Search

Abstract Corynebacterium glutamicum is used as an industrial platform organism for amino acid production. Previously, the organism was utilized to produce l‐histidine with research focusing on metabolic engineering approaches to increase titer and yield. Only a few studies have been published that provide information on bioprocess development, with media optimization and fed‐batch cultivation procedure being particularly promising areas. In this work, we show how experimental setups such as miniature cultivation technology, dynamic and time‐optimized LC‐MS/MS metabolic footprinting tools, and automated workflows for the detection of local and global metabolic patterns can significantly accelerate bioprocess development. Potential media bottlenecks in form of phosphate and magnesium availability were identified by sensitivity analysis in parallelized microscale cultivation assisted by lab automation. A rapid dilute‐and‐shoot flow‐injection‐analysis tandem mass spectrometry approach was used to cope with the resulting cultivation throughput and allowed to quantify amino acids with 1 min per sample. We were able to increase the l‐histidine titer of a C. glutamicum random mutagenesis mutant by a factor of 5.8 through process optimization while also identifying both known and previously unknown targets for additional strain improvements. The presented methodology can be seen as a supplement to traditional approaches in the field of bioprocess development.

Abstract Background Filamentous fungi have long been recognized for their exceptional enzyme production capabilities. Among these, Trichoderma reesei has emerged as a key producer of various industrially relevant enzymes and is particularly known for the production of cellulases. Despite the availability of advanced gene editing techniques for T. reesei, the cultivation and characterization of resulting strain libraries remain challenging, necessitating well-defined and controlled conditions with higher throughput. Small-scale cultivation devices are popular for screening bacterial strain libraries. However, their current use for filamentous fungi is limited due to their complex morphology. Results This study addresses this research gap through the development of a batch cultivation protocol using a microbioreactor for cellulase-producing T. reesei strains (wild type, RutC30 and RutC30 TR3158) with offline cellulase activity analysis. Additionally, the feasibility of a microscale fed-batch cultivation workflow is explored, crucial for mimicking industrial cellulase production conditions. A batch cultivation protocol was developed and validated using the BioLector microbioreactor, a Round Well Plate, adapted medium and a shaking frequency of 1000 rpm. A strong correlation between scattered light intensity and cell dry weight underscores the reliability of this method in reflecting fungal biomass formation, even in the context of complex fungal morphology. Building on the batch results, a fed-batch strategy was established for T. reesei RutC30. Starting with a glucose concentration of 2.5 g l $$^{-1}$$ - 1 in the batch phase, we introduced a dual-purpose lactose feed to induce cellulase production and prevent carbon catabolite repression. Investigating lactose feeding rates from 0.3 to 0.75 g (l h) $$^{-1}$$ - 1 , the lowest rate of 0.3 g (l h) $$^{-1}$$ - 1 revealed a threefold increase in cellobiohydrolase and a fivefold increase in $$\upbeta$$ β -glucosidase activity compared to batch processes using the same type and amount of carbon sources. Conclusion We successfully established a robust microbioreactor batch cultivation protocol for T. reesei wild type, RutC30 and RutC30 TR3158, overcoming challenges associated with complex fungal morphologies. The study highlights the effectiveness of microbioreactor workflows in optimizing cellulase production with T. reesei, providing a valuable tool for simultaneous assessment of critical bioprocess parameters and facilitating efficient strain screening. The findings underscore the potential of microscale fed-batch strategies for enhancing enzyme production capabilities, revealing insights for future industrial applications in biotechnology.

Abstract 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.

Abstract Background In recent years, the production of inclusion bodies that retain substantial catalytic activity was demonstrated. These catalytically active inclusion bodies (CatIBs) are formed by genetic fusion of an aggregation-inducing tag to a gene of interest via short linker polypeptides. The resulting CatIBs are known for their easy and cost-efficient production, recyclability as well as their improved stability. Recent studies have outlined the cooperative effects of linker and aggregation-inducing tag on CatIB activities. However, no a priori prediction is possible so far to indicate the best combination thereof. Consequently, extensive screening is required to find the best performing CatIB variant. Results In this work, a semi-automated cloning workflow was implemented and used for fast generation of 63 CatIB variants with glucose dehydrogenase of Bacillus subtilis (BsGDH). Furthermore, the variant BsGDH-PT-CBDCell was used to develop, optimize and validate an automated CatIB screening workflow, enhancing the analysis of many CatIB candidates in parallel. Compared to previous studies with CatIBs, important optimization steps include the exclusion of plate position effects in the BioLector by changing the cultivation temperature. For the overall workflow including strain construction, the manual workload could be reduced from 59 to 7 h for 48 variants (88%). After demonstration of high reproducibility with 1.9% relative standard deviation across 42 biological replicates, the workflow was performed in combination with a Bayesian process model and Thompson sampling. While the process model is crucial to derive key performance indicators of CatIBs, Thompson sampling serves as a strategy to balance exploitation and exploration in screening procedures. Our methodology allowed analysis of 63 BsGDH-CatIB variants within only three batch experiments. Because of the high likelihood of TDoT-PT-BsGDH being the best CatIB performer, it was selected in 50 biological replicates during the three screening rounds, much more than other, low-performing variants. Conclusions At the current state of knowledge, every new enzyme requires screening for different linker/aggregation-inducing tag combinations. For this purpose, the presented CatIB toolbox facilitates fast and simplified construction and screening procedures. The methodology thus assists in finding the best CatIB producer from large libraries in short time, rendering possible automated Design-Build-Test-Learn cycles to generate structure/function learnings.

Abstract 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.

Abstract Background Adaptive laboratory evolution (ALE) is known as a powerful tool for untargeted engineering of microbial strains and genomics research. It is particularly well suited for the adaptation of microorganisms to new environmental conditions, such as alternative substrate sources. Since the probability of generating beneficial mutations increases with the frequency of DNA replication, ALE experiments are ideally free of constraints on the required duration of cell proliferation. Results Here, we present an extended robotic workflow for performing long-term evolution experiments based on fully automated repetitive batch cultures (rbALE) in a well-controlled microbioreactor environment. Using a microtiter plate recycling approach, the number of batches and thus cell generations is technically unlimited. By applying the validated workflow in three parallel rbALE runs, ethanol utilization by Corynebacterium glutamicum ATCC 13032 (WT) was significantly improved. The evolved mutant strain WT_EtOH-Evo showed a specific ethanol uptake rate of 8.45 ± 0.12 mmolEtOH gCDW −1 h−1 and a growth rate of 0.15 ± 0.01 h−1 in lab-scale bioreactors. Genome sequencing of this strain revealed a striking single nucleotide variation (SNV) upstream of the ald gene (NCgl2698, cg3096) encoding acetaldehyde dehydrogenase (ALDH). The mutated basepair was previously predicted to be part of the binding site for the global transcriptional regulator GlxR, and re-engineering demonstrated that the identified SNV is key for enhanced ethanol assimilation. Decreased binding of GlxR leads to increased synthesis of the rate-limiting enzyme ALDH, which was confirmed by proteomics measurements. Conclusions The established rbALE technology is generally applicable to any microbial strain and selection pressure that fits the small-scale cultivation format. In addition, our specific results will enable improved production processes with C. glutamicum from ethanol, which is of particular interest for acetyl-CoA-derived products.

Abstract 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.

Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling constraint-based models, a highly relevant use-case in systems biology, we here demonstrate that thinning boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. By benchmarking CHRR with thinning with simplices and genome-scale metabolic networks of up to thousands of dimensions, we find a substantial increase in computational efficiency compared to unthinned CHRR, in our examples by orders of magnitude, as measured by the effective sample size per time (ESS/t), with performance gains growing with polytope (effective network) dimension. Using a set of benchmark models we derive a ready-to-apply guideline for tuning thinning to efficient and effective use of compute resources without requiring additional coding effort. Our guideline is validated using three (out-of-sample) large-scale networks and we show that it allows sampling convex polytopes uniformly to convergence in a fraction of time, thereby unlocking the rigorous investigation of hitherto intractable models. The derivation of our guideline is explained in detail, allowing future researchers to update it as needed as new model classes and more training data becomes available. CHRR with deliberate utilization of thinning thereby paves the way to keep pace with progressing model sizes derived with the constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.

Abstract Background Catalytically active inclusion bodies (CatIBs) are known for their easy and cost efficient production, recyclability as well as high stability and provide an alternative purely biological technology for enzyme immobilization. Due to their ability to self-aggregate in a carrier-free, biodegradable form, no further laborious immobilization steps or additional reagents are needed. These advantages put CatIBs in a beneficial position in comparison to traditional immobilization techniques. Recent studies outlined the impact of cooperative effects of the linker and aggregation inducing tag on the activity level of CatIBs, requiring to test many combinations to find the best performing CatIB variant. Results Here, we present the formation of 14 glucose dehydrogenase CatIB variants of Bacillus subtilis, a well-known enzyme in biocatalysis due to its capability for substrate coupled regeneration of reduced cofactors with cheap substrate glucose. Nine variants revealed activity, with highest productivity levels for the more rigid PT-Linker combinations. The best performing CatIB, BsGDH-PT-CBDCell, was characterized in more detail including long-term storage at −20 °C as well as NADH cofactor regeneration performance in repetitive batch experiments with CatIB recycling. After freezing, BsGDH-PT-CBDCell CatIB only lost approx. 10% activity after 8 weeks of storage. Moreover, after 11 CatIB recycling cycles in repetitive batch operation 80% of the activity was still present. Conclusions This work presents a method for the effective formation of a highly active and long-term stable BsGDH-CatIB as an immobilized enzyme for robust and convenient NADH regeneration.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract Background In recent years, the production of inclusion bodies that retained substantial catalytic activity was demonstrated. These catalytically active inclusion bodies (CatIBs) were formed by genetic fusion of an aggregation inducing tag to a gene of interest via short linker polypeptides and overproduction of the resulting gene fusion in Escherichia coli. The resulting CatIBs are known for their high stability, easy and cost efficient production, and recyclability and thus provide an interesting alternative to conventionally immobilized enzymes. Results Here, we present the construction and characterization of a CatIB set of the lysine decarboxylase from Escherichia coli (EcLDCc), constructed via Golden Gate Assembly. A total of ten EcLDCc variants consisting of combinations of two linker and five aggregation inducing tag sequences were generated. A flexible Serine/Glycine (SG)- as well as a rigid Proline/Threonine (PT)-Linker were tested in combination with the artificial peptides (18AWT, L6KD and GFIL8) or the coiled-coil domains (TDoT and 3HAMP) as aggregation inducing tags. The linkers were fused to the C-terminus of the EcLDCc to form a linkage between the enzyme and the aggregation inducing tags. Comprehensive morphology and enzymatic activity analyses were performed for the ten EcLDCc-CatIB variants and a wild type EcLDCc control to identify the CatIB variant with the highest activity for the decarboxylation of l-lysine to 1,5-diaminopentane. Interestingly, all of the CatIB variants possessed at least some activity, whilst most of the combinations with the rigid PT-Linker showed the highest conversion rates. EcLDCc-PT-L6KD was identified as the best of all variants allowing a volumetric productivity of 457 g L− 1 d− 1 and a specific volumetric productivity of 256 g L− 1 d− 1 gCatIB −1. Noteworthy, wild type EcLDCc, without specific aggregation inducing tags, also partially formed CatIBs, which, however showed lower activity compared to most of the newly constructed CatIB variants (volumetric productivity: 219 g L− 1 d− 1, specific volumetric activity: 106 g L− 1 d− 1 gCatIB − 1). Furthermore, we demonstrate that microscopic analysis can serve as a tool to find CatIB producing strains and thus allow for prescreening at an early stage to save time and resources. Conclusions Our results clearly show that the choice of linker and aggregation inducing tag has a strong influence on the morphology and the enzymatic activity of the CatIBs. Strikingly, the linker had the most pronounced influence on these characteristics.

Summary 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.

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.

Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.

Abstract Background Streptomycetes are filamentous microorganisms of high biotechnological relevance, especially for the production of antibiotics. In submerged cultures, the productivity of these microorganisms is closely linked to their growth morphology. Microfluidic lab-on-a-chip cultivation systems, coupled with automated time-lapse imaging, generate spatio-temporal insights into the mycelium development of streptomycetes, therewith extending the biotechnological toolset by spatio-temporal screening under well-controlled and reproducible conditions. However, the analysis of the complex mycelial structure formation is limited by the extent of manual interventions required during processing of the acquired high-volume image data. These interventions typically lead to high evaluation times and, therewith, limit the analytic throughput and exploitation of microfluidic-based screenings. Results We present the tool mycelyso (MYCElium anaLYsis SOftware), an image analysis system tailored to fully automated hyphae-level processing of image stacks generated by time-lapse microscopy. With mycelyso, the developing hyphal streptomycete network is automatically segmented and tracked over the cultivation period. Versatile key growth parameters such as mycelium network structure, its development over time, and tip growth rates are extracted. Results are presented in the web-based exploration tool mycelyso Inspector, allowing for user friendly quality control and downstream evaluation of the extracted information. In addition, 2D and 3D visualizations show temporal tracking for detailed inspection of morphological growth behaviors. For ease of getting started with mycelyso, bundled Windows packages as well as Docker images along with tutorial videos are available. Conclusion mycelyso is a well-documented, platform-independent open source toolkit for the automated end-to-end analysis of Streptomyces image stacks. The batch-analysis mode facilitates the rapid and reproducible processing of large microfluidic screenings, and easy extraction of morphological parameters. The objective evaluation of image stacks is possible by reproducible evaluation workflows, useful to unravel correlations between morphological, molecular and process parameters at the hyphae- and mycelium-levels with statistical power.

Abstract Background Filamentously growing microorganisms offer unique advantages for biotechnological processes, such as extraordinary secretion capacities, going along with multiple obstacles due to their complex morphology. However, limited experimental throughput in bioprocess development still hampers taking advantage of their full potential. Miniaturization and automation are powerful tools to accelerate bioprocess development, but so far the application of such technologies has mainly been focused on non-filamentous systems. During cultivation, filamentous fungi can undergo remarkable morphological changes, creating challenging cultivation conditions. Depending on the process and product, only one specific state of morphology may be advantageous to achieve e.g. optimal productivity or yield. Different approaches to control morphology have been investigated, such as microparticle enhanced cultivation. However, the addition of solid microparticles impedes the optical measurements typically used by microbioreactor systems and thus alternatives are needed. Results Aspergillus giganteus IfGB 0902 was used as a model system to develop a time-efficient and robust workflow allowing microscale cultivation with increased throughput. The effect of microtiter plate geometry, shaking frequency and medium additives (talc and calcium chloride) on homogeneity of culture morphology as well as reproducibility were analyzed via online biomass measurement, microscopic imaging and cell dry weight. While addition of talc severely affected online measurements, 2% (w v−1) calcium chloride was successfully applied to obtain a highly reproducible growth behavior with homogenous morphology. Furthermore, the influence of small amounts of complex components was investigated for the applied model strain. By correlation to cell dry weight, it could be shown that optical measurements are a suitable signal for biomass concentration. However, each correlation is only applicable for a specific set of cultivation parameters. These optimized conditions were used in micro as well as lab-scale bioreactor cultivation in order to verify the reproducibility and scalability of the setup. Conclusion A robust workflow for A. giganteus was developed, allowing for reproducible microscale cultivation with online monitoring, where calcium chloride is an useful alternative to microparticle enhanced cultivation in order to control the morphology. Independent of the cultivation volume, comparable phenotypes were observed in microtiter plates and in lab-scale bioreactor.

Abstract 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.

Wild-type C. glutamicum ATCC 13032 is known to possess two enzymes with anaplerotic (C4-directed) carboxylation activity, namely phosphoenolpyruvate carboxylase (PEPCx) and pyruvate carboxylase (PCx). On the other hand, C3-directed decarboxylation can be catalyzed by the three enzymes phosphoenolpyruvate carboxykinase (PEPCk), oxaloacetate decarboxylase (ODx), and malic enzyme (ME). The resulting high metabolic flexibility at the anaplerotic node compromises the unambigous determination of its carbon and energy flux in C. glutamicum wild type. To circumvent this problem we performed a comprehensive analysis of selected single or double deletion mutants in the anaplerosis of wild-type C. glutamicum under defined d-glucose conditions. By applying well-controlled lab-scale bioreactor experiments in combination with untargeted proteomics, quantitative metabolomics and whole-genome sequencing hitherto unknown, and sometimes counter-intuitive, genotype-phenotype relationships in these mutants could be unraveled. In comparison to the wild type the four mutants C. glutamiucm Δpyc, C. glutamiucm Δpyc Δodx, C. glutamiucm Δppc Δpyc, and C. glutamiucm Δpck showed lowered specific growth rates and d-glucose uptake rates, underlining the importance of PCx and PEPCk activity for a balanced carbon and energy flux at the anaplerotic node. Most interestingly, the strain C. glutamiucm Δppc Δpyc could be evolved to grow on d-glucose as the only source of carbon and energy, whereas this combination was previously considered lethal. The prevented anaplerotic carboxylation activity of PEPCx and PCx was found in the evolved strain to be compensated by an up-regulation of the glyoxylate shunt, potentially in combination with the 2-methylcitrate cycle.

Increasing the growth rate of the industrial host Corynebacterium glutamicum is a promising target to rise productivities of growth coupled product formation. As a prerequisite, detailed knowledge about the tight regulation network is necessary for identifying promising metabolic engineering goals. Here, we present comprehensive metabolic and transcriptional analysis of C. glutamicum ATCC 13032 growing under glucose limited chemostat conditions with μ = 0.2, 0.3, and 0.4 h–1. Intermediates of central metabolism mostly showed rising pool sizes with increasing growth. 13C-metabolic flux analysis (13C-MFA) underlined the fundamental role of central metabolism for the supply of precursors, redox, and energy equivalents. Global, growth-associated, concerted transcriptional patterns were not detected giving rise to the conclusion that glycolysis, pentose-phosphate pathway, and citric acid cycle are predominately metabolically controlled under glucose-limiting chemostat conditions. However, evidence is found that transcriptional regulation takes control over glycolysis once glucose-rich growth conditions are installed.

Abstract Background Expanding the application of technical enzymes, e.g., in industry and agriculture, commands the acceleration and cost-reduction of bioprocess development. Microplates and shake flasks are massively employed during screenings and early phases of bioprocess development, although major drawbacks such as low oxygen transfer rates are well documented. In recent years, miniaturization and parallelization of stirred and shaken bioreactor concepts have led to the development of novel microbioreactor concepts. They combine high cultivation throughput with reproducibility and scalability, and represent promising tools for bioprocess development. Results Parallelized microplate cultivation of the eukaryotic protein production host Pichia pastoris was applied effectively to support miniaturized phenotyping of clonal libraries in batch as well as fed-batch mode. By tailoring a chemically defined growth medium, we show that growth conditions are scalable from microliter to 0.8 L lab-scale bioreactor batch cultivation with different carbon sources. Thus, the set-up allows for a rapid physiological comparison and preselection of promising clones based on online data and simple offline analytics. This is exemplified by screening a clonal library of P. pastoris constitutively expressing AppA phytase from Escherichia coli. The protocol was further modified to establish carbon-limited conditions by employing enzymatic substrate-release to achieve screening conditions relevant for later protein production processes in fed-batch mode. Conclusion The comparison of clonal rankings under batch and fed-batch-like conditions emphasizes the necessity to perform screenings under process-relevant conditions. Increased biomass and product concentrations achieved after fed-batch microscale cultivation facilitates the selection of top producers. By reducing the demand to conduct laborious and cost-intensive lab-scale bioreactor cultivations during process development, this study will contribute to an accelerated development of protein production processes.

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.

Abstract 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.

13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.

Alternative sigma factors control numerous aspects of bacterial life, including adaptation to physiological stresses, morphological development, persistence states and virulence. This is especially true for the physiologically complex actinobacteria. Here we report the development of a robust gene deletions system for Streptomyces lividans TK24 based on a BAC library combined with the λ-Red recombination technique. The developed system was validated by systematically deleting the most highly expressed genes encoding alternative sigma factors and several other regulatory genes within the chromosome of S. lividans TK24. To demonstrate the possibility of large scale genomic manipulations, the major part of the undecylprodigiosin gene cluster was deleted as well. The resulting mutant strains were characterized in terms of morphology, growth parameters, secondary metabolites production and response to thiol-oxidation and cell-wall stresses. Deletion of SLIV_12645 gene encoding S. coelicolor SigR1 ortholog has the most prominent phenotypic effect, resulted in overproduction of actinorhodin and coelichelin P1 and increased sensitivity to diamide. The secreted proteome analysis of SLIV_12645 mutant revealed SigR1 influence on trafficking of proteins involved in cell wall biogenesis and refactoring. The reported here gene deletion system will further facilitate work on S. lividans strain improvement as a host for either secondary metabolites or protein production and will contribute to basic research in streptomycetes physiology, morphological development, secondary metabolism. On the other hand, the systematic deletion of sigma factors encoding genes demonstrates the complexity and conservation of regulatory processes conducted by sigma factors in streptomycetes.

Quantitative single-cell cultivation has provided fundamental contributions to our understanding of heterogeneity among industrially used microorganisms. Filamentous growing Streptomyces species are emerging platform organisms for industrial production processes, but their exploitation is still limited due to often reported high batch-to-batch variations and unexpected growth and production differences. Population heterogeneity is suspected to be one responsible factor, which is so far not systematically investigated at the single-cell level. Novel microfluidic single-cell cultivation devices offer promising solutions to investigate these phenomena. In this study, we investigated the germination and growth behavior of Streptomyces lividans TK24 under varying medium compositions on different complexity levels (i.e., mycelial growth, hyphal growth and tip elongation) on single-cell level. Our analysis reveals a remarkable stability within growth and germination of spores and early mycelium development when exposed to constant and defined environments. We show that spores undergo long metabolic adaptation processes of up to > 30 h to adjust to new medium conditions, rather than using a “persister” strategy as a possibility to cope with rapidly changing environments. Due to this uniform behavior, we conclude that S. lividans can be cultivated quite robustly under constant environmental conditions as provided by microfluidic cultivation approaches. Failure and non-reproducible cultivations are thus most likely to be found in less controllable larger-scale cultivation workflows and as a result of environmental gradients within large-scale cultivations.

Science revolves around the best way of conducting an experiment to obtain insightful results. Experiments with maximal information content can be found by computational experimental design (ED) strategies that identify optimal conditions under which to perform the experiment. Several criteria have been proposed to measure the information content, each emphasizing different aspects of the design goal, i.e., reduction of uncertainty. Where experiments are complex or expensive, second sight is at the budget governing the achievable amount of information. In this context, the design objectives cost and information gain are often incommensurable, though dependent. By casting the ED task into a multiple-criteria optimization problem, a set of trade-off designs is derived that approximates the Pareto-frontier which is instrumental for exploring preferable designs. In this work, we present a computational methodology for multiple-criteria ED of information-rich experiments that accounts for virtually any set of design criteria. The methodology is implemented for the case of 13C metabolic flux analysis (MFA), which is arguably the most expensive type among the 'omics' technologies, featuring dozens of design parameters (tracer composition, analytical platform, measurement selection etc.). Supported by an innovative visualization scheme, we demonstrate with two realistic showcases that the use of multiple criteria reveals deep insights into the conflicting interplay between information carriers and cost factors that are not amendable to single-objective ED. For instance, tandem mass spectrometry turns out as best-in-class with respect to information gain, while it delivers this information quality cheaper than the other, routinely applied analytical technologies. Therewith, our Pareto approach to ED offers the investigator great flexibilities in the conception phase of a study to balance costs and benefits.

Gram-positive Streptomyces bacteria are profuse secretors of polypeptides using complex, yet unknown mechanisms. Many of their secretory proteins are proteases that play important roles in the acquisition of amino acids from the environment. Other proteases regulate cellular proteostasis. To begin dissecting the possible role of proteases in Streptomyces secretion, we applied a multi-omics approach. We probed the role of the 190 proteases of Streptomyces lividans strain TK24 in protein secretion in defined media at different stages of growth. Transcriptomics analysis revealed transcripts for 93% of these proteases and identified that 41 of them showed high abundance. Proteomics analysis identified 57 membrane-embedded or secreted proteases with variations in their abundance. We focused on 17 of these proteases and putative inhibitors and generated strains deleted of their genes. These were characterized in terms of their fitness, transcriptome and secretome changes. In addition, we performed a targeted analysis in deletion strains that also carried a secretion competent mRFP. One strain, carrying a deletion of the gene for the regulatory protease FtsH, showed significant global changes in overall transcription and enhanced secretome and secreted mRFP levels. These data provide a first multi-omics effort to characterize the complex regulatory mechanisms of protein secretion in Streptomyces lividans and lay the foundations for future rational manipulation of this process.

Microalgae currently receive growing attention as promising candidates for future bio‐economy concepts. However, the reliable maintenance of production strains remains challenging. The well‐established serial subculturing techniques suffer from low long‐time stability and high effort and are therefore stepwise being replaced by cryopreservation. Currently, available protocols are often deduced from cell culture technology and are rather complex. This study aimed to investigate if less complex approaches can be applied. We introduce an easy‐to‐use cryopreservation protocol based on the model organism Chlorella vulgaris. To overcome error‐prone viability estimation by plating techniques, an alternative method using growth pattern analysis was developed. As revealed by growth pattern analysis, the preservation of stationary phase cells proved superior to the commonly applied concept of freezing cells from the growing phase. Controlled‐rate cooling using simple devices resulted in reproducibly high post‐thawing viabilities in the range of 63 ± 2%. Moreover, the presented protocol highlights the potential of simplifying microalgal cryo‐preservation procedures, thereby reducing the required labor and material need to a minimum. Apart from the viability analysis of the cryopreserved microalga C. vulgaris, this approach seems to have the potential to be applied for other algae species and microorganisms, as well.

Gluconobacter oxydans 621H is used as an industrial production organism due to its exceptional ability to incompletely oxidize a great variety of carbohydrates in the periplasm. With glucose as the carbon source, up to 90% of the initial concentration is oxidized periplasmatically to gluconate and ketogluconates. Growth on glucose is biphasic and intracellular sugar catabolism proceeds via the Entner–Doudoroff pathway (EDP) and the pentose phosphate pathway (PPP). Here we studied the in vivo contributions of the two pathways to glucose catabolism on a microtiter scale. In our approach we applied specifically 13C labeled glucose, whereby a labeling pattern in alanine was generated intracellularly. This method revealed a dynamic growth phase-dependent pathway activity with increased activity of EDP in the first and PPP in the second growth phase, respectively. Evidence for a growth phase-independent decarboxylation-carboxylation cycle around the pyruvate node was obtained from 13C fragmentation patterns of alanine. For the first time, down-scaled microtiter plate cultivation together with 13C-labeled substrate was applied for G. oxydans to elucidate pathway operation, exhibiting reasonable labeling costs and allowing for sufficient replicate experiments.

Microfluidics has become an essential tool in single-cell analysis assays for gaining more accurate insights into cell behavior. Various microfluidics methods have been introduced facilitating single-cell analysis of a broad range of cell types. However, the study of prokaryotic cells such as Escherichia coli and others still faces the challenge of achieving proper single-cell immobilization simply due to their small size and often fast growth rates. Recently, new approaches were presented to investigate bacteria growing in monolayers and single-cell tracks under environmental control. This allows for high-resolution time-lapse observation of cell proliferation, cell morphology and fluorescence-coupled bioreporters. Inside microcolonies, interactions between nearby cells are likely and may cause interference during perturbation studies. In this paper, we present a microfluidic device containing hundred sub-micron sized trapping barrier structures for single E. coli cells. Descendant cells are rapidly washed away as well as components secreted by growing cells. Experiments show excellent growth rates, indicating high cell viability. Analyses of elongation and growth rates as well as morphology were successfully performed. This device will find application in prokaryotic single-cell studies under constant environment where by-product interference is undesired.

Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected.

BACKGROUND:Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. RESULTS:We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. CONCLUSION:Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso.

Phase contrast microscopy cannot give sufficient information on bacterial metabolic activity, or if a cell is dead, it has the fate to die or it is in a viable but non-growing state. Thus, a reliable sensing of the metabolic activity helps to distinguish different categories of viability. We present a non-invasive instantaneous sensing method using a fluorogenic substrate for online monitoring of esterase activity and calcein efflux changes in growing wild type bacteria. The fluorescent conversion product of calcein acetoxymethyl ester (CAM) and its efflux indicates the metabolic activity of cells grown under different conditions at real-time. The dynamic conversion of CAM and the active efflux of fluorescent calcein were analyzed by combining microfluidic single cell cultivation technology and fluorescence time lapse microscopy. Thus, an instantaneous and non-invasive sensing method for apparent esterase activity was created without the requirement of genetic modification or harmful procedures. The metabolic activity sensing method consisting of esterase activity and calcein secretion was demonstrated in two applications. Firstly, growing colonies of our model organism Corynebacterium glutamicum were confronted with intermittent nutrient starvation by interrupting the supply of iron and carbon, respectively. Secondly, bacteria were exposed for one hour to fatal concentrations of antibiotics. Bacteria could be distinguished in growing and non-growing cells with metabolic activity as well as non-growing and non-fluorescent cells with no detectable esterase activity. Microfluidic single cell cultivation combined with high temporal resolution time-lapse microscopy facilitated monitoring metabolic activity of stressed cells and analyzing their descendants in the subsequent recovery phase. Results clearly show that the combination of CAM with a sampling free microfluidic approach is a powerful tool to gain insights in the metabolic activity of growing and non-growing bacteria.

Background: The fast development of microbial production strains for basic and fine chemicals is increasingly carried out in small scale cultivation systems to allow for higher throughput. Such parallelized systems create a need for new rapid online detection systems to quantify the respective target compound. In this regard, biosensors, especially genetically encoded Förster resonance energy transfer (FRET)-based biosensors, offer tremendous opportunities. As a proof-of-concept, we have created a toolbox of FRET-based biosensors for the ratiometric determination of l-lysine in fermentation broth. Methods: The sensor toolbox was constructed based on a sensor that consists of an optimized central lysine-/arginine-/ornithine-binding protein (LAO-BP) flanked by two fluorescent proteins (enhanced cyan fluorescent protein (ECFP), Citrine). Further sensor variants with altered affinity and sensitivity were obtained by circular permutation of the binding protein as well as the introduction of flexible and rigid linkers between the fluorescent proteins and the LAO-BP, respectively. Results: The sensor prototype was applied to monitor the extracellular l-lysine concentration of the l-lysine producing Corynebacterium glutamicum (C. glutamicum) strain DM1933 in a BioLector® microscale cultivation device. The results matched well with data obtained by HPLC analysis and the Ninhydrin assay, demonstrating the high potential of FRET-based biosensors for high-throughput microbial bioprocess optimization.