Genome-scale metabolic modeling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production

CFSA: Comparative flux sampling analysis as a guide for strain design

Genome-scale metabolic models of microbial metabolism have extensively been used to guide the design of microbial cell factories, still, many of the available strain design algorithms often fail to produce a reduced list of targets for improved performance that can be implemented and validated in a step-wise manner. We present Comparative Flux Sampling Analysis (CFSA), a strain design method based on the extensive comparison of complete metabolic spaces corresponding to maximal or near-maximal growth and production phenotypes. The comparison is complemented by statistical analysis to identify reactions with altered flux that are suggested as targets for genetic interventions including up-regulations, down-regulations and gene deletions. We applied CFSA to the production of lipids by Cutaneotrichosporon oleaginosus and naringenin by Saccharomyces cerevisiae identifying engineering targets in agreement with previous studies as well as new interventions. CFSA is an easy-to-use, robust method that suggests potential metabolic engineering targets for growth-uncoupled production that can be applied to the design of microbial cell factories.

Advances in genome-scale metabolic models of industrially important fungi

Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing.

Expanding the genetic toolbox for Cutaneotrichosporon oleaginosus employing newly identified promoters and a novel antibiotic resistance marker

BACKGROUND

Cutaneotrichosporon oleaginosus is an oleaginous yeast that can produce up to 80% lipid per dry weight. Its high capacity for the biosynthesis of single cell oil makes it highly interesting for the production of engineered lipids or oleochemicals for industrial applications. However, the genetic toolbox for metabolic engineering of this non-conventional yeast has not yet been systematically expanded. Only three long endogenous promoter sequences have been used for heterologous gene expression, further three dominant and one auxotrophic marker have been established.

RESULTS

In this study, the structure of putative endogenous promoter sequences was analyzed based on more than 280 highly expressed genes. The identified motifs of regulatory elements and translational initiation sites were used to annotate the four endogenous putative promoter sequences D9FADp, UBIp, PPIp, and 60Sp. The promoter sequences were tested in a construct regulating the known dominant marker hygromycin B phosphotransferase. The four newly described promoters and the previously established GAPDHp successfully initiated expression of the resistance gene and PPIp was selected for further marker development. The geneticin G418 resistance (aminoglycoside 3'-phosphotransferase, APH) and the nourseothricin resistance gene N-acetyl transferase (NAT) were tested for applicability in C. oleaginosus. Both markers showed high transformation efficiency, positive rate, and were compatible for combined use in a successive and simultaneous manner.

CONCLUSIONS

The implementation of four endogenous promoters and one novel dominant resistance markers for C. oleaginosus opens up new opportunities for genetic engineering and strain development. In combination with recently developed methods for targeted genomic integration, the established toolbox allows a wide spectrum of new strategies for genetic and metabolic engineering of the industrially highly relevant yeast.

Heterologous Expression of CFL1 Confers Flocculating Ability to Cutaneotrichosporon oleaginosus Lipid-Rich Cells

Lipid extraction from microbial and microalgae biomass requires the separation of oil-rich cells from the production media. This downstream procedure represents a major bottleneck in biodiesel production, increasing the cost of the final product. Flocculation is a rapid and cheap system for removing solid particles from a suspension. This natural characteristic is displayed by some microorganisms due to the presence of lectin-like proteins (called flocculins/adhesins) in the cell wall. In this work, we showed, for the first time, that the heterologous expression of the adhesin Cfl1p endows the oleaginous species Cutaneotrichosporon oleaginosus with the capacity of cell flocculation. We used Helm's test to demonstrate that the acquisition of this trait allows for reducing the time required for the separation of lipid-rich cells from liquid culture by centrifugation without altering the productivity. This improves the lipid production process remarkably by providing a more efficient downstream.

Tailoring and optimizing fatty acid production by oleaginous yeasts through the systematic exploration of their physiological fitness

Background

The use of palm oil for our current needs is unsustainable. Replacing palm oil with oils produced by microbes through the conversion of sustainable feedstocks is a promising alternative. However, there are major technical challenges that must be overcome to enable this transition. Foremost among these challenges is the stark increase in lipid accumulation and production of higher content of specific fatty acids. Therefore, there is a need for more in-depth knowledge and systematic exploration of the oil productivity of the oleaginous yeasts. In this study, we cultivated Cutaneotrichosporon oleaginosus and Yarrowia lipolytica at various C/N ratios and temperatures in a defined medium with glycerol as carbon source and urea as nitrogen source. We ascertained the synergistic effect between various C/N ratios of a defined medium at different temperatures with Response Surface Methodology (RSM) and explored the variation in fatty acid composition through Principal Component Analysis.

Results

By applying RSM, we determined a temperature of 30 °C and a C/N ratio of 175 g/g to enable maximal oil production by C. oleaginosus and a temperature of 21 °C and a C/N ratio of 140 g/g for Y. lipolytica. We increased production by 71% and 66% respectively for each yeast compared to the average lipid accumulation in all tested conditions. Modulating temperature enabled us to steer the fatty acid compositions. Accordingly, switching from higher temperature to lower cultivation temperature shifted the production of oils from more saturated to unsaturated by 14% in C. oleaginosus and 31% in Y. lipolytica. Higher cultivation temperatures resulted in production of even longer saturated fatty acids, 3% in C. oleaginosus and 1.5% in Y. lipolytica.

Conclusions

In this study, we provided the optimum C/N ratio and temperature for C. oleaginosus and Y. lipolytica by RSM. Additionally, we demonstrated that lipid accumulation of both oleaginous yeasts was significantly affected by the C/N ratio and temperature. Furthermore, we systematically analyzed the variation in fatty acids composition and proved that changing the C/N ratio and temperature steer the composition. We have further established these oleaginous yeasts as platforms for production of tailored fatty acids.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01956-5.

Recycling Food Waste and Saving Water: Optimization of the Fermentation Processes from Cheese Whey Permeate to Yeast Oil

With the aim of developing bioprocesses for waste valorization and a reduced water footprint, we optimized a two-step fermentation process that employs the oleaginous yeast Cutaneotrichosporon oleaginosus for the production of oil from liquid cheese whey permeate. For the first step, the addition of urea as a cost-effective nitrogen source allowed an increase in yeast biomass production. In the second step, a syrup from candied fruit processing, another food waste supplied as carbon feeding, triggered lipid accumulation. Consequently, yeast lipids were produced at a final concentration and productivity of 38 g/L and 0.57 g/L/h respectively, which are among the highest reported values. Through this strategy, based on the valorization of liquid food wastes (WP and mango syrup) and by recovering not only nutritional compounds but also the water necessary for yeast growth and lipid production, we addressed one of the main goals of the circular economy. In addition, we set up an accurate and fast-flow cytometer method to quantify the lipid content, avoiding the extraction step and the use of solvents. This can represent an analytical improvement to screening lipids in different yeast strains and to monitoring the process at the single-cell level.

Exploring Yeast Diversity to Produce Lipid-Based Biofuels from Agro-Forestry and Industrial Organic Residues

Exploration of yeast diversity for the sustainable production of biofuels, in particular biodiesel, is gaining momentum in recent years. However, sustainable, and economically viable bioprocesses require yeast strains exhibiting: (i) high tolerance to multiple bioprocess-related stresses, including the various chemical inhibitors present in hydrolysates from lignocellulosic biomass and residues; (ii) the ability to efficiently consume all the major carbon sources present; (iii) the capacity to produce lipids with adequate composition in high yields. More than 160 non-conventional (non-Saccharomyces) yeast species are described as oleaginous, but only a smaller group are relatively well characterised, including Lipomyces starkeyi, Yarrowia lipolytica, Rhodotorula toruloides, Rhodotorula glutinis, Cutaneotrichosporonoleaginosus and Cutaneotrichosporon cutaneum. This article provides an overview of lipid production by oleaginous yeasts focusing on yeast diversity, metabolism, and other microbiological issues related to the toxicity and tolerance to multiple challenging stresses limiting bioprocess performance. This is essential knowledge to better understand and guide the rational improvement of yeast performance either by genetic manipulation or by exploring yeast physiology and optimal process conditions. Examples gathered from the literature showing the potential of different oleaginous yeasts/process conditions to produce oils for biodiesel from agro-forestry and industrial organic residues are provided.

Repressed Central Carbon Metabolism and Its Effect on Related Metabolic Pathways in Cefoperazone/Sulbactam-Resistant Pseudomonas aeruginosa

Metabolic shift and antibiotic resistance have been reported in Pseudomonas aeruginosa. However, the global metabolic characteristics remain largely unknown. The present study characterizes the central carbon metabolism and its effect on other metabolic pathways in cefoperazone-sulbactam (SCF)-resistant P. aeruginosa (PA-R_SCF). GC-MS-based metabolomics shows a repressed central carbon metabolism in PA-R_SCF, which is confirmed by measuring expression of genes and activity of enzymes in the metabolism. Furthermore, expression of the genes that encode the enzymes for the first step of fatty acid biosynthesis, glutamate metabolism, and electron transport chain is reduced, confirmed by their enzymatic activity assay, and the key enzyme for riboflavin metabolism is also reduced, indicating the decreased metabolic flux to the four related metabolic pathways. Moreover, the role of the reduced riboflavin metabolism, being related to ROS generation, in SCF resistance is explored. Exogenous H₂O₂ potentiates SCF-mediated killing in a dose-dependent manner, suggesting that the decreased ROS resulted from the reduced riboflavin metabolism that contributed to the resistance. These results indicate that the repressed central carbon metabolism and related riboflavin metabolism contribute to SCF resistance, but increasing ROS can restore SCF sensitivity. These findings characterize the repressed central carbon metabolism and its effect on other metabolic pathways as the global metabolic features in PA-R_SCF.

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

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

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

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

Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction

Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R² = 0.87). However, the model had reduced predictability for low performance strains (0.01-1 g/L, R² = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.

A metabolic model of Lipomyces starkeyi for predicting lipogenesis potential from diverse low-cost substrates

BACKGROUND

Lipomyces starkeyi has been widely regarded as a promising oleaginous yeast with broad industrial application prospects because of its wide substrate spectrum, good adaption to fermentation inhibitors, excellent fatty acid composition for high-quality biodiesel, and negligible lipid remobilization. However, the currently low experimental lipid yield of L. starkeyi prohibits its commercial success. Metabolic model is extremely valuable to comprehend the complex biochemical processes and provide great guidance for strain modification to facilitate the lipid biosynthesis.

RESULTS

A small-scale metabolic model of L. starkeyi NRRL Y-11557 was constructed based on the genome annotation information. The theoretical lipid yields of glucose, cellobiose, xylose, glycerol, and acetic acid were calculated according to the flux balance analysis (FBA). The optimal flux distribution of the lipid synthesis showed that pentose phosphate pathway (PPP) independently met the necessity of NADPH for lipid synthesis, resulting in the relatively low lipid yields. Several targets (NADP-dependent oxidoreductases) beneficial for oleaginicity of L. starkeyi with significantly higher theoretical lipid yields were compared and elucidated. The combined utilization of acetic acid and other carbon sources and a hypothetical reverse β-oxidation (RBO) pathway showed outstanding potential for improving the theoretical lipid yield.

CONCLUSIONS

The lipid biosynthesis potential of L. starkeyi can be significantly improved through appropriate modification of metabolic network, as well as combined utilization of carbon sources according to the metabolic model. The prediction and analysis provide valuable guidance to improve lipid production from various low-cost substrates.