Scale-up of microbial processes: Impacts, tools and open questions

Genetic heterogeneity of engineered Escherichia coli Nissle 1917 strains during scale-up simulation

Advanced microbiome therapeutics have emerged as a powerful approach for the treatment of numerous diseases. While the genetic instability of genetically engineered microorganisms is a well-known challenge in the scale-up of biomanufacturing processes, it has not yet been investigated for advanced microbiome therapeutics. Here, the evolution of engineered Escherichia coli Nissle 1917 strains producing Interleukin 2 and Aldafermin were investigated in two strain backgrounds with and without the three error-prone DNA polymerases polB, dinB, and umuDC, which contribute to the mutation rate of the host strain. Whole genome short-read sequencing revealed the genetic instability of the pMUT-based production plasmid after serial passaging for approximately 150 generations using an automated platform for high-throughput microbial evolution in five independent lineages for six distinct strains. While a reduction of the number of mutations of 12%-43% could be observed after the deletion of the error-prone DNA polymerases, the interruption of production-relevant genes could not be prevented, highlighting the need for additional strategies to improve the stability of advanced microbiome therapeutics.

Analyzing of hydrodynamic stress and mass transfer requirements of a fermentation process carried out in a coaxial bioreactor: a scale-up study

Fluid hydrodynamic stress has a deterministic effect on the morphology of filamentous fungi. Although the coaxial mixer has been recognized as a suitable gas dispersion system for minimizing inhomogeneities within a bioreactor, its performance for achieving enhanced oxygen transfer while operating at a reduced shear environment has not been investigated yet, specifically upon scale-up. Therefore, the influence of the impeller type, aeration rate, and central impeller retrofitting on the efficacy of an abiotic coaxial system containing a shear-thinning fluid was examined. The aim was to assess the hydrodynamic parameters, including stress, mass transfer, bubble size, and gas hold-up, upon conducting a scale-up study. The investigation was conducted through dynamic gassing-in, tomography, and computational fluid dynamics combined with population balance methods. It was observed that the coaxial bioreactor performance was strongly influenced by the agitator type. In addition, coaxial bioreactors are scalable in terms of shear environment and oxygen transfer rate.

Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation

BACKGROUND

Microorganisms must respond to changes in their environment. Analysing the robustness of functions (i.e. performance stability) to such dynamic perturbations is of great interest in both laboratory and industrial settings. Recently, a quantification method capable of assessing the robustness of various functions, such as specific growth rate or product yield, across different conditions, time frames, and populations has been developed for microorganisms grown in a 96-well plate. In micro-titer-plates, environmental change is slow and undefined. Dynamic microfluidic single-cell cultivation (dMSCC) enables the precise maintenance and manipulation of microenvironments, while tracking single cells over time using live-cell imaging. Here, we combined dMSCC and a robustness quantification method to a pipeline for assessing performance stability to changes occurring within seconds or minutes.

RESULTS

Saccharomyces cerevisiae CEN.PK113-7D, harbouring a biosensor for intracellular ATP levels, was exposed to glucose feast-starvation cycles, with each condition lasting from 1.5 to 48 min over a 20 h period. A semi-automated image and data analysis pipeline was developed and applied to assess the performance and robustness of various functions at population, subpopulation, and single-cell resolution. We observed a decrease in specific growth rate but an increase in intracellular ATP levels with longer oscillation intervals. Cells subjected to 48 min oscillations exhibited the highest average ATP content, but the lowest stability over time and the highest heterogeneity within the population.

CONCLUSION

The proposed pipeline enabled the investigation of function stability in dynamic environments, both over time and within populations. The strategy allows for parallelisation and automation, and is easily adaptable to new organisms, biosensors, cultivation conditions, and oscillation frequencies. Insights on the microbial response to changing environments will guide strain development and bioprocess optimisation.

Synergic kinetic and physiological control to improve the efficiency of Komagataella phaffii recombinant protein production bioprocesses

The yeast Komagataella phaffii (Pichia pastoris) is currently considered a versatile and highly efficient host for recombinant protein production (RPP). Interestingly, the regulated application of specific stress factors as part of bioprocess engineering strategies has proven potential for increasing the production of recombinant products. This study aims to evaluate the impact of controlled oxygen-limiting conditions on the performance of K. phaffii bioprocesses for RPP in combination with the specific growth rate (μ) in fed-batch cultivations. In this work, Candida rugosa lipase 1 (Crl1) production, regulated by the constitutive GAP promoter, growing at different nominal μ (0.030, 0.065, 0.100 and 0.120 h-1 ) under both normoxic and hypoxic conditions in carbon-limiting fed-batch cultures is analysed. Hypoxic fermentations were controlled at a target respiratory quotient (RQ) of 1.4, with excellent performance, using an innovative automated control based on the stirring rate as the manipulated variable developed during this study. The results conclude that oxygen limitation positively affects bioprocess efficiency under all growing conditions compared. The shift from respiratory to respiro-fermentative metabolism increases bioprocess productivity by up to twofold for the specific growth rates evaluated. Moreover, the specific product generation rate (qp ) increases linearly with μ, regardless of oxygen availability. Furthermore, this hypoxic boosting effect was also observed in the production of Candida antarctica lipase B (CalB) and pro-Rhizopus oryzae lipase (proRol), thus proving the synergic effect of kinetic and physiological stress control. Finally, the Crl1 production scale-up was conducted successfully, confirming the strategy's scalability and the robustness of the results obtained at the bench-scale level.

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Exploring the Feasibility of Cell-Free Synthesis as a Platform for Polyhydroxyalkanoate (PHA) Production: Opportunities and Challenges

The extensive utilization of traditional petroleum-based plastics has resulted in significant damage to the natural environment and ecological systems, highlighting the urgent need for sustainable alternatives. Polyhydroxyalkanoates (PHAs) have emerged as promising bioplastics that can compete with petroleum-based plastics. However, their production technology currently faces several challenges, primarily focused on high costs. Cell-free biotechnologies have shown significant potential for PHA production; however, despite recent progress, several challenges still need to be overcome. In this review, we focus on the status of cell-free PHA synthesis and compare it with microbial cell-based PHA synthesis in terms of advantages and drawbacks. Finally, we present prospects for the development of cell-free PHA synthesis.

Experimental kS estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale

Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as k_S , is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate k_S . In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of k_S values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity k_S of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A k_S of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial k_S estimation, sl-MSCC allows an affinity estimation by determining t_D at concentrations less or equal to the k_S value. Thus, sl-MSCC lays the foundation for strain-specific k_S estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity.

Biomanufacturing by In Vitro Biotransformation (ivBT) Using Purified Cascade Multi-enzymes

In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development.

Cloning and Expression of Metagenomic DNA in Streptomyces lividans and Its Subsequent Fermentation for Optimized Production

The choice of an expression system for the metagenomic DNA of interest is of vital importance for the detection of any particular gene or gene cluster. Most of the screens to date have used the Gram-negative bacterium Escherichia coli as a host for metagenomic gene libraries. However, the use of E. coli introduces a potential host bias since only 40% of the enzymatic activities may be readily recovered by random cloning in E. coli. To recover some of the remaining 60%, alternative cloning hosts such as Streptomyces spp. have been used. Streptomycetes are high-GC Gram-positive bacteria belonging to the Actinomycetales and they have been studied extensively for more than 25 years as an alternative expression system. They are extremely well suited for the expression of DNA from other actinomycetes and genomes of high GC content. Furthermore, due to its high innate, extracellular secretion capacity, Streptomyces can be a better system than E. coli for the production of many extracellular proteins. In this article, an overview is given about the materials and methods for growth and successful expression and secretion of heterologous proteins from diverse origin using Streptomyces lividans as a host. More in detail, an overview is given about the protocols of transformation, type of plasmids used and of vectors useful for integration of DNA into the host chromosome, and accompanying cloning strategies. In addition, various control elements for gene expression including synthetic promoters are discussed, and methods to compare their strength are described. Stable and efficient marker-less integration of the gene of interest under the control of the promoter of choice into S. lividans chromosome via homologous recombination using pAMR23A-based system will be explained. Finally, a basic protocol for bench-top bioreactor experiments which can form the start in the production process optimization and up-scaling will be provided.

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

CO2-elevated cell-free protein synthesis

Gases are the vital nutrition of all organisms as the precursor of metabolism pathways. As a potential biological process, protein synthesis is inevitably regulated by gas transport and utilization. However, the effect of carbon dioxide (CO₂) present in many metabolic pathways on protein synthesis has not been studied well. In this work, carbon dioxide combined with oxygen was employed for cell-free protein synthesis (CFPS) in the tube-in-tube reactor with precise control of gas concentration. In this in vitro system, gases could directly affect the protein synthesis process without transmembrane transport. Varied concentrations of carbon dioxide (0–1%) and constant oxygen concentration (21%) were employed for CFPS to assess the effects. The cell-free reactions with 0.3% CO₂ and 21% O₂ showed the highest protein yields. The combined effect of CO₂ and O₂ also resulted in relatively high protein expression under high oxygen conditions (0.3% CO₂ and 100% O₂). Moreover, metabolomics assays were performed to gain insight into metabolic changes, which showed that CO₂ slightly improved energy metabolism and redox balance. In particular, the extra supplied CO₂ activated the decarboxylating reactions and removed toxic metabolites to recover the protein synthesis activity. The exploration of CO₂ on protein synthesis could provide guiding implications for basic studies and biomanufacturing.

Microfluidic single-cell scale-down bioreactors: A proof-of-concept for the growth of Corynebacterium glutamicum at oscillating pH values

In large-scale bioreactors, gradients in cultivation parameter such as oxygen, substrate and pH result in fluctuating cell environments. pH fluctuations were identified as a critical parameter for bioprocess performance. Traditionally, scale-down systems at the laboratory scale are used to analyze the effects of fluctuating pH values on strain and thus process performance. Here, we demonstrate the application of dynamic microfluidic single-cell cultivation (dMSCC) as a novel scale-down system for the characterization of Corynebacterium glutamicum growth using oscillating pH conditions as a model stress-factor. A detailed comparison between two-compartment reactor (two-CR) scale-down experiments and dMSCC was performed for one specific pH oscillation between reference pH 7 (~ 8 min) and disturbed pH 6 (~2 min). Similar reductions in growth rates were observed in both systems (dMSCC 21% and two-CR 27%) compared to undisturbed cultivation at pH 7. Afterwards, systematic experiments at symmetric and asymmetric pH oscillations between pH ranges of 4-6 and 8-11 and different intervals from 1 minute to 20 minutes, were performed to demonstrate the unique application range and throughput of the dMSCC system. Finally, the strength of the dMSCC application was demonstrated by mimicking fluctuating environmental conditions of a putative large-scale bioprocesse, which is difficult to conduct using two-CRs. This article is protected by copyright. All rights reserved.

Continuous feeding strategy for polyhydroxyalkanoate production from solid waste animal fat at laboratory- and pilot-scale

Bioconversion of waste animal fat (WAF) to polyhydroxyalkanoates (PHAs) is an approach to lower the production costs of these plastic alternatives. However, the solid nature of WAF requires a tailor-made process development. In this study, a double-jacket feeding system was built to thermally liquefy the WAF to employ a continuous feeding strategy. During laboratory-scale cultivations with Ralstonia eutropha Re2058/pCB113, 70% more PHA (45 g_PHA L^-1 ) and a 75% higher space-time yield (0.63 g_PHA L^-1  h^-1 ) were achieved compared to previously reported fermentations with solid WAF. During the development process, growth and PHA formation were monitored in real-time by in-line photon density wave spectroscopy. The process robustness was further evaluated during scale-down fermentations employing an oscillating aeration, which did not alter the PHA yield although cells encountered periods of oxygen limitation. Flow cytometry with propidium iodide staining showed that more than two-thirds of the cells were viable at the end of the cultivation and viability was even little higher in the scale-down cultivations. Application of this feeding system at 150-L pilot-scale cultivation yielded in 31.5 g_PHA L^-1 , which is a promising result for the further scale-up to industrial scale.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

Development of a ribosome profiling protocol to study translation in Kluyveromyces marxianus

Kluyveromyces marxianus is an interesting and important yeast because of particular traits such as thermotolerance and rapid growth, and for applications in food and industrial biotechnology. For both understanding its biology and developing bioprocesses, it is important to understand how K. marxianus responds and adapts to changing environments. For this, a full suite of omics tools to measure and compare global patterns of gene expression and protein synthesis is needed. We report here the development of a ribosome profiling method for K. marxianus, which allows codon resolution of translation on a genome-wide scale by deep sequencing of ribosome locations on mRNAs. To aid in the analysis and sharing of ribosome profiling data, we added the K. marxianus genome as well as transcriptome and ribosome profiling data to the publicly accessible GWIPS-viz and Trips-Viz browsers. Users are able to upload custom ribosome profiling and RNA-Seq data to both browsers, therefore allowing easy analysis and sharing of data. We also provide a set of step-by-step protocols for the experimental and bioinformatic methods that we developed.

Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli

Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L−1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.

Robustness: linking strain design to viable bioprocesses

Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.

Synthesis as an Expanding Resource in Human Milk Science

Few classes of natural products rival the structural audacity of oligosaccharides. Their complexity, however, has stood as an immense roadblock to translational research, as access to homogeneous material from nature is challenging. Thus, while carbohydrates are critical to the myriad functional and structural aspects of the biological sciences, their behavior is largely descriptive. This challenge presents an attractive opportunity for synthetic chemistry, particularly in the area of human milk science. First, there is an inordinate need for synthesizing homogeneous human milk oligosaccharides (HMOs). Superimposed on this goal is the mission of conducting syntheses at scale to enable animal studies. Herein, we present a personalized rumination of our involvement, and that of our colleagues, which has led to the synthesis and characterization of HMOs and mechanistic probes. Along the way, we highlight chemical, chemoenzymatic, and synthetic biology based approaches. We close with a discussion on emergent challenges and opportunities for synthesis, broadly defined, in human milk science.

Evolving a New Efficient Mode of Fructose Utilization for Improved Bioproduction in Corynebacterium glutamicum

Fructose utilization in Corynebacterium glutamicum starts with its uptake and concomitant phosphorylation via the phosphotransferase system (PTS) to yield intracellular fructose 1-phosphate, which enters glycolysis upon ATP-dependent phosphorylation to fructose 1,6-bisphosphate by 1-phosphofructokinase. This is known to result in a significantly reduced oxidative pentose phosphate pathway (oxPPP) flux on fructose (∼10%) compared to glucose (∼60%). Consequently, the biosynthesis of NADPH demanding products, e.g., L-lysine, by C. glutamicum is largely decreased when fructose is the only carbon source. Previous works reported that fructose is partially utilized via the glucose-specific PTS presumably generating fructose 6-phosphate. This closer proximity to the entry point of the oxPPP might increase oxPPP flux and, consequently, NADPH availability. Here, we generated deletion strains lacking either the fructose-specific PTS or 1-phosphofructokinase activity. We used these strains in short-term evolution experiments on fructose minimal medium and isolated mutant strains, which regained the ability of fast growth on fructose as a sole carbon source. In these fructose mutants, the deletion of the glucose-specific PTS as well as the 6-phosphofructokinase gene, abolished growth, unequivocally showing fructose phosphorylation via glucose-specific PTS to fructose 6-phosphate. Gene sequencing revealed three independent amino acid substitutions in PtsG (M260V, M260T, and P318S). These three PtsG variants mediated faster fructose uptake and utilization compared to native PtsG. In-depth analysis of the effects of fructose utilization via these PtsG variants revealed significantly increased ODs, reduced side-product accumulation, and increased L-lysine production by 50%.

Engineering of a robust Escherichia coli chassis and exploitation for large-scale production processes

In large-scale bioprocesses microbes are exposed to heterogeneous substrate availability reducing the overall process performance. A series of deletion strains was constructed from E. coli MG1655 aiming for a robust phenotype in heterogeneous fermentations with transient starvation. Deletion targets were hand-picked based on a list of genes derived from previous large-scale simulation runs. Each gene deletion was conducted on the premise of strict neutrality towards growth parameters in glucose minimal medium. The final strain of the series, named E. coli RM214, was cultivated continuously in an STR-PFR (stirred tank reactor - plug flow reactor) scale-down reactor. The scale-down reactor system simulated repeated passages through a glucose starvation zone. When exposed to nutrient gradients, E. coli RM214 had a significantly lower maintenance coefficient than E. coli MG1655 (Δm_s = 0.038 g_Glucose/g_CDW/h, p < 0.05). In an exemplary protein production scenario E. coli RM214 remained significantly more productive than E. coli MG1655 reaching 44% higher eGFP yield after 28 h of STR-PFR cultivation. This study developed E. coli RM214 as a robust chassis strain and demonstrated the feasibility of engineering microbial hosts for large-scale applications.

Implementation of kLa-Based Strategy for Scaling Up Porphyridium purpureum (Red Marine Microalga) to Produce High-Value Phycoerythrin, Fatty Acids, and Proteins

Porphyridium purpureum is a well-known Rhodophyta that recently has attracted enormous attention because of its capacity to produce many high-value metabolites such as the pigment phycoerythrin and several high-value fatty acids. Phycoerythrin is a fluorescent red protein-pigment commercially relevant with antioxidant, antimicrobial activity, and fluorescent properties. The volumetric mass transfer coefficient (k_La) was kept constant within the different scaling-up stages in the present study. This scaling-up strategy was sought to maintain phycoerythrin production and other high-value metabolites by Porphyridium purpureum, using hanging-bag photobioreactors. The k_La was monitored to ensure the appropriate mixing and CO₂ diffusion in the entire culture during the scaling process (16, 80, and 400 L). Then, biomass concentration, proteins, fatty acids, carbohydrates, and phycoerythrin were determined in each step of the scaling-up process. The k_La at 16 L reached a level of 0.0052 s^-1, while at 80 L, a value of 0.0024 s^-1 was achieved. This work result indicated that at 400 L, 1.22 g L^-1 of biomass was obtained, and total carbohydrates (117.24 mg L^-1), proteins (240.63 mg L^-1), and lipids (17.75% DW) were accumulated. Regarding fatty acids production, 46.03% palmitic, 8.03% linoleic, 22.67% arachidonic, and 2.55% eicosapentaenoic acid were identified, principally. The phycoerythrin production was 20.88 mg L^-1 with a purity of 2.75, making it viable for food-related applications. The results of these experiments provide insight into the high-scale production of phycoerythrin via the cultivation of P. purpureum in an inexpensive and straightforward culture system.

Transcriptional profiling of the stringent response mutant strain E. coli SR reveals enhanced robustness to large-scale conditions

In large-scale fed-batch production processes, microbes are exposed to heterogeneous substrate availability caused by long mixing times. Escherichia coli, the most common industrial host for recombinant protein production, reacts by recurring accumulation of the alarmone ppGpp and energetically wasteful transcriptional strategies. Here, we compare the regulatory responses of the stringent response mutant strain E. coli SR and its parent strain E. coli MG1655 to repeated nutrient starvation in a two-compartment scale-down reactor. Our data show that E. coli SR can withstand these stress conditions without a ppGpp-mediated stress response maintaining fully functional ammonium uptake and biomass formation. Furthermore, E. coli SR exhibited a substantially reduced short-term transcriptional response compared to E. coli MG1655 (less than half as many differentially expressed genes). E. coli SR proceeded adaptation via more general SOS response pathways by initiating negative regulation of transcription, translation and cell division. Our results show that locally induced stress responses propagating through the bioreactor do not result in cyclical induction and repression of genes in E. coli SR, but in a reduced and coordinated response, which makes it potentially suitable for large-scale production processes.

Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors

Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.

A physiological characterization in controlled bioreactors reveals a novel survival strategy for Debaryomyces hansenii at high salinity

Debaryomyces hansenii is traditionally described as a halotolerant non-conventional yeast and has served as a model organism for the study of osmotolerance and salt tolerance mechanisms in eukaryotic systems for the past 30 years. However, unraveling of D. hansenii's biotechnological potential has always been difficult due to the persistent limitations in the availability of efficient molecular tools described for this yeast. Additionally, there is a lack of consensus and contradictory information along the recent years that limits a comprehensive understanding of its central carbon metabolism, mainly due to a lack of physiological studies in controlled and monitored environments. Moreover, there is little consistency in the culture conditions (media composition, temperature, and pH among others) used by different groups, which makes it complicated when trying to get prevalent conclusions on behavioral patterns. In this work, we present for the first time a characterization of D. hansenii in batch cultivations using highly controlled lab-scale bioreactors. Our findings contribute to a more complete picture of the central carbon metabolism and the external pH influence on the yeast's ability to tolerate high Na⁺ and K⁺ concentrations, pointing to a differential effect of both salts, as well as a positive effect in cell performance when low environmental pH values are combined with a high sodium concentration in the media. Finally, a novel survival strategy at very high salinity (2 M) is proposed for this yeast, as well as potential outcomes for its use in industrial biotechnology applications. TAKE AWAY: High salt concentrations stimulate respiration in Debaryomyces hansenii. Sodium exerts a stronger positive impact on cell performance than potassium. μ_max is higher at a combination of low pH, high salt, and high temperature. Concentrations of 2 M salt result in slower growth but increased biomass yield. The positive effect of salts is enhanced at low glucose concentration.

Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach

Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.

Short and long-read ultra-deep sequencing profiles emerging heterogeneity across five platform Escherichia coli strains

Reprogramming organisms for large-scale bioproduction counters their evolutionary objectives of fast growth and often leads to mutational collapse of the engineered production pathways during cultivation. Yet, the mutational susceptibility of academic and industrial Escherichia coli bioproduction host strains are poorly understood. In this study, we apply 2^nd and 3^rd generation deep sequencing to profile simultaneous modes of genetic heterogeneity that decimate engineered biosynthetic production in five popular E. coli hosts BL21(DE3), TOP10, MG1655, W, and W3110 producing 2,3-butanediol and mevalonic acid. Combining short-read and long-read sequencing, we detect strain and sequence-specific mutational modes including single nucleotide polymorphism, inversion, and mobile element transposition, as well as complex structural variations that disrupt the integrity of the engineered biosynthetic pathway. Our analysis suggests that organism engineers should avoid chassis strains hosting active insertion sequence (IS) subfamilies such as IS1 and IS10 present in popular E. coli TOP10. We also recommend monitoring for increased mutagenicity in the pathway transcription initiation regions and recombinogenic repeats. Together, short and long sequencing reads identified latent low-frequency mutation events such as a short detrimental inversion within a pathway gene, driven by 8-bp short inverted repeats. This demonstrates the power of combining ultra-deep DNA sequencing technologies to profile genetic heterogeneities of engineered constructs and explore the markedly different mutational landscapes of common E. coli host strains. The observed multitude of evolving variants underlines the usefulness of early mutational profiling for new synthetic pathways designed to sustain in organisms over long cultivation scales.

The future of self-selecting and stable fermentations

Unfavorable cell heterogeneity is a frequent risk during bioprocess scale-up and characterized by rising frequencies of low-producing cells. Low-producing cells emerge by both non-genetic and genetic variation and will enrich due to their higher specific growth rate during the extended number of cell divisions of large-scale bioproduction. Here, we discuss recent strategies for synthetic stabilization of fermentation populations and argue for their application to make cell factory designs that better suit industrial needs. Genotype-directed strategies leverage DNA-sequencing data to inform strain design. Self-selecting phenotype-directed strategies couple high production with cell proliferation, either by redirected metabolic pathways or synthetic product biosensing to enrich for high-performing cell variants. Evaluating production stability early in new cell factory projects will guide heterogeneity-reducing design choices. As good initial metrics, we propose production half-life from standardized serial-passage stability screens and production load, quantified as production-associated percent-wise growth rate reduction. Incorporating more stable genetic designs will greatly increase scalability of future cell factories through sustaining a high-production phenotype and enabling stable long-term production.

Data-driven in silico prediction of regulation heterogeneity and ATP demands of Escherichia coli in large-scale bioreactors

Escherichia coli exposed to industrial-scale heterogeneous mixing conditions respond to external stress by initiating short-term metabolic and long-term strategic transcriptional programs. In native habitats, long-term strategies allow survival in severe stress but are of limited use in large bioreactors, where microenvironmental conditions may change right after said programs are started. Related on/off switching of genes causes additional ATP burden that may reduce the cellular capacity for producing the desired product. Here, we present an agent-based data-driven model linked to computational fluid dynamics, finally allowing to predict additional ATP needs of Escherichia coli K12 W3110 exposed to realistic large-scale bioreactor conditions. The complex model describes transcriptional up- and downregulation dynamics of about 600 genes starting from subminute range covering 28 h. The data-based approach was extracted from comprehensive scale-down experiments. Simulating mixing and mass transfer conditions in a 54 m³ stirred bioreactor, 120,000 E. coli cells were tracked while fluctuating between different zones of glucose availability. It was found that cellular ATP demands rise between 30% and 45% of growth decoupled maintenance needs, which may limit the production of ATP-intensive product formation accordingly. Furthermore, spatial analysis of individual cell transcriptional patterns reveal very heterogeneous gene amplifications with hot spots of 50%-80% messenger RNA upregulation in the upper region of the bioreactor. The phenomenon reflects the time-delayed regulatory response of the cells that propagate through the stirred tank. After 4.2 h, cells adapt to environmental changes but still have to bear an additional 6% ATP demand.

Investigation of Bar-seq as a method to study population dynamics of Saccharomyces cerevisiae deletion library during bioreactor cultivation

Background

Despite the latest advancements in metabolic engineering for genome editing and characterization of host performance, the successful development of robust cell factories used for industrial bioprocesses and accurate prediction of the behavior of microbial systems, especially when shifting from laboratory-scale to industrial conditions, remains challenging. To increase the probability of success of a scale-up process, data obtained from thoroughly performed studies mirroring cellular responses to typical large-scale stimuli may be used to derive crucial information to better understand potential implications of large-scale cultivation on strain performance. This study assesses the feasibility to employ a barcoded yeast deletion library to assess genome-wide strain fitness across a simulated industrial fermentation regime and aims to understand the genetic basis of changes in strain physiology during industrial fermentation, and the corresponding roles these genes play in strain performance.

Results

We find that mutant population diversity is maintained through multiple seed trains, enabling large scale fermentation selective pressures to act upon the community. We identify specific deletion mutants that were enriched in all processes tested in this study, independent of the cultivation conditions, which include MCK1, RIM11, MRK1, and YGK3 that all encode homologues of mammalian glycogen synthase kinase 3 (GSK-3). Ecological analysis of beta diversity between all samples revealed significant population divergence over time and showed feed specific consequences of population structure. Further, we show that significant changes in the population diversity during fed-batch cultivations reflect the presence of significant stresses. Our observations indicate that, for this yeast deletion collection, the selection of the feeding scheme which affects the accumulation of the fermentative by-product ethanol impacts the diversity of the mutant pool to a higher degree as compared to the pH of the culture broth. The mutants that were lost during the time of most extreme population selection suggest that specific biological processes may be required to cope with these specific stresses.

Conclusions

Our results demonstrate the feasibility of Bar-seq to assess fermentation associated stresses in yeast populations under industrial conditions and to understand critical stages of a scale-up process where variability emerges, and selection pressure gets imposed. Overall our work highlights a promising avenue to identify genetic loci and biological stress responses required for fitness under industrial conditions.

Saccharomyces cerevisiae (Baker's Yeast) as an Interfering RNA Expression and Delivery System

The broad application of RNA interference for disease prevention is dependent upon the production of dsRNA in an economically feasible, scalable, and sustainable fashion, as well as the identification of safe and effective methods for RNA delivery. Current research has sparked interest in the use of Saccharomyces cerevisiae for these applications. This review examines the potential for commercial development of yeast interfering RNA expression and delivery systems. S. cerevisiae is a genetic model organism that lacks a functional RNA interference system, which may make it an ideal system for expression and accumulation of high levels of recombinant interfering RNA. Moreover, recent studies in a variety of eukaryotic species suggest that this microbe may be an excellent and safe system for interfering RNA delivery. Key areas for further research and development include optimization of interfering RNA expression in S. cerevisiae, industrial-sized scaling of recombinant yeast cultures in which interfering RNA molecules are expressed, the development of methods for large-scale drying of yeast that preserve interfering RNA integrity, and identification of encapsulating agents that promote yeast stability in various environmental conditions. The genetic tractability of S. cerevisiae and a long history of using this microbe in both the food and pharmaceutical industry will facilitate further development of this promising new technology, which has many potential applications of medical importance.

Pseudomonas putida KT2440 is naturally endowed to withstand industrial-scale stress conditions

Pseudomonas putida is recognized as a very promising strain for industrial application due to its high redox capacity and frequently observed tolerance towards organic solvents. In this research, we studied the metabolic and transcriptional response of P. putida KT2440 exposed to large-scale heterogeneous mixing conditions in the form of repeated glucose shortage. Cellular responses were mimicked in an experimental setup comprising a stirred tank reactor and a connected plug flow reactor. We deciphered that a stringent response-like transcriptional regulation programme is frequently induced, which seems to be linked to the intracellular pool of 3-hydroxyalkanoates (3-HA) that are known to serve as precursors for polyhydroxyalkanoates (PHA). To be precise, P. putida is endowed with a survival strategy likely to access cellular PHA, amino acids and glycogen in few seconds under glucose starvation to obtain ATP from respiration, thereby replenishing the reduced ATP levels and the adenylate energy charge. Notably, cells only need 0.4% of glucose uptake to build those 3-HA-based energy buffers. Concomitantly, genes that are related to amino acid catabolism and β-oxidation are upregulated during the transient absence of glucose. Furthermore, we provide a detailed list of transcriptional short- and long-term responses that increase the cellular maintenance by about 17% under the industrial-like conditions tested.

Escherichia coli metabolism under short-term repetitive substrate dynamics: adaptation and trade-offs

Background

Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions.

Results

In this study we applied a repetitive feast–famine regime in an aerobic Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast–famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast–famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/g_CDW/s) than reported during batch growth (3.3 μmol/g_CDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions.

Conclusions

The adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.

Effect of production process scale-up on the characteristics and properties of bacterial nanocellulose obtained from overripe Banana culture medium

In this study, the effect of bioreactor size was evaluated with respect to the production and characteristics of the nanocellulose membranes produced by two different bioreactors: one with an 1800 cm² cross-sectional area (BC-B44) and a lab-scale bioreactor with a 41 cm² cross-sectional area (BC-B1). The culture conditions were kept the same, and the substrate consisted of overripe bananas, which are inexpensive because they are unsuitable for human consumption. The X-ray diffraction pattern showed that the two samples had similar crystalline structures, but changes were observed at the morphological level in the nanofibers that make up the BNC membranes. These changes generated, in turn, variations in the mechanical and thermal properties of the samples. This result represents a novel scale-up effect related to the static mode fermentation of BNC.

Scale-down studies for the scale-up of a recombinant Corynebacterium glutamicum fed-batch fermentation: loss of homogeneity leads to lower levels of cadaverine production

BACKGROUND

The loss of efficiency and performance of bioprocesses on scale-up is well known, but not fully understood. This work addresses this problem, by studying the effect of some fermentation gradients (pH, glucose and oxygen) that occur at the larger scale in a bench-scale two-compartment reactor [plug flow reactor (PFR) + stirred tank reactor (STR)] using the cadaverine-producing recombinant Corynebacterium glutamicum DM1945 Δact3 Ptuf-ldcC_OPT. The new scale-down strategy developed here studied the effect of increasing the magnitude of fermentation gradients by considering not only the average cell residence time in the PFR (τ _PFR), but also the mean frequency at which the bacterial cells entered the PFR (f _m) section of the two-compartment reactor.

RESULTS

On implementing this strategy the cadaverine production decreased on average by 26%, 49% and 59% when the τ _PFR was increased from 1 to 2 min and then 5 min respectively compared to the control fermentation. The carbon dioxide productivity was highest (3.1-fold that of the control) at a τ _PFR of 5 min, but no losses were observed in biomass production. However, the population of viable but non-culturable cells increased as the magnitude of fermentation gradients was increased. The new scale-down approach was also shown to have a bigger impact on fermentation performance than the traditional one.

CONCLUSION

This study demonstrated that C. glutamicum DM1945 Δact3 Ptuf-ldcC_OPT physiological response was a function of the magnitude of fermentation gradients simulated. The adaptations of a bacterial cell within a heterogeneous environment ultimately result in losses in fermentation productivity as observed here. © 2019 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.