Rational Genetic Engineering Of Ascomycin-producing Strain Based On Metabolic Pathway Analysis Together With ¹³C Parallel Labeling Expriments

This research was carried out mainly centered on the line of “extracellular mutagenesis screening – metabolic pathway analysis together with ¹³C-labeling experiments to predict target genes related to high- yield ascomycin- genes engineering guided with models – fermentation optimization”. A high-yield ascomycin producing engineering strain was achieved. The main results were as follows:First, a shikimic acid resistant strain SA68 was obtained with ascomycin production of 330 mg/L, 22.2% higher than the parental strain FS35（270 mg/L）. Under the optimal condition of shikimic acid addition, the ascomycin production up to 450 mg/L, displaying further 36.4% increment. To get deep insights into the effects of shikimic acid resistance and addition on ascomycin biosynthesis, fermentation properties, enzymes activities, metabolites and genes transcriptional levels were analyzed and evaluated. The potential targets were revealed for higher ascomycin production, which were the amplification of carbon flux toward shikimic acid by enhancing 3-deoxy-d-arabino- heptulosonate-7-phosphate synthase（DAHPS） activities and eliminating the feedback inhibition of aromatic amino acids on DAHPS activities, and the elevation of FkbO activities by amplifying fkbO gene to increase the flux of shikimic acid to chorismic acid further to 4,5-dihydroxycyclohex-1-enecarboxylic acid in Streptomyces hygroscopicus var. ascomyceticus.In order to further increase the capacity of mutant SA68 for ascomycin biosynthesis, a rational strain engineering approach was implemented guided by the systematic metabolic pathway analysis. Based on the metabolic network of S. coelicolor and bioinformatics databases of KEGG and BioCyc, the initial central metabolic network of S. hygroscopicus var. ascomyceticus SA68 was constructed. Using the 13 C parallel labeling expriments, the network was verified and calibrated with statistically acceptable minimum residual sum of squares as index, acquiring high precision center metabolic network that meet all 13 C labeling experimental data. The specific confirmed reactions for ascomycin biosynthesis were supplemented, and center metabolic network model involved in ascomycin biosynthesis was obtained, which contained 81 reactions. Then, the p recise model with 81 reactions was analyzed by elementary mode analysis and Flux Design algorithm, and a series of potential targets that could improve the ascomycin production were identified. E leven of them were potential target genes dependent on the cell physiological condition, and the twenty not dependent on the cell physiological condition. These target genes were quantized and ranked.With the guidance of model, the fkbO gene and pyc gene that significantly positively and negatively influenced ascomycin biosynthesis, respectively, were selected as candidates for rational molecular mod ification of mutant SA68. The fkbO gene and pyc gene were overexpressed and knocked out, respectively, according to the model prediction, resulted in a 15.6% and 8.9% improvement of ascomycin, up to 520 mg/L and 490 mg/L, compared with the control strain S A68, which were in accordance with the prediction results of model. Moreover, the combined effect of the genetic modifications was evaluated. Results showed that the strain TD-ΔPyc- fkbO with pyc deletion and fkbO overexpression successfully increased the titer of ascomycin, up to 550 mg/L, 22.2% higher than SA68. After fermentation optimization, the production of ascomycin by strain TD-ΔPyc-fkbO was improved to 610 mg/L, further 35.6% increment with yield elevated by 45.5%, up to 12.8 mg/g glucose. These results demonstrated the central metabolic network and the approach for targets identification were efficient and could guide the ascomycin production improvement.