Engineering improved ethylene production: Leveraging systems biology and adaptive laboratory evolution

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

“… Chromosomal deletion of Δ proB predicted based on iJO1366. Growth-coupled production of ethylene Optimized ethylene bioproduction for further engineering Systems metabolic [17] E. coli W3110 M9 glucose with increasing concentrations of NiCl 2 L -lysine-sensitive biosensor with tetA actuator (conferring resistance against Ni 2+ ) Random promoter libraries targeting ppc expression Growth-coupled production of L -lysine Increased L -lysine production (0.6 g/L) Biosensor-assisted [5] E. coli W A modified glycerol minimal medium with increasing concentration of tetracycline 3-hydroxypropionic acid (3-HB) biosensor with tetA actuator (resistance against tetracycline). Growth-coupled production of 3-hydroxypropionic acid 3-HB production yield closest to the theoretical max (0.91 g/g glycerol) [85] P. putida KT2400 M9 glucose Muconate biosensor with fluorescent reporter actuator Restoration of fitness defect (but failed to isolate high-muconate producing strain) 3-fold increase in muconate production FACS-mediated isolation of growth-restored, high muconate producer mutant [58] * IPTG - isopropyl-β- D -thiogalactopyranoside.…”

“…2 C). Initially applied in E. coli for the overproduction of native and heterologous compounds [15] , [16] , [17] , [18] , [19] , [20] , [70] , [73] , [74] , [75] , [76] , this approach now extends to a diverse array of “GEM-accessible” microorganisms [77] . Evolutionary engineering is increasingly implemented in tandem with GEMs to enhance the target product titer, largely by optimizing cellular metabolism or compensating for fitness defects that would result from metabolic engineering.…”

“…Evolutionary engineering is increasingly implemented in tandem with GEMs to enhance the target product titer, largely by optimizing cellular metabolism or compensating for fitness defects that would result from metabolic engineering. For instance, in an attempt to achieve growth-coupled ethylene production in E. coli , a constraint-based simulation using the model, iJO1366, was employed to identify novel growth-coupling targets [17] . Assuming that growth rate was maintained, deletion of proB (glutamate-5-semialdehyde dehydrogenase) was predicted to render the ethylene pathway as the sole passage for L -proline production, thereby coupling ethylene production with growth via L -proline biosynthesis.…”

“…Assuming that growth rate was maintained, deletion of proB (glutamate-5-semialdehyde dehydrogenase) was predicted to render the ethylene pathway as the sole passage for L -proline production, thereby coupling ethylene production with growth via L -proline biosynthesis. Subsequently, the deletion strain was subjected to ALE with random mutagenesis in the absence of L -proline to select for a high ethylene-producing strain, resulting in a final strain with enhanced solubility of the ethylene-producing enzyme and a 49-fold increase in ethylene production [17] .…”

“…The computational framework of cellular metabolism, known as the genome-scale metabolic model (GEM), provides for a highly accurate description and interpretation of metabolic phenotypes of adaptively evolved strains [13] , [14] . As a result, various GEM-derived systems biology frameworks have been used together with the ALE techniques to improve target compound production [15] , [16] , [17] , [18] , [19] , [20] and acquire complex phenotypes [21] , [22] . Thus, ALE demonstrates extended utility in strain engineering and serves a tool that informs users of the best possible optimization scenarios.…”