Computer-assisted multilevel optimization of malonyl-CoA availability in Pseudomonas putida

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Industrial Biotechnology

Background:

• Malonyl-CoA is crucial for producing valuable compounds like fatty acids and polyketides.
• Limited intracellular malonyl-CoA hinders microbial production of these chemicals.
• Pseudomonas putida is a versatile host for industrial chemical synthesis.

Purpose of the Study:

• To systematically increase malonyl-CoA levels in Pseudomonas putida.
• To develop a multilevel optimization workflow for microbial hosts.
• To enhance the production of industrially relevant compounds.

Main Methods:

• Utilized a multilevel optimization workflow involving gene downregulation and chassis selection.
• Engineered the acetyl-CoA carboxylase complex via ribosome binding site modification.
• Employed computational tools and high-throughput screening with a malonyl-CoA biosensor.

Main Results:

• Achieved a 5.8-fold enhancement in the production titer of the polyketide phloroglucinol.
• Successfully identified and combined beneficial genetic targets for malonyl-CoA overproduction.
• Demonstrated rapid evaluation of genetic targets using computational and biosensor approaches.

Conclusions:

• The study effectively integrated computational and genetic engineering for P. putida.
• The developed workflow significantly increases malonyl-CoA availability in microbial hosts.
• This research opens new possibilities for industrial strain development and fundamental biological studies.