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.
Introduction: A predisposition to nephronophthisis (NPHP) is inherited and typically presents with cysts in the kidney and liver, leading to end-stage kidney disease. The mechanisms underlying onset and progression of cyst growth remain unknown and detection of NPHP and other polycystic kidney diseases (PKDs) is not sensitive or specific. For these reasons, management and treatment are limited to renal replacement therapy and transplantation. The LPK rat phenotype has been characterized and classified as a model of the PKD, NPHP9, caused by mutation of the nek8 gene. Using this model, the aim of this collection of studies was to use a GC-MS-based untargeted metabolomic analysis to determine key biochemical changes in kidney and liver tissue of the LPK rat and to investigate biomarkers in the blood plasma and urine. Furthermore, the study determined whether sample derivatisation could be streamlined in an automated process to improve reproducibility and investigated the use of BSTFA as a derivatisation reagent, as an alternative to MSTFA.
Methods: Following a pilot study using 4 LPK and 4 Lewis controls, 11 LPK and 11 Lewis age- and sex-matched control animals aged 5 to 16 weeks were used. Blood and urine were sampled weekly and organs harvested at the conclusion of the study. Metabolites were extracted with methanol and water containing 13C6-sorbitol (IS) and derivatised with methoxyamine-HCl and MSTFA. A Shimadzu QP2010 Ultra GC-MS was used for sample analysis, and for data analysis, AnalyzerPro, The Unscrambler X and SPSS were used. Features were matched to an in-house library of metabolites and the NIST mass spectral database.
Results: For metabolomic analysis of the kidney and liver tissue, principal component analysis (PCA) distinguished signal corrected metabolite profiles from Lewis and LPK rats iv for kidney (PC-1 77%) and liver (PC-1 46%) tissue. In kidney tissue, 122 metabolites were found to be significantly different between the LPK and Lewis strains and 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. In the liver, 30 metabolites were found to be significantly different. Urine and plasma metabolites were tested for age (Kruskal-Wallis; p < 0.05) and strain effects (Mann-Whitney U-test; p < 0.05). Fifty-nine putatively identified metabolites from the LPK plasma and urine were found to be significantly different from Lewis controls. These results were concomitant with data from kidney and liver tissue analyses. The results of these studies validate and complement the current literature and are consistent with suggestions relating to the pathobiology of PKD. Most notably, myo-inositol was suggested as an early marker of renal dysfunction in PKD. Derivatised metabolite responses were highly variable throughout the ten analytical batches of urine samples compared to the 12 batches of plasma samples, even for test mixtures, which were not affected by sample concentration or matrix. Derivatization reagent and protocol are key factors affecting the reproducibility and intensity of individual urinary metabolites, so we tested both BSTFA as an alternate to MSTFA and the use of automated protocols (batch and in-time) using a CTC CombiPAL auto sampler. Of 249 features detected in rat urine, 40 features were significantly different (p < 0.05) based upon reagent and 154 features were significantly different (p < 0.05) based upon protocol. The overall reproducibility of the methods was similar, although highly feature dependent.
The purpose of this research was to use metabolomics to investigate the cystic phenotype in the Lewis polycystic kidney rat.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.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.The findings of this study demonstrate the potential of metabolomics to further investigate kidney disease.
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.
Some investigators have reported an excess risk of venous thromboembolism (VTE) associated with depression and with use of antidepressant drugs. We explored these associations in a large prospective study of UK women.
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.
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