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Ending all forms of hunger by 2030, as set forward in the UN-Sustainable Development Goal 2 (UN-SDG2), is a daunting but essential task, given the limited timeline ahead and the negative global health and socio-economic impact of hunger. Malnutrition or hidden hunger due to micronutrient deficiencies affects about one third of the world population and severely jeopardizes economic development. Staple crop biofortification through gene stacking, using a rational combination of conventional breeding and metabolic engineering strategies, should enable a leap forward within the coming decade. A number of specific actions and policy interventions are proposed to reach this goal. Biofortification is an effective means to reduce micronutrient malnutrition. Here, the authors review recent advances in biofortification and propose stacking multiple micronutrient traits into high-yielding varieties through the combination of conventional breeding and genetic engineering approaches.

Background Erythritol is a four-carbon sugar alcohol with sweetening properties that is used by the agro-food industry as a food additive. In the yeast Yarrowia lipolytica , the last step of erythritol synthesis involves the reduction of erythrose by specific erythrose reductase(s). In the earlier report, an erythrose reductase gene ( YALI0F18590g ) from erythritol-producing yeast Y. lipolytica MK1 was identified (Janek et al. in Microb Cell Fact 16:118, 2017 ). However, deletion of the gene in Y. lipolytica MK1 only resulted in some lower erythritol production but the erythritol synthesis process was still maintained, indicating that other erythrose reductase gene(s) might exist in the genome of Y. lipolytica . Results In this study, we have isolated genes g141.t1 ( YALI0D07634g ) and g3023.t1 ( YALI0C13508g ) encoding two novel erythrose reductases (ER). The biochemical characterization of the purified enzymes showed that they have a strong affinity for erythrose. Deletion of the two ER genes plus g801.t1 ( YALI0F18590g ) did not prevent erythritol synthesis, suggesting that other ER or ER-like enzymes remain to be discovered in this yeast. Overexpression of the newly isolated two genes (ER10 or ER25) led to an average 14.7% higher erythritol yield and 31.2% higher productivity compared to the wild-type strain. Finally, engineering NADPH cofactor metabolism by overexpression of genes ZWF1 and GND1 encoding glucose-6-phosphate dehydrogenase and 6-phosphogluconate dehydrogenase, respectively, allowed a 23.5% higher erythritol yield and 50% higher productivity compared to the wild-type strain. The best of our constructed strains produced an erythritol titer of 190 g/L in baffled flasks using glucose as main carbon source. Conclusions Our results highlight that in the Y. lipolytica genome several genes encode enzymes able to reduce erythrose into erythritol. The catalytic properties of these enzymes and their cofactor dependency are different from that of already known erythrose reductase of Y. lipolytica . Constitutive expression of the newly isolated genes and engineering of NADPH cofactor metabolism led to an increase in erythritol titer. Development of fermentation strategies will allow further improvement of this productivity in the future.

The GH10 xylanase XYNIII is expressed in the hyper-cellulase-producing mutant PC-3-7, but not in the standard strain QM9414 of Trichoderma reesei. The GH11 xylanase gene xyn1 is induced by cellulosic and xylanosic carbon sources while xyn3 is induced only by cellulosic carbon sources in the PC-3-7 strain. In this study, we constructed a modified xyn3 promoter in which we replaced the cis-acting region of the xyn3 promoter by the cis-acting region of the xyn1 promoter. The resulting xyn3 chimeric promoter exhibited improved inductivity against cellulosic carbon over the wild-type promoter and acquired inductivity against xylanosic carbon. Furthermore, PC-3-7 expressing the heterologous β-glycosidase gene, Aspergillus aculeatus bgl1, under the control of the xyn3 chimeric promoter, showed enhanced saccharification ability through increased cellobiase activity. We also show that the xyn3 chimeric promoter is also functional in the QM9414 strain. Our results indicate that the xyn3 chimeric promoter is very efficient for enzyme expression.

Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), 13C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs. To commercialize recombinant organisms for renewable chemical production, it is essential to characterize the cost and benefit of metabolic burden using metabolic flux analysis tools. Genome-scale modeling can incorporate 13C-fluxome information and machine learning to predict the metabolic burden of synthetic biology modules. Modularized expression of native or recombinant pathways using a variety of experimental tools for controlling expression can substantially reduce the metabolic burden introduced by these pathways. The development of a standard synthetic-biology publication database may allow the use of machine learning or artificial intelligence to harness past knowledge for future rational design. Detailed computational methods have been developed to model macromolecule synthesis (DNA, RNA, proteins) to account for the maintenance costs associated with basal cellular function. Systems-level dynamic simulations and design algorithms can inform new approaches to engineering microbial production strains.

Metabolic engineering can have a pivotal role in increasing the environmental sustainability of the transportation and chemical manufacturing sectors. The field has already developed engineered microorganisms that are currently being used in industrial-scale processes. However, it is often challenging to achieve the titres, yields and productivities required for commercial viability. The efficiency of microbial chemical production is usually dependent on the physiological traits of the host organism, which may either impose limitations on engineered biosynthetic pathways or, conversely, boost their performance. In this Review, we discuss different aspects of microbial physiology that often create obstacles for metabolic engineering, and present solutions to overcome them. We also describe various instances in which natural or engineered physiological traits in host organisms have been harnessed to benefit engineered metabolic pathways for chemical production.In this Review, Avalos and colleagues discuss different aspects of microbial physiology that can have an impact on engineered metabolic pathways, and they describe instances in which natural or engineered physiological traits in host organisms have been harnessed to benefit engineered metabolic pathways for chemical production.

The design and optimization of biosynthetic pathways for industrially relevant, non-model organisms is challenging due to transformation idiosyncrasies, reduced numbers of validated genetic parts and a lack of high-throughput workflows. Here we describe a platform for in vitro prototyping and rapid optimization of biosynthetic enzymes (iPROBE) to accelerate this process. In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE by screening 54 different cell-free pathways for 3-hydroxybutyrate production and optimizing a six-step butanol pathway across 205 permutations using data-driven design. Observing a strong correlation ( r  = 0.79) between cell-free and cellular performance, we then scaled up our highest-performing pathway, which improved in vivo 3-HB production in Clostridium by 20-fold to 14.63 ± 0.48 g l −1 . We expect iPROBE to accelerate design–build–test cycles for industrial biotechnology. The iPROBE platform accelerates the design and optimization of engineered biosynthetic pathways using a combination of cell-free protein synthesis, in vitro pathway assembly and a scoring system to identify high-performing combinations.

Advances in synthetic biology enable microbial hosts to synthesize valuable natural products in an efficient, cost-competitive and safe manner. However, current engineering endeavors focus mainly on enzyme engineering and pathway optimization, leaving the role of cofactors in microbial production of natural products and cofactor engineering largely ignored. Here we systematically engineered the supply and recycling of three cofactors (FADH2, S-adenosyl-l-methion and NADPH) in the yeast Saccharomyces cerevisiae, for high-level production of the phenolic acids caffeic acid and ferulic acid, the precursors of many pharmaceutical molecules. Tailored engineering strategies were developed for rewiring biosynthesis, compartmentalization and recycling of the cofactors, which enabled the highest production of caffeic acid (5.5 ± 0.2 g l−1) and ferulic acid (3.8 ± 0.3 g l−1) in microbial cell factories. These results demonstrate that cofactors play an essential role in driving natural product biosynthesis and the engineering strategies described here can be easily adopted for regulating the metabolism of other cofactors.Engineering the biosynthesis, compartmentalization and recycling of three cofactors enables increased production of caffeic acid and ferulic acid in yeast, suggesting that these strategies could improve metabolic flux to other desirable compounds.

Metabolic engineers endeavor to create a bio-based manufacturing industry using microbes to produce fuels, chemicals, and medicines. Plant natural products (PNPs) are historically challenging to produce and are ubiquitous in medicines, flavors, and fragrances. Engineering PNP pathways into new hosts requires finding or modifying a suitable host to accommodate the pathway, planning and implementing a biosynthetic route to the compound, and discovering or engineering enzymes for missing steps. In this review, we describe recent developments in metabolic engineering at the level of host, pathway, and enzyme, and discuss how the field is approaching ever more complex biosynthetic opportunities. Engineering microbial cell factories for the production of useful plant natural products (PNPs) is a resource-conserving and environmentally-friendly synthesis route. Here, the authors review recent developments that enable engineering of hosts, pathways, and enzymes to make PNPs and PNP derivatives.

Metabolic engineering seeks to reprogram microbial cells to efficiently and sustainably produce value-added compounds. Since chemical production can be at odds with the cell’s natural objectives, strategies have been developed to balance conflicting goals. For example, dynamic regulation modulates gene expression to favor biomass and metabolite accumulation at low cell densities before diverting key metabolic fluxes toward product formation. To trigger changes in gene expression in a pathway-independent manner without the need for exogenous inducers, researchers have coupled gene expression to quorum-sensing (QS) circuits, which regulate transcription based on cell density. While effective, studies thus far have been limited to one control point. More challenging pathways may require layered dynamic regulation strategies, motivating the development of a generalizable tool for regulating multiple sets of genes. We have developed a QS-based regulation tool that combines components of the lux and esa QS systems to simultaneously and dynamically up- and down-regulate expression of 2 sets of genes. Characterization of the circuit revealed that varying the expression level of 2 QS components leads to predictable changes in switching dynamics and that using components from 2 QS systems allows for independent tuning capability. We applied the regulation tool to successfully address challenges in both the naringenin and salicylic acid synthesis pathways. Through these case studies, we confirmed the benefit of having multiple control points, predictable tuning capabilities, and independently tunable regulation modules.

Systems metabolic engineering is facilitating the development of high-performing microbial cell factories for producing chemicals and materials. However, constructing an efficient microbial cell factory still requires exploring and selecting various host strains, as well as identifying the best-suited metabolic engineering strategies, which demand significant time, effort, and costs. Here, we comprehensively evaluate the capacities of various microbial cell factories and propose strategies for systems metabolic engineering steps, including host strain selection, metabolic pathway reconstruction, and metabolic flux optimization. We analyze the metabolic capacities of five representative industrial microorganisms as cell factories for the production of 235 different bio-based chemicals and suggest the most suitable host strain for the corresponding chemical production. To improve the innate metabolic capacity by constructing more efficient metabolic pathways, heterologous metabolic reactions, and cofactor exchanges are systematically analyzed. Additionally, we present metabolic engineering strategies, which include up- and down-regulation target reactions, for the improved production of chemicals. Altogether, this study will serve as a comprehensive resource for the systems metabolic engineering of microorganisms in the bio-based production of chemicals. Constructing an efficient microbial cell factory still requires exploring and selecting various host strains, as well as identifying the best-suited metabolic engineering strategies, which demand significant time, effort, and costs. Here the authors calculate the maximum yields of 235 bio-based chemicals in 5 different microbes, evaluated heterologous reactions and cofactor swaps, and predicted engineering strategies, providing a comprehensive resource for systems metabolic engineering.