Expanding Pseudomonas taiwanensis VLB120's acyl-CoA portfolio: Propionate production in mineral salt medium.

As one of the main precursors, acetyl-CoA leads to the predominant production of even-chain products. From an industrial biotechnology perspective, extending the acyl-CoA portfolio of a cell factory is vital to producing industrial relevant odd-chain alcohols, acids, ketones and polyketides. The bioproduction of odd-chain molecules can be facilitated by incorporating propionyl-CoA into the metabolic network. The shortest pathway for propionyl-CoA production, which relies on succinyl-CoA catabolism encoded by the sleeping beauty mutase operon, was evaluated in Pseudomonas taiwanensis VLB120. A single genomic copy of the sleeping beauty mutase genes scpA, argK and scpB combined with the deletion of the methylcitrate synthase PVLB_08385 was sufficient to observe propionyl-CoA accumulation in this Pseudomonas. The chassis' capability for odd-chain product synthesis was assessed by expressing an acyl-CoA hydrolase, which enabled propionate synthesis. Three fed-batch strategies during bioreactor fermentations were benchmarked for propionate production, in which a maximal propionate titre of 2.8 g L-1 was achieved. Considering that the fermentations were carried out in mineral salt medium under aerobic conditions and that a single genome copy drove propionyl-CoA production, this result highlights the potential of Pseudomonas to produce propionyl-CoA derived, odd-chain products.

Cross-Species Synthetic Promoter Library: Finding Common Ground between Pseudomonas taiwanensis VLB120 and Escherichia coli.

The potential of nonmodel organisms for industrial biotechnology is increasingly becoming evident since advances in systems and synthetic biology have made it possible to explore their unique traits. However, the lack of adequately characterized genetic elements that drive gene expression impedes benchmarking nonmodel with model organisms. Promoters are one of the genetic elements that contribute significantly to gene expression, but information about their performance in different organisms is limited. This work addresses this bottleneck by characterizing libraries of synthetic σ70-dependent promoters controlling the expression of msfGFP, a monomeric, superfolder green fluorescent protein, in both Escherichia coli TOP10 and Pseudomonas taiwanensis VLB120, a less explored microbe with industrially attractive attributes. We adopted a standardized method for comparing gene promoter strength across species and laboratories. Our approach uses fluorescein calibration and adjusts for cell growth variation, enabling accurate cross-species comparisons. The quantitative description of promoter strength is a valuable expansion of P. taiwanensis VLB120's genetic toolbox, while the comparison with the performance in E. coli facilitates the evaluation of P. taiwanensis VLB120's potential as a chassis for biotechnology applications.

Proteome Regulation Patterns Determine Escherichia coli Wild-Type and Mutant Phenotypes.

It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes.IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today's whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli Being equally computationally lightweight as classical stoichiometric models and allowing for the application of established in silico tools, the PAM and related simulation approaches will foster the use of a model-driven metabolic research. Applications range from the investigation of mechanisms of microbial evolution to the determination of optimal strain design strategies in metabolic engineering, thus supporting basic scientists and engineers alike.

High titer methyl ketone production with tailored Pseudomonas taiwanensis VLB120.

Methyl ketones present a group of highly reduced platform chemicals industrially produced from petroleum-derived hydrocarbons. They find applications in the fragrance, flavor, pharmacological, and agrochemical industries, and are further discussed as biodiesel blends. In recent years, intense research has been carried out to achieve sustainable production of these molecules by re-arranging the fatty acid metabolism of various microbes. One challenge in the development of a highly productive microbe is the high demand for reducing power. Here, we engineered Pseudomonas taiwanensis VLB120 for methyl ketone production as this microbe has been shown to sustain exceptionally high NAD(P)H regeneration rates. The implementation of published strategies resulted in 2.1 g Laq-1 methyl ketones in fed-batch fermentation. We further increased the production by eliminating competing reactions suggested by metabolic analyses. These efforts resulted in the production of 9.8 g Laq-1 methyl ketones (corresponding to 69.3 g Lorg-1 in the in situ extraction phase) at 53% of the maximum theoretical yield. This represents a 4-fold improvement in product titer compared to the initial production strain and the highest titer of recombinantly produced methyl ketones reported to date. Accordingly, this study underlines the high potential of P. taiwanensis VLB120 to produce methyl ketones and emphasizes model-driven metabolic engineering to rationalize and accelerate strain optimization efforts.

Determination of growth-coupling strategies and their underlying principles.

BACKGROUND: Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts. RESULTS: We adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies. CONCLUSION: Maximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.

Genetic Optimization Algorithm for Metabolic Engineering Revisited.

To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives; (ii) the identification of gene target-sets according to logical gene-protein-reaction associations; (iii) minimization of the number of network perturbations; and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.