Genome-scale metabolic modeling and in silico analysis of lipid accumulating yeast Candida tropicalis for dicarboxylic acid production

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Whole-cell one-pot biosynthesis of dodecanedioic acid from renewable linoleic acid

BACKGROUND

Dodecanedioic acid (DDA), a typical medium-chain dicarboxylic fatty acid with widespread applications, has a great synthetic value and a huge industrial market demand. Currently, a sustainable, eco-friendly and efficient process is desired for dodecanedioic acid production.

RESULTS

Herein, a multi-enzymatic cascade was designed and constructed for the production of DDA from linoleic acid based on the lipoxygenase pathway in plants. The cascade is composed of lipoxygenase, hydroperoxide lyase, aldehyde dehydrogenase, and unidentified double-bond reductase in E. coli for the main cascade reactions, as well as NADH oxidase for cofactor recycling. The four component enzymes involved in the cascade were co-expressed in E. coli, together with the endogenous double-bond reductase of E. coli. After optimizing the reaction conditions of the rate-limiting step, 43.8 g L- 1 d- 1 of DDA was obtained by a whole-cell one-pot process starting from renewable linoleic acid.

CONCLUSIONS

Through engineering of the reaction system and co-expressing the component enzymes, a sustainable and eco-friendly DDA biosynthesis route was set up in E. coli, which afforded the highest space time yield for DDA production among the current artificial multi-enzymatic routes derived from the LOX-pathway, and the productivity achieved here ranks the second highest among the current research progress in DDA biosynthesis.

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.

An Eclectic Review on Dicarboxylic Acid Production Through Yeast Cell Factories and Its Industrial Prominence

Dicarboxylic acid (DCA) is a multifaceted chemical intermediate, recoursed to produce many industrially important products such as adhesives, plasticizers, lubricants, polymers, etc. To bypass the shortcomings of the chemical methods of synthesis of DCA and to reduce fossil fuel footprints, bio-based synthesis is gaining attention. In pursuit of an eco-friendly sustainable alternative method of DCA production, microbial cell factories, and renewable organic resources are gaining popularity. Among the plethora of microbial communities, yeast is being favored industrially compared to bacterial fermentation due to its hyperosmotic and low pH tolerance and flexibility for gene manipulations. By application of rapidly evolving genetic manipulation techniques, the bio-based DCA production could be made more precise and economical. To bridge the gap between supply and demand of DCA, many strategies are employed to improve the fermentation. This review briefly outlines the advancements in DCA production using yeast cell factories with the exemplification of strain improvement strategies.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

Rational genome and metabolic engineering of Candida viswanathii by split CRISPR to produce hundred grams of dodecanedioic acid

Candida viswanathii is a promising cell factory for producing dodecanedioic acid (DDA) and other long chain dicarboxylic acids. However, metabolic engineering of C. viswanathii is difficult partly due to the lack of synthetic biology toolkits. Here we developed CRISPR-based approaches for rational genome and metabolic engineering of C. viswanathii. We first optimized the CRISPR system and protocol to promote the homozygous gene integration efficiency to >60%. We also designed a split CRISPR system for one-step integration of multiple genes into C. viswanathii. We uncovered that co-expression of CYP52A19, CPRb and FAO2 that catalyze different steps in the biotransformation enhances DDA production and abolishes accumulation of intermediates. We also unveiled that co-expression of additional enzyme POS5 further promotes DDA production and augments cell growth. We harnessed the split CRISPR system to co-integrate these 4 genes (13.6 kb) into C. viswanathii and generated a stable strain that doubles the DDA titer (224 g/L), molar conversion (83%) and productivity (1.87 g/L/h) when compared with the parent strain. This study altogether identifies appropriate enzymes/promoters to augment dodecane conversion to DDA and implicates the potential of split CRISPR system for metabolic engineering of C. viswanathii.

Metabolic reconstruction of the human pathogen Candida auris: using a cross-species approach for drug target prediction

Candida auris is an emerging human pathogen, associated with antifungal drug resistance and hospital candidiasis outbreaks. In this work, we present iRV973, the first reconstructed Genome-scale metabolic model (GSMM) for C. auris. The model was manually curated and experimentally validated, being able to accurately predict the specific growth rate of C. auris and the utilization of several sole carbon and nitrogen sources. The model was compared to GSMMs available for other pathogenic Candida species and exploited as a platform for cross-species comparison, aiming the analysis of their metabolic features and the identification of potential new antifungal targets common to the most prevalent pathogenic Candida species. From a metabolic point of view, we were able to identify unique enzymes in C. auris in comparison with other Candida species, which may represent unique metabolic features. Additionally, 50 enzymes were identified as potential drug targets, given their essentiality in conditions mimicking human serum, common to all four different Candida models analysed. These enzymes represent interesting drug targets for antifungal therapy, including some known targets of antifungal agents used in clinical practice, but also new potential drug targets without any human homolog or drug association in Candida species.

A Genome-Scale Metabolic Model for the Human Pathogen Candida Parapsilosis and Early Identification of Putative Novel Antifungal Drug Targets

Candida parapsilosis is an emerging human pathogen whose incidence is rising worldwide, while an increasing number of clinical isolates display resistance to first-line antifungals, demanding alternative therapeutics. Genome-Scale Metabolic Models (GSMMs) have emerged as a powerful in silico tool for understanding pathogenesis due to their systems view of metabolism, but also to their drug target predictive capacity. This study presents the construction of the first validated GSMM for C. parapsilosis—iDC1003—comprising 1003 genes, 1804 reactions, and 1278 metabolites across four compartments and an intercompartment. In silico growth parameters, as well as predicted utilisation of several metabolites as sole carbon or nitrogen sources, were experimentally validated. Finally, iDC1003 was exploited as a platform for predicting 147 essential enzymes in mimicked host conditions, in which 56 are also predicted to be essential in C. albicans and C. glabrata. These promising drug targets include, besides those already used as targets for clinical antifungals, several others that seem to be entirely new and worthy of further scrutiny. The obtained results strengthen the notion that GSMMs are promising platforms for drug target discovery and guide the design of novel antifungal therapies.

Global Transcriptome Profile of the Oleaginous Yeast Saitozyma podzolica DSM 27192 Cultivated in Glucose and Xylose

Unlike conventional yeasts, several oleaginous yeasts, including Saitozyma podzolica DSM 27192, possess the innate ability to grow and produce biochemicals from plant-derived lignocellulosic components such as hexose and pentose sugars. To elucidate the genetic basis of S. podzolica growth and lipid production on glucose and xylose, we performed comparative temporal transcriptome analysis using RNA-seq method. Approximately 3.4 and 22.2% of the 10,670 expressed genes were differentially (FDR < 0.05, and log2FC > 1.5) expressed under batch and fed batch modes, respectively. Our analysis revealed that a higher number of sugar transporter genes were significantly overrepresented in xylose relative to glucose-grown cultures. Given the low homology between proteins encoded by most of these genes and those of the well-characterised transporters, it is plausible to conclude that S. podzolica possesses a cache of putatively novel sugar transporters. The analysis also suggests that S. podzolica potentially channels carbon flux from xylose via both the non-oxidative pentose phosphate and potentially via the first steps of the Weimberg pathways to yield xylonic acid. However, only the ATP citrate lyase (ACL) gene showed significant upregulation among the essential oleaginous pathway genes under nitrogen limitation in xylose compared to glucose cultivation. Combined, these findings pave the way toward the design of strategies or the engineering of efficient biomass hydrolysate utilization in S. podzolica for the production of various biochemicals.

A Brief Journey into the History of and Future Sources and Uses of Fatty Acids

Fats and lipids have always had a primary role in the history of humankind, from ancient civilisations to the modern and contemporary time, going from domestic and cosmetic uses, to the first medical applications and later to the large-scale industrial uses for food, pharmaceutical, cosmetics, and biofuel production. Sources and uses of those have changed during time following the development of chemical sciences and industrial technological advances. Plants, fish, and animal fats have represented the primary source of lipids and fats for century. Nowadays, the use of fatty acid sources has taken a turn: industries are mainly interested in polyunsaturated fatty acids (PUFAs), which have beneficial properties in human health; and also, for high-value fatty acids product for innovative and green production of biofuel and feedstocks. Thus, the constant increase in demand of fatty acids, the fact that marine and vegetable sources are not adequate to meet the high level of fatty acids required worldwide and climate change, have determined the necessity of the search for renewable and sustainable sources for fatty acids. Biotechnological advances and bioengineering have started looking at the genetic modification of algae, bacteria, yeasts, seeds, and plants to develop cell factory able to produce high value fatty acid products in a renewable and sustainable manner. This innovative approach applied to FA industry is a peculiar example of how biotechnology can serve as a powerful mean to drive the production of high value fatty acid derivatives on the concept of circular bioeconomy, based on the reutilisation of organic resources for alternative and sustainable productive patterns that are environmentally friendly.

Metabolic pathway analysis of walnut endophytic bacterium Bacillus subtilis HB1310 related to lipid production from fermentation of cotton stalk hydrolysate based on genome sequencing

OBJECTIVES

In this study, genome sequencing and metabolic analysis were used to identify and verify the key metabolic pathways for glucose and xylose utilization and fatty acid synthesis in the walnut endophytic bacterium (WEB) Bacillus subtilis HB1310.

RESULTS

The genome sequence of WEB HB1310 was generated with a size of 4.1 Mb and GC content of 43.5%. Genome annotation indicated that the Embden-Meyerhof-Parnas, pentose phosphate, and fatty acid synthesis pathways were mainly involved in mixed sugar utilization and lipid production. In particular, diverse and abundant fatty acid synthesis genes were observed in a higher number than in other Bacillus strains. The tricarboxylic acid cycle competitively shared the carbon flux flowing before 48 h, and the acetic acid fermentation competed after 72 h. Moreover, fatty acid synthase activity was highly correlated with lipid titer with a correlation coefficient of 0.9626, and NADPH might be more utilized for the lipid synthesis within 48 h.

CONCLUSIONS

This study is the first attempt to explain the metabolic mechanism of mixed sugar utilization and lipid production based on genomic information, which provides a theoretical basis for the metabolic regulation of bacterial lipid production from lignocellulosic hydrolysates.

Lipid production by oleaginous yeasts

Microbial lipid production has been studied extensively for years; however, lipid metabolic engineering in many of the extraordinarily high lipid-accumulating yeasts was impeded by inadequate understanding of the metabolic pathways including regulatory mechanisms defining their oleaginicity and the limited genetic tools available. The aim of this review is to highlight the prominent oleaginous yeast genera, emphasizing their oleaginous characteristics, in conjunction with diverse other features such as cheap carbon source utilization, withstanding the effect of inhibitory compounds, commercially favorable fatty acid composition-all supporting their future development as economically viable lipid feedstock. The unique aspects of metabolism attributing to their oleaginicity are accentuated in the pretext of outlining the various strategies successfully implemented to improve the production of lipid and lipid-derived metabolites. A large number of in silico data generated on the lipid accumulation in certain oleaginous yeasts have been carefully curated, as suggestive evidences in line with the exceptional oleaginicity of these organisms. The different genetic elements developed in these yeasts to execute such strategies have been scrupulously inspected, underlining the major types of newly-found and synthetically constructed promoters, transcription terminators, and selection markers. Additionally, there is a plethora of advanced genetic toolboxes and techniques described, which have been successfully used in oleaginous yeasts in the recent years, promoting homologous recombination, genome editing, DNA assembly, and transformation at remarkable efficiencies. They can accelerate and effectively guide the rational designing of system-wide metabolic engineering approaches pinpointing the key targets for developing industrially suitable yeast strains.

Increasing Long-Chain Dicarboxylic Acid Production in Candida tropicalis by Engineering Fatty Transporters

Candida tropicalis can metabolize alkanes or fatty acids to produce long-chain dicarboxylic acids (DCAs). Fatty acid transporters located on the cell or peroxisome membrane may play an important role in this process. Using amino acid sequence homologous alignment, two putative proteins, CtFat1p and CtPxa1p, located on the cell and peroxisome membrane were found, respectively. Moreover, single- and double-knockout homologous recombination technology was used to study ctfat1p and ctpxa1p gene effects on DCA synthesis. In comparison to the wild-type strain, long-chain DCA yield decreased by 65.14%, 88.38% and 56.19% after single and double-copy knockout of ctfat1p genes and double-copy knockout of ctpxa1p genes, respectively, indicating that the knockout of ctfat1p and ctpxa1p genes had a significant effect on the conversion of oils and fats into long-chain DCAs by C. tropicalis. However, the yield of long-chain DCAs increased by 21.90% after single-knockout of the ctpxa1p gene, indicating that the single-knockout of the ctpxa1p gene may reduce fatty acid transport to peroxisome for further oxidation. Moreover, to improve the intracellular transport rate of fatty acids, ctfat1p copy number increased, increasing DCA yield by 30.10%. These results may provide useful information for enhancing the production of long-chain DCAs by C. tropicalis.

High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

The long-chain, ω-3 polyunsaturated fatty acids (PUFAs) (e.g., eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]), are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists with the capability to synthesize and accrue large amounts of DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66's metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

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 lipid metabolic engineering of oleaginous yeasts

With the increasing demand to develop a renewable and sustainable biolipid feedstock, several species of non-conventional oleaginous yeasts are being explored. Apart from the platform oleaginous yeast Yarrowia lipolytica, the understanding of metabolic pathway and, therefore, exploiting the engineering prospects of most of the oleaginous species are still in infancy. However, in the past few years, enormous efforts have been invested in Rhodotorula, Rhodosporidium, Lipomyces, Trichosporon, and Candida genera of yeasts among others, with the rapid advancement of engineering strategies, significant improvement in genetic tools and techniques, generation of extensive bioinformatics and omics data. In this review, we have collated these recent progresses to make a detailed and insightful summary of the major developments in metabolic engineering of the prominent oleaginous yeast species. Such a comprehensive overview would be a useful resource for future strain improvement and metabolic engineering studies for enhanced production of lipid and lipid-derived chemicals in oleaginous yeasts.

Exploring the druggable proteome of Candida species through comprehensive computational analysis

Candida albicans and non-albicans Candida spp. are major cause of systemic mycoses. Antifungal drugs such as azoles and polyenes are not efficient to successfully eradicate Candida infection owing to their fungistatic nature or low bioavailability. Here, we have adopted a comprehensive computational workflow for identification, prioritization and validation of targets from proteomes of Candida albicans and Candida tropicalis. The protocol involves identification of essential drug-target candidates using subtractive genomics, protein-protein interaction network properties and systems biology based methods. The essentiality of the novel metabolic and non-metabolic targets was established by performing in silico gene knockouts, under aerobic as well as anaerobic conditions, and in vitro drug inhibition assays respectively. Deletion of twelve genes that are involved in amino acid, secondary metabolite, and carbon metabolism showed zero growth in metabolic model under simulated conditions. The algorithm, used in this study, can be downloaded from http://pbit.bicnirrh.res.in/offline.php and executed locally.

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.

Characterization of two sugar transporters responsible for efficient xylose uptake in an oleaginous yeast Candida tropicalis SY005

Microbial conversion of lignocellulosic feedstock to the target bioproduct requires efficient assimilation of its constituent sugars, a large part of which comprises of glucose and xylose. This study aims to identify and characterize sugar transporters capable of xylose uptake in an oleaginous strain of the industrially relevant yeast Candida tropicalis. In silico database mining resulted in two sugar transporter proteins- CtStp1 and CtStp2, containing conserved amino acid residues and motifs that have been previously reported to be involved in xylose transport in other organisms. Several softwares predicted the likelihood of 10-12 transmembrane (TM) helices to be present in both the Stps, while molecular modelling showed 12 TM helices that were organized into a typical structure found in the major facilitator superfamily of transporters. Docking with different sugars also predicted favorable interactions. Heterologous expression in a Saccharomyces cerevisiae strain harboring functional xylose metabolic genes validated the broad substrate specificity of the two Stps. Each transporter supported prominent growth of recombinant S. cerevisiae strains on six sugars including xylose at various concentrations. Expression of CtSTP1 and CtSTP2 along with the xylose metabolic genes in yeast transformants grown in presence of xylose was confirmed by transcript detection. Growth curve and sugar consumption profiles revealed uptake of both glucose and xylose simultaneously by the recombinant yeast strains, though CtStp1 showed relatively less effect of glucose repression in mixed sugars and was a better transporter of xylose than CtStp2. Such glucose-xylose utilizing efficient transporters can be effective tools for developing co-fermenting yeasts through genetic engineering in future, with noteworthy applications in renewable biomass utilization.

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.

Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.

Genome-scale modeling for Bacillus coagulans to understand the metabolic characteristics

Lactic acid is widely used in many industries, especially in the production of poly-lactic acid. Bacillus coagulans is a promising lactic acid producer in industrial fermentation due to its thermophilic property. In this study, we developed the first genome-scale metabolic model (GEM) of B. coagulans iBag597, together with an enzyme-constrained model ec-iBag597. We measured strain-specific biomass composition and integrated the data into a biomass equation. Then, we validated iBag597 against experimental data generated in this study, including amino acid requirements and carbon source utilization, showing that simulations were generally consistent with the experimental results. Subsequently, we carried out chemostats to investigate the effects of specific growth rate and culture pH on metabolism of B. coagulans. Meanwhile, we used iBag597 to estimate the intracellular metabolic fluxes for those conditions. The results showed that B. coagulans was capable of generating ATP via multiple pathways, and switched among them in response to various conditions. With ec-iBag597, we estimated the protein cost and protein efficiency for each ATP-producing pathway to investigate the switches. Our models pave the way for systems biology of B. coagulans, and our findings suggest that maintaining a proper growth rate and selecting an optimal pH are beneficial for lactate fermentation.

Quantitative multi-omics analysis of the effects of mitochondrial dysfunction on lipid metabolism in Saccharomyces cerevisiae

In this study, combined genome, transcriptome, and metabolome analysis was performed for eight Saccharomyces cerevisiae mitochondrial respiration-deficient mutants. Each mutant exhibited a unique nuclear genome mutation pattern; the nuclear genome mutations, and thus potentially affected genes and metabolic pathways, showed a co-occurrence frequency of ≤ 3 among the eight mutants. For example, only a lipid metabolism-related pathway was likely to be affected by the nuclear genome mutations in one of the mutants. However, large deletions in the mitochondrial genome were the shared characteristic among the eight mutants. At the transcriptomic level, lipid metabolism was the most significantly enriched Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway for differentially expressed genes (DEGs) co-occurring in both ≥ 4 and ≥ 5 mutants. Any identified DEG enriched in lipid metabolism showed the same up-/down-regulated pattern among nearly all eight mutants. Further, 126 differentially expressed lipid species (DELS) were identified, which also showed the same up-/down-regulated pattern among nearly all investigated mutants. It was conservatively demonstrated that the similar change pattern of lipid metabolism in the entire investigated mutant population was attributed to mitochondrial dysfunction. The change spectrum of lipid species was presented, suggesting that the number and change degree of up-regulated lipid species were higher than those of down-regulated lipid species. Additionally, energy storage lipids increased in content and plasma-membrane phospholipid compositions varied in the relative proposition. The results for the genome, transcriptome, and lipidome were mutually validated, which provides quantitative data revealing the roles of mitochondria from a global cellular perspective.

Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

Bacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using ¹³C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

Genomic sequencing, genome-scale metabolic network reconstruction, and in silico flux analysis of the grape endophytic fungus Alternaria sp. MG1

BACKGROUND

Alternaria sp. MG1, an endophytic fungus isolated from grape, is a native producer of resveratrol, which has important application potential. However, the metabolic characteristics and physiological behavior of MG1 still remains mostly unraveled. In addition, the resveratrol production of the strain is low. Thus, the whole-genome sequencing is highly required for elucidating the resveratrol biosynthesis pathway. Furthermore, the metabolic network model of MG1 was constructed to provide a computational guided approach for improving the yield of resveratrol.

RESULTS

Firstly, a draft genomic sequence of MG1 was generated with a size of 34.7 Mbp and a GC content of 50.96%. Genome annotation indicated that MG1 possessed complete biosynthesis pathways for stilbenoids, flavonoids, and lignins. Eight secondary metabolites involved in these pathways were detected by GC-MS analysis, confirming the metabolic diversity of MG1. Furthermore, the first genome-scale metabolic network of Alternaria sp. MG1 (named iYL1539) was reconstructed, accounting for 1539 genes, 2231 metabolites, and 2255 reactions. The model was validated qualitatively and quantitatively by comparing the in silico simulation with experimental data, and the results showed a high consistency. In iYL1539, 56 genes were identified as growth essential in rich medium. According to constraint-based analysis, the importance of cofactors for the resveratrol biosynthesis was successfully demonstrated. Ethanol addition was predicted in silico to be an effective method to improve resveratrol production by strengthening acetyl-CoA synthesis and pentose phosphate pathway, and was verified experimentally with a 26.31% increase of resveratrol. Finally, 6 genes were identified as potential targets for resveratrol over-production by the recently developed methodology. The target-genes were validated using salicylic acid as elicitor, leading to an increase of resveratrol yield by 33.32% and the expression of gene 4CL and CHS by 1.8- and 1.6-fold, respectively.

CONCLUSIONS

This study details the diverse capability and key genes of Alternaria sp. MG1 to produce multiple secondary metabolites. The first model of the species Alternaria was constructed, providing an overall understanding of the physiological behavior and metabolic characteristics of MG1. The model is a highly useful tool for enhancing productivity by rational design of the metabolic pathway for resveratrol and other secondary metabolites.

Advances in bio-based production of dicarboxylic acids longer than C4

Growing concerns of environmental pollution and fossil resource shortage are major driving forces for bio-based production of chemicals traditionally from petrochemical industry. Dicarboxylic acids (DCAs) are important platform chemicals with large market and wide applications, and here the recent advances in bio-based production of straight-chain DCAs longer than C4 from biological approaches, especially by synthetic biology, are reviewed. A couple of pathways were recently designed and demonstrated for producing DCAs, even those ranging from C5 to C15, by employing respective starting units, extending units, and appropriate enzymes. Furthermore, in order to achieve higher production of DCAs, enormous efforts were made in engineering microbial hosts that harbored the biosynthetic pathways and in improving properties of biocatalytic elements to enhance metabolic fluxes toward target DCAs. Here we summarize and discuss the current advantages and limitations of related pathways, and also provide perspectives on synthetic pathway design and optimization for hyper-production of DCAs.

Comprehensive analysis of a metabolic model for lipid production in Rhodosporidium toruloides

The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with flux balance analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism.

Effect of decanoic acid and 10-hydroxydecanoic acid on the biotransformation of methyl decanoate to sebacic acid

Biotransformation of fatty acid methyl esters to dicarboxylic acids has attracted much attention in recent years; however, reports of sebacic acid production using such biotransformation remain few. The toxicity of decanoic acid is the main challenge for this process. Decane induction has been reported to be essential to activate the enzymes involved in the α,ω-oxidation pathway before initiating the biotransformation of methyl decanoate to sebacic acid. However, we observed the accumulation of intermediates (decanoic acid and 10-hydroxydecanoic acid) during the induction period. In this study, we examined the effects of these intermediates on the biotransformation process. The presence of decanoic acid, even at a low concentration (0.2 g/L), inhibited the transformation of 10-hydroxydecanoic acid to sebacic acid. Moreover, about 24–32% reduction in the decanoic acid oxidation was observed in the presence of 0.5–1.5 g/L 10-hydroxydecanoic acid. To eliminate these inhibitory effects, we applied substrate-limiting conditions during the decane induction process, which eliminated the accumulation of decanoic acid. Although the productivity of sebacic acid (34.5 ± 1.10 g/L) was improved, by 28% over that achieved using the previously methods, after 54 h, the accumulation of 10-hydroxydecanoic acid was still detected. The accumulation of 10-hydroxydecanoic acid even under the decane limiting conditions could be an evidence that oxidation of 10-hydroxydecanoic acid could be the rate-limiting step in this process. The improvement of this reaction should be an important objective for further development of the production of sebacic acid using biotransformation.

Toward Synthetic Biology Strategies for Adipic Acid Production: An in Silico Tool for Combined Thermodynamics and Stoichiometric Analysis of Metabolic Networks

Adipic acid, a nylon-6,6 precursor, has recently gained popularity in synthetic biology. Here, 16 different production routes to adipic acid were evaluated using a novel tool for network-embedded thermodynamic analysis of elementary flux modes. The tool distinguishes between thermodynamically feasible and infeasible modes under determined metabolite concentrations, allowing the thermodynamic feasibility of theoretical yields to be assessed. Further, patterns that always caused infeasible flux distributions were identified, which will aid the development of tailored strain design. A review of cellular efflux mechanisms revealed that significant accumulation of extracellular product is only possible if coupled with ATP hydrolysis. A stoichiometric analysis demonstrated that the maximum theoretical product carbon yield heavily depends on the metabolic route, ranging from 32 to 99% on glucose and/or palmitate in Escherichia coli and Saccharomyces cerevisiae metabolic models. Equally important, metabolite concentrations appeared to be thermodynamically restricted in several pathways. Consequently, the number of thermodynamically feasible flux distributions was reduced, in some cases even rendering whole pathways infeasible, highlighting the importance of pathway choice. Only routes based on the shikimate pathway were thermodynamically favorable over a large concentration and pH range. The low pH capability of S. cerevisiae shifted the thermodynamic equilibrium of some pathways toward product formation. One identified infeasible-pattern revealed that the reversibility of the mitochondrial malate dehydrogenase contradicted the current state of knowledge, which imposes a major restriction on the metabolism of S. cerevisiae. Finally, the evaluation of industrially relevant constraints revealed that two shikimate pathway-based routes in E. coli were the most robust.

Effects of glucose concentration on 1,18-cis-octadec-9-enedioic acid biotransformation efficiency and lipid body formation in Candida tropicalis

The unsaturated long-chain α,ω-dicarboxylic acid 1,18-cis-octadec-9-enedioic acid (cis-ODA) is a versatile precursor of various valuable compounds, such as polymers, and can be obtained from renewable resources. This makes cis-ODA highly attractive for the chemical industry where there is a growing interest in sustainable processes. However, chemical synthesis of the cis isomers is currently not feasible. In contrast, biotechnological production allows for highly specific and selective reactions. Therefore, we developed an efficient production strategy for cis-ODA using Candida tropicalis as a whole-cell biocatalyst for the biotransformation of oleic acid, which naturally occurs in various fats and oils. Applying a bench-top system comprising eight parallel bioreactors, the production process was characterised and optimised for high productivity. Glucose feed rate was identified as the most crucial process parameter influencing product yield, with high rates inducing oleic acid incorporation into triacylglycerols and storage in lipid bodies. Conversely, application of medium-chain length fatty acid as a substrate did not show any occurrence of lipid bodies. Applying the lowest possible molar ratio of glucose to oleic acid (1.5) resulted in marginal lipid body formation, but led to a peak volumetric productivity of 0.56 g/L/h and a final titre of approximately 45 g/L with a corresponding yield of 70%.

Biotechnological production of bio-based long-chain dicarboxylic acids with oleogenious yeasts

Long-chain α,ω-dicarboxylic acids (DCAs) are versatile chemical intermediates of industrial importance used as building blocks for the production of polymers, lubricants, or adhesives. The majority of industrial long-chain DCAs is produced from petro-chemical resources. An alternative is their biotechnological production from renewable materials like plant oil fatty acids by microbial fermentation using oleogenious yeasts. Oleogenious yeasts are natural long-chain DCA producers, which have to be genetically engineered for high-yield DCA production. Although, some commercialized fermentation processes using engineered yeasts are reported, bio-based long-chain DCAs are still far from being a mass product. Further progress in bioprocess engineering and rational strain design is necessary to advance their further commercialization. The present article reviews the basic strategies, as well as novel approaches in the strain design of oleogenious yeasts, such as the combination of traditional metabolic engineering with system biology strategies for high-yield long-chain DCA production. Therefore a detailed overview of the involved metabolic processes for the biochemical long-chain DCA synthesis is given.

Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus

Background

Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production.

Results

The metabolic conservation of Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA-Hcd-Ccr with hcd and ccr overexpression exhibited the highest ascomycin production (up to 438.95 mg/L), 1.43-folds improvement than that of the parent strain FS35 (305.56 mg/L).

Conclusions

The successful constructing and experimental validation of the metabolic model of S. hygroscopicus var. ascomyceticus showed that the general metabolic network model of Streptomyces species could be used to analyze the intracellular metabolism and predict the potential key limiting steps for target metabolites overproduction. The corresponding overexpression strains of the two identified genes (hcd and ccr) using the constructed model all displayed higher ascomycin titer. The strategy for yield improvement developed here could also be extended to the improvement of other secondary metabolites in Streptomyces species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-017-0787-5) contains supplementary material, which is available to authorized users.

Reconstruction of a Genome-scale Metabolic Network of Komagataeibacter nataicola RZS01 for Cellulose Production

Bacterial cellulose (BC) is widely used in industries owing to its high purity and strength. Although Komagataeibacter nataicola is a representative species for BC production, its intracellular metabolism leading to BC secretion is unclear. In the present study, a genome-scale metabolic network of cellulose-producing K. nataicola strain RZS01 was reconstructed to understand its metabolic behavior. This model iHZ771 comprised 771 genes, 2035 metabolites, and 2014 reactions. Constraint-based analysis was used to characterize and evaluate the critical intracellular pathways. The analysis revealed that a total of 71 and 30 genes are necessary for cellular growth in a minimal medium and complex medium, respectively. Glycerol was identified as the optimal carbon source for the highest BC production. The minimization of metabolic adjustment algorithm identified 8 genes as potential targets for over-production of BC. Overall, model iHZ771 proved to be a useful platform for understanding the physiology and BC production of K. nataicola.

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.

Production of dodecanedioic acid via biotransformation of low cost plant-oil derivatives using Candida tropicalis

Dodecanedioic acid (DDA) is highly useful to the chemical industry as a versatile precursor for producing the polyamide nylon-6,12, which is used for many technical applications, such as heat and chemical-resistant sheaths. However, DDA synthesis has several drawbacks, such as high energy input and cost-intensive removal of by-products. Therefore, alternative bio-based production routes are required due to increasing industrial demand for green chemicals and renewable products. Candida tropicalis converts petrochemical-based n-dodecanes to the corresponding dicarboxylic acids by targeted functionalization. To increase sustainability of the DDA production process, we tested dodecanoic acid methyl ester, which can be easily obtained from transesterification of coconut oil, in whole-cell biotransformation by C. tropicalis. By modifying selected process parameters, a final DDA concentration of 66 g/L was achieved using a highly reliable, small-scale bioreactor system. Crucial process development included a gradual pH shift, an optimized substrate feeding strategy, and monitoring the transcriptional profile.

Characterization of the newly isolated ω-oxidizing yeast Candida sorbophila DS02 and its potential applications in long-chain dicarboxylic acid production

α, ω-Dicarboxylic acids (DCAs) are multipurpose chemicals widely used in polymers, perfumes, plasticizers, lubricants, and adhesives. The biotransformation of DCAs from alkanes and fatty acids by microorganisms has attracted recent interest, since synthesis via chemical oxidation causes problems in terms of the environment and safety. We isolated an ω-oxidizing yeast from a wastewater disposal facility of a petrochemical factory by chemostat enrichment culture. The haploid strain identified as Candida sorbophila DS02 grew on glucose and dodecane, exhibiting greater cell shrinkage on the latter. In flask cultures with mixed alkanes (C10-16) and fatty acid methyl esters (C10-16), DS02 used mixed alkanes simultaneously unlike Candida tropicalis and Yarrowia lipolytica and showed high substrate resistance. In flask cultures with acrylic acid-a known inhibitor of β-oxidation-DS02 produced 0.28 g/l dodecanedioic acid (DDDA) from dodecane, similar to wild-type C. tropicalis ATCC 20336. In fed-batch fermentation, DS02 produced 9.87 g/l DDDA, which was 5.7-fold higher than wild-type C. tropicalis. These results suggest that C. sorbophila strain DS02 has potential applications for the large-scale production of DCA.

Adipic acid tolerance screening for potential adipic acid production hosts

BACKGROUND

Biobased processes for the production of adipic acid are of great interest to replace the current environmentally detrimental petrochemical production route. No efficient natural producer of adipic acid has yet been identified, but several approaches for pathway engineering have been established. Research has demonstrated that the microbial production of adipic acid is possible, but the yields and titres achieved so far are inadequate for commercialisation. A plausible explanation may be intolerance to adipic acid. Therefore, in this study, selected microorganisms, including yeasts, filamentous fungi and bacteria, typically used in microbial cell factories were considered to evaluate their tolerance to adipic acid.

RESULTS

Screening of yeasts and bacteria for tolerance to adipic acid was performed in microtitre plates, and in agar plates for A. niger in the presence of adipic acid over a broad range of concentration (0-684 mM). As the different dissociation state(s) of adipic acid may influence cells differently, cultivations were performed with at least two pH values. Yeasts and A. niger were found to tolerate substantially higher concentrations of adipic acid than bacteria, and were less affected by the undissociated form of adipic acid than bacteria. The yeast exhibiting the highest tolerance to adipic acid was Candida viswanathii, showing a reduction in maximum specific growth rate of no more than 10-15% at the highest concentration of adipic acid tested and the tolerance was not dependent on the dissociation state of the adipic acid.

CONCLUSIONS

Tolerance to adipic acid was found to be substantially higher among yeasts and A. niger than bacteria. The explanation of the differences in adipic acid tolerance between the microorganisms investigated are likely related to fundamental differences in their physiology and metabolism. Among the yeasts investigated, C. viswanathii showed the highest tolerance and could be a potential host for a future microbial cell factory for adipic acid.

Reconstruction of a genome-scale metabolic model and in silico analysis of the polymalic acid producer Aureobasidium pullulans CCTCC M2012223

Aureobasidium pullulans is a yeast-like fungus used for producing biopolymers e.g. polymalic acid (PMA) and pullulan. A high PMA producing strain, A. pullulans CCTCC M2012223, was isolated and sequenced in our previous study. To understand its metabolic performance, a genome-scale metabolic model, iZX637, consisting of 637 genes, 1347 reactions and 1133 metabolites, was reconstructed based on genome annotation and literature mining studies. The iZX637 model was validated by simulating cell growth, utilization of carbon and nitrogen sources, and gene essentiality analysis in A. pullulans. We further validated our model, designed a simulation program for the prediction of PMA production using experimental data, and further analyzed the carbon flux distribution and change with increasing PMA synthesis rates. Through the calculated flux distribution, NADPH- and NADH-dependent methylenetetrahydrofolate dehydrogenase (MTHFD) were found to be associated with the transfer of reducing equivalents from NADPH to NADH for supplementing NADH in the metabolic network. Furthermore, under the high PMA synthesis rate, a large amount of carbon flux was through pyruvate into malic acid via the reductive TCA cycle. Thus, pyruvate carboxylase, which can convert pyruvate to oxaloacetate with CO₂ fixation, may also be an important target for PMA synthesis. These results illustrated that the model iZX637 was a powerful tool for optimization of A. pullulans metabolism and identification of targets for guiding metabolic engineering.