The CHO VRC01 cell line produces an anti-HIV IgG1 monoclonal antibody containing N-linked glycans on both the Fab (variable) and Fc (constant) regions. Site-specific glycan analysis was used to measure the complex effects of cell culture process conditions on Fab and Fc glycosylation. Experimental data revealed major differences in glycan fractions across the two sites. Bioreactor pH was found to influence fucosylation, galactosylation, and sialylation in the Fab region and galactosylation in the Fc region. To understand the complex effects of process conditions on site-specific N-linked glycosylation, a kinetic model of site-specific N-linked glycosylation was developed. The model parameters provided mechanistic insights into the differences in glycan fractions observed in the Fc and Fab regions. Enzyme activities calculated from the model provided insights into the effect of bioreactor pH on site-specific N-linked glycosylation. Model predictions were experimentally tested by measuring glycosyltransferase-enzyme mRNA-levels and intracellular nucleotide sugar concentrations. The model was used to demonstrate the effect of increasing galactosyltransferase activity on site-specific N-linked glycan fractions. Experiments involving galactose and MnCl 2 supplementation were used to test model predictions. The model is capable of providing insights into experimentally measured data and also of making predictions that can be used to design media supplementation strategies.

Recent advancements in three-dimensional (3D) cell culture technologies, such as cell spheroids, organoids, and 3D bioprinted tissue constructs, have significantly improved the physiological relevance of in vitro models. These models better mimic tissue structure and function, closely emulating in vivo characteristics and enhancing phenotypic analysis, critical for basic research and drug screening in personalized cancer therapy. Despite their potential, current 3D cell culture platforms face technical challenges, which include user unfriendliness in long-term dynamic cell culture, incompatibility with rapid cell encapsulation in biomimetic hydrogels, and low throughput for compound screening. To address these issues, we developed a 144-pillar plate with sidewalls and slits (144PillarPlate) and a complementary 144-perfusion plate with perfusion wells and reservoirs (144PerfusionPlate) for dynamic 3D cell culture and predictive compound screening. To accelerate biomimetic tissue formation, small Hep3B liver tumor spheroids suspended in alginate were printed and encapsulated on the 144PillarPlate rapidly by using microsolenoid valve-driven 3D bioprinting technology. The microarray bioprinting technology enabled precise and rapid loading of small spheroids in alginate on the pillar plate, facilitating reproducible and scalable formation of large tumor spheroids with minimal manual intervention. The bioprinted Hep3B spheroids on the 144PillarPlate were dynamically cultured in the 144PerfusionPlate and tested with anticancer drugs to measure drug effectiveness and determine the concentration required to inhibit 50% of the cell viability (IC 50 value). The perfusion plate enabled the convenient dynamic culture of tumor spheroids and facilitated the dynamic testing of anticancer drugs with increased sensitivity. It is envisioned that the integration of microarray bioprinting of tumor spheroids onto the pillar plate, along with dynamic 3D cell culture in the perfusion plate, could more accurately replicate tumor microenvironments. This advancement has the potential to enhance the predictive drug screening process in personalized cancer therapy significantly.

The bioaugmentation performance is severely reduced in the treatment of high saline pesticide wastewater because the growth and degradation activity of pesticide degraders are significantly inhibited by high salt concentrations. In this study, an artificial halotolerant degrader J9U-MP capable of mineralizing p-nitrophenol (PNP) substituted organophosphorus pesticides (OPs) [e.g., methyl parathion (MP)] was created by integrating a MP-mineralizing pathway into the genome of a salt-tolerant chassis Halomonas cupida J9. MP degradation coupled with stable isotope analysis indicated that J9U-MP was able to metabolize MP as a sole carbon source to finally produce CO 2 and H 2O in high-salt media (up to 120 g/L NaCl). J9U-MP was genetically stable during passage culture and exogenous gene integration did not negatively influence growth and metabolism of J9U-MP. A real-time monitoring system was established with enhanced green fluorescent protein (EGFP) to track the movement and activity of J9U-MP in environmental remediation. A low-oxygen tolerant system was developed by enhancing oxygen utilization, which makes J9U-MP maintain the MP-mineralizing activity under oxygen-limited conditions. More importantly, efficient mineralization of MP by J9U-MP in high saline wastewater was demonstrated. This study highlights that synthetic biology has opened up new avenues for creating stress-resistant pollutants-mineralizing microbes. Competitive advantages of J9U-MP in high-salinity and low-oxygen environments make this degrader suitable for bioaugmentation of pesticide wastewater.

Detergent-mediated viral inactivation is an important process step for ensuring viral safety of parenteral biotherapeutics, including plasma proteins and monoclonal antibodies (mAb). The conventional Triton™ X-100 detergent has ecological toxicity concerns and REACH classification that mandate replacement in the biopharmaceutical industry. Criteria for a replacement detergent include viral inactivation efficacy, acceptable safety and biodegradation profile, process removal and quality suitable for parenteral drug product manufacturing. A non-ionic, C11-15 secondary alcohol ethoxylate, Deviron ® 13-S9 detergent, has been demonstrated to meet the necessary requirements for detergent performance. Benchmarking studies with Triton™ X-100 demonstrate comparable performance with a panel of enveloped viruses in multiple matrices, including human IgG, clarified cell culture harvest, and fractionated plasma. Deviron ® 13-S9 detergent demonstrated viral inactivation efficiency comparable to or better than, Triton™ X-100 detergent, achieving > 5 log reduction values (LRV). Critical micelle concentration (CMC) was determined across different temperatures and media. Deviron ® 13-S9 detergent was demonstrated to be readily biodegradable according to OECD 301B guidelines. Effective removal with typical chromatography processes used in downstream purification was confirmed. These findings support Deviron ® 13-S9 detergent as a viable alternative to Triton™ X-100 detergent, ensuring robust viral inactivation, environmental compatibility, and alignment with regulatory requirements.

Chinese hamster ovary (CHO) bioprocesses, the dominant platform for therapeutic protein production, are increasingly used to produce complex multispecific proteins. Product quantity and quality are affected by intracellular conditions, but these are challenging to measure and often overlooked during process optimization studies. pH is known to impact quality attributes like protein aggregation across upstream and downstream processes, yet the effects of intracellular pH on cell culture performance are largely unknown. Recently, advances in protein biosensors have enabled investigations of intracellular environments with high spatiotemporal resolution. In this study, we integrated a fluorescent pH-sensitive biosensor into a bispecifc (bisAb)-producing cell line to investigate changes in endoplasmic reticulum pH (pH ER). We then investigated how changes in lactate metabolism impacted pH ER, cellular redox, and product quality in fed-batch and perfusion bioreactors. Our data show pH ER rapidly increased during exponential growth to a maximum of pH 7.7, followed by a sharp drop in stationary phase in all perfusion and fed-batch conditions. pH ER decline in stationary phase was driven by an apparent loss of cellular pH regulation that occurred despite differences in redox profiles. Protein aggregate levels correlated positively with pH ER, indicating that aggregation is not solely driven by oxidative stress and cellular protein load. Improved characterization of intracellular changes in bioprocesses can help guide media optimizations, improve bioprocess control strategies or provide new targets for cell engineering.

Pterostilbene (PST), a 3,5- O-methylated derivative of resveratrol (RSV), is a natural potent antioxidant produced by some plants in trace amounts as defense compounds. It exhibits various health-promoting activities, such as anticancer, antiviral, and antimicrobial effects. Large-scale biosynthesis of PST is crucial due to the challenges associated with extracting it from plants. This study aims to develop an efficient method for PST production using an engineered E. coli strain by feeding RSV as an intermediate compound. We introduced a two-step substrate addition strategy combined with immobilized RSV (IMRSV) on macroporous adsorption resin (MAR) to enhance PST production. Five MARs were selected for RSV immobilization, and the substrate additions strategy and fermentation parameters for PST synthesis were optimized. A maximum PST concentration of 402.78 ± 9.20 mg/L was achieved, representing a 239% increase over the control. The PST titer reached 395.20 ± 23.89 mg/L in a 3-L bioreactor. In conclusion, the combination of a two-step substrate adding system and IMRSV is a promising approach for the economical and industrial-scale production of PST.

Monoclonal antibodies (mAbs) are a major class of biopharmaceuticals manufactured by well-established processes using Chinese Hamster Ovary (CHO) cells. Next-generation biomanufacturing using alternative hosts like Komagataella phaffii could improve the accessibility of these medicines, address broad societal goals for sustainability, and offer financial advantages for accelerated development of new products. Antibodies produced by K. phaffii, however, may manifest unique molecular quality attributes, like host-dependent, product-related variants, that could raise potential concerns for clinical use. We demonstrate here conservative modifications to the amino acid sequence of aglycosylated antibodies based on the human IgG1 isotype that minimize product-related variations when secreted by K. phaffii. A combination of 2-3 changes of amino acids reduced variations across six different aglycosylated versions of commercial mAbs. Expression of a modified sequence of NIST mAb in both K. phaffii and CHO cells showed comparable biophysical properties and molecular variations. These results suggest a path towards production of high-quality mAbs that could be expressed interchangeably by either yeast or mammalian cells. Improving molecular designs of proteins to enable a range of manufacturing strategies for well-characterized biopharmaceuticals could accelerate global accessibility and innovations.

Scale-up of microbial bioprocesses to production scale is accompanied by significant changes in growth conditions. One aspect are rapidly changing substrate concentrations due to prolonged mixing times. The impact of substrate gradients on the physiology of Saccharomyces cerevisiae has been studied for glucose but no other industrially relevant carbohydrate. With different transport mechanism, non-glucose sugars will lead to a different intracellular response. Here we studied the response of S. cerevisiae to gradients for four different sugars: glucose, fructose, sucrose and maltose. To study the impact of the carbon sources as well as the large-scale gradients, steady-state and dynamic feast/famine cultivation conditions were applied. The physiology, intracellular metabolome as well as the proteome were compared. Especially, gradients of maltose lead to a significant decrease in biomass yield. Under dynamic glucose, fructose and sucrose conditions S. cerevisiae was able to maintain the biomass yield of steady-state conditions. Although the physiology was very comparable for these sugars, the intracellular metabolome and proteome changed. The concentration of upper glycolytic enzymes decreased for glucose and maltose (up to -60% and -40% respectively), while an increase was observed for sucrose and fructose when exposed to gradients. At the same time, enzymes of lower glycolysis were increased. Interestingly, common stress-related proteins were decreased during dynamic conditions. The observed adaptations to repeating gradients highlight the importance to study physiology and metabolism under dynamic conditions to obtain results that are relevant for the envisioned large-scale process.

Natural Killer (NK) cells, integral to the innate immune system, are notable in cell therapies because for their applicability in allogeneic treatments, distinguishing them from T cells typically employed in conventional cell therapies. However, their limited half-life poses a challenge for therapy. Although attempts to leverage feeder cells are common, safer methods are needed to mitigate the associated risks. In our study, an upregulation in the expression of 4-1BBL in Colo-205 cells under extracellular stresses such as hypoxia and cytochalasin D was observed. This enhanced binding to the 4-1BB receptors on NK cells promotes proliferation in NK cells. Elevated CD56 expression of a marker strongly linked to NK cell proliferation in co-culture further supports this process. Applying extracellular stressors, specifically hypoxia and cytochalasin D, to Colo-205 cells successfully tailored feeder cells, significantly enhancing NK cell proliferation.

Gamma-aminobutyric acid (GABA) is widely applied in the food and pharmaceutical industries, and glutamate decarboxylase (GAD) is the core enzyme for the biosynthesis of GABA. GAD exhibits high activity but poor stability under acidic conditions, and low activity under neutral conditions. In this study, GAD from Priestia flexa ( PfGAD) with high activity was screened out and characterized, and a variant with significantly improved stability was obtained through molecular modification strategies. We constructed an engineered strain that coexpressed PfGAD and a transporter protein for GABA and L-glutamic acid. Furthermore, the consumption of GABA was suppressed by knocking out the pepD gene, confirming a novel downstream metabolic pathway of GABA in Escherichia coli. The continuous accumulation of GABA in the engineered strain led to a yield of 154.7 g·L -1, with a conversation rate of 100%, meanwhile the cell growth was remained normal. The problem of the trade-off between cell growth and GABA accumulation was solved, which was one of the urgent problems according to previous reports. Our study provides important insights into the optimization of GABA production through enzyme engineering and strain modification, which could have significant implications for the industrial application of GABA.

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

Substantial wealth of knowledge about anaerobic fungi has accumulated in recent years, which may guide the attention of the biotechnology-oriented scientists towards the possible exploitation of their fascinating capabilities. Very efficient, unique and complex enzyme systems of anaerobic fungi play determining role in the conversion of lignocellulosic fodder to milk and meat in mammalian herbivores. Mitigation of the concomitant greenhouse gas emission by ruminants is a major environmental, climate change issue. In turn, controlled management of the inter-kingdom syntrophic co-operations among the eukaryotic anaerobic fungi, bacteria and archaea can lead to the production of valuable bio-fuels, e.g. bio-methane, bio-hydrogen, bio-ethanol, and organic acids, the latter could serve as building blocks in numerous biosynthetic processes in circular bioeconomy.

Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome scale metabolic models (GSMM) and by assuming instantaneous pseudo steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry (MS) coupled with liquid (LCMS) and gas chromatography (GCMS) for a fed-batch, high cell density cultivation of E. coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in TCA cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

In this paper, Long Short-Term Memory (LSTM) networks and multilayered feedforward neural networks (FFNNs) were combined with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed-batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system. The adaptive moment estimation method (ADAM) with stochastic regularization and cross-validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network (DNN) architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed hybrid FFNN models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with 7 internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R 2=0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by-products for around 10 cultivation days.

The aim of this study was to investigate the survival, distribution and reaction of different cell types on a monolayer disk, as well as their behavior under bioreactor treatment. Specifically, porcine EEC and porcine fibroblasts (PCF) were labeled with GFT and Texas Red, respectively, to track their viability and distribution. The experiments involved monitoring the cells using various microscopy techniques and comparing the results with controls. These findings have important implications for understanding cell behavior and potential applications for Discrete Subaortic Stenosis. This paper aims to discuss the implications of the findings in the context of existing literature and future research directions.

Bioreactor scale-up is complicated by dynamic interactions between mixing, reaction, mass transfer, and biological phenomena, the effects of which are usually predicted with simple correlations or case-specific simulations. This two-part study investigated whether axial diffusion equations could be used to calculate mixing times and to model and characterize large-scale stirred bioreactors in a general and predictive manner without fitting the diffusivity parameter. In this first part, a resistances-in-series model analogous to basic heat transfer theory was developed to estimate the diffusivity such that only available hydrodynamic numbers and literature data were needed in calculations. For model validation, over 800 previously published experimentally determined mixing times were predicted with the transient axial diffusion equation. The collected data covered reactor sizes up to 160 m 3, single- and multi-impeller configurations, aerated and non-aerated operation in turbulent and transition flow regimes, and various mixing time quantification methods. The model performed excellently for typical multi-impeller configurations as long as flooding conditions were avoided. Mixing times for single-impeller and few non-standard bioreactors were not predicted equally well. The transient diffusion equation together with the developed transfer resistance analogy proved to be a convenient and predictive model of mixing in typical large-scale bioreactors.

Large-scale fermentation processes involve complex dynamic interactions between mixing, reaction, mass transfer, and the suspended biomass. Empirical correlations or case-specific computational simulations are usually used to predict and estimate the performance of large-scale bioreactors based on data acquired at bench scale. In this two-part-study, one-dimensional axial diffusion equations were studied as a general and predictive model of large-scale bioreactors. This second part focused on typical fed-batch operations where substrate gradients are known to occur, and characterized the profiles of substrate, pH, oxygen, carbon dioxide, and temperature. The physically grounded steady-state axial diffusion equations with first- and zeroth-order kinetics yielded analytical solutions to the relevant variables. The results were compared with large-scale Escherichia coli and Saccharomyces cerevisiae experiments and simulations from the literature, and good agreement was found in substrate profiles. The analytical profiles obtained for dissolved oxygen, temperature, pH, and CO 2 were also consistent with the available data. Distribution functions for the substrate were defined, and efficiency factors for biomass growth and oxygen uptake rate were derived. In conclusion, this study demonstrated that axial diffusion equations can be used to model the effects of mixing and reaction on the relevant variables of typical large-scale fed-batch fermentations.

Enabling real-time monitoring and control of the biomanufacturing processes through product quality insights continues to be an area of focus in the biopharmaceutical industry. The goal is to manufacture products with the desired quality attributes. To realize this rigorous attribute-focused Quality by Design (QbD) approach, it’s critical to support the development of processes that consistently deliver high-quality products and facilitate product commercialization. Time delays associated with off-line analytical testing can limit the speed of process development. Thus, developing and deploying analytical technology is necessary to accelerate process development. In this study, we have developed the Micro Sequential Injection (µSI) process analyzer and the Automatic Assay Preparation Platform (A2P2) system. These innovations address the unmet need for an automatic, online, real-time sample acquisition and preparation platform system for in-process monitoring, control, and release of biopharmaceuticals. These systems can also be deployed in laboratory areas as an off-line analytical system and on the manufacturing floor to enable rapid testing and release of products manufactured in a GMP environment.