A constraint-based model of Scheffersomyces stipitis for improved ethanol production

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Phenotype-specific estimation of metabolic fluxes using gene expression data

A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.

Comprehensive Characterization of Mutant Pichia stipitis Co-Fermenting Cellobiose and Xylose through Genomic and Transcriptomic Analyses

The development of a yeast strain capable of fermenting mixed sugars efficiently is crucial for producing biofuels and value-added materials from cellulosic biomass. Previously, a mutant Pichia stipitis YN14 strain capable of co-fermenting xylose and cellobiose was developed through evolutionary engineering of the wild-type P. stipitis CBS6054 strain, which was incapable of cofermenting xylose and cellobiose. In this study, through genomic and transcriptomic analyses, we sought to investigate the reasons for the improved sugar metabolic performance of the mutant YN14 strain in comparison with the parental CBS6054 strain. Unfortunately, comparative wholegenome sequencing (WGS) showed no mutation in any of the genes involved in the cellobiose metabolism between the two strains. However, comparative RNA sequencing (RNA-seq) revealed that the YN14 strain had 101.2 times and 5.9 times higher expression levels of HXT2.3 and BGL2 genes involved in cellobiose metabolism, and 6.9 times and 75.9 times lower expression levels of COX17 and SOD2.2 genes involved in respiration, respectively, compared with the CBS6054 strain. This may explain how the YN14 strain enhanced cellobiose metabolic performance and shifted the direction of cellobiose metabolic flux from respiration to fermentation in the presence of cellobiose compared with the CBS6054 strain.

Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production

Many platform chemicals can be produced from renewable biomass by microorganisms, with organic acids making up a large fraction. Intolerance to the resulting low pH growth conditions, however, remains a challenge for the industrial production of organic acids by microorganisms. Issatchenkia orientalis SD108 is a promising host for industrial production because it is tolerant to acidic conditions as low as pH 2.0. With the goal to systematically assess the metabolic capabilities of this non-model yeast, we developed a genome-scale metabolic model for I. orientalis SD108 spanning 850 genes, 1826 reactions, and 1702 metabolites. In order to improve the model’s quantitative predictions, organism-specific macromolecular composition and ATP maintenance requirements were determined experimentally and implemented. We examined its network topology, including essential genes and flux coupling analysis and drew comparisons with the Yeast 8.3 model for Saccharomyces cerevisiae. We explored the carbon substrate utilization and examined the organism’s production potential for the industrially-relevant succinic acid, making use of the OptKnock framework to identify gene knockouts which couple production of the targeted chemical to biomass production. The genome-scale metabolic model iIsor850 is a data-supported curated model which can inform genetic interventions for overproduction.

•

Genome-scale metabolic model iIsor850 describes metabolism of I. orientalis SD108.

•

Customized biomass reaction highlights differences with S. cerevisiae.

•

Chemostat data elucidate growth-associated ATP maintenance.

•

Substrate utilization and CRISPR/Cas9 gene knockout phenotypes validate model.

•

Model pinpoints candidate gene deletions coupling succinic acid production to growth.

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.

Advances in yeast alcoholic fermentations for the production of bioethanol, beer and wine

Yeasts have a long-standing relationship with humankind that has widened in recent years to encompass production of diverse foods, beverages, fuels and medicines. Here, key advances in the field of yeast fermentation applied to alcohol production, which represents the predominant product of industrial biotechnology, will be presented. More specifically, we have selected industries focused in producing bioethanol, beer and wine. In these bioprocesses, yeasts from the genus Saccharomyces are still the main players, with Saccharomyces cerevisiae recognized as the preeminent industrial ethanologen. However, the growing demand for new products has opened the door to diverse yeasts, including non-Saccharomyces strains. Furthermore, the development of synthetic media that successfully simulate industrial fermentation medium will be discussed along with a general overview of yeast fermentation modeling.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Ganoderma lucidum for Improved Extracellular Polysaccharide Production

In this study, we reconstructed for the first time a genome-scale metabolic model (GSMM) of Ganoderma lucidum strain CGMCC5.26, termed model iZBM1060, containing 1060 genes, 1202 metabolites, and 1404 reactions. Important findings based on model iZBM1060 and its predictions are as follows: (i) The extracellular polysaccharide (EPS) biosynthetic pathway was elucidated completely. (ii) A new fermentation strategy is proposed: addition of phenylalanine increased EPS production by 32.80% in simulations and by 38.00% in experiments. (iii) Eight genes for key enzymes were proposed for EPS overproduction. Model iZBM1060 provides a useful platform for regulating EPS production in terms of system metabolic engineering for G. lucidum, as well as a guide for future metabolic pathway construction of other high value-added edible/ medicinal mushroom species.

Systems Biology Approach to Bioremediation of Nitroaromatics: Constraint-Based Analysis of 2,4,6-Trinitrotoluene Biotransformation by Escherichia coli

Microbial remediation of nitroaromatic compounds (NACs) is a promising environmentally friendly and cost-effective approach to the removal of these life-threating agents. Escherichia coli (E. coli) has shown remarkable capability for the biotransformation of 2,4,6-trinitro-toluene (TNT). Efforts to develop E. coli as an efficient TNT degrading biocatalyst will benefit from holistic flux-level description of interactions between multiple TNT transforming pathways operating in the strain. To gain such an insight, we extended the genome-scale constraint-based model of E. coli to account for a curated version of major TNT transformation pathways known or evidently hypothesized to be active in E. coli in present of TNT. Using constraint-based analysis (CBA) methods, we then performed several series of in silico experiments to elucidate the contribution of these pathways individually or in combination to the E. coli TNT transformation capacity. Results of our analyses were validated by replicating several experimentally observed TNT degradation phenotypes in E. coli cultures. We further used the extended model to explore the influence of process parameters, including aeration regime, TNT concentration, cell density, and carbon source on TNT degradation efficiency. We also conducted an in silico metabolic engineering study to design a series of E. coli mutants capable of degrading TNT at higher yield compared with the wild-type strain. Our study, therefore, extends the application of CBA to bioremediation of nitroaromatics and demonstrates the usefulness of this approach to inform bioremediation research.

Genome-scale modeling of yeast: chronology, applications and critical perspectives

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.

Insertion of a xylanase in xylose binding protein results in a xylose-stimulated xylanase

BACKGROUND

Product inhibition can reduce catalytic performance of enzymes used for biofuel production. Different mechanisms can cause this inhibition and, in most cases, the use of classical enzymology approach is not sufficient to overcome this problem. Here we have used a semi-rational protein fusion strategy to create a product-stimulated enzyme.

RESULTS

A semi-rational protein fusion strategy was used to create a protein fusion library where the Bacillus subtilis GH11 xylanase A (XynA) was inserted at 144 surface positions of the Escherichia coli xylose binding protein (XBP). Two XynA insertions at XBP positions 209 ([209]XBP-Xyn-XBP) and 262 ([262]XBP-Xyn-XBP) showed a 20% increased xylanolytic activity in the presence of xylose, conditions where native XynA is inhibited. Random linkers of 1-4 Gly/Ala residues were inserted at the XynA N- and C-termini in the [209]XBP and [262]XBP, and the chimeras 2091A and 2621B were isolated, showing a twofold increased xylanolytic activity in the presence of xylose and k cat values of 200 and 240 s(-1) in the 2091A and 2621B, respectively, as compared to 70 s(-1) in the native XynA. The xylose affinity of the XBP was unchanged in the chimeras, showing that the ~3- to 3.5-fold stimulation of catalytic efficiency by xylose was the result of allosteric coupling between the XBP and XynA domains. Molecular dynamics simulations of the chimeras suggested conformation alterations in the XynA on xylose binding to the XBP resulted in exposure of the catalytic cavity and increased mobility of catalytic site residues as compared to the native XynA.

CONCLUSIONS

These results are the first report of engineered glycosyl hydrolase showing allosteric product stimulation and suggest that the strategy may be more widely employed to overcome enzyme product inhibition and to improve catalytic performance. Graphical abstractProtein fusion of a GH11 xylanase (in red) and a xylose binding protein (XBP, in blue) results in a xylanase-XBP chimera that presents allosteric activation of the xylanase activity by xylose (shown as a space-filled molecule bound to the xylanase-XBP chimera).

Current state of genome-scale modeling in filamentous fungi

The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi.

Characterization of newly isolated oleaginous yeasts - Cryptococcus podzolicus, Trichosporon porosum and Pichia segobiensis

The yeast strains Cryptococcus podzolicus, Trichosporon porosum and Pichia segobiensis were isolated from soil samples and identified as oleaginous yeast strains beneficial for the establishment of microbial production processes for sustainable lipid production suitable for several industrial applications. When cultured in bioreactors with glucose as the sole carbon source C. podzolicus yielded 31.8% lipid per dry biomass at 20°C, while T. porosum yielded 34.1% at 25°C and P. segobiensis 24.6% at 25°C. These amounts correspond to lipid concentrations of 17.97 g/L, 17.02 g/L and 12.7 g/L and volumetric productivities of 0.09 g/Lh, 0.1 g/Lh and 0.07 g/Lh, respectively. During the culture of C. podzolicus 30 g/l gluconic acid was detected as by-product in the culture broth and 12 g/L gluconic acid in T. porosum culture. The production of gluconic acid was eliminated for both strains when glucose was substituted by xylose as the carbon source. Using xylose lipid yields were 11.1 g/L and 13.9 g/L, corresponding to 26.8% and 33.4% lipid per dry biomass and a volumetric productivity of 0.07 g/Lh and 0.09 g/Lh, for C. podzolicus and T. porosum respectively. The fatty acid profile analysis showed that oleic acid was the main component (39.6 to 59.4%) in all three strains and could be applicable for biodiesel production. Palmitic acid (18.4 to 21.1%) and linolenic acid (7.5 to 18.7%) are valuable for cosmetic applications. P. segobiensis had a considerable amount of palmitoleic acid (16% content) and may be suitable for medical applications.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

Metabolic reconstruction and flux analysis of industrial Pichia yeasts

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.