Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources

Mitigating toxic formaldehyde to promote efficient utilization of C1 resources

The C1 resource is widely considered because of its abundance and affordability. In the context of extensive utilization of C1 resources by methylotrophic microorganisms, especially for methanol, formaldehyde is an important intermediate metabolite that is at the crossroads of assimilation and dissimilation pathways. However, formaldehyde is an exceedingly reactive compound that can form covalent cross-linked complexes with amine and thiol groups in cells, which causes irreversible damage to the organism. Thus, it is important to balance the intensity of the assimilation and dissimilation pathways of formaldehyde, which can avoid formaldehyde toxicity and improve the full utilization of C1 resources. This review details the source of endogenous formaldehyde and its toxicity mechanism, explaining the harm of excessive accumulation of formaldehyde to metabolism. Importantly, the self-detoxification and various feasible strategies to mitigate formaldehyde toxicity are discussed and proposed. These strategies are meant to help appropriately handle formaldehyde toxicity and accelerate the effective use of C1 resources.

Integrating Experimental and Computational Analyses of Yeast Protein Profiles for Optimizing the Production of High-Quality Microbial Proteins

Microbial proteins represent a promising solution to address the escalating global demand for protein, particularly in regions with limited arable land. Yeasts, such as Saccharomyces cerevisiae, are robust and safe protein-producing strains. However, the utilization of non-conventional yeast strains for microbial protein production has been hindered, partly due to a lack of comprehensive understanding of protein production traits. In this study, we conducted experimental analyses focusing on the growth, protein content, and amino acid composition of nine yeast strains, including one S. cerevisiae strain, three Yarrowia lipolytica strains, and five Pichia spp. strains. We identified that, though Y. lipolytica and Pichia spp. strains consumed glucose at a slower rate compared to S. cerevisiae, Pichia spp. strains showed a higher cellular protein content, and Y. lipolytica strains showed a higher glucose-to-biomass/protein yield and methionine content. We further applied computational approaches to explain that metabolism economy was the main underlying factor for the limited amount of scarce/carbon-inefficient amino acids (such as methionine) within yeast cell proteins. We additionally verified that the specialized metabolism was a key reason for the high methionine content in Y. lipolytica strains, and proposed Y. lipolytica strain as a potential producer of high-quality single-cell protein rich in scarce amino acids. Through experimental evaluation, we identified Pichia jadinii CICC 1258 as a potential strain for high-quality protein production under unfavorable pH/temperature conditions. Our work suggests a promising avenue for optimizing microbial protein production, identifying the factors influencing amino acid composition, and paving the way for the use of unconventional yeast strains to meet the growing protein demands.

Ogataea polymorpha as a next-generation chassis for industrial biotechnology

Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Candida boidinii isolates from olive curation water: a promising platform for methanol-based biomanufacturing

Methanol is a promising feedstock for biomanufacturing, but the efficiency of methanol-based bioprocesses is limited by the low rate of methanol utilization pathways and methanol toxicity. Yeast diversity is an attractive biological resource to develop efficient bioprocesses since any effort with strain improvement is more deserving if applied to innate robust strains with relevant catabolic and biosynthetic potential. The present study is in line with such rational and describes the isolation and molecular identification of seven isolates of the methylotrophic species Candida boidinii from waters derived from the traditional curation of olives, in different years, and from contaminated superficial soil near fuel stations. The yeast microbiota from those habitats was also characterized. The four C. boidinii isolates obtained from the curation of olives' water exhibited significantly higher maximum specific growth rates (range 0.15-0.19 h-1), compared with the three isolates obtained from the fuel contaminated soils (range 0.05-0.06 h-1) when grown on methanol as the sole C-source (1% (v/v), in shake flasks, at 30°C). The isolates exhibit significant robustness towards methanol toxicity that increases as the cultivation temperature decreases from 30°C to 25°C. The better methanol-based growth performance exhibited by C. boidinii isolates from olives´ soaking waters could not be essentially attributed to higher methanol tolerance. These methanol-efficient catabolizing isolates are proposed as a promising platform to develop methanol-based bioprocesses.

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.

Protein production dynamics and physiological adaptation of recombinant Komagataella phaffii at near-zero growth rates

BACKGROUND

Specific productivity (qP) in yeast correlates with growth, typically peaking at intermediate or maximum specific growth rates (μ). Understanding the factors limiting productivity at extremely low μ might reveal decoupling strategies, but knowledge of production dynamics and physiology in such conditions is scarce. Retentostats, a type of continuous cultivation, enable the well-controlled transition to near-zero µ through the combined retention of biomass and limited substrate supply. Recombinant Komagataella phaffii (syn Pichia pastoris) secreting a bivalent single domain antibody (VHH) was cultivated in aerobic, glucose-limited retentostats to investigate recombinant protein production dynamics and broaden our understanding of relevant physiological adaptations at near-zero growth conditions.

RESULTS

By the end of the retentostat cultivation, doubling times of approx. two months were reached, corresponding to µ = 0.00047 h-1. Despite these extremely slow growth rates, the proportion of viable cells remained high, and de novo synthesis and secretion of the VHH were observed. The average qP at the end of the retentostat was estimated at 0.019 mg g-1 h-1. Transcriptomics indicated that genes involved in protein biosynthesis were only moderately downregulated towards zero growth, while secretory pathway genes were mostly regulated in a manner seemingly detrimental to protein secretion. Adaptation to near-zero growth conditions of recombinant K. phaffii resulted in significant changes in the total protein, RNA, DNA and lipid content, and lipidomics revealed a complex adaptation pattern regarding the lipid class composition. The higher abundance of storage lipids as well as storage carbohydrates indicates that the cells are preparing for long-term survival.

CONCLUSIONS

In conclusion, retentostat cultivation proved to be a valuable tool to identify potential engineering targets to decouple growth and protein production and gain important insights into the physiological adaptation of K. phaffii to near-zero growth conditions.

Methanol bioconversion into C3, C4, and C5 platform chemicals by the yeast Ogataea polymorpha

BACKGROUND

One carbon (C1) molecules such as methanol have the potential to become sustainable feedstocks for biotechnological processes, as they can be derived from CO2 and green hydrogen, without the need for arable land. Therefore, we investigated the suitability of the methylotrophic yeast Ogataea polymorpha as a potential production organism for platform chemicals derived from methanol. We selected acetone, malate, and isoprene as industrially relevant products to demonstrate the production of compounds with 3, 4, or 5 carbon atoms, respectively.

RESULTS

We successfully engineered O. polymorpha for the production of all three molecules and demonstrated their production using methanol as carbon source. We showed that the metabolism of O. polymorpha is well suited to produce malate as a product and demonstrated that the introduction of an efficient malate transporter is essential for malate production from methanol. Through optimization of the cultivation conditions in shake flasks, which included pH regulation and constant substrate feeding, we were able to achieve a maximum titer of 13 g/L malate with a production rate of 3.3 g/L/d using methanol as carbon source. We further demonstrated the production of acetone and isoprene as additional heterologous products in O. polymorpha, with maximum titers of 13.6 mg/L and 4.4 mg/L, respectively.

CONCLUSION

These findings highlight how O. polymorpha has the potential to be applied as a versatile cell factory and contribute to the limited knowledge on how methylotrophic yeasts can be used for the production of low molecular weight biochemicals from methanol. Thus, this study can serve as a point of reference for future metabolic engineering in O. polymorpha and process optimization efforts to boost the production of platform chemicals from renewable C1 carbon sources.

Advances in genome-scale metabolic models of industrially important fungi

Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing.

Deciphering cell wall sensors enabling the construction of robust P. pastoris for single-cell protein production

Single-cell protein (SCP) production in the methylotrophic yeast Pichia pastoris has the potential to achieve a sustainable protein supply. However, improving the methanol fermentation efficiency and reducing carbon loss has been a long-standing challenge with far-reaching scientific and practical implications. Here, comparative transcriptomics revealed that PAS_0305, a gene directly associated with cell wall thickness under methanol stress, can be used as a target for unlocking cell wall sensors. Intracellular trehalose accumulation confirmed that cell wall sensors were activated after knocking out PAS_0305, which resulted in increased cell wall permeability. Genome-wide signal perturbations were transduced through the HOG module and the CWI pathway, which was confirmed to connected by Pbs2-Mkk. As a consequence of CWI pathway activation, ΔPAS_0305 elicited a rescue response of cell wall remodeling by increasing the β-1,3-glucan content and decreasing the chitin/mannose content. Remarkably, perturbations in global stress signals led to a fine-tuning of the metabolic network of ΔPAS_0305, resulting in a superior phenotype with highest crude protein and methanol conversion rate of 67.21% and 0.46 gDCW/g. Further genome-scale metabolic models were constructed to validate the experimental results, confirming that unlocking cell wall sensors resulted in maximized flux from methanol towards SCP and effectively addressing the issue of carbon loss in methanol fermentation. This work sheds new light on the potential of manipulating cellular signaling pathways to optimize metabolic networks and achieve exceptional phenotypic characteristics, providing new strategies for constructing versatile cell factories in P. pastoris.

Engineering the synthetic β-alanine pathway in Komagataella phaffii for conversion of methanol into 3-hydroxypropionic acid

BACKGROUND

Methanol is increasingly gaining attraction as renewable carbon source to produce specialty and commodity chemicals, as it can be generated from renewable sources such as carbon dioxide (CO2). In this context, native methylotrophs such as the yeast Komagataella phaffii (syn Pichia pastoris) are potentially attractive cell factories to produce a wide range of products from this highly reduced substrate. However, studies addressing the potential of this yeast to produce bulk chemicals from methanol are still scarce. 3-Hydroxypropionic acid (3-HP) is a platform chemical which can be converted into acrylic acid and other commodity chemicals and biopolymers. 3-HP can be naturally produced by several bacteria through different metabolic pathways.

RESULTS

In this study, production of 3-HP via the synthetic β-alanine pathway has been established in K. phaffii for the first time by expressing three heterologous genes, namely panD from Tribolium castaneum, yhxA from Bacillus cereus, and ydfG from Escherichia coli K-12. The expression of these key enzymes allowed a production of 1.0 g l-1 of 3-HP in small-scale cultivations using methanol as substrate. The addition of a second copy of the panD gene and selection of a weak promoter to drive expression of the ydfG gene in the PpCβ21 strain resulted in an additional increase in the final 3-HP titer (1.2 g l-1). The 3-HP-producing strains were further tested in fed-batch cultures. The best strain (PpCβ21) achieved a final 3-HP concentration of 21.4 g l-1 after 39 h of methanol feeding, a product yield of 0.15 g g-1, and a volumetric productivity of 0.48 g l-1 h-1. Further engineering of this strain aiming at increasing NADPH availability led to a 16% increase in the methanol consumption rate and 10% higher specific productivity compared to the reference strain PpCβ21.

CONCLUSIONS

Our results show the potential of K. phaffii as platform cell factory to produce organic acids such as 3-HP from renewable one-carbon feedstocks, achieving the highest volumetric productivities reported so far for a 3-HP production process through the β-alanine pathway.

Genome-scale metabolic model analysis of Pichia pastoris for enhancing the production of S-adenosyl-L-methionine

Komagataella phaffii, formerly Pichia pastoris (P. pastoris), is a promising methylotrophic yeast used in industry to produce recombinant protein and valuable metabolites. In this study, a genome-scale metabolic model (GEMs) was reconstructed and used to assess P. pastoris' metabolic capabilities for the production of S-adenosyl-L-methionine (AdoMet or SAM or SAMe) from individual carbon sources along with the addition of L-methionine. In a model-driven P. pastoris strain, the well-established genome-scale metabolic model iAUKM can be implemented to predict high valuable metabolite production. The model, iAUKM, was created by merging the previously published iMT1026 model and the draught model generated using Raven toolbox from the KEGG database which covered 2309 enzymatic reactions associated with 1033 metabolic genes and 1750 metabolites. The highly curated model was successful in capturing P. pastoris growth on various carbon sources, as well as AdoMet production under various growth conditions. Many overexpression gene targets for increasing AdoMet accumulation in the cell have been predicted for various carbon sources. Inorganic phosphatase (IPP) was one of the predicted overexpression targets as revealed from simulations using iAUKM. When IPP gene was integrated into P. pastoris, we found that AdoMet accumulation increased by 16% and 14% using glucose and glycerol as carbon sources, respectively. Our in silico results shed light on the factors limiting AdoMet production, as well as key pathways for rationalized engineering to increase AdoMet yield.

Bioconversion of C1 feedstocks for chemical production using Pichia pastoris

Bioconversion of C1 feedstocks for chemical production offers a promising solution to global challenges such as the energy and food crises and climate change. The methylotroph Pichia pastoris is an attractive host system for the production of both recombinant proteins and chemicals from methanol. Recent studies have also demonstrated its potential for utilizing CO2 through metabolic engineering or coupling with electrocatalysis. This review focuses on the bioconversion of C1 feedstocks for chemical production using P. pastoris. Herein the challenges and feasible strategies for chemical production in P. pastoris are discussed. The potential of P. pastoris to utilize other C1 feedstocks - including CO2 and formate - is highlighted, and new insights from the perspectives of synthetic biology and material science are proposed.

High throughput 13C-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux

BACKGROUND

Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput ¹³C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris.

RESULTS

We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5.

CONCLUSIONS

This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris's central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes.

Nitrogen recovery from low-value biogenic feedstocks via steam gasification to methylotrophic yeast biomass

Carbon and nitrogen are crucial elements for life and must be efficiently regenerated in a circular economy. Biomass streams at the end of their useful life, such as sewage sludge, are difficult to recycle even though they contain organic carbon and nitrogen components. Gasification is an emerging technology to utilize such challenging waste streams and produce syngas that can be further processed into, e.g., Fischer-Tropsch fuels, methane, or methanol. Here, the objective is to investigate if nitrogen can be recovered from product gas cleaning in a dual fluidized bed (DFB) after gasification of softwood pellets to form yeast biomass. Yeast biomass is a protein-rich product, which can be used for food and feed applications. An aqueous solution containing ammonium at a concentration of 66 mM was obtained and by adding other nutrients it enables the growth of the methylotrophic yeast Komagataella phaffii to form 6.2 g.L^-1 dry yeast biomass in 3 days. To further integrate the process, it is discussed how methanol can be obtained from syngas by chemical catalysis, which is used as a carbon source for the yeast culture. Furthermore, different gas compositions derived from the gasification of biogenic feedstocks including sewage sludge, bark, and chicken manure are evaluated for their ability to yield methanol and yeast biomass. The different feedstocks are compared based on their potential to yield methanol and ammonia, which are required for the generation of yeast biomass. It was found that the gasification of bark and chicken manure yields a balanced carbon and nitrogen source for the formation of yeast biomass. Overall, a novel integrated process concept based on renewable, biogenic feedstocks is proposed connecting gasification with methanol synthesis to enable the formation of protein-rich yeast biomass.

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 ).

Enabling growth-decoupled Komagataella phaffii recombinant protein production based on the methanol-free PDH promoter

The current transition towards the circular bioeconomy requires a rational development of biorefineries to sustainably fulfill the present demands. The use of Komagataella phaffii (Pichia pastoris) can meet this challenge, since it has the capability to use crude glycerol as a carbon-source, a by-product from the biodiesel industry, while producing high- and low-added value products. Recombinant protein production (RPP) using K. phaffii has often been driven either by the methanol induced AOX1 promoter (P_AOX1) and/or the constitutive GAP promoter (P_GAP). In the last years, strong efforts have been focused on developing novel expression systems that expand the toolbox variety of K. phaffii to efficiently produce diverse proteins that requires different strategies. In this work, a study was conducted towards the development of methanol-free expression system based on a heat-shock gene promoter (P_DH) using glycerol as sole carbon source. Using this promoter, the recombinant expression is strongly induced in carbon-starving conditions. The classical P_GAP was used as a benchmark, taking for both strains the lipase B from Candida antarctica (CalB) as model protein. Titer of CalB expressed under P_DH outperformed P_GAP controlled expression in shake-flask cultivations when using a slow-release continuous feeding technology, confirming that P_DH is induced under pseudo-starving conditions. This increase was also confirmed in fed-batch cultivations. Several optimization rounds were carried out for P_DH under different feeding and osmolarity conditions. In all of them the P_DH controlled process outperformed the P_GAP one in regard to CalB titer. The best P_DH approach reached 3.6-fold more specific productivity than P_GAP fed-batch at low μ. Compared to the optimum approach for P_GAP-based process, the best P_DH fed-batch strategy resulted in 2.3-fold higher titer, while the specific productivity was very similar. To summarize, P_DH is an inducible promoter that exhibited a non-coupled growth regulation showing high performance, which provides a methanol-free additional solution to the usual growth-coupled systems for RPP. Thus, this novel system emerges as a potential alternative for K. phaffii RPP bioprocess and for revaluing crude glycerol, promoting the transition towards a circular economy.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

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.

Designing Microbial Cell Factories for the Production of Chemicals

The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.

Role of Dissimilative Pathway of Komagataella phaffii (Pichia pastoris): Formaldehyde Toxicity and Energy Metabolism

Komagataella phaffii (aka Pichia pastoris) is a yeast able to grow in methanol as the sole carbon and energy source. This substrate is converted into formaldehyde, a toxic intermediary that can either be assimilated to biomass or dissimilated to CO₂ through the enzymes formaldehyde dehydrogenase (FLD) and formate dehydrogenase, also producing energy in the form of NADH. The dissimilative pathway has been described as an energy producing and a detoxifying route, but conclusive evidence has not been provided for this. In order to elucidate this theory, we generated mutants lacking the FLD activity (Δfld1) and used flux analysis to evaluate the metabolic impact of this disrupted pathway. Unexpectedly, we found that the specific growth rate of the Δfld1 strain was only slightly lower (92%) than the control. In contrast, the sensitivity to formaldehyde pulses (up to 8mM) was significantly higher in the Δfld1 mutant strain and was associated with a higher maintenance energy. In addition, the intracellular flux estimation revealed a high metabolic flexibility of K. phaffii in response to the disrupted pathway. Our results suggest that the role of the dissimilative pathway is mainly to protect the cells from the harmful effect of formaldehyde, as they were able to compensate for the energy provided from this pathway when disrupted.

From a Hetero- to a Methylotrophic Lifestyle: Flash Back on the Engineering Strategies to Create Synthetic Methanol-User Strains

Engineering microorganisms to grow on alternative feedstocks is crucial not just because of the indisputable biotechnological applications but also to deepen our understanding of microbial metabolism. One-carbon (C1) substrate metabolism has been the focus of extensive research for the prominent role of C1 compounds in establishing a circular bioeconomy. Methanol in particular holds great promise as it can be produced directly from greenhouse gases methane and carbon dioxide using renewable resources. Synthetic methylotrophy, i.e. introducing a non-native methanol utilization pathway into a model host, has therefore been the focus of long-time efforts and is perhaps the pinnacle of metabolic engineering. It entails completely changing a microorganism's lifestyle, from breaking up multi-carbon nutrients for growth to building C-C bonds from a single-carbon molecule to obtain all metabolites necessary to biomass formation as well as energy. The frontiers of synthetic methylotrophy have been pushed further than ever before and in this review, we outline the advances that paved the way for the more recent accomplishments. These include optimizing the host's metabolism, "copy and pasting" naturally existing methylotrophic pathways, "mixing and matching" enzymes to build new pathways, and even creating novel enzymatic functions to obtain strains that are able to grow solely on methanol. Finally, new approaches are contemplated to further advance the field and succeed in obtaining a strain that efficiently grows on methanol and allows C1-based production of added-value compounds.

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.

Elementary vectors and autocatalytic sets for resource allocation in next-generation models of cellular growth

Traditional (genome-scale) metabolic models of cellular growth involve an approximate biomass “reaction”, which specifies biomass composition in terms of precursor metabolites (such as amino acids and nucleotides). On the one hand, biomass composition is often not known exactly and may vary drastically between conditions and strains. On the other hand, the predictions of computational models crucially depend on biomass. Also elementary flux modes (EFMs), which generate the flux cone, depend on the biomass reaction. To better understand cellular phenotypes across growth conditions, we introduce and analyze new classes of elementary vectors for comprehensive (next-generation) metabolic models, involving explicit synthesis reactions for all macromolecules. Elementary growth modes (EGMs) are given by stoichiometry and generate the growth cone. Unlike EFMs, they are not support-minimal, in general, but cannot be decomposed “without cancellations”. In models with additional (capacity) constraints, elementary growth vectors (EGVs) generate a growth polyhedron and depend also on growth rate. However, EGMs/EGVs do not depend on the biomass composition. In fact, they cover all possible biomass compositions and can be seen as unbiased versions of elementary flux modes/vectors (EFMs/EFVs) used in traditional models. To relate the new concepts to other branches of theory, we consider autocatalytic sets of reactions. Further, we illustrate our results in a small model of a self-fabricating cell, involving glucose and ammonium uptake, amino acid and lipid synthesis, and the expression of all enzymes and the ribosome itself. In particular, we study the variation of biomass composition as a function of growth rate. In agreement with experimental data, low nitrogen uptake correlates with high carbon (lipid) storage.

Next-generation, genome-scale metabolic models allow to study the reallocation of cellular resources upon changing environmental conditions, by not only modeling flux distributions, but also expression profiles of the catalyzing proteome. In particular, they do no longer assume a fixed biomass composition. Methods to identify optimal solutions in such comprehensive models exist, however, an unbiased understanding of all feasible allocations is missing so far. Here we develop new concepts, called elementary growth modes and vectors, that provide a generalized definition of minimal pathways, thereby extending classical elementary flux modes (used in traditional models with a fixed biomass composition). The new concepts provide an understanding of all possible flux distributions and of all possible biomass compositions. In other words, elementary growth modes and vectors are the unique functional units in any comprehensive model of cellular growth. As an example, we show that lipid accumulation upon nitrogen starvation is a consequence of resource allocation and does not require active regulation. Our work puts current approaches on a theoretical basis and allows to seamlessly transfer existing workflows (e.g. for the design of cell factories) to next-generation metabolic models.

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.

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of ¹³C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.

There is an increasing demand for protein pharmaceuticals, especially monoclonal antibodies. Chinese Hamster Ovary (CHO) are currently the leading production host due to their ability to perform human-like post-translational modifications. However, it typically takes several months of trial-and-error approaches to develop a high-producer cell line. Metabolic modelling has the potential to make cell line and process development faster and cheaper by predicting targeted modifications to the cell line genome, cultivation medium or bioprocess. In fact, genome-scale metabolic reconstructions of CHO are already available, and ready for use in cell line development. However, in order to successfully use these models, we need to make sure that they are able to accurately predict metabolic phenotypes. Here we use genome-scale metabolic models of CHO to evaluate the models’ ability to correctly predict intracellular flux distributions. We find that a crucial key ingredient for the correct estimation of central carbon fluxes is the non-growth associated maintenance energy (mATP). We estimated mATP computationally and confirmed it experimentally. Adding this single constraint leads to significantly better predictions of intracellular fluxes, especially in glycolysis and the tricarboxylic acid cycle.

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.

A subcellular proteome atlas of the yeast Komagataella phaffii

The compartmentalization of metabolic and regulatory pathways is a common pattern of living organisms. Eukaryotic cells are subdivided into several organelles enclosed by lipid membranes. Organelle proteomes define their functions. Yeasts, as simple eukaryotic single cell organisms, are valuable models for higher eukaryotes and frequently used for biotechnological applications. While the subcellular distribution of proteins is well studied in Saccharomyces cerevisiae, this is not the case for other yeasts like Komagataella phaffii (syn. Pichia pastoris). Different to most well-studied yeasts, K. phaffii can grow on methanol, which provides specific features for production of heterologous proteins and as a model for peroxisome biology. We isolated microsomes, very early Golgi, early Golgi, plasma membrane, vacuole, cytosol, peroxisomes and mitochondria of K. phaffii from glucose- and methanol-grown cultures, quantified their proteomes by liquid chromatography-electrospray ionization-mass spectrometry of either unlabeled or tandem mass tag-labeled samples. Classification of the proteins by their relative enrichment, allowed the separation of enriched proteins from potential contaminants in all cellular compartments except the peroxisomes. We discuss differences to S. cerevisiae, outline organelle specific findings and the major metabolic pathways and provide an interactive map of the subcellular localization of proteins in K. phaffii.

Bioprocess performance analysis of novel methanol-independent promoters for recombinant protein production with Pichia pastoris

Background

Pichia pastoris is a powerful and broadly used host for recombinant protein production (RPP), where past bioprocess performance has often been directed with the methanol regulated AOX1 promoter (P_AOX1), and the constitutive GAP promoter (P_GAP). Since promoters play a crucial role in an expression system and the bioprocess efficiency, innovative alternatives are constantly developed and implemented. Here, a thorough comparative kinetic characterization of two expression systems based on the commercial PDF and UPP promoters (P_PDF, P_UPP) was first conducted in chemostat cultures. Most promising conditions were subsequently tested in fed-batch cultivations. These new alternatives were compared with the classical strong promoter P_GAP, using the Candida antarctica lipase B (CalB) as model protein for expression system performance.

Results

Both the P_PDF and P_UPP-based expression systems outperformed similar P_GAP-based expression in chemostat cultivations, reaching ninefold higher specific production rates (q_p). CALB transcription levels were drastically higher when employing the novel expression systems. This higher expression was also correlated with a marked upregulation of unfolded protein response (UPR) related genes, likely from an increased protein burden in the endoplasmic reticulum (ER). Based on the chemostat results obtained, best culture strategies for both P_PDF and P_UPP expression systems were also successfully implemented in 15 L fed-batch cultivations where q_p and product to biomass yield (Y_P/X*) values were similar than those obtained in chemostat cultivations.

Conclusions

As an outcome of the macrokinetic characterization presented, the novel P_PDF and P_UPP were observed to offer much higher efficiency for CalB production than the widely used P_GAP-based methanol-free alternative. Thus, both systems arise as highly productive alternatives for P. pastoris-based RPP bioprocesses. Furthermore, the different expression regulation patterns observed indicate the level of gene expression can be adjusted, or tuned, which is interesting when using Pichia pastoris as a cell factory for different products of interest.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01564-9.

Genome-scale model reconstruction of the methylotrophic yeast Ogataea polymorpha

Background

Ogataea polymorpha is a thermotolerant, methylotrophic yeast with significant industrial applications. While previously mainly used for protein synthesis, it also holds promise for producing platform chemicals. O. polymorpha has the distinct advantage of using methanol as a substrate, which could be potentially derived from carbon capture and utilization streams. Full development of the organism into a production strain and estimation of the metabolic capabilities require additional strain design, guided by metabolic modeling with a genome-scale metabolic model. However, to date, no genome-scale metabolic model is available for O. polymorpha.

Results

To overcome this limitation, we used a published reconstruction of the closely related yeast Komagataella phaffii as a reference and corrected reactions based on KEGG and MGOB annotation. Additionally, we conducted phenotype microarray experiments to test the suitability of 190 substrates as carbon sources. Over three-quarter of the substrate use was correctly reproduced by the model and 27 new substrates were added, that were not present in the K. phaffii reference model.

Conclusion

The developed genome-scale metabolic model of O. polymorpha will support the engineering of synthetic metabolic capabilities and enable the optimization of production processes, thereby supporting a sustainable future methanol economy.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12896-021-00675-w).

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.

Beyond alcohol oxidase: the methylotrophic yeast Komagataella phaffii utilizes methanol also with its native alcohol dehydrogenase Adh2

Methylotrophic yeasts are considered to use alcohol oxidases to assimilate methanol, different to bacteria which employ alcohol dehydrogenases with better energy conservation. The yeast Komagataella phaffii carries two genes coding for alcohol oxidase, AOX1 and AOX2. The deletion of the AOX1 leads to the Mut^S phenotype and the deletion of AOX1 and AOX2 to the Mut^– phenotype. The Mut^– phenotype is commonly regarded as unable to utilize methanol. In contrast to the literature, we found that the Mut^– strain can consume methanol. This ability was based on the promiscuous activity of alcohol dehydrogenase Adh2, an enzyme ubiquitously found in yeast and normally responsible for ethanol consumption and production. Using ¹³C labeled methanol as substrate we could show that to the largest part methanol is dissimilated to CO₂ and a small part is incorporated into metabolites, the biomass, and the secreted recombinant protein. Overexpression of the ADH2 gene in K. phaffii Mut^– increased both the specific methanol uptake rate and recombinant protein production, even though the strain was still unable to grow. These findings imply that thermodynamic and kinetic constraints of the dehydrogenase reaction facilitated the evolution towards alcohol oxidase-based methanol metabolism in yeast.

Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O₂/CO₂, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut⁺ strain) to significant (for Mut^S strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut⁺ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O₂ uptake-based (10%), CO₂ production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.

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.

Crude glycerol impurities improve Rhizomucor miehei lipase production by Pichia pastoris

Crude glycerol, a by-product of biodiesel production, was employed as the carbon source to produce lipase using Pichia pastoris. Under identical fermentation conditions, cell growth and lipase activity were improved using crude glycerol instead of pure glycerol. The impacts of crude glycerol impurities (methyl ester, grease, glycerol, methanol, and metal ions Na⁺, Ca²⁺, and Fe³⁺) on lipase production were investigated. Impurities accelerated P. pastoris entering the stationary phase. Na⁺, Ca²⁺, and grease in waste crude glycerol were the main factors influencing higher lipase activity. Through response surface optimization of Ca²⁺, Na⁺, and grease concentrations, lipase activity reached 1437 U/mL (15,977 U/mg), which was 2.5 times that of the control. This study highlights the economical and highly efficient valorization of crude glycerol, demonstrating its possible utilization as a carbon source to produce lipase by P. pastoris without pretreatment.

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.

Continuous Cultivation as a Tool Toward the Rational Bioprocess Development With Pichia Pastoris Cell Factory

The methylotrophic yeast Pichia pastoris (Komagataella phaffii) is currently considered one of the most promising hosts for recombinant protein production (RPP) and metabolites due to the availability of several tools to efficiently regulate the recombinant expression, its ability to perform eukaryotic post-translational modifications and to secrete the product in the extracellular media. The challenge of improving the bioprocess efficiency can be faced from two main approaches: the strain engineering, which includes enhancements in the recombinant expression regulation as well as overcoming potential cell capacity bottlenecks; and the bioprocess engineering, focused on the development of rational-based efficient operational strategies. Understanding the effect of strain and operational improvements in bioprocess efficiency requires to attain a robust knowledge about the metabolic and physiological changes triggered into the cells. For this purpose, a number of studies have revealed chemostat cultures to provide a robust tool for accurate, reliable, and reproducible bioprocess characterization. It should involve the determination of key specific rates, productivities, and yields for different C and N sources, as well as optimizing media formulation and operating conditions. Furthermore, studies along the different levels of systems biology are usually performed also in chemostat cultures. Transcriptomic, proteomic and metabolic flux analysis, using different techniques like differential target gene expression, protein description and ¹³C-based metabolic flux analysis, are widely described as valued examples in the literature. In this scenario, the main advantage of a continuous operation relies on the quality of the homogeneous samples obtained under steady-state conditions, where both the metabolic and physiological status of the cells remain unaltered in an all-encompassing picture of the cell environment. This contribution aims to provide the state of the art of the different approaches that allow the design of rational strain and bioprocess engineering improvements in Pichia pastoris toward optimizing bioprocesses based on the results obtained in chemostat cultures. Interestingly, continuous cultivation is also currently emerging as an alternative operational mode in industrial biotechnology for implementing continuous process operations.

Evaluation of Product Distribution in Chemostat and Batch Fermentation in Lactic Acid-Producing Komagataella phaffii Strains Utilizing Glycerol as Substrate

Lactic acid is the monomeric unit of polylactide (PLA), a bioplastic widely used in the packaging, automotive, food, and pharmaceutical industries. Previously, the yeast Komagataella phaffii was genetically modified for the production of lactate from glycerol. For this, the bovine L-lactate dehydrogenase- (LDH)-encoding gene was inserted and the gene encoding the pyruvate decarboxylase (PDC) was disrupted, resulting in the GLp strain. This showed a yield of 67% L-lactic acid and 20% arabitol as a by-product in batches with oxygen limitation. Following up on these results, the present work endeavored to perform a detailed study of the metabolism of this yeast, as well as perturbing arabitol synthesis in an attempt to increase lactic acid titers. The GLp strain was cultivated in a glycerol-limited chemostat at different dilution rates, confirming that the production of both lactic acid and arabitol is dependent on the specific growth rate (and consequently on the concentration of the limiting carbon source) as well as on the oxygen level. Moreover, disruption of the gene encoding arabitol dehydrogenase (ArDH) was carried out, resulting in an increase of 20% in lactic acid and a 50% reduction in arabitol. This study clarifies the underlying metabolic reasons for arabitol formation in K. phaffii and points to ways for improving production of lactic acid using K. phaffii as a biocatalyst.

Characterization of methanol utilization negative Pichia pastoris for secreted protein production: New cultivation strategies for current and future applications

The methanol utilization (Mut) phenotype in the yeast Pichia pastoris (syn. Komagataella spp.) is defined by the deletion of the genes AOX1 and AOX2. The Mut- phenotype cannot grow on methanol as a single carbon source. We assessed the Mut- phenotype for secreted recombinant protein production. The methanol inducible AOX1 promoter (PAOX1 ) was active in the Mut- phenotype and showed adequate eGFP fluorescence levels and protein yields (YP/X ) in small-scale screenings. Different bioreactor cultivation scenarios with methanol excess concentrations were tested using PAOX1 HSA and PAOX1 vHH expression constructs. Scenario B comprising a glucose-methanol phase and a 72-hr-long methanol only phase was the best performing, producing 531 mg/L HSA and 1631 mg/L vHH. 61% of the HSA was produced in the methanol only phase where no biomass growth was observed, representing a special case of growth independent production. By using the Mut- phenotype, the oxygen demand, heat output, and specific methanol uptake (qmethanol ) in the methanol phase were reduced by more than 80% compared with the MutS phenotype. The highlighted improved process parameters coupled with growth independent protein production are overlooked benefits of the Mut- strain for current and future applications in the field of recombinant protein production.

Redox Engineering by Ectopic Overexpression of NADH Kinase in Recombinant Pichia pastoris (Komagataella phaffii): Impact on Cell Physiology and Recombinant Production of Secreted Proteins

High-level expression and secretion of heterologous proteins in yeast cause an increased energy demand, which may result in altered metabolic flux distributions. Moreover, recombinant protein overproduction often results in endoplasmic reticulum (ER) stress and oxidative stress, causing deviations from the optimal NAD(P)H regeneration balance. In this context, overexpression of genes encoding enzymes catalyzing endogenous NADPH-producing reactions, such as the oxidative branch of the pentose phosphate pathway, has been previously shown to improve protein production in Pichia pastoris (syn. Komagataella spp.). In this study, we evaluate the overexpression of the Saccharomyces cerevisiaePOS5-encoded NADH kinase in a recombinant P. pastoris strain as an alternative approach to overcome such redox constraints. Specifically, POS5 was cooverexpressed in a strain secreting an antibody fragment, either by directing Pos5 to the cytosol or to the mitochondria. The physiology of the resulting strains was evaluated in continuous cultivations with glycerol or glucose as the sole carbon source, as well as under hypoxia (on glucose). Cytosolic targeting of Pos5 NADH kinase resulted in lower biomass-substrate yields but allowed for a 2-fold increase in product specific productivity. In contrast, Pos5 NADH kinase targeting to the mitochondria did not affect growth physiology and recombinant protein production significantly. Growth physiological parameters were in silico evaluated using the recent upgraded version (v3.0) of the P. pastoris consensus genome-scale metabolic model iMT1026, providing insights on the impact of POS5 overexpression on metabolic flux distributions.IMPORTANCE Recombinant protein overproduction often results in oxidative stress, causing deviations from the optimal redox cofactor regeneration balance. This becomes one of the limiting factors in obtaining high levels of heterologous protein production. Overexpression of redox-affecting enzymes has been explored in other organisms, such as Saccharomyces cerevisiae, as a means to fine tune the cofactor regeneration balance in order to obtain higher protein titers. In the present work, this strategy is explored in P. pastoris In particular, one NADH kinase enzyme from S. cerevisiae (Pos5) is used, either in the cytosol or in mitochondria of P. pastoris, and its impact on the production of a model protein (antibody fragment) is evaluated. A significant improvement in the production of the model protein is observed when the kinase is directed to the cytosol. These results are significant in the field of heterologous protein production in general and in particular in the development of improved metabolic engineering strategies for P. pastoris.

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.

CO2 to succinic acid - Estimating the potential of biocatalytic routes

Microbial carbon dioxide assimilation and conversion to chemical platform molecules has the potential to be developed as economic, sustainable processes. The carbon dioxide assimilation can proceed by a variety of natural pathways and recently even synthetic CO2 fixation routes have been designed. Early assessment of the performance of the different carbon fixation alternatives within biotechnological processes is desirable to evaluate their potential. Here we applied stoichiometric metabolic modeling based on physiological and process data to evaluate different process variants for the conversion of C1 carbon compounds to the industrial relevant platform chemical succinic acid. We computationally analyzed the performance of cyanobacteria, acetogens, methylotrophs, and synthetic CO2 fixation pathways in Saccharomyces cerevisiae in terms of production rates, product yields, and the optimization potential. This analysis provided insight into the economic feasibility and allowed to estimate the future industrial applicability by estimating overall production costs. With reported, or estimated data of engineered or wild type strains, none of the simulated microbial succinate production processes showed a performance allowing competitive production. The main limiting factors were identified as gas and photon transfer and metabolic activities whereas metabolic network structure was not restricting. In simulations with optimized parameters most process alternatives reached economically interesting values, hence, represent promising alternatives to sugar-based fermentations.

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.