Implementation of Synthetic Pathways to Foster Microbe-Based Production of Non-Naturally Occurring Carboxylic Acids and Derivatives.

Microbially produced carboxylic acids (CAs) are considered key players in the implementation of more sustainable industrial processes due to their potential to replace a set of oil-derived commodity chemicals. Most CAs are intermediates of microbial central carbon metabolism, and therefore, a biochemical production pathway is described and can be transferred to a host of choice to enable/improve production at an industrial scale. However, for some CAs, the implementation of this approach is difficult, either because they do not occur naturally (as is the case for levulinic acid) or because the described production pathway cannot be easily ported (as it is the case for adipic, muconic or glucaric acids). Synthetic biology has been reshaping the range of molecules that can be produced by microbial cells by setting new-to-nature pathways that leverage on enzyme arrangements not observed in vivo, often in association with the use of substrates that are not enzymes' natural ones. In this review, we provide an overview of how the establishment of synthetic pathways, assisted by computational tools for metabolic retrobiosynthesis, has been applied to the field of CA production. The translation of these efforts in bridging the gap between the synthesis of CAs and of their more interesting derivatives, often themselves non-naturally occurring molecules, is also reviewed using as case studies the production of methacrylic, methylmethacrylic and poly-lactic acids.

Prospecting Biochemical Pathways to Implement Microbe-Based Production of the New-to-Nature Platform Chemical Levulinic Acid.

Levulinic acid is a versatile platform molecule with potential to be used as an intermediate in the synthesis of many value-added products used across different industries, from cosmetics to fuels. Thus far, microbial biosynthetic pathways having levulinic acid as a product or an intermediate are not known, which restrains the development and optimization of a microbe-based process envisaging the sustainable bioproduction of this chemical. One of the doors opened by synthetic biology in the design of microbial systems is the implementation of new-to-nature pathways, that is, the assembly of combinations of enzymes not observed in vivo, where the enzymes can use not only their native substrates but also non-native ones, creating synthetic steps that enable the production of novel compounds. Resorting to a combined approach involving complementary computational tools and extensive manual curation, in this work, we provide a thorough prospect of candidate biosynthetic pathways that can be assembled for the production of levulinic acid in Escherichia coli or Saccharomyces cerevisiae. Out of the hundreds of combinations screened, five pathways were selected as best candidates on the basis of the availability of substrates and of candidate enzymes to catalyze the synthetic steps (that is, those steps that involve conversions not previously described). Genome-scale metabolic modeling was used to assess the performance of these pathways in the two selected hosts and to anticipate possible bottlenecks. Not only does the herein described approach offer a platform for the future implementation of the microbial production of levulinic acid but also it provides an organized research strategy that can be used as a framework for the implementation of other new-to-nature biosynthetic pathways for the production of value-added chemicals, thus fostering the emerging field of synthetic industrial microbiotechnology.

Metabolic engineering of an acid-tolerant yeast strain Pichia kudriavzevii for itaconic acid production.

Itaconic acid (IA), or 2-methylenesuccinic acid, has a broad spectrum of applications in the biopolymer industry owing to the presence of one vinyl bond and two acid groups in the structure. Its polymerization can follow a similar mechanism as acrylic acid but additional functionality can be incorporated into the extra beta acid group. Currently, the bio-based production of IA in industry relies on the fermentation of the filamentous fungus Aspergillus terreus. However, the difficulties associated with the fermentation undertaken by filamentous fungi together with the pathogenic potential of A. terreus pose a serious challenge for industrial-scale production. In recent years, there has been increasing interest in developing alternative production hosts for fermentation processes that are more homogenous in the production of organic acids. Pichia kudriavzevii is a non-conventional yeast with high acid tolerance to organic acids at low pH, which is a highly desirable trait by easing downstream processing. We introduced cis-aconitic acid decarboxylase gene (cad) from A. terreus (designated At_cad) into this yeast and established the initial titer of IA at 135 â€‹± â€‹5 â€‹mg/L. Subsequent overexpression of a native mitochondrial tricarboxylate transporter (herein designated Pk_mttA) presumably delivered cis-aconitate efficiently to the cytosol and doubled the IA production. By introducing the newly invented CRISPR-Cas9 system into P. kudriavzevii, we successfully knocked out both copies of the gene encoding isocitrate dehydrogenase (ICD), aiming to increase the availability of cis-aconitate. The resulting P. kudriavzevii strain, devoid of ICD and overexpressing Pk_mttA and At_cad on its genome produced IA at 505 â€‹± â€‹17.7 â€‹mg/L in shake flasks, and 1232 â€‹± â€‹64 â€‹mg/L in fed-batch fermentation. Because the usage of an acid-tolerant species does not require pH adjustment during fermentation, this work demonstrates the great potential of engineering P. kudriavzevii as an industrial chassis for the production of organic acid.

OptPipe - a pipeline for optimizing metabolic engineering targets.

BACKGROUND: We propose OptPipe - a Pipeline for Optimizing Metabolic Engineering Targets, based on a consensus approach. The method generates consensus hypotheses for metabolic engineering applications by combining several optimization solutions obtained from distinct algorithms. The solutions are ranked according to several objectives, such as biomass and target production, by using the rank product tests corrected for multiple comparisons. RESULTS: OptPipe was applied in a genome-scale model of Corynebacterium glutamicum for maximizing malonyl-CoA, which is a valuable precursor for many phenolic compounds. In vivo experimental validation confirmed increased malonyl-CoA level in case of ΔsdhCAB deletion, as predicted in silico. CONCLUSIONS: A method was developed to combine the optimization solutions provided by common knockout prediction procedures and rank the suggested mutants according to the expected growth rate, production and a new adaptability measure. The implementation of the pipeline along with the complete documentation is freely available at https://github.com/AndrasHartmann/OptPipe .