Production of 2,3-butanediol in Saccharomyces cerevisiae by in silico aided metabolic engineering

Self-controlled in silico gene knockdown strategies to enhance the sustainable production of heterologous terpenoid by Saccharomyces cerevisiae

Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1, fol2, fol3, abz1, and abz2, or a combined knockout of hfd1, ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△fol3), EK5 (OA07△abz1), and EK6 (OA07△abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1, abz2, and at acetyl-CoA carboxylase-encoding gene acc1, to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L-1 in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design.

Evaluation of enzyme-constrained genome-scale model through metabolic engineering of anaerobic co-production of 2,3-butanediol and glycerol by Saccharomyces cerevisiae

Enzyme-constrained genome-scale models (ecGEMs) have potential to predict phenotypes in a variety of conditions, such as growth rates or carbon sources. This study investigated if ecGEMs can guide metabolic engineering efforts to swap anaerobic redox-neutral ATP-providing pathways in yeast from alcoholic fermentation to equimolar co-production of 2,3-butanediol and glycerol. With proven pathways and low product toxicity, the ecGEM solution space aligned well with observed phenotypes. Since this catabolic pathway provides only one-third of the ATP of alcoholic fermentation (2/3 versus 2 ATP per glucose), the ecGEM predicted a growth decrease from 0.36 h-1 in the reference to 0.175 h-1 in the engineered strain. However, this <3-fold decrease would require the specific glucose consumption rate to increase. Surprisingly, after the pathway swap the engineered strain immediately grew at 0.15 h-1 with a glucose consumption rate of 29 mmol (g CDW)-1 h-1, which was indeed higher than reference (23 mmol (g CDW)-1 h-1) and one of the highest reported for S. cerevisiae. The accompanying 2,3-butanediol- (15.8 mmol (g CDW)-1 h-1) and glycerol (19.6 mmol (g CDW)-1 h-1) production rates were close to predicted values. Proteomics confirmed that this increased consumption rate was facilitated by enzyme reallocation from especially ribosomes (from 25.5 to 18.5 %) towards glycolysis (from 28.7 to 43.5 %). Subsequently, 200 generations of sequential transfer did not improve growth of the engineered strain, showing the use of ecGEMs in predicting opportunity space for laboratory evolution. The observations in this study illustrate both the current potential, as well as future improvements, of ecGEMs as a tool for both metabolic engineering and laboratory evolution.

Snapshot of the Probiotic Potential of Kluveromyces marxianus DMKU-1042 Using a Comparative Probiogenomics Approach

Kluyveromyces marxianus is a rapidly growing thermotolerant yeast that secretes a variety of lytic enzymes, utilizes different sugars, and produces ethanol. The probiotic potential of this yeast has not been well explored. To evaluate its probiotic potential, the yeast strain Kluyveromyces marxianus DMKU3-1042 was analyzed using next-generation sequencing technology. Analysis of the genomes showed that the yeast isolates had a GC content of 40.10-40.59%. The isolates had many genes related to glycerol and mannose metabolism, as well as genes for acetoin and butanediol metabolism, acetolactate synthase subunits, and lactic acid fermentation. The strain isolates were also found to possess genes for the synthesis of different vitamins and Coenzyme A. Genes related to heat and hyperosmotic shock tolerance, as well as protection against reactive oxygen species were also found. Additionally, the isolates contained genes for the synthesis of lysine, threonine, methionine, and cysteine, as well as genes with anticoagulation and anti-inflammatory properties. Based on our analysis, we concluded that the strain DMKU3-1042 possesses probiotic properties that make it suitable for use in food and feed supplementation.

Improved 2,3-Butanediol Production Rate of Metabolically Engineered Saccharomyces cerevisiae by Deletion of RIM15 and Activation of Pyruvate Consumption Pathway

Saccharomyces cerevisiae is a promising host for the bioproduction of higher alcohols, such as 2,3-butanediol (2,3-BDO). Metabolically engineered S. cerevisiae strains that produce 2,3-BDO via glycolysis have been constructed. However, the specific 2,3-BDO production rates of engineered strains must be improved. To identify approaches to improving the 2,3-BDO production rate, we investigated the factors contributing to higher ethanol production rates in certain industrial strains of S. cerevisiae compared to laboratory strains. Sequence analysis of 11 industrial strains revealed the accumulation of many nonsynonymous substitutions in RIM15, a negative regulator of high fermentation capability. Comparative metabolome analysis suggested a positive correlation between the rate of ethanol production and the activity of the pyruvate-consuming pathway. Based on these findings, RIM15 was deleted, and the pyruvate-consuming pathway was activated in YHI030, a metabolically engineered S. cerevisiae strain that produces 2,3-BDO. The titer, specific production rate, and yield of 2,3-BDO in the test tube-scale culture using the YMS106 strain reached 66.4 ± 4.4 mM, 1.17 ± 0.017 mmol (g dry cell weight h)-1, and 0.70 ± 0.03 mol (mol glucose consumed)-1. These values were 2.14-, 2.92-, and 1.81-fold higher than those of the vector control, respectively. These results suggest that bioalcohol production via glycolysis can be enhanced in a metabolically engineered S. cerevisiae strain by deleting RIM15 and activating the pyruvate-consuming pathway.

Improving ribonucleic acid production in Saccharomyces pastorianus via in silico genome-scale metabolic network model

Ribonucleic acid (RNA) and its degradation products are important biomolecules widely used in the food and pharmaceutical industries for their flavoring and nutritional functions. In this study, we used a genome-scale metabolic network model (GSMM) to explore genetic targets for nucleic acid synthesis in a Saccharomyces pastorianus strain (G03). Yeast 8.5.0 was used as the base model, which accurately predicted G03's growth. Using OptForce, we found that overexpression of ARO8 and ATP1 among six different strategies increased the RNA content of G03 by 58.0% and 74.8%, respectively. We also identified new metabolic targets for improved RNA production using a modified GSMM called TissueModel, constructed using the GIMME transcriptome constraint tool to remove low-expressed reactions in the model. After running OptKnock, the RNA content of G03-△BNA1 and G03-△PMA1 increased by 44.6% and 39.8%, respectively, compared to G03. We suggest that ATP1, ARO8, BNA1, and PMA1 regulate cell fitness, which affects RNA content. This study is the first to identify strategies for RNA overproduction using GSMM and to report that regulation of ATP1, ARO8, BNA1, and PMA1 can increase RNA content in S. pastorianus. These findings also provide valuable knowledge on model reconstruction for S. pastorianus.

Sustainable metabolic engineering requires a perfect trifecta

The versatility of cellular metabolism in converting various substrates to products inspires sustainable alternatives to conventional chemical processes. Metabolism can be engineered to maximize the yield, rate, and titer of product generation. However, the numerous combinations of substrate, product, and organism make metabolic engineering projects difficult to navigate. A perfect trifecta of substrate, product, and organism is prerequisite for an environmentally and economically sustainable metabolic engineering endeavor. As a step toward this endeavor, we propose a reverse engineering strategy that starts with product selection, followed by substrate and organism pairing. While a large bioproduct space has been explored, the top-ten compounds have been synthesized mainly using glucose and model organisms. Unconventional feedstocks (e.g. hemicellulosic sugars and CO2) and non-model organisms are increasingly gaining traction for advanced bioproduct synthesis due to their specialized metabolic modes. Judicious selection of the substrate-organism-product combination will illuminate the untapped territory of sustainable metabolic engineering.

Identification of Bacterial Metabolites Modulating Breast Cancer Cell Proliferation and Epithelial-Mesenchymal Transition

Breast cancer patients are characterized by the oncobiotic transformation of multiple microbiome communities, including the gut microbiome. Oncobiotic transformation of the gut microbiome impairs the production of antineoplastic bacterial metabolites. The goal of this study was to identify bacterial metabolites with antineoplastic properties. We constructed a 30-member bacterial metabolite library and screened the library compounds for effects on cell proliferation and epithelial-mesenchymal transition. The metabolites were applied to 4T1 murine breast cancer cells in concentrations corresponding to the reference serum concentrations. However, yric acid, glycolic acid, d-mannitol, 2,3-butanediol, and trans-ferulic acid exerted cytostatic effects, and 3-hydroxyphenylacetic acid, 4-hydroxybenzoic acid, and vanillic acid exerted hyperproliferative effects. Furthermore, 3-hydroxyphenylacetic acid, 4-hydroxybenzoic acid, 2,3-butanediol, and hydrocinnamic acid inhibited epithelial-to-mesenchymal (EMT) transition. We identified redox sets among the metabolites (d-mannitol-d-mannose, 1-butanol-butyric acid, ethylene glycol-glycolic acid-oxalic acid), wherein only one partner within the set (d-mannitol, butyric acid, glycolic acid) possessed bioactivity in our system, suggesting that changes to the local redox potential may affect the bacterial secretome. Of the nine bioactive metabolites, 2,3-butanediol was the only compound with both cytostatic and anti-EMT properties.

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (k_app) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

A highly efficient transcriptome-based biosynthesis of non-ethanol chemicals in Crabtree negative Saccharomyces cerevisiae

BACKGROUND

Owing to the Crabtree effect, Saccharomyces cerevisiae produces a large amount of ethanol in the presence of oxygen and excess glucose, leading to a loss of carbon for the biosynthesis of non-ethanol chemicals. In the present study, the potential of a newly constructed Crabtree negative S. cerevisiae, as a chassis cell, was explored for the biosynthesis of various non-ethanol compounds.

RESULTS

To understand the metabolic characteristics of Crabtree negative S. cerevisiae sZJD-28, its transcriptional profile was compared with that of Crabtree positive S. cerevisiae CEN.PK113-11C. The reporter GO term analysis showed that, in sZJD-28, genes associated with translational processes were down-regulated, while those related to carbon metabolism were significantly up-regulated. To verify a potential increase in carbon metabolism for the Crabtree negative strain, the production of non-ethanol chemicals, derived from different metabolic nodes, was then undertaken for both sZJD-28 and CEN.PK113-11C. At the pyruvate node, production of 2,3-butanediol and lactate in sZJD-28-based strains was remarkably higher than that of CEN.PK113-11C-based ones, representing 16.8- and 1.65-fold increase in titer, as well as 4.5-fold and 0.65-fold increase in specific titer (mg/L/OD), respectively. Similarly, for shikimate derived p-coumaric acid, the titer of sZJD-28-based strain was 0.68-fold higher than for CEN.PK113-11C-based one, with a 0.98-fold increase in specific titer. While farnesene and lycopene, two acetoacetyl-CoA derivatives, showed 0.21- and 1.88-fold increases in titer, respectively. From malonyl-CoA, the titer of 3-hydroxypropionate and fatty acids in sZJD-28-based strains were 0.19- and 0.76-fold higher than that of CEN.PK113-11C-based ones, respectively. In fact, yields of products also improved by the same fold due to the absence of residual glucose. Fed-batch fermentation further showed that the titer of free fatty acids in sZJD-28-based strain 28-FFA-E reached 6295.6 mg/L with a highest reported specific titer of 247.7 mg/L/OD in S. cerevisiae.

CONCLUSIONS

Compared with CEN.PK113-11C, the Crabtree negative sZJD-28 strain displayed a significantly different transcriptional profile and obvious advantages in the biosynthesis of non-ethanol chemicals due to redirected carbon and energy sources towards metabolite biosynthesis. The findings, therefore, suggest that a Crabtree negative S. cerevisiae strain could be a promising chassis cell for the biosynthesis of various chemicals.

Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment

The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.

Development of an industrial yeast strain for efficient production of 2,3-butanediol

As part of the transition from a fossil resources-based economy to a bio-based economy, the production of platform chemicals by microbial cell factories has gained strong interest. 2,3-butanediol (2,3-BDO) has various industrial applications, but its production by microbial fermentation poses multiple challenges. We have engineered the bacterial 2,3-BDO synthesis pathway, composed of AlsS, AlsD and BdhA, in a pdc-negative version of an industrial Saccharomyces cerevisiae yeast strain. The high concentration of glycerol caused by the excess NADH produced in the pathway from glucose to 2,3-BDO was eliminated by overexpression of NoxE and also in a novel way by combined overexpression of NDE1, encoding mitochondrial external NADH dehydrogenase, and AOX1, encoding a heterologous alternative oxidase expressed inside the mitochondria. This was combined with strong downregulation of GPD1 and deletion of GPD2, to minimize glycerol production while maintaining osmotolerance. The HGS50 strain produced a 2,3-BDO titer of 121.04 g/L from 250 g/L glucose, the highest ever reported in batch fermentation, with a productivity of 1.57 g/L.h (0.08 g/L.h per gCDW) and a yield of 0.48 g/g glucose or with 96% the closest to the maximum theoretical yield ever reported. Expression of Lactococcus lactis NoxE, encoding a water-forming NADH oxidase, combined with similar genetic modifications, as well as expression of Candida albicans STL1, also minimized glycerol production while maintaining high osmotolerance. The HGS37 strain produced 130.64 g/L 2,3-BDO from 280 g/L glucose, with productivity of 1.58 g/L.h (0.11 g/L.h per gCDW). Both strains reach combined performance criteria adequate for industrial implementation.

Diversity and sex differences in rectal gland volatiles of Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)

Rectal gland volatiles are key mediators of sexual interactions in tephritid fruit flies. We used solid-phase microextraction (SPME) plus gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID) to substantially expand rectal gland chemical characterisation of the Queensland fruit fly (Bactrocera tryoni (Diptera: Tephritidae); Qfly). The SPME GC-MS analysis identified 24 of the 30 compounds previously recorded from Qfly rectal glands, plus another 21 compounds that had not previously been reported. A few amides and fatty acid esters dominated the chromatograms of males and females respectively, but we also found other esters, alcohols and aldehydes and a ketone. The GC-FID analyses also revealed over 150 others, as yet unidentified, volatiles, generally in lesser amounts. The GC-FID analyses also showed 49 and 12 compounds were male- and female-specific, respectively, both in single sex (virgin) and mixed sex (mostly mated) groups. Another ten compounds were male-specific among virgins but undetected in mixed sex groups, and 29 were undetected in virgins but male-specific in mixed sex groups. The corresponding figures for females were four and zero, respectively. Most short retention time peaks (including a ketone and an ester) were male-specific, whereas most female-biased peaks (including five fatty acid esters) had long retention times. Our results indicate previously unsuspected diversity of rectal gland volatiles that might have pheromone functions in males, but far fewer in females.

Enhancement of 2,3-Butanediol Production by Klebsiella pneumoniae: Emphasis on the Mediation of sRNA-SgrS on the Carbohydrate Utilization

The demand for renewable energy is increasing. Klebsiella pneumoniae is one of the most promising strains to produce 2,3-butanediol (2,3-BD). Compared with chemical methods, the biological production of 2,3-BD has the characteristics of substrate safety, low cost, and low energy consumption. However, excessive glucose concentrations can cause damage to cells. Therefore, this study investigated the effect of sRNA-SgrS as a sugar transport regulator on the fermentative production of 2,3-BD by K. pneumoniae in response to sugar stress. We designed multiple mutants of K. pneumoniae HD79 to redistribute its carbon flux to produce 2,3-BD. It was found that the 2,3-BD yield of sgrS overexpressed strain decreased by 44% compared with the original strain. The results showed that a high concentration of sRNA-SgrS could accelerate the degradation of ptsG mRNA (encoding the glucose transporter EIICBGlc) and downregulate the expression levels of the budA gene (encoding the α-acetyllactate decarboxylase) and the budB gene (encoding the α-acetyllactate synthase) and budC gene (encoding the 2,3-BD dehydrogenase) but had no effect on the ack gene (encoding the acetate kinase) and the ldh gene (encoding the lactate dehydrogenase). It provides a theoretical basis and a technical reference for understanding the complex regulation mechanism of sRNA in microorganisms and the genetics and breeding in industrial fermentation engineering.

In Silico Design Strategies for the Production of Target Chemical Compounds Using Iterative Single-Level Linear Programming Problems

The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use.

In silico target-based strain engineering of Saccharomyces cerevisiae for terpene precursor improvement

Systems-based metabolic engineering enables cells to enhance product formation by predicting gene knockout and overexpression targets using modeling tools. FOCuS, a novel metaheuristic tool, was used to predict flux improvement targets in terpenoid pathway using the genome-scale model of Saccharomyces cerevisiae, iMM904. Some of the key knockout target predicted includes LYS1, GAP1, AAT1, AAT2, TH17, KGD-m, MET14, PDC1 and ACO1. It was also observed that the knockout reactions belonged either to fatty acid biosynthesis, amino acid synthesis pathways or nucleotide biosynthesis pathways. Similarly, overexpression targets such as PFK1, FBA1, ZWF1, TDH1, PYC1, ALD6, TPI1, PDX1 and ENO1 were established using three different existing gene amplification algorithms. Most of the overexpression targets belonged to glycolytic and pentose phosphate pathways. Each of these targets had plausible role for improving flux toward sterol pathway and were seemingly not artifacts. Moreover, an in vitro study as validation was carried with overexpression of ALD6 and TPI1. It was found that there was an increase in squalene synthesis by 2.23- and 4.24- folds, respectively, when compared with control. In general, the rationale for predicting these in silico targets was attributed to either increasing the acetyl-CoA precursor pool or regeneration of NADPH, which increase the sterol pathway flux.

Bioengineering for the industrial production of 2,3-butanediol by the yeast, Saccharomyces cerevisiae

Owing to issues, such as the depletion of petroleum resources and price instability, the development of biorefinery related technologies that produce fuels, electric power, chemical substances, among others, from renewable resources is being actively promoted. 2,3-Butanediol (2,3-BDO) is a key compound that can be used to produce various chemical substances. In recent years, 2,3-BDO production using biological processes has attracted extensive attention for achieving a sustainable society through the production of useful compounds from renewable resources. With the development of genetic engineering, metabolic engineering, synthetic biology, and other research field, studies on 2,3-BDO production by the yeast, Saccharomyces cerevisiae, which is safe and can be fabricated using an established industrial-scale cultivation technology, have been actively conducted. In this review, we sought to describe 2,3-BDO and its derivatives; discuss 2,3-BDO production by microorganisms, in particular S. cerevisiae, whose research and development has made remarkable progress; describe a method for separating and recovering 2,3-BDO from a microbial culture medium; and propose future prospects for the industrial production of 2,3-BDO by microorganisms.

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

An integrated in vivo/in vitro framework to enhance cell-free biosynthesis with metabolically rewired yeast extracts

Cell-free systems using crude cell extracts present appealing opportunities for designing biosynthetic pathways and enabling sustainable chemical synthesis. However, the lack of tools to effectively manipulate the underlying host metabolism in vitro limits the potential of these systems. Here, we create an integrated framework to address this gap that leverages cell extracts from host strains genetically rewired by multiplexed CRISPR-dCas9 modulation and other metabolic engineering techniques. As a model, we explore conversion of glucose to 2,3-butanediol in extracts from flux-enhanced Saccharomyces cerevisiae strains. We show that cellular flux rewiring in several strains of S. cerevisiae combined with systematic optimization of the cell-free reaction environment significantly increases 2,3-butanediol titers and volumetric productivities, reaching productivities greater than 0.9 g/L-h. We then show the generalizability of the framework by improving cell-free itaconic acid and glycerol biosynthesis. Our coupled in vivo/in vitro metabolic engineering approach opens opportunities for synthetic biology prototyping efforts and cell-free biomanufacturing.

Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.

Metabolic engineering of non-pathogenic microorganisms for 2,3-butanediol production

2,3-Butanediol (2,3-BDO) is a promising commodity chemical with various industrial applications. While petroleum-based chemical processes currently dominate the industrial production of 2,3-BDO, fermentation-based production of 2,3-BDO provides an attractive alternative to chemical-based processes with regards to economic and environmental sustainability. The achievement of high 2,3-BDO titer, yield, and productivity in microbial fermentation is a prerequisite for the production of 2,3-BDO at large scales. Also, enantiopure production of 2,3-BDO production is desirable because 2,3-BDO stereoisomers have unique physicochemical properties. Pursuant to these goals, many metabolic engineering strategies to improve 2,3-BDO production from inexpensive sugars by Klebsiella oxytoca, Bacillus species, and Saccharomyces cerevisiae have been developed. This review summarizes the recent advances in metabolic engineering of non-pathogenic microorganisms to enable efficient and enantiopure production of 2,3-BDO. KEY POINTS: • K. oxytoca, Bacillus species, and S. cerevisiae have been engineered to achieve efficient 2,3-BDO production. • Metabolic engineering of non-pathogenic microorganisms enabled enantiopure production of 2,3-BDO. • Cost-effective 2,3-BDO production can be feasible by using renewable biomass.

Prospects on bio-based 2,3-butanediol and acetoin production: Recent progress and advances

The bio-based platform chemicals 2,3-butanediol (BDO) and acetoin have various applications in chemical, cosmetics, food, agriculture, and pharmaceutical industries, whereas the derivatives of BDO could be used as fuel additives, polymer and synthetic rubber production. This review summarizes the novel technological developments in adapting genetic and metabolic engineering strategies for selection and construction of chassis strains for BDO and acetoin production. The valorization of renewable feedstocks and bioprocess development for the upstream and downstream stages of bio-based BDO and acetoin production are discussed. The techno-economic aspects evaluating the viability and industrial potential of bio-based BDO production are presented. The commercialization of bio-based BDO and acetoin production requires the utilization of crude renewable resources, the chassis strains with high fermentation production efficiencies and development of sustainable purification or conversion technologies.

Recent advances in microbial biosynthesis of C3 - C5 diols: Genetics and process engineering approaches

Diols derived from renewable feedstocks have significant commercial interest in polymer, pharmaceutical, cosmetics, flavors and fragrances, food and feed industries. In C3-C5 diols biological processes of 1,3-propanediol, 1,2-propanediol and 2,3-butanediol have been commercialized as other isomers are non-natural metabolites and lack natural biosynthetic pathways. However, the developments in the field of systems and synthetic biology paved a new path to learn, build, construct, and test for efficient chassis strains. The current review addresses the recent advancements in metabolic engineering, construction of novel pathways, process developments aimed at enhancing in production of C3-C5 diols. The requisites on developing an efficient and sustainable commercial bioprocess for C3-C5 diols were also discussed.

Evaluation of 2,3-Butanediol Production from Red Seaweed Gelidium amansii Hydrolysates Using Engineered Saccharomyces cerevisiae

Hyper-thermal (HT) acid hydrolysis of red seaweed Gelidium amansii was performed using 12% (w/v) slurry and an acid mix concentration of 180 mM at 150°C for 10 min. Enzymatic saccharification when using a combination of Celluclast 1.5 L and CTec2 at a dose of 16 U/ml led to the production of 12.0 g/l of reducing sugar with an efficiency of enzymatic saccharification of 13.2%. After the enzymatic saccharification, 2,3-butanediol (2,3-BD) fermentation was carried out using an engineered S. cerevisiae strain. The use of HT acid-hydrolyzed medium with 1.9 g/l of 5-hydroxymethylfurfural showed a reduction in the lag time from 48 to 24 h. The 2,3-BD concentration and yield coefficient at 72 h were 14.8 g/l and 0.30, respectively. Therefore, HT acid hydrolysis and the use of the engineered S. cerevisiae strain can enhance the overall 2,3-BD yields from G. amansii seaweed.

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

Lignocellulosic bio-refinery approach for microbial 2,3-Butanediol production

Bio-refinery approach using agricultural and industrial waste material as feedstock is becoming a preferred area of interest in biotechnology in the current decades. The reasons for this trend are mainly because of the declining petroleum resources, greenhouse gas emission risks and fluctuating market price of crude oil. Most chemicals synthesized petro chemically, can be produced using microbial biocatalysts. 2,3-Butanediol (BDO) is such an important platform bulk chemical with numerous industrial applications including as a fuel additive. Although microbial production of BDO is well studied, strategies that could successfully upgrade the current lab-scale researches to an industrial level have to be developed. This review presents an overview of the recent trends and developments in the microbial production of BDO from different lignocellulose biomass.

In Vitro Selection of Peptides and Proteins-Advantages of mRNA Display

mRNA display is a robust in vitro selection technique that allows the selection of peptides and proteins with desired functions from libraries of trillions of variants. mRNA display relies upon a covalent linkage between a protein and its encoding mRNA molecule; the power of the technique stems from the stability of this link, and the large degree of control over experimental conditions afforded to the researcher. This article describes the major advantages that make mRNA display the method of choice among comparable in vivo and in vitro methods, including cell-surface display, phage display, and ribosomal display. We also describe innovative techniques that harness mRNA display for directed evolution, protein engineering, and drug discovery.

High-copy genome integration of 2,3-butanediol biosynthesis pathway in Saccharomyces cerevisiae via in vivo DNA assembly and replicative CRISPR-Cas9 mediated delta integration

CRISPR Cas9 system is becoming an emerging genome-editing platform and has been widely used for multiplex genome engineering of Saccharomyces cerevisiae. In this study, we developed a novel replicative and integrative CRISPR Cas9 genome-editing platform for large DNA construct in vivo assembly, replication, and high-copy genome integration in Saccharomyces cerevisiae. It harnessed advantages of autonomous replicative sequence in S. cerevisiae, in vivo DNA assembly, CRISPR Cas9, and delta integration. Enhanced green fluorescent protein was used as a marker to confirm large DNA construct in vivo assembly and genome integration. Based on this platform, an efficient 2,3- BDO producing yeast strain was rapidly constructed with up to 25-copy genome integration of 2,3-BDO biosynthesis pathway. Further strain engineering was conducted by multiplex disruption of ADH1, PDC1, PDC5 and MTH1 using a 2μ-based replicative CRISPR Cas9 plasmid containing donor DNAs. As a result, the 2,3-BDO titer was improved by 3.9 folds compared to that obtained by the initially engineered yeast and 50.5 g/L 2,3-BDO was produced by the final engineered yeast strain 36aS5-CFBDO in fed-batch fermentation without strain evolution and process optimization. This study demonstrated that the new replicative and integrative CRISPR Cas9 genome-editing platform was promising in generating an efficient 2,3-BDO-producing S. cerevisiae strain.

The current strategies and parameters for the enhanced microbial production of 2,3-butanediol

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2,3-Butanediol (2,3-BD) is a propitious compound with many industrial uses.

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2,3-BD production has always been hampered by low fermentation yields and high production costs.

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2,3-BD production may be enhanced by optimization of culture conditions and use of high-producing strains.

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TMetabolic engineering tools are currently used to generate high-yielding strains.

2,3-Butanediol (2,3-BD) is a propitious compound with many industrial uses ranging from rubber, fuels, and cosmetics to food additives. Its microbial production has especially attracted as an alternative way to the petroleum-based production. However, 2,3-BD production has always been hampered by low yields and high production costs. The enhanced production of 2,3-butanediol requires screening of the best strains and a systematic optimization of fermentation conditions. Moreover, the metabolic pathway engineering is essential to achieve the best results and minimize the production costs by rendering the strains to use efficiently low cost substrates. This review is to provide up-to-date information on the current strategies and parameters for the enhanced microbial production of 2,3-BD.

OptFill: A Tool for Infeasible Cycle-Free Gapfilling of Stoichiometric Metabolic Models

Stoichiometric metabolic modeling, particularly genome-scale models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions but cannot avoid thermodynamically infeasible cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill, which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle-free gapfilling solutions. In addition, OptFill can be adapted to automate inherent TICs identification in any GSM. Overall, OptFill can address critical issues in automated development of high-quality GSMs.

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This work presents an alternative to state-of-the-art methods for gapfilling

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Unlike current methods, this method is holistic and infeasible cycle free

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This method is applied to three tests and one published model

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This method might also be used to address infeasible cycling

Comprehensive analysis of metabolic sensitivity of 1,4-butanediol producing Escherichia coli toward substrate and oxygen availability

Nowadays, chemical production of 1,4-butanediol is supplemented by biotechnological processes using a genetically modified Escherichia coli strain, which is an industrial showcase of successful application of metabolic engineering. However, large scale bioprocess performance can be affected by presence of physical and chemical gradients in bioreactors which are a consequence of imperfect mixing and limited oxygen transfer. Hence, upscaling comes along with local and time dependent fluctuations of cultivation conditions. This study emphasizes on scale-up related effects of microbial 1,4-butanediol production by comprehensive bioprocess characterization in lab scale. Due to metabolic network constraints 1,4-butanediol formation takes place under oxygen limited microaerobic conditions, which can be hardly realized in large scale bioreactor. The purpose of this study was to assess the extent to which substrate and oxygen availability influence the productivity. It was found, that the substrate specific product yield and the production rate are higher under substrate excess than under substrate limitation. Furthermore, the level of oxygen supply within microaerobic conditions revealed strong effects on product and by-product formation. Under strong oxygen deprivation nearly 30% of the consumed carbon is converted into 1,4-butanediol, whereas an increase in oxygen supply results in 1,4-butanediol reduction of 77%. Strikingly, increasing oxygen availability leads to strong increase of main by-product acetate as well as doubled carbon dioxide formation. The study provides clear evidence that scale-up of microaerobic bioprocesses constitute a substantial challenge. Although oxygen is strictly required for product formation, the data give clear evidence that terms of anaerobic and especially aerobic conditions strongly interfere with 1,4-butanediol production.

Metabolic model guided strain design of cyanobacteria

Cyanobacteria are oxygenic photoautotrophs that serve as potential platforms for the production of biochemicals from cheap and renewable raw materials - sunlight, water, and carbon dioxide. Systems level analysis of the metabolic network of these organisms could enable the successful engineering of these organisms for the enhanced production of target chemicals. Metabolic modeling techniques including both stoichiometric and kinetic modeling with a genome-wide coverage enable a global assessment of metabolic capabilities. Recent studies guided by such modeling techniques have engineered strains for the enhanced production of valuable chemicals such as ethanol, n-butanol, 1,3-propanediol, glycerol, limonene, and isoprene from CO₂.

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

Background

Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation.

Results

The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae, defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L⁻¹ h⁻¹), with a final ethanol concentration of 87 g L⁻¹ and yield of 0.46 g_ethanol g_substrate⁻¹. The other fermentation strategies showed lower yields (close to 0.40 g_ethanol g_substrate⁻¹) and ethanol productivity around 4.0 g L⁻¹ h⁻¹.

Conclusion

The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.

Synthetic engineering of Corynebacterium crenatum to selectively produce acetoin or 2,3-butanediol by one step bioconversion method

BACKGROUND

Acetoin (AC) and 2,3-butanediol (2,3-BD) as highly promising bio-based platform chemicals have received more attentions due to their wide range of applications. However, the non-efficient substrate conversion and mutually transition between AC and 2,3-BD in their natural producing strains not only led to a low selectivity but also increase the difficulty of downstream purification. Therefore, synthetic engineering of more suitable strains should be a reliable strategy to selectively produce AC and 2,3-BD, respectively.

RESULTS

In this study, the respective AC (alsS and alsD) and 2,3-BD biosynthesis pathway genes (alsS, alsD, and bdhA) derived from Bacillus subtilis 168 were successfully expressed in non-natural AC and 2,3-BD producing Corynebacterium crenatum, and generated recombinant strains, C. crenatum SD and C. crenatum SDA, were proved to produce 9.86 g L-1 of AC and 17.08 g L-1 of 2,3-BD, respectively. To further increase AC and 2,3-BD selectivity, the AC reducing gene (butA) and lactic acid dehydrogenase gene (ldh) in C. crenatum were then deleted. Finally, C. crenatumΔbutAΔldh SD produced 76.93 g L-1 AC in one-step biocatalysis with the yield of 0.67 mol mol-1. Meanwhile, after eliminating the lactic acid production and enhancing 2,3-butanediol dehydrogenase activity, C. crenatumΔldh SDA synthesized 88.83 g L-1 of 2,3-BD with the yield of 0.80 mol mol-1.

CONCLUSIONS

The synthetically engineered C. crenatumΔbutAΔldh SD and C. crenatumΔldh SDA in this study were proved as an efficient microbial cell factory for selective AC and 2,3-BD production. Based on the insights from this study, further synthetic engineering of C. crenatum for AC and 2,3-BD production is suggested.

Prediction of Rate-Limiting Reactions for Growth-Associated Production Using a Constraint-Based Approach

Identification of a rate-limiting step in pathways is a key challenge in metabolic engineering. Although the prediction of rate-limiting steps using a kinetic model is a powerful approach, there are several technical hurdles for developing a kinetic model. In this study, an in silico screening algorithm of key enzyme for metabolic engineering is developed to identify the possible rate-limiting reactions for the growth-coupled target production using a stoichiometric model without any experimental data and kinetic parameters. In this method, for each reaction, an upper-bound flux constraint is imposed and the target production is predicted by linear programming. When the constraint decreases the target production at the optimal growth state, the reaction is thought to be a possible rate-limiting step. For validation, this method is applied to the production of succinate or 1,4-butanediol (1,4-BDO) in Escherichia coli, in which the experimental engineering for eliminating rate-limiting steps has been previously reported. In succinate production from glycerol, nine reactions including phosphoenolpyruvate carboxylase are predicted as the rate-limiting steps. In 1,4-BDO production from glucose, eight reactions including pyruvate dehydrogenase are predicted as the rate-limiting steps. These predictions include experimentally identified rate-limiting steps, which would contribute to metabolic engineering as a practical tool for screening candidates of rate-limiting reactions.

OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.

Production of 2,3-butanediol from glucose and cassava hydrolysates by metabolically engineered industrial polyploid Saccharomyces cerevisiae

BACKGROUND

2,3-Butanediol (2,3-BDO) is a valuable chemical for industrial applications. Bacteria can produce 2,3-BDO with a high productivity, though most of their classification as pathogens makes them undesirable for the industrial-scale production. Though Saccharomyces cerevisiae (GRAS microorganism) was engineered to produce 2,3-BDO efficiently in the previous studies, their 2,3-BDO productivity, yield, and titer were still uncompetitive compared to those of bacteria production. Thus, we propose an industrial polyploid S. cerevisiae as a host for efficient production of 2,3-BDO with high growth rate, rapid sugar consumption rate, and resistance to harsh conditions. Genetic manipulation tools for polyploid yeast had been limited; therefore, we engineered an industrial polyploid S. cerevisiae strain based on the CRISPR-Cas9 genome-editing system to produce 2,3-BDO instead of ethanol.

RESULTS

Endogenous genes coding for pyruvate decarboxylase and alcohol dehydrogenase were partially disrupted to prevent declined growth rate and C₂-compound limitation. A bacterial 2,3-BDO-producing pathway was also introduced in engineered polyploid S. cerevisiae. A fatal redox imbalance was controlled through the heterologous NADH oxidase from Lactococcus lactis during the 2,3-BDO production. The resulting strain (YG01_SDBN) still retained the beneficial traits as polyploid strains for the large-scale fermentation. The combination of partially disrupted PDC (pyruvate decarboxylase) and ADH (alcohol dehydrogenase) did not cause the severe growth defects typically found in all pdc- or adh-deficient yeast. The YG01_SDBN strain produced 178 g/L of 2,3-BDO from glucose with an impressive productivity (2.64 g/L h). When a cassava hydrolysate was used as a sole carbon source, this strain produced 132 g/L of 2,3-BDO with a productivity of 1.92 g/L h.

CONCLUSIONS

The microbial production of 2,3-BDO has been limited to bacteria and haploid laboratorial S. cerevisiae strains. This study suggests that an industrial polyploid S. cerevisiae (YG01_SDBN) can produce high concentration of 2,3-BDO with various advantages. Integration of metabolic engineering of the industrial yeast at the gene level with optimization of fed-batch fermentation at the process scale resulted in a remarkable achievement of 2,3-BDO production at 178 g/L of 2,3-BDO concentration and 2.64 g/L h of productivity. Furthermore, this strain could make a bioconversion of a cassava hydrolysate to 2,3-BDO with economic and environmental benefits. The engineered industrial polyploid strain could be applicable to production of biofuels and biochemicals in large-scale fermentations particularly when using modified CRISPR-Cas9 tools.

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.

Methods for automated genome-scale metabolic model reconstruction

In the era of next-generation sequencing and ubiquitous assembly and binning of metagenomes, new putative genome sequences are being produced from isolate and microbiome samples at ever-increasing rates. Genome-scale metabolic models have enormous utility for supporting the analysis and predictive characterization of these genomes based on sequence data. As a result, tools for rapid automated reconstruction of metabolic models are becoming critically important for supporting the analysis of new genome sequences. Many tools and algorithms have now emerged to support rapid model reconstruction and analysis. Here, we are comparing and contrasting the capabilities and output of a variety of these tools, including ModelSEED, Raven Toolbox, PathwayTools, SuBliMinal Toolbox and merlin.

A synthetic pathway for the production of 2-hydroxyisovaleric acid in Escherichia coli

Synthetic biology, encompassing the design and construction of novel artificial biological pathways and organisms and the redesign of existing natural biological systems, is rapidly expanding the number of applications for which biological systems can play an integral role. In the context of chemical production, the combination of synthetic biology and metabolic engineering approaches continues to unlock the ability to biologically produce novel and complex molecules from a variety of feedstocks. Here, we utilize a synthetic approach to design and build a pathway to produce 2-hydroxyisovaleric acid in Escherichia coli and demonstrate how pathway design can be supplemented with metabolic engineering approaches to improve pathway performance from various carbon sources. Drawing inspiration from the native pathway for the synthesis of the 5-carbon amino acid l-valine, we exploit the decarboxylative condensation of two molecules of pyruvate, with subsequent reduction and dehydration reactions enabling the synthesis of 2-hydroxyisovaleric acid. Key to our approach was the utilization of an acetolactate synthase which minimized kinetic and regulatory constraints to ensure sufficient flux entering the pathway. Critical host modifications enabling maximum product synthesis from either glycerol or glucose were then examined, with the varying degree of reduction of these carbons sources playing a major role in the required host background. Through these engineering efforts, the designed pathway produced 6.2 g/L 2-hydroxyisovaleric acid from glycerol at 58% of maximum theoretical yield and 7.8 g/L 2-hydroxyisovaleric acid from glucose at 73% of maximum theoretical yield. These results demonstrate how the combination of synthetic biology and metabolic engineering approaches can facilitate bio-based chemical production.

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.

Engineering and Evolution of Saccharomyces cerevisiae to Produce Biofuels and Chemicals

To mitigate global climate change caused partly by the use of fossil fuels, the production of fuels and chemicals from renewable biomass has been attempted. The conversion of various sugars from renewable biomass into biofuels by engineered baker's yeast (Saccharomyces cerevisiae) is one major direction which has grown dramatically in recent years. As well as shifting away from fossil fuels, the production of commodity chemicals by engineered S. cerevisiae has also increased significantly. The traditional approaches of biochemical and metabolic engineering to develop economic bioconversion processes in laboratory and industrial settings have been accelerated by rapid advancements in the areas of yeast genomics, synthetic biology, and systems biology. Together, these innovations have resulted in rapid and efficient manipulation of S. cerevisiae to expand fermentable substrates and diversify value-added products. Here, we discuss recent and major advances in rational (relying on prior experimentally-derived knowledge) and combinatorial (relying on high-throughput screening and genomics) approaches to engineer S. cerevisiae for producing ethanol, butanol, 2,3-butanediol, fatty acid ethyl esters, isoprenoids, organic acids, rare sugars, antioxidants, and sugar alcohols from glucose, xylose, cellobiose, galactose, acetate, alginate, mannitol, arabinose, and lactose.