Comparative metabolomics reveals the mechanism of avermectin production enhancement by S-adenosylmethionine

  • Pingping Tian
  • Peng Cao
  • Dong Hu
  • Depei Wang
  • Jian Zhang
  • Lin Wang
  • Yan Zhu
  • Qiang Gao
Applied Genomics & Systems Biotechnology - Original Paper

Abstract

It was found that S-adenosylmethionine (SAM) could effectively improve avermectin titer with 30–60 μg/mL addition to FH medium. To clearly elucidate the mechanism of SAM on intracellular metabolites of Streptomyces avermitilis, a GC–MS-based comparative metabolomics approach was carried out. First, 230 intracellular metabolites were identified and 14 of them remarkably influenced avermectin biosynthesis were discriminative biomarkers between non-SAM groups and SAM-treated groups by principal components analysis (PCA) and partial least squares (PLS). Based on further key metabolic pathway analyses, these biomarkers, such as glucose, oxaloacetic acid, fatty acids (in soybean oil), threonine, valine, and leucine, were identified as potentially beneficial precursors and added in medium. Compared with single-precursor feeding, the combined feeding of the precursors and SAM markedly increased the avermectin titer. The co-feeding approach not only directly verified our hypothesis on the mechanism of SAM by comparative metabolomics, but also provided a novel strategy to increase avermectin production.

Keywords

Avermectin S-adenosylmethionine Comparative metabolomics Metabolites Rational feeding 

Notes

Acknowledgements

This work was financially supported by the National Basic Research Program (973 Program) of China (2013CB734004), and the Natural Science Foundation of China (31370075, 31471725, 61603273), and the Youth Innovation Fund of Tianjin University of Science and Technology of China (2014CXLG28). We also appreciate Dr. Arnold L. Demain for his valuable advice with the manuscript, Dr. Lixin Zhang for providing the experimental strain and Mr. Gang Guo for his technical support.

Supplementary material

10295_2016_1883_MOESM1_ESM.ppt (160 kb)
Fig. 1S PCA score plots of different SAM additions at different timepoints. PCA score plots (PC1, 72.35% of total variance; PC2, 21.47% of total variance). In the score plots, the confidence interval is defined by Hotelling’s T2 ellipse (95% confidence interval), and observations outside the ellipse are considered outliers (PPT 160 kb)
10295_2016_1883_MOESM2_ESM.pptx (134 kb)
Fig. 2S PLS score plots (a, c, e) and loading plots (b, d, f) of samples with different contents of SAM. Sampling time (a, b) at 24 h, (c, d) at 48 h, and (e, f) at 72 h. In the score plots, the confidence interval is defined by Hotelling’s T2 ellipse (95% confidence interval), and observations outside the ellipse are considered outliers. Green icons represent the non-SAM groups; blue icons represent the 30 μg/mL SAM-treated groups; and red icons represent the 60 μg/mL SAM-treated groups (PPTX 134 kb)
10295_2016_1883_MOESM3_ESM.pptx (464 kb)
Fig. 3S PLS score plots (a, c, e) and loading plots (b, d, f) of samples with different contents of SAM. Sampling time (a, b) at 24 h, (c, d) at 48 h, and (e, f) at 72 h. In the score plots, the confidence interval is defined by Hotelling’s T2 ellipse (95% confidence interval), and observations outside the ellipse are considered outliers. Green icons represent the non-SAM groups; blue icons represent the 30 μg/mL SAM-treated groups; and red icons represent the 60 μg/mL SAM-treated groups (PPTX 464 kb)
10295_2016_1883_MOESM4_ESM.pptx (57 kb)
Table 1S Statistical data from PLS at different sampling timepoints (PPTX 58 kb)

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Copyright information

© Society for Industrial Microbiology and Biotechnology 2016

Authors and Affiliations

  1. 1.Key Laboratory of Industrial Fermentation MicrobiologyMinistry of Education, Tianjin Key Laboratory of Industrial Fermentation Microbiology, College of Biotechnology, Tianjin University of Science and TechnologyTianjinPeople’s Republic of China
  2. 2.School of Computer Sciences and Information EngineeringTianjin University of Science and TechnologyTianjinPeople’s Republic of China
  3. 3.Logistics Service GroupTianjin University of Science and TechnologyTianjinPeople’s Republic of China

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