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.

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.

13 C-MFA helps to identify metabolic bottlenecks for improving malic acid production in Myceliophthora thermophila.

BACKGROUND: Myceliophthora thermophila has been engineered as a significant cell factory for malic acid production, yet strategies to further enhance production remain unclear and lack rational guidance. 13C-MFA (13C metabolic flux analysis) offers a means to analyze cellular metabolic mechanisms and pinpoint critical nodes for improving product synthesis. Here, we employed 13C-MFA to investigate the metabolic flux distribution of a high-malic acid-producing strain of M. thermophila and attempted to decipher the crucial bottlenecks in the metabolic pathways. RESULTS: Compared with the wild-type strain, the high-Malic acid-producing strain M. thermophila JG207 exhibited greater glucose uptake and carbon dioxide evolution rates but lower oxygen uptake rates and biomass yields. Consistent with these phenotypes, the 13C-MFA results showed that JG207 displayed elevated flux through the EMP pathway and downstream TCA cycle, along with reduced oxidative phosphorylation flux, thereby providing more precursors and NADH for malic acid synthesis. Furthermore, based on the 13C-MFA results, we conducted oxygen-limited culture and nicotinamide nucleotide transhydrogenase (NNT) gene knockout experiments to increase the cytoplasmic NADH level, both of which were shown to be beneficial for malic acid accumulation. CONCLUSIONS: This work elucidates and validates the key node for achieving high malic acid production in M. thermophila. We propose effective fermentation strategies and genetic modifications for enhancing malic acid production. These findings offer valuable guidance for the rational design of future cell factories aimed at improving malic acid yields.

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.

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.

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.

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.

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.

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.

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.

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.

Power input effects on degeneration in prolonged penicillin chemostat cultures: A systems analysis at flux, residual glucose, metabolite, and transcript levels.

In the present work, by performing chemostat experiments at 400 and 600 RPM, two typical power inputs representative of industrial penicillin fermentation (P/V, 1.00 kW/m3 in more remote zones and 3.83 kW/m3 in the vicinity of the impellers, respectively) were scaled-down to bench-scale bioreactors. It was found that at 400 RPM applied in prolonged glucose-limited chemostat cultures, the previously reported degeneration of penicillin production using an industrial Penicillium chrysogenum strain was virtually absent. To investigate this, the cellular response was studied at flux (stoichiometry), residual glucose, intracellular metabolite and transcript levels. At 600 RPM, 20% more cell lysis was observed and the increased degeneration of penicillin production was accompanied by a 22% larger ATP gap and an unexpected 20-fold decrease in the residual glucose concentration (Cs,out ). At the same time, the biomass specific glucose consumption rate (qs ) did not change but the intracellular glucose concentration was about sixfold higher, which indicates a change to a higher affinity glucose transporter at 600 RPM. In addition, power input differences cause differences in the diffusion rates of glucose and the calculated Batchelor diffusion length scale suggests the presence of a glucose diffusion layer at the glucose transporting parts of the hyphae, which was further substantiated by a simple proposed glucose diffusion-uptake model. By analysis of calculated mass action ratios (MARs) and energy consumption, it indicated that at 600 RPM glucose sensing and signal transduction in response to the low Cs,out appear to trigger a gluconeogenic type of metabolic flux rearrangement, a futile cycle through the pentose phosphate pathway (PPP) and a declining redox state of the cytosol. In support of the change in glucose transport and degeneration of penicillin production at 600 RPM, the transcript levels of the putative high-affinity glucose/hexose transporter genes Pc12g02880 and Pc06g01340 increased 3.5- and 3.3-fold, respectively, and those of the pcbC gene encoding isopenicillin N-synthetase (IPNS) were more than twofold lower in the time range of 100-200 hr of the chemostat cultures. Summarizing, changes at power input have unexpected effects on degeneration and glucose transport, and result in significant metabolic rearrangements. These findings are relevant for the industrial production of penicillin, and other fermentations with filamentous microorganisms.

Hydrodynamic investigation of a novel shear-generating device for the measurement of anchorage-dependent cell adhesion intensity.

Proliferation of anchorage-dependent cells occurs after adhesion to a suitable surface. Thus, quantitative information about the force of cells adhesion to microcarriers at early culture phases is vital for scaling up such system. In this work, a newly designed shear-generating device was proposed, based on a previously proposed contraction flow device designed for suspended cells. A design equation for the new device was also proposed to correlate the generated energy dissipation rate (EDR) with the cross-sectional area and flow rate. Microscopic-particle image velocimetry was measured to validate the simulation results, and good agreement was achieved. The newly designed device was then used to measure the adhesion force of MDCK and PK cells, and the results showed that the critical EDR was 3000 W/m3 for MDCK and 5000 W/m3 for PK cells. This quantitative information is of great value for better understanding shearing effects during the scaling up of anchorage-dependent cell cultures.

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.

A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum.

A powerful approach for the optimization of industrial bioprocesses is to perform detailed simulations integrating large-scale computational fluid dynamics (CFD) and cellular reaction dynamics (CRD). However, complex metabolic kinetic models containing a large number of equations pose formidable challenges in CFD-CRD coupling and computation time afterward. This necessitates to formulate a relatively simple but yet representative model structure. Such a kinetic model should be able to reproduce metabolic responses for short-term (mixing time scale of tens of seconds) and long-term (fed-batch cultivation of hours/days) dynamics in industrial bioprocesses. In this paper, we used Penicillium chrysogenum as a model system and developed a metabolically structured kinetic model for growth and production. By lumping the most important intracellular metabolites in 5 pools and 4 intracellular enzyme pools, linked by 10 reactions, we succeeded in maintaining the model structure relatively simple, while providing informative insight into the state of the organism. The performance of this 9-pool model was validated with a periodic glucose feast-famine cycle experiment at the minute time scale. Comparison of this model and a reported black box model for this strain shows the necessity of employing a structured model under feast-famine conditions. This proposed model provides deeper insight into the in vivo kinetics and, most importantly, can be straightforwardly integrated into a computational fluid dynamic framework for simulating complete fermentation performance and cell population dynamics in large scale and small scale fermentors. Biotechnol. Bioeng. 2017;114: 1733-1743. © 2017 Wiley Periodicals, Inc.

Integrated isotope-assisted metabolomics and (13)C metabolic flux analysis reveals metabolic flux redistribution for high glucoamylase production by Aspergillus niger.

BACKGROUND: Aspergillus niger is widely used for enzyme production and achievement of high enzyme production depends on the comprehensive understanding of cell's metabolic regulation mechanisms. RESULTS: In this paper, we investigate the metabolic differences and regulation mechanisms between a high glucoamylase-producing strain A. niger DS03043 and its wild-type parent strain A. niger CBS513.88 via an integrated isotope-assisted metabolomics and (13)C metabolic flux analysis approach. We found that A. niger DS03043 had higher cell growth, glucose uptake, and glucoamylase production rates but lower oxalic acid and citric acid secretion rates. In response to above phenotype changes, A. niger DS03043 was characterized by an increased carbon flux directed to the oxidative pentose phosphate pathway in contrast to reduced flux through TCA cycle, which were confirmed by consistent changes in pool sizes of metabolites. A higher ratio of ATP over AMP in the high producing strain might contribute to the increase in the PP pathway flux as glucosephosphate isomerase was inhibited at higher ATP concentrations. A. niger CBS513.88, however, was in a higher redox state due to the imbalance of NADH regeneration and consumption, resulting in the secretion of oxalic acid and citric acid, as well as the accumulation of intracellular OAA and PEP, which may in turn result in the decrease in the glucose uptake rate. CONCLUSIONS: The application of integrated metabolomics and (13)C metabolic flux analysis highlights the regulation mechanisms of energy and redox metabolism on flux redistribution in A. niger. Graphical abstract An integrated isotope-assisted metabolomics and (13)C metabolic flux analysis was was firstly systematically performed in A. niger. In response to enzyme production, the metabolic flux in A. niger DS03043 (high-producing) was redistributed, characterized by an increased carbon flux directed to the oxidative pentose phosphate pathway as well as an increased pool size of pentose. The consistency in (13)C metabolic flux analysis and metabolites quantification indicated that an imbalance of NADH formation and consumption led to the accumulation and secretion of organic acids in A. niger CBS513.88 (wild-type).

Dependence of fungal characteristics on seed morphology and shear stress in bioreactors.

The fungal morphology during submerged cultivations has a profound influence on the overall performance of bioreactors. In this research, glucoamylase production by Aspergillus niger has been taken as a model to improve more insights. The morphology engineering could be conducted effectively by changing the seed morphology, as well as specific power input. During the fed-batch cultivations, pellet formation under milder shear stress field helped to reduce the broth viscosity, thus relieving oxygen limitation and promoting the enzyme production. Furthermore, we found that the relation between the shear stress field, which was characterized by energy dissipation rate/circulation function (EDCF), and enzyme activity was consistent with quadratic parabola, which threw light on the process optimization and scale-up for industrial enzyme production.

Prelude to rational scale-up of penicillin production: a scale-down study.

Penicillin is one of the best known pharmaceuticals and is also an important member of the ß-lactam antibiotics. Over the years, ambitious yields, titers, productivities, and low costs in the production of the ß-lactam antibiotics have been stepwise realized through successive rounds of strain improvement and process optimization. Penicillium chrysogenum was proven to be an ideal cell factory for the production of penicillin, and successful approaches were exploited to elevate the production titer. However, the industrial production of penicillin faces the serious challenge that environmental gradients, which are caused by insufficient mixing and mass transfer limitations, exert a considerably negative impact on the ultimate productivity and yield. Scale-down studies regarding diverse environmental gradients have been carried out on bacteria, yeasts, and filamentous fungi as well as animal cells. In accordance, a variety of scale-down devices combined with fast sampling and quenching protocols have been established to acquire the true snapshots of the perturbed cellular conditions. The perturbed metabolome information stemming from scale-down studies contributed to the comprehension of the production process and the identification of improvement approaches. However, little is known about the influence of the flow field and the mechanisms of intracellular metabolism. Consequently, it is still rather difficult to realize a fully rational scale-up. In the future, developing a computer framework to simulate the flow field of the large-scale fermenters is highly recommended. Furthermore, a metabolically structured kinetic model directly related to the production of penicillin will be further coupled to the fluid flow dynamics. A mathematical model including the information from both computational fluid dynamics and chemical reaction dynamics will then be established for the prediction of detailed information over the entire period of the fermentation process and thereby for the optimization of penicillin production, and subsequently also benefiting other fermentation products.

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.

Transcriptional Downregulation of Methanol Metabolism Key Genes During Yeast Death in Engineered Pichia pastoris.

Pichia pastoris possesses the unique ability to utilize methanol as its sole carbon source, which makes it a proper host for producing various high-value-added products via metabolic engineering. Nevertheless, cell death has been observed during the fermentation of modified P. pastoris, with limited literature elucidating the underlying causes and mechanisms. Understanding the death mechanisms during methanol-based fermentation is crucial for optimizing fermentation strategies, enhancing the accumulation of target products, and reducing production costs. Here, we first sought to eliminate the potential causes of cell death during fermentation, such as inadequate inorganic salts and toxic by-product accumulation. The elimination of these potential causes was achieved efficiently utilizing the high-throughput fermentation equipment. Subsequently, we established a correlation between yeast cell death and the duration of the methanol metabolism period by monitoring the growth of the yeast at different fermentation stages. A critical revelation from this work came from analyzing the yeast's transcriptomic data at various stages of methanol metabolism. It was observed that a significant characteristic of yeast cell death during fermentation was the marked down-regulation of transcript levels of key enzymes involved in the methanol assimilation pathway and genes related to their biosynthesis process. The findings of this work are crucial for better understanding the causes and mechanisms of cell death for engineered P. pastoris during methanol-utilized fermentation.