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Metabolomics. 2018 May 25;14(6):75. doi: 10.1007/s11306-018-1373-5.

Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites.

Author information

1
Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA. bbmisraccb@gmail.com.
2
Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, 78227, USA. bbmisraccb@gmail.com.
3
Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, 78227, USA.
4
Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
5
Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, 78227, USA.

Abstract

INTRODUCTION:

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

OBJECTIVES:

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

METHODS:

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC-ToF-MS) platform after methoximation and silylation.

RESULTS:

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

CONCLUSION:

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

KEYWORDS:

Extraction; GC–MS; Kidney; Non-human primate; Optimization; Solvent

PMID:
30830353
DOI:
10.1007/s11306-018-1373-5
[Indexed for MEDLINE]

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