Industrial glucoamylase fed-batch benefits from oxygen limitation and high osmolarity

An industrial perspective on metabolic responses of Penicillium chrysogenum to periodic dissolved oxygen feast-famine cycles in a scale-down system

While traveling through different zones in large-scale bioreactors, microbes are most likely subjected to fluctuating dissolved oxygen (DO) conditions at the timescales of global circulation time. In this study, to mimic industrial-scale spatial DO gradients, we present a scale-down setup based on dynamic feast/famine regime (150 s) that leads to repetitive cycles with rapid changes in DO availability in glucose-limited chemostat cultures of Penicillium chrysogenum. Such DO feast/famine regime induced a stable and repetitive pattern with a reproducible metabolic response in time, and the dynamic response of intracellular metabolites featured specific differences in terms of both coverage and magnitude in comparison to other dynamic conditions, for example, substrate feast/famine cycles. Remarkably, intracellular sugar polyols were considerably increased as the hallmark metabolites along with a dynamic and higher redox state (NADH/NAD+) of the cytosol. Despite the increased availability of NADPH for penicillin production under the oscillatory DO conditions, this positive effect may be counteracted by the decreased ATP supply. Moreover, it is interesting to note that not only the penicillin productivity was reduced under such oscillating DO conditions, but also that of the unrecyclable byproduct ortho-hydroxyphenyl acetic acid and degeneration of penicillin productivity. Furthermore, dynamic flux profiles showed the most pronounced variations in central carbon metabolism, amino acid (AA) metabolism, energy metabolism and fatty acid metabolism upon the DO oscillation. Taken together, the metabolic responses of P. chrysogenum to DO gradients reported here are important for elucidating metabolic regulation mechanisms, improving bioreactor design and scale-up procedures as well as for constructing robust cell strains to cope with heterogenous industrial culture conditions.

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with a normalized root mean squared error (N-RMSE) of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

Enhancement of fatty acid degradation pathway promoted glucoamylase synthesis in Aspergillus niger

Background

Our recent multi-omics analyses of glucoamylase biosynthesis in Aspergillus niger (A. niger) suggested that lipid catabolism was significantly up-regulated during high-yield period under oxygen limitation. Since the catabolism of fatty acids can provide energy compounds such as ATP and important precursors such as acetyl-CoA, we speculated that enhancement of this pathway might be beneficial to glucoamylase overproduction.

Results

Based on previous transcriptome data, we selected and individually overexpressed five candidate genes involved in fatty acid degradation under the control of the Tet-on gene switch in A. niger. Overexpression of the fadE, fadA and cyp genes increased the final specific enzyme activity and total secreted protein on shake flask by 21.3 ~ 31.3% and 16.0 ~ 24.2%, respectively. And a better inducible effect by doxycycline was obtained from early logarithmic growth phase (18 h) than stationary phase (42 h). Similar with flask-level results, the glucoamylase content and total extracellular protein in engineered strains OE-fadE (overexpressing fadE) and OE-fadA (overexpressing fadA) on maltose-limited chemostat cultivation were improved by 31.2 ~ 34.1% and 35.1 ~ 38.8% compared to parental strain B36. Meanwhile, intracellular free fatty acids were correspondingly decreased by 41.6 ~ 44.6%. The metabolomic analysis demonstrated intracellular amino acids pools increased 24.86% and 18.49% in two engineered strains OE-fadE and OE-fadA compared to B36. Flux simulation revealed that increased ATP, acetyl-CoA and NADH was supplied into TCA cycle to improve amino acids synthesis for glucoamylase overproduction.

Conclusion

This study suggested for the first time that glucoamylase production was significantly improved in A. niger by overexpression of genes fadE and fadA involved in fatty acids degradation pathway. Harnessing the intracellular fatty acids could be a strategy to improve enzyme production in Aspergillus niger cell factory.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01966-3.

Reconstruction and analysis of genome-scale metabolic model for thermophilic fungus Myceliophthora thermophila

Myceliophthora thermophila, a thermophilic fungus that can degrade and utilize all major polysaccharides in plant biomass, has great potential in biotechnological industries. Here, the first manually curated genome-scale metabolic model iDL1450 for M. thermophila was reconstructed using an auto-generating pipeline with thorough manual curation. The model contains 1450 genes, 2592 reactions and 1784 unique metabolites. High accuracy was shown in predictions related to carbon and nitrogen source utilization based on data obtained from Biolog experiments. Besides, metabolism profiles were analyzed using iDL1450 integrated with transcriptomics data of M. thermophila at various growth temperatures. The refined model provides new insights into thermophilic fungi metabolism and sheds light on model-driven strain design to improve biotechnological applications of this thermophilic lignocellulosic fungus. This article is protected by copyright. All rights reserved.

Changes in Oxygen Availability during Glucose-Limited Chemostat Cultivations of Penicillium chrysogenum Lead to Rapid Metabolite, Flux and Productivity Responses

Bioreactor scale-up from the laboratory scale to the industrial scale has always been a pivotal step in bioprocess development. However, the transition of a bioeconomy from innovation to commercialization is often hampered by performance loss in titer, rate and yield. These are often ascribed to temporal variations of substrate and dissolved oxygen (for instance) in the environment, experienced by microorganisms at the industrial scale. Oscillations in dissolved oxygen (DO) concentration are not uncommon. Furthermore, these fluctuations can be exacerbated with poor mixing and mass transfer limitations, especially in fermentations with filamentous fungus as the microbial cell factory. In this work, the response of glucose-limited chemostat cultures of an industrial Penicillium chrysogenum strain to different dissolved oxygen levels was assessed under both DO shift-down (60% → 20%, 10% and 5%) and DO ramp-down (60% → 0% in 24 h) conditions. Collectively, the results revealed that the penicillin productivity decreased as the DO level dropped down below 20%, while the byproducts, e.g., 6-oxopiperidine-2-carboxylic acid (OPC) and 6-aminopenicillanic acid (6APA), accumulated. Following DO ramp-down, penicillin productivity under DO shift-up experiments returned to its maximum value in 60 h when the DO was reset to 60%. The result showed that a higher cytosolic redox status, indicated by NADH/NAD+, was observed in the presence of insufficient oxygen supply. Consistent with this, flux balance analysis indicated that the flux through the glyoxylate shunt was increased by a factor of 50 at a DO value of 5% compared to the reference control, favoring the maintenance of redox status. Interestingly, it was observed that, in comparison with the reference control, the penicillin productivity was reduced by 25% at a DO value of 5% under steady state conditions. Only a 14% reduction in penicillin productivity was observed as the DO level was ramped down to 0. Furthermore, intracellular levels of amino acids were less sensitive to DO levels at DO shift-down relative to DO ramp-down conditions; this difference could be caused by different timescales between turnover rates of amino acid pools (tens of seconds to minutes) and DO switches (hours to days at steady state and minutes to hours at ramp-down). In summary, this study showed that changes in oxygen availability can lead to rapid metabolite, flux and productivity responses, and dynamic DO perturbations could provide insight into understanding of metabolic responses in large-scale bioreactors.

Exploration and characterization of hypoxia-inducible endogenous promoters in Aspergillus niger

Aspergillus niger is widely used for the efficient production of organic acids and enzyme preparations. However, this organism lacks basic genetic elements for dynamic control, especially inducible promoters that can respond to specific environmental signals. Since these are desirable for better adaptation of fermentation to large-scale industrial production, herein, we have identified the two first hypoxia-inducible promoters in A. niger, PsrbB and PfhbA. Their performance under high or low oxygen conditions was monitored using two reporter proteins, green fluorescent protein (EGFP) and β-glucuronidase (GUS). For comparison, basal expression of the general strong promoter PgpdA was lower than PsrbB but higher than PfhbA. However, under hypoxia, both promoters showed higher expression than under hyperoxia, and these values were also higher than those observed for PgpdA. For PsrbB, strength under hypoxia was ~2-3 times higher than under hyperoxia (for PfhbA, 3-9 times higher) and ~2.5-5 times higher than for PgpdA (for PfhbA, 2-3 times higher). Promoter truncation analysis showed that the PsrbB fragment -1024 to -588 bp is the core region that determines hypoxia response. KEY POINTS: The first identification of two hypoxia-inducible promoters in A. niger is a promising tool for modulation of target genes under hypoxia. Two reporter genes revealed a different activity and responsiveness to hypoxia of PfhbA and PsrbB promoters, which is relevant for the development of dynamic metabolic regulation of A. niger fermentation. PsrbB promoter truncation and bioinformatics analysis is the foundation for further research.

Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Background

Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.

Results

Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{{cat}}$$\end{document}kcat values, predicting the differential expression of enzymes under different growth conditions.

Conclusions

This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01614-2.

Improving cytosolic aspartate biosynthesis increases glucoamylase production in Aspergillus niger under oxygen limitation

Background

Glucoamylase is one of the most industrially applied enzymes, produced by Aspergillus species, like Aspergillus niger. Compared to the traditional ways of process optimization, the metabolic engineering strategies to improve glucoamylase production are relatively scarce.

Results

In the previous study combined multi-omics integrative analysis and amino acid supplementation experiment, we predicted four amino acids (alanine, glutamate, glycine and aspartate) as the limited precursors for glucoamylase production in A. niger. To further verify this, five mutants namely OE-ala, OE-glu, OE-gly, OE-asp1 and OE-asp2, derived from the parental strain A. niger CBS 513.88, were constructed respectively for the overexpression of five genes responsible for the biosynthesis of the four kinds of amino acids (An11g02620, An04g00990, An05g00410, An04g06380 and An16g05570). Real-time quantitative PCR revealed that all these genes were successfully overexpressed at the mRNA level while the five mutants exhibited different performance in glucoamylase production in shake flask cultivation. Notably, the results demonstrated that mutant OE-asp2 which was constructed for reinforcing cytosolic aspartate synthetic pathway, exhibited significantly increased glucoamylase activity by 23.5% and 60.3% compared to CBS 513.88 in the cultivation of shake flask and the 5 L fermentor, respectively. Compared to A. niger CBS 513.88, mutant OE-asp2 has a higher intracellular amino acid pool, in particular, alanine, leucine, glycine and glutamine, while the pool of glutamate was decreased.

Conclusion

Our study combines the target prediction from multi-omics analysis with the experimental validation and proves the possibility of increasing glucoamylase production by enhancing limited amino acid biosynthesis. In short, this systematically conducted study will surely deepen the understanding of resources allocation in cell factory and provide new strategies for the rational design of enzyme production strains.

Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of Aspergillus niger under industrial enzyme production condition

Oxygen limitation is regarded as a useful strategy to improve enzyme production by mycelial fungus like Aspergillus niger. However, the intracellular metabolic response of A. niger to oxygen limitation is still obscure. To address this, the metabolism of A. niger was studied using multi-omics integrated analysis based on the latest GEMs (genome-scale metabolic model), including metabolomics, fluxomics and transcriptomics. Upon sharp reduction of the oxygen supply, A. niger metabolism shifted to higher redox level status, as well as lower energy supply, down-regulation of genes for fatty acid synthesis and a rapid decrease of the specific growth rate. The gene expression of the glyoxylate bypass was activated, which was consistent with flux analysis using the A. niger GEMs iHL1210. The increasing flux of the glyoxylate bypass was assumed to reduce the NADH formation from TCA cycle and benefit maintenance of the cellular redox balance under hypoxic conditions. In addition, the relative fluxes of the EMP pathway were increased, which possibly relieved the energy demand for cell metabolism. The above multi-omics integrative analysis provided new insights on metabolic regulatory mechanisms of A. niger associated with enzyme production under oxygen-limited condition, which will benefit systematic design and optimization of the A. niger microbial cell factory.

Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs

Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, ¹³ C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from ¹³ C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc.

Current state of genome-scale modeling in filamentous fungi

The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi.

Advances and Practices of Bioprocess Scale-up

: This chapter addresses the update progress in bioprocess engineering. In addition to an overview of the theory of multi-scale analysis for fermentation process, examples of scale-up practice combining microbial physiological parameters with bioreactor fluid dynamics are also described. Furthermore, the methodology for process optimization and bioreactor scale-up by integrating fluid dynamics with biokinetics is highlighted. In addition to a short review of the heterogeneous environment in large-scale bioreactor and its effect, a scale-down strategy for investigating this issue is addressed. Mathematical models and simulation methodology for integrating flow field in the reactor and microbial kinetics response are described. Finally, a comprehensive discussion on the advantages and challenges of the model-driven scale-up method is given at the end of this chapter.

Evaluation of glucosidases of Aspergillus niger strain comparing with other glucosidases in transformation of ginsenoside Rb1 to ginsenosides Rg3

The transformation of ginsenoside Rb1 into a specific minor ginsenoside using Aspergillus niger KCCM 11239, as well as the identification of the transformed products and the pathway via thin layer chromatography and high performance liquid chromatography were evaluated to develop a new biologically active material. The conversion of ginsenoside Rb1 generated Rd, Rg3, Rh2, and compound K although the reaction rates were low due to the low concentration. In enzymatic conversion, all of the ginsenoside Rb1 was converted to ginsenoside Rd and ginsenoside Rg3 after 24 h of incubation. The crude enzyme (β-glucosidase) from A. niger KCCM 11239 hydrolyzed the β-(1→6)-glucosidic linkage at the C-20 of ginsenoside Rb1 to generate ginsenoside Rd and ginsenoside Rg3. Our experimental demonstration showing that A. niger KCCM 11239 produces the ginsenoside-hydrolyzing β-glucosidase reflects the feasibility of developing a specific bioconversion process to obtain active minor ginsenosides.

Comparing cellular performance of Yarrowia lipolytica during growth on glucose and glycerol in submerged cultivations

Yarrowia lipolytica is an attractive host for sustainable bioprocesses due to its ability to utilize a variety of carbon substrates and convert them to a range of different product types (including lipids, organic acids and polyols) under specific conditions. Despite an increasing number of applications for this yeast, relatively few studies have focused on uptake and metabolism of carbon sources, and the metabolic basis for carbon flow to the different products. The focus of this work was quantification of the cellular performance of Y. lipolytica during growth on glycerol, glucose or a mixture of the two. Carbon substrate uptake rate, growth rate, oxygen utilisation (requirement and uptake rate) and polyol yields were estimated in batch cultivations at 1 litre scale. When glucose was used as the sole carbon and energy source, the growth rate was 0.24 h^-1 and biomass and CO₂ were the only products. Growth on glycerol proceeded at approximately 0.30 h^-1, and the substrate uptake rate was 0.02 mol L^-1 h^-1 regardless of the starting glycerol concentration (10, 20 or 45 g L^-1). Utilisation of glycerol was accompanied by higher oxygen uptake rates compared to glucose growth, indicating import of glycerol occurred initially via phosphorylation of glycerol into glycerol-3-phosphate. Based on these results it could be speculated that once oxygen limitation was reached, additional production of NADH created imbalance in the cofactor pools and the polyol formation observed could be a result of cofactor recycling to restore the balance in metabolism.