Combining metabolomics and network analysis to improve tacrolimus production in Streptomyces tsukubaensis using different exogenous feedings

Abstract

Tacrolimus is widely used as an immunosuppressant in the treatment of various autoimmune diseases. However, the low fermentation yield of tacrolimus has thus far restricted its industrial applications. To solve this problem, the time-series response mechanisms of the intracellular metabolism that were highly correlated with tacrolimus biosynthesis were investigated using different exogenous feeding strategies in S. tsukubaensis. The metabolomic datasets, which contained 93 metabolites, were subjected to weighted correlation network analysis (WGCNA), and eight distinct metabolic modules and seven hub metabolites were identified to be specifically associated with tacrolimus biosynthesis. The analysis of metabolites within each metabolic module suggested that the pentose phosphate pathway (PPP), shikimate and aspartate pathway might be the main limiting factors in the rapid synthesis phase of tacrolimus accumulation. Subsequently, all possible key-limiting steps in the above metabolic pathways were further screened using a genome-scale metabolic network model (GSMM) of S. tsukubaensis. Based on the prediction results, two newly identified targets (aroC and dapA) were overexpressed experimentally, and both of the engineered strains showed higher tacrolimus production. Moreover, the best strain, HT-aroC/dapA, that was engineered to simultaneously enhanced chorismate and lysine biosynthesis was able to produce 128.19 mg/L tacrolimus, 1.64-fold higher than control (78.26 mg/L). These findings represent a valuable addition to our understanding of tacrolimus accumulation in S. tsukubaensis, and pave the way to further production improvements.

Genome‐Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production

“…iMAT was also used to integrate transcriptome data obtained from four different time points of the cultivation profile with the GEM of S. ambofaciens in order to understand changes in overall metabolic flux distributions during mycelial growth and spiramycin production along the time . In another study, metabolome data together with the GEM simulation were useful in narrowing a list of candidate gene manipulation targets . Analysis of metabolome data covering 93 metabolites suggested that pentose phosphate pathway, shikimate pathway, and aspartate pathway need to be better streamlined for the enhanced production of tacrolimus using S. tsukubaensis .…”

“…In another study, metabolome data together with the GEM simulation were useful in narrowing a list of candidate gene manipulation targets . Analysis of metabolome data covering 93 metabolites suggested that pentose phosphate pathway, shikimate pathway, and aspartate pathway need to be better streamlined for the enhanced production of tacrolimus using S. tsukubaensis . This initial information from the metabolome data was further analyzed using the S. tsukubaensis GEM in order to pinpoint individual rate‐limiting reactions in the abovementioned metabolic pathways; simultaneous overexpression of two newly identified targets, aroC and dapA from shikimate and lysine biosynthetic pathways, respectively, led to 1.64‐fold increase in the tacrolimus production in comparison with the wild‐type .…”

“…Dynamic FBA and MOMA for the tacrolimus production; [23] and GEM-based prediction of knockout and overexpression targets for the tacrolimus production [22] 129.8% Increase in the tacrolimus production titer as a result of gcdh knockout and overexpression of tktB, msdh and ask; [23] 1.64-fold increase in the tacrolimus production titer via overexpression of (Continued) aroC and dapA; [42] 1.47 fold increase in the tacrolimus titer via gdhA knockout and overexpression of dahp, accA2, and zwf2; [22] 146% increase in the tacrolimus production titer via overexpression of fkbOPLMD, and 70% further increase via feed optimization [57] Streptomyces lividans TK24 4: D'Huys et al…”

Toward Systems Metabolic Engineering of Streptomycetes for Secondary Metabolites Production

“…Some noteworthy examples include the production of tacrolimus (FK506) using Streptomyces tsukubaensis, [67][68][69] ascomycin (FK520) using Streptomyces hygroscopicus var. ascomyceticus, [70][71][72] avermectin using S. avermitilis, [73] and pristinamycin I (PI) and II (PII) using Streptomyces pristinaespiralis.…”

“…ascomyceticus, [70][71][72] avermectin using S. avermitilis, [73] and pristinamycin I (PI) and II (PII) using Streptomyces pristinaespiralis. [74,75] To solve the issue of low production yield of tacrolimus in the natural producer, Wang et al [67] first determined the intracellular response of S. tsukubaensis to exogenous feeding of four precursors known to promote the tacrolimus biosynthesis. Using a weighted correlation network analysis (WGCNA) on a dataset containing 93 different intracellular metabolites measured with gas chromatography (GC)-MS and LC-MS/MS, metabolites highly associated with the tacrolimus biosynthesis were identified.…”