Next-generation genome-scale models for metabolic engineering

Ogataea polymorpha as a next-generation chassis for industrial biotechnology

Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.

Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

Effect of folA gene in human breast milk-derived Limosilactobacillus reuteri on its folate biosynthesis

Introduction

Folate supplementation is crucial for the human body, and the chemically synthesized folic acid might have undesirable side effects. The use of molecular breeding methods to modify the genes related to the biosynthesis of folate by probiotics to increase folate production is currently a focus of research.

Methods

In this study, the folate-producing strain of Limosilactobacillus reuteri B1-28 was isolated from human breast milk, and the difference between B1-28 and folA gene deletion strain ΔFolA was investigated by phenotyping, in vitro probiotic evaluation, metabolism and transcriptome analysis.

Results

The results showed that the folate producted by the ΔFolA was 2-3 folds that of the B1-28. Scanning electron microscope showed that ΔFolA had rougher surface, and the acid-producing capacity (p = 0.0008) and adhesion properties (p = 0.0096) were significantly enhanced than B1-28. Transcriptomic analysis revealed that differentially expressed genes were mainly involved in three pathways, among which the biosynthesis of ribosome and aminoacyl-tRNA occurred in the key metabolic pathways. Metabolomics analysis showed that folA affected 5 metabolic pathways, involving 89 different metabolites.

Discussion

In conclusion, the editing of a key gene of folA in folate biosynthesis pathway provides a feasible pathway to improve folate biosynthesis in breast milk-derived probiotics.

Bacterial glycobiotechnology: A biosynthetic route for the production of biopharmaceutical glycans

The recent advancement in the human glycome and progress in the development of an inclusive network of glycosylation pathways allow the incorporation of suitable machinery for protein modification in non-natural hosts and explore novel opportunities for constructing next-generation tailored glycans and glycoconjugates. Fortunately, the emerging field of bacterial metabolic engineering has enabled the production of tailored biopolymers by harnessing living microbial factories (prokaryotes) as whole-cell biocatalysts. Microbial catalysts offer sophisticated means to develop a variety of valuable polysaccharides in bulk quantities for practical clinical applications. Glycans production through this technique is highly efficient and cost-effective, as it does not involve expensive initial materials. Metabolic glycoengineering primarily focuses on utilizing small metabolite molecules to alter biosynthetic pathways, optimization of cellular processes for glycan and glycoconjugate production, characteristic to a specific organism to produce interest tailored glycans in microbes, using preferably cheap and simple substrate. However, metabolic engineering faces one of the unique challenges, such as the need for an enzyme to catalyze desired substrate conversion when natural native substrates are already present. So, in metabolic engineering, such challenges are evaluated, and different strategies have been developed to overcome them. The generation of glycans and glycoconjugates via metabolic intermediate pathways can still be supported by glycol modeling achieved through metabolic engineering. It is evident that modern glycans engineering requires adoption of improved strain engineering strategies for creating competent glycoprotein expression platforms in bacterial hosts, in the future. These strategies include logically designing and introducing orthogonal glycosylation pathways, identifying metabolic engineering targets at the genome level, and strategically improving pathway performance (for example, through genetic modification of pathway enzymes). Here, we highlight current strategies, applications, and recent progress in metabolic engineering for producing high-value tailored glycans and their applications in biotherapeutics and diagnostics.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

A systematic overexpression approach reveals native targets to increase squalene production in Synechocystis sp. PCC 6803

Cyanobacteria are a promising platform for the production of the triterpene squalene (C30), a precursor for all plant and animal sterols, and a highly attractive intermediate towards triterpenoids, a large group of secondary plant metabolites. Synechocystis sp. PCC 6803 natively produces squalene from CO2 through the MEP pathway. Based on the predictions of a constraint-based metabolic model, we took a systematic overexpression approach to quantify native Synechocystis gene's impact on squalene production in a squalene-hopene cyclase gene knock-out strain (Δshc). Our in silico analysis revealed an increased flux through the Calvin-Benson-Bassham cycle in the Δshc mutant compared to the wildtype, including the pentose phosphate pathway, as well as lower glycolysis, while the tricarboxylic acid cycle predicted to be downregulated. Further, all enzymes of the MEP pathway and terpenoid synthesis, as well as enzymes from the central carbon metabolism, Gap2, Tpi and PyrK, were predicted to positively contribute to squalene production upon their overexpression. Each identified target gene was integrated into the genome of Synechocystis Δshc under the control of the rhamnose-inducible promoter Prha. Squalene production was increased in an inducer concentration dependent manner through the overexpression of most predicted genes, which are genes of the MEP pathway, ispH, ispE, and idi, leading to the greatest improvements. Moreover, we were able to overexpress the native squalene synthase gene (sqs) in Synechocystis Δshc, which reached the highest production titer of 13.72 mg l-1 reported for squalene in Synechocystis sp. PCC 6803 so far, thereby providing a promising and sustainable platform for triterpene production.

Machine learning-guided prediction of potential engineering targets for microbial production of lycopene

The process of designing streamlined workflows for developing microbial strains using classical methods from vast amounts of biological big data has reached its limits. With the continuous increase in the amount of biological big data, data-driven machine learning approaches are being used to overcome the limits of classical approaches for strain development. Here, machine learning-guided engineering of Deinococcus radiodurans R1 for high-yield production of lycopene was demonstrated. The multilayer perceptron models were first trained using the mRNA expression levels of the key genes along with lycopene titers and yields obtained from 17 strains. Then, the potential overexpression targets from 2,047 possible combinations were predicted by the multilayer perceptron combined with a genetic algorithm. Through the machine learning-aided fine-tuning of the predicted genes, the final-engineered LY04 strain resulted in an 8-fold increase in the lycopene production, up to 1.25 g/L from glycerol, and a 6-fold increase in the lycopene yield.

Development and Perspective of Rhodotorula toruloides as an Efficient Cell Factory

Rhodotorula toruloides is receiving significant attention as a novel cell factory because of its high production of lipids and carotenoids, fast growth and high cell density, as well as the ability to utilize a wide variety of substrates. These attractive traits of R. toruloides make it possible to become a low-cost producer that can be engineered for the production of various fuels and chemicals. However, the lack of understanding and genetic engineering tools impedes its metabolic engineering applications. A number of research efforts have been devoted to filling these gaps. This review focuses on recent developments in genetic engineering tools, advances in systems biology for improved understandings, and emerging engineered strains for metabolic engineering applications. Finally, future trends and barriers in developing R. toruloides as a cell factory are also discussed.

Investigating ethanol production using the Zymomonas mobilis crude extract

Cell-free systems have become valuable investigating tools for metabolic engineering research due to their easy access to metabolism without the interference of the membrane. Therefore, we applied Zymomonas mobilis cell-free system to investigate whether ethanol production is controlled by the genes of the metabolic pathway or is limited by cofactors. Initially, different glucose concentrations were added to the extract to determine the crude extract's capability to produce ethanol. Then, we investigated the genes of the metabolic pathway to find the limiting step in the ethanol production pathway. Next, to identify the bottleneck gene, a systemic approach was applied based on the integration of gene expression data on a cell-free metabolic model. ZMO1696 was determined as the bottleneck gene and an activator for its enzyme was added to the extract to experimentally assess its effect on ethanol production. Then the effect of NAD+ addition at the high concentration of glucose (1 M) was evaluated, which indicates no improvement in efficiency. Finally, the imbalance ratio of ADP/ATP was found as the controlling factor by measuring ATP levels in the extract. Furthermore, sodium gluconate as a carbon source was utilized to investigate the expansion of substrate consumption by the extract. 100% of the maximum theoretical yield was obtained at 0.01 M of sodium gluconate while it cannot be consumed by Z. mobilis. This research demonstrated the challenges and advantages of using Z. mobilis crude extract for overproduction.

Construction and application of the genome-scale metabolic model of Streptomyces radiopugnans

Geosmin is one of the most common earthy-musty odor compounds, which is mainly produced by Streptomyces. Streptomyces radiopugnans was screened in radiation-polluted soil, which has the potential to overproduce geosmin. However, due to the complex cellular metabolism and regulation mechanism, the phenotypes of S. radiopugnans were hard to investigate. A genome-scale metabolic model of S. radiopugnans named iZDZ767 was constructed. Model iZDZ767 involved 1,411 reactions, 1,399 metabolites, and 767 genes; its gene coverage was 14.1%. Model iZDZ767 could grow on 23 carbon sources and five nitrogen sources, which achieved 82.1% and 83.3% prediction accuracy, respectively. For the essential gene prediction, the accuracy was 97.6%. According to the simulation of model iZDZ767, D-glucose and urea were the best for geosmin fermentation. The culture condition optimization experiments proved that with D-glucose as the carbon source and urea as the nitrogen source (4 g/L), geosmin production could reach 581.6 ng/L. Using the OptForce algorithm, 29 genes were identified as the targets of metabolic engineering modification. With the help of model iZDZ767, the phenotypes of S. radiopugnans could be well resolved. The key targets for geosmin overproduction could also be identified efficiently.

Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges

Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.

Pareto Optimal Metabolic Engineering for the Growth-coupled Overproduction of Sustainable Chemicals

Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta‐carotene (β‐carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of β‐carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution space to highlight near‐optimal strains on Pareto Fronts, balancing the above‐cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this study produced robust results: seven significant strains of Y. lipolytica and seven strains of S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. This study offers solutions for developing an efficient in vitro mutagenesis for microorganisms and demonstrates the efficiency of multiobjective optimization in automatizing metabolic engineering processes.

Recent Developments in Lignocellulosic Biofuels, a Renewable Source of Bioenergy

Biofuel consists of non-fossil fuel derived from the organic biomass of renewable resources, including plants, animals, microorganisms, and waste. Energy derived from biofuel is known as bioenergy. The reserve of fossil fuels is now limited and continuing to decrease, while at the same time demand for energy is increasing. In order to overcome this scarcity, it is vital for human beings to transfer their dependency on fossil fuels to alternative types of fuel, including biofuels, which are effective methods of fulfilling present and future demands. The current review therefore focusses on second-generation lignocellulosic biofuels obtained from non-edible plant biomass (i.e., cellulose, lignin, hemi-celluloses, non-food material) in a more sustainable manner. The conversion of lignocellulosic feedstock is an important step during biofuel production. It is, however, important to note that, as a result of various technical restrictions, biofuel production is not presently cost efficient, thus leading to the need for improvement in the methods employed. There remain a number of challenges for the process of biofuel production, including cost effectiveness and the limitations of various technologies employed. This leads to a vital need for ongoing and enhanced research and development, to ensure market level availability of lignocellulosic biofuel.

Interrogation of Essentiality in the Reconstructed Haemophilus influenzae Metabolic Network Identifies Lipid Metabolism Antimicrobial Targets: Preclinical Evaluation of a FabH β-Ketoacyl-ACP Synthase Inhibitor

Expediting drug discovery to fight antibacterial resistance requires holistic approaches at system levels. In this study, we focused on the human-adapted pathogen Haemophilus influenzae, and by constructing a high-quality genome-scale metabolic model, we rationally identified new metabolic drug targets in this organism. Contextualization of available gene essentiality data within in silico predictions identified most genes involved in lipid metabolism as promising targets. We focused on the β-ketoacyl-acyl carrier protein synthase III FabH, responsible for catalyzing the first step in the FASII fatty acid synthesis pathway and feedback inhibition. Docking studies provided a plausible three-dimensional model of FabH in complex with the synthetic inhibitor 1-(5-(2-fluoro-5-(hydroxymethyl)phenyl)pyridin-2-yl)piperidine-4-acetic acid (FabHi). Validating our in silico predictions, FabHi reduced H. influenzae viability in a dose- and strain-dependent manner, and this inhibitory effect was independent of fabH gene expression levels. fabH allelic variation was observed among H. influenzae clinical isolates. Many of these polymorphisms, relevant for stabilization of the dimeric active form of FabH and/or activity, may modulate the inhibitory effect as part of a complex multifactorial process with the overall metabolic context emerging as a key factor tuning FabHi activity. Synergies with antibiotics were not observed and bacteria were not prone to develop resistance. Inhibitor administration during H. influenzae infection on a zebrafish septicemia infection model cleared bacteria without signs of host toxicity. Overall, we highlight the potential of H. influenzae metabolism as a source of drug targets, metabolic models as target-screening tools, and FASII targeting suitability to counteract this bacterial infection. IMPORTANCE Antimicrobial resistance drives the need of synergistically combined powerful computational tools and experimental work to accelerate target identification and drug development. Here, we present a high-quality metabolic model of H. influenzae and show its usefulness both as a computational framework for large experimental data set contextualization and as a tool to discover condition-independent drug targets. We focus on β-ketoacyl-acyl carrier protein synthase III FabH chemical inhibition by using a synthetic molecule with good synthetic and antimicrobial profiles that specifically binds to the active site. The mechanistic complexity of FabH inhibition may go beyond allelic variation, and the strain-dependent effect of the inhibitor tested supports the impact of metabolic context as a key factor driving bacterial cell behavior. Therefore, this study highlights the systematic metabolic evaluation of individual strains through computational frameworks to identify secondary metabolic hubs modulating drug response, which will facilitate establishing synergistic and/or more precise and robust antibacterial treatments.

ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.

Metabolic alterations are often used as hallmarks of observable phenotypes. In this regard, reconstructed genome-scale metabolic models (GEMs) provide a rich and computable representation of the entire set of biochemical reactions in a cell. However, the performance of analytical tools for predicting metabolic reaction rates or fluxes using GEMs is sensitive to the assumed metabolic objective that is often unknown and likely context-specific. Here, we propose a novel method called ΔFBA that combines differential gene expression data and GEMs to evaluate differences in the metabolic fluxes between two conditions (perturbation vs. control) without the need for specifying a metabolic objective. In our demonstration, ΔFBA outperformed other existing methods in predicting metabolic flux alterations.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Recurrent Urinary Tract Infections: Unraveling the Complicated Environment of Uncomplicated rUTIs

Urinary tract infections (UTIs) are frequent in humans, affecting the upper and lower urinary tract. Present diagnosis relies on the positive culture of uropathogenic bacteria from urine and clinical markers of inflammation of the urinary tract. The bladder is constantly challenged by adverse environmental stimuli which influence urinary tract physiology, contributing to a dysbiotic environment. Simultaneously, pathogens are primed by environmental stressors such as antibiotics, favoring recurrent UTIs (rUTIs), resulting in chronic illness. Due to different confounders for UTI onset, a greater understanding of the fundamental environmental mechanisms and microbial ecology of the human urinary tract is required. Such advancements could promote the tandem translation of bench and computational studies for precision treatments and clinical management of UTIs. Therefore, there is an urgent need to understand the ecological interactions of the human urogenital microbial communities which precede rUTIs. This review aims to outline the mechanistic aspects of rUTI ecology underlying dysbiosis between both the human microbiome and host physiology which predisposes humans to rUTIs. By assessing the applications of next generation and systems level methods, we also recommend novel approaches to elucidate the systemic consequences of rUTIs which requires an integrated approach for successful treatment. To this end, we will provide an outlook towards the so-called 'uncomplicated environment of UTIs', a holistic and systems view that applies ecological principles to define patient-specific UTIs. This perspective illustrates the need to withdraw from traditional reductionist perspectives in infection biology and instead, a move towards a systems-view revolving around patient-specific pathophysiology during UTIs.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.

A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).

In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.

Changes in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments.

In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell.

Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition.

Genetically Intact Bioengineered Spores of Bacillus subtilis

Developments in genome editing offer potential solutions to challenges in agriculture, industry, medicine, and the environment. However, many technologies remain unexploited due to limitations in the use of genetically altered organisms. In this study, we use B. subtilis spores to explore the possibility of bioengineering organisms while leaving their genome intact. Taking advantage of the differential expression between the mother cell and the fore-spore compartments during sporulation, we created plasmids programmed to modify the spore phenotype from the mother cell compartment, but to "self-digest" in the fore-spore. At the end of sporulation, the mother cell undergoes lysis and releases the phenotypically engineered, genetically unaltered spores. Using this approach, we demonstrated the potential to express foreign proteins in B. subtilis spores without genome alterations by producing spores expressing GFP in their protective coats, where approximately 90% of the spore population had no detectable plasmid or chromosome alterations. In a separate demonstration, we programmed KinA overexpression during vegetative growth to artificially induce sporulation, and also obtained spores with nearly 90% of them free of detectable plasmid. Artificial induction of sporulation could potentially simplify the bioprocess for industrial spore production, as it reduces the number of steps involved. Overall, these findings demonstrate the potential to create genetically intact bioengineered organisms.

Integrative characterization of the near-minimal bacterium Mesoplasma florum

The near-minimal bacterium Mesoplasma florum is an interesting model for synthetic genomics and systems biology due to its small genome (~ 800 kb), fast growth rate, and lack of pathogenic potential. However, fundamental aspects of its biology remain largely unexplored. Here, we report a broad yet remarkably detailed characterization of M. florum by combining a wide variety of experimental approaches. We investigated several physical and physiological parameters of this bacterium, including cell size, growth kinetics, and biomass composition of the cell. We also performed the first genome-wide analysis of its transcriptome and proteome, notably revealing a conserved promoter motif, the organization of transcription units, and the transcription and protein expression levels of all protein-coding sequences. We converted gene transcription and expression levels into absolute molecular abundances using biomass quantification results, generating an unprecedented view of the M. florum cellular composition and functions. These characterization efforts provide a strong experimental foundation for the development of a genome-scale model for M. florum and will guide future genome engineering endeavors in this simple organism.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Optimal Ratio of Carbon Flux between Glycolysis and the Pentose Phosphate Pathway for Amino Acid Accumulation in Corynebacterium glutamicum

Glucose is metabolized through central metabolic pathways such as glycolysis and the pentose phosphate pathway (PPP) to synthesize downstream metabolites including amino acids. However, how the split ratio of carbon flux between glycolysis and PPP specifically affects the formation of downstream metabolites remains largely unclear. Here, we conducted a comprehensive metabolomic analysis to investigate the effect of the split ratio between glycolysis and the PPP on the intracellular concentration of amino acids and their derivatives in Corynebacterium glutamicum. The split ratio was varied by exchanging the promoter of a gene encoding glucose 6-phosphate isomerase (PGI). The ratio was correlated with the pgi transcription level and the enzyme activity. Concentrations of threonine and lysine-derivative 1,5-diaminopentane increased with an increase of the split ratio into the PPP. In contrast, concentrations of alanine, leucine, and valine were increased with an increase of the split ratio into glycolysis. These results could provide a new engineering target for improving the production of the amino acids and the derivatives.

Beyond natural: synthetic expansions of botanical form and function

Powered by developments that enabled genome-scale investigations, systems biology emerged as a field aiming to understand how phenotypes emerge from network functions. These advances fuelled a new engineering discipline focussed on synthetic reconstructions of complex biological systems with the goal of predictable rational design and control. Initially, progress in the nascent field of synthetic biology was slow due to the ad hoc nature of molecular biology methods such as cloning. The application of engineering principles such as standardisation, together with several key technical advances, enabled a revolution in the speed and accuracy of genetic manipulation. Combined with mathematical and statistical modelling, this has improved the predictability of engineering biological systems of which nonlinearity and stochasticity are intrinsic features leading to remarkable achievements in biotechnology as well as novel insights into biological function. In the past decade, there has been slow but steady progress in establishing foundations for synthetic biology in plant systems. Recently, this has enabled model-informed rational design to be successfully applied to the engineering of plant gene regulation and metabolism. Synthetic biology is now poised to transform the potential of plant biotechnology. However, reaching full potential will require conscious adjustments to the skillsets and mind sets of plant scientists.

Understanding the metabolism of the tetralin degrader Sphingopyxis granuli strain TFA through genome-scale metabolic modelling

Sphingopyxis granuli strain TFA is an α-proteobacterium that belongs to the sphingomonads, a group of bacteria well-known for its degradative capabilities and oligotrophic metabolism. Strain TFA is the only bacterium in which the mineralisation of the aromatic pollutant tetralin has been completely characterized at biochemical, genetic, and regulatory levels and the first Sphingopyxis characterised as facultative anaerobe. Here we report additional metabolic features of this α-proteobacterium using metabolic modelling and the functional integration of genomic and transcriptomic data. The genome-scale metabolic model (GEM) of strain TFA, which has been manually curated, includes information on 743 genes, 1114 metabolites and 1397 reactions. This represents the largest metabolic model for a member of the Sphingomonadales order thus far. The predictive potential of this model was validated against experimentally calculated growth rates on different carbon sources and under different growth conditions, including both aerobic and anaerobic metabolisms. Moreover, new carbon and nitrogen sources were predicted and experimentally validated. The constructed metabolic model was used as a platform for the incorporation of transcriptomic data, generating a more robust and accurate model. In silico flux analysis under different metabolic scenarios highlighted the key role of the glyoxylate cycle in the central metabolism of strain TFA.

Organizing genome engineering for the gigabase scale

Genome-scale engineering holds great potential to impact science, industry, medicine, and society, and recent improvements in DNA synthesis have enabled the manipulation of megabase genomes. However, coordinating and integrating the workflows and large teams necessary for gigabase genome engineering remains a considerable challenge. We examine this issue and recommend a path forward by: 1) adopting and extending existing representations for designs, assembly plans, samples, data, and workflows; 2) developing new technologies for data curation and quality control; 3) conducting fundamental research on genome-scale modeling and design; and 4) developing new legal and contractual infrastructure to facilitate collaboration.

Experimental Challenges for Reduced Genomes: The Cell Model Escherichia coli

Genome reduction, as a top-down approach to obtain the minimal genetic information essential for a living organism, has been conducted with bacterial cells for decades. The most popular and well-studied cell models for genome reduction are Escherichia coli strains. As the previous literature intensively introduced the genetic construction and application of the genome-reduced Escherichia coli strains, the present review focuses the design principles and compares the reduced genome collections from the specific viewpoint of growth, which represents a fundamental property of living cells and is an important feature for their biotechnological application. For the extended simplification of the genomic sequences, the approach of experimental evolution and concern for medium optimization are newly proposed. The combination of the current techniques of genomic construction and the newly proposed methodologies could allow us to acquire growing Escherichia coli cells carrying the extensively reduced genome and to address the question of what the minimal genome essential for life is.

A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data

Rhodosporidium toruloides is a red, basidiomycetes yeast that can accumulate a large amount of lipids and produce carotenoids. To better assess this non-model yeast's metabolic capabilities, we reconstructed a genome-scale model of R. toruloides IFO0880's metabolic network (iRhto1108) accounting for 2204 reactions, 1985 metabolites and 1108 genes. In this work, we integrated and supplemented the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism's metabolic capabilities. Predictions of genotype-phenotype relations were improved through manual curation of gene-protein-reaction rules for 543 reactions leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloides's utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model's fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for triacylglycerol overproduction. Suggested interventions recapitulated many of the previous successful implementations of genetic modifications and put forth a few new ones.

Impact framework: A python package for writing data analysis workflows to interpret microbial physiology

Microorganisms can be genetically engineered to solve a range of challenges in diverse including health, environmental protection and sustainability. The natural complexity of biological systems makes this an iterative cycle, perturbing metabolism and making stepwise progress toward a desired phenotype through four major stages: design, build, test, and data interpretation. This cycle has been accelerated by advances in molecular biology (e.g. robust DNA synthesis and assembly techniques), liquid handling automation and scale-down characterization platforms, generating large heterogeneous data sets. Here, we present an extensible Python package for scientists and engineers working with large biological data sets to interpret, model, and visualize data: the IMPACT (Integrated Microbial Physiology: Analysis, Characterization and Translation) framework. Impact aims to ease the development of Python-based data analysis workflows for a range of stakeholders in the bioengineering process, offering open-source tools for data analysis, physiology characterization and translation to visualization. Using this framework, biologists and engineers can opt for reproducible and extensible programmatic data analysis workflows, mediating a bottleneck limiting the throughput of microbial engineering. The Impact framework is available at https://github.com/lmse/impact.

Basics of genome-scale metabolic modeling and applications on C1-utilization

It is fundamental to understand the relationship between genotype and phenotype in biology. This requires comprehensive knowledge of metabolic pathways, genetic information and well-defined mathematic modeling. Integration of knowledge on metabolism with mathematical modeling results in genome-scale metabolic models which have proven useful to investigate bacterial metabolism and to engineer bacterial strains capable of producing value-added biochemical. Single carbon substrates such as methane and carbon monoxide have drawn interests and they assumed one of next-generation feedstocks because of their high abundance and low price. The methylotroph and acetogen-based biorefineries hold promises for bioconversion of C1 substrates into biofuels and high value compounds. As an effort on expanding our knowledge on C1 utilization approaches, in silico computational framework of C1-metabolism in methylotrophic and acetogenic bacteria has been developed. In this review, genome-scale metabolic models for C1-utilizing bacteria and well-established analysis tools are presented for potential uses for study of C1 metabolism at the genome scale and its application in metabolic engineering.

A Protocol for the Construction and Curation of Genome-Scale Integrated Metabolic and Regulatory Network Models

Genome-scale metabolic network models have been widely used over the last decade and have been shown to successfully predict the metabolic behavior of many organisms. Yet the complexity of metabolic regulation often limits the accuracy of these models. Integrative modeling approaches have recently been developed that combine metabolic and regulatory networks, thereby expanding the capabilities and accuracy of genome-scale modeling. This chapter provides a guide to reconstruct and curate such integrated network models. Specifically, this protocol describes the PROM (Probabilistic Regulation of Metabolism) and GEMINI (Gene Expression and Metabolism Integrated for Network Inference) approaches. PROM is an automated method for the construction of integrated metabolic and transcriptional regulatory network models, while the GEMINI approach curates the integrated network models using transcriptomics and phenomics data. GEMINI represents the first attempt at applying well-established curation tools that exist for metabolic networks to be applied for curating regulatory networks. The integrated network models generated by these approaches enable the mechanistic integration of diverse biological data and can identify novel strategies to engineer cellular metabolism.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Modular design: Implementing proven engineering principles in biotechnology

Modular design is at the foundation of contemporary engineering, enabling rapid, efficient, and reproducible construction and maintenance of complex systems across applications. Remarkably, modularity has recently been discovered as a governing principle in natural biological systems from genes to proteins to complex networks within a cell and organism communities. The convergent knowledge of natural and engineered modular systems provides a key to drive modern biotechnology to address emergent challenges associated with health, food, energy, and the environment. Here, we first present the theory and application of modular design in traditional engineering fields. We then discuss the significance and impact of modular architectures on systems biology and biotechnology. Next, we focus on the very recent theoretical and experimental advances in modular cell engineering that seeks to enable rapid and systematic development of microbial catalysts capable of efficiently synthesizing a large space of useful chemicals. We conclude with an outlook towards theoretical and practical opportunities for a more systematic and effective application of modular cell engineering in biotechnology.

Risk-Based Bioengineering Strategies for Reliable Bacterial Vaccine Production

Design of a reliable process for bacterial antigen production requires understanding of and control over critical process parameters. Current methods for process design use extensive screening experiments for determining ranges of critical process parameters yet fail to give clear insights into how they influence antigen potency. To address this gap, we propose to apply constraint-based, genome-scale metabolic models to reduce the need of experimental screening for strain selection and to optimize strains based on model driven iterative Design-Build-Test-Learn (DBTL) cycles. Application of these systematic methods has not only increased the understanding of how metabolic network properties influence antigen potency, but also allows identification of novel critical process parameters that need to be controlled to achieve high process reliability.

Population Genetics Based Phylogenetics Under Stabilizing Selection for an Optimal Amino Acid Sequence: A Nested Modeling Approach

We present a new phylogenetic approach, selection on amino acids and codons (SelAC), whose substitution rates are based on a nested model linking protein expression to population genetics. Unlike simpler codon models that assume a single substitution matrix for all sites, our model more realistically represents the evolution of protein-coding DNA under the assumption of consistent, stabilizing selection using a cost-benefit approach. This cost-benefit approach allows us to generate a set of 20 optimal amino acid-specific matrix families using just a handful of parameters and naturally links the strength of stabilizing selection to protein synthesis levels, which we can estimate. Using a yeast data set of 100 orthologs for 6 taxa, we find SelAC fits the data much better than popular models by 104-105 Akike information criterion units adjusted for small sample bias. Our results also indicated that nested, mechanistic models better predict observed data patterns highlighting the improvement in biological realism in amino acid sequence evolution that our model provides. Additional parameters estimated by SelAC indicate that a large amount of nonphylogenetic, but biologically meaningful, information can be inferred from existing data. For example, SelAC prediction of gene-specific protein synthesis rates correlates well with both empirical (r=0.33-0.48) and other theoretical predictions (r=0.45-0.64) for multiple yeast species. SelAC also provides estimates of the optimal amino acid at each site. Finally, because SelAC is a nested approach based on clearly stated biological assumptions, future modifications, such as including shifts in the optimal amino acid sequence within or across lineages, are possible.

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

BACKGROUND

Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype.

RESULTS

We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics ("inertia") alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation.

CONCLUSIONS

Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

Predicting the metabolic capabilities of Synechococcus elongatus PCC 7942 adapted to different light regimes

There is great interest in engineering photoautotrophic metabolism to generate bioproducts of societal importance. Despite the success in employing genome-scale modeling coupled with flux balance analysis to engineer heterotrophic metabolism, the lack of proper constraints necessary to generate biologically realistic predictions has hindered broad application of this methodology to phototrophic metabolism. Here we describe a methodology for constraining genome-scale models of photoautotrophy in the cyanobacteria Synechococcus elongatus PCC 7942. Experimental photophysiology parameters coupled to genome-scale flux balance analysis resulted in accurate predictions of growth rates and metabolic reaction fluxes at low and high light conditions. Additionally, by constraining photon uptake fluxes, we characterized the metabolic cost of excess excitation energy. The predicted energy fluxes were consistent with known light-adapted phenotypes in cyanobacteria. Finally, we leveraged the modeling framework to characterize existing photoautotrophic and photomixtotrophic engineering strategies for 2,3-butanediol production in S. elongatus. This methodology, applicable to genome-scale modeling of all phototrophic microorganisms, can facilitate the use of flux balance analysis in the engineering of light-driven metabolism.

Effective models and the search for quantitative principles in microbial evolution

Microbes evolve rapidly. Yet they do so in idiosyncratic ways, which depend on the specific mutations that are beneficial or deleterious in a given situation. At the same time, some population-level patterns of adaptation are strikingly similar across different microbial systems, suggesting that there may also be simple, quantitative principles that unite these diverse scenarios. We review the search for simple principles in microbial evolution, ranging from the biophysical level to emergent evolutionary dynamics. A key theme has been the use of effective models, which coarse-grain over molecular and cellular details to obtain a simpler description in terms of a few effective parameters. Collectively, these theoretical approaches provide a set of quantitative principles that facilitate understanding, prediction, and potentially control of evolutionary phenomena, though formidable challenges remain due to the ecological complexity of natural populations.

Opportunities, challenges, and future perspectives of succinic acid production by Actinobacillus succinogenes

Due to environmental issues and the depletion of fossil-based resources, ecofriendly sustainable biomass-based chemical production has been given more attention recently. Succinic acid (SA) is one of the top value added bio-based chemicals. It can be synthesized through microbial fermentation using various waste steam bioresources. Production of chemicals from waste streams has dual function as it alleviates environmental concerns; they could have caused because of their improper disposal and transform them into valuable products. To date, Actinobacillus succinogenes is termed as the best natural SA producer. However, few reviews regarding SA production by A. succinogenes were reported. Herewith, pathways and metabolic engineering strategies, biomass pretreatment and utilization, and process optimization related with SA fermentation by A. succinogenes were discussed in detail. In general, this review covered vital information including merits, achievements, progresses, challenges, and future perspectives in SA production using A. succinogenes. Therefore, it is believed that this review will provide platform to understand the potential of the strain and tackle existing hurdles so as to develop superior strain for industrial applications. It will also be used as a baseline for identification, isolation, and improvement of other SA-producing microbes.

Elucidating redox balance shift in Scheffersomyces stipitis' fermentative metabolism using a modified genome-scale metabolic model

Background

Scheffersomyces stipitis is an important yeast species in the field of biorenewables due to its desired capacity for xylose utilization. It has been recognized that redox balance plays a critical role in S. stipitis due to the different cofactor preferences in xylose assimilation pathway. However, there has not been any systems level understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. Genome-scale metabolic network models (GEMs) offer the opportunity to gain such systems level understanding; however, currently the two published GEMs for S. stipitis cannot be used for this purpose, as neither of them is able to capture the strain’s fermentative metabolism reasonably well due to their poor prediction of xylitol production, a key by-product under oxygen limited conditions.

Results

A system identification-based (SID-based) framework that we previously developed for GEM validation is expanded and applied to refine a published GEM for S. stipitis, iBB814. After the modified GEM, named iDH814, was validated using literature data, it is used to obtain genome-scale understanding on how redox cofactor shifts when cells respond to reduced oxygen supply. The SID-based framework for GEM analysis was applied to examine how the environmental perturbation (i.e., reduced oxygen supply) propagates through the metabolic network, and key reactions that contribute to the shifts of redox and metabolic state were identified. Finally, the findings obtained through GEM analysis were validated using transcriptomic data.

Conclusions

iDH814, the modified model, was shown to offer significantly improved performance in terms of matching available experimental results and better capturing available knowledge on the organism. More importantly, our analysis based on iDH814 provides the first genome-scale understanding on how redox balance in S. stipitis was shifted as a result of reduced oxygen supply. The systems level analysis identified the key contributors to the overall metabolic state shift, which were validated using transcriptomic data. The analysis confirmed that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the overall shift in metabolic states.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-0983-y) contains supplementary material, which is available to authorized users.