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

Characterising the metabolic rewiring of extremely slow growing Komagataella phaffii

Retentostat cultivations have enabled investigations into substrate-limited near-zero growth for a number of microbes. Quantitative physiology at these near-zero growth conditions has been widely discussed, yet characterisation of the fluxome is relatively under-reported. We investigated the rewiring of metabolism in the transition of a recombinant protein-producing strain of Komagataella phaffii to glucose-limited near-zero growth rates. We used cultivation data from a 200-fold range of growth rates and comprehensive biomass composition data to integrate growth rate dependent biomass equations, generated using a number of different approaches, into a K. phaffii genome-scale metabolic model. Here, we show that a non-growth-associated maintenance value of 0.65 mmol ATP g CDW - 1 h - 1 and a growth-associated maintenance value of 108 mmol ATP g CDW - 1 lead to accurate growth rate predictions. In line with its role as energy source, metabolism is rewired to increase the yield of ATP per glucose. This includes a reduction of flux through the pentose phosphate pathway, and a greater utilisation of glycolysis and the TCA cycle. Interestingly, we observed activity of an external, non-proton translocating NADH dehydrogenase in addition to the malate-aspartate shuttle. Regardless of the method used for the generation of biomass equations, a similar, yet different, growth rate dependent rewiring was predicted. As expected, these differences between the different methods were clearer at higher growth rates, where the biomass equation provides a much greater constraint than at slower growth rates. When placed on an increasingly limited glucose diet, the metabolism of K. phaffii adapts, enabling it to continue to drive critical processes sustaining its high viability at near-zero growth rates.

Improving microbial bioproduction under low-oxygen conditions

Microbial bioconversion provides access to a wide range of sustainably produced chemicals and commodities. However, industrial-scale bioproduction process operations are preferred to be anaerobic due to the cost associated with oxygen transfer. Anaerobic bioconversion generally offers limited substrate utilization profiles, lower product yields, and reduced final product diversity compared with aerobic processes. Bioproduction under conditions of reduced oxygen can overcome the limitations of fully aerobic and anaerobic bioprocesses, but many microbial hosts are not developed for low-oxygen bioproduction. Here, we describe advances in microbial strain engineering involving the use of redox cofactor engineering, genome-scale metabolic modeling, and functional genomics to enable improved bioproduction processes under low oxygen and provide a viable path for scaling these bioproduction systems to industrial scales.

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.

β-glucosidase production by recombinant Pichia pastoris strain Y1433 under optimal feed profiles of fed-batch cultivation

Pichia pastoris, a methylotrophic yeast, is known to be an efficient host for heterologous proteins production. In this study, a recombinant P. pastoris Y11430 was found better for β-glucosidase activity in comparison with a wild type P. pastoris Y11430 strain, and thereby, subjected to methanol intermittent feed profiling for β-glucosidase production. The results showed that at 72 h of cultivation time, the cultures with 16.67% and 33.33% methanol feeding with constant rate could produce the total dry cell weight of 52.23 and 118.55 g/L, respectively, while the total mutant β-glucosidase activities were 1001.59 and 3259.82 units, respectively. The methanol feeding profile was kept at 33% with three methanol feeding strategies such as constant feed rate, linear feed rate, and exponential feed rate which were used in fed-batch fermentation. At 60 h of cultivation, the highest total mutant β-glucosidase activity was 2971.85 units for exponential feed rate culture. On the other hand, total mutant β-glucosidase activity of the constant feed rate culture and linear feed rate culture were 1682.25 and 1975.43 units, respectively. The kinetic parameters of exponential feed rate culture were specific growth rate on glycerol 0.228/h, specific growth of methanol 0.061/h, maximum total dry cell weight 196.73 g, yield coefficient biomass per methanol ([Formula: see text]) 0.57 g_cell/g_MeOH, methanol consumption rate ([Formula: see text]) 5.76 g_MeOH/h, and enzyme productivity ([Formula: see text]) 75.96 units/h. In conclusion, higher cell mass and β- glucosidase activity were produced under exponential feed rate than constant and linear feed rates.

The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility

The topology of metabolic networks is recognisably modular with modules weakly connected apart from sharing a pool of currency metabolites. Here, we defined modules as sets of reversible reactions isolated from the rest of metabolism by irreversible reactions except for the exchange of currency metabolites. Our approach identifies topologically independent modules under specific conditions associated with different metabolic functions. As case studies, the E.coli iJO1366 and Human Recon 2.2 genome-scale metabolic models were split in 103 and 321 modules respectively, displaying significant correlation patterns in expression data. Finally, we addressed a fundamental question about the metabolic flexibility conferred by reversible reactions: “Of all Directed Topologies (DTs) defined by fixing directions to all reversible reactions, how many are capable of carrying flux through all reactions?”. Enumeration of the DTs for iJO1366 model was performed using an efficient depth-first search algorithm, rejecting infeasible DTs based on mass-imbalanced and loopy flux patterns. We found the direction of 79% of reversible reactions must be defined before all directions in the network can be fixed, granting a high degree of flexibility.

Genome-scale metabolic reconstructions represent all biochemical reactions that an organism can accomplish. These reconstructions are complex and often difficult to study in great detail. A way to overcome this limitation is to focus on specific pathways or subsystems. We present a novel method to identify metabolic modules based on the network topology. The method relies on reaction directions and ignores currency metabolites, which artificially connect distant metabolic reactions. In this way, topologically independent modules are built, where inputs and outputs are controlled by irreversible reactions. The method is automatic and unbiased, and, the result is a set of condition specific modules with defined metabolic functions. As a proof-of-concept we generated biologically relevant modules for the E.coli and Human genome-scale metabolic reconstructions supported by transcriptomic data. Finally, we applied the novel approach to study the network flexibility conferred by reversible reactions. In the case of the E. coli model, we found that the direction of 79% of structurally reversible reactions (those not directionally constrained by surrounding irreversible reactions) must be fixed to determine all the reaction directions in the network. Therefore, reversible reactions operate practically independent of each other.

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.

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

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.

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol^-1, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

Improved production of recombinant Rhizomucor miehei lipase by coexpressing protein folding chaperones in Pichia pastoris, which triggered ER stress

Rhizomucor miehei lipase (RML) is a biocatalyst that widely used in laboratory and industrial. Previously, RML with a 70-amino acid propeptide (pRML) was cloned and expressed in P. pastoris. Recombinant strains with (strain containing 4-copy prml) and without ER stress (strain containing 2-copy prml) were obtained. However, the effective expression of pRML in P. pastoris by coexpressing ER-related elements in pRML-produced strain with or without ER stress has not been reported to date. In this study, an efficient way to produce functional pRML was explored in P. pastoris. The coexpression of protein folding chaperones, including PDI and ERO1, in different strains with or without ER stress, was investigated. PDI overexpression only increased pRML production in 4-copy strain from 705 U/mL to 1430 U/mL because it alleviated the protein folded stress, increased the protein concentration from 0.56  mg/mL to 0.65 mg/mL, and improved enzyme-specific activity from 1238 U/mg to 2186 U/mg. However, PDI coexpression could not improve pRML production in the 2-copy strain because it increased protein folded stress, while ERO1 coexpression in the two strains all had a negative effect on pRML expression. We also investigated the effect of the propeptide on the substrate specificity and the condition for pRML enzyme powder preparation. Results showed that the relative activity exceeded 80% when the substrates C8–C10 were detected at 35°C and pH 6, and C8–C12 at 45°C and pH 8. The optimal enzyme powder preparation pH was 7, and the maximum recovery rate for pRML was 73.19%.

Inferring composition and function of the human gut microbiome in time and space: A review of genome-scale metabolic modelling tools

The human gut hosts a complex community of microorganisms that directly influences gastrointestinal physiology, playing a central role in human health. Because of its importance, the metabolic interplay between the gut microbiome and host metabolism has gained special interest. While there has been great progress in the field driven by metagenomics and experimental studies, the mechanisms underpinning microbial composition and interactions in the microbiome remain poorly understood. Genome-scale metabolic models are mathematical structures capable of describing the metabolic potential of microbial cells. They are thus suitable tools for probing the metabolic properties of microbial communities. In this review, we discuss the most recent and relevant genome-scale metabolic modelling tools for inferring the composition, interactions, and ultimately, biological function of the constituent species of a microbial community with special emphasis in the gut microbiota. Particular attention is given to constraint-based metabolic modelling methods as well as hybrid agent-based methods for capturing the interactions and behavior of the community in time and space. Finally, we discuss the challenges hindering comprehensive modelling of complex microbial communities and its application for the in-silico design of microbial consortia with therapeutic functions.

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.

Engineering Saccharomyces cerevisiae for the Overproduction of β-Ionone and Its Precursor β-Carotene

β-ionone is a commercially attractive industrial fragrance produced naturally from the cleavage of the pigment β-carotene in plants. While the production of this ionone is typically performed using chemical synthesis, environmentally friendly and consumer-oriented biotechnological production is gaining increasing attention. A convenient cell factory to address this demand is the yeast Saccharomyces cerevisiae. However, current β-ionone titers and yields are insufficient for commercial bioproduction. In this work, we optimized S. cerevisiae for the accumulation of high amounts of β-carotene and its subsequent conversion to β-ionone. For this task, we integrated systematically the heterologous carotenogenic genes (CrtE, CrtYB and CrtI) from Xanthophyllomyces dendrorhous using markerless genome editing CRISPR/Cas9 technology; and evaluated the transcriptional unit architecture (bidirectional or tandem), integration site, and impact of gene dosage, first on β-carotene accumulation, and later, on β-ionone production. A single-copy insertion of the carotenogenic genes in high expression loci of the wild-type yeast CEN.Pk2 strain yielded 4 mg/gDCW of total carotenoids, regardless of the transcriptional unit architecture employed. Subsequent fine-tuning of the carotenogenic gene expression enabled reaching 16 mg/gDCW of total carotenoids, which was further increased to 32 mg/gDCW by alleviating the known pathway bottleneck catalyzed by the hydroxymethylglutaryl-CoA reductase (HMGR1). The latter yield represents the highest total carotenoid concentration reported to date in S. cerevisiae for a constitutive expression system. For β-ionone synthesis, single and multiple copies of the carotene cleavage dioxygenase 1 (CCD1) gene from Petunia hybrida (PhCCD1) fused with a membrane destination peptide were expressed in the highest β-carotene-producing strains, reaching up to 33 mg/L of β-ionone in the culture medium after 72-h cultivation in shake flasks. Finally, interrogation of a contextualized genome-scale metabolic model of the producer strains pointed to PhCCD1 unspecific cleavage activity as a potentially limiting factor reducing β-ionone production. Overall, the results of this work constitute a step toward the industrial production of this ionone and, more broadly, they demonstrate that biotechnological production of apocarotenoids is technically feasible.