Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain.

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management of product yield and quality. Metabolomics data provides a read-out of these interactions at a given moment in time and is informative of an organism's biochemical status. Further, individual metabolites or panels of metabolites can be used as precise biomarkers for yield and quality prediction and management. The plant metabolome is predicted to contain thousands of small molecules with varied physicochemical properties that provide an opportunity for a biochemical insight into physiological traits and biomarker discovery. To exploit this, a key aim for metabolomics researchers is to capture as much of the physicochemical diversity as possible within a single analysis. Here we present a liquid chromatography-mass spectrometry-based untargeted metabolomics method for the analysis of field-grown wheat grain. The method uses the liquid chromatograph quaternary solvent manager to introduce a third mobile phase and combines a traditional reversed-phase gradient with a lipid-amenable gradient. Grain preparation, metabolite extraction, instrumental analysis and data processing workflows are described in detail. Good mass accuracy and signal reproducibility were observed, and the method yielded approximately 500 biologically relevant features per ionization mode. Further, significantly different metabolite and lipid feature signals between wheat varieties were determined.

Untargeted gas chromatography-mass spectrometry-based metabolomics analysis of kidney and liver tissue from the Lewis Polycystic Kidney rat.

Polycystic kidney disease (PKD) encompasses a spectrum of inherited disorders that lead to end-stage renal disease (ESRD). There is no cure for PKD and current treatment options are limited to renal replacement therapy and transplantation. A better understanding of the pathobiology of PKD is needed for the development of new, less invasive treatments. The Lewis Polycystic Kidney (LPK) rat phenotype has been characterized and classified as a model of nephronophthisis (NPHP9, caused by mutation of the Nek8 gene) for which polycystic kidneys are one of the main pathologic features. The aim of this study was to use a GC-MS-based untargeted metabolomics approach to determine key biochemical changes in kidney and liver tissue of the LPK rat. Tissues from 16-week old LPK (n = 10) and Lewis age- and sex-matched control animals (n = 11) were used. Principal component analysis (PCA) distinguished signal corrected metabolite profiles from Lewis and LPK rats for kidney (PC-1 77%) and liver (PC-1 46%) tissue. There were marked differences in the metabolite profiles of the kidney tissues with 122 deconvoluted features significantly different between the LPK and Lewis strains. The metabolite profiles were less marked between strains for liver samples with 30 features significantly different. Five biochemical pathways showed three or more significantly altered metabolites: transcription/translation, arginine and proline metabolism, alpha-linolenic and linoleic acid metabolism, the citric acid cycle, and the urea cycle. The results of this study validate and complement the current literature and are consistent with the understood pathobiology of PKD.

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases.

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Development of a non-targeted metabolomics method to investigate urine in a rat model of polycystic kidney disease.

AIM: The purpose of this research was to use metabolomics to investigate the cystic phenotype in the Lewis polycystic kidney rat. METHODS: Spot urine samples were collected from four male Lewis control and five male Lewis polycystic kidney rats aged 5 weeks, before kidney function was significantly impaired. Metabolites were extracted from urine and analysed using gas chromatography-mass spectrometry. Principal component analysis was used to determine key metabolites contributing to the variance observed between sample groups. RESULTS: With the development of a metabolomics method to analyse Lewis and Lewis polycystic kidney rat urine, 2-ketoglutaric acid, allantoin, uric acid and hippuric acid were identified as potential biomarkers of cystic disease in the rat model. CONCLUSION: The findings of this study demonstrate the potential of metabolomics to further investigate kidney disease.