Optimizing microbioreactor cultivation strategies for Trichoderma reesei: from batch to fed-batch operations.

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 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 , the lowest rate of 0.3 g (l h) - 1 revealed a threefold increase in cellobiohydrolase and a fivefold increase in ß -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.

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.

"High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian optimization".

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.

Analysis of protein secretion in Bacillus subtilis by combining a secretion stress biosensor strain with an in vivo split GFP assay.

BACKGROUND: Bacillus subtilis is one of the workhorses in industrial biotechnology and well known for its secretion potential. Efficient secretion of recombinant proteins still requires extensive optimization campaigns and screening with activity-based methods. However, not every protein can be detected by activity-based screening. We therefore developed a combined online monitoring system, consisting of an in vivo split GFP assay for activity-independent target detection and an mCherry-based secretion stress biosensor. The split GFP assay is based on the fusion of a target protein to the eleventh ß-sheet of sfGFP, which can complement a truncated sfGFP that lacks this ß-sheet named GFP1-10. The secretion stress biosensor makes use of the CssRS two component quality control system, which upregulates expression of mCherry in the htrA locus thereby allowing a fluorescence readout of secretion stress. RESULTS: The biosensor strain B. subtilis PAL5 was successfully constructed by exchanging the protease encoding gene htrA with mCherry via CRISPR/Cas9. The Fusarium solani pisi cutinase Cut fused to the GFP11 tag (Cut11) was used as a model enzyme to determine the stress response upon secretion mediated by signal peptides SPPel, SPEpr and SPBsn obtained from naturally secreted proteins of B. subtilis. An in vivo split GFP assay was developed, where purified GFP1-10 is added to the culture broth. By combining both methods, an activity-independent high-throughput method was created, that allowed optimization of Cut11 secretion. Using the split GFP-based detection assay, we demonstrated a good correlation between the amount of secreted cutinase and the enzymatic activity. Additionally, we screened a signal peptide library and identified new signal peptide variants that led to improved secretion while maintaining low stress levels. CONCLUSION: Our results demonstrate that the combination of a split GFP-based detection assay for secreted proteins with a secretion stress biosensor strain enables both, online detection of extracellular target proteins and identification of bottlenecks during protein secretion in B. subtilis. In general, the system described here will also enable to monitor the secretion stress response provoked by using inducible promoters governing the expression of different enzymes.

Engineered coumarin accumulation reduces mycotoxin-induced oxidative stress and disease susceptibility.

Coumarins can fight pathogens and are thus promising for crop protection. Their biosynthesis, however, has not yet been engineered in crops. We tailored the constitutive accumulation of coumarins in transgenic Nicotiana benthamiana, Glycine max and Arabidopsis thaliana plants, as well as in Nicotiana tabacum BY-2 suspension cells. We did so by overexpressing A. thaliana feruloyl-CoA 6-hydroxylase 1 (AtF6'H1), encoding the key enzyme of scopoletin biosynthesis. Besides scopoletin and its glucoside scopolin, esculin at low level was the only other coumarin detected in transgenic cells. Mechanical damage of scopolin-accumulating tissue led to a swift release of scopoletin, presumably from the scopolin pool. High scopolin levels in A. thaliana roots coincided with reduced susceptibility to the root-parasitic nematode Heterodera schachtii. In addition, transgenic soybean plants were more tolerant to the soil-borne pathogenic fungus Fusarium virguliforme. Because mycotoxin-induced accumulation of reactive oxygen species and cell death were reduced in the AtF6'H1-overexpressors, the weaker sensitivity to F. virguliforme may be caused by attenuated oxidative damage of coumarin-hyperaccumulating cells. Together, engineered coumarin accumulation is promising for enhanced disease resilience of crops.

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.

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.

Identification and Characterization of Corynaridin, a Novel Linaridin from Corynebacterium lactis.

Genome analysis of Corynebacterium lactis revealed a bacteriocin gene cluster encoding a putative bacteriocin of the linaridin family of ribosomally synthesized and posttranslationally modified peptides (RiPPs). The locus harbors typical linaridin modification enzymes but lacks genes for a decarboxylase and methyltransferase, which is unusual for type B linaridins. Supernatants of Corynebacterium lactis RW3-42 showed antimicrobial activity against Corynebacterium glutamicum. Deletion of the precursor gene crdA clearly linked the antimicrobial activity of the producer strain to the identified gene cluster. Following purification, we observed potent activity of the peptide against Actinobacteria, mainly other members of the genus Corynebacterium, including the pathogenic species Corynebacterium striatum and Corynebacterium amycolatum. Also, low activity against some Firmicutes was observed, but there was no activity against Gram-negative species. The peptide is resilient towards heat but sensitive to proteolytic degradation by trypsin and proteinase K. Analysis by mass spectrometry indicates that corynaridin is processed by cleaving off the leader sequence at a conserved motif and posttranslationally modified by dehydration of all threonine and serin residues, resulting in a monoisotopic mass of 3,961.19 Da. Notably, time-kill kinetics and experiments using live biosensors to monitor membrane integrity suggest bactericidal activity that does not involve formation of pores in the cytoplasmic membrane. As Corynebacterium species are ubiquitous in nature and include important commensals and pathogens of mammalian organisms, secretion of bacteriocins by species of this genus could be a hitherto neglected trait with high relevance for intra- and interspecies competition and infection. IMPORTANCE Bacteriocins are antimicrobial peptides produced by bacteria to fend off competitors in ecological niches and are considered to be important factors influencing the composition of microbial communities. However, bacteriocin production by bacteria of the genus Corynebacterium has been a hitherto neglected trait, although its species are ubiquitous in nature and make up large parts of the microbiome of humans and animals. In this study, we describe and characterize a novel linaridin family bacteriocin from Corynebacterium lactis and show its narrow-spectrum activity, mainly against other actinobacteria. Moreover, we were able to extend the limited knowledge on linaridin bioactivity in general and for the first time describe the bactericidal activity of such a bacteriocin. Interestingly, the peptide, which was named corynaridin, appears bactericidal, but without formation of pores in the bacterial membrane.

Optimized recombinant production of the bacteriocin garvicin Q by Corynebacterium glutamicum.

Bacteriocins are antimicrobial peptides applied in food preservation and are interesting candidates as alternatives to conventional antibiotics or as microbiome modulators. Recently, we established Corynebacterium glutamicum as a suitable production host for various bacteriocins including garvicin Q (GarQ). Here, we establish secretion of GarQ by C. glutamicum via the Sec translocon achieving GarQ titers of about 7 mg L-1 in initial fermentations. At neutral pH, the cationic peptide is efficiently adsorbed to the negatively charged envelope of producer bacteria limiting availability of the bacteriocin in culture supernatants. A combination of CaCl2 and Tween 80 efficiently reduces GarQ adsorption to C. glutamicum. Moreover, cultivation in minimal medium supplemented with CaCl2 and Tween 80 improves GarQ production by C. glutamicum to about 15 mg L-1 but Tween 80 resulted in reduced GarQ activity at later timepoints. Using a reporter strain and proteomic analyses, we identified HtrA, a protease associated with secretion stress, as another potential factor limiting GarQ production. Transferring production to HtrA-deficient C. glutamicum K9 improves GarQ titers to close to 40 mg L-1. Applying conditions of low aeration prevented loss in activity at later timepoints and improved GarQ titers to about 100 mg L-1. This is about 50-fold higher than previously shown with a C. glutamicum strain employing the native GarQ transporter GarCD for secretion and in the range of levels observed with the native producer Lactococcus petauri B1726. Additionally, we tested several synthetic variants of GarQ and were able to show that exchange of the methionine in position 5 to a phenylalanine (GarQM5F) results in markedly increased activity against Lactococcus lactis and Listeria monocytogenes. In summary, our findings shed light on several aspects of recombinant GarQ production that may also be of relevance for production with natural producers and other bacteriocins.

Garvicin Q: characterization of biosynthesis and mode of action.

Bacteriocins are ribosomally synthesized antimicrobial peptides, that either kill target bacteria or inhibit their growth. Bacteriocins are used in food preservation and are of increasing interest as potential alternatives to conventional antibiotics. In the present study, we show that Lactococcus petauri B1726, a strain isolated from fermented balsam pear, produces a heat-stable and protease-sensitive compound. Following genome sequencing, a gene cluster for production of a class IId bacteriocin was identified consisting of garQ (encoding for the bacteriocin garvicin Q), garI (for a putative immunity protein), garC, and garD (putative transporter proteins). Growth conditions were optimized for increased bacteriocin activity in supernatants of L. petauri B1726 and purification and mass spectrometry identified the compound as garvicin Q. Further experiments suggest that garvicin Q adsorbs to biomass of various susceptible and insusceptible bacteria and support the hypothesis that garvicin Q requires a mannose-family phosphotransferase system (PTSMan) as receptor to kill target bacteria by disruption of membrane integrity. Heterologous expression of a synthetic garQICD operon was established in Corynebacterium glutamicum demonstrating that genes garQICD are responsible for biosynthesis and secretion of garvicin Q. Moreover, production of garvicin Q by the recombinant C. glutamicum strain was improved by using a defined medium yet product levels were still considerably lower than with the natural L. petauri B1726 producer strain.Collectively, our data identifies the genetic basis for production of the bacteriocin garvicin Q by L. petauri B1726 and provides insights into the receptor and mode of action of garvicin Q. Moreover, we successfully performed first attempts towards biotechnological production of this interesting bacteriocin using natural and heterologous hosts.

Control of parallelized bioreactors II: probabilistic quantification of carboxylic acid reductase activity for bioprocess optimization.

Autonomously operated parallelized mL-scale bioreactors are considered the key to reduce bioprocess development cost and time. However, their application is often limited to products with very simple analytics. In this study, we investigated enhanced protein expression conditions of a carboxyl reductase from Nocardia otitidiscaviarum in E. coli. Cells were produced with exponential feeding in a L-scale bioreactor. After the desired cell density for protein expression was reached, the cells were automatically transferred to 48 mL-scale bioreactors operated by a liquid handling station where protein expression studies were conducted. During protein expression, the feed rate and the inducer concentration was varied. At the end of the protein expression phase, the enzymatic activity was estimated by performing automated whole-cell biotransformations in a deep-well-plate. The results were analyzed with hierarchical Bayesian modelling methods to account for the biomass growth during the biotransformation, biomass interference on the subsequent product assay, and to predict absolute and specific enzyme activities at optimal expression conditions. Lower feed rates seemed to be beneficial for high specific and absolute activities. At the optimal investigated expression conditions an activity of [Formula: see text] was estimated with a [Formula: see text] credible interval of [Formula: see text]. This is about 40-fold higher than the highest published data for the enzyme under investigation. With the proposed setup, 192 protein expression conditions were studied during four experimental runs with minimal manual workload, showing the reliability and potential of automated and digitalized bioreactor systems.

Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems.

We here propose an automated pipeline for the microscopy image-based characterization of catalytically active inclusion bodies (CatIBs), which includes a fully automatic experimental high-throughput workflow combined with a hybrid approach for multi-object microbial cell segmentation. For automated microscopy, a CatIB producer strain was cultivated in a microbioreactor from which samples were injected into a flow chamber. The flow chamber was fixed under a microscope and an integrated camera took a series of images per sample. To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation. The experimental setup in combination with an automated image analysis unlocks high-throughput screening of CatIB production, saving time and resources. Biotechnological relevance- CatIBs have wide application in synthetic chemistry and biocatalysis, but also could have future biomedical applications such as therapeutics. The proposed hybrid automatic image processing pipeline can be adjusted to treat comparable biological microorganisms, where fully data-driven ML-based segmentation approaches are not feasible due to the lack of training data. Our work is the first step towards image- based bioprocess control.

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

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

Construction and characterization of BsGDH-CatIB variants and application as robust and highly active redox cofactor regeneration module for biocatalysis.

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.

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.

Catalytically Active Inclusion Bodies─Benchmarking and Application in Flow Chemistry.

In industries, enzymes are often immobilized to obtain stable preparations that can be utilized in batch and flow processes. In contrast to traditional immobilization methods that rely on carrier binding, various immobilization strategies have been recently presented that enable the simultaneous production and in vivo immobilization of enzymes. Catalytically active inclusion bodies (CatIBs) are a promising example for such in vivo enzyme immobilizates. CatIB formation is commonly induced by fusion of aggregation-inducing tags, and numerous tags, ranging from small synthetic peptides to protein domains or whole proteins, have been successfully used. However, since these systems have been characterized by different groups employing different methods, a direct comparison remains difficult, which prompted us to benchmark different CatIB-formation-inducing tags and fusion strategies. Our study highlights that important CatIB properties like yield, activity, and stability are strongly influenced by tag selection and fusion strategy. Optimization enabled us to obtain alcohol dehydrogenase CatIBs with superior activity and stability, which were subsequently applied for the first time in a flow synthesis approach. Our study highlights the potential of CatIB-based immobilizates, while at the same time demonstrating the robust use of CatIBs in flow chemistry.

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

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

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.

Biosensor-Based Optimization of Cutinase Secretion by Corynebacterium glutamicum.

The industrial microbe Corynebacterium glutamicum is gaining substantial importance as a platform host for recombinant protein secretion. We recently developed a fluorescence-based (eYFP) C. glutamicum reporter strain for the quantification of Sec-dependent protein secretion by monitoring the secretion-related stress response and now demonstrate its applicability in optimizing the secretion of the heterologous enzyme cutinase from Fusarium solani pisi. To drive secretion, either the poor-performing PelSP or the potent NprESP Sec signal peptide from Bacillus subtilis was used. To enable easy detection and quantification of the secreted cutinase we implemented the split green fluorescent protein (GFP) assay, which relies on the GFP11-tag fused to the C-terminus of the cutinase, which can complement a truncated GFP thereby reconstituting its fluorescence. The reporter strain was transformed with different mutant libraries created by error-prone PCR, which covered the region of the signal peptide and the N-terminus of the cutinase. Fluorescence-activated cell sorting (FACS) was performed to isolate cells that show increased fluorescence in response to increased protein secretion stress. Five PelSP variants were identified that showed a 4- to 6-fold increase in the amount and activity of the secreted cutinase (up to 4,100 U/L), whereas two improved NprESP variants were identified that showed a ∼35% increase in secretion, achieving ∼5,500 U/L. Most of the isolated variants carried mutations in the h-region of the signal peptide that increased its overall hydrophobicity. Using site-directed mutagenesis it was shown that the combined mutations F11I and P16S within the hydrophobic core of the PelSP are sufficient to boost cutinase secretion in batch cultivations to the same level as achieved by the NprESP. Screening of a PelSP mutant library in addition resulted in the identification of a cutinase variant with an increased specific activity, which was attributed to the mutation A85V located within the substrate-binding region. Taken together the biosensor-based optimization approach resulted in a substantial improvement of cutinase secretion by C. glutamicum, and therefore represents a valuable tool that can be applied to any secretory protein of interest.