Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials

<p>Abstract</p> <p>Background</p> <p><it>Pichia stipitis </it>and <it>Pichia pastoris </it>have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research.</p> <p>Results</p> <p>In this work, genome-scale metabolic models (GEMs) of <it>P. stipitis </it>(iSS884) and <it>P. pastoris </it>(iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of <it>P. pastoris</it>, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of <it>P. pastoris </it>expressing a recombinant protein. The potential of <it>P. stipitis </it>for the conversion of xylose and glucose into ethanol using reactors in series, and of <it>P. pastoris </it>to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed.</p> <p>Conclusions</p> <p>In conclusion the first GEM of <it>P. stipitis </it>(iSS884) was reconstructed and validated. The expanded version of the <it>P. pastoris </it>GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials.</p

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 addressedThis work was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of a Ph.D. grant (PD/BD/52336/2013), of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01–0145FEDER-006684) and also in the context of the EU-funded initiative ERA-NET for Industrial Biotechnology (ERA-IB-2/0003/2013), in addition to the BioTecNorte operation (NORTE-01–0145FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

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

Integration and Validation of the GenomeScale Metabolic Models of Pichia pastoris : a Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism

Motivation: Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. Results: In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the iMT1026 model has shown a better performance than the previous existing models

iOD907, the first genome-scale metabolic model for the milk yeast Kluyveromyces lactis

We describe here the first genome-scale metabolic model of Kluyveromyces lactis, iOD907. It is partially compartmentalized (4 compartments), composed of 1867 reactions and 1476 metabolites. The iOD907 model performed well when assessing the positive growth of K. lactis to Biolog experiments and to an online catalogue of strains that provides information on carbon sources in which K. lactis is able to grow. Chemostat experiments were used to adjust non-growth-associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. In silico knockouts predicted in vivo phenotypes accurately when compared to published experiments. The iOD907 genome-scale metabolic model complies with the MIRIAM standards for the annotation of enzymes, transporters, metabolites and reactions. Moreover, it contains direct links to KEGG (for enzymes, metabolites and reactions) and to TCDB for transporters, allowing easy comparisons to other models. Furthermore, this model is provided in the well-established SBML format, which means that it can be used in most metabolic engineering platforms, such as OptFlux or Cobra. The model is able to predict the behavior of K. lactis under different environmental conditions and genetic perturbations. Furthermore, it can also be important in the design of minimal media and will allow insights on the milk yeast's metabolism, as well as identifying metabolic engineering targets for the improvement of the production of products of interest by performing simulations and optimizations.The authors thank strategic Project PEst-OE/EQB/LA0023/2013 and project "BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF. NORTE-07-0124-FEDER-000028" co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER. The authors would also like to acknowledge Steve Sheridan for proof reading this manuscript

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

<p>Background:</p> <p>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.</p> <p>Results:</p> <p>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.</p> <p>Conclusion:</p> <p>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.</p

Scheffersomyces stipitis: a comparative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p><it>Scheffersomyces stipitis</it> is a Crabtree negative yeast, commonly known for its capacity to ferment pentose sugars. Differently from Crabtree positive yeasts such as <it>Saccharomyces cerevisiae</it>, the onset of fermentation in <it>S. stipitis</it> is not dependent on the sugar concentration, but is regulated by a decrease in oxygen levels. Even though <it>S. stipitis</it> has been extensively studied due to its potential application in pentoses fermentation, a limited amount of information is available about its metabolism during aerobic growth on glucose. Here, we provide a systems biology based comparison between the two yeasts, uncovering the metabolism of <it>S. stipitis</it> during aerobic growth on glucose under batch and chemostat cultivations.</p> <p>Results</p> <p>Starting from the analysis of physiological data, we confirmed through ¹³C-based flux analysis the fully respiratory metabolism of <it>S. stipitis</it> when growing both under glucose limited or glucose excess conditions. The patterns observed showed similarity to the fully respiratory metabolism observed for <it>S. cerevisiae</it> under chemostat cultivations however, intracellular metabolome analysis uncovered the presence of several differences in metabolite patterns. To describe gene expression levels under the two conditions, we performed RNA sequencing and the results were used to quantify transcript abundances of genes from the central carbon metabolism and compared with those obtained with <it>S. cerevisiae</it>. Interestingly, genes involved in central pathways showed different patterns of expression, suggesting different regulatory networks between the two yeasts. Efforts were focused on identifying shared and unique families of transcription factors between the two yeasts through <it>in silico</it> transcription factors analysis, suggesting a different regulation of glycolytic and glucoenogenic pathways.</p> <p>Conclusions</p> <p>The work presented addresses the impact of high-throughput methods in describing and comparing the physiology of Crabtree positive and Crabtree negative yeasts. Based on physiological data and flux analysis we identified the presence of one metabolic condition for <it>S. stipitis</it> under aerobic batch and chemostat cultivations, which shows similarities to the oxidative metabolism observed for <it>S. cerevisiae</it> under chemostat cultivations. Through metabolome analysis and genome-wide transcriptomic analysis several differences were identified. Interestingly, <it>in silico</it> analysis of transciption factors was useful to address a different regulation of mRNAs of genes involved in the central carbon metabolism. To our knowledge, this is the first time that the metabolism of <it>S. stiptis</it> is investigated in details and is compared to <it>S. cerevisiae</it>. Our study provides useful results and allows for the possibility to incorporate these data into recently developed genome-scaled metabolic, thus contributing to improve future industrial applications of <it>S. stipitis</it> as cell factory.</p

Established tools and emerging trends for the production of recombinant proteins and metabolites in Pichia pastoris

Besides bakers' yeast, the methylotrophic yeast Komagataella phaffii (also known as Pichia pastoris) has been developed into the most popular yeast cell factory for the production of heterologous proteins. Strong promoters, stable genetic constructs and a growing collection of freely available strains, tools and protocols have boosted this development equally as thorough genetic and cell biological characterization. This review provides an overview of state-of-the-art tools and techniques for working with P. pastoris, as well as guidelines for the production of recombinant proteins with a focus on small-scale production for biochemical studies and protein characterization. The growing applications of P. pastoris for in vivo biotransformation and metabolic pathway engineering for the production of bulk and specialty chemicals are highlighted as well

Contextualized genome-scale model unveils high-order metabolic effects of the specific growth rate and oxygenation level in recombinant Pichia pastoris

Pichia pastoris is recognized as a biotechnological workhorse for recombinant protein expression. The metabolic performance of this microorganism depends on genetic makeup and culture conditions, amongst which the specific growth rate and oxygenation level are critical. Despite their importance, only their individual effects have been assessed so far, and thus their combined effects and metabolic consequences still remain to be elucidated. In this work, we present a comprehensive framework for revealing high-order (i.e., individual and combined) metabolic effects of the above parameters in glucose-limited continuous cultures of P. pastoris, using thaumatin production as a case study. Specifically, we employed a rational experimental design to calculate statistically significant metabolic effects from multiple chemostat data, which were later contextualized using a refined and highly predictive genome-scale metabolic model of this yeast under the simulated conditions. Our results revealed a negative effect of the oxygenation on the specific product formation rate (thaumatin), and a positive effect on the biomass yield. Notably, we identified a novel positive combined effect of both the specific growth rate and oxygenation level on the specific product formation rate. Finally, model predictions indicated an opposite relationship between the oxygenation level and the growth-associated maintenance energy (GAME) requirement, suggesting a linear GAME decrease of 0.56 mmol ATP/g per each 1% increase in oxygenation level, which translated into a 44% higher metabolic cost under low oxygenation compared to high oxygenation. Overall, this work provides a systematic framework for mapping high-order metabolic effects of different culture parameters on the performance of a microbial cell factory. Particularly in this case, it provided valuable insights about optimal operational conditions for protein production in P. pastoris

Whole-genome metabolic model of Trichoderma reesei built by comparative reconstruction

Background: Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis. Results: A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model. Conclusions: The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1on-strate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.Peer reviewe