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Evaluating different extraction solvents for GC-MS based metabolomic analysis of the fecal metabolome of adult and baby giant pandas.

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posted on 2019-09-04, 11:41 authored by Y Yang, Y Yin, X Chen, C Chen, Y Xia, H Qi, PN Baker, H Zhang, T-L Han
The gut microbiome plays a fundamental role in host health and the fecal metabolome can be analysed to assess microbial activity and can be used as an intermediate phenotype monitoring the host-microbiome relationship. However, there is no established extraction protocol to study the fecal metabolome of giant pandas. The aim of this research is to optimize extraction of the fecal metabolome from adult and baby pandas for high throughput metabolomics analysis using gas chromatography-mass spectrometry (GC-MS). Fecal samples were collected from eight adult pandas and a pair of twin baby pandas. Six different extraction solvents were investigated and evaluated for their reproducibility, metabolite coverage, and extraction efficiency, particularly in relation to the biochemical compound classes such as amino acids, tricarboxylic acid (TCA) cycle intermediates, fatty acids, secondary metabolites, and vitamin and cofactors. Our GC-MS results demonstrated that the extraction solvents with isopropanol: acetonitrile: water (3:2:2 ratio) and 80% methanol were the most appropriate for studying the fecal metabolome of adult and baby giant pandas respectively. These extraction solvents can be used in future study protocols for the analysis of the fecal metabolome in giant pandas.

Funding

The authors would like to acknowledge the support of the mass spectrometry center of the Maternal and Fetal Medicine of Chongqing Medical University and the Chongqing Zoo. This research was supported by the National Natural Science Foundation of China (No. 81871185, 81571453, 81771607, 81701477), The 111 Project (Yuwaizhuan (2016)32 The National Key Research and Development Program of Reproductive Health & Major Birth Defects Control and Prevention (2016YFC1000407), Health Commission (2018ZDXM024, 2017ZDXM008), and Chongqing Science & Technology Commission (cstc2017jcyjBX0062).

History

Citation

Scientific Reports, 2019, volume 9, Article number: 12017

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES

Version

  • VoR (Version of Record)

Published in

Scientific Reports

Publisher

Nature Research (part of Springer Nature)

eissn

2045-2322

Acceptance date

2019-08-06

Copyright date

2019

Available date

2019-09-04

Publisher version

https://www.nature.com/articles/s41598-019-48453-1

Notes

The data that support the findings of this study are available from the corresponding author upon reasonable request. Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-48453-1.

Language

en

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