In recent years, bacteria from genus Clostridia have attracted attention of research community because of their biofuel production capabilities. Present study reports comparative genomic (CG) analysis of 48 genomes of solventogenic and saccharolytic Clostridia. We have focused on central carbon metabolism and general stress response in the analysis. Comprehensive summaries on comparison of general genome features, COG categories, CDSs of the energy, catabolic, and sporulation pathways are given. Furthermore, we have proposed two new genome-scale metabolic (GSM) models iKK848 and iKK1425 for Clostridium pasteurianum DSM 525 = ATCC 6013 and Clostridium acetobutylicum ATCC 824, respectively. These GSM models are most comprehensive in that they account for the largest number of reactions, metabolites, and genes as compared to previous models. Model quality and metabolic flux optimization for biomass growth using iKK1425 and iKK848 are compared with previous literature. Our models had the highest quality score of 61% and 77%.

Engineering biological systems to test new pathway variants containing different enzyme homologs is laborious and time-consuming. To tackle this challenge, a novel strategy was developed for rapidly prototyping enzyme homologs by combining cell-free protein synthesis (CFPS) with split GFP. This strategy featured two main advantages: 1) dozens of enzyme homologs were parallelly produced by CFPS within hours, and 2) the expression level and activity of each homolog was determined [simultaneously](javascript:;) by using the split GFP assay. As a model, this strategy was applied to optimize a 3-step pathway for nicotinamide mononucleotide (NMN) synthesis. Ten enzyme homologs from different organisms were selected for each step. Here, the most productive homolog of each step was identified within 24 h rather than weeks or months. Finally, the titer of NMN was increased to 1213 mg/L by improving physiochemical conditions, tuning enzyme ratios and cofactor concentrations, and decreasing the feedback inhibition, which was a more than 12-fold improvement over the initial setup. This strategy would provide a promising way to accelerate design-build-test cycles for metabolic engineering to improve the production of desired products.

Next generation manufacturing (NGM) has evolved over the past decade to a point where large biopharmaceutical organizations are making large investments in the technology and considering implementation in clinical and commercial processes. There are many well-considered reasons to implement NGM. For the most part, organizations will not fund NGM unless the implementation benefits the funding organization by providing reduced costs, reduced time or additional needed capabilities. Productivity improvements gained from continuous purification are shown in this work, which used a new system that fully integrates and automates several downstream unit operations of a biopharmaceutical process to provide flexibility and easy implementation of NGM. The equipment and automation supporting NGM can be complicated and expensive. Biopharmaceutical Process Development considered two options: (1) design its own NGM system or (2) buy a pre-built system. PAK BioSolutions (Virginia, US), provides a turn-key automated and integrated system that can operate up to four continuous purification stages simultaneously, while maintaining a small footprint in the manufacturing plant. The PAK system provides significant cost benefits (~10x lower) compared to the alternative – integration of many different pieces of equipment through a Distributed Control System (DCS) that would require significant engineering time for design, automation and integration. Integrated and Continuous Biomanufacturing can lead to significant reductions in facility size, reduced manufacturing costs, and enhanced product quality when compared to the traditional batch mode of operation. The PAK system uses new automation strategies that robustly link unit operations. We present the optimized process fit, sterility and bioburden control strategy, and automation features (such as pH feedback control and in-line detergent addition) that enabled continuous operation of a 14 day end-to-end monoclonal antibody purification process at the clinical manufacturing scale.

The liver is one of the vital organs in the body, and the gold standard of treatment for liver function impairment is liver transplantation, which poses many challenges. The specific 3D structure of liver, which significantly impacts the growth and function of its cells, has made biofabrication with the 3D printing of scaffolds suitable for this approach. In this study, to investigate the effect of scaffold geometry on the performance of HepG2 cells, Poly-Lactic acid (PLA) polymer was used as the input of the Fused Deposition Modeling (FDM) 3D-printing machine. Samples with simple square and bioinspired hexagonal cross-section designs were printed. 1% and 2% of gelatin-coating were applied to the 3D printed PLA to improve the wettability and surface properties of the scaffold. SEM pictures were used to analyze the structural properties of PLA-Gel hybrid scaffolds, EDS to investigate the presence of gelatin, water contact angle measurement for wettability, and weight loss for degradation. In vitro tests were performed by culturing HepG2 cells on the scaffold to evaluate the cell adhesion, viability, cytotoxicity, and specific liver functions. Then, high-precision scaffolds were printed and the presence of gelatin was detected. Also, the effect of geometry on cell function was confirmed in viability, adhesion, and functional tests. The albumin and urea production of the Hexagonal PLA scaffold was about 1.22 ­±0.02 fold higher than the square design in 3 days. This study will hopefully advance our understanding of liver tissue engineering toward a promising perspective for liver regeneration.

Herein we report the use of Pseudomonas putida F1 biofilms grown on carbonized cellulosic fibers to achieve biodegradation of airborne VOCs in the absence of any bulk aqueous phase media. It is believed that direct exposure of gaseous VOC substrates to biomass may eliminate aqueous phase mass transfer resistance and facilitate VOC capture and degradation. When tested with toluene vapor as a model VOC, the supported biofilm could grow optimally at 300 ppm toluene and 80% relative humidity, with a specific growth rate of 0.425 day -1. During long-term VOC biodegradation tests in a tubular packed bed reactor, biofilms achieved a toluene degradation rate of 2.5 mg g DCW -1 h -1 during the initial exponential growth phase. Interestingly, the P. putida F1 film kept biodegrading activity even at the subsequent stationary non-growth phase. The supported biofilms with a biomass loading of 20% (wt) could degrade toluene at a rate of 1.9 mg g DCW -1 h -1 during the stationary phase, releasing CO 2 at a rate of 6.4 mg g DCW -1 h -1 at the same time (indicating 100% conversion of substrate carbon to CO 2). All the specific degradation rates are much higher than what can be gleaned from previously reported work. It also demonstrates the feasibility of biofilm growth and direct gas phase degradation of VOCs without requiring any bulk aqueous phase.

Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.

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 (PE-H) 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.

Pichia pastoris ( Komagataella phaffii) is a fast-growing methylotrophic yeast with the ability to assimilate several carbon sources such as methanol, glucose, or glycerol. It has been shown to have outstanding secretion capability with a variety of heterologous proteins. In previous studies, we engineered P. pastoris to co-express E. coli AppA phytase and the HAC1 transcriptional activator using a bidirectional promoter. Phytase production was characterised in shake flasks and did not reflect industrial conditions. In the present study, phytase expression was explored and optimised using instrumented fermenters in continuous and fed-batch modes. First, the production of phytase was investigated under glucose de-repression in continuous culture at three dilution factors, 0.5 d -1, 1 d -1, and 1.5 d -1. The fermenter parameters of these cultures were used to inform a kinetic model in batch and fed-batch modes for growth and phytase production. The kinetic model developed aided to design the glucose feeding profile of a fed-batch culture. Kinetic model simulations under glucose de-repression and fed-batch conditions identified an optimal phytase productivity at the specific growth rate of 0.041 h -1. Validation of the model simulation with experimental data confirmed the feasibility of the model to predict phytase production in our newly engineered strain. Methanol was used only to induce the expression of phytase at high cell densities. Our results showed that high phytase production required two stages, the first stage used glucose under de-repression conditions to generate biomass while expressing phytase, and stage two used methanol to induce phytase expression. The production of phytase was improved 3.5-fold by methanol induction compared to the expression with glucose alone under de-repression conditions to a final phytase activity of 12.65 MU/L. This final volumetric phytase production represented an approximate 36-fold change compared to the flask fermentations. This two-phase strategy allowed to optimise phytase productivity by producing phytase during both growth and methanol induction phases. Finally, the phytase protein produced was assayed to confirm its molecular weight, and pH and temperature profiles. This study highlights the importance of optimising protein production in P. pastoris when using novel promoters and presents a general approach to performing bioprocess optimisation in this important production host.

Thermobifida fusca cutinase ( TfC ) is a carboxylesterase (CE) that degrades the environmental pollutant, polyethylene terephthalate (PET). TfC also acts upon PET’s degradation intermediates (DIs), such as oligoethylene terephthalate (OET), and bis-/mono-hydroxyethyl terephthalate (BHET/MHET), to convert these into terephthalic acid (TPA), the terminal product of PET degradation. We examined TfC’s surface, compared it to that of other CEs, and performed molecular docking and MD simulations with an OET, 2HE-(MHET) 3, to understand interactions between TfC’s surface and the OET, at TfC’s active site as well as vicinal regions. We mutated 17 residues on TfC’s surface, mostly individually, but sometimes using pairs of mutations, to see how these modulate TfC’s activity. Most mutants/variants showed a decrease in activity against solid PET. Some killed activity completely. However, three mutations (L90F, F209I and F249R), made using a background mutation (G62A) already reported to improve activity by almost ~2.0-fold, yielded increases in activity that were between ~1.3- and ~2.0-fold higher than that of G62A TfC (which we found to display a ~1.7-fold increase in activity over TfC, in our own experiments). TfC variants, G62A/F249R, and G62A/F209I, exhibit the highest activities yet observed in any TfC mutants/variants, against PET, and BHET, respectively.

Autologous cell therapy has proven to be an effective treatment for hematological malignancies. Cell therapies for solid tumors are on the horizon, however the high cost and complexity of manufacturing these therapies remain a challenge. Routinely used open steps to transfer cells and reagents through unit operations further burden the workflow reducing efficiency and increasing the chance for human error. Here we describe a fully closed, autologous bioprocess generating MAGE-B2 TCR-T cells. This bioprocess yielded 5 – 12e9 MAGE-B2-specific TCR-expressing T cells, transduced at low MOIs, within 7 to 10 days, and cells exhibited an enriched memory T cell phenotype and enhanced metabolic fitness. It was demonstrated that activating, transducing, and expanding leuko-apheresed cells in a single bioreactor without a T cell enrichment step promoted lentivirus transduction efficiency while resulting in comparable level of T cell purity (~97%) as that of leukopak cells that went through CD8+ and CD4+ positive selection. The critical process parameters of the bioreactor, including culturing at a high cell density (7e6 cells/mL), adjusting rocking agitations during phases of scale up, lowering glycolysis through addition of 2-Deoxy-D-glucose (2-DG), and modulating IL-2 levels, were shown to positively regulate TCR expression and cell doubling time, and promote resistance to effector-associated apoptosis of TCR-T cells. The bioprocess described herein supports scale-out feasibility by enabling processing of multiple patients’ batches in parallel within a Grade C cleanroom.

Biobutanol produced in acetone-butanol-ethanol fermentation at batch mode cannot compete with chemically derived butanol because of the low reactor productivity. Continuous fermentation can dramatically enhance productivity and lower capital and operating costs but are rarely used in industrial fermentation because of increased risks in culture degeneration, cell washout, and contamination. In this study, cells of the asporogenous Clostridium acetobutylicum ATCC55025 were immobilized in a single-pass fibrous-bed bioreactor (FBB) for continuous production of butanol from glucose and butyrate at various dilution rates. Butyric acid in the feed medium helped maintaining cells in the solventogenic phase for stable continuous butanol production. At the dilution rate of 1.88 h -1, butanol was produced at 9.55 g/L with a yield of 0.24 g/g and productivity of 16.8 g/L∙h, which was the highest ever achieved for biobutanol fermentation and an 80-fold improvement over the conventional ABE fermentation. The extremely high productivity was attributed to the high density of viable cells (~100 g/L at >70% viability) immobilized in the fibrous matrix, which also enabled the cells to better tolerate butanol and butyric acid. The FBB was stable for continuous operation for an extended period of over one month.

Isoprenoids are a large family of natural products with diverse structures, which allow them to play diverse and important roles in the physiology of plants and animals. They also have important commercial uses as pharmaceuticals, flavouring agents, fragrances, and nutritional supplements. Recently, metabolic engineering has been intensively investigated and emerged as the technology of choice for the production of isoprenoids through microbial fermentation. Isoprenoid biosynthesis typically originates in plants from acetyl-coA in central carbon metabolism, however, a recent study reported an alternative pathway, the Isopentenol Utilization pathway (IUP), that can provide the building blocks of isoprenoid biosynthesis from affordable C5 substrates. In this work, we expressed the IUP in Escherichia coli to efficiently convert isopentenols into geranate, a valuable isoprenoid compound. We first established a geraniol-producing strain in E. coli that uses the IUP. Then, we extended the geraniol synthesis pathway to produce geranate through two oxidation reactions catalysed by two alcohol/aldehyde dehydrogenases from Castellaniella defragrans. The geranate titer was further increased by optimizing the expression of the two dehydrogenases and also parameters of the fermentation process. The best strain produced 764 mg/L geranate in 24 h from 2 g/L isopentenols (a mixture of isoprenol and prenol). We also investigated if the dehydrogenases could accept other isoprenoid alcohols as substrates.

The dominant method for generating Chinese hamster ovary (CHO) cell lines that produce high titers of biotherapeutic proteins utilizes selectable markers such as dihydrofolate reductase (Dhfr) or glutamine synthetase (Gs), alongside inhibitory compounds like methotrexate (MTX) or methionine sulfoximine (MSX), respectively. Recent work has shown the importance of asparaginase (Aspg) for growth in media lacking glutamine–the selection medium for Gs-based selection systems. We generated a Gs/Aspg double knockout CHO cell line and evaluated its utility as a novel dual selectable system via co-transfection of Gs-Enbrel and Aspg-Enbrel plasmids. Using the same selection conditions as the standard Gs system, the resulting cells from the Gs/Aspg dual selection showed substantially improved specific productivity and titer compared to the standard Gs selection method, however, with reduced growth rate and viability. Following adaptation in selection medium, the cells improved viability and growth while still achieving ~5-fold higher specific productivity and ~3-fold higher titer than Gs selection alone. We anticipate that with further optimization of culture medium and selection conditions this approach would serve as an effective addition to workflows for the industrial production of recombinant biotherapeutics.

Microorganisms build fatty acids with biocatalytic assembly lines, or fatty acid synthases (FASs), that can be repurposed to produce a broad set of fuels and chemicals. Despite their versatility, the product profiles of FAS-based pathways are challenging to adjust without experimental iteration, and off-target products are common. This study uses a detailed kinetic model of the E. coli FAS as a foundation to model nine oleochemical pathways. These models provide good fits to experimental data and help explain unexpected results from in vivo studies. An analysis of pathways for alkanes and fatty acid ethyl esters, for example, suggests that reductions in titer caused by enzyme overexpression can result from shifts in pools of metabolic intermediates that are incompatible with the substrate specificities of downstream enzymes. In general, different engineering objectives (i.e., production, unsaturated fraction, and average chain length) show experimentally consistent sensitivities to pathway enzymes, and model-based compositional analyses indicate simple shifts in enzyme concentrations can alter the product profiles of pathways with promiscuous components. The study concludes by integrating all models into a graphical user interface. The models supplied by this work provide a versatile kinetic framework for studying oleochemical pathways in different biochemical contexts.

7-Methylxanthine, a derivative of caffeine (1,3,7-trimethylxanthine), is a high-value compound that has multiple medical applications, particularly with respect to eye health. Here, we demonstrate the biocatalytic production of 7-methylxanthine from caffeine using Escherichia coli strain MBM019, which was constructed for production of paraxanthine (1,7-dimethylxanthine). The mutant N-demethylase NdmA4, which was previously shown to catalyze N 3-demethylation of caffeine to produce paraxanthine, also retains N 1-demethylation activity toward paraxanthine. This work demonstrates that whole cell biocatalysts containing NdmA4 are more active toward paraxanthine than caffeine. We used four serial resting cell assays, with spent cells exchanged for fresh cells between each round, to produce 2,120 μM 7-methylxanthine and 552 μM paraxanthine from 4,331 μM caffeine. The purified 7-methylxanthine and paraxanthine were then isolated via preparatory-scale HPLC, resulting in 177.3 mg 7-methylxanthine and 48.1 mg paraxanthine at high purity. This is the first reported strain genetically optimized for the biosynthetic production of 7-methylxanthine from caffeine.

Glioblastoma (GBM) is the most common form of brain cancer. Even with aggressive treatment, tumor recurrence is almost universal and patient prognosis is poor because many GBM cell subpopulations, especially the mesenchymal and glioma stem cell populations, are resistant to temozolomide (TMZ) the most commonly used chemotherapeutic in GBM. For this reason, there is an urgent need for the development of new therapies that can more effectively treat GBM. Several recent studies have indicated that high expression of connexin 43 (Cx43) in GBM is associated with poor patient outcomes. It has been hypothesized that inhibition of the Cx43 hemichannels could prevent TMZ efflux and sensitize otherwise resistance cells to the treatment. In this study, we use a 3-dimensional organoid model of GBM to demonstrate that combinatorial treatment with TMZ and αCT1, a Cx43 mimetic peptide, significantly improves treatment efficacy in certain populations of GBM. Confocal imaging was used to analyze changes in Cx43 expression in response to combinatorial treatment. These results indicate that Cx43 inhibition should be pursued further as an improved treatment for GBM.

We report an automated cell-isolation system based on fluorescence image analysis of cell aggregates cultured in a photodegradable hydrogel. The system incorporates cell culture in a humidified atmosphere with controlled CO 2 concentration and temperature, image acquisition and analysis, micropatterned light exposure, and cell collection by pipetting. Cell aggregates were cultured on hydrogels, and target cells were selected by phase contrast and fluorescence image analysis. After degradation of the hydrogel by exposure to micropatterned ultraviolet light, cell aggregates were transferred to a collection vessel by robotic pipetting. We assessed the system for hydrogel degradation, recovery of target cells, and contamination by off-target cells. We demonstrated two practical applications of our method: (i) in cell aggregates from MCF-7-RFP strains in which 18.8% of cells produced red fluorescent protein (RFP), we successfully obtained 14 proliferative fluorescence-positive cell aggregates from 31 wells, and all of the isolated strains produced a higher proportion of RFP than the original populations; (ii) after fluorescent immunostaining of human epidermal growth factor receptor 2 (HER2) in cancer cells, we successfully isolated HER2-positive cells from a mixed population of HER2-positive and -negative cells, and gene sequence analysis confirmed that the isolated cells mainly contained the target cells.

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a co-culture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.