Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Microbiome distribution modeling using gradient descent strategies for mock, in vitro and clinical community distributions

The human gut is home to a complex array of microorganisms interacting with the host and each other, forming a community known as the microbiome. This community has been linked to human health and disease, but understanding the underlying interactions is still challenging for researchers. Standard studies typically use high-throughput sequencing to analyze microbiome distribution in patient samples. Recent advancements in meta-omic data analysis have enabled computational modeling strategies to integrate this information into an in silico model. However, there is a need for improved parameter fitting and data integration features in microbial community modeling. This study proposes a novel alternative strategy utilizing state-of-the-art dynamic flux balance analysis (dFBA) to provide a simple protocol enabling accurate replication of abundance data composition through dynamic parameter estimation and integration of metagenomic data. We used a recurrent optimization algorithm to replicate community distributions from three different sources: mock, in vitro, and clinical microbiome. Our results show an accuracy of 98% and 96% when using in vitro and clinical bacterial abundance distributions, respectively. The proposed modeling scheme allowed us to observe the evolution of metabolites. It could provide a deeper understanding of metabolic interactions while taking advantage of the high contextualization features of GEM schemes to fit the study case. The proposed modeling scheme could improve the approach in cases where external factors determine specific bacterial distributions, such as drug intake.

Developing a metabolic model-based fed-batch feeding strategy for Pichia pastoris fermentation through fine-tuning of the methanol utilization pathway

Pichia pastoris is a commonly used microbial host for recombinant protein production. It is mostly cultivated in fed-batch mode, in which the environment of the cell is continuously changing. Hence, it is vital to understand the influence of feeding strategy parameters on the intracellular reaction network to fine-tune bioreactor performance. This study used dynamic flux balance analysis (DFBA) integrated with transcriptomics data to simulate the recombinant P. pastoris (Mut^s ) growth during the induction phase for three fed-batch strategies, conducted at constant specific growth rates (μ-stat). The induction phase was split into equal time intervals, and the correlated reactions with protein yield were identified in the three fed-batch strategies using the Pearson correlation coefficient. Subsequently, principal component analysis (PCA) was applied to cluster induction phase time intervals and identify the role of correlated reactions on metabolic differentiation of time intervals. It was found that increasing fluxes through the methanol dissimilation pathway increased protein yield. By adding a methanol assimilation pathway inhibitor (HgCl₂ ) to the shake flask medium growing on glycerol: methanol mixture (10%: 90%, v/v), the protein titre increased by 60%. As per DFBA, the higher the methanol to biomass flux ratio (R_meoh/Δx ), the higher the protein yield. Finally, a novel feeding strategy was developed to increase the amount of R_meoh/Δx compared to the three feeding strategies. The concentration of recombinant human growth hormone (rhGH), used as the model protein, increased by 16% compared to the optimal culture result obtained previously (800 mg L^-1 to 928 mg L^-1 ), while production yield improved by 85% (24.8 mg g_DCW ^-1 to 46 mg g_DCW ^-1 ).

Current advances of Pichia pastoris as cell factories for production of recombinant proteins

Pichia pastoris (syn. Komagataella spp.) has attracted extensive attention as an efficient platform for recombinant protein (RP) production. For obtaining a higher protein titer, many researchers have put lots of effort into different areas and made some progress. Here, we summarized the most recent advances of the last 5 years to get a better understanding of its future direction of development. The appearance of innovative genetic tools and methodologies like the CRISPR/Cas9 gene-editing system eases the manipulation of gene expression systems and greatly improves the efficiency of exploring gene functions. The integration of novel pathways in microorganisms has raised more ideas of metabolic engineering for enhancing RP production. In addition, some new opportunities for the manufacture of proteins have been created by the application of novel mathematical models coupled with high-throughput screening to have a better overview of bottlenecks in the biosynthetic process.

Advances in Komagataella phaffii Engineering for the Production of Renewable Chemicals and Proteins

The need for a more sustainable society has prompted the development of bio-based processes to produce fuels, chemicals, and materials in substitution for fossil-based ones. In this context, microorganisms have been employed to convert renewable carbon sources into various products. The methylotrophic yeast Komagataella phaffii has been extensively used in the production of heterologous proteins. More recently, it has been explored as a host organism to produce various chemicals through new metabolic engineering and synthetic biology tools. This review first summarizes Komagataella taxonomy and diversity and then highlights the recent approaches in cell engineering to produce renewable chemicals and proteins. Finally, strategies to optimize and develop new fermentative processes using K. phaffii as a cell factory are presented and discussed. The yeast K. phaffii shows an outstanding performance for renewable chemicals and protein production due to its ability to metabolize different carbon sources and the availability of engineering tools. Indeed, it has been employed in producing alcohols, carboxylic acids, proteins, and other compounds using different carbon sources, including glycerol, glucose, xylose, methanol, and even CO2.

Advances in Cell Engineering of the Komagataella phaffii Platform for Recombinant Protein Production

Komagataella phaffii (formerly known as Pichia pastoris) has become an increasingly important microorganism for recombinant protein production. This yeast species has gained high interest in an industrial setting for the production of a wide range of proteins, including enzymes and biopharmaceuticals. During the last decades, relevant bioprocess progress has been achieved in order to increase recombinant protein productivity and to reduce production costs. More recently, the improvement of cell features and performance has also been considered for this aim, and promising strategies with a direct and substantial impact on protein productivity have been reported. In this review, cell engineering approaches including metabolic engineering and energy supply, transcription factor modulation, and manipulation of routes involved in folding and secretion of recombinant protein are discussed. A lack of studies performed at the higher-scale bioreactor involving optimisation of cultivation parameters is also evidenced, which highlights new research aims to be considered.

Burden Imposed by Heterologous Protein Production in Two Major Industrial Yeast Cell Factories: Identifying Sources and Mitigation Strategies

Production of heterologous proteins, especially biopharmaceuticals and industrial enzymes, in living cell factories consumes cellular resources. Such resources are reallocated from normal cellular processes toward production of the heterologous protein that is often of no benefit to the host cell. This competition for resources is a burden to host cells, has a negative impact on cell fitness, and may consequently trigger stress responses. Importantly, this often causes a reduction in final protein titers. Engineering strategies to generate more burden resilient production strains offer sustainable opportunities to increase production and profitability for this growing billion-dollar global industry. We review recently reported impacts of burden derived from resource competition in two commonly used protein-producing yeast cell factories: Saccharomyces cerevisiae and Komagataella phaffii (syn. Pichia pastoris). We dissect possible sources of burden in these organisms, from aspects related to genetic engineering to protein translation and export of soluble protein. We also summarize advances as well as challenges for cell factory design to mitigate burden and increase overall heterologous protein production from metabolic engineering, systems biology, and synthetic biology perspectives. Lastly, future profiling and engineering strategies are highlighted that may lead to constructing robust burden-resistant cell factories. This includes incorporation of systems-level data into mathematical models for rational design and engineering dynamical regulation circuits in production strains.

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

This review is a part of the SI 'Genome-Scale Modeling of Microorganisms in the Real World'. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

Recombinant protein production in Pichia pastoris: From transcriptionally redesigned strains to bioprocess optimization and metabolic modelling

Pichia pastoris is one of the most widely used host for the production of recombinant proteins. Expression systems that rely mostly on promoters from genes encoding alcohol oxidase 1 or glyceraldehyde-3-phosphate dehydrogenase have been developed together with related bioreactor operation strategies based on carbon sources such as methanol, glycerol, or glucose. Although, these processes are relatively efficient and easy to use, there have been notable improvements over the last twenty years to better control gene expression from these promoters and their engineered variants. Methanol-free and more efficient protein production platforms have been developed by engineering promoters and transcription factors. The production window of P. pastoris has been also extended by using alternative feedstocks including ethanol, lactic acid, mannitol, sorbitol, sucrose, xylose, gluconate, formate, or rhamnose. Herein, the specific aspects that are emerging as key parameters for recombinant protein synthesis are discussed. For this purpose, a holistic approach has been considered to scrutinize protein production processes from strain design to bioprocess optimization, particularly focusing on promoter engineering, transcriptional circuitry redesign. This review also considers the optimization of bioprocess based on alternative carbon sources and derived co-feeding strategies. Optimization strategies for recombinant protein synthesis through metabolic modelling are also discussed.

A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of the Saccharomyces Genus in Fermentation

Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories.

IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories.

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.

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.

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

Into the wild: new yeast genomes from natural environments and new tools for their analysis

Genomic studies of yeasts from the wild have increased considerably in the past few years. This revolution has been fueled by advances in high-throughput sequencing technologies and a better understanding of yeast ecology and phylogeography, especially for biotechnologically important species. The present review aims to first introduce new bioinformatic tools available for the generation and analysis of yeast genomes. We also assess the accumulated genomic data of wild isolates of industrially relevant species, such as Saccharomyces spp., which provide unique opportunities to further investigate the domestication processes associated with the fermentation industry and opportunistic pathogenesis. The availability of genome sequences of other less conventional yeasts obtained from the wild has also increased substantially, including representatives of the phyla Ascomycota (e.g. Hanseniaspora) and Basidiomycota (e.g. Phaffia). Here, we review salient examples of both fundamental and applied research that demonstrate the importance of continuing to sequence and analyze genomes of wild yeasts.

Yeast synthetic biology for designed cell factories producing secretory recombinant proteins

Yeasts are prominent hosts for the production of recombinant proteins from industrial enzymes to therapeutic proteins. Particularly, the similarity of protein secretion pathways between these unicellular eukaryotic microorganisms and higher eukaryotic organisms has made them a preferential host to produce secretory recombinant proteins. However, there are several bottlenecks, in terms of quality and quantity, restricting their use as secretory recombinant protein production hosts. In this mini-review, we discuss recent developments in synthetic biology approaches to constructing yeast cell factories endowed with enhanced capacities of protein folding and secretion as well as designed targeted post-translational modification process functions. We focus on the new genetic tools for optimizing secretory protein expression, such as codon-optimized synthetic genes, combinatory synthetic signal peptides and copy number-controllable integration systems, and the advanced cellular engineering strategies, including endoplasmic reticulum and protein trafficking pathway engineering, synthetic glycosylation, and cell wall engineering, for improving the quality and yield of secretory recombinant proteins.

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_DCW 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.

Systems biology approach in the formulation of chemically defined media for recombinant protein overproduction

The cell culture medium is an intricate mixture of components which has a tremendous effect on cell growth and recombinant protein production. Regular cell culture medium includes various components, and the decision about which component should be included in the formulation and its optimum amount is an underlying issue in biotechnology industries. Applying conventional techniques to design an optimal medium for the production of a recombinant protein requires meticulous and immense research. Moreover, since the medium formulation for the production of one protein could not be the best choice for another protein, hence, the most suitable media should be determined for each recombinant cell line. Accordingly, medium formulation becomes a laborious, time-consuming, and costly process in biomanufacturing of recombinant protein, and finding alternative strategies for medium development seems to be crucial. In silico modeling is an attractive concept to be adapted for medium formulation due to its high potential to supersede laboratory examinations. By emerging the high-throughput datasets, scientists can disclose the knowledge about the effect of medium components on cell growth and metabolism, and via applying this information through systems biology approach, medium formulation optimization could be accomplished in silico with no need of significant amount of experimentation. This review demonstrates some of the applications of systems biology as a powerful tool for medium development and illustrates the effect of medium optimization with system-level analysis on the production of recombinant proteins in different host cells.

Genetic engineering approaches to improve posttranslational modification of biopharmaceuticals in different production platforms

The number of approved biopharmaceuticals, where product quality attributes remain of major importance, is increasing steadily. Within the available variety of expression hosts, the production of biopharmaceuticals faces diverse limitations with respect to posttranslational modifications (PTM), while different biopharmaceuticals demand different forms and specifications of PTMs for proper functionality. With the growing toolbox of genetic engineering technologies, it is now possible to address general as well as host- or biopharmaceutical-specific product quality obstacles. In this review, we present diverse expression systems derived from mammalians, bacteria, yeast, plants, and insects as well as available genetic engineering tools. We focus on genes for knockout/knockdown and overexpression for meaningful approaches to improve biopharmaceutical PTMs and discuss their applicability as well as future trends in the field.

Bioproduction of the Recombinant Sweet Protein Thaumatin: Current State of the Art and Perspectives

There is currently a worldwide trend to reduce sugar consumption. This trend is mostly met by the use of artificial non-nutritive sweeteners. However, these sweeteners have also been proven to have adverse health effects such as dizziness, headaches, gastrointestinal issues, and mood changes for aspartame. One of the solutions lies in the commercialization of sweet proteins, which are not associated with adverse health effects. Of these proteins, thaumatin is one of the most studied and most promising alternatives for sugars and artificial sweeteners. Since the natural production of these proteins is often too expensive, biochemical production methods are currently under investigation. With these methods, recombinant DNA technology is used for the production of sweet proteins in a host organism. The most promising host known today is the methylotrophic yeast, Pichia pastoris. This yeast has a tightly regulated methanol-induced promotor, allowing a good control over the recombinant protein production. Great efforts have been undertaken for improving the yields and purities of thaumatin productions, but a further optimization is still desired. This review focuses on (i) the motivation for using and producing sweet proteins, (ii) the properties and history of thaumatin, (iii) the production of recombinant sweet proteins, and (iv) future possibilities for process optimization based on a systems biology approach.

Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant Escherichia coli

Escherichia coli has been the host organism most frequently investigated for efficient recombinant protein production. However, the production of a foreign protein in recombinant E. coli often leads to growth deterioration and elevated secretion of acetic acid. Such observed phenomena have been widely linked with cell stress responses and metabolic burdens originated particularly from the increased energy demand. In this study, flux balance analysis and dynamic flux balance analysis were applied to investigate the observed growth physiology of recombinant E. coli, incorporating the proteome allocation theory and an adjustable maintenance energy level (ATPM) to capture the proteomic and energetic burdens introduced by recombinant protein synthesis. Model predictions of biomass growth, substrate consumption, acetate excretion, and protein production with two different strains were in good agreement with the experimental data, indicating that the constraint on the available proteomic resource and the change in ATPM might be important contributors governing the growth physiology of recombinant strains. The modeling framework developed in this work, currently with several limitations to overcome, offers a starting point for the development of a practical, model-based tool to guide metabolic engineering decisions for boosting recombinant protein production.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Carbon metabolism influenced for promoters and temperature used in the heterologous protein production using Pichia pastoris yeast

Nowadays, it is necessary to search for different high-scale production strategies to produce recombinant proteins of economic interest. Only a few microorganisms are industrially relevant for recombinant protein production: methylotrophic yeasts are known to use methanol efficiently as the sole carbon and energy source. Pichia pastoris is a methylotrophic yeast characterized as being an economical, fast and effective system for heterologous protein expression. Many factors can affect both the product and the production, including the promoter, carbon source, pH, production volume, temperature, and many others; but to control all of them most of the time is difficult and this depends on the initial selection of each variable. Therefore, this review focuses on the selection of the best promoter in the recombination process, considering different inductors, and the temperature as a culture medium variable in methylotrophic Pichia pastoris yeast. The goal is to understand the effects associated with different factors that influence its cell metabolism and to reach the construction of an expression system that fulfills the requirements of the yeast, presenting an optimal growth and development in batch, fed-batch or continuous cultures, and at the same time improve its yield in heterologous protein production.

Dynamic flux balance analysis for microbial conversion of glycerol into 1,3-propanediol by Klebsiella pneumoniae

To investigate the relationship between the yield of 1,3-propanediol (1,3-PD) and the flux variation in metabolic pathways of Klebsiella pneumoniae, an optimized calculation method was constructed on basis of dynamic flux balance analysis by combining genome-scale flux balance analysis with a kinetic model of extracellular metabolites. Through optimizing calculations, a more completely expanded metabolic pathway was obtained, which includes the previously reported metabolic pathway and additional three pathways or site: a pentose phosphate pathway (PPP) elicited at the dihydroxyacetone (DHA) node to provide more reducing equivalents; a branch of synthetic amino acids at the 3-phosphoglycerate (3PG) node; and the α-ketoglutarate site in the tricarboxylic acid (TCA) cycle leading to anabolic pathways for glutamate and other amino acids. On this basis, the relationships between the dynamic flux distribution of the important nodes in the metabolic pathway and the yield of 1,3-propanediol were analyzed. First, dynamic flux change from DHA to the PPP is positively correlated with the yield. Second, variation in flux in the TCA cycle is also positively correlated with the yield of 1,3-propanediol. In addition, the influence of the feedback loop formed by the cofactor tetrahydrofolate on the flux change of TCA in the amino acid anabolic pathway was examined. These results are of important reference value and have guiding significance for the extension of the glycerol metabolism pathway in K. pneumoniae, the rational transformation of genetic engineering in bacteria, and the optimization of metabolic pathways for industrial production.

Metabolic engineering of Pichia pastoris

Besides its use for efficient production of recombinant proteins the methylotrophic yeast Pichia pastoris (syn. Komagataella spp.) has been increasingly employed as a platform to produce metabolites of varying origin. We summarize here the impressive methodological developments of the last years to model and analyze the metabolism of P. pastoris, and to engineer its genome and metabolic pathways. Efficient methods to insert, modify or delete genes via homologous recombination and CRISPR/Cas9, supported by modular cloning techniques, have been reported. An outstanding early example of metabolic engineering in P. pastoris was the humanization of protein glycosylation. More recently the cell metabolism was engineered also to enhance the productivity of heterologous proteins. The last few years have seen an increased number of metabolic pathway design and engineering in P. pastoris, mainly towards the production of complex (secondary) metabolites. In this review, we discuss the potential role of P. pastoris as a platform for metabolic engineering, its strengths, and major requirements for future developments of chassis strains based on synthetic biology principles.

Integrating metabolic modeling and population heterogeneity analysis into optimizing recombinant protein production by Komagataella (Pichia) pastoris

The methylotrophic yeast Komagataella (Pichia) pastoris has become one of the most utilized cell factories for the production of recombinant proteins over the last three decades. This success story is linked to its specific physiological traits, i.e., the ability to grow at high cell density in inexpensive culture medium and to secrete proteins at high yield. Exploiting methanol metabolism is at the core of most P. pastoris-based processes but comes with its own challenges. Co-feeding cultures with glycerol/sorbitol and methanol is a promising approach, which can benefit from improved understanding and prediction of metabolic response. The development of profitable processes relies on the construction and selection of efficient producing strains from less efficient ones but also depends on the ability to master the bioreactor process itself. More specifically, how a bioreactor processes could be monitored and controlled to obtain high yield of production. In this review, new perspectives are detailed regarding a multi-faceted approach to recombinant protein production processes by P. pastoris; including gaining improved understanding of the metabolic pathways involved, accounting for variations in transcriptional and translational efficiency at the single cell level and efficient monitoring and control of methanol levels at the bioreactor level.

Engineering strategies for enhanced production of protein and bio-products in Pichia pastoris: A review

Pichia pastoris has been recognized as one of the most industrially important hosts for heterologous protein production. Despite its high protein productivity, the optimization of P. pastoris cultivation is still imperative due to strain- and product-specific challenges such as promoter strength, methanol utilization type and oxygen demand. To address the issues, strategies involving genetic and process engineering have been employed. Optimization of codon usage and gene dosage, as well as engineering of promoters, protein secretion pathways and methanol metabolic pathways have proved beneficial to innate protein expression levels. Large-scale production of proteins via high cell density fermentation additionally relies on the optimization of process parameters including methanol feed rate, induction temperature and specific growth rate. Recent progress related to the enhanced production of proteins in P. pastoris via various genetic engineering and cultivation strategies are reviewed. Insight into the regulation of the P. pastoris alcohol oxidase 1 (AOX1) promoter and the development of methanol-free systems are highlighted. Novel cultivation strategies such as mixed substrate feeding are discussed. Recent advances regarding substrate and product monitoring techniques are also summarized. Application of P. pastoris to the production of biodiesel and other value-added products via metabolic engineering are also reviewed. P. pastoris is becoming an indispensable platform through the use of these combined engineering strategies.

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.