A gram-scale limonene production process with engineered escherichia coli

Monoterpenes, such as the cyclic terpene limonene, are valuable and important natural products widely used in food, cosmetics, household chemicals, and pharmaceutical applications. The biotechnological production of limonene with microorganisms may complement traditional plant extraction methods. For this purpose, the bioprocess needs to be stable and ought to show high titers and space-time yields. In this study, a limonene production process was developed with metabolically engineered Escherichia coli at the bioreactor scale. Therefore, fed-batch fermentations in minimal medium and in the presence of a non-toxic organic phase were carried out with E. coli BL21 (DE3) pJBEI-6410 harboring the optimized genes for the mevalonate pathway and the limonene synthase from Mentha spicata on a single plasmid. The feasibility of glycerol as the sole carbon source for cell growth and limonene synthesis was examined, and it was applied in an optimized fermentation setup. Titers on a gram-scale of up to 7.3 g·Lorg−1 (corresponding to 3.6 g·L−1 in the aqueous production phase) were achieved with industrially viable space-time yields of 0.15 g·L−1·h−1. These are the highest monoterpene concentrations obtained with a microorganism to date, and these findings provide the basis for the development of an economic and industrially relevant bioprocess

Functional analysis of genes involved in the biosynthesis of isoprene in Bacillus subtilis

In comparison to other bacteria Bacillus subtilis emits the volatile compound isoprene in high concentrations. Isoprene is the smallest representative of the natural product group of terpenoids. A search in the genome of B. subtilis resulted in a set of genes with yet unknown function, but putatively involved in the methylerythritol phosphate (MEP) pathway to isoprene. Further identification of these genes would give the possibility to engineer B. subtilis as a host cell for the production of terpenoids like the valuable plant-produced drugs artemisinin and paclitaxel. Conditional knock-out strains of putative genes were analyzed for the amount of isoprene emitted. Differences in isoprene emission were used to identify the function of the enzymes and of the corresponding selected genes in the MEP pathway. We give proof on a biochemical level that several of these selected genes from this species are involved in isoprene biosynthesis. This opens the possibilities to investigate the physiological function of isoprene emission and to increase the endogenous flux to the terpenoid precursors, isopentenyl diphosphate and dimethylallyl diphosphate, for the heterologous production of more complex terpenoids in B. subtilis

Enrichment and identification of Δ9-Tetrahydrocannabinolic acid synthase from Pichia pastoris culture supernatants

This data article refers to the report Δ9-Tetrahydrocannabinolic acid synthase (THCAS) production in Pichia pastoris enables chemical synthesis of cannabinoids (Lange et. al. 2015) [2]. THCAS was produced on a 2 L lab scale using recombinant P. pastoris KM71 KE1. Enrichment of THCAS as a technically pure enzyme was realized using dialysis and cationic exchange chromatography. nLC-ESI-MS/MS analysis identified THCAS in different fractions obtained by cationic exchange chromatography

A gram-scale limonene production process with engineered Escherichia coli

Monoterpenes, such as the cyclic terpene limonene, are valuable and important natural products widely used in food, cosmetics, household chemicals, and pharmaceutical applications. The biotechnological production of limonene with microorganisms may complement traditional plant extraction methods. For this purpose, the bioprocess needs to be stable and ought to show high titers and space-time yields. In this study, a limonene production process was developed with metabolically engineered Escherichia coli at the bioreactor scale. Therefore, fed-batch fermentations in minimal medium and in the presence of a non-toxic organic phase were carried out with E. coli BL21 (DE3) pJBEI-6410 harboring the optimized genes for the mevalonate pathway and the limonene synthase from Mentha spicata on a single plasmid. The feasibility of glycerol as the sole carbon source for cell growth and limonene synthesis was examined, and it was applied in an optimized fermentation setup. Titers on a gram-scale of up to 7.3 g·Lorg –1 (corresponding to 3.6 g·L–1 in the aqueous production phase) were achieved with industrially viable space-time yields of 0.15 g·L–1·h–1. These are the highest monoterpene concentrations obtained with a microorganism to date, and these findings provide the basis for the development of an economic and industrially relevant bioprocess.</p

Combinatorial biosynthesis of medicinal plant secondary metabolites

Combinatorial biosynthesis is a new tool in the generation of novel natural products and for the production of rare and expensive natural products. The basic concept is combining metabolic pathways in different organisms on a genetic level. As a consequence heterologous organisms provide precursors from their own primary and secondary metabolism that are metabolised to the desired secondary product due to the expression of foreign genes. In this review we discuss the possibilities and limitations of combining genes from different organisms and the expression of heterologous genes. Major focuses are fundamentals of the genetic work, used expression systems and latest progress in this field. Combinatorial biosynthesis is discussed for important classes of natural products, including alkaloids (vinblastine, vincristine), terpenoids (artemisinin, paclitaxel) and flavonoids. The role and importance of today's used host organisms is critically described, and the latest approaches discussed to give an outlook for future trends and possibilities. (c) 2006 Elsevier B.V. All rights reserved

Back-to-monomer recycling of polycondensation polymers : Opportunities for chemicals and enzymes

The use of plastics in a wide range of applications has grown substantially over recent decades, resulting in enormous growth in production volumes to meet demand. Though a wide range of biomass-derived chemicals and materials are available on the market, the production volumes of such renewable alternatives are currently not sufficient to replace their fossil-based analogues due to various factors, in particular cost-effectiveness. Hence, the majority of plastics are still industrially produced from fossil-based feedstocks. Moreover, various reports have clearly raised concern about the plastics that are not recycled at their end-of-life and instead end up in landfills or the oceans. To avoid further pollution of our planet, it is highly desirable to develop recycling processes that use plastic waste as feedstock. Chemical recycling processes could potentially offer a solution, since they afford monomers from which new polymers can be produced, with the same performance as virgin plastics. In this manuscript, the opportunities for using either chemical or biochemical (i.e., enzymatic) approaches in the depolymerization of polycondensation polymers for recycling purposes are reviewed. Our aim is to highlight the strategies that have been developed so far to break down plastic waste into monomers, providing the first step in the development of chemical recycling processes for plastic waste, and to create a renewed awareness of the need to valorize plastic waste by efficiently transforming it into virgin plastics

Designing Eukaryotic Gene Expression Regulation Using Machine Learning

Controlling the expression of genes is one of the key challenges of synthetic biology. Until recently fine-tuned control has been out of reach, particularly in eukaryotes owing to their complexity of gene regulation. With advances in machine learning (ML) and in particular with increasing dataset sizes, models predicting gene expression levels from regulatory sequences can now be successfully constructed. Such models form the cornerstone of algorithms that allow users to design regulatory regions to achieve a specific gene expression level. In this review we discuss strategies for data collection, data encoding, ML practices, design algorithm choices, and finally model interpretation. Ultimately, these developments will provide synthetic biologists with highly specific genetic building blocks to rationally engineer complex pathways and circuits.</p

Designing Eukaryotic Gene Expression Regulation Using Machine Learning

Controlling the expression of genes is one of the key challenges of synthetic biology. Until recently fine-tuned control has been out of reach, particularly in eukaryotes owing to their complexity of gene regulation. With advances in machine learning (ML) and in particular with increasing dataset sizes, models predicting gene expression levels from regulatory sequences can now be successfully constructed. Such models form the cornerstone of algorithms that allow users to design regulatory regions to achieve a specific gene expression level. In this review we discuss strategies for data collection, data encoding, ML practices, design algorithm choices, and finally model interpretation. Ultimately, these developments will provide synthetic biologists with highly specific genetic building blocks to rationally engineer complex pathways and circuits.</p