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

AutoBioTech─A Versatile Biofoundry for Automated Strain Engineering

The inevitable transition from petrochemical production processes to renewable alternatives has sparked the emergence of biofoundries in recent years. Manual engineering of microbes will not be sufficient to meet the ever-increasing demand for novel producer strains. Here we describe the AutoBioTech platform, a fully automated laboratory system with 14 devices to perform operations for strain construction without human interaction. Using modular workflows, this platform enables automated transformations of Escherichia coli with plasmids assembled via modular cloning. A CRISPR/Cas9 toolbox compatible with existing modular cloning frameworks allows automated and flexible genome editing of E. coli. In addition, novel workflows have been established for the fully automated transformation of the Gram-positive model organism Corynebacterium glutamicum by conjugation and electroporation, with the latter proving to be the more robust technique. Overall, the AutoBioTech platform excels at versatility due to the modularity of workflows and seamless transitions between modules. This will accelerate strain engineering of Gram-negative and Gram-positive bacteria.

Automated workflow for characterization of bacteriocin production in natural producers Lactococcus lactis and Latilactobacillus sakei

BACKGROUND

Lactic acid bacteria are commonly used as protective starter cultures in food products. Among their beneficial effects is the production of ribosomally synthesized peptides termed bacteriocins that kill or inhibit food-spoiling bacteria and pathogens, e.g., members of the Listeria species. As new bacteriocins and producer strains are being discovered rapidly, modern automated methods for strain evaluation and bioprocess development are required to accelerate screening and development processes.

RESULTS

In this study, we developed an automated workflow for screening and bioprocess optimization for bacteriocin producing lactic acid bacteria, consisting of microcultivation, sample processing and automated antimicrobial activity assay. We implemented sample processing workflows to minimize bacteriocin adsorption to producer cells via addition of Tween 80 and divalent cations to the cultivation media as well as acidification of culture broth prior to cell separation. Moreover, we demonstrated the applicability of the automated workflow to analyze influence of media components such as MES buffer or yeast extract for bacteriocin producers Lactococcus lactis B1629 and Latilactobacillus sakei A1608.

CONCLUSIONS

Our automated workflow provides advanced possibilities to accelerate screening and bioprocess optimization for natural bacteriocin producers. Based on its modular concept, adaptations for other strains, bacteriocin products and applications are easily carried out and a unique tool to support bacteriocin research and bioprocess development is provided.

Itaconate Production from Crude Substrates with U. maydis: Scale-up of an Industrially Relevant Bioprocess

BACKGROUND

Industrial by-products accrue in most agricultural or food-related production processes, but additional value chains have already been established for many of them. Crude glycerol has a 60% lower market value than commercial glucose, as large quantities are produced in the biodiesel industry, but its valorisation is still underutilized. Due to its high carbon content and the natural ability of many microorganisms to metabolise it, microbial upcycling is a suitable option for this waste product.

RESULTS

In this work, the use of crude glycerol for the production of the value-added compound itaconate is demonstrated using the smut fungus Ustilago maydis. Starting with a highly engineered strain, itaconate production from an industrial glycerol waste stream was quickly established on a small scale, and the resulting yields were already competitive with processes using commercial sugars. Adaptive laboratory evolution resulted in an evolved strain with a 72% increased growth rate on glycerol. In the subsequent development and optimisation of a fed-batch process on a 1.5-2 L scale, the use of molasses, a side stream of sugar beet processing, eliminated the need for other expensive media components such as nitrogen or vitamins for biomass growth. The optimised process was scaled up to 150 L, achieving an overall titre of 72 g L- 1, a yield of 0.34 g g- 1, and a productivity of 0.54 g L- 1 h- 1.

CONCLUSIONS

Pilot-scale itaconate production from the complementary waste streams molasses and glycerol has been successfully established. In addition to achieving competitive performance indicators, the proposed dual feedstock strategy offers lower process costs and carbon footprint for the production of bio-based itaconate.

Introducing molasses as an alternative feedstock into itaconate production using Ustilago sp

In this work, we established an efficient process for the production of itaconate from the regionally sourced industrial side-stream molasses using Ustilago cynodontis and Ustilago maydis. While being relatively cheap and more environmentally friendly than refined sugars, there are some major challenges to overcome when working with molasses. Some of those challenges are a high nitrogen load, unknown impurities in the feedstock, and high amounts of ill-favoured carbon sources, such as sucrose or lactate. We could show that the activity of the sucrose-hydrolysing enzyme invertase plays a crucial role in the efficiency of the process and that the fructose utilisation differs between the two strains used in this work. Thus, with a higher invertase activity, the ability to convert fructose into the desired product itaconate, and an overall higher tolerance towards undesired substances in molasses, U. maydis is better equipped for the process on the alternative feedstock molasses than U. cynodontis. The established process with U. maydis reached competitive yields of up to 0.38 g g-1 and a titre of more than 37 g L-1. This shows that an efficient and cost-effective itaconate production process is generally feasible using U. maydis, which has the potential to greatly increase the sustainability of industrial itaconate production.

From frozen cell bank to product assay: high-throughput strain characterisation for autonomous Design-Build-Test-Learn cycles

BACKGROUND

Modern genome editing enables rapid construction of genetic variants, which are further developed in Design-Build-Test-Learn cycles. To operate such cycles in high throughput, fully automated screening, including cultivation and analytics, is crucial in the Test phase. Here, we present the required steps to meet these demands, resulting in an automated microbioreactor platform that facilitates autonomous phenotyping from cryo culture to product assay.

RESULTS

First, an automated deep freezer was integrated into the robotic platform to provide working cell banks at all times. A mobile cart allows flexible docking of the freezer to multiple platforms. Next, precultures were integrated within the microtiter plate for cultivation, resulting in highly reproducible main cultures as demonstrated for Corynebacterium glutamicum. To avoid manual exchange of microtiter plates after cultivation, two clean-in-place strategies were established and validated, resulting in restored sterile conditions within two hours. Combined with the previous steps, these changes enable a flexible start of experiments and greatly increase the walk-away time.

CONCLUSIONS

Overall, this work demonstrates the capability of our microbioreactor platform to perform autonomous, consecutive cultivation and phenotyping experiments. As highlighted in a case study of cutinase-secreting strains of C. glutamicum, the new procedure allows for flexible experimentation without human interaction while maintaining high reproducibility in early-stage screening processes.

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha

BACKGROUND

Non-invasive online fluorescence monitoring in high-throughput microbioreactors is a well-established method to accelerate early-stage bioprocess development. Recently, single-wavelength fluorescence monitoring in microtiter plates was extended to measurements of highly resolved 2D fluorescence spectra, by introducing charge-coupled device (CCD) detectors. Although introductory experiments demonstrated a high potential of the new monitoring technology, an assessment of the capabilities and limits for practical applications is yet to be provided.

RESULTS

In this study, three experimental sets introducing secondary substrate limitations of magnesium, potassium, and phosphate to cultivations of a GFP-expressing H. polymorpha strain were conducted. This increased the complexity of the spectral dynamics, which were determined by 2D fluorescence measurements. The metabolic responses upon growth limiting conditions were assessed by monitoring of the oxygen transfer rate and extensive offline sampling. Using only the spectral data, subsequently, partial least-square (PLS) regression models for the key parameters of glycerol, cell dry weight, and pH value were generated. For model calibration, spectral data of only two cultivation conditions were combined with sparse offline sampling data. Applying the models to spectral data of six cultures not used for calibration, resulted in an average relative root-mean-square error (RMSE) of prediction between 6.8 and 6.0%. Thus, while demanding only sparse offline data, the models allowed the estimation of biomass accumulation and glycerol consumption, even in the presence of more or less pronounced secondary substrate limitation.

CONCLUSION

For the secondary substrate limitation experiments of this study, the generation of data-driven models allowed a considerable reduction in sampling efforts while also providing process information for unsampled cultures. Therefore, the practical experiments of this study strongly affirm the previously claimed advantages of 2D fluorescence spectroscopy in microtiter plates.

Explore or exploit? A model-based screening strategy for PETase secretion by Corynebacterium glutamicum

Extracellular production of target proteins simplifies downstream processing due to obsolete cell disruption. However, optimal combinations of a heterologous protein, suitable signal peptide, and secretion host can currently not be predicted, resulting in large strain libraries that need to be tested. On the experimental side, this challenge can be tackled by miniaturization, parallelization, and automation, which provide high-throughput screening data. These data need to be condensed into a candidate ranking for decision-making to focus bioprocess development on the most promising candidates. We screened for Bacillus subtilis signal peptides mediating Sec secretion of two polyethylene terephthalate degrading enzymes (PETases), leaf-branch compost cutinase (LCC) and polyester hydrolase mutants, by Corynebacterium glutamicum. We developed a fully automated screening process and constructed an accompanying Bayesian statistical modeling framework, which we applied in screenings for highest activity in 4-nitrophenyl palmitate degradation. In contrast to classical evaluation methods, batch effects and biological errors are taken into account and their uncertainty is quantified. Within only two rounds of screening, the most suitable signal peptide was identified for each PETase. Results from LCC secretion in microliter-scale cultivation were shown to be scalable to laboratory-scale bioreactors. This work demonstrates an experiment-modeling loop that can accelerate early-stage screening in a way that experimental capacities are focused to the most promising strain candidates. Combined with high-throughput cloning, this paves the way for using large strain libraries of several hundreds of strains in a Design-Build-Test-Learn approach.

Rational metabolic engineering of Corynebacterium glutamicum to create a producer of L-valine

L-Valine is one of the nine amino acids that cannot be synthesized de novo by higher organisms and must come from food. This amino acid not only serves as a building block for proteins, but also regulates protein and energy metabolism and participates in neurotransmission. L-Valine is used in the food and pharmaceutical industries, medicine and cosmetics, but primarily as an animal feed additive. Adding L-valine to feed, alone or mixed with other essential amino acids, allows for feeds with lower crude protein content, increases the quality and quantity of pig meat and broiler chicken meat, as well as improves reproductive functions of farm animals. Despite the fact that the market for L-valine is constantly growing, this amino acid is not yet produced in our country. In modern conditions, the creation of strains-producers and organization of L-valine production are especially relevant for Russia. One of the basic microorganisms most commonly used for the creation of amino acid producers, along with Escherichia coli, is the soil bacterium Corynebacterium glutamicum. This review is devoted to the analysis of the main strategies for the development of L- valine producers based on C. glutamicum. Various aspects of L-valine biosynthesis in C. glutamicum are reviewed: process biochemistry, stoichiometry and regulation, enzymes and their corresponding genes, export and import systems, and the relationship of L-valine biosynthesis with central cell metabolism. Key genetic elements for the creation of C. glutamicum-based strains-producers are identified. The use of metabolic engineering to enhance L-valine biosynthesis reactions and to reduce the formation of byproducts is described. The prospects for improving strains in terms of their productivity and technological characteristics are shown. The information presented in the review can be used in the production of producers of other amino acids with a branched side chain, namely L-leucine and L-isoleucine, as well as D-pantothenate.

Stratifications and foliations in phase portraits of gene network models

Periodic processes of gene network functioning are described with good precision by periodic trajectories (limit cycles) of multidimensional systems of kinetic-type differential equations. In the literature, such systems are often called dynamical, they are composed according to schemes of positive and negative feedback between components of these networks. The variables in these equations describe concentrations of these components as functions of time. In the preparation of numerical experiments with such mathematical models, it is useful to start with studies of qualitative behavior of ensembles of trajectories of the corresponding dynamical systems, in particular, to estimate the highest likelihood domain of the initial data, to solve inverse problems of parameter identification, to list the equilibrium points and their characteristics, to localize cycles in the phase portraits, to construct stratification of the phase portraits to subdomains with different qualities of trajectory behavior, etc. Such an à priori geometric analysis of the dynamical systems is quite analogous to the basic section "Investigation of functions and plot of their graphs" of Calculus, where the methods of qualitative studies of shapes of curves determined by equations are exposed. In the present paper, we construct ensembles of trajectories in phase portraits of some dynamical systems. These ensembles are 2-dimensional surfaces invariant with respect to shifts along the trajectories. This is analogous to classical construction in analytic mechanics, i. e. the level surfaces of motion integrals (energy, kinetic moment, etc.). Such surfaces compose foliations in phase portraits of dynamical systems of Hamiltonian mechanics. In contrast with this classical mechanical case, the foliations considered in this paper have singularities: all their leaves have a non-empty intersection, they contain limit cycles on their boundaries. Description of the phase portraits of these systems at the level of their stratifications, and that of ensembles of trajectories allows one to construct more realistic gene network models on the basis of methods of statistical physics and the theory of stochastic differential equations.

Accelerated strain construction and characterization of C. glutamicum protein secretion by laboratory automation

Secretion of bacterial proteins into the culture medium simplifies downstream processing by avoiding cell disruption for target protein purification. However, a suitable signal peptide for efficient secretion needs to be identified, and currently, there are no tools available to predict optimal combinations of signal peptides and target proteins. The selection of such a combination is influenced by several factors, including protein biosynthesis efficiency and cultivation conditions, which both can have a significant impact on secretion performance. As a result, a large number of combinations must be tested. Therefore, we have developed automated workflows allowing for targeted strain construction and secretion screening using two platforms. Key advantages of this experimental setup include lowered hands-on time and increased throughput. In this study, the automated workflows were established for the heterologous production of Fusarium solani f. sp. pisi cutinase in Corynebacterium glutamicum. The target protein was monitored in culture supernatants via enzymatic activity and split GFP assay. Varying spacer lengths between the Shine-Dalgarno sequence and the start codon of Bacillus subtilis signal peptides were tested. Consistent with previous work on the secretory cutinase production in B. subtilis, a ribosome binding site with extended spacer length to up to 12 nt, which likely slows down translation initiation, does not necessarily lead to poorer cutinase secretion by C. glutamicum. The best performing signal peptides for cutinase secretion with a standard spacer length were identified in a signal peptide screening. Additional insights into the secretion process were gained by monitoring secretion stress using the C. glutamicum K9 biosensor strain. KEY POINTS: • Automated workflows for strain construction and screening of protein secretion • Comparison of spacer, signal peptide, and host combinations for cutinase secretion • Signal peptide screening for secretion by C. glutamicum using the split GFP assay.

Multi-analyte High-Throughput Microplate-SERS Reader with Controllable Liquid Interfacial Arrays

High-throughput surface-enhanced Raman scattering (SERS) reader, especially for liquid sample testing, is of great significance and huge demand in biology, environment, and other analytical fields. Inspired by the principle of microplate reader, herein, we developed a microplate-SERS reader for semiautomatic and high-throughput assays by virtue of three-dimensional liquid interfacial arrays (LIAs). For the first time, the formation of LIA in oil-in-water state, water-in-oil state, and two-dimensional plane state is realized by operating the hydrophilicity (contact angle) of the container. Through the force analysis of LIA, the effect of organic (O) phase density on the relative position of LIA was quantified. In addition, the optimized reader offers fast and continuous semiautomatic detection of 12 samples below 10 min with great signal reproducibility (calibration with the characteristic peak of O phase as the internal standard). The isolated wells in the microplate prevent analyte cross talk, allowing accurate quantification of each sample. Multiplex analysis capability highlights that this reader has the ability of rapid identification and quantification of samples containing various analytes and concentrations. The results demonstrate high-resolution dual and triple analyte detection with fully preserved signal and Raman features of individual analytes in a mixture, which implies that it also has excellent anticounterfeiting applications. This microplate-SERS reader combines the superior advantages of the LIA, microplate, and SERS techniques to retrieve the molecular vibrational fingerprints of various chemicals in complex media.

Hot isopropanol quenching procedure for automated microtiter plate scale 13C-labeling experiments

Background

Currently, the generation of genetic diversity for microbial cell factories outpaces the screening of strain variants with omics-based phenotyping methods. Especially isotopic labeling experiments, which constitute techniques aimed at elucidating cellular phenotypes and supporting rational strain design by growing microorganisms on substrates enriched with heavy isotopes, suffer from comparably low throughput and the high cost of labeled substrates.

Results

We present a miniaturized, parallelized, and automated approach to ¹³C-isotopic labeling experiments by establishing and validating a hot isopropanol quenching method on a robotic platform coupled with a microbioreactor cultivation system. This allows for the first time to conduct automated labeling experiments at a microtiter plate scale in up to 48 parallel batches. A further innovation enabled by the automated quenching method is the analysis of free amino acids instead of proteinogenic ones on said microliter scale. Capitalizing on the latter point and as a proof of concept, we present an isotopically instationary labeling experiment in Corynebacterium glutamicum ATCC 13032, generating dynamic labeling data of free amino acids in the process.

Conclusions

Our results show that a robotic liquid handler is sufficiently fast to generate informative isotopically transient labeling data. Furthermore, the amount of biomass obtained from a sub-milliliter cultivation in a microbioreactor is adequate for the detection of labeling patterns of free amino acids. Combining the innovations presented in this study, isotopically stationary and instationary automated labeling experiments can be conducted, thus fulfilling the prerequisites for ¹³C-metabolic flux analyses in high-throughput.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01806-4.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences.

Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications.

In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences.

Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

In experimental fields like biotechnology, scientists need to quantify process parameters such as concentrations and state the uncertainty around them. However, measurements rarely yield the desired quantity directly; for example, the measurement of scattered light is just an indirect measure for the number of cells in a suspension. For reliable interpretation, scientists must determine the uncertainty around the underlying quantities of interest using statistical methods.

A key step in these workflows is the establishment of calibration models to describe the relation between the quantities of interest and measurement outcomes. This is typically done using measurements of reference samples for which the true quantities are known. However, implementing and applying these statistical models often requires skills that are not commonly taught.

We therefore developed two software packages, calibr8 and murefi, to simplify such calibration and modeling procedures. To showcase our work, we performed an experiment commonly seen in microbiology: the acquisition of a microbial growth curve, in this case of Corynebacterium glutamicum, in an online measurement device. Using our software, we built a mathematical model of the overall process to quantify relevant parameters with uncertainty, e.g. the growth rate or the yield of biomass per amount of glucose.

Impedance flow cytometry for viability analysis of Corynebacterium glutamicum

Corynebacterium glutamicum efficiently produces glutamate when growth is inhibited. Analyses of viability in this non-growing state requires time consuming plating and determination of colony forming units. We here establish impedance flow cytometry measurements to assess the viability of non-growing, glutamate producing C. glutamicum cultures within minutes.

Scaling production of GFP1-10 detector protein in E. coli for secretion screening by split GFP assay

BACKGROUND

The split GFP assay is a well-known technology for activity-independent screening of target proteins. A superfolder GFP is split into two non-fluorescent parts, GFP11 which is fused to the target protein and GFP1-10. In the presence of both, GFP1-10 and the GFP11-tag are self-assembled and a functional chromophore is formed. However, it relies on the availability and quality of GFP1-10 detector protein to develop fluorescence by assembly with the GFP11-tag connected to the target protein. GFP1-10 detector protein is often produced in small scale shake flask cultivation and purified from inclusion bodies.

RESULTS

The production of GFP1-10 in inclusion bodies and purification was comprehensively studied based on Escherichia coli as host. Cultivation in complex and defined medium as well as different feed strategies were tested in laboratory-scale bioreactor cultivation and a standardized process was developed providing high quantity of GFP1-10 detector protein with suitable quality. Split GFP assay was standardized to obtain robust and reliable assay results from cutinase secretion strains of Corynebacterium glutamicum with Bacillus subtilis Sec signal peptides NprE and Pel. Influencing factors from environmental conditions, such as pH and temperature were thoroughly investigated.

CONCLUSIONS

GFP1-10 detector protein production could be successfully scaled from shake flask to laboratory scale bioreactor. A single run yielded sufficient material for up to 385 96-well plate screening runs. The application study with cutinase secretory strains showed very high correlation between measured cutinase activity to split GFP fluorescence signal proofing applicability for larger screening studies.

Lab on a Chip: Bioreactors miniaturization for rapid optimization of biomedical processes and its impact on SARS-CoV-2 diagnosis

Lab on a Chip (LoC) as part of Microbioreactors (MBRs) constitute an emergent technology to carry out micro-bioprocesses based on microfluidics research. In this review, the usefulness of LoCs is exposed since its inception, demonstrating that it is a multidisciplinary research field, gathering different science branches to develop this technology. As a result, a beneficial point of advancement is reached, producing useful consumables for humanity. Some of the described LoCs throughout this work are also used to detect infectious diseases caused by bacteria or viruses, allowing accelerated studies on emerging or high-impact diseases, such as COVID-19. Here are also displayed with an updated panorama, different strategies to improve the use, applications in the biomedical field, and spread of these devices aimed at their availability to solve social problems.

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.

Need for speed: evaluation of dilute and shoot-mass spectrometry for accelerated metabolic phenotyping in bioprocess development

With the utilization of small-scale and highly parallelized cultivation platforms embedded in laboratory robotics, microbial phenotyping and bioprocess development have been substantially accelerated, thus generating a bottleneck in bioanalytical bioprocess sample analytics. While microscale cultivation platforms allow the monitoring of typical process parameters, only limited information about product and by-product formation is provided without comprehensive analytics. The use of liquid chromatography mass spectrometry can provide such a comprehensive and quantitative insight, but is often limited by analysis runtime and throughput. In this study, we developed and evaluated six methods for amino acid quantification based on two strong cation exchanger columns and a dilute and shoot approach in hyphenation with either a triple-quadrupole or a quadrupole time-of-flight mass spectrometer. Isotope dilution mass spectrometry with 13C15N labeled amino acids was used to correct for matrix effects. The versatility of the methods for metabolite profiling studies of microbial cultivation supernatants is confirmed by a detailed method validation study. The methods using chromatography columns showed a linear range of approx. 4 orders of magnitude, sufficient response factors, and low quantification limits (7-443 nM) for single analytes. Overall, relative standard deviation was comparable for all analytes, with < 8% and < 11% for unbuffered and buffered media, respectively. The dilute and shoot methods with an analysis time of 1 min provided similar performance but showed a factor of up to 35 times higher throughput. The performance and applicability of the dilute and shoot method are demonstrated using a library of Corynebacterium glutamicum strains producing L-histidine, obtained from random mutagenesis, which were cultivated in a microscale cultivation platform.

Automated Rational Strain Construction Based on High-Throughput Conjugation

Molecular cloning is the core of synthetic biology, as it comprises the assembly of DNA and its expression in target hosts. At present, however, cloning is most often a manual, time-consuming, and repetitive process that highly benefits from automation. The automation of a complete rational cloning procedure, i.e., from DNA creation to expression in the target host, involves the integration of different operations and machines. Examples of such workflows are sparse, especially when the design is rational (i.e., the DNA sequence design is fixed and not based on randomized libraries) and the target host is less genetically tractable (e.g., not sensitive to heat-shock transformation). In this study, an automated workflow for the rational construction of plasmids and their subsequent conjugative transfer into the biotechnological platform organism Corynebacterium glutamicum is presented. The whole workflow is accompanied by a custom-made software tool. As an application example, a rationally designed library of transcription factor-biosensors based on the regulator Lrp was constructed and characterized. A sensor with an improved dynamic range was obtained, and insights from the screening provided evidence for a dual regulator function of C. glutamicum Lrp.

A fully automated pipeline for the dynamic at-line morphology analysis of microscale Aspergillus cultivation

Background

Morphology, being one of the key factors influencing productivity of filamentous fungi, is of great interest during bioprocess development. With increasing demand of high-throughput phenotyping technologies for fungi due to the emergence of novel time-efficient genetic engineering technologies, workflows for automated liquid handling combined with high-throughput morphology analysis have to be developed.

Results

In this study, a protocol allowing for 48 parallel microbioreactor cultivations of Aspergillus carbonarius with non-invasive online signals of backscatter and dissolved oxygen was established. To handle the increased cultivation throughput, the utilized microbioreactor is integrated into a liquid handling platform. During cultivation of filamentous fungi, cell suspensions result in either viscous broths or form pellets with varying size throughout the process. Therefore, tailor-made liquid handling parameters such as aspiration/dispense height, velocity and mixing steps were optimized and validated. Development and utilization of a novel injection station enabled a workflow, where biomass samples are automatically transferred into a flow through chamber fixed under a light microscope. In combination with an automated image analysis concept, this enabled an automated morphology analysis pipeline. The workflow was tested in a first application study, where the projected biomass area was determined at two different cultivation temperatures and compared to the microbioreactor online signals.

Conclusions

A novel and robust workflow starting from microbioreactor cultivation, automated sample harvest and processing via liquid handling robots up to automated morphology analysis was developed. This protocol enables the determination of projected biomass areas for filamentous fungi in an automated and high-throughput manner. This measurement of morphology can be applied to describe overall pellet size distribution and heterogeneity.

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.

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.

Development of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators

Bioreactors of various forms have been widely used in environmental protection, healthcare, industrial biotechnology, and space exploration. Robust demand in the field stimulated the development of novel designs of bioreactor geometries and process control strategies and the evolution of the physical structure of the control system. After the introduction of digital computers to bioreactor process control, a hierarchical structure control system (HSCS) for bioreactors has become the dominant physical structure, having high efficiency and robustness. However, inherent drawbacks of the HSCS for bioreactors have produced a need for a more consolidated solution of the control system. With the fast progress in sensors, machinery, and information technology, the development of a flat organizational control system (FOCS) for bioreactors based on parallel distributed smart sensors and actuators may provide a more concise solution for process control in bioreactors. Here, we review the evolution of the physical structure of bioreactor control systems and discuss the properties of the novel FOCS for bioreactors and related smart sensors and actuators and their application circumstances, with the hope of further improving the efficiency, robustness, and economics of bioprocess control.

A FRET-based biosensor for the quantification of glucose in culture supernatants of mL scale microbial cultivations

BACKGROUND

In most microbial cultivations D-glucose is the main carbon and energy source. However, quantification of D-glucose especially in small scale is still challenging. Therefore, we developed a FRET-based glucose biosensor, which can be applied in microbioreactor-based cultivations. This sensor consists of a glucose binding protein sandwiched between two fluorescent proteins, constituting a FRET pair. Upon D-glucose binding the sensor undergoes a conformational change which is translated into a FRET-ratio change.

RESULTS

The selected sensor shows an apparent Kd below 1.5 mM D-glucose and a very high sensitivity of up to 70% FRET-ratio change between the unbound and the glucose-saturated state. The soluble sensor was successfully applied online to monitor the glucose concentration in an Escherichia coli culture. Additionally, this sensor was utilized in an at-line process for a Corynebacterium glutamicum culture as an example for a process with cell-specific background (e.g. autofluorescence) and medium-induced quenching. Immobilization of the sensor via HaloTag® enabled purification and covalent immobilization in one step and increased the stability during application, significantly.

CONCLUSION

A FRET-based glucose sensor was used to quantify D-glucose consumption in microtiter plate based cultivations. To the best of our knowledge, this is the first method reported for online quantification of D-glucose in microtiter plate based cultivations. In comparison to D-glucose analysis via an enzymatic assay and HPLC, the sensor performed equally well, but enabled much faster measurements, which allowed to speed up microbial strain development significantly.

FeedER: a feedback-regulated enzyme-based slow-release system for fed-batch cultivation in microtiter plates

With the advent of modern genetic engineering methods, microcultivation systems have become increasingly important tools for accelerated strain phenotyping and bioprocess engineering. While these systems offer sophisticated capabilities to screen batch processes, they lack the ability to realize fed-batch processes, which are used more frequently in industrial bioprocessing. In this study, a novel approach to realize a feedback-regulated enzyme-based slow-release system (FeedER), allowing exponential fed-batch for microscale cultivations, was realized by extending our existing Mini Pilot Plant technology with a customized process control system. By continuously comparing the experimental growth rates with predefined set points, the automated dosage of Amyloglucosidase enzyme for the cleavage of dextrin polymers into d-glucose monomers is triggered. As a prerequisite for stable fed-batch operation, a constant pH is maintained by automated addition of ammonium hydroxide. We show the successful application of FeedER to study fed-batch growth of different industrial model organisms including Corynebacterium glutamicum, Pichia pastoris, and Escherichia coli. Moreover, the comparative analysis of a C. glutamicum GFP producer strain, cultivated under microscale batch and fed-batch conditions, revealed two times higher product yields under slow growing fed-batch operation. In summary, FeedER enables to run 48 parallel fed-batch experiments in an automated and miniaturized manner, and thereby accelerates industrial bioprocess development at the screening stage.