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Harnessing the intracellular triacylglycerols for titer improvement of polyketides in Streptomyces

Nature Biotechnology volume 38, pages 76–83 (2020)Cite this article

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A Publisher Correction to this article was published on 18 December 2019

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Abstract

Pharmaceutically important polyketides such as avermectin are mainly produced as secondary metabolites during the stationary phase of growth of Streptomyces species in fermenters. The source of intracellular metabolites that are funneled into polyketide biosynthesis has proven elusive. We applied multi-omics to reveal that intracellular triacylglycerols (TAGs), which accumulates in primary metabolism, are degraded during stationary phase. This process could channel carbon flux from both intracellular TAGs and extracellular substrates into polyketide biosynthesis. We devised a strategy named ‘dynamic degradation of TAG’ (ddTAG) to mobilize the TAG pool and increase polyketide biosynthesis. Using ddTAG we increased the titers of actinorhodin, jadomycin B, oxytetracycline and avermectin B1a in Streptomyces coelicolor, Streptomyces venezuelae, Streptomyces rimosus and Streptomyces avermitilis. Application of ddTAG increased the titer of avermectin B1a by 50% to 9.31 g l−1 in a 180-m3 industrial-scale fermentation, which is the highest titer ever reported. Our strategy could improve polyketide titers for pharmaceutical production.

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Fig. 1: Identification of intracellular metabolites with relationship to polyketides production.
Fig. 2: Cellular TAG pool contributes to Act yield during stationary phase.
Fig. 3: High-yielding mechanism of mobilizing cellular TAG pool.
Fig. 4: Identification of SCO6196 responsible for cellular TAG mobilization.
Fig. 5: The ddTAG strategy for polyketide titer improvement.

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Data availability

The genome sequence of S. coelicolor strain is available at NCBI (accession number NC_003888). Time-series transcriptome data of S. coelicolor stains are available at NCBI GEO database (accession numbers GSE2983, GSE18489, GSE30569, GSE30570 and GSE53562). Other data supporting the findings of this study are included in the published article and supplementary information. Requests for any additional information can be made to the corresponding authors.

Code availability

The custom Python scripts used for flux sampling analysis are provided in Supplementary Note 4.

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Acknowledgements

We dedicate this paper to the memory of our beloved mentor, friend and colleague, K. Yang. We thank H. Tan, Y. Tao, Y. Chen, M. Li, S. Chen, S. Gao and Q. Gao for helpful discussions. We acknowledge the financial support from the National Natural Science Foundation of China (grants 31430002 and 31720103901 to L.Z.; 31922002 and 31570031 to W.W.; 31772242 to S.L. and 31672092 to W.X.). This work was also supported by the Open Project Funding of the State Key Laboratory of Bioreactor Engineering, the 111 Project (B18022), the Major Basic Program of the Natural Science Foundation of Shandong Province (ZR2017ZB0206), the Shanghai Science and Technology Commission (18JC1411900) and the Shandong Taishan Scholar Program of China to L.Z. and the International Partnership Program of Chinese Academy of Sciences (153211KYSB20170014) and the Young Scientists Innovation Promotion Association of CAS (2016087) to W.W.

Author information

Author notes

  1. These authors contributed equally: Weishan Wang, Shanshan Li, Zilong Li, Jingyu Zhang.

Authors and Affiliations

  1. State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China

    Weishan Wang, Zilong Li, Keqiang Fan, Guomin Ai & Yihong Li

  2. State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China

    Weishan Wang, Jingyu Zhang, Gaoyi Tan, Zhiheng Yang, Hongzhong Lu, Xiangyin Chen, Xueting Liu, Yi Yang, Siliang Zhang & Lixin Zhang

  3. State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China

    Shanshan Li, Pinjiao Jin & Wensheng Xiang

  4. State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Sin Man Lam & Guanghou Shui

  5. Key Biosensor Laboratory of Shandong Province, Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Xuekui Xia

  6. Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

    Xueting Liu

  7. Indiana State University, Terre Haute, IN, USA

    H. Kathleen Dannelly

  8. CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China

    Chen Yang

  9. Boston Children’s Hospital, Boston, MA, USA

    Gil Alterovitz

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  1. Weishan Wang
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Contributions

L.Z., W.W. and S.L. conceived and supervised the project. W.W., S.L., Z.L. and W.X. designed and performed the main experiments. J.Z. performed the large-scale fermentations. G.A. performed the GC–MS analyses. G.S. and S.M.L. collected LC–MS/MS data for isotope tracing experiments. K.F., G.T., Z.Y., H.L., P. J., Y.L., X.C., X.X., X.L., H.K.D., C.Y., Y.Y., G.A. and S.Z. participated in the experiments. W.W., S.L. and L.Z. wrote the manuscript.

Corresponding authors

Correspondence to Weishan Wang, Wensheng Xiang or Lixin Zhang.

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Competing interests

The authors have filed a provisional patent for this work to the China National Intellectual Property Administration (CNIPA). L.Z., W.W., S.L., Z.L., G.T., K,F. and J.Z. are inventors on the provisional patent application (CN201910411123.7, filed 17 May 2019).

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Integrated supplementary information

Supplementary Figure 1 Trends of the metabolic pathways in M145 and HY01.

Black bold lines indicate the overall trends of the metabolites involved in the five investigated pathways, and light gray lines show the trends of individual metabolites. Pink background indicates the late stationary phase. (a and g) Overall trends of metabolites in different metabolic pathways in M145 and HY01, respectively. Trend of each metabolite investigated in different pathways were as follows: (b and h) glucose-6-phosphate, dihydroxyacetone phosphate, glyceraldehyde 3-phosphate, D-glycerate 3-phosphate, and pyruvate for glycolysis (EMP) in M145 and HY01, respectively; (c and i) D-altro-heptulose 7-phosphate, D-erythrose 4-phosphate, 6-phospho-D-gluconate, D-ribose, and D-ribulose 5-phosphate for pentose phosphate pathway (PPP) in M145 and HY01, respectively; (d and j) succinic acid, isocitric acid, fumaric acid, cirtrate, DL-malic acid, and α-ketoglutarate for tricarboxylic acid cycle (TCA) in M145 and HY01, respectively; (e and k) serine, glycine, L-valine, L-threonine, L-pyroglutamic acid, L-proline, L-leucine, L-isoleucine, L-aspartic acid, L-alanine, DL-ornithine, 5-oxo-L-proline, 3-phosphoserine, N-acetyl-L-glutamic acid, and α-ketoadipic acid for amino acid metabolism (AAM) in M145 and HY01, respectively; (f and l) myristic acid, pentadecanoic acid, hexadecanoic acid, margaric acid, octadecanoic acid, MG(16:0/0:0/0:0), and MG(18:0/0:0/0:0) for lipid metabolism (LPM) in M145 and HY01, respectively. Levels of metabolites determined at 20 h were set as one. Data were the average and s.d. of five independent experimental replicates.

Supplementary Figure 2 Analysis of cellular TAGs by GC-MS.

(a) Fatty acid moieties of cellular TAG pool identified by GC-MS. Fatty acid abbreviations used were as follows: iC12:0, isolauric acid; iC13:0, isotridecanoic acid; aiC13:0, anteisotridecanoic acid; iC14:0, isomyristic acid; C14:0, myristic acid; iC15:0, isopentadecanoic acid ; aiC15:0, anteisopentadecanoic acid ; C15:0, pentadeclic acid; iC16:0, isopalmitic acid; C16:1-Δ9, 9-hexadecenoic acid; C16:0, palmitic acid; iC17:0, isoheptadecanoic acid; aiC17:0, anteisoheptadecanoic acid; C17:0, heptadecanoic acid; C18:1-Δ9, 9-octadecenoic acid; and C18:0, stearic acid. (b) Trends of fatty acid moieties of cellular TAG pool in M145. The cellular TAGs from the time-course samples were firstly hydrolyzed to release fatty acid moieties, and then the fatty acids were quantified using GC-MS. Peak areas of the mass chromatograms were used to indicate the relative levels of the corresponding fatty acid moieties. Results shown are average and s.d. of five independent experimental replicates.

Supplementary Figure 3 Change of cellular TAGs and their derived intermediate metabolite during stationary phase in M145 and HY01.

(a) Levels of TAG pools in M145 and HY01 at transition phase. Columns show the consumed amount of fatty acid moieties from TAGs at 48 h. The level of each fatty acid moiety in M145 and HY01 shows no significant difference (Student t-test, p < 0.05). (b-e) Levels of the metabolites derived from TAG degradation. (b) Schematic of the metabolic pathway related to TAG degradation. (c-e) Levels of metabolites related to TAG degradation. Metabolite levels are determined by LC-MS/MS. G3P, glycerol-3-phosphate; 3PG, 3-phosphoglycerate; DHAP, dihydroxyacetone phosphate. Data shown are the average and s.d. of three independent experiments. The levels of significance are *** p < 0.001, ** p < 0.01, * p < 0.05.

Supplementary Figure 4 Contributions of metabolites on Act production during stationary phase.

(a) Contributions of fatty acid moieties of cellular TAG pool on Act production during stationary phase. (b) Contributions of intercellular amino acids on Act production during stationary phase. VIP score is used to identify important features from the examined samples (Nat. Protoc. 6, 743-760, 2011). Here we aimed to identify important metabolites for Act production using VIP score. VIP score was obtained by comparing the data matrix of M145 and HY01 from different period of stationary phase (48-60 h, 60-72 h, and 72-96 h) using MetaboAnalyst Software.

Supplementary Figure 5 Dynamic profile of genes and metabolites related to TAG metabolism.

(a-d) Transcript profiles of the genes in fatty acid metabolism under diverse culture conditions. The selected genes are the same as those shown in Fig. 2e, f. Time-series transcriptome data were obtained from NCBI GEO database: (a) GSE2983, M145 cultivated in modified R5 medium (Genes Dev. 15, 3183-3192, 2001); (b) GSE18489, M145 cultivated in a fermentation medium (BMC Genomics 11, 10, 2010); (c) GSE30569, the glnK mutant of M145 cultivated in SSBM-E medium (Appl. Microbiol. Biotechnol. 92, 1219-1236, 2011); (d) GSE30570, M145 cultivated in SSBM-E medium (Appl. Microbiol. Biotechnol. 92, 1219-1236, 2011). (e-g) Profiles of cellular TAG pools in industrial Streptomyces avermitilis A56 (Proc. Natl. Acad. Sci. U. S. A. 107, 11250-11254, 2010; Synth. Syst. Biotechnol. 1, 7-16, 2016), Streptomyces bingchenggesis BC04 (Microb. Cell Fact. 15, 152, 2016), and Streptomyces rimosus M2R (M4018::2SFOtcR) (Microb. Cell Fact. 14, 46, 2015), respectively. The amount of cellular TAG pool was analyzed by TLC.

Supplementary Figure 6 Isotopic feeding experiments in M145 and HY01.

(a) Feeding experiments with [U-13C] oleate. 2.5 nM [U-13C] oleate was added in cultures of M145 and HY01 at 72 h. The samples were harvested after 24 h. (b) Feeding experiments with [U-13C] glucose. 8.6 nM [U-13C] glucose was added in cultures of M145 and HY01 at 72 h. The samples were harvested after 48 h labeling. Data shown are the average and s.d. of three independent experiments.

Supplementary Figure 7 Levels of intermediates.

The level is indicated by the peak area of metabolites. (a) Levels of acetyl-CoA obtained from [U-13C] oleate feeding experiments. (b) Levels of acetyl-CoA obtained from [U-13C] glucose feeding experiments. (c–f) Levels of the TCA intermediates obtained from [U-13C] oleate feeding experiments. (g–j) Levels of the TCA intermediates obtained from [U-13C] glucose feeding experiments. Data of oleate and glucose labeling experiments were collected at 96 h and 120 h, respectively (Supplementary Fig. 6). Data shown are the average and s.d. of three independent experiments.

Supplementary Figure 8 Comparison of labeling ratio of TCA intermediates in M145 and HY01.

(a) Labeling ratio obtained from [U-13C] oleate feeding experiments. (b) Labeling ratio obtained from [U-13C] glucose feeding experiments. The labeling ratio of TCA intermediates in HY01 were normalized with that of the direct input acetyl-CoA using the following equation: normalized labeling ratio = labeling_ratio × (labeling ratio of acetyl-CoAHY01 / labeling ratio of acetyl-CoAM145). Differences were analyzed by Student t-test, and p < 0.05 was considered statistically significant. The levels of significance are *** p < 0.001, ** p < 0.01, * p < 0.05. Data shown are the average and s.d. of three independent experiments.

Supplementary Figure 9 Cellular levels of reducing equivalents and ATP.

(a–c) Cellular levels of NADH, ATP and NADPH, respectively, in M145 and HY01 during stationary phase (72, and 96 h). Differences were analyzed by Student t-test, and p < 0.05 was considered statistically significant. The levels of significance are *** p < 0.001, ** p < 0.01, * p < 0.05. Data shown are the average and s.d. of three independent experiments.

Supplementary Figure 10 Carbon flux distribution of S. coelicolor by metabolic flux analysis.

(a) Simplified metabolic network of S. coelicolor. Flux for biomass biosynthesis from the G6P node (v2) including fluxes from five different precursors (glucose-6-phosphate, fructose-6-phosphate, ribose-5-phosphate, erythrose-4-phosphate, glyceraldehyde-3-phosphate). 15 fatty acid moieties from cellular TAGs are detected in S. coelicolor, here flux v19 represents the sum of specific production rate of AcCoA from degradation of these fatty acids. The biochemical reactions, stoichiometric equations and experimentally determined fluxes are shown in Supplementary Table 6–8. (b) Actual fluxes of M145, HY01 and M145-DT in fermentation with (+) or without (-) glucose feeding. Data for metabolic flux analysis were collected during stationary phase (72–120 h). Data shown here were the average of three independent experiments. Abbreviations: Glc, glucose; G6P, glucose-6-phosphate; 3PG, 3-phosphate-glycerate; PEP, phosphoenolpyruvate; PYR, pyruvate; AcCoA, acetyl coenzyme A; AKG, alpha-ketoglutaric acid; OAA, oxaloacetic acid; TAG, triacylglycerol; Act, actinorhodin.

Supplementary Figure 11 Sampling analysis of flux distribution in M145 and HY01.

The genome-scale metabolic model (GEM) of S. coelicolor (iKS1317) (Biotechnol. J 14, e1800180, 2019) was constrained by the experimentally measured rate (Supplementary Table 8) with the minimization of glucose uptake rate as the objective function. During the simulation, the sampling algorithm optGpSampler (PLoS One 9, e86587, 2014) was adopted. For each condition, 1000 samples were used for valid sampling distributions. Here we showed two reactions from TCA cycle as examples. (a) Flux changes of reactions catalyzed by citrate synthase (CS) in batch fermentation (without glucose feeding). (b) Flux changes of reactions catalyzed by CS in fed-batch fermentation (with glucose feeding). (c) Flux changes of reactions catalyzed by aconitase (ACONTa) in batch fermentation (without glucose feeding). (d) Flux changes of reactions catalyzed by ACONTa in fed-batch fermentation (with glucose feeding).

Supplementary Figure 12 Construction of sco6196 deletion and overexpression strains.

6196DM, sco6196 deletion strain; 6196OE, sco6196 overexpression strain. (a) Confirmation of 6196DM.The primer pair 96vF/96vR for PCR amplification is listed in Supplementary table 14. (b) Effect of SCO6196 on cell growth. Data shown here were the average of three independent experiments. (c) Effect of SCO6196 on Act production. The picture shown here was the bottom of the solid plates. (d) Act production of M145, 6196OE and 6196DM in liquid SMM medium. (e) TLC assay of the cellular TAG pools in M145 and 6196DM at transition phase (48 h).

Supplementary Figure 13 Biochemical analysis of substrate specificity of SCO6196 in vitro.

(a) SDS-PAGE of the purified recombinant protein SCO6196. (b) Relative activities of SCO6196 when adding different fatty acids. When using C18:1-Δ9 as a substrate, the activity of SCO6196 was set to one. Data shown here were the average of three independent experiments. (c) Specific daughter ions used for the quantification of the corresponding acyl-CoAs from C18:1-Δ9, iC14:0, iC15:0, aiC15:0, iC16:0, aiC17:0, respectively. These daughter ions were selected based on m/z 425.9 and 408 in common and specific ions of [M-H-80] - and [M-H-347]-.

Supplementary Figure 14 Construction and application of the ddTAG module.

(a) Map of the plasmid used to construct the ddTAG module. The ddTAG module is controlled by a cumate inducible system (Appl. Microbiol. Biotechnol. 98, 8641-8655, 2014). (b) Induction range of sco6196 transcripts quantified by RT-qPCR. (c) Comparison of the Act titers of M145, HY01 and M145-DT. (d) Comparison of the amount of consumed TAGs between M145 and M145-DT. Columns show the consumed amount of fatty acid moieties from cellular TAG pool between 48 h and 96 h. (e) Comparison of the redox status (NADH/NAD+) in M145-DT and M145. (f) Effects of cumate on oxytetracycline (Otc) titer of M4018 and M-DT. (f) Effects of cumate on B1a titer of A56 and A56-DT. Data shown in (b-g) are the average and s.d. of three independent experiments. Significant differences were analyzed by one-way ANOVA, and p < 0.05 was considered statistically significant. The levels of significance are *** p < 0.001, ** p < 0.01, * p < 0.05.

Supplementary Figure 15 Carbon flux distribution of S. avermitilis by metabolic flux analysis.

(a) Simplified metabolic network of S. avermitilis. Flux for biomass biosynthesis from the G6P node (v12) including fluxes from five different precursors (glucose-6-phosphate, fructose-6-phosphate, ribose-5-phosphate, erythrose-4-phosphate, glyceraldehyde-3-phosphate). 15 detected fatty acid moieties from cellular TAGs in S. avermitilis were detected, and flux v21 represents the sum of specific production rate of AcCoA from degradation of these 15 fatty acids. Similarly, v22 and v23 represent the sum of specific production rate of PCoA from degradation of two fatty acids (C15:0 and C17:0) and the sum of specific production rate of MBCoA from degradation of three fatty acids (aiC13:0, aiC15:0, and aiC17:0), respectively. The biochemical reactions, stoichiometric equations and experimentally determined fluxes are shown in Supplementary Table 11–13. (b) Actual fluxes of A56 and A56-DT in fed-batch fermentation. The inset showed the mass balance of the AcCoA node, and fluxes were expressed in unit of the acetyl unit. Data for metabolic flux analysis were collected during stationary phase (120–196 h). Data shown here were the average of three independent experiments. Abbreviations: PCoA, propionyl coenzyme A; MCoA, malonyl coenzyme A; MMCoA, methylmalonyl coenzyme A; MBCoA, 2-methylbutyryl coenzyme A; SucCoA, succinyl coenzyme A; dTDP-Ole, deoxythymidine diphosphate-oleandrose; L-Asp, L-aspartate; L-Thr, L-threonine; Ile, L-isoleucine; B1a, avermectin B1a; other abbreviations were shown in Supplementary Fig. 10.

Supplementary information

Supplementary Materials

Supplementary Figs. 1–15, Tables 1, 3–9 and 11–14 and Notes 1–4.

Reporting Summary

Supplementary Table 2

The identified metabolites in this work.

Supplementary Table 10

Details of 888 ACSs.

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Wang, W., Li, S., Li, Z. et al. Harnessing the intracellular triacylglycerols for titer improvement of polyketides in Streptomyces. Nat Biotechnol 38, 76–83 (2020). https://doi.org/10.1038/s41587-019-0335-4

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