Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

Biodegradation of low-density polyethylene plastic waste by a constructed tri-culture yeast consortium from wood-feeding termite: Degradation mechanism and pathway

Polyethylene (PE) is one of the most common synthetic polymers, and PE waste pollution has been an environmental and health concern for decades. Biodegradation is the most eco-friendly and effective approach for plastic waste management. Recently, an emphasis has been placed on novel symbiotic yeasts isolated from termite guts as promising microbiomes for multiple biotechnological applications. This study might be the first to explore the potential of a constructed tri-culture yeast consortium, designated as DYC, isolated from termites for the degradation of low-density polyethylene (LDPE). The yeast consortium DYC stands for the molecularly identified species Sterigmatomyces halophilus, Meyerozyma guilliermondii, and Meyerozyma caribbica. The LDPE-DYC consortium showed a high growth rate on UV-sterilized LDPE as a sole carbon source, resulting in a reduction in tensile strength (TS) of 63.4% and a net LDPE mass reduction of 33.2% compared to the individual yeasts. All yeasts, individually and in consortium, showed a high production rate for LDPE-degrading enzymes. The hypothetical LDPE biodegradation pathway that was proposed revealed the formation of several metabolites, including alkanes, aldehydes, ethanol, and fatty acids. This study emphasizes a novel concept for using LDPE-degrading yeasts from wood-feeding termites for plastic waste biodegradation.

Comprehensive genome-scale metabolic model of the human pathogen Cryptococcus neoformans: A platform for understanding pathogen metabolism and identifying new drug targets

Introduction: The fungal priority pathogen Cryptococcus neoformans causes cryptococcal meningoencephalitis in immunocompromised individuals and leads to hundreds of thousands of deaths per year. The undesirable side effects of existing treatments, the need for long application times to prevent the disease from recurring, the lack of resources for these treatment methods to spread over all continents necessitate the search for new treatment methods. Methods: Genome-scale models have been shown to be valuable in studying the metabolism of many organisms. Here we present the first genome-scale metabolic model for C. neoformans, iCryptococcus. This comprehensive model consists of 1,270 reactions, 1,143 metabolites, 649 genes, and eight compartments. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources and growth rate in comparison to experimental data. Results and Discussion: The compatibility of the in silico Cryptococcus metabolism under infection conditions was assessed. The steroid and amino acid metabolisms found in the essentiality analyses have the potential to be drug targets for the therapeutic strategies to be developed against Cryptococcus species. iCryptococcus model can be applied to explore new targets for antifungal drugs along with essential gene, metabolite and reaction analyses and provides a promising platform for elucidation of pathogen metabolism.

Strain engineering and metabolic flux analysis of a probiotic yeast Saccharomyces boulardii for metabolizing L-fucose, a mammalian mucin component

Background

Saccharomyces boulardii is a probiotic yeast that exhibits antimicrobial and anti-toxin activities. Although S. boulardii has been clinically used for decades to treat gastrointestinal disorders, several studies have reported weak or no beneficial effects of S. boulardii administration in some cases. These conflicting results of S. boulardii efficacity may be due to nutrient deficiencies in the intestine that make it difficult for S. boulardii to maintain its metabolic activity.

Results

To enable S. boulardii to overcome any nutritional deficiencies in the intestine, we constructed a S. boulardii strain that could metabolize l-fucose, a major component of mucin in the gut epithelium. The fucU, fucI, fucK, and fucA from Escherichia coli and HXT4 from S. cerevisiae were overexpressed in S. boulardii. The engineered S. boulardii metabolized l-fucose and produced 1,2-propanediol under aerobic and anaerobic conditions. It also produced large amounts of 1,2-propanediol under strict anaerobic conditions. An in silico genome-scale metabolic model analysis was performed to simulate the growth of S. boulardii on l-fucose, and elementary flux modes were calculated to identify critical metabolic reactions for assimilating l-fucose. As a result, we found that the engineered S. boulardii consumes l-fucose via (S)-lactaldehyde-(S)-lactate-pyruvate pathway, which is highly oxygen dependent.

Conclusion

To the best of our knowledge, this is the first study in which S. cerevisiae and S. boulardii strains capable of metabolizing l-fucose have been constructed. This strategy could be used to enhance the metabolic activity of S. boulardii and other probiotic microorganisms in the gut.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01926-x.

A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast

The fission yeast, Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker's yeast, Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually curated high-quality reconstruction of S. pombe's metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behavior and the role of resource allocation in driving different strategies in fission yeast. IMPORTANCE Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species thus remains largely unexplored in mechanistic terms. Computational modeling of metabolic networks offers an attractive platform to bridge the knowledge gap and gain new insights into physiology of lesser-studied organisms. Here we showcase an example of successful knowledge transfer from the budding yeast Saccharomyces cerevisiae to a popular model organism in molecular and cell biology, fission yeast Schizosaccharomyces pombe, using computational models.

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.

Improved ammonia production from soybean residues by cell surface-displayed l-amino acid oxidase on yeast

Ammonia is critical for agricultural and chemical industries. The extracellular production of ammonia by yeast (Saccharomyces cerevisiae) using cell surface engineering can be efficient approach because yeast can avoid growth deficiencies caused by knockout of genes for ammonia assimilation. In this study, we produced ammonia outside the yeast cells by displaying an l-amino acid oxidase with a wide substrate specificity derived from Hebeloma cylindrosporum (HcLAAO) on yeast cell surfaces. The HcLAAO-displaying yeast successfully produced 12.6 m m ammonia from a mixture of 20 proteinogenic amino acids (the theoretical conversion efficiency was 63%). We also succeeded in producing ammonia from a food processing waste, soybean residues (okara) derived from tofu production. The conversion efficiency was 88.1%, a higher yield than reported in previous studies. Our study demonstrates that ammonia production outside of yeast cells is a promising strategy to utilize food processing wastes.

PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models

Targeted identification of cellular processes responsible for a phenotype is of major importance in guiding efforts in bioengineering and medicine. Genome-scale metabolic models (GEMs) are widely used to integrate various types of omics data and study the cellular physiology under different conditions. Here, we present PhenoMapping, a protocol that uses GEMs, omics, and phenotypic data to map cellular processes and observed phenotypes. PhenoMapping also classifies genes as conditionally and unconditionally essential and guides a comprehensive curation of GEMs. For complete details on the use and execution of this protocol, please refer to Stanway et al. (2019) and Krishnan et al. (2020).

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

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

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

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

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

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

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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.

Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage

Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.

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1,342 barcoded P. berghei knockout (KO) mutants analyzed for stage-specific phenotypes

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Life-stage-specific metabolic models reveal reprogramming of cellular function

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High agreement between blood/liver stage metabolic models and genetic screening data

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Essential metabolic pathways for parasite development and mechanistic origin revealed

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.

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation

Metabolomics is a modern field of chemical biology that strives to simultaneously quantify hundreds of cellular metabolites. Techniques for metabolomic analysis in Schizosaccharomyces pombe have only recently been developed. Here we introduce methods that provide a complete workflow for metabolomic analysis in S. pombe Based on available literature, we estimate the yeast metabolome to comprise on the order of several thousand different metabolites. We discuss the feasibility of extraction and detection of such a large number of metabolites, and the influences of various parameters on the results. Among the parameters addressed are cell cultivation conditions, metabolite extraction techniques, and detection and quantification methods. Further, we provide recommendations on data management and data processing for metabolomic experiments, and describe possible pitfalls regarding the interpretation of metabolomic data. Finally, we briefly discuss potential future developments of this technique.

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

Kinetics of aerobic cometabolic biodegradation of chlorinated and brominated aliphatic hydrocarbons: A review

This review analyses kinetic studies of aerobic cometabolism (AC) of halogenated aliphatic hydrocarbons (HAHs) from 2001-2015 in order to (i) compare the different kinetic models proposed, (ii) analyse the estimated model parameters with a focus on novel HAHs and the identification of general trends, and (iii) identify further research needs. The results of this analysis show that aerobic cometabolism can degrade a wide range of HAHs, including HAHs that were not previously tested such as chlorinated propanes, highly chlorinated ethanes and brominated methanes and ethanes. The degree of chlorine mineralization was very high for the chlorinated HAHs. Bromine mineralization was not determined for studies with brominated aliphatics. The examined research period led to the identification of novel growth substrates of potentially high interest. Decreasing performance of aerobic cometabolism were found with increasing chlorination, indicating the high potential of aerobic cometabolism in the presence of medium- and low-halogenated HAHs. Further research is needed for the AC of brominated aliphatic hydrocarbons, the potential for biofilm aerobic cometabolism processes, HAH-HAH mutual inhibition and the identification of the enzymes responsible for each aerobic cometabolism process. Lastly, some indications for a possible standardization of future kinetic studies of HAH aerobic cometabolism are provided.

PSAMM: A Portable System for the Analysis of Metabolic Models

The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.

The broad application of genome-scale metabolic modeling has made it a useful technique for tackling fundamental questions in biological research and engineering. Today over 100 models have been constructed for organisms that carry out a diverse array of metabolic activities spanning all three kingdoms of life. These models, however, have been curated independently following different conventions. The maintenance of model consistency has been challenging due to the lack of consensus in model representation and the absence of integrated modeling software for associating mathematical simulations with the annotation and biological interpretation of metabolic models. To solve this problem, we developed a new software package, PSAMM, and a new model format that incorporates heterogeneous, model-specific annotation information into modular representations of model definitions and simulation settings. PSAMM provides significant advances in standardizing the workflow of model annotation and consistency checking. Compared to existing tools, PSAMM supports more flexible configurations and is more efficient in running constraint-based simulations. All functions of PSAMM are freely available for academic users and can be downloaded from a public Git repository (https://zhanglab.github.io/psamm/) under the GNU General Public License.

Plant genome-scale modeling and implementation

Considerable progress has been made in plant genome-scale metabolic reconstruction and modeling in recent years. Such reconstructions made it possible to explore metabolic phenotypes through appropriate model formulation and optimization methods. As a result, plant genome-scale modeling has increasingly attracted interest from the plant research community. In this chapter, the first generation of plant genome-scale metabolic reconstructions is presented, along with the important concepts behind model and constraint formulation. A brief protocol describing the use of constraint-based reconstruction and analysis (COBRA) Toolbox in flux simulation and model modification is provided. This is followed by a presentation of metabolic constraints required to generate fluxes in AraGEM using COBRA that describe photosynthesis, photorespiration, and respiration, respectively. Overall, plant genome-scale modeling is a powerful approach that is accessible and readily adopted.

Genome-scale metabolic models of yeast, methods for their reconstruction, and other applications

Here, we present the concept of genome-scale metabolic models and some of their applications in metabolic engineering of yeast and in the analysis of gene expression data. The yeast species for which there are available genome-scale metabolic models are reviewed, as well as the methods for the reconstruction of genome-scale metabolic models for new species. Some commonly used algorithms for metabolic engineering and data integration are described.

Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

Background

The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network.

Results

Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold for integrating transcriptomics, and flux data from four different conditions in order to identify transcriptionally controlled reactions, i.e. reactions that change both in flux and transcription between the compared conditions.

Conclusion

We present a new yeast model that represents a comprehensive up-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition-dependent changes of metabolism between different environmental conditions.

Flux-coupled genes and their use in metabolic flux analysis

As large volumes of omics data have become available, systems biology is playing increasingly important roles in elucidating new biological phenomena, especially through genome-scale metabolic network modeling and simulation. Much effort has been exerted on integrating omics data with metabolic flux simulation, but further development is necessary for more accurate flux estimation. To move one step forward, we adopted the concept of flux-coupled genes (FCGs), which show that their expression transition patterns upon perturbations are correlated with their corresponding flux values, as additional constraints in metabolic flux analysis. It was found that gnd, pfkB, rpe, sdhB, sdhD, sucA, and zwf genes, mostly associated with pentose phosphate pathway and TCA cycle, were the most consistent FCGs in Escherichia coli based on its transcriptome and (13) C-flux data obtained from the chemostat cultivation at five different dilution rates. Consequently, constraints-based flux analyses with FCGs as additional constraints were conducted for the seven single-gene knockout mutants, compared with those obtained without using FCGs. This strategy of constraining the metabolic flux analysis with FCGs is expected to be useful due to the relative ease in obtaining transcriptional information in the functional genomics era.

Plant genome-scale metabolic reconstruction and modelling

Genome-scale metabolic reconstructions are used extensively in the study of microbial metabolism and have proven powerful tools to guide rational pathway design of industrial strains. Generation and curation of plant genome-scale metabolic models has proven far more challenging, not the least of which is our incomplete knowledge of compartmentation and organelle transporters in plants. Conversely, the potential value of modelling is far greater when exploring a complex, multi-organelle and multi-tissue metabolism. The first generation of plant genome-scale metabolic reconstructions have proven surprisingly functional and robust as well as capable of predicting many observed complex phenotypes. With further refinement, the application of these models promises to make important contributions to plant biology and metabolic engineering.