

MetaboHunter is a freely accessible, easy to use and user friendly 1H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.

Identification and quantification of metabolites in (1)H NMR spectra by Bayesian model selection

MOTIVATION

Nuclear magnetic resonance (NMR) spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, accurate interpretation of the spectra in terms of identities and abundances of metabolites can be challenging, in particular in crowded regions with heavy peak overlap. Although a number of computational approaches for this task have recently been proposed, they are not entirely satisfactory in either accuracy or extent of automation.

RESULTS

We introduce a probabilistic approach Bayesian Quantification (BQuant), for fully automated database-based identification and quantification of metabolites in local regions of (1)H NMR spectra. The approach represents the spectra as mixtures of reference profiles from a database, and infers the identities and the abundances of metabolites by Bayesian model selection. We show using a simulated dataset, a spike-in experiment and a metabolomic investigation of plasma samples that BQuant outperforms the available automated alternatives in accuracy for both identification and quantification.

AVAILABILITY

The R package BQuant is available at: http://www.stat.purdue.edu/~ovitek/BQuant-Web/.

Efficient recovery of electrophoretic profiles of nucleoside metabolites from urine samples by multivariate curve resolution

Chemometric techniques usually employed in purity assessment and resolution of multicomponent peaks have been applied to analytical data from complex biological samples obtained with CE-DAD. In the assessment of the purity of the electrophoretic peaks, the orthogonal projection approach, the orthogonal projection approach with Durbin-Watson criterion, and the simple-to-use interactive self-modeling mixture analysis method have been employed. Multivariate curve resolution with alternating least squares has been successfully implemented to resolve co-migrating peaks of metabolites in CE-DAD and to recover qualitative and quantitative information about co-migrating components of urine extract. The main challenge consisted of developing high-quality multivariate curve resolution with alternating least squares models of multicomponent peaks acquired during the CE analysis of nucleoside patterns in 18 urine samples. The recovered ultraviolet visible (UV-Vis) spectra have been employed to identify additional nucleosides, such as 1-methylinosine, 2-methylguanosine, and 1-methylguanosine, whose presence in the metabolic profile produced by the applied CE-DAD method has not yet been recognized. Concentration profiles of these compounds can be used in metabonomic studies.