As a novel cofactor of oxidoreductase, pyrroloquinoline quinone (PQQ) has a great potential of application in medicine, food industries. In order to improve the efficiency of the PQQ production by Methylobacterium extorquens AM1, the strain was treated by atmospheric and room temperature plasma (ARTP). Positive mutants with changes in PQQ yield were obtained based on a high-throughput screening approach. After ARTP treatment, analysis data show that the positive mutation rate was 31.6%. Furthermore, we obtained an excellent positive mutant M. extorquens AM1 (E-F3) with the yield of 54.0 mg/L PQQ, which was approximately 3 times as much compared with that of the wild-type strain. The robust high-throughput screening method for mutagenesis by ARTP improves PQQ production. In addition, this method also provides a new strategy for further strain improvement.
Bacterial Proteins ; biosynthesis ; High-Throughput Screening Assays ; Methylobacterium extorquens ; enzymology ; genetics ; Mutagenesis ; PQQ Cofactor ; biosynthesis ; Plasma Gases ; Temperature

Here we report a systematic method for constructing a large scale kinetic metabolic model and its initial application to the modeling of central metabolism of Methylobacterium extorquens AM1, a methylotrophic and environmental important bacterium. Its central metabolic network includes formaldehyde metabolism, serine cycle, citric acid cycle, pentose phosphate pathway, gluconeogensis, PHB synthesis and acetyl-CoA conversion pathway, respiration and energy metabolism. Through a systematic and consistent procedure of finding a set of parameters in the physiological range we overcome an outstanding difficulty in large scale kinetic modeling: the requirement for a massive number of enzymatic reaction parameters. We are able to construct the kinetic model based on general biological considerations and incomplete experimental kinetic parameters. Our method consists of the following major steps: (1) using a generic enzymatic rate equation to reduce the number of enzymatic parameters to a minimum set while still preserving their characteristics; (2) using a set of steady state fluxes and metabolite concentrations in the physiological range as the expected output steady state fluxes and metabolite concentrations for the kinetic model to restrict the parametric space of enzymatic reactions; (3) choosing enzyme constants K's and K'(eqS) optimized for reactions under physiological concentrations, if their experimental values are unknown; (4) for models which do not cover the entire metabolic network of the organisms, designing a dynamical exchange for the coupling between the metabolism represented in the model and the rest not included.
Citric Acid ; metabolism ; Computer Simulation ; Energy Metabolism ; Formaldehyde ; metabolism ; Kinetics ; Metabolic Networks and Pathways ; Methylobacterium extorquens ; genetics ; growth & development ; metabolism ; Models, Biological ; Serine ; metabolism ; Systems Biology ; methods