Comprehensive analysis of metabolic changes in spontaneously hypertensive rats

OBJECTIVES

Hypertension is a chronic disease with multiple causative factors that involve metabolic disturbances and can cause various complications. However, the metabolic characteristics of hypertension at different stages are still unclear. This study aimed to explore the metabolic changes induced by hypertension at different ages.

METHODS

Spontaneously hypertensive rats (SHR) and Wistar Kyoto (WKY) rats were divided into four groups according to age: 5-week-old SHR (n = 6), 5-week-old WKY rats (n = 6), 32-week-old SHR (n = 6), and 32-week-old WKY rats (n = 6). Metabolites were analyzed in primary tissues (serum, heart, lung, kidney, brain, and brown adipose) using a non-targeted metabolomics approach.

RESULTS

Thirty-five metabolites and nine related metabolic pathways were identified in 5-week-old SHR, mainly related to the metabolism of amino acids. Fifty-one metabolites and seven related metabolic pathways were identified in the 32-week-old SHR, involving glycolysis, lipid, and amino acid metabolisms.

CONCLUSION

This experiment elucidates the metabolic profile of SHR at different ages and provides a basis for predicting and diagnosing hypertension. It also provides a reference for the pathogenesis of hypertension.

Urinary volatile Organic compounds in non-alcoholic fatty liver disease (NAFLD), type two diabetes mellitus (T2DM) and NAFLD-T2DM coexistence

INTRODUCTION

Accumulating evidence have shown a significant correlation between urinary volatile organic compounds (VOCs) profile and the manifestation of several physiological and pathological states, including liver diseases. Previous studies have investigated the urinary metabolic signature as a non-invasive tool for the early discrimination between non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH), which nowadays represents one of the most important challenges in this context, feasible only by carrying out liver biopsy.

OBJECTIVES

The aim of the study was to investigate the differences in the urinary VOCs profiles of non-alcoholic fatty liver disease (NAFLD) patients, diabetes mellitus (T2DM) subjects and NAFLD/T2DM patients.

METHODS

Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) was applied to profile the urinary VOCs. Urine samples were analysed both under acid and alkaline conditions, to obtain a range of urinary volatiles with different physicochemical properties.

RESULTS

Urinary VOCs profiles of 13 NAFLD patients, 13 T2DM subjects and 13 NAFLD/T2DM patients were investigated by multivariate and univariate data analysis techniques which allowed to identify 21 volatiles under alkaline conditions able to describe the NAFLD/T2DM group concerning the other two groups.

CONCLUSION

Our results suggest that VOCs signatures can improve the knowledge of the pathological condition where NAFLD coexists with T2DM and discovering new features that are not simply the sum of the two diseases. These preliminary findings may be considered as hypothesis-generating, to be clearly confirmed by larger prospective investigations.

Identification of Putative Biomarkers Specific to Foodborne Pathogens Using Metabolomics

Metabolomics is one of the more recently developed "omics" that measures low molecular weight (typically < 1500 Da) compounds in biological samples. Metabolomics has been widely explored in environmental, clinical, and industrial biotechnology applications. However, its application to the area of food safety has been limited but preliminary work has demonstrated its value. This chapter describes an untargeted (nontargeted) metabolomics workflow using gas chromatography coupled to mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for extraction of polar metabolites from media, analyzing the extracts using GC-MS and, finally, chemometric data analysis using the software "SIMCA" to identify potential pathogen-specific biomarkers.