Roseoflavin, a promising broad-spectrum antibiotic, is naturally produced by the bacteria Streptomyces davaonensis and Streptomyces cinnabarinus. The key enzymes responsible for roseoflavin biosynthesis and the corresponding genes were recently identified. In this study we aimed to enhance roseoflavin production in S. davaonensis and to synthesize roseoflavin in the heterologous hosts Bacillus subtilis and Corynebacterium glutamicum by (over)expression of the roseoflavin biosynthesis genes.

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

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.

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.

Ashbya gossypii is a filamentous fungus that is currently exploited for the industrial production of riboflavin. In addition, metabolically engineered strains of A. gossypii have also been described as valuable biocatalysts for the production of different metabolites such as folic acid, nucleosides, and biolipids. Hence, bioproduction in A. gossypii relies on the availability of well-performing gene expression systems both for endogenous and heterologous genes. In this regard, the identification of novel promoters, which are critical elements for gene expression, decisively helps to expand the A. gossypii molecular toolbox. In this work, we present an adaptation of the Dual Luciferase Reporter (DLR) Assay for promoter analysis in A. gossypii using integrative cassettes. We demonstrate the efficiency of the analysis through the identification of 10 new promoters with different features, including carbon source-regulatable abilities, that will highly improve the gene expression platforms used in A. gossypii. Three novel strong promoters (PCCW12, PSED1, and PTSA1) and seven medium/weak promoters (PHSP26, PAGL366C, PTMA10, PCWP1, PAFR038W, PPFS1, and PCDA2) are presented. The functionality of the promoters was further evaluated both for the overexpression and for the underexpression of the A. gossypii MSN2 gene, which induced significant changes in the sporulation ability of the mutant strains.

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.

Terpenoids are the largest group of small-molecule natural products, with more than 60,000 compounds made from isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP). As the most diverse group of small-molecule natural products, terpenoids play an important role in the pharmaceutical, food, and cosmetic industries. For decades, Escherichia coli (E. coli) and Saccharomyces cerevisiae (S. cerevisiae) were extensively studied to biosynthesize terpenoids, because they are both fully amenable to genetic modifications and have vast molecular resources. On the other hand, our literature survey (20 years) revealed that terpenoids are naturally more widespread in Bacillales. In the mid-1990s, an inherent methylerythritol phosphate (MEP) pathway was discovered in Bacillus subtilis (B. subtilis). Since B. subtilis is a generally recognized as safe (GRAS) organism and has long been used for the industrial production of proteins, attempts to biosynthesize terpenoids in this bacterium have aroused much interest in the scientific community. This review discusses metabolic engineering of B. subtilis for terpenoid production, and encountered challenges will be discussed. We will summarize some major advances and outline future directions for exploiting the potential of B. subtilis as a desired "cell factory" to produce terpenoids.

The pink or red ketocarotenoids, canthaxanthin and astaxanthin, are used as feed additives in the poultry and aquaculture industries as a source of egg yolk and flesh pigmentation, as farmed animals do not have access to the carotenoid sources of their wild counterparts. Because soybean is already an important component in animal feed, production of these carotenoids in soybean could be a cost-effective means of delivery. In order to characterize the ability of soybean seed to produce carotenoids, soybean cv. Jack was transformed with the crtB gene from Pantoea ananatis, which codes for phytoene synthase, an enzyme which catalyzes the first committed step in the carotenoid pathway. The crtB gene was engineered together in combinations with ketolase genes (crtW from Brevundimonas sp. strain SD212 and bkt1 from Haematococcus pluvialis) to produce ketocarotenoids; all genes were placed under the control of seed-specific promoters. HPLC results showed that canthaxanthin is present in the transgenic seeds at levels up to 52 μg/g dry weight. Transgenic seeds also accumulated other compounds in the carotenoid pathway, such as astaxanthin, lutein, β-carotene, phytoene, α-carotene, lycopene, and β-cryptoxanthin, whereas lutein was the only one of these detected in non-transgenic seeds. The accumulation of astaxanthin, which requires a β-carotene hydroxylase in addition to a β-carotene ketolase, in the transgenic seeds suggests that an endogenous soybean enzyme is able to work in combination with the ketolase transgene. Soybean seeds that accumulate ketocarotenoids could potentially be used in animal feed to reduce or eliminate the need for the costly addition of these compounds.

Ester-linked p-hydroxybenzoate occurs naturally in poplar lignin as pendent groups that can be released by mild alkaline hydrolysis. These 'clip-off' phenolics can be separated from biomass and upgraded into diverse high-value bioproducts. We introduced a bacterial chorismate pyruvate lyase gene into transgenic poplar trees with the aim of producing more p-hydroxybenzoate from chorismate, itself a metabolic precursor to lignin. By driving heterologous expression specifically in the plastids of cells undergoing secondary wall formation, this strategy achieved a 50% increase in cell-wall-bound p-hydroxybenzoate in mature wood and nearly 10 times more in developing xylem relative to control trees. Comparable amounts also remained as soluble p-hydroxybenzoate-containing xylem metabolites, pointing to even greater engineering potential. Mass spectrometry imaging showed that the elevated p-hydroxybenzoylation was largely restricted to the cell walls of fibres. Finally, transgenic lines outperformed control trees in assays of saccharification potential. This study highlights the biotech potential of cell-wall-bound phenolate esters and demonstrates the importance of substrate supply in lignin engineering.

Anthocyanins such as cyanidin 3-O-glucoside (C3G) have wide applications in industry as food colorants. Their current production heavily relies on extraction from plant tissues. Development of a sustainable method to produce anthocyanins is of considerable interest for industrial use. Previously, E. coli-based microbial production of anthocyanins has been investigated extensively. However, safety concerns on E. coli call for the adoption of a safe production host. In the present study, a GRAS bacterium, Corynebacterium glutamicum, was introduced as the host strain to synthesize C3G. We adopted stepwise metabolic engineering strategies to improve the production titer of C3G.

The integration of biotechnology into chemical manufacturing has been recognized as a key technology to build a sustainable society. However, the practical applications of biocatalytic chemical conversions are often restricted due to their complexities involving the unpredictability of product yield and the troublesome controls in fermentation processes. One of the possible strategies to overcome these limitations is to eliminate the use of living microorganisms and to use only enzymes involved in the metabolic pathway. Use of recombinant mesophiles producing thermophilic enzymes at high temperature results in denaturation of indigenous proteins and elimination of undesired side reactions; consequently, highly selective and stable biocatalytic modules can be readily prepared. By rationally combining those modules together, artificial synthetic pathways specialized for chemical manufacturing could be designed and constructed.

The ability of metabolic engineers to conceptualize, implement, and evaluate strain designs has dramatically increased in the last decade. Unlike other engineering fields, no centralized, open-access, and easily searched repository exists for cataloging these designs and the lessons learned from their construction and evaluation. To address this issue, we have developed a repository for metabolic engineering strain designs, known as LASER (Learning Assisted Strain EngineeRing, laser.colorado.edu) and a formal standard for disseminating designs to metabolic engineers. Curation of every available genetically-defined E. coli and S. cerevisiae strain from 310 metabolic engineering papers published over the last 21 years yields a total of 417 designs containing a total of 2661 genetic modifications. This collection has been deposited in LASER and represents the known bibliome of genetically defined and tested metabolic engineering designs in the academic literature. Properties of LASER designs and the analysis pipeline are examined to provide insight into LASER capabilities. Several future research directions utilizing LASER capabilities are discussed to highlight the potential of the LASER database for metabolic engineering.

L-DOPA, also known as Levodopa or L-3,4-dihydroxyphenylalanine, is a non-standard amino acid, and the gold standard drug for the treatment for Parkinson's Disease (PD). Recently, a gene encoding the enzyme that is responsible for its synthesis, as a precursor of the coloured pigment group betalains, was identified in beetroot, BvCYP76AD6. We have engineered tomato fruit enriched in L-DOPA through overexpression of BvCYP76AD6 in a fruit specific manner. Analysis of the transgenic fruit revealed the feasibility of accumulating L-DOPA in a non-naturally betalain-producing plant. Fruit accumulating L-DOPA also showed major effects on the fruit metabolome. Some of these changes included elevation of amino acids levels, changes in the levels of intermediates of the TCA and glycolysis pathways and reductions in the levels of phenolic compounds and nitrogen-containing specialised metabolites. Furthermore, we were able to increase the L-DOPA levels further by elevating the expression of the metabolic master regulator, MYB12, specifically in tomato fruit, together with BvCYP76AD6. Our study elucidated new roles for L-DOPA in plants, because it impacted fruit quality parameters including antioxidant capacity and firmness. The L-DOPA levels achieved in tomato fruit were comparable to the levels in other non-seed organs of L-DOPA - accumulating plants, offering an opportunity to develop new biological sources of L-DOPA by widening the repertoire of L-DOPA-accumulating plants. These tomato fruit could be used as an alternative source of this important pharmaceutical.

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.

We previously introduced the LASER database (Learning Assisted Strain EngineeRing, https://bitbucket.org/jdwinkler/laser_release) (Winkler et al. 2015) to serve as a platform for understanding past and present metabolic engineering practices. Over the past year, LASER has been expanded by 50% to include over 600 engineered strains from 450 papers, including their growth conditions, genetic modifications, and other information in an easily searchable format. Here, we present the results of our efforts to use LASER as a means for defining the complexity of a metabolic engineering "design". We evaluate two complexity metrics based on the concepts of construction difficulty and novelty. No correlation is observed between expected product yield and complexity, allowing minimization of complexity without a performance trade-off. We envision the use of such complexity metrics to filter and prioritize designs prior to implementation of metabolic engineering efforts, thereby potentially reducing the time, labor, and expenses of large-scale projects. Possible future developments based on an expanding LASER database are then discussed.

Gas fermentation using acetogenic bacteria such as Clostridium autoethanogenum offers an attractive route for production of fuel ethanol from industrial waste gases. Acetate reduction to acetaldehyde and further to ethanol via an aldehyde: ferredoxin oxidoreductase (AOR) and alcohol dehydrogenase has been postulated alongside the classic pathway of ethanol formation via a bi-functional aldehyde/alcohol dehydrogenase (AdhE). Here we demonstrate that AOR is critical to ethanol formation in acetogens and inactivation of AdhE led to consistently enhanced autotrophic ethanol production (up to 180%). Using ClosTron and allelic exchange mutagenesis, which was demonstrated for the first time in an acetogen, we generated single mutants as well as double mutants for both aor and adhE isoforms to confirm the role of each gene. The aor1+2 double knockout strain lost the ability to convert exogenous acetate, propionate and butyrate into the corresponding alcohols, further highlighting the role of these enzymes in catalyzing the thermodynamically unfavourable reduction of carboxylic acids into alcohols.

Synthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.

Triacylglycerols (TAGs) are valuable versatile compounds that can be used as metabolites for nutrition and health, as well as feedstocks for biofuel production. Although Saccharomyces cerevisiae is the favored microbial cell factory for industrial production of biochemicals, it does not produce large amounts of lipids and TAGs comprise only ~1% of its cell dry weight. Here, we engineered S. cerevisiae to reorient its metabolism for overproduction of TAGs, by regulating lipid droplet associated-proteins involved in TAG synthesis and hydrolysis. We implemented a push-and-pull strategy by overexpressing genes encoding a deregulated acetyl-CoA carboxylase, ACC1S659A/S1157A(ACC1**), as well as the last two steps of TAG formation: phosphatidic phosphatase (PAH1) and diacylglycerol acyltransferase (DGA1), ultimately leading to 129 mg∙gCDW-1 of TAGs. Disruption of TAG lipase genes TGL3, TGL4, TGL5 and sterol acyltransferase gene ARE1 increased the TAG content to 218 mg∙gCDW-1. Further disruption of the beta-oxidation by deletion of POX1, as well as glycerol-3-phosphate utilization through deletion of GUT2, did not affect TAGs levels. Finally, disruption of the peroxisomal fatty acyl-CoA transporter PXA1 led to accumulation of 254 mg∙gCDW-1. The TAG levels achieved here are the highest titer reported in S. cerevisiae, reaching 27.4% of the maximum theoretical yield in minimal medium with 2% glucose. This work shows the potential of using an industrially established and robust yeast species for high level lipid production.

7-Dehydrocholesterol (7-DHC) has attracted increasing attentions due to its great medical value and the enlarging market demand of its ultraviolet-catalyzed product vitamin D3. Microbial production of 7-DHC from simple carbon has been recognized as an attractive complement to the traditional sources. Even though our previous work realized 7-DHC biosynthesis in Saccharomyces cerevisiae, the current productivity of 7-DHC is still too low to satisfy the demand of following industrialization. As increasing the compatibility between heterologous pathway and host cell is crucial to realize microbial overproduction of natural products with complex structure and relative long pathway, in this study, combined efforts in tuning the heterologous Δ24-dehydrocholesterol reductase (DHCR24) and manipulating host cell were applied to promote 7-DHC accumulation.

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.