Enhanced ε-Poly-L-Lysine Production in Streptomyces albulus through Multi-Omics-Guided Metabolic Engineering

Abstract

Safe and eco-friendly preservatives are crucial to preventing food spoilage and illnesses, as foodborne diseases caused by pathogens result in approximately 600 million cases of illness and 420,000 deaths annually. ε-Poly-L-lysine (ε-PL) is a novel food preservative widely used in many countries. However, its commercial application has been hindered by high costs and low production. In this study, ε-PL's biosynthetic capacity was enhanced in Streptomyces albulus WG608 through metabolic engineering guided by multi-omics techniques. Based on transcriptome and metabolome data, differentially expressed genes (fold change >2 or <0.5; p < 0.05) and differentially expressed metabolites (fold change >1.2 or <0.8) were separately subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The integrative analysis of transcriptome, metabolome, and overexpression revealed the essential roles of isocitrate lyase, succinate dehydrogenase, flavoprotein subunit, diaminopimelate dehydrogenase, polyphosphate kinase, and polyP:AMP phosphotransferase in ε-PL biosynthesis. Subsequently, a strain with enhanced ATP supply, L-lysine supply, and ε-PL synthetase expression was constructed to improve its production. Finally, the resulting strain, S. albulus WME10, achieved an ε-PL production rate of 77.16 g/L in a 5 L bioreactor, which is the highest reported ε-PL production to date. These results suggest that the integrative analysis of the transcriptome and metabolome can facilitate the identification of key pathways and genetic elements affecting ε-PL synthesis, guiding further metabolic engineering and thus significantly enhancing ε-PL production. The method presented in this study could be applicable to other valuable natural antibacterial agents.

Keywords: antimicrobial; metabolic engineering; multi-omics; preservatives; ε-Poly-L-lysine.

Figure 4

Effect of the increasing ATP supply and overexpressing ε-PL synthetase on ε-PL production. (A) Engineering for enhancing ε-PL synthesis in S. albulus WG608. (B) Schematic of the construction of strains WME1-WME4. (C) Effect of the overexpressing different modules on ε-PL production. pls, ppk, pap, and vgb represent the genes encoding ε-PL synthetase, polyphosphate kinase, polyP: AMP phosphotransferase and Vitreoscilla hemoglobin, respectively. All data were from biological triplicates. Error bars represent standard deviation. The statistical analysis was performed by one-way ANOVA analysis; *, **, and *** indicate p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001 relative to the control (WG608), respectively.

Figure 1

Overview of transcriptome results at different fermentation phases. (A) Venn diagrams of differentially expressed genes. (B) Pearson correlation heat map of gene expression amount. (C) Numbers of differentially expressed genes.

Figure 2

Overview of metabolome results at different fermentation phases. (A) Principal component analysis of the metabolome in positive ion mode. (B) Principal component analysis of the metabolome in negative ion mode. (C) Numbers of differential expressed metabolites.

Figure 3

Transcriptional and metabolic changes in the ε-PL biosynthesis pathway. Squares represent the metabolome; circles represent the transcriptome; red represents upregulated expression of genes/increased accumulation of metabolites; green represents downregulated expression of genes/decreased accumulation of metabolites.

Figure 5

Effect of the overexpression of zwf, aceA, and sdhA on ε-PL production. (A) NADPH concentration of strains WG608 and M-Z18 during fermentation. (B) NADPH/NADP⁺ ratio of strains WG608 and M-Z18 during fermentation. (C) Schematic of the construction of strains WME5-9. (D) Effect of the overexpressing different genes on ε-PL production. aceA, sdhA, ddh^bg, and ddh^cg represent the genes encoding isocitrate lyase, succinate dehydrogenase flavoprotein subunit, diaminopimelic dehydrogenase from Bacillus sphaericus, and diaminopimelic dehydrogenase from Corynebacterium glutamicum. All data were from biological triplicates. Error bars represent standard deviation. The statistical analysis was performed by one-way ANOVA analysis; *, **, ***, and **** indicate p ≤ 0.05, p ≤ 0.01, p ≤ 0.001, and p ≤ 0.0001 relative to the control (WG608), respectively.

Figure 6

Construction of engineering WME10 and its fermentation performance. (A) ε-PL biosynthesis pathway in S. albulus, red lines represent genes overexpressed in WME10. (B) Shake-flask fermentation performance of WME10 and WG608. (C–E) ε-PL production, DCW, and ATP concentration of WME10 and WG608 in the fed-batch fermentation. ε-PL, ε-poly-L-lysine; DCW, cell dry weight; ATP, adenosine triphosphate. The red dots represent WG608, and the purple square represents WME10. The data are presented as averages, and the error bars represent standard deviations (n = 3 independent experiments). The statistical analysis was performed by one-way ANOVA analysis; *** indicates p ≤ 0.001 relative to the control (WG608), respectively.