Metabonomic Study of Host–Phage Interaction by Nuclear Magnetic Resonance- and Statistical Total Correlation Spectroscopy-Based Analysis

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Profiling analysis of amino acids from hyperlipidaemic rats treated with Gynostemma pentaphyllum and atorvastatin

Context Gynostemma pentaphyllum (Thunb.) Makino has been used in traditional medicine for the treatment of hyperlipidaemic with a long history.

OBJECTIVE

The objective of this study was to evaluate the influence of Gynostemma pentaphyllum (GP) and atorvastatin on amino acids from the plasma and liver tissue of hyperlipidaemic rats. Materials and methods The rats were fed a high-fat diet continuously for 11 weeks for the construction of hyperlipidaemic model. The hyperlipidaemic rats were treated with Gynostemma pentaphyllum (120 mg/kg) and atorvastatin (1.8 mg/kg) for 4 weeks, and the rats were intragastric administration one time every day. Chromatographic separation was performed on a Shim-pack XR-ODSIII C18 analytical column (75 mm × 2.0 mm i.d., 1.6 μm, Shmadazu Corp., Tokyo, Japan). The biomarkers of amino acids were identified by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Results After feeding with a high-fat diet, the TC and LDL-C values of the hyperlipidaemic mode rats increased dramatically (p < 0.01). The established method allowed a target analysis of 12 kinds of amino acids. PCA studies showed that the plasma amino acids had not returned to normal after GP treatment, but those had recovered slightly after atorvastatin treatment. GP has almost no impact on the metabolism of amino acids, while atorvastatin can modify the metabolism of amino acids via self-regulatory mechanisms. Discussion and conclusion UPLC/DAD combined with SCX-SPE can be successfully used for profiling analysis of amino acids. By the comparison of biomarkers following treatment with GP and atorvastatin, the influence of the two drugs on biomarkers is revealed.

Assessment of early triage for acute radiation injury in rat model based on urinary amino acid target analysis

Rapid radiation injury early triage after a radiological or nuclear exposure is vital for treatment of a large number of wounded people. Owing to the high-throughput analysis and minimally invasive nature of sample collection, radiation metabolomics has been recently applied to radiation damage research. In the present study, exploring the feasibility of estimating the acute radiation injury for early triage by means of urinary amino acid target analysis was attempted using a high performance liquid chromatography electrospray tandem mass spectrometry (HPLC-ESI-MS/MS) technique combined with multivariate statistical analysis. The non-linear kernel partial least squares (KPLS) model was used to separate the control and different radiation dose groups. The classification of different groups was performed after feature selection instead of before feature selection, because of its better separation. The classification accuracy at various radiation injury levels at different time points (5, 24, 48 and 72 h) post-irradiation exposure was investigated. For most of the radiation damage levels, the classification accuracy at 72 h after exposure was superior to that at earlier time points. Additionally, the potential radiation injury biomarkers selected suggested that the urea cycle, glycine, serine and threonine metabolism, alanine, aspartate and glutamine metabolism and related metabolic pathways were involved. The findings suggest that non-invasive urinary biomarkers have great potential for serving as an effective tool for rapid triage of mass casualties in nuclear accidents and understanding the pathogenesis of radiation injury.