A Hybrid Model Simulating Multi-Stage Continuous Fermentation of Saccharomyces cerevisiae.

The performance of industrial strains has gradually improved with the rapid development of synthetic biotechnology. The production efficiency of traditional batch and fed-batch culture is limited and product quality varies since both are dynamic processes, whereas multi-stage continuous culture can maximise the production efficiency of specific fermentation processes and achieve consistent product quality. However, each single-stage fermentation under multi-stage continuous fermentation requires accurate steady-state control, and a model with adequate accuracy is required for designing and controlling a multi-stage continuous fermentation process. At present, there are few reports on kinetic models for the control of multi-stage continuous fermentation. In this work, we constructed a hybrid model for Saccharomyces cerevisiae multi-stage continuous culture, taking both oxygen limitation and Crabtree effect. The accuracy of the model was ∼80%, the advantages and limitations of the model are discussed and a potential improvement strategy is proposed.

Increased CO2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast.

CO2 fixation plays a key role to make biobased production cost competitive. Here, we use 3-hydroxypropionic acid (3-HP) to showcase how CO2 fixation enables approaching theoretical-yield production. Using genome-scale metabolic models to calculate the production envelope, we demonstrate that the provision of bicarbonate, formed from CO2, restricts previous attempts for high yield production of 3-HP. We thus develop multiple strategies for bicarbonate uptake, including the identification of Sul1 as a potential bicarbonate transporter, domain swapping of malonyl-CoA reductase, identification of Esbp6 as a potential 3-HP exporter, and deletion of Uga1 to prevent 3-HP degradation. The combined rational engineering increases 3-HP production from 0.14 g/L to 11.25 g/L in shake flask using 20 g/L glucose, approaching the maximum theoretical yield with concurrent biomass formation. The engineered yeast forms the basis for commercialization of bio-acrylic acid, while our CO2 fixation strategies pave the way for CO2 being used as the sole carbon source.

Construction and testing of Yarrowia lipolytica recombinant protein expression chassis cells based on the high-throughput screening and secretome.

BACKGROUND: In the recombinant protein market with broad economic value, the rapid development of synthetic biology has made it necessary to construct an efficient exocrine expression system for the different heterologous proteins. Yarrowia lipolytica possesses unique advantages in nascent protein transport and glycosylation modification, so it can serve as a potential protein expression platform. Although the Po1 series derived from W29 is often used for the expression of the various heterologous proteins, the ability of W29 to secrete proteins has not been verified and the Po1 series has been found to be not convenient for further gene editing. RESULTS: A total of 246 Y. lipolytica strains were evaluated for their secretory capacity through performing high-throughput screening in 48-well plate. Thereafter, following two rounds of shake flask re-screening, a high-secreting protein starting strain DBVPG 5851 was obtained. Subsequently, combined with the extracellular protein types and relative abundance information provided by the secretome of the starting strain, available chassis cell for heterologous protein expression were preliminarily constructed, and it was observed that the most potential signal peptide was derived from YALI0D20680g. CONCLUSIONS: This study offers a novel perspective on the diversification of Y. lipolytica host cells for the heterologous protein expression and provides significant basis for expanding the selection space of signal peptide tools in the future research.

Improved production of andrimid in Erwinia persicina BST187 strain by fermentation optimization.

BACKGROUND: Andrimid is reported to be a novel kind of polyketide-nonribosomal peptide hybrid product (PK-NRPs) that inhibits fatty acid biosynthesis in bacteria. Considering its great potential in biomedicine and biofarming, intensive studies have been conducted to increase the production of andrimid to overcome the excessive costs of chemosynthesis. In screening for species with broad-spectrum antibacterial activity, we detected andrimid in the fermentation products of Erwinia persicina BST187. To increase andrimid production, the BST187 fermentation medium formulation and fermentation conditions were optimized by using systematic design of experiments (One-Factor-At-A-Time, Plackett-Burman design, Response Surface Methodology). RESULTS: The results indicate that the actual andrimid production reached 140.3 ± 1.28 mg/L under the optimized conditions (trisodium citrate dihydrate-30 g/L, beef extract-17.1 g/L, MgCl2·6H2O-100 mM, inoculation amount-1%, initial pH-7.0, fermentation time-36 h, temperature-19.7℃), which is 20-fold greater than the initial condition without optimization (7.00 ± 0.40 mg/L), consistent with the improved antibacterial effect of the fermentation supernatant. CONCLUSIONS: The present study provides valuable information for improving andrimid production via optimization of the fermentation process, which will be of great value in the future industrialization of andrimid production.

Yeast increases glycolytic flux to support higher growth rates accompanied by decreased metabolite regulation and lower protein phosphorylation.

Supply of Gibbs free energy and precursors are vital for cellular function and cell metabolism have evolved to be tightly regulated to balance their supply and consumption. Precursors and Gibbs free energy are generated in the central carbon metabolism (CCM), and fluxes through these pathways are precisely regulated. However, how fluxes through CCM pathways are affected by posttranslational modification and allosteric regulation remains poorly understood. Here, we integrated multi-omics data collected under nine different chemostat conditions to explore how fluxes in the CCM are regulated in the yeast Saccharomyces cerevisiae. We deduced a pathway- and metabolism-specific CCM flux regulation mechanism using hierarchical analysis combined with mathematical modeling. We found that increased glycolytic flux associated with an increased specific growth rate was accompanied by a decrease in flux regulation by metabolite concentrations, including the concentration of allosteric effectors, and a decrease in the phosphorylation level of glycolytic enzymes.

Flux regulation through glycolysis and respiration is balanced by inositol pyrophosphates in yeast.

Although many prokaryotes have glycolysis alternatives, it's considered as the only energy-generating glucose catabolic pathway in eukaryotes. Here, we managed to create a hybrid-glycolysis yeast. Subsequently, we identified an inositol pyrophosphatase encoded by OCA5 that could regulate glycolysis and respiration by adjusting 5-diphosphoinositol 1,2,3,4,6-pentakisphosphate (5-InsP7) levels. 5-InsP7 levels could regulate the expression of genes involved in glycolysis and respiration, representing a global mechanism that could sense ATP levels and regulate central carbon metabolism. The hybrid-glycolysis yeast did not produce ethanol during growth under excess glucose and could produce 2.68 g/L free fatty acids, which is the highest reported production in shake flask of Saccharomyces cerevisiae. This study demonstrated the significance of hybrid-glycolysis yeast and determined Oca5 as an inositol pyrophosphatase controlling the balance between glycolysis and respiration, which may shed light on the role of inositol pyrophosphates in regulating eukaryotic metabolism.

Micro-electrochemical DO sensor with ultra-micropore matrix fabricated with femtosecond laser processing successfully applied in on-line DO monitoring for yeast culture.

Accurate monitoring of dissolved oxygen (DO) is vital for aerobic fermentation process control. This work presents an autoclavable Micro-Dissolved oxygen Sensor (MDS) that can monitor real time DO. The proposed sensor is much cheaper to be manufactured (< $35) and can be adapted to varying measurement environments. An ultra-micropore matrix was created using femtosecond laser processing technology to reduce flow dependency of probe signals. The validity of the proposed DO sensor was verified by testing it under different DO levels. The result revealed consistency between the new designed sensor and a commercial DO sensor. The obtained sensitivity was- 7.93 µA·L·mg-1 (MDS with ultra-micropore matrix). Moreover, the MDS can function without an oxygen-permeable membrane and a solid electrolyte was used which reduced the response time (4.6 s). For real-time monitoring, the stability of the MDS was validated during a yeast batch fermentation carried out until 18 h.

[Construction and application of natural stable isotope correction matrix in 13C-labeled metabolic flux analysis].

Stable isotope 13C labeling is an important tool to analyze cellular metabolic flux. The 13C distribution in intracellular metabolites can be detected via mass spectrometry and used as a constraint in intracellular metabolic flux calculations. Then, metabolic flux analysis algorithms can be employed to obtain the flux distribution in the corresponding metabolic reaction network. However, in addition to carbon, other elements such as oxygen in the nature also have natural stable isotopes (e.g., 17O, 18O). This makes the isotopic information of elements other than the 13C marker interspersed in the isotopic distribution measured by the mass spectrometry, especially that of the molecules containing many other elements, which leads to large errors. Therefore, it is essential to correct the mass spectrometry data before performing metabolic flux calculations. In this paper, we proposed a method for construction of correction matrix based on Python language for correcting the measurement errors due to natural isotope distribution. The method employed a basic power method for constructing the correction matrix with simple structure and easy coding implementation, which can be directly applied to data pre-processing in 13C metabolic flux analysis. The correction method was then applied to the intracellular metabolic flux analysis of 13C-labeled Aspergillus niger. The results showed that the proposed method was accurate and effective, which can serve as a reliable data correction method for accurate microbial intracellular metabolic flux analysis.

Multi-omics analyses of the transition to the Crabtree effect in S. cerevisiae reveals a key role for the citric acid shuttle.

The Crabtree effect in the yeast, Saccharomyces cerevisiae, has been extensively studied, but only few studies have analyzed the dynamic conditions across the critical specific growth rate where the Crabtree effect sets in. Here, we carried out a multi-omics analysis of S. cerevisiae undergoing a specific growth rate transition from 0.2 h-1 to 0.35 h-1. The extracellular metabolome, the transcriptome and the proteome were analyzed in an 8-h transition period after the specific growth rate shifted from 0.2 h-1 to 0.35 h-1. The changing trends of both the transcriptome and proteome were analyzed using principal component analysis, which showed that the transcriptome clustered together after 60 min, while the proteome reached steady-state much later. Focusing on central carbon metabolism, we analyzed both the changes in the transcriptome and proteome, and observed an interesting changing pattern in the tricarboxylic acid (TCA) pathway, which indicates an important role for citric acid shuttling across the mitochondrial membrane for α-ketoglutarate accumulation during the transition from respiratory to respiro-fermentative metabolism. This was supported by a change in the oxaloacetate and malate shuttle. Together, our findings shed new light into the onset of the Crabtree effect in S. cerevisiae.

Proteome allocations change linearly with the specific growth rate of Saccharomyces cerevisiae under glucose limitation.

Saccharomyces cerevisiae is a widely used cell factory; therefore, it is important to understand how it organizes key functional parts when cultured under different conditions. Here, we perform a multiomics analysis of S. cerevisiae by culturing the strain with a wide range of specific growth rates using glucose as the sole limiting nutrient. Under these different conditions, we measure the absolute transcriptome, the absolute proteome, the phosphoproteome, and the metabolome. Most functional protein groups show a linear dependence on the specific growth rate. Proteins engaged in translation show a perfect linear increase with the specific growth rate, while glycolysis and chaperone proteins show a linear decrease under respiratory conditions. Glycolytic enzymes and chaperones, however, show decreased phosphorylation with increasing specific growth rates; at the same time, an overall increased flux through these pathways is observed. Further analysis show that even though mRNA levels do not correlate with protein levels for all individual genes, the transcriptome level of functional groups correlates very well with its corresponding proteome. Finally, using enzyme-constrained genome-scale modeling, we find that enzyme usage plays an important role in controlling flux in amino acid biosynthesis.

Genome-Scale Metabolic Model's multi-objective solving algorithm based on the inflexion point of Pareto front including maximum energy utilization and its application in Aspergillus niger DS03043.

The solution of Genome-Scale Metabolic Model (GSMM) directly affects the simulation accuracy of the metabolic process in digital cells. Single-objective optimization methods, such as flux balance analysis (FBA), which is widely used in solving GSMM, have limitations when simulating actual biological processes, which leads to unrealistic results due to other biological constraints being ignored. A novel multi-objective differential evolution algorithm based on general FBA (i.e., differential evolution FBA [DEFBA]) is hence proposed to solve GSMM. First, in accordance with the assumption that cells minimize resource consumption and maximize resource utilization, the maximum specific growth rate and the minimum cellular production rate of ATP, NADPH, and NADH are defined as the multi-objective functions of DEFBA. Second, FBA is used to produce the initial individuals of DEFBA by changing the upper bound of biomass reaction in GSMM. Third, mutation and selection operations help in generating new individuals in the solution space to search the Pareto front. Finally, the optimal solution is selected by analyzing the inflexion point of the Pareto front. In DEFBA, multi-objective technology and optimal solution judging technology can introduce the biological constraints into the GSMM solving method, such that the solution can be more consistent with the essential biological mechanism. DEFBA is applied to solve Aspergillus niger's GSMM. The improved results show that DEFBA can be an effective general solving algorithm for GSMM.

γ-PGA Fermentation by Bacillus subtilis PG-001 with Glucose Feedback Control pH-stat Strategy.

Poly-γ-glutamic acid (γ-PGA) is an important biopolymer with many applications due to its biodegradable and non-toxic characteristics. γ-PGA is produced industrially by fermentation of Bacillus species. The optimal pH range for producing γ-PGA by Bacillus subtilis PG-001 was firstly studied by glucose fed-batch fermentation with non-controlled pH. Result showed that both cell growth and γ-PGA synthesis were repressed when pH was lower than pH 6. Further investigation with γ-PGA fed-batch fermentation showed that pH 6.5 is more suitable for γ-PGA fermentation than pH 7. Under comparable consumption of glutamic acid and glucose, 11.8 g/L γ-PGA and 0.7 g/g yield were achieved by fermentation at pH 6.5, which was significantly higher than 10.5 g/L and 0.56 g/g yield of fermentation at pH 7. In addition, γ-PGA degradation during later phase of fermentation was repressed at pH 6.5 as 9238cP of final broth viscosity was achieved from fermentation at pH 6.5 while it was only 346 cP for fermentation at pH 7. Finally, a glucose feedback control pH-stat strategy was performed for reducing alkali consumption during γ-PGA fermentation, which further increased final γ-PGA concentration to 15.5 g/L with much higher viscosity (11458 cP); meanwhile the consumption of alkali decreased 57%. The fed-batch γ-PGA fermentation with glucose feedback control pH-stat strategy showed high feasibility for industrial scaling-up.

[Opportunities and challenges for fermentation optimization and scale-up technology in the artificial intelligence era].

Artificial intelligence (AI) technology is booming up a new industrial revolution, and its successful application is rapidly spreading from the information industry to many other fields. The traditional fermentation industry also faces more opportunities and great challenges for reforming. First of all, the rapid development of synthetic biotechnology has greatly enhanced the availability and efficiency for obtaining high-performance strains, which poses great opportunities to the traditional fermentation optimization and scale-up technology. It is urgent to upgrade fermentation optimization technology to cope with the requirement for high-throughput verification of strain performance. Secondly, the development of fermentation equipment technology has laid a good foundation for advancing fermentation optimization technology. The application of AI technology, especially the digital twin and knowledge graph technology, will further boost the upgrade of the traditional fermentation technology. This review summarizes the challenges of fermentation optimization technology in the era of synthetic biology, the core technology of fermentation optimization and scale-up, the equipment technology of high-throughput fermentation, data visualization technology, as well as the application of digital twin and knowledge graph in fermentation optimization and scale-up. This review also prospects future industrial fermentation technology, and the associated new requirements for personnel training.

A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models.

Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.

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 [Formula: see text] 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.

[Advances in the development of constraint-based genome-scale metabolic network models].

Genome-scale metabolic network model (GSMM) is becoming an important tool for studying cellular metabolic characteristics, and remarkable advances in relevant theories and methods have been made. Recently, various constraint-based GSMMs that integrated genomic, transcriptomic, proteomic, and thermodynamic data have been developed. These developments, together with the theoretical breakthroughs, have greatly contributed to identification of target genes, systems metabolic engineering, drug discovery, understanding disease mechanism, and many others. This review summarizes how to incorporate transcriptomic, proteomic, and thermodynamic-constraints into GSMM, and illustrates the shortcomings and challenges of applying each of these methods. Finally, we illustrate how to develop and refine a fully integrated GSMM by incorporating transcriptomic, proteomic, and thermodynamic constraints, and discuss future perspectives of constraint-based GSMM.

[New opportunities and challenges for hybrid data and model driven bioprocess optimization and scale-up].

Currently, biomanufacturing technology and industry are receiving worldwide attention. However, there are still great challenges on bioprocess optimization and scale-up, including: lacing the process detection methods, which makes it difficult to meet the requirement of monitoring of key indicators and parameters; poor understanding of cell metabolism, which arouses problems to rationally achieve process optimization and regulation; the reactor environment is very different across the scales, resulting in low efficiency of stepwise scale-up. Considering the above key issues that need to be resolved, here we summarize the key technological innovations of the whole chain of fermentation process, i.e., real-time detection-dynamic regulation-rational scale-up, through case analysis. In the future, bioprocess design will be guided by a full lifecycle in-silico model integrating cellular physiology (spatiotemporal multiscale metabolic models) and fluid dynamics (CFD models). This will promote computer-aided design and development, accelerate the realization of large-scale intelligent production and serve to open a new era of green biomanufacturing.

Dynamic response of Aspergillus niger to periodical glucose pulse stimuli in chemostat cultures.

In industrial large-scale bioreactors, microorganisms encounter heterogeneous substrate concentration conditions, which can impact growth or product formation. Here we carried out an extended (12 h) experiment of repeated glucose pulsing with a 10-min period to simulate fluctuating glucose concentrations with Aspergillus niger producing glucoamylase, and investigated its dynamic response by rapid sampling and quantitative metabolomics. The 10-min period represents worst-case conditions, as in industrial bioreactors the average cycling duration is usually in the order of 1 min. We found that cell growth and the glucoamylase productivity were not significantly affected, despite striking metabolomic dynamics. Periodical dynamic responses were found across all central carbon metabolism pathways, with different time scales, and the frequently reported ATP paradox was confirmed for this A. niger strain under the dynamic conditions. A thermodynamics analysis revealed that several reactions of the central carbon metabolism remained in equilibrium even under periodical dynamic conditions. The dynamic response profiles of the intracellular metabolites did not change during the pulse exposure, showing no significant adaptation of the strain to the more than 60 perturbation cycles applied. The apparent high tolerance of the glucoamylase producing A. niger strain for extreme variations in the glucose availability presents valuable information for the design of robust industrial microbial hosts.

Real-time and on-line monitoring of ethanol fermentation process by viable cell sensor and electronic nose.

In this study, introduction of a viable cell sensor and electronic nose into ethanol fermentation was investigated, which could be used in real-time and on-line monitoring of the amount of living cells and product content, respectively. Compared to the conventional off-line biomass determination, the capacitance value exhibited a completely consistent trend with colony forming units, indicating that the capacitance value could reflect the living cells in the fermentation broth. On the other hand, in comparison to the results of off-line determination by high-performance liquid chromatography, the ethanol concentration measured by electronic nose presented an excellent consistency, so as to realize the on-line monitoring during the whole process. On this basis, a dynamic feeding strategy of glucose guided by the changes of living cells and ethanol content was developed. And consequently, the ethanol concentration, productivity and yield were enhanced by 15.4%, 15.9% and 9.0%, respectively. The advanced sensors adopted herein to monitor the key parameters of ethanol fermentation process could be readily extended to an industrial scale and other similar fermentation processes.

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.