Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints

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 finegrained 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

Extracting novel hypotheses and findings from RNA-seq data

Over the past decade, improvements in technology and methods have enabled rapid and relatively inexpensive generation of high-quality RNA-seq datasets. These datasets have been used to characterize gene expression for several yeast species and have provided systems-level insights for basic biology, biotechnology and medicine. Herein, we discuss new techniques that have emerged and existing techniques that enable analysts to extract information from multifactorial yeast RNA-seq datasets. Ultimately, this minireview seeks to inspire readers to query datasets, whether previously published or freshly obtained, with creative and diverse methods to discover and support novel hypotheses

Systems-level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high-value chemicals. In this regard, Yarrowia lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid, and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data-driven approaches, in-depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems-level studies and the related cutting-edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica-based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development

Nitrogen as the major factor influencing gene expression in Yarrowia lipolytica

Yarrowia lipolytica is an important industrial microorganism used for the production of oleochemicals. The design of effective biotechnological processes with this cell factory requires an in-depth knowledge of its metabolism. Here we present a transcriptomic study of Y. lipolytica grown in the presence of glycerol and glucose, and mixture of both at different carbon to nitrogen ratios. It emerged that the transcriptomic landscape of Y. lipolytica is more sensitive to the nitrogen availability than to the utilized carbon source, as evidenced by more genes being differentially expressed in lower carbon to nitrogen ratio. Specifically, expression of hexokinase (HXK1) is significantly susceptible to changes in nitrogen concentrations. High HXK1 expression in low nitrogen seems to impact other genes which are implicated in tricarboxylic acid cycle and erythritol biosynthesis. We further show that expression of HXK1 and two genes belonging to the sugar porter family might be controlled by GATA-like zinc-finger proteins

A Pan-Draft Metabolic Model Reflects Evolutionary Diversity across 332 Yeast Species

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

Extending a dynamic mathematical model of metabolism in Trypanosoma brucei

There is an urgent need for new chemotherapies against human African trypanosomiasis (HAT), caused by the protozoan parasite Trypanosoma brucei. It is anticipated that the parasites’ divergent biochemistry will enable development of novel therapies. To study the behaviour of a complex network as metabolism, one can employ mathematical models. In this thesis, metabolism of bloodstream form T. brucei was investigated. Cellular metabolism consists as a complex system connecting enzymes with metabolites, and to study such a network one can construct mathematical models that describe the connections within the biological system. A previously published, and well-curated model of glycolysis in bloodstream form T. brucei (Bakker BM, et al. (1997) J Biol Chem 272:3207 15), was extended here with the pentose phosphate pathway (PPP), the second major pathway in glucose metabolism in most life forms. Several hypotheses were derived during the model building process and these were tested experimentally. It became apparent that the glycosomal bound-phosphate balance is essential for the parasite. Extension of the glycolytic model with the PPP introduced the risk of a so-called ’phosphate leak’, where bound-phosphates are depleted in the glycosome. Two hypotheses were investigated in silico, while one hypothesis could also be tested experimentally; (i) a glycosomal ATP:ADP antiporter was proposed, but in silico analysis indicated that the activity of such an antiporter requires tight regulation. (ii) A glycosomal ribokinase was investigated both in silico and experimentally. Genetic mutants indicated that ribokinase is essential to bloodstream form T. brucei, albeit at low levels. Additional analysis of the generated models indicated that ablation of 6-phosphogluconate dehydrogenase (6PGDH) in T. brucei is lethal by a different mechanism as seen in other organisms. Overall, extension of the glycolytic model with the PPP demonstrated the fragility of the model regarding the bound-phosphate balance and indicated that future analysis on glycosomal metabolism should be focused on this. Important in the use of mathematical models of metabolism is that the underlying stoichiometry of the model reflects (albeit with simplification) the in vivo system. It is therefore paramount to know what enzyme activities are present in the organism of interest. In this thesis a metabolomics approach was used to elucidate the function of three T. brucei genes. These genes were putatively annotated as arginase (ARG), N-acetylornithine deacetylase (NAO) and nicotinamidase (NAM). The results suggested that ARG has catalytic activity as tryptophan monooxygenase (EC 1.3.12.3), while substrate promiscuity was indicated for NAO and NAM. The work presented in this thesis has provided us with new insights on trypanosomal metabolism. The extended model now allows us to research a larger part of T. brucei metabolism with mathematical modelling, and will thereby aid in the identification and further investigation of (proposed) drug targets

Validated Growth Rate-Dependent Regulation of Lipid Metabolism in Yarrowia lipolytica

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

Urea is a drop-in nitrogen source alternative to ammonium sulphate in Yarrowia lipolytica

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

Regulation of amino-acid metabolism controls flux to lipid accumulation in <i>Yarrowia lipolytica</i>

Yarrowia lipolytica is a promising microbial cell factory for the production of lipids to be used as fuels and chemicals, but there are few studies on regulation of its metabolism. Here we performed the first integrated data analysis of Y. lipolytica grown in carbon and nitrogen limited chemostat cultures. We first reconstructed a genome-scale metabolic model and used this for integrative analysis of multilevel omics data. Metabolite profiling and lipidomics was used to quantify the cellular physiology, while regulatory changes were measured using RNAseq. Analysis of the data showed that lipid accumulation in Y. lipolytica does not involve transcriptional regulation of lipid metabolism but is associated with regulation of amino-acid biosynthesis, resulting in redirection of carbon flux during nitrogen limitation from amino acids to lipids. Lipid accumulation in Y. lipolytica at nitrogen limitation is similar to the overflow metabolism observed in many other microorganisms, e.g. ethanol production by Sacchromyces cerevisiae at nitrogen limitation

Editorial: Multi-Omics Technologies for Optimizing Synthetic Biomanufacturing

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