Systems metabolic engineering of Corynebacterium glutamicum for the efficient production of ß-alanine.

ß-Alanine is an important ß-amino acid with a growing demand in a wide range of applications in chemical and food industries. However, current industrial production of ß-alanine relies on chemical synthesis, which usually involves harmful raw materials and harsh production conditions. Thus, there has been increasing demand for more sustainable, yet efficient production process of ß-alanine. In this study, we constructed Corynebacterium glutamicum strains for the highly efficient production of ß-alanine through systems metabolic engineering. First, aspartate 1-decarboxylases (ADCs) from seven different bacteria were screened, and the Bacillus subtilis ADC showing the most efficient ß-alanine biosynthesis was used to construct a ß-alanine-producing base strain. Next, genome-scale metabolic simulations were conducted to optimize multiple metabolic pathways in the base strain, including phosphotransferase system (PTS)-independent glucose uptake system and the biosynthesis of key precursors, including oxaloacetate and L-aspartate. TCA cycle was further engineered for the streamlined supply of key precursors. Finally, a putative ß-alanine exporter was newly identified, and its overexpression further improved the ß-alanine production. Fed-batch fermentation of the final engineered strain BAL10 (pBA2_tr18) produced 166.6 g/L of ß-alanine with the yield and productivity of 0.28 g/g glucose and 1.74 g/L/h, respectively. To our knowledge, this production performance corresponds to the highest titer, yield and productivity reported to date for the microbial fermentation.

Predicting the effects of cultivation condition on gene regulation in Escherichia coli by using deep learning.

Cell's physiology is affected by cultivation conditions at varying degrees, including carbon sources and inorganic nutrients in growth medium, and the presence or absence of aeration. When examining the effects of cultivation conditions on the cell, the cell's transcriptional response is often examined first among other phenotypes (e.g., proteome and metabolome). In this regard, we developed DeepMGR, a deep learning model that predicts the effects of culture media on gene regulation in Escherichia coli. DeepMGR specifically classifies the direction of gene regulation (i.e., upregulation, no regulation, or downregulation) for an input gene in comparison with M9 minimal medium with glucose as a control condition. For this classification task, DeepMGR uses a feedforward neural network to process: i) DNA sequence of a target gene, ii) presence or absence of aeration and trace elements, and iii) concentration and structural information (SMILES) of up to ten nutrients. The complete DeepMGR showed accuracy of 0.867 and F1 score of 0.703 for a test set from the gold standard dataset. DeepMGR was further subjected to simulation studies for validation where regulation directions for groups of homologous genes were predicted, and the DeepMGR results were compared with the literature with focus on carbon sources that upregulate specific genes. DeepMGR will be useful for designing experiments to understand gene regulations, especially in the context of metabolic engineering.

Comparative genomic analysis of Streptomyces rapamycinicus NRRL 5491 and its mutant overproducing rapamycin.

Streptomyces rapamycinicus NRRL 5491 is a well-known producer of rapamycin, a secondary metabolite with useful bioactivities, including antifungal, antitumor, and immunosuppressive functions. For the enhanced rapamycin production, a rapamycin-overproducing strain SRMK07 was previously obtained as a result of random mutagenesis. To identify genomic changes that allowed the SRMK07 strain's enhanced rapamycin production, genomes of the NRRL 5491 and SRMK07 strains were newly sequenced in this study. The resulting genome sequences of the wild-type and SRMK07 strains showed the size of 12.47 Mbp and 9.56 Mbp, respectively. Large deletions were observed at both end regions of the SRMK07 strain's genome, which cover 17 biosynthetic gene clusters (BGCs) encoding secondary metabolites. Also, genes in a genomic region containing the rapamycin BGC were shown to be duplicated. Finally, comparative metabolic network analysis using these two strains' genome-scale metabolic models revealed biochemical reactions with different metabolic fluxes, which were all associated with NADPH generation. Taken together, the genomic and computational approaches undertaken in this study suggest biological clues for the enhanced rapamycin production of the SRMK07 strain. These clues can also serve as a basis for systematic engineering of a production host for further enhanced rapamycin production.