Single cell mutant selection for metabolic engineering of actinomycetes

Actinomycetes are important producers of pharmaceuticals and industrial enzymes. However, wild type strains require laborious development prior to industrial usage. Here we present a generally applicable reporter-guided metabolic engineering tool based on random mutagenesis, selective pressure, and single-cell sorting. We developed fluorescence-activated cell sorting (FACS) methodology capable of reproducibly identifying high-performing individual cells from a mutant population directly from liquid cultures. Genome-mining based drug discovery is a promising source of bioactive compounds, which is complicated by the observation that target metabolic pathways may be silent under laboratory conditions. We demonstrate our technology for drug discovery by activating a silent mutaxanthene metabolic pathway in Amycolatopsis. We apply the method for industrial strain development and increase mutaxanthene yields 9-fold to 99 mg l-1 in a second round of mutant selection. Actinomycetes are an important source of catabolic enzymes, where product yields determine industrial viability. We demonstrate 5-fold yield improvement with an industrial cholesterol oxidase ChoD producer Streptomyces lavendulae to 20.4 U g-1 in three rounds. Strain development is traditionally followed by production medium optimization, which is a time-consuming multi-parameter problem that may require hard to source ingredients. Ultra-high throughput screening allowed us to circumvent medium optimization and we identified high ChoD yield production strains directly from mutant libraries grown under preset culture conditions. In summary, the ability to screen tens of millions of mutants in a single cell format offers broad applicability for metabolic engineering of actinomycetes for activation of silent metabolic pathways and to increase yields of proteins and natural products.

Enhancement of Carrimycin Production Via Traditional Mutagenesis with Metabolic Engineering in Streptomyces Spiramyceticus 54IA

Background: Carrimycin is a new approved class I antibiotic in China. The novel carrimycin producing strain, Streptomyces spiramyceticus 54IA, was constructed by CRISPR-Cas9 editing system without insertion of antibiotics resistant gene. The problem of low yield limits this strain in large scale fermentation. In this study, the carrimycin production was significantly improved by strain mutagenesis coupled metabolic engineering. Results: The sspD gene is responsible for degradation of triacylglycerol to provide precursors of the polyketide biosynthesis. The extra sspD gene controlled by the promoters of pks and bsm42 genes could moderately enhance carrimycin production. The Bsm42 was identified to play a pathway-specific positive regulator for carrimycin biosynthesis. Due to production of carrimycin significantly enhanced by bsm42 overexpression, the two different length promoters of bsm42 individually ligated with two reporter genes were used to monitor bsm42 expression for screening the higher carrimycin production mutants treated by plasma and ultraviolet. 47% of the 608 selected mutants had higher fermentation titer than the starting strain. The shorter promoter of bsm42 displayed more appropriate for selection of the carrimycin production improved mutants. The F2R-15 mutant had highest titer (1010±30 μg/mL), which was about 9 times higher than that of 54IA strain. Comparative analysis of transcriptome profiles of F2R-15 mutant and 54IA strains found 158 differential expression genes with more than 2 fold-changes. The up-regulated genes were associated with macrolide precursor biosynthesis, macrolide-inactivation, antibiotics transporter, oxidative phosphorylation; while the most down-regulated genes were referring to the primary metabolites synthetic genes and biosynthetic genes of other secondary metabolites. Conclusion: These results suggested that manipulation of the positive regulatory gene bsm42 and traditional mutagenesis coupled with reporter-guided mutant selection method facilitated selection of carrimycin high-yielding mutants.

A Computational Framework to Identify Metabolic Engineering Strategies for the Co-Production of Metabolites

Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.