Evaluating Different Extraction Approaches for GC-MS Based Metabolomics Analysis of the Giant Pandas' Fur.

Abstract

Giant pandas in zoo captivity are situated in residential areas, where environmental pollutants and anthropogenic factors have an impact on their health. Hair metabolomics has been applied in numerous environmental toxicological studies. Therefore, the panda fur metabolome could be a reliable approach to reflect endogenous and exogenous metabolic changes related to environmental exposure. However, there is no established extraction protocol to study the fur metabolome of pandas. The aim of this research was to optimize the extraction of panda fur metabolome for high-throughput metabolomics analysis using gas chromatography-mass spectrometry. Fur samples were collected from five pandas. Eight different extraction methods 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 cycle derivatives, fatty acids, and secondary metabolites. Our results demonstrated that HCl + ACN were the superior extraction solvents for amino acid and secondary metabolite extraction, and NaOH + MeOH was ideal for fatty acid extraction. Interestingly, the metabolomic analysis of panda fur was capable of discriminating the longitudinal metabolite profile between black and white furs. These extraction protocols can be used in future study protocols for the analysis of the fur metabolome in pandas.