Garcia-Morales, L. | Search bioRxiv

bioRxiv2024

Combinatorial engineering reveals shikimate pathway bottlenecks in para-aminobenzoic acid production in Pseudomonas putida

Combinatorial approaches in metabolic engineering enable the optimization of multigene pathways, thereby improving product titers. However, the optimization of complex metabolic pathways is hindered by their multiple interactions. Testing all possible combinations of suitable genetic parts is often prevented by the large number of possible variants. A valuable alternative to this is to use statistical design of experiments and linear modeling to collect important information for optimization without testing every possible combination. The shikimate pathway is an example of a complex metabolic pathway involved in the production of aromatic compounds, which are prevalent in industry. In this study, we explore the impact of the modulation of the expression levels of all the genes in the shikimate and para-aminobenzoic acid (pABA) biosynthesis pathways for pABA production (a widely used industrial intermediate) in Pseudomonas putida. We used this approach to select 14 representative strains from a total of 512 possible combinations. We obtained a range of product titers from 2 to 186.2 mg/l. This information was used to guide a second round of strain construction to further increase the production to 232.1 mg/l. Using this strategy, we demonstrate that aroB expression, encoding 3-dehydroquinate synthase, is a significant limiting factor in the production of pABA.

synthetic biology↗

bioRxiv2019

Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media

Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective, and vaccination is required. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation for vaccine production challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict and validate in vitro two serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach enabled us to optimize medium composition, capacitated growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive vaccine production.

systems biology↗