Biotechnology and Bioengineering

During manufacturing batches, Chinese hamster ovary (CHO) cells encounter critical levels of environmental stressors such as ammonia, lactate, and osmolality accumulation that can significantly reduce cell health and productivity. It is therefore crucial that stress adaptation and resistance be factored into cell line development (CLD). In this study, we employee population-based transcriptomic and differential gene expression analysis on stress-induced CHO cells to identify biomarkers displaying both heritable and stress-responsive properties. Using this workflow, 199 genes displayed transcriptional variability characteristic of a bistable system that formed four network communities of co-fluctuating genes. These communities were enriched in genes related to the regulation of apoptotic processes and gene expression/metabolic pathways. Seven genes were identified as promising biomarkers for engineering a stress-resistant phenotype. Genetic engineering methods may be employed in the future to bias clonal populations for higher stress tolerance to manufacturing stress, therefore increasing cell health and productivity in at-scale bioreactors.

Chinese Hamster Ovary (CHO) cell monoclonal antibody (mAb) production in continuous perfusion has witnessed a renewed interest within the biopharmaceutical industry. Widespread implementation of perfusion biomanufacturing, however, remains hindered by long process development timelines and high costs. Use of predictive scale-down platforms to generate large informative metabolic datasets and guide process development decisions is critical to decreasing a molecules time to market. While scale-down platforms based on the pseudo perfusion concept have been previously reported, they have not been rigorously validated. They are often limited by oxygen transport or insufficient metabolic characterization, reducing their role to a preliminary screening tool. Here, we report the design and validation of a pseudo perfusion platform based on a phenotype-driven approach to ascertain that the process emulates continuous perfusion characteristics and is not oxygen limited. Beyond metabolic and cell size steady state, we show that our pseudo perfusion design enables cell cycle subpopulation and intracellular antibody expression steady state. We also demonstrate that pseudo perfusion robustly predicts amino acid demands in continuous perfusion bioreactors with exceptional linear correlation across a broad range of cell-specific perfusion rates (CSPRs). When coupling the pseudo perfusion platform developed here with a workflow for metabolic characterization, we significantly augment the dimensionality and reliability of data which can be generated at this scale to gain actionable insights towards perfusion process design, ultimately reducing process development timelines and the associated costs. HighlightsResidual lactate is a key proxy for oxygen transport in scale down platform design Novel flow cytometry workflow confirms cell cycle and intracellular steady state Pseudo perfusion robustly predicts metabolic phenotypes in continuous perfusion K-means clustering analysis of nutrient rates provides insight into media design

Small extracellular vesicles (sEV) released by healthy beta cells are promising candidates for diabetes therapy thanks to their aptitude to modulate inflammation, to induce or maintain pancreatic function and to prevent pathogenic mechanisms. To advance the clinical development of therapeutics, there is a crucial need for scalable production methods. Stirred tank bioreactors (STR) are widely used in the industry due to their ability to provide homogeneous gas and nutrient supply, online monitoring, and efficient scale up. Anchorage- dependent cells can be cultured in STR on microcarriers or as spheroids, but may experience shear stress, which can affect sEV phenotype and function. Using pancreatic beta cells, this study identifies critical cell culturing parameters, including culture mode (monolayer vs. spheroids), medium formulation (with or without serum, glucose control), and process parameters (stirring, duration, cell density). The findings show that small spheroid culture promotes beta cell maturation without decreasing the yield of sEV per cell, despite a reduced cell surface exchange area. However, stirring increased expression of cellular stress markers and decreased cell viability. Set up of a three-step bioprocess allowed to maximize cell viability and sEV yields at high cell density over short production duration. sEV produced under these conditions maintained high purity, membrane integrity, and the aptitude to reduce T- lymphocyte proliferation and IFN-{gamma} cytokine secretion in a mixed lymphocyte reaction. Flow cytometry analysis revealed lower CD63/CD81 ratios in STR, indicating enhanced ectosome production. Switch from high glucose expansion to low glucose production medium further allowed to direct sorting of the antigen insulin into beta-sEV. This study demonstrates the feasibility of producing functional sEV from mature beta cells cultured as small spheroids, suitable for upscale. Production of sEV in STR may be particularly beneficial for ectosome- enriched compound loading for therapeutic applications. Graphical abstractRational development of a scalable bioprocess to control extracellular vesicles production & function. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/611247v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@6eaefeorg.highwire.dtl.DTLVardef@a2da3borg.highwire.dtl.DTLVardef@1a58f35org.highwire.dtl.DTLVardef@5cfd1d_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISmall 3D-spheroid culture promotes cell maturation C_LIO_LIStirring induces cellular stress responses & promotes ectosome release from the cytoplasmic membrane C_LIO_LIDesign of experiment efficiently enhances cell viability and EV yield with preserved immune-modulatory properties C_LIO_LIGlucose-starvation during the production phase directs insulin-sorting into extracellular vesicles C_LI

PurposeLarge quantities of human pluripotent stem cells (hPSCs) are required for clinical applications. 3D suspension cultures are suitable for large scale manufacturing of hPSCs but yield, viability and quality are affected by the hydrodynamic environment. This paper characterizes the hydrodynamic environment inside vertical wheel bioreactors (VWBRs) as a function of size and agitation rates, measures its effect on cell aggregation and proliferation, and proposes the use of Lagrangian-based shear stress and energy dissipation rate (EDR) exposures to support scale-up. MethodsIn silico: Transient, 3D, turbulent flow simulations are conducted for two VWBR sizes (100, 500 mL) at five agitation rates between 20 and 80 rpm. Trajectories of cell aggregates of sizes from 200 to 1,000 microns are calculated, and shear stress and EDR exposures are collected along these trajectories. In vitro: ESI-017 hPSCs were cultured in VWBRs for 6 days. Aggregation efficiency and daily fold ratios were calculated based on cell counts and initial inoculation density. ResultsAggregate size, agitation rate and bioreactor size modulate cell aggregate exposures to EDR and shear stress, which significantly depart from maximum or volume average metrics used for scale-up. Combined in vitro/in silico results show EDR affects aggregation efficiency, cell counts and aggregate size, and has a small effect on daily fold ratios but a significant effect on total fold ratio. ConclusionHistory of trajectory-based cell aggregate exposures to EDRs provide a better scale-up basis for VWBRs than volume-averaged EDR. Shear stress does not significantly affect hPSC aggregation, proliferation and expansion in VWBRs under the tested conditions.

Hyaluronic acid (HA) is a biopolymer with wide applications in the field of medicine and cosmetics. Bacterial production of HA has a huge market globally. Certain species of Streptococcus are native producers of HA but they are pathogenic. Therefore, safer organisms such as L. lactis are engineered for HA production. However, there are challenges such as low yield, low molecular weight and polydispersity of HA obtained from these cultures. Optimisation of bioprocess parameters and downstream purification parameters are being addressed to overcome these challenges. We explore these problems from the perspective of microbial heterogeneity, since variations in phenotype affect the yield and properties of the product in a bioreactor. For this perspective, a method to quantitatively assess the occurrence of heterogenous phenotypes depending on the amount of HA produced at the single-cell level is required. Here, we evaluated for the first time the use of calcofluor white staining method combined with in vivo fluorescence confocal microscopy to quantify the heterogeneity in phenotypes of L. lactis cells engineered for HA production. From the microscopy image analysis, we found that the population harbours significant heterogeneity with respect to HA production and our novel approach successfully differentiates these phenotypes. Using the fluorescence intensity levels, first we were able to confidently differentiate cells not expressing HA (Host cells without HA genes for expression) from cells with genes for HA production (GJP2) and induced for expression, as there is a consistently two-fold higher level of expression in the GJP2 cells independently of the cell size. Further, this method revealed the occurrence of two different phenotypes in GJP2 cultures, one of a high-expression phenotype (40% of the population) and the other one of a low-expression (remaining 60% of the population), and it is the high expression phenotype that contributes to the increase in the HA expression of the GJP2 population compared with the host cells. Thus, it is essential to identify the extrinsic and intrinsic factors that can favour most of the cells in the population to switch and stabilise into the high-expression phenotype state in a bioreactor, for higher yield and possibly reduced heterogeneity of the product, such as polydispersity in chain lengths. For such optimisation studies, this in vivo method serves as a promising tool for rapid detection of phenotypes in the bioreactor samples under varying conditions, allowing fine tuning of the factors to stabilise high-expression phenotypes thereby maximizing the yield. Graphical Abstractdone. Key PointsO_LICalcofluor staining successfully differentiated the phenotypes based on HA levels. C_LIO_LIThis study revealed the occurrence of significant heterogeneity in HA expression. C_LIO_LIThis method will aid for rapid optimization of factors for improved HA production. C_LI

Bioreactor pH can significantly affect Chinese Hamster Ovary (CHO) cell metabolism, thus impacting glycoprotein titers. However, there is very limited literature on incorporating pH in mathematical models for CHO cell metabolism. To address this limitation, guided by recently published experimental data, we have curated a stoichiometric network and formulated phenotype-driven kinetic expressions to develop a Dynamic Metabolic Flux Analysis (DMFA) model. The DMFA model incorporates Critical Process Parameters (CPPs), notably bioreactor pH, basal and feed media nutrient composition, feeding times, and inoculation cell densities to predict bioreactor performance: cell growth rates, antibody titers, and nutrient and metabolite profiles. The DMFA model was trained on diverse fed-batch data of the CHO VRC01 cell line to regress the kinetic parameters. The models utility was demonstrated through experimentally validated model predictions of CHO-cell performance in intensified fed-batch cultures, perfusion cultures, and cultures with different media. Experimentally validated predictions of a culture with high initial cell density and increased feed addition (intensified fed-batch culture) showed that mAb titers similar to fed-batch culture can be achieved with shorter culture durations. Similarly, experimentally validated predictions of perfusion bioreactor performance showed that coupling historical fed-batch data with computational tools can be leveraged to predict continuous biomanufacturing performance. We thus demonstrate that the developed mathematical model can simulate culture performance outside of the training data set. This supports the predictive robustness of the framework and provides a valuable tool for bioprocess development of diverse culture modes. HighlightsO_LIExperimentally measured fed-batch cell culture data was used to curate a reaction network. This reaction network was integrated with phenotypically driven kinetic expressions to yield a dynamic metabolic flux analysis (DMFA) model. C_LIO_LIThe DMFA model can predict bioprocess performance indicators such as concentration of viable cells, mAb, amino acids, glucose, lactate, and ammonia. C_LIO_LIThe model was developed to make these predictions under various process conditions such as bioreactor pH, media concentrations, feed supplementation schedule, and initial cell densities. C_LIO_LIPredicting and experimentally validating the impact of high initial cell density and increased feed media supplementation yielded in mAb titers similar to traditional fed-batch processes with much shorter culture durations. C_LIO_LIThe application of the DMFA model trained on data from a traditional fed-batch process to predict perfusion bioreactor culture performance was successfully demonstrated and experimentally verified. C_LIO_LIThe impact of AMBIC reference media on cell culture process performance was also predicted and experimentally validated. The predictions of amino acid metabolism yielded insights into improving the media. C_LI

A computational fluid dynamics (CFD) model was developed and validated against experiments for a laboratory-scale 5-L bioreactor. Numerical simulation was performed to describe the bioreaction of Streptomyces atratus SCSIO ZH16 fermentation for ilamycin E production with the dynamic changes in viscosity of the fermentation broth due to biomass growth and decay. This model can account for the two-way coupling between the fermentation environment and medium, which allowed for the elucidation of the impact of the flow field in the bioreactor on bacterial growth and production, as well as the influence of viscosity changes on the flow field. This work represents the first integration of Streptomyces fermentation with CFD, enabling the simulation of flow field and mass transfer under varying stirring speed, aeration rate, and viscosity during Streptomyces fermentation. An optimum range of fermentation broth viscosity (10-30 mPa s) was identified for ilamycin E production by S. atratus SCSIO ZH16 fermentation. Furthermore, the addition of sorbitol to optimize the viscosity of the fermentation broth in the later stages of fermentation. The enhanced mass transfer efficiency strengthens the respiration, energy supply, and carbon source consumption in Streptomyces, thereby increasing the ilamycin E production. This research offers a practical strategy for the process intensification and industrial scale-up for such bioreactors. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/630827v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@3d463corg.highwire.dtl.DTLVardef@ce3859org.highwire.dtl.DTLVardef@d55c7forg.highwire.dtl.DTLVardef@1abaee5_HPS_FORMAT_FIGEXP M_FIG C_FIG

Understanding Chinese hamster ovary (CHO) cell metabolism through mathematical models is essential for optimizing culture media and biomanufacturing processes. Current mechanistic models rely primarily on either flux balance analysis (FBA), estimating intracellular fluxes while assuming steady state, or kinetic modeling, capturing dynamic behavior but typically for a limited number of reactions. Dynamic FBA (dFBA) integrates both approaches in a hybrid framework, but challenges remain in integrating the two formats to describe bioprocesses. In this study, we first enhanced an existing dynamic CHO-metabolism model by incorporating 13C-labeled data to refine kinetic expressions and stoichiometric constraints of amino acid pathways, including the asparagine-aspartate network and serine biosynthesis. We next evaluated the impact of prioritizing either stoichiometry, through the pseudo steady state assumption (PSSA), or the kinetic expressions of fluxes. Comparing error and predictive performance for both models for two industrially relevant fed-batch CHO culture conditions involving varying initial concentrations of nutrients and three feed streams, demonstrated that the kinetic-oriented model (KOM) yielded superior predictions for viable cell density (VCD), antibody production, and a range of amino acids and metabolites compared to the stoichiometric oriented model (SOM). Indeed, the KOM was able to predict production-to-consumption shifts of lactate and alanine, fluctuating levels of ammonia based on reversible kinetic expressions, and amino acids like asparagine and the serine-glycine pool. The KOM also provided better predictions for a third case including lactate-supplemented (LS) feed; however, slight parameter adjustments helped to improve model fidelity, likely due to the impact of high lactate on kinetic expressions of antibody (directly) and VCD (indirectly). In summary, our findings demonstrate that hybrid models emphasizing empirical kinetics over strict pseudo-steady-state constraints capture biologically realistic dynamics such as transient shifts for key metabolites like lactate, alanine, and ammonia, and also produce parameters useful across varying conditions, making them a practical and powerful tool for characterizing CHO cell culture performance in the future.

Extracellular vesicles (EVs) are nano-scale structures produced by cells that transport biological substances for intercellular communication. The tetraspanins CD9, CD81, and CD63 are crucial to EV biogenesis and function. This study uses CRISPR-Cas9 system to knock out (KO) CD9, CD63, and CD81 in HEK293T cells. The goal is to investigate the role of these tetraspanins in EV bioengineering with the hypothesis that repressing endogenous production may increase the availability of exogenously introduced tetraspanin-fusion constructs and increase engineered EV production. Firstly, it is observed that individually knocking out a tetraspanin does not significantly affect EV formation. However, when all three tetraspanins are simultaneously knocked out, there is a marked decrease in EV production, as measured by nanoparticle tracking analysis (NTA). Secondly, upon reintroduction of the corresponding tetraspanins fused to firefly ThermoLuc (Tluc) or neon green (mNG) into the PanKO-, CD9KO, CD63KO-, and CD81KO-cells, the engineered EVs display a significant increase in production by 50% to 70% compared to transduction of wild-type (WT) cells, as measured by luminometer and imaging flow cytometry. These findings emphasize the potential of tetraspanin KO in the bioengineering of EVs, paving the way for new therapeutic applications by enhancing production and potentially modifying their cargo.

Scalable, continuous biomanufacturing processes have grown in importance to meet demand for smaller bioreactor sizes, lowered production costs, and improved quality attributes. The Sf9/recombinant baculovirus (rBV) expression system demonstrates promise for virus-like particle (VLP) vaccine and gene therapy production. Here, we present a continuous rBV platform integrating an infection plug flow reactor (PFR) between stirred tank growth (gCSTR) and production (pCSTR) bioreactors. Cell expansion in the gCSTR included a ramp-up stage followed by continuous growth, reaching a steady state of 5x106 cells/mL and >90% viability. Peclet number-fit tracer studies confirmed near-ideal plug flow in the PFR, yielding a 10 h residence time and progressive infection as measured by gp64 signaling. Finally, a pCSTR with a residence time of 48 h exhibited sustained recombinant protein production. An integrated pilot cascade incorporating all reactors ran continuously for 5 days, maintaining stable CSTR cell densities and a measurable increase in infected cell diameter from 14.5 m to 16.1 m. Western blotting and EM of [~]100 nm VLPs in pCSTR effluent demonstrated platform success. Digital twin mechanistic models across four distinct stages of bioreactor operation and Hill-type relationships for rBV infection kinetics predicted cell growth and death for a 7-day run, demonstrating promise for designing continuous systems in silico and building a quantitative framework for scale-up and optimization. Our multi-stage reactor configuration represents a cell host- and product-agnostic production scheme, particularly for processes prone to product heterogeneity, and paves the way towards a true end-to-end continuous platform for myriad modalities in the future.

The activation of gene circuits can impose a significant burden on cells, leading to heterogeneous expression and reduced productivity. In this work, we focused on the T7 production system in E. coli BL21, a prime example of a burdensome gene circuit, to investigate the main cause for this gene expression heterogeneity and methods to mitigate it. Based on continuous cultivation analyzed and control by automated flow cytometry, we quantified the trade-off between cellular growth and gene expression and tracked the cell-to-cell heterogeneity in gene expression (measured as entropy). We concluded that the growth reduction associated to the activation of the burdensome gene circuit, i.e., the switching cost, is at the origin of the population heterogeneity. The loss of growth rate imposed by the burdensome activation of the gene is compensated at the population level by the overgrowth of less induced cells that safeguard the population by generating entropy. We tried to homogenize the population by pulsing the inducer with increasing frequency but found that the population escapes control through promoter mutation, leading to a genotype exhibiting reduced gene expression, but also, reduced entropy. To engineer a more homogeneous population without sacrificing gene expression, we decreased the switching cost associated to the induction by lowering the quality of the main carbon source. This strategy successfully led to a more homogeneous and productive population. Our approach allows for a precise quantification of the trade-off between growth and gene expression in cell population cultivated under dynamic conditions and highlights the importance of the switching cost for designing efficient approaches of cell population control.

Escherichia coli is a bacterium that has been widely used as host in industrial fermentation processes. Sugars and glycerol are currently used as feedstocks in most of such applications. To reduce the associated carbon footprint, there are many ongoing efforts in engineering the bacterium to utilize formate, a molecule that can be obtained from CO2 easily. Glycine is a key intermediate in a formate utilization pathway that has been reconstituted in E. coli. This study focuses on engineering E. coli to assimilate glycine into the central metabolism. We systematically compared three glycine utilization pathways and found that the glycine dehydrogenase pathway yielded the most stable strain. Through rational promoter engineering and evolution in a continuous stirred tank reactor (CSTR) with a mutator plasmid, we isolated a strain that was able to use glycine as the sole carbon and nitrogen source. It consumed 8 g/L glycine within 48 h. Whole genome sequencing revealed 40 changes in its genome, including a few in critical genes such as those encoding glutamate synthase and ATP synthase. The expression of the genes around the glyoxylate node was also found by RNA sequencing to be fine-tuned, presumably for reducing accumulation of the toxic aldehyde intermediate (glyoxylate). The strain obtained in this study could be useful in improving formate utilization in E. coli. The methods and equipment developed in this study (e.g., the customized, low-cost CSTR) could also facilitate training E. coli to utilize other non-conventional substrates.

Recent research endeavors have turned to sustainably generating useful chemicals from biological platforms. However, conventional model organisms, such as Escherichia coli and Saccharomyces cerevisiae, face limitations, particularly in terms of substrate range and yield for certain metabolites. In this study, we share our work toward the development of the non-model bacterium, Paraburkholderia sacchari (hereafter P. sacchari), as a microbial factory for the production of polyhydroxyalkanoates (PHAs), which are precursors for biodegradable plastic. The particular PHAs of interest produced by P. sacchari include poly(3-hydroxybutyrate) (PHB) and the co-polymer produced by the combination of PHB and 3-hydroxyvalerate (3HV) called poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). P. sacchari produces PHB from mixtures of hexose and pentose sugars commonly found in lignocellulosic biomass, however PHBV requires co-feeding with propionate. Both plastic precursors have industrial interest, so both PHB and 3HV were chosen as production targets. Due to studies in other bacteria demonstrating PHB yield can be improved by overexpressing genes for critical pathway enzymes, we hypothesized there is a bottleneck in the production pathway leading to PHB in P. sacchari as well. To explore this, heterologous genes coding for the three critical enzymes were taken from Cupriavidus necator H16 (hereafter C. necator) and inserted via plasmid; phaA and bktb (homologous genes for {beta}-ketothiolase), phaB (acetyl-CoA reductase), and phaC (PHA polymerase). PHB production increased following overexpression of phaB, indicating acetoacetyl-CoA as the limiting enzyme. In fact, overexpression of phaB with the synthetic Anderson promoter, BBa_J23 104, increased titer by 162% over wildtype. On the other hand, strategies to improve 3HV had mixed results. Heterologous overexpression of propionyl-CoA transferase (pct from C. necator), which converts propionate into propionyl-CoA-the starting substrate for the 3HVproduction, showed a 145% increase in 3HV. Yet, internal sourcing of propionyl-CoA from succinyl-CoA following introduction of the sleeping beauty mutase (sbm) operon from E. coli showed no 3HV production. To this end, Max/Min Driving Force (MDF) thermodynamic analysis of critical PHBV pathways revealed two major limitations of 3HV production: 1) internal sourcing is not thermodynamically favorable; and 2) recycling of propionyl-CoA through the methyl citrate cycle (MCC) is more favorable than 3HV formation. Overall, we have shown promising progress and suggest future directions toward an industrially useful strain of P. sacchari for PHB and PHBV production. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/624694v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1942ebaorg.highwire.dtl.DTLVardef@187ec83org.highwire.dtl.DTLVardef@b8b439org.highwire.dtl.DTLVardef@4015d6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsParaburkholderia sacchari has interest as a bioproduction platform for PHAs from complex feedstocks. Removal of PHA pathway bottleneck increases PHB yield by 162%. Improved conversion of fed-in propionate increases 3HV yield by 145%. Internal sourcing of propionyl-CoA does not successfully yield 3HV. Thermodynamic analysis provides insight into difficult conversion of propionyl-CoA to 3HV.

Bioprocess development is commonly characterized by long development times, especially in the early screening phase. After promising candidates have been pre-selected in screening campaigns, an optimal operating strategy has to be found and verified under conditions similar to production. Cultivating cells with pulse-based feeding and thus exposing them to oscillating feast and famine phases has shown to be a powerful approach to study microorganisms closer to industrial bioreactor conditions. In view of the large number of strains and the process conditions to be tested, high-throughput cultivation systems provide an essential tool to sample the large design space in short time. We have recently presented a comprehensive platform, consisting of two liquid handling stations coupled with a model-based experimental design and operation framework to increase the efficiency in High Throughput bioprocess development. Using calibrated macro-kinetic growth models, the platform has been successfully used for the development of scale-down fed-batch cultivations in parallel mini-bioreactor systems. However, it has also been shown that parametric uncertainties in the models can significantly affect the prediction accuracy and thus the reliability of optimized cultivation strategies. To tackle this issue, we implemented a multi-stage Model Predictive Control (MPC) strategy to fulfill the experimental objectives under tight constraints despite the uncertainty in the parameters and the measurements. Dealing with uncertainties in the parameters is of major importance, since constraint violation would easily occur otherwise, which in turn could have adverse effects on the quality of the heterologous protein produced. Multi-stage approaches build up scenario tree, based on the uncertainty that can be encountered and computing optimal inputs that satisfy the constrains despite of such uncertainties. Using the feedback information gained through the evolution along the tree, the control approach is significantly more robust than standard MPC approaches without being overly conservative. We show in this study that the application of multi-stage MPC can increase the number of successful experiments, by applying this methodology to a mini-bioreactor cultivation operated in parallel.

For decades, expression systems based on the methanol-regulated AOX1 promoter (pAOX1) from the alcohol oxidase 1 gene have served as a benchmark for recombinant protein (rProt) production in Komagataella phaffii. However, methanol-free processes are increasingly being developed to overcome the drawbacks of methanol utilization, particularly its toxicity and flammability. The use of formate as a pAOX1 inducer in combination with sorbitol, a non-repressive carbon source, has emerged as a promising alternative to methanol-based expression systems. Recently, we demonstrated that formate derived from the tetrahydrofolate-mediated one-carbon (THF-C1) metabolism accumulates in K. phaffii cells deficient in formate dehydrogenase (FdhKO) when grown in sorbitol-based methanol-free medium. Using the lipase CalB from Candida antarctica as a model protein, we observed that rProt productivity in an FdhKO strain grown on sorbitol was comparable to that of an Fdh-proficient strain grown on methanol. However, sorbitol is inefficiently metabolized in K. phaffii, leading to a low growth rate and potentially limiting rProt productivity due to insufficient energy and carbon supply. Here, we increased sorbitol uptake rate, and thus improved sorbitol metabolism, by overexpressing the gene encoding sorbitol dehydrogenase (SOR1) in an FdhKO strain. Our results demonstrate that while increased sorbitol metabolism promotes biomass formation, it reduces pAOX1 induction, as evidenced by lower formate accumulation and decreased rProt productivity, both for intracellular eGFP and secreted proteins namely CalB lipase and glucose oxidase GOx from Aspergillus niger in SOR1-overexpressing strains. Additionally, oxygen availability for cells influences these dynamics, with lower oxygen transfer favoring higher pAOX1 induction due to increased formate accumulation in an FdhKO strain. Our data also suggests that at low oxygen transfer and low sorbitol uptake rate, the proportion of cells in an induced state increased significantly. This work provides valuable insights into the interplay between sorbitol metabolism and oxygen transfer conditions, contributing to the development of improved recombinant protein production strategies in K. phaffii.

With advancements in synthetic biology and metabolic engineering, microorganisms can now be engineered to perform increasingly complex functions, which may be limited by the resources available in individual cells. Division of labour in synthetic microbial communities offers a promising approach to enhance metabolic efficiency and resilience in bioproduction. By distributing complex metabolic pathways across multiple subpopulations, the resource competition and metabolic burden imposed on an individual cell is reduced, potentially enabling more efficient production of target compounds. Violacein is a high-value pigment with anti-tumour properties that exemplifies such a challenge due to its complex bioproduction pathway, imposing a significant metabolic burden on host cells. In this study we investigated the benefits of division of labour for violacein production by splitting the violacein bioproduction pathway between two subpopulations of Escherichia coli based synthetic communities. We tested several pathway splitting strategies and reported that splitting the pathway into two subpopulations expressing VioABE and VioDC at a final composition of 60:40 yields a 2.5 fold increase in violacein production as compared to a monoculture. We demonstrated that the coculture outperforms the monoculture when both subpopulations exhibit similar metabolic burden levels, resulting in comparable growth rates, and when both subpopulations are present in sufficiently high proportions.

Heparosan is a precursor molecule for the widely used anticoagulant heparin, which also has other uses such as certain drug delivery applications and as a scaffold for tissue engineering in biomaterials. Traditionally, pathogenic bacteria such as E.Coli have been used as a host to produce heparosan as an alternative to animal and chemoenzymatic synthesis. Using GRAS status organisms like Lactococcus Lactis as the host for production of heparosan provides a safe alternative as well as being a well-established organism for genetic manipulation and reengineering. In this study, a functional heparosan synthesis pathway was successfully expressed in Lactococcus Lactis by the expression of E.coli K5 genes KfiA and KfiC, along with the overexpression of ugd, glmu and pgma genes present natively in the host organism. The genes were activated using the tightly controlled NICE expression system. The genes were cloned into plasmid p8148 and transformed into two strains, Lactococcus Lactis NZ9000 and Lactococcus Lactis NZ9020, totaling six different recombinant strains were created using these two hosts and various combinations of the heterologous genes. The recombinant Lactococcus Lactis SH6 strain, expressing the genes ugd-KfiA-KfiC-pgma yielded a maximum concentration of 754 mg/l in batch bioreactor experiments and the titer was increased to 1263 mg/l in fed-batch fermentation. NMR imaging successfully determined that the structure of the product derived from Lactococcus Lactis was indeed similar to E.coli heparosan. The molecular weight of heparosan varied from 10-20 KDa, indicating its potential use for chemoenzymatic heparin biosynthesis.

Efficient, large-scale heterologous production of enzymes is a crucial component of the biomass valorization industry. Whereas cellulose utilization has been successful in applications such as bioethanol, its counterpart lignin remains significantly underutilized despite being an abundant potential source of aromatic commodity chemicals. Fungal lignin-degrading heme peroxidases are thought to be the major agents responsible for lignin depolymerization in nature, but their large-scale production remains inaccessible due to the genetic intractability of basidiomycete fungi and the challenges in the heterologous production of these enzymes. In this study, we employ a strain engineering approach based on functional genomics to identify mutants of the model yeast Saccharomyces cerevisiae with enhanced heterologous production of lignin-degrading heme peroxidases. We show that our screening method coupling an activity-based readout with fluorescence-assisted cell sorting enables identification of two single null mutants of S. cerevisiae, pmt2 and cyt2, with up to 11-fold improved secretion of a versatile peroxidase from the lignin-degrading fungus Pleurotus eryngii. We demonstrate that the double deletion strain pmt2cyt2 displays positive epistasis, improving and even enabling production of members from all three classes of lignin-degrading fungal peroxidases. We anticipate that these mutant strains will be broadly applicable for improved heterologous production of this biotechnologically important class of enzymes.

Retinal organoids (ROs), derived from human pluripotent stem cells (hPSCs), simulate in vivo development and retinal morphology, providing a platform to study retinal development and diseases. However, current differentiation protocols often yield inconsistent results with substantial cell line and batch variability. These protocols utilize static culture methods that rely on passive oxygen diffusion to reach the vessel bottom, where adherent hPSCs initially differentiate. Static culture is standard for adherent monolayer cells and is presumed suitable for RO differentiation. We questioned this assumption given that, during differentiation, the monolayer hPSCs become highly structured and multi-layered, first as neural rosettes and then as optic vesicles (OVs). We hypothesized that the cellular oxygen consumption rate would exceed the rate of delivery via passive diffusion, particularly to inner regions of emerging OVs. To test this hypothesis, we measured dissolved oxygen concentrations at the vessel bottom and found that within hours of media change, oxygen dropped to < 1 %, a level considered non-physiologically hypoxic, which imperils cell viability. This non-physiological hypoxia caused OV degeneration, hypoxic marker expression, and necrosis. To address this problem, we developed a novel 3D-printed stirred bioreactor (SBR) that maintains physiological oxygen levels between [~]4-6%. This approach significantly improved organoid yield, quality, and reproducibility while being easily adaptable to typical laboratory cell culture workflows. We conclude that non-physiological hypoxia, a previously unappreciated condition, is a limiting factor underlying inconsistent yield and quality in RO production. Physiological oxygenation levels can be restored by the SBR platform, resulting in greater consistency and improved production outcomes.

There is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of CHO cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.