Reconstruction and exploitation of a dedicated Genome-Scale Metabolic Model of the human pathogen C. neoformans

C. neoformans is notorious for causing severe pulmonary and central nervous system infections, particularly in immunocompromised patients. High mortality rates, associated with its tropism and adaptation to the brain microenvironment and its drug resistance profile, makes this pathogen a public health threat and a World Health Organization (WHO) priority. In this study, we reconstructed GSMM iRV890 for C. neoformans var. grubii , providing a promising platform for the comprehensive understanding of the unique metabolic features of C. neoformans , and subsequently shedding light on its complex tropism for the brain microenvironment and potentially informing the discovery of new drug targets. The GSMM iRV890 model is openly available in the SBML format, and underwent validation using experimental data for nitrogen and carbon assimilation, as well as specific growth and glucose consumption rates. Based on the comparison with GSMMs available for other pathogenic yeasts, unique metabolic features were predicted for C. neoformans , including key pathways shaping the dynamics between C. neoformans and the human host, and underlying its adaptation to the brain environment. Finally, predicted essential genes from the validated model are explored herein as potential novel antifungal drug targets.

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.

Host-mycobiome metabolic interactions in health and disease

Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease.

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.

Reconstruction and analysis of a genome-scale metabolic model for Agrobacterium tumefaciens

The plant pathogen Agrobacterium tumefaciens causes crown gall disease and is a widely used tool for generating transgenic plants owing to its virulence. The pathogenic process involves a shift from an independent to a living form within a host plant. However, comprehensive analyses of metabolites, genes, and reactions contributing to this complex process are lacking. To gain new insights about the pathogenicity from the viewpoints of physiology and cellular metabolism, a genome-scale metabolic model (GSMM) was reconstructed for A. tumefaciens. The model, referred to as iNX1344, contained 1,344 genes, 1,441 reactions, and 1,106 metabolites. It was validated by analyses of in silico cell growth on 39 unique carbon or nitrogen sources and the flux distribution of carbon metabolism. A. tumefaciens metabolic characteristics under three ecological niches were modelled. A high capacity to access and metabolize nutrients is more important for rhizosphere colonization than in the soil, and substantial metabolic changes were detected during the shift from the rhizosphere to tumour environments. Furthermore, by integrating transcriptome data for tumour conditions, significant alterations in central metabolic pathways and secondary metabolite metabolism were identified. Overall, the GSMM and constraint-based analysis could decode the physiological and metabolic features of A. tumefaciens as well as interspecific interactions with hosts, thereby improving our understanding of host adaptation and infection mechanisms.

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.

Transcriptomic and proteomic profiling revealed reprogramming of carbon metabolism in acetate-grown human pathogen Candida glabrata

BACKGROUND

Emergence of Candida glabrata, which causes potential life-threatening invasive candidiasis, has been widely associated with high morbidity and mortality. In order to cause disease in vivo, a robust and highly efficient metabolic adaptation is crucial for the survival of this fungal pathogen in human host. In fact, reprogramming of the carbon metabolism is believed to be indispensable for phagocytosed C. glabrata within glucose deprivation condition during infection.

METHODS

In this study, the metabolic responses of C. glabrata under acetate growth condition was explored using high-throughput transcriptomic and proteomic approaches.

RESULTS

Collectively, a total of 1482 transcripts (26.96%) and 242 proteins (24.69%) were significantly up- or down-regulated. Both transcriptome and proteome data revealed that the regulation of alternative carbon metabolism in C. glabrata resembled other fungal pathogens such as Candida albicans and Cryptococcus neoformans, with up-regulation of many proteins and transcripts from the glyoxylate cycle and gluconeogenesis, namely isocitrate lyase (ICL1), malate synthase (MLS1), phosphoenolpyruvate carboxykinase (PCK1) and fructose 1,6-biphosphatase (FBP1). In the absence of glucose, C. glabrata shifted its metabolism from glucose catabolism to anabolism of glucose intermediates from the available carbon source. This observation essentially suggests that the glyoxylate cycle and gluconeogenesis are potentially critical for the survival of phagocytosed C. glabrata within the glucose-deficient macrophages.

CONCLUSION

Here, we presented the first global metabolic responses of C. glabrata to alternative carbon source using transcriptomic and proteomic approaches. These findings implicated that reprogramming of the alternative carbon metabolism during glucose deprivation could enhance the survival and persistence of C. glabrata within the host.

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.

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

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Customized biomass reaction highlights differences with S. cerevisiae.

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Chemostat data elucidate growth-associated ATP maintenance.

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

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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.

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.

New Therapeutic Candidates for the Treatment of Malassezia pachydermatis -Associated Infections

The opportunistic pathogen Malassezia pachydermatis causes bloodstream infections in preterm infants or individuals with immunodeficiency disorders and has been associated with a broad spectrum of diseases in animals such as seborrheic dermatitis, external otitis and fungemia. The current approaches to treat these infections are failing as a consequence of their adverse effects, changes in susceptibility and antifungal resistance. Thus, the identification of novel therapeutic targets against M. pachydermatis infections are highly relevant. Here, Gene Essentiality Analysis and Flux Variability Analysis was applied to a previously reported M. pachydermatis metabolic network to identify enzymes that, when absent, negatively affect biomass production. Three novel therapeutic targets (i.e., homoserine dehydrogenase (MpHSD), homocitrate synthase (MpHCS) and saccharopine dehydrogenase (MpSDH)) were identified that are absent in humans. Notably, L-lysine was shown to be an inhibitor of the enzymatic activity of MpHCS and MpSDH at concentrations of 1 mM and 75 mM, respectively, while L-threonine (1 mM) inhibited MpHSD. Interestingly, L- lysine was also shown to inhibit M. pachydermatis growth during in vitro assays with reference strains and canine isolates, while it had a negligible cytotoxic activity on HEKa cells. Together, our findings form the bases for the development of novel treatments against M. pachydermatis infections.

Enhancement of pyruvic acid production in Candida glabrata by engineering hypoxia-inducible factor 1

Dissolved oxygen (DO) supply plays essential roles in microbial organic acid production. Candida glabrata, as a dominant strain for producing pyruvic acid, principally converts glucose to pyruvic acid through glycolysis. However, this process relies excessively on high extracellular DO content. In this study, in combination with specific motif analysis of gene promoters, hypoxia-inducible factor 1 (HIF1) was engineered to improve the transcription level of some enzymes related to pyruvic acid synthesis under low DO level and directly led to increased pyruvic acid production and glycolysis efficiency. Moreover, the intracellular stability of HIF1 was further optimized from different aspects to maximize pyruvic acid accumulation. Finally, the pyruvic acid titer in a 5-L batch bioreactor with 10% DO level reached 53.1 g/L. As pyruvic acid is involved in the biosynthesis of various products, these findings suggest that HIF1-enabled regulation method has significant potential for increasing the synthesis of other chemicals in microorganisms.

Enhanced Pyruvate Production in Candida glabrata by Engineering ATP Futile Cycle System

Energy metabolism plays an important role in the growth and central metabolic pathways of cells. Manipulating energy metabolism is an efficient strategy to improve the formation of target products and to understand the effects of altering intracellular energy levels on global metabolic networks. Candida glabrata, as a dominant yeast strain for producing pyruvate, principally converts glucose to pyruvate through the glycolytic pathway. However, this process can be severely inhibited by a high intracellular ATP content. Here, in combination with the physiological characteristics of C. glabrata, efforts have been made to construct an ATP futile cycle system (ATP-FCS) in C. glabrata to decrease the intracellular ATP level without destroying F₀F₁-ATPase function. ATP-FCS was capable of decreasing the intracellular ATP level by 51.0% in C. glabrata. The decrease in the ATP level directly led to an increased pyruvate production and glycolysis efficiency. Moreover, we further optimized different aspects of the ATP-FCS to maximize pyruvate accumulation. Combining ATP-FCS with further genetic optimization strategies, we achieved a final pyruvate titer of 40.2 g/L, with 4.35 g pyruvate/g dry cell weight and a 0.44 g/g substrate conversion rate in 500 mL flasks, which represented increases of 98.5%, 322.3%, and 160%, respectively, compared with the original strain. Thus, these strategies hold great potential for increasing the synthesis of other organic acids in microbes.

Current advance in biological production of malic acid using wild type and metabolic engineered strains

Malic acid (2-hydroxybutanedioic acid) is a four-carbon dicarboxylic acid, which has attracted great interest due to its wide usage as a precursor of many industrially important chemicals in the food, chemicals, and pharmaceutical industries. Several mature routes for malic acid production have been developed, such as chemical synthesis, enzymatic conversion and biological fermentation. With depletion of fossil fuels and concerns regarding environmental issues, biological production of malic acid has attracted more attention, which mainly consists of three pathways, namely non-oxidative pathway, oxidative pathway and glyoxylate cycle. In recent decades, metabolic engineering of model strains, and process optimization for malic acid production have been rapidly developed. Hence, this review comprehensively introduces an overview of malic acid producers and highlight some of the successful metabolic engineering approaches.

Recent advances and strategies in process and strain engineering for the production of butyric acid by microbial fermentation

Butyric acid is an important platform chemical, which is widely used in the fields of food, pharmaceutical, energy, etc. Microbial fermentation as an alternative approach for butyric acid production is attracting great attention as it is an environmentally friendly bioprocessing. However, traditional fermentative butyric acid production is still not economically competitive compared to chemical synthesis route, due to the low titer, low productivity, and high production cost. Therefore, reduction of butyric acid production cost by utilization of alternative inexpensive feedstock, and improvement of butyric acid production and productivity has become an important target. Recently, several advanced strategies have been developed for enhanced butyric acid production, including bioprocess techniques and metabolic engineering methods. This review provides an overview of advances and strategies in process and strain engineering for butyric acid production by microbial fermentation. Additionally, future perspectives on improvement of butyric acid production are also proposed.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

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.

Biotechnological production of alpha-keto acids: Current status and perspectives

Alpha-keto (α-keto) acids are used widely in feeds, food additives, pharmaceuticals, and in chemical synthesis processes. Although most α-keto acids are currently produced by chemical synthesis, their biotechnological production from renewable carbohydrates is a promising new approach. In this mini-review, we first present the different types of α-keto acids as well as their applications; next, we summarize the recent progresses in the biotechnological production of some important α-keto acids; namely, pyruvate, α-ketoglutarate, α-ketoisovalerate, α-ketoisocaproate, phenylpyruvate, α-keto-γ-methylthiobutyrate, and 2,5-diketo-d-gluconate. Finally, we discuss the future prospects as well as favorable directions for the biotechnological production of keto acids that ultimately would be more environment-friendly and simpler compared with the production by chemical synthesis.

Genome Sequencing of the Pyruvate-producing Strain Candida glabrata CCTCC M202019 and Genomic Comparison with Strain CBS138

Candida glabrata CCTCC M202019 as an industrial yeast strain that is widely used to produce α-oxocarboxylic acid. Strain M202019 has been proven to have a higher pyruvate-producing capacity than the reference strain CBS138. To characterize the genotype of the M202019 strain, we generated a draft sequence of its genome, which has a size of 12.1 Mbp and a GC content of 38.47%. Evidence accumulated during genome annotation suggests that strain M202019 has strong capacities for glucose transport and pyruvate biosynthesis, defects in pyruvate catabolism, as well as variations in genes involved in nutrient and dicarboxylic acid transport, oxidative phosphorylation, and other relevant aspects of carbon metabolism, which might promote pyruvate accumulation. In addition to differences in its central carbon metabolism, a genomic analysis revealed genetic differences in adhesion metabolism. Forty-nine adhesin-like proteins of strain M202019 were identified classified into seven subfamilies. Decreased amounts of adhesive proteins, and deletions or changes of low-complexity repeats and functional domains might lead to lower adhesion and reduced pathogenicity. Further virulence experiments validated the biological safety of strain M202019. Analysis of the C. glabrata CCTCC M202019 genome sequence provides useful insights into its genetic context, physical characteristics, and potential metabolic capacity.

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

Recently, the bio-production of α,ω-dicarboxylic acids (DCAs) has gained significant attention, which potentially leads to the replacement of the conventional petroleum-based products. In this regard, the lipid accumulating yeast Candida tropicalis, has been recognized as a promising microbial host for DCA biosynthesis: it possess the unique ω-oxidation pathway where the terminal carbon of α-fatty acids is oxidized to form DCAs with varying chain lengths. However, despite such industrial importance, its cellular physiology and lipid accumulation capability remain largely uncharacterized. Thus, it is imperative to better understand the metabolic behavior of this lipogenic yeast, which could be achieved by a systems biological approach. To this end, herein, we reconstructed the genome-scale metabolic model of C. tropicalis, iCT646, accounting for 646 unique genes, 945 metabolic reactions, and 712 metabolites. Initially, the comparative network analysis of iCT646 with other yeasts revealed several distinctive metabolic reactions, mainly within the amino acid and lipid metabolism including the ω-oxidation pathway. Constraints-based flux analysis was, then, employed to predict the in silico growth rates of C. tropicalis which are highly consistent with the cellular phenotype observed in glucose and xylose minimal media chemostat cultures. Subsequently, the lipid accumulation capability of C. tropicalis was explored in comparison with Saccharomyces cerevisiae, indicating that the formation of "citrate pyruvate cycle" is essential to the lipid accumulation in oleaginous yeasts. The in silico flux analysis also highlighted the enhanced ability of pentose phosphate pathway as NADPH source rather than malic enzyme during lipogenesis. Finally, iCT646 was successfully utilized to highlight the key directions of C. tropicalis strain design for the whole cell biotransformation application to produce long-chain DCAs from alkanes. Biotechnol. Bioeng. 2016;113: 1993-2004. © 2016 Wiley Periodicals, Inc.

Mitochondrial fusion and fission are involved in stress tolerance of Candida glabrata

Background

Recently, cell tolerance toward environmental stresses has become the major problem in the development of industrial microbial fermentation. Acetoin is an important chemical that can be synthesized by microbes. Its toxicity was investigated using Candida glabrata as the model in this study.

Results

A series of physiological and biochemical experiments demonstrated that the organic solvent acetoin can inhibit cell growth by increasing intracellular reactive oxygen species (ROS) production and inducing damage to mitochondria and cell apoptosis. Integrating RT-PCR experiments, the genes fzo1 and dnm1 were overexpressed to regulate the balance between mitochondrial fusion and fission. Enhancement of mitochondrial fusion was shown to significantly increase cell tolerance toward acetoin stress by inhibiting ROS production and increasing the intracellular adenosine triphosphate (ATP) supply, which was also demonstrated by the addition of citrate.

Conclusions

Regulating mitochondrial fusion-fission may be an alternative strategy for rationally improving the growth performance of eukaryotes under high environmental stress conditions, and also expands our knowledge of the mechanisms of cell tolerance through the processes of energy-related metabolic pathways.

Mitochondrial engineering of the TCA cycle for fumarate production

Microbial fumarate production from renewable feedstock is a promising and sustainable alternative to petroleum-based chemical synthesis. Here, mitochondrial engineering was used to construct the oxidative pathway for fumarate production starting from the TCA cycle intermediate α-ketoglutarate in Candida glabrata. Accordingly, α-ketoglutarate dehydrogenase complex (KGD), succinyl-CoA synthetase (SUCLG), and succinate dehydrogenase (SDH) were selected to be manipulated for strengthening the oxidative pathway, and the engineered strain T.G-K-S-S exhibited increased fumarate biosynthesis (1.81 g L(-1)). To further improve fumarate production, the oxidative route was optimized. First, three fusion proteins KGD2-SUCLG2, SUCLG2-SDH1 and KGD2-SDH1 were constructed, and KGD2-SUCLG2 led to improved fumarate production (4.24 g L(-1)). In addition, various strengths of KGD2-SUCLG2 and SDH1 expression cassettes were designed by combinations of promoter strengths and copy numbers, resulting in a large increase in fumarate production (from 4.24 g L(-1) to 8.24 g L(-1)). Then, through determining intracellular amino acids and its related gene expression levels, argininosuccinate lyase in the urea cycle was identified as the key factor for restricting higher fumarate production. Correspondingly, after overexpression of it, the fumarate production was further increased to 9.96 g L(-1). Next, two dicarboxylic acids transporters facilitated an improvement of fumarate production, and, as a result, the final strain T.G-KS(H)-S(M)-A-2S reached fumarate titer of 15.76 g L(-1). This strategy described here paves the way to the development of an efficient pathway for microbial production of fumarate.

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.

Fumaric acid production by Torulopsis glabrata: engineering the urea cycle and the purine nucleotide cycle

A multi-vitamin auxotrophic Torulopsis glabrata strain, a pyruvate producer, was further engineered to produce fumaric acid. Using the genome-scale metabolic model iNX804 of T. glabrata, four fumaric acid biosynthetic pathways, involving the four cytosolic enzymes, argininosuccinate lyase (ASL), adenylosuccinate lyase (ADSL), fumarylacetoacetase (FAA), and fumarase (FUM1), were found. Athough single overexpression of each of the four enzymes in the cytosol improved fumaric acid production, the highest fumaric acid titer (5.62 g L(-1) ) was obtained with strain T.G-ASL(H) -ADSL(L) by controlling the strength of ASL at a high level and ADSL at a low level. In order to further improve the production of fumaric acid, the SpMAE1 gene encoding the C4 -dicarboxylic acids transporter was overexpressed in strain T.G-ASL(H) -ADSL(L) -SpMAE1 and the final fumaric acid titer increased to 8.83 g L(-1) . This study provides a novel strategy for fumaric acid biosynthesis by utilizing the urea cycle and the purine nucleotide cycle to enhance the bridge between carbon metabolism and nitrogen metabolism.

Urea enhances cell growth and pyruvate production in Torulopsis glabrata

Torulopsis glabrata is a strain of yeast that is used for the industrial production of pyruvate. Determination of the optimal nutrient environment is vital for obtaining the most efficient production system. In this study, the fermentation parameters, gene transcription levels, activities of key enzymes and metabolites levels were analyzed when either urea or ammonium chloride was used as the sole source of nitrogen. Urea caused an increase in the dry cell weight (18%) and pyruvate productivity was significantly increased (14%). The transcription levels of CAGL0M05533g (DUR1,2), CAGL0J07612g (ZWF1), and CAGL0I02200g (SOL3) were upregulated, but CAGL0G05698g (GDH2) and CAGL0L01089g (GLT1) were down-regulated. The activities of urea amidolyase, NADPH dependent glutamate dehydrogenase and glucose-6-phosphate dehydrogenase were increased by 380, 430, and 140%, respectively. The activities of arginase and glutamate synthase were decreased by 40 and 35%, respectively. The NADPH content was increased by 33%, whilst ATP content was decreased by 37%. This changed the intracellular levels of organic acids and amino acids. The results expand the understanding of the physiological characteristics of yeast species grown with different sources of nitrogen.

Enhancement of acetoin production in Candida glabrata by in silico-aided metabolic engineering

BACKGROUND

Acetoin is a promising chemical compound that can potentially serve as a high value-added platform for a broad range of applications. Many industrial biotechnological processes are moving towards the use of yeast as a platform. The multi-auxotrophic yeast, Candida glabrata, can accumulate a large amount of pyruvate, but produces only trace amounts of acetoin. Here, we attempted to engineer C. glabrata to redirect the carbon flux of pyruvate to increase acetoin production.

RESULTS

Based on an in silico strategy, a synthetic, composite metabolic pathway involving two distinct enzymes, acetolactate synthase (ALS) and acetolactate decarboxylase (ALDC), was constructed, leading to the accumulation of acetoin in C. glabrata. Further genetic modifications were introduced to increase the carbon flux of the heterologous pathway, increasing the production of acetoin to 2.08 g/L. Additionally, nicotinic acid was employed to regulate the intracellular NADH level, and a higher production of acetoin (3.67 g/L) was obtained at the expense of 2,3-butanediol production under conditions of a lower NADH/NAD+ ratio.

CONCLUSION

With the aid of in silico metabolic engineering and cofactor engineering, C. glabrata was designed and constructed to improve acetoin production.

Metabolic engineering of Candida glabrata for diacetyl production

In this study, Candida glabrata, an efficient pyruvate-producing strain, was metabolically engineered for the production of the food ingredient diacetyl. A diacetyl biosynthetic pathway was reconstructed based on genetic modifications and medium optimization. The former included (i) channeling carbon flux into the diacetyl biosynthetic pathway by amplification of acetolactate synthase, (ii) elimination of the branched pathway of α-acetolactate by deleting the ILV5 gene, and (iii) restriction of diacetyl degradation by deleting the BDH gene. The resultant strain showed an almost 1∶1 co-production of α-acetolactate and diacetyl (0.95 g L(-1)). Furthermore, addition of Fe3+ to the medium enhanced the conversion of α-acetolactate to diacetyl and resulted in a two-fold increase in diacetyl production (2.1 g L(-1)). In addition, increased carbon flux was further channeled into diacetyl biosynthetic pathway and a titer of 4.7 g L(-1) of diacetyl was achieved by altering the vitamin level in the flask culture. Thus, this study illustrates that C. glabrata could be tailored as an attractive platform for enhanced biosynthesis of beneficial products from pyruvate by metabolic engineering strategies.

Two non-exclusive strategies employed to protect Torulopsis glabrata against hyperosmotic stress

Several recent reports described an apoptosis-like programmed cell death (PCD) process in yeast in response to different environmental challenges. In this study, hyperosmotic stress caused by high NaCl concentration in culture medium induced cell death in the haploid yeast Torulopsis glabrata. Propidium iodide (PI) and PI/rhodamine-123 (Rh123) dual staining with flow cytometry showed that high salinity decreased intact cells by 16.5 %, increased necrotic cells by nearly twofold, and altered fermentative parameters appreciably. Morphological and biochemical indicators of apoptosis were apparent, specifically a decrease in mitochondrial membrane potential (∆Ψm), translocation of phosphatidylserine (PS) from the inner to the outer side of the plasma membrane, generation of reactive oxygen species (ROS), and involvement of caspase all while plasma membrane integrity was maintained. Additionally, it was found that overexpression of YCA1 drastically stimulated cell death, indicating that activation of metacaspase might lead to cell death. However, T. glabrata growth under hyperosmotic stress was enhanced when FIS1, HOG1, and GPD2 were overexpressed, or when exogenous proline or glutathione (GSH) were added into the cultures, both of which could repress caspase-3 activity. Thus, in these concrete cases of overexpression of anti-apoptotic or anti-necrotic factors and pharmacological manipulations, it decreased T. glabrata cell death that might help to achieve higher fermentative efficiency.

Engineering of carboligase activity reaction in Candida glabrata for acetoin production

Utilization of Candida glabrata overproducing pyruvate is a promising strategy for high-level acetoin production. Based on the known regulatory and metabolic information, acetaldehyde and thiamine were fed to identify the key nodes of carboligase activity reaction (CAR) pathway and provide a direction for engineering C. glabrata. Accordingly, alcohol dehydrogenase, acetaldehyde dehydrogenase, pyruvate decarboxylase, and butanediol dehydrogenase were selected to be manipulated for strengthening the CAR pathway. Following the rational metabolic engineering, the engineered strain exhibited increased acetoin biosynthesis (2.24 g/L). In addition, through in silico simulation and redox balance analysis, NADH was identified as the key factor restricting higher acetoin production. Correspondingly, after introduction of NADH oxidase, the final acetoin production was further increased to 7.33 g/L. By combining the rational metabolic engineering and cofactor engineering, the acetoin-producing C. glabrata was improved stepwise, opening a novel pathway for rational development of microorganisms for bioproduction.

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.