In silico reconstruction and experimental validation of Saccharopolyspora erythraea genome-scale metabolic model iZZ1342 that accounts for 1685 ORFs

Background: Saccharopolyspora erythraea (S. erythraea) is a Gram-positive erythromycin-producing filamentous bacterium. The lack of comprehensive S. erythraea genome-scale metabolic models (GEMs) hinders the efficiency of metabolic engineering as well as fermentation process optimization. Results: In this study, the GEMs model of S. erythraea iZZ1342 was reconstructed according to the latest genome annotations, omics databases, and literatures. Compared with the previous S. erythraea model-GSMR, the new model iZZ1342 presented great improvements both on scope and coverage in the number of reactions, metabolites, and annotated genes. In detail, the number of unique reactions in iZZ1342 was increased from 1482 to 1684, the number of metabolites was increased from 1546 to 1614, and the number of unique genes was increased from 1272 to 1342. We also added 1441 gene-protein-reaction associations in iZZ1342 which lacks in the previous model to overcome the limitation in the application of strain designing. Compared with the transcriptomics data obtained from the published literature, 86.3% ORFs and 92.9% reactions in iZZ1342 can be verified. The results of the sensitivity analysis showed the similar trend in the E. coli GEMs. The prediction of growth on available 27 kinds of carbon sources and 33 kinds of nitrogen sources showed the accuracy rate was 77.8 and 87.9%, respectively. Compared with the physiological data obtained from chemostat cultivation, the simulation results showed good consistency. The correlation coefficient between the C-13-labeled experiment data and the flux simulation result was 0.97. All the above results showed that the iZZ1342 model has good performance. Furthermore, four genes are in the range of successful knockout by comparing these targets with the results which have been earlier published. Conclusion: The new model iZZ1342 improved significantly in model size and prediction performance, which will lay a good foundation to study the systematic metabolic engineering of S. erythraea system in vivo.