ABSTRACT
Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.
ABSTRACT
Itaconic acid is an emerging platform chemical with extensive applications. Itaconic acid is currently produced by Aspergillus terreus through biological fermentation. However, A. terreus is a fungal pathogen that needs additional morphology controls, making itaconic acid production on industrial scale problematic. Here, we reprogrammed the Generally Recognized As Safe (GRAS) yeast Yarrowia lipolytica for competitive itaconic acid production. After preventing carbon sink into lipid accumulation, we evaluated itaconic acid production both inside and outside the mitochondria while fine-tuning its biosynthetic pathway. We then mimicked the regulation of nitrogen limitation in nitrogen-replete conditions by down-regulating NAD+-dependent isocitrate dehydrogenase through weak promoters, RNA interference, or CRISPR interference. Ultimately, we optimized fermentation parameters for fed-batch cultivations and produced itaconic acid titers of 130.1 grams per liter in 1-liter bioreactors and 94.8 grams per liter in a 50-liter bioreactor on semipilot scale. Our findings provide effective approaches to harness the GRAS microorganism Y. lipolytica for competitive industrial-scale production of itaconic acid.
Subject(s)
Bioreactors , Fermentation , Succinates , Yarrowia , Yarrowia/metabolism , Yarrowia/genetics , Succinates/metabolism , Metabolic Engineering/methods , Nitrogen/metabolism , Biosynthetic Pathways , Isocitrate Dehydrogenase/metabolism , Isocitrate Dehydrogenase/geneticsABSTRACT
Metabolic engineering for high productivity and increased robustness is needed to enable sustainable biomanufacturing of lactic acid from lignocellulosic biomass. Lactic acid is an important commodity chemical used for instance as a monomer for production of polylactic acid, a biodegradable polymer. Here, rational and model-based optimization was used to engineer a diploid, xylose fermenting Saccharomyces cerevisiae strain to produce L-lactic acid. The metabolic flux was steered towards lactic acid through the introduction of multiple lactate dehydrogenase encoding genes while deleting ERF2, GPD1, and CYB2. A production of 93 g/L of lactic acid with a yield of 0.84 g/g was achieved using xylose as the carbon source. To increase xylose utilization and reduce acetic acid synthesis, PHO13 and ALD6 were also deleted from the strain. Finally, CDC19 encoding a pyruvate kinase was overexpressed, resulting in a yield of 0.75 g lactic acid/g sugars consumed, when the substrate used was a synthetic lignocellulosic hydrolysate medium, containing hexoses, pentoses and inhibitors such as acetate and furfural. Notably, modeling also provided leads for understanding the influence of oxygen in lactic acid production. High lactic acid production from xylose, at oxygen-limitation could be explained by a reduced flux through the oxidative phosphorylation pathway. On the contrast, higher oxygen levels were beneficial for lactic acid production with the synthetic hydrolysate medium, likely as higher ATP concentrations are needed for tolerating the inhibitors therein. The work highlights the potential of S. cerevisiae for industrial production of lactic acid from lignocellulosic biomass.
Subject(s)
Lactic Acid , Lignin , Metabolic Engineering , Saccharomyces cerevisiae , Saccharomyces cerevisiae/genetics , Saccharomyces cerevisiae/metabolism , Lactic Acid/metabolism , Lactic Acid/biosynthesis , Lignin/metabolism , Biomass , Xylose/metabolism , Xylose/genetics , Saccharomyces cerevisiae Proteins/genetics , Saccharomyces cerevisiae Proteins/metabolismABSTRACT
Flavonoids exhibit various bioactivities including anti-oxidant, anti-tumor, anti-inflammatory, and anti-viral properties. Methylated flavonoids are particularly significant due to their enhanced oral bioavailability, improved intestinal absorption, and greater stability. The heterologous production of plant flavonoids in bacterial factories involves the need for enough biosynthetic precursors to allow for high production levels. These biosynthetic precursors are malonyl-CoA and l-tyrosine. In this work, to enhance flavonoid biosynthesis in Streptomyces albidoflavus, we conducted a transcriptomics study for the identification of candidate genes involved in l-tyrosine catabolism. The hypothesis was that the bacterial metabolic machinery would detect an excess of this amino acid if supplemented with the conventional culture medium and would activate the genes involved in its catabolism towards energy production. Then, by inactivating those overexpressed genes (under an excess of l-tyrosine), it would be possible to increase the intracellular pools of this precursor amino acid and eventually the final flavonoid titers in this bacterial factory. The RNAseq data analysis in the S. albidoflavus wild-type strain highlighted the hppD gene encoding 4-hydroxyphenylpyruvate dioxygenase as a promising target for knock-out, exhibiting a 23.2-fold change (FC) in expression upon l-tyrosine supplementation in comparison to control cultivation conditions. The subsequent knock-out of the hppD gene in S. albidoflavus resulted in a 1.66-fold increase in the naringenin titer, indicating enhanced flavonoid biosynthesis. Leveraging the improved strain of S. albidoflavus, we successfully synthesized the methylated flavanones hesperetin, homoeriodictyol, and homohesperetin, achieving titers of 2.52 mg/L, 1.34 mg/L, and 0.43 mg/L, respectively. In addition, the dimethoxy flavanone homohesperetin was produced as a byproduct of the endogenous metabolism of S. albidoflavus. To our knowledge, this is the first time that hppD deletion was utilized as a strategy to augment the biosynthesis of flavonoids. Furthermore, this is the first report where hesperetin and homoeriodictyol have been synthesized from l-tyrosine as a precursor. Therefore, transcriptomics is, in this case, a successful approach for the identification of catabolism reactions affecting key precursors during flavonoid biosynthesis, allowing the generation of enhanced production strains.
Subject(s)
Craniofacial Abnormalities , Flavones , Flavonoids , Gene Expression Profiling , Hesperidin , Streptomyces , Amino Acids , TyrosineABSTRACT
Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
Subject(s)
Genome , Models, Biological , Genome/genetics , Metabolic Networks and PathwaysABSTRACT
Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.
Subject(s)
Metabolic Engineering , Models, Biological , Metabolic Engineering/methods , Computer Simulation , Saccharomyces cerevisiae/genetics , Metabolic Networks and PathwaysABSTRACT
Saccharomyces cerevisiae has been conveniently used to produce Taxol® anticancer drug early precursors. However, the harmful impact of oxidative stress by the first cytochrome P450-reductase enzymes (CYP725A4-POR) of Taxol® pathway has hampered sufficient progress in yeast. Here, we evolved an oxidative stress-resistant yeast strain with three-fold higher titre of their substrate, taxadiene. The performance of the evolved and parent strains were then evaluated in galactose-limited chemostats before and under the oxidative stress by an oxidising agent. The interaction of evolution and oxidative stress was comprehensively evaluated through transcriptomics and metabolite profiles integration in yeast enzyme-constrained genome scale model. Overall, the evolved strain showed improved respiration, reduced overflow metabolites production and oxidative stress re-induction tolerance. The cross-protection mechanism also potentially contributed to better heme, flavin and NADPH availability, essential for CYP725A4 and POR optimal activity in yeast. The results imply that the evolved strain is a robust cell factory for future efforts towards Taxol© production.
ABSTRACT
The bark is the outermost defense of trees against microbial attack, largely thanks to toxicity and prevalence of extractive compounds. Nevertheless, bark decomposes in nature, though by which species and mechanisms remains unknown. Here, we have followed the development of microbial enrichments growing on spruce bark over six months, by monitoring both chemical changes in the material and performing community and metagenomic analyses. Carbohydrate metabolism was unexpectedly limited, and instead a key activity was metabolism of extractives. Resin acid degradation was principally linked to community diversification with specific bacteria revealed to dominate the process. Metagenome-guided isolation facilitated the recovery of the dominant enrichment strain in pure culture, which represents a new species (Pseudomonas abieticivorans sp. nov.), that can grow on resin acids as a sole carbon source. Our results illuminate key stages in degradation of an abundant renewable resource, and how defensive extractive compounds have major roles in shaping microbiomes.
Subject(s)
Microbiota , Picea , Plant Bark , Metagenome , Bacteria/geneticsABSTRACT
BACKGROUND: Integrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol® in engineered Saccharomyces cerevisiae cells. RESULTS: Using constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol® biosynthetic pathway. Under different cultivation conditions, most single genomic modifications resulted in increased taxadiene production. The strain named KM32, which contained four overexpressed genes (ILV2, TRR1, ADE13, and ECM31) involved in branched-chain amino acid biosynthesis, the thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively, exhibited the best performance. KM32 achieved a 50% increase in taxadiene production, reaching 215 mg/L. Furthermore, KM32 produced the highest reported yields of taxa-4(20),11-dien-5α-ol (T5α-ol) at 43.65 mg/L and taxa-4(20),11-dien-5-α-yl acetate (T5αAc) at 26.2 mg/L among early-step Taxol® metabolites in S. cerevisiae. CONCLUSIONS: This study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol®.
Subject(s)
Diterpenes , Paclitaxel , Saccharomyces cerevisiae/genetics , Saccharomyces cerevisiae/metabolism , Metabolic Engineering , Diterpenes/metabolismABSTRACT
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.
Subject(s)
Models, Biological , Proteome , Fungi/genetics , Metabolic Networks and Pathways/geneticsABSTRACT
Yarrowia lipolytica naturally saves excess carbon as storage lipids. Engineering efforts allow redirecting the high precursor flux required for lipid synthesis toward added-value chemicals such as polyketides, flavonoids, and terpenoids. To redirect precursor flux from storage lipids to other products, four genes involved in triacylglycerol and sterol ester synthesis (DGA1, DGA2, LRO1, and ARE1) can be deleted. To elucidate the effect of the deletions on cell physiology and regulation, we performed chemostat cultivations under carbon and nitrogen limitations, followed by transcriptome analysis. We found that storage lipid-free cells show an enrichment of the unfolded protein response, and several biological processes related to protein refolding and degradation are enriched. Additionally, storage lipid-free cells show an altered lipid class distribution with an abundance of potentially cytotoxic free fatty acids under nitrogen limitation. Our findings not only highlight the importance of lipid metabolism on cell physiology and proteostasis, but can also aid the development of improved chassy strains of Y. lipolytica for commodity chemical production.
Subject(s)
Yarrowia , Yarrowia/genetics , Yarrowia/metabolism , Fungal Proteins/genetics , Fungal Proteins/metabolism , Lipid Metabolism , Triglycerides/metabolism , Nitrogen/metabolism , Carbon/metabolismABSTRACT
Rhodotorula toruloides is a non-conventional, oleaginous yeast able to naturally accumulate high amounts of microbial lipids. Constraint-based modeling of R. toruloides has been mainly focused on the comparison of experimentally measured and model predicted growth rates, while the intracellular flux patterns have been analyzed on a rather general level. Hence, the intrinsic metabolic properties of R. toruloides that make lipid synthesis possible are not thoroughly understood. At the same time, the lack of diverse physiological data sets has often been the bottleneck to predict accurate fluxes. In this study, we collected detailed physiology data sets of R. toruloides while growing on glucose, xylose, and acetate as the sole carbon source in chemically defined medium. Regardless of the carbon source, the growth was divided into two phases from which proteomic and lipidomic data were collected. Complemental physiological parameters were collected in these two phases and altogether implemented into metabolic models. Simulated intracellular flux patterns demonstrated the role of phosphoketolase in the generation of acetyl-CoA, one of the main precursors during lipid biosynthesis, while the role of ATP citrate lyase was not confirmed. Metabolic modeling on xylose as a carbon substrate was greatly improved by the detection of chirality of D-arabinitol, which together with D-ribulose were involved in an alternative xylose assimilation pathway. Further, flux patterns pointed to metabolic trade-offs associated with NADPH allocation between nitrogen assimilation and lipid biosynthetic pathways, which was linked to large-scale differences in protein and lipid content. This work includes the first extensive multi-condition analysis of R. toruloides using enzyme-constrained models and quantitative proteomics. Further, more precise kcat values should extend the application of the newly developed enzyme-constrained models that are publicly available for future studies.
Subject(s)
Proteomics , Xylose , Carbon , LipidsABSTRACT
BACKGROUND: Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species. RESULTS: In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species. CONCLUSIONS: The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.
Subject(s)
Gene Transfer, Horizontal , Genomics , Phylogeny , Genome , Evolution, MolecularABSTRACT
Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
Subject(s)
Gene Expression Profiling , Single-Cell Gene Expression Analysis , Animals , Mice , Humans , Gene Expression Profiling/methods , Algorithms , RNA-Seq , Genome/genetics , Single-Cell Analysis/methods , Sequence Analysis, RNA/methodsABSTRACT
Media components, including the nitrogen source, are significant cost factors in cultivation processes. The nitrogen source also influences cell behavior and production performance. Ammonium sulfate is a widely used nitrogen source for microorganisms' cultivation. Urea is a sustainable and cheap alternative nitrogen source. We investigated the influence of urea as a nitrogen source compared to ammonium sulfate by cultivating phenotypically different Yarrowia lipolytica strains in chemostats under carbon or nitrogen limitation. We found no significant coherent changes in growth and lipid production. RNA sequencing revealed no significant concerted changes in the transcriptome. The genes involved in urea uptake and degradation are not upregulated on a transcriptional level. Our findings support urea usage, indicating that previous metabolic engineering efforts where ammonium sulfate was used are likely translatable to the usage of urea and can ease the way for urea as a cheap and sustainable nitrogen source in more applications.
ABSTRACT
Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using two alternative procedures from the toolbox RAVEN v 2.0. We found that draft GEMs could reflect the difference in yeast metabolic potentials, and therefore, could be utilized to probe the evolutionary trend of metabolism among 332 yeast species. We created a pan-draft metabolic model to account for the metabolic capacity of every sequenced yeast species by merging all draft GEMs. Further analysis showed that the pan-reactome of yeast has a "closed" property, which confirmed the great conservatism that exists in yeast metabolic evolution. Lastly, the quantitative correlations among trait similarity, evolutionary distances, genotype, and model similarity were thoroughly investigated. The results suggest that the evolutionary distance and genotype, to some extent, determine model similarity, but not trait similarity, indicating that multiple mechanisms shape yeast trait evolution. A large-scale reconstruction and integrative analysis of yeast draft GEMs would be a valuable resource to probe the evolutionary mechanism behind yeast trait variety and to further refine the existing yeast species-specific GEMs for the community.
Subject(s)
Genomics , Yeasts , Phylogeny , Yeasts/genetics , Genomics/methods , Phenotype , GenomeABSTRACT
Given the strong potential of Yarrowia lipolytica to produce lipids for use as renewable fuels and oleochemicals, it is important to gain in-depth understanding of the molecular mechanism underlying its lipid accumulation. As cellular growth rate affects biomass lipid content, we performed a comparative proteomic analysis of Y. lipolytica grown in nitrogen-limited chemostat cultures at different dilution rates. After confirming the correlation between growth rate and lipid accumulation, we were able to identify various cellular functions and biological mechanisms involved in oleaginousness. Inspection of significantly up- and downregulated proteins revealed nonintuitive processes associated with lipid accumulation in this yeast. This included proteins related to endoplasmic reticulum (ER) stress, ER-plasma membrane tether proteins, and arginase. Genetic engineering of selected targets validated that some genes indeed affected lipid accumulation. They were able to increase lipid content and were complementary to other genetic engineering strategies to optimize lipid yield.
Subject(s)
Yarrowia , Biomass , Lipid Metabolism/genetics , Lipids/genetics , Proteomics , Yarrowia/metabolismABSTRACT
Genome-scale metabolic models (GEMs) provide a useful framework for modeling the metabolism of microorganisms. While the applications of GEMs are wide and far reaching, the reconstruction and continuous curation of such models can be perceived as a tedious and time-consuming task. Using RAVEN, a MATLAB-based toolbox designed to facilitate the reconstruction analysis of metabolic networks, this protocol practically demonstrates how researchers can create their own GEMs using a homology-based approach. To provide a complete example, a draft GEM for the industrially relevant yeast Hansenula polymorpha is reconstructed.
Subject(s)
Metabolic Networks and Pathways , Saccharomycetales , Genome, Fungal , Metabolic Networks and Pathways/genetics , Models, Biological , Saccharomycetales/genetics , Saccharomycetales/metabolismABSTRACT
The concept of metabolic models with resource allocation constraints has been around for over a decade and has clear advantages even when implementation is relatively rudimentary. Nonetheless, the number of organisms for which such a model is reconstructed is low. Various approaches exist, from coarse-grained consideration of enzyme usage to fine-grained description of protein translation. These approaches are reviewed here, with a particular focus on user-friendly solutions that can introduce resource allocation constraints to metabolic models of any organism. The availability of kcat data is a major hurdle, where recent advances might help to fill in the numerous gaps that exist for this data, especially for nonmodel organisms.
Subject(s)
Resource AllocationABSTRACT
Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.