Enhanced FK506 production in Streptomyces tsukubaensis by rational feeding strategies based on comparative metabolic profiling analysis

Abstract

FK506, a widely used immunosuppressant, is produced by industrial fermentation processes using various Streptomyces species. However, the low titer becomes a bottleneck for its application and industrialization. It urgently required a full understanding of the biological mechanisms for FK506 overproduction. Towards this end, comparative metabolomics approach was employed to analyze metabolite concentrations difference of Streptomyces tsukubaensis cultivated in two media with low and high productivities. Initially, 98 intracellular metabolites were identified and 13 metabolites involved in five pathways were determined to be directly correlated with FK506 biosynthesis. Then in-depth analysis elucidated how those key factors exerted influence on FK506 biosynthesis. Many previously unreported metabolites were shown to play an important role in FK506 biosynthesis and provided potential regulation points for external manipulation. Based on such key information, rationally designed feeding strategy was carried out. Results showed that the FK506 yield increased from 251 to 405 mg/L, whereas, by-products FK520 and 37,38-dihydro-FK506 decreased by 31% and 39%, respectively, compared with the values of control. To our knowledge, it is the first study to apply the comparative metabolomics method to identify key metabolites to promote the FK506 production. The strategies developed here can easily be extended to titer improvement of other important microbial natural products and process optimization.

Comparative metabolic profiling reveals the key role of amino acids metabolism in the rapamycin overproduction by Streptomyces hygroscopicus

“…However, the pH value of the mutant and wild-type strain suddenly increased to 5.2 and 5.8 after 120 h, while rapamycin production began to decrease. It is speculated that it might be due to an effect of organic acids' consumption [45]. And previous research has reported that a weak alkaline final pH value was unfavorable for the accumulation of rapamycin [25].…”

“…On the other hand, high intracellular abundance of aromatic amino acids may influence other metabolic reactions. For example, as the rate-limiting step in shikimate synthesis originating from erythrose 4-phosphate and phosphoenolpyruvate, 3-deoxy-7-phosphoheptulosonate synthase could be inhibited due to high intracellular abundance of aromatic amino acids [45].…”

Integrating multi-omics analyses of Nonomuraea dietziae to reveal the role of soybean oil in [(4′-OH)MeLeu]4-CsA overproduction

“…Interestingly, addition of 0.1% (w/v) soybean oil could further increase the conversion rate by 55.6%. Although soybean oil has been proved to accelerate the strain’s growth and improve the antibiotic production [18–20], the specific effects on [(4′-OH)MeLeu] 4 -CsA production may be complex and comprehensive because the conversion process from CsA to [(4′-OH)MeLeu] 4 -CsA is a monooxygenation reaction and seems irrelevant to soybean oil. Hence, to unveil the potential mechanism, in this study, the two-dimensional gel electrophoresis (2-DE) coupled with matrix-assisted laser-desorption/ionization time-of-flight/time-of-flight mass spectrometry (MALDI-TOF/TOF–MS) and gas chromatography–mass spectrometry (GC–MS), were employed for proteomic and metabolomic analyses, respectively.…”

A dynamic pathway analysis approach reveals a limiting futile cycle in N-acetylglucosamine overproducing Bacillus subtilis

“…The current repertoire to answer these questions ranges from computational methods that are often based on genome-scale models678 to omics methods that monitor cellular responses at the level of molecular component concentrations or metabolic fluxes91011121314. A particular problem is diagnosis of rate-limiting steps in supply or within synthetic pathways from omics-type data.…”

“…A particular problem is diagnosis of rate-limiting steps in supply or within synthetic pathways from omics-type data. As most diagnosis tools are rather time-consuming, typical applications focus on steady-state analyses, such as transcriptome-based identification of an intermediate for pool size increase to improve riboflavin production10, proteome-based identification of two rate-limiting steps in isopentenol synthesis11 or metabolomics-based identification of an optimal intermediate feeding approach to improve tacrolimus production12. In particular, dynamic metabolomics has great diagnostic potential as was elegantly demonstrated by bottleneck identification within in vitro reconstituted glycolysis15 and a new method for near real-time metabolomics16.…”