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. 2022 May 27;31(7):827-841.
doi: 10.1007/s10068-022-01098-9. eCollection 2022 Jul.

Influence of thermophilic microorganism on non-volatile metabolites during high-temperature pile-fermentation of Chinese dark tea based on metabolomic analysis

Wen Zhu  1 , Wenfeng Wang  1 , Wencan Xu  1 , Shuang Wu  1 , Wenjun Chen  1 , Youyi Huang  1 , Shengpeng Wang  2
Affiliations

Influence of thermophilic microorganism on non-volatile metabolites during high-temperature pile-fermentation of Chinese dark tea based on metabolomic analysis

Wen Zhu et al. Food Sci Biotechnol. .

Abstract

Pile-fermentation is a critical procedure for producing Chinese dark tea, during which thermophilic microorganisms would play an irreplaceable role. However, there have been little researches on the influences of thermophilic microorganism pile-fermentation (TMPF) in high-temperature of Chinese dark tea. Thus, we conducted high-performance liquid chromatography and nontargeted metabolomic to analyze the non-volatile metabolites of TMPF. Our results discovered that the amounts of ( -)-epigallocatechin gallate, ( -)-epigallocatechin, ( -)-epicatechin gallate, and ( -)-epicatechin were decreased significantly (p < 0.05) after TMPF. By using nontargeted metabolomic analysis, a total of 1733 ion features were detected. KEGG pathway enrichment analysis showed that TMPF had a significant impact on caffeine metabolism. Also, theophylline, 3-methylxanthine, and 1,3,7-trimethyluric acid were increased significantly after TMPF, which suggested that demethylation and oxidation reaction might be the main pathways of caffeine metabolism. This study provides a better understanding of the mechanism of TMPF during high-temperature for Chinese dark tea and lays a foundation for further research.

Supplementary information: The online version contains supplementary material available at 10.1007/s10068-022-01098-9.

Keywords: Chinese dark tea; High-temperature; Nontargeted metabolomic analysis; Pile-fermentation; Thermophilic microorganism.

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Conflict of interest statement

Conflict of interestThe authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Analysis of identified metabolites. A Pie chart of the superclass’s classification of all metabolites; B column chart of fold changes of different classification; C relative concentrations of pharmacological components
Fig. 2
Fig. 2
PLS-DA analysis of the tea samples. A Negative model; B positive model
Fig. 3
Fig. 3
Venn plot of significantly different metabolites
Fig. 4
Fig. 4
KEGG pathway analysis for different metabolites. A differential metabolites shared in all tea samples; B unique differential metabolites in FT vs CK; C unique differential metabolites in FTCK vs CK; D unique differential metabolites in FT vs FTCK
Fig. 4
Fig. 4
KEGG pathway analysis for different metabolites. A differential metabolites shared in all tea samples; B unique differential metabolites in FT vs CK; C unique differential metabolites in FTCK vs CK; D unique differential metabolites in FT vs FTCK
Fig. 5
Fig. 5
Metabolic pathway of phenolics and heatmap of flavonol/flavone glycosides in tea samples. A Metabolic pathway. Relative concentration of each metabolite is an average data of three biological replicates obtained through nontargeted metabolomic analysis. B Heatmap
Fig. 5
Fig. 5
Metabolic pathway of phenolics and heatmap of flavonol/flavone glycosides in tea samples. A Metabolic pathway. Relative concentration of each metabolite is an average data of three biological replicates obtained through nontargeted metabolomic analysis. B Heatmap
See this image and copyright information in PMC

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