High Cell Density Cultivation Combined with high Specific Enzyme Activity: Cultivation Protocol for the Production of an Amine Transaminase from Bacillus megaterium in E. coli.

High cell density cultivation is an established method for the production of various industrially important products such as recombinant proteins. However, these protocols are not always suitable for biocatalytic processes as the focus often lies on biomass production rather than high specific activities of the enzyme inside the cells. In contrast, a range of shake flask protocols are well known with high specific activities but rather low cell densities. To overcome this gap, we established a tailor-made fed-batch protocol combining both aspects: high cell density and high specific activities of heterologously produced enzyme. Using the example of an industrially relevant amine transaminase from Bacillus megaterium, we describe a strategy to optimize the cultivation yield based on the feed rate, IPTG concentration, and post-induction temperature. By adjusting these key parameters, we were able to increase the specific activity by 2.6-fold and the wet cell weight by even 17-fold compared to shake flasks. Finally, we were able to verify our established protocol by transferring it to another experimenter. With that, our optimization strategy can serve as a template for the production of high titers of heterologously produced, active enzymes and might enable the availability of these catalysts for upscaling biocatalytic processes.

Development of an itaconic acid production process with Ustilaginaceae on alternative feedstocks.

BACKGROUND: Currently, Aspergillus terreus is used for the industrial production of itaconic acid. Although, alternative feedstock use in fermentations is crucial for cost-efficient and sustainable itaconic acid production, their utilisation with A. terreus most often requires expensive pretreatment. Ustilaginacea are robust alternatives for itaconic acid production, evading the challenges, including the pretreatment of crude feedstocks regarding reduction of manganese concentration, that A. terreus poses. RESULTS: In this study, five different Ustilago strains were screened for their growth and production of itaconic acid on defined media. The most promising strains were then used to find a suitable alternative feedstock, based on the local food industry. U. cynodontis ITA Max pH, a highly engineered production strain, was selected to determine the biologically available nitrogen concentration in thick juice and molasses. Based on these findings, thick juice was chosen as feedstock to ensure the necessary nitrogen limitation for itaconic acid production. U. cynodontis ITA Max pH was further characterised regarding osmotolerance and product inhibition and a successful scale-up to a 2 L stirred tank reactor was accomplished. A titer of 106.4 gitaconic acid/L with a theoretical yield of 0.50 gitaconic acid/gsucrose and a space-time yield of 0.72 gitaconic acid/L/h was reached. CONCLUSIONS: This study demonstrates the utilisation of alternative feedstocks to produce ITA with Ustilaginaceae, without drawbacks in either titer or yield, compared to glucose fermentations.

Scaling eukaryotic cell-free protein synthesis achieved with the versatile and high-yielding tobacco BY-2 cell lysate.

Eukaryotic cell-free protein synthesis (CFPS) can accelerate expression and high-throughput analysis of complex proteins with functionally relevant post-translational modifications (PTMs). However, low yields and difficulties scaling such systems have prevented their widespread adoption in protein research and manufacturing. Here, we provide detailed demonstrations for the capabilities of a CFPS system derived from Nicotiana tabacum BY-2 cell culture (BY-2 lysate; BYL). BYL is able to express diverse, functional proteins at high yields in 48 h, complete with native disulfide bonds and N-glycosylation. An optimized version of the technology is commercialized as ALiCE® and advances in scaling of BYL production methodologies now allow scaling of eukaryotic CFPS reactions. We show linear, lossless scale-up of batch mode protein expression from 100 µL microtiter plates to 10 and 100 mL volumes in Erlenmeyer flasks, culminating in preliminary data from a litre-scale reaction in a rocking-type bioreactor. Together, scaling across a 20,000x range is achieved without impacting product yields. Production of multimeric virus-like particles from the BYL cytosolic fraction were then shown, followed by functional expression of multiple classes of complex, difficult-to-express proteins using the native microsomes of the BYL CFPS. Specifically: a dimeric enzyme; a monoclonal antibody; the SARS-CoV-2 receptor-binding domain; a human growth factor; and a G protein-coupled receptor membrane protein. Functional binding and activity are demonstrated, together with in-depth PTM characterization of purified proteins through disulfide bond and N-glycan analysis. Taken together, BYL is a promising end-to-end R&D to manufacturing platform with the potential to significantly reduce the time-to-market for high value proteins and biologics.

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.

ALiCE ® : A versatile, high yielding and scalable eukaryotic cell-free protein synthesis (CFPS) system.

Eukaryotic cell-free protein synthesis (CFPS) systems have the potential to simplify and speed up the expression and high-throughput analysis of complex proteins with functionally relevant post-translational modifications (PTMs). However, low yields and the inability to scale such systems have so far prevented their widespread adoption in protein research and manufacturing. Here, we present a detailed demonstration for the capabilities of a CFPS system derived from Nicotiana tabacum BY-2 cell culture (BY-2 lysate; BYL). BYL is able to express diverse, functional proteins at high yields in under 48 hours, complete with native disulfide bonds and N-glycosylation. An optimised version of the technology is commercialised as 'ALiCE ® ', engineered for high yields of up to 3 mg/mL. Recent advances in the scaling of BYL production methodologies have allowed scaling of the CFPS reaction. We show simple, linear scale-up of batch mode reporter proten expression from a 100 µL microtiter plate format to 10 mL and 100 mL volumes in standard Erlenmeyer flasks, culminating in preliminary data from 1 L reactions in a CELL-tainer® CT20 rocking motion bioreactor. As such, these works represent the first published example of a eukaryotic CFPS reaction scaled past the 10 mL level by several orders of magnitude. We show the ability of BYL to produce the simple reporter protein eYFP and large, multimeric virus-like particles directly in the cytosolic fraction. Complex proteins are processed using the native microsomes of BYL and functional expression of multiple classes of complex, difficult-to-express proteins is demonstrated, specifically: a dimeric, glycoprotein enzyme, glucose oxidase; the monoclonal antibody adalimumab; the SARS-Cov-2 receptor-binding domain; human epidermal growth factor; and a G protein-coupled receptor membrane protein, cannabinoid receptor type 2. Functional binding and activity are shown using a combination of surface plasmon resonance techniques, a serology-based ELISA method and a G protein activation assay. Finally, in-depth post-translational modification (PTM) characterisation of purified proteins through disulfide bond and N-glycan analysis is also revealed - previously difficult in the eukaryotic CFPS space due to limitations in reaction volumes and yields. Taken together, BYL provides a real opportunity for screening of complex proteins at the microscale with subsequent amplification to manufacturing-ready levels using off-the-shelf protocols. This end-to-end platform suggests the potential to significantly reduce cost and the time-to-market for high value proteins and biologics.

bletl - A Python package for integrating BioLector microcultivation devices in the Design-Build-Test-Learn cycle.

Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rate µ ⃗ t based on unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switch-points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model-driven process analysis.

Given its geometric similarity to large-scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom-made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale.

pyFOOMB: Python framework for object oriented modeling of bioprocesses.

Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black-box bioprocess modeling with non-linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi-purpose programing language, ODEs can be formulated in an object-oriented manner, which facilitates their modular design, reusability, and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi-stage processes, discontinuities in the right-hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programing language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.

Screening of a genome-reduced Corynebacterium glutamicum strain library for improved heterologous cutinase secretion.

The construction of microbial platform organisms by means of genome reduction is an ongoing topic in biotechnology. In this study, we investigated whether the deletion of single or multiple gene clusters has a positive effect on the secretion of cutinase from Fusarium solani pisi in the industrial workhorse Corynebacterium glutamicum. A total of 22 genome-reduced strain variants were compared applying two Sec signal peptides from Bacillus subtilis. High-throughput phenotyping using robotics-integrated microbioreactor technology with automated harvesting revealed distinct cutinase secretion performance for a specific combination of signal peptide and genomic deletions. The biomass-specific cutinase yield for strain GRS41_51_NprE was increased by ~ 200%, although the growth rate was reduced by ~ 60%. Importantly, the causative deletions of genomic clusters cg2801-cg2828 and rrnC-cg3298 could not have been inferred a priori. Strikingly, bioreactor fed-batch cultivations at controlled growth rates resulted in a complete reversal of the screening results, with the cutinase yield for strain GRS41_51_NprE dropping by ~ 25% compared to the reference strain. Thus, the choice of bioprocess conditions may turn a 'high-performance' strain from batch screening into a 'low-performance' strain in fed-batch cultivation. In conclusion, future studies are needed in order to understand metabolic adaptations of C. glutamicum to both genomic deletions and different bioprocess conditions.

Less Sacrifice, More Insight: Repeated Low-Volume Sampling of Microbioreactor Cultivations Enables Accelerated Deep Phenotyping of Microbial Strain Libraries.

With modern genetic engineering tools, high number of potentially improved production strains can be created in a short time. This results in a bottleneck in the succeeding step of bioprocess development, which can be handled by accelerating quantitative microbial phenotyping. Miniaturization and automation are key technologies to achieve this goal. In this study, a novel workflow for repeated low-volume sampling of BioLector-based cultivation setups is presented. Six samples of 20 µL each can be taken automatically from shaken 48-well microtiter plates without disturbing cell population growth. The volume is sufficient for quantification of substrate and product concentrations by spectrophotometric-based enzyme assays. From transient concentration data and replicate cultures, valid performance indicators (titers, rates, yields) are determined through process modeling and random error propagation analysis. Practical relevance of the workflow is demonstrated with a set of five genome-reduced Corynebacterium glutamicum strains that are engineered for Sec-mediated heterologous cutinase secretion. Quantitative phenotyping of this strain library led to the identification of a strain with a 1.6-fold increase in cutinase yield. The prophage-free strain carries combinatorial deletions of three gene clusters (Δ3102-3111, Δ3263-3301, and Δ3324-3345) of which the last two likely contain novel target genes to foster rational engineering of heterologous cutinase secretion in C. glutamicum.

Combinatorial impact of Sec signal peptides from Bacillus subtilis and bioprocess conditions on heterologous cutinase secretion by Corynebacterium glutamicum.

The impact of Sec signal peptides (SPs) from Bacillus subtilis in combination with isopropyl-ß- d-1-thiogalactopyranoside concentration and feeding profile was investigated for heterologous protein secretion performance by Corynebacterium glutamicum using cutinase as model enzyme. Based on a comprehensive data set of about 150 bench-scale bioreactor cultivations in fed-batch mode and choosing the cutinase yield as objective, it was shown that relative secretion performance for bioprocesses remains very similar, irrespective of the applied SP enabling Sec-mediated cutinase secretion. However, to achieve the maximal absolute cutinase yield, careful adjustment of bioprocess conditions was found to be necessary. A model-based, two-step multiple regression approach resembled the collected data in a comprehensive way. The corresponding results suggest that the choice of the heterologous Sec SP and its interaction with the adjusted exponential feeding profile is highly relevant to maximize absolute cutinase yield in this study. For example, the impact of Sec SP is high at low growth rates and low at high growth rates. However, promising Sec SPs could be inferred from less complex batch cultivations. The extensive data were also evaluated in terms of cutinase productivity, highlighting the well-known trade-off between yield and productivity in bioprocess development in detail. Conclusively, only the right combination of target protein, Sec SP, and bioprocess conditions is the key to success.

Microbioreactor Systems for Accelerated Bioprocess Development.

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess engineering tools. They provide a unique solution to combine higher experimental throughput with extensive bioprocess monitoring and control, which is indispensable to develop economically and ecologically competitive bioproduction processes. MBR systems are based either on down-scaled stirred tank reactors or on advanced shaken microtiter plate cultivation devices. Importantly, MBR systems make use of optical measurements for non-invasive, online monitoring of important process variables like biomass concentration, dissolved oxygen, pH, and fluorescence. The application range of MBR systems can be further increased by integration into liquid handling robots, enabling automatization and, thus standardization, of various handling and operation procedures. Finally, the tight integration of quantitative strain phenotyping with bioprocess development under industrially relevant conditions greatly increases the probability of finding the right combination of producer strain and bioprocess control strategy. This review will discuss the current state of the art in the field of MBR systems and we can readily conclude that their importance for industrial biotechnology will further increase in the near future.

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology.

A core business in industrial biotechnology using microbial production cell factories is the iterative process of strain engineering and optimization of bioprocess conditions. One important aspect is the improvement of cultivation medium to provide an optimal environment for microbial formation of the product of interest. It is well accepted that the media composition can dramatically influence overall bioprocess performance. Nutrition medium optimization is known to improve recombinant protein production with microbial systems and thus, this is a rewarding step in bioprocess development. However, very often standard media recipes are taken from literature, since tailor-made design of the cultivation medium is a tedious task that demands microbioreactor technology for sufficient cultivation throughput, fast product analytics, as well as support by lab robotics to enable reliability in liquid handling steps. Furthermore, advanced mathematical methods are required for rationally analyzing measurement data and efficiently designing parallel experiments such as to achieve optimal information content. The generic nature of the presented protocol allows for easy adaption to different lab equipment, other expression hosts, and target proteins of interest, as well as further bioprocess parameters. Moreover, other optimization objectives like protein production rate, specific yield, or product quality can be chosen to fit the scope of other optimization studies. The applied Kriging Toolbox (KriKit) is a general tool for Design of Experiments (DOE) that contributes to improved holistic bioprocess optimization. It also supports multi-objective optimization which can be important in optimizing both upstream and downstream processes.

Automated growth rate determination in high-throughput microbioreactor systems.

OBJECTIVE: The calculation of growth rates provides basic metric for biological fitness and is standard task when using microbioreactors (MBRs) in microbial phenotyping. MBRs easily produce huge data at high frequency from parallelized high-throughput cultivations with online monitoring of biomass formation at high temporal resolution. Resulting high-density data need to be processed efficiently to accelerate experimental throughput. RESULTS: A MATLAB code is presented that detects the exponential growth phase from multiple microbial cultivations in an iterative procedure based on several criteria, according to the model of exponential growth. These were obtained with Corynebacterium glutamicum showing single exponential growth phase and Escherichia coli exhibiting diauxic growth with exponential phase followed by retarded growth. The procedure reproducibly detects the correct biomass data subset for growth rate calculation. The procedure was applied on data set detached from growth phenotyping of library of genome reduced C. glutamicum strains and results agree with previously reported results where manual effort was needed to pre-process the data. Thus, the automated and standardized method enables a fair comparison of strain mutants for biological fitness evaluation. The code is easily parallelized and greatly facilitates experimental throughout in biological fitness testing from strain screenings conducted with MBR systems.

Use of a Sec signal peptide library from Bacillus subtilis for the optimization of cutinase secretion in Corynebacterium glutamicum.

BACKGROUND: Technical bulk enzymes represent a huge market, and the extracellular production of such enzymes is favorable due to lowered cost for product recovery. Protein secretion can be achieved via general secretion (Sec) pathway. Specific sequences, signal peptides (SPs), are necessary to direct the target protein into the translocation machinery. For example, >150 Sec-specific SPs have been identified for Bacillus subtilis alone. As the best SP for a target protein of choice cannot be predicted a priori, screening of homologous SPs has been shown to be a powerful tool for different expression organisms. While SP libraries between closely related species were successfully applied to optimize recombinant protein secretion, this was not investigated for distantly related species. Therefore, in this study a Sec SP library from low-GC firmicutes B. subtilis is investigated to optimize protein secretion in high-GC actinobacterium Corynebacterium glutamicum using cutinase from Fusarium solani pisi as model protein. RESULTS: A homologous SP library (~150 SP) for recombinant cutinase secretion in B. subtilis was successfully transferred to C. glutamicum as alternative secretion host. Cutinase secretion in C. glutamicum was quantified using an automated micro scale cultivation system for online growth monitoring, cell separation and cutinase activity determination. Secretion phenotyping results were correlated to those from a previous study, in which the same SP library was used to optimize secretion of the same cutinase but using B. subtilis as host. Strikingly, behavior of specific SP-cutinase combinations was changed dramatically between B. subtilis and C. glutamicum. Some SPs showed comparable cutinase secretion performances in both hosts, whereas other SPs caused diametrical extracellular cutinase activities. CONCLUSION: The optimal production strain for a specific target protein of choice still cannot be designed in silico. Not only the best SP for a target protein has to be evaluated each time from scratch, the expression host also affects which SP is best. Thus, (heterologous) SP library screening using high-throughput methods is considered to be crucial to construct an optimal production strain for a target protein.

Comprehensive clone screening and evaluation of fed-batch strategies in a microbioreactor and lab scale stirred tank bioreactor system: application on Pichia pastoris producing Rhizopus oryzae lipase.

BACKGROUND: In Pichia pastoris bioprocess engineering, classic approaches for clone selection and bioprocess optimization at small/micro scale using the promoter of the alcohol oxidase 1 gene (PAOX1), induced by methanol, present low reproducibility leading to high time and resource consumption. RESULTS: An automated microfermentation platform (RoboLector) was successfully tested to overcome the chronic problems of clone selection and optimization of fed-batch strategies. Different clones from Mut+P. pastoris phenotype strains expressing heterologous Rhizopus oryzae lipase (ROL), including a subset also overexpressing the transcription factor HAC1, were tested to select the most promising clones.The RoboLector showed high performance for the selection and optimization of cultivation media with minimal cost and time. Syn6 medium was better than conventional YNB medium in terms of production of heterologous protein.The RoboLector microbioreactor was also tested for different fed-batch strategies with three clones producing different lipase levels. Two mixed substrates fed-batch strategies were evaluated. The first strategy was the enzymatic release of glucose from a soluble glucose polymer by a glucosidase, and methanol addition every 24 hours. The second strategy used glycerol as co-substrate jointly with methanol at two different feeding rates. The implementation of these simple fed-batch strategies increased the levels of lipolytic activity 80-fold compared to classical batch strategies used in clone selection. Thus, these strategies minimize the risk of errors in the clone selection and increase the detection level of the desired product.Finally, the performance of two fed-batch strategies was compared for lipase production between the RoboLector microbioreactor and 5 liter stirred tank bioreactor for three selected clones. In both scales, the same clone ranking was achieved. CONCLUSION: The RoboLector showed excellent performance in clone selection of P. pastoris Mut+ phenotype. The use of fed-batch strategies using mixed substrate feeds resulted in increased biomass and lipolytic activity. The automated processing of fed-batch strategies by the RoboLector considerably facilitates the operation of fermentation processes, while reducing error-prone clone selection by increasing product titers.The scale-up from microbioreactor to lab scale stirred tank bioreactor showed an excellent correlation, validating the use of microbioreactor as a powerful tool for evaluating fed-batch operational strategies.

Growth independent rhamnolipid production from glucose using the non-pathogenic Pseudomonas putida KT2440.

BACKGROUND: Rhamnolipids are potent biosurfactants with high potential for industrial applications. However, rhamnolipids are currently produced with the opportunistic pathogen Pseudomonas aeruginosa during growth on hydrophobic substrates such as plant oils. The heterologous production of rhamnolipids entails two essential advantages: Disconnecting the rhamnolipid biosynthesis from the complex quorum sensing regulation and the opportunity of avoiding pathogenic production strains, in particular P. aeruginosa. In addition, separation of rhamnolipids from fatty acids is difficult and hence costly. RESULTS: Here, the metabolic engineering of a rhamnolipid producing Pseudomonas putida KT2440, a strain certified as safety strain using glucose as carbon source to avoid cumbersome product purification, is reported. Notably, P. putida KT2440 features almost no changes in growth rate and lag-phase in the presence of high concentrations of rhamnolipids (> 90 g/L) in contrast to the industrially important bacteria Bacillus subtilis, Corynebacterium glutamicum, and Escherichia coli. P. putida KT2440 expressing the rhlAB-genes from P. aeruginosa PAO1 produces mono-rhamnolipids of P. aeruginosa PAO1 type (mainly C(10):C(10)). The metabolic network was optimized in silico for rhamnolipid synthesis from glucose. In addition, a first genetic optimization, the removal of polyhydroxyalkanoate formation as competing pathway, was implemented. The final strain had production rates in the range of P. aeruginosa PAO1 at yields of about 0.15 g/g(glucose) corresponding to 32% of the theoretical optimum. What's more, rhamnolipid production was independent from biomass formation, a trait that can be exploited for high rhamnolipid production without high biomass formation. CONCLUSIONS: A functional alternative to the pathogenic rhamnolipid producer P. aeruginosa was constructed and characterized. P. putida KT24C1 pVLT31_rhlAB featured the highest yield and titer reported from heterologous rhamnolipid producers with glucose as carbon source. Notably, rhamnolipid production was uncoupled from biomass formation, which allows optimal distribution of resources towards rhamnolipid synthesis. The results are discussed in the context of rational strain engineering by using the concepts of synthetic biology like chassis cells and orthogonality, thereby avoiding the complex regulatory programs of rhamnolipid production existing in the natural producer P. aeruginosa.