Abstract
Pseudomonas putida is evolutionarily endowed with features relevant for bioproduction, especially under harsh operating conditions. The rich metabolic versatility of this species, however, comes at the price of limited formation of acetyl-coenzyme A (CoA) from sugar substrates. Since acetyl-CoA is a key metabolic precursor for a number of added-value products, in this work we deployed an in silico-guided rewiring program of central carbon metabolism for upgrading P. putida as a host for acetyl-CoA-dependent bioproduction. An updated kinetic model, integrating fluxomics and metabolomics datasets in addition to manually-curated information of enzyme mechanisms, identified targets that would lead to increased acetyl-CoA levels. Based on these predictions, a set of plasmids based on clustered regularly interspaced short palindromic repeats (CRISPR) and dead CRISPR-associated protein 9 (dCas9) was constructed to silence genes by CRISPR interference (CRISPRi). Dynamic reduction of gene expression of two key targets (gltA, encoding citrate synthase, and the essential accA gene, encoding subunit A of the acetyl-CoA carboxylase complex) mediated an 8-fold increase in the acetyl-CoA content of rewired P. putida. Poly(3-hydroxybutyrate) (PHB) was adopted as a proxy of acetyl-CoA availability, and two synthetic pathways were engineered for biopolymer accumulation. By including cell morphology as an extra target for the CRISPRi approach, fully rewired P. putida strains programmed for PHB accumulation had a 5-fold increase in PHB titers in bioreactor cultures using glucose. Thus, the strategy described herein allowed for rationally redirecting metabolic fluxes in P. putida from central metabolism towards product biosynthesis-especially relevant when deletion of essential pathways is not an option.
Highlights
After decades of continuous and intensive technological develop ment, a broad range of bulk and fine chemicals, drugs and biofuels can be accessed by metabolic engineering of microorganisms (Choi et al, 2019; Ko et al, 2020; Nielsen and Keasling, 2016; Smanski et al, 2016)
Building on the latest genome-scale metabolic reconstructions for strain KT2440 (Belda et al, 2016; Nogales et al, 2020), our core kinetic model comprising a detailed description of the reactions involved in glucose uptake, glycolysis, pentose phosphate pathway and tricarboxylic acid (TCA) cycle (Fig. 1A and Table S3 in the Supplementary Data)
Canonical approaches for prediction of targets for manipulation include flux balance analysis-guided methods based on genome-scale or kinetic models and metabolic control analysis based on kinetic models (Banerjee et al, 2020; Gopalakrishnan et al, 2020; Lloyd et al, 2018; Maia et al, 2016)
Summary
After decades of continuous and intensive technological develop ment, a broad range of bulk and fine chemicals, drugs and biofuels can be accessed by metabolic engineering of microorganisms (Choi et al, 2019; Ko et al, 2020; Nielsen and Keasling, 2016; Smanski et al, 2016). Multiple strategies have been deployed to this end, including the se lection of suitable hosts for strain engineering (Becker and Wittmann, 2018; Calero and Nikel, 2019), modelling control of biochemical net works designed in silico (Fang et al, 2020; Volkova et al, 2020) and elimination or fine-tuned balancing of competing pathways (Nishida and Kondo, 2020; Shen et al, 2019)—an aspect supported by the ever expanding synthetic biology toolbox (Chen et al, 2020; Lammens et al., 2020; Young et al, 2021) In this sense, the inception of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and the CRISPR-associated protein 9 (Cas9) of Streptococcus pyogenes played a central role in our ability to access and harness the metabolic potential of microbial species (Jakociunas et al, 2017; Tarasava et al, 2018; Zhao et al, 2021), including non-traditional bacterial platforms (Banerjee et al, 2020; Batianis et al, 2020; Kim et al, 2020; Tan et al, 2018; Weimer et al, 2020).
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