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Metabolomics

, 14:75 | Cite as

Optimized GC–MS metabolomics for the analysis of kidney tissue metabolites

  • Biswapriya B. MisraEmail author
  • Ram P. Upadhayay
  • Laura A. Cox
  • Michael Olivier
Original Article

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

Kidney Non-human primate Solvent Extraction Optimization GC–MS 

Notes

Acknowledgements

This work was supported by a Forum Grant (BM-17-04629) awarded to BBM by the Texas Biomedical Research Institute, San Antonio, Texas.

Author contributions

BBM envisioned the project, BBM, and LAC designed the research; BBM and RPU performed the experiments; MO provided essential reagents and materials, BBM analyzed the data, BBM, MO, LAC wrote the manuscript, and BBM interpreted the data, has the primary responsibility for the final content, and edits. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interest and no conflicts of interest.

Ethical approval

The baboon kidney samples were collected under IACUC approved protocols at facilities located at the Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, United States. All applicable international, national, and institutional guidelines for the care and use of animals were followed.

Supplementary material

11306_2018_1373_MOESM1_ESM.doc (42 kb)
Supplementary material 1 (DOC 42 KB)
11306_2018_1373_MOESM2_ESM.doc (1.6 mb)
Supplementary material 2 (DOC 1670 KB)
11306_2018_1373_MOESM3_ESM.xlsx (215 kb)
Supplementary material 3 (XLSX 215 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Precision Medicine, Department of Internal Medicine, Section of Molecular MedicineWake Forest School of MedicineWinston-SalemUSA
  2. 2.Department of GeneticsTexas Biomedical Research InstituteSan AntonioUSA
  3. 3.Southwest National Primate Research CenterTexas Biomedical Research InstituteSan AntonioUSA

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