Engineering in Life Sciences最新文献

Adherent cells, mammalian or human, are ubiquitous for production of viral vaccines, in gene therapy and in immuno-oncology. The development of a cell-expansion process with adherent cells is challenging as scale-up requires the expansion of the cell culture surface. Microcarrier (MC)-based cultures are still predominate. However, the development of MC processes from scratch possesses particular challenges due to their complexity. A novel approach for the reduction of development times and costs of cell propagation processes is the combination of mathematical process models with statistical optimization methods, called model-assisted Design of Experiments (mDoE). In this study, an mDoE workflow was evaluated successfully for the design of a MC-based expansion process of adherent L929 cells at a very early stage of development with limited prior knowledge. At the start, the analytical methods and the screening of appropriate MCs were evaluated. Then, cause-effect relationships (e.g., cell growth related to medium conditions) were worked out, and a mathematical process model was set-up and adapted to experimental data for modeling purposes. The model was subsequently used in mDoE to identify optimized process conditions, which were proven experimentally. An eight-fold increase in cell yield was achieved basically by reducing the initial MC concentration.

The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to model biomass growth. Three metrics: coefficient of determination (R²), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the model. Sensitivity analysis was applied to confirm variables that have a strong influence on biomass growth. The results of the study showed that MLP ANN algorithm was able to model biomass growth successfully. R² values were 0.844, 0.853, and 0.823 during training, validation, and testing phases, respectively. RMSE values were 0.7476, 1.1641, and 0.7798 during training, validation, and testing phases respectively. MSE values were 0.5589, 1.3551, and 0.6081 during training, validation, and testing phases, respectively. Sensitivity analysis results showed that temperature (47.2%) and dissolved oxygen (DO) concentration (40.2%) were the biggest drivers of biomass growth. Aeration period (4.3%), chemical oxygen demand (COD) concentration (3.2%), and oxygen uptake rate (OUR) (5.1%) contributed minimally. The biomass growth model can be applied at different wastewater treatment plants by different plant managers/operators in order to achieve optimum biomass growth. The optimum biomass growth will improve the removal of organic and inorganic matter in the biological treatment process.

CRISPR/Cas9-mediated targeted gene integration (TI) has been used to generate recombinant mammalian cell lines with predictable transgene expression. Identifying genomic hot spots that render high and stable transgene expression and knock-in (KI) efficiency is critical for fully implementing TI-mediated cell line development (CLD); however, such identification is cumbersome. In this study, we developed an artificial KI construct that can be used as a hot spot at different genomic loci. The ubiquitous chromatin opening element (UCOE) was employed because of its ability to open chromatin and enable stable and site-independent transgene expression. UCOE KI cassettes were randomly integrated into CHO-K1 and HEK293T cells, followed by TI of enhanced green fluorescent protein (EGFP) onto the artificial UCOE KI site. The CHO-K1 random pool harboring 5′2.2A2UCOE-CMV displayed a significant increase in EGFP expression level and KI efficiency compared with that of the control without UCOE. In addition, 5′2.2A2UCOE-CMV showed improved Cas9 accessibility in the HEK293T genome, leading to an increase in indel frequency and homology-independent KI. Overall, this assessment revealed the potential of UCOE KI constructs as artificial integration sites in streamlining the screening of high-production targeted integrants by mitigating the selection of genomic hot spots.

γ-Aminobutyric acid (GABA) is a kind of non-proteinogenic amino acid which is highly soluble in water and widely used in the food and pharmaceutical industries. Enzymatic conversion is an efficient method to produce GABA, whereby glutamic acid decarboxylase (GAD) is the key enzyme that catalyzes the process. The activity of wild-type GAD is usually limited by temperature, pH or biotin concentration, and hence directional modification is applied to improve its catalytic properties and practical application. GABA was produced using whole cell transformation of the recombinant strains Escherichia coli BL21(DE3)-Gad B, E. coli BL21(DE3)-Gad B-T62S and E. coli BL21(DE3)-Gad B-Q309A. The corresponding GABA concentrations in the fermentation broth were 219.09, 238.42, and 276.66 g/L, and the transformation rates were 78.02%, 85.04%, and 98.58%, respectively. The results showed that Gad B-T62S and Gad B-Q309A are two effective mutation sites. These findings may contribute to ideas for constructing potent recombinant strains for GABA production.

Practical Application: Enzymatic properties of the GAD from Escherichia coli and GAD site-specific mutants were examined by analyzing their conserved sequences, substrate contacts, contact between GAD amino acid residues and mutation energy (ΔΔG) of the GAD mutants. The enzyme activity and stability of Gad B-T62S and Gad B-Q309A mutants were improved compared to Gad B. The kinetic parameters K_m and V_max of Gad B, Gad B-T62S, and Gad B-Q309A mutants were 11.3 ± 2.1 mM and 32.1 ± 2.4 U/mg, 7.3 ± 2.5 mM and 76.1 ± 3.1 U/mg, and 7.2 ± 3.8 mM and 87.3 ± 1.1 U/mg, respectively. GABA was produced using whole cell transformation of the recombinant strains E. coli BL21(DE3)-Gad B, E. coli BL21(DE3)-Gad B-T62S, and E. coli BL21(DE3)-Gad B-Q309A. The corresponding GABA concentrations in the fermentation broth were 219.09, 238.42, and 276.66 g/L, and the transformation rates were 78.02%, 85.04%, and 98.58%, respectively.

The cultivation of algae either in open raceway ponds or in closed bioreactors could allow the renewable production of biomass for food, pharmaceutical, cosmetic, or chemical industries. Optimal cultivation conditions are however required to ensure that the production of these compounds is both efficient and economical. Therefore, high-frequency analytical measurements are required to allow timely process control and to detect possible disturbances during algae growth. Such analytical methods are only available to a limited extent. Therefore, we introduced a method for monitoring algae release volatile organic compounds (VOCs) in the headspace above a bioreactor in real time. This method is based on ion mobility spectrometry (IMS) in combination with a membrane inlet (MI). The unique feature of IMS is that complete spectra are detected in real time instead of sum signals. These spectral patterns produced in the ion mobility spectrum were evaluated automatically via principal component analysis (PCA). The detected peak patterns are characteristic for the respective algae culture; allow the assignment of the individual growth phases and reflect the influence of experimental parameters. These results allow for the first time a continuous monitoring of the algae cultivation and thus an early detection of possible disturbances in the biotechnological process.

Multiple control strategies, including a downstream purification process with well-controlled parameters and a comprehensive release or characterization for intermediates or drug substances, were implemented to mitigate the potential risk of host cell proteins (HCPs) in one concentrated fed-batch (CFB) mode manufactured product. A host cell process specific enzyme-linked immunosorbent assay (ELISA) method was developed for the quantitation of HCPs. The method was fully validated and showed good performance including high antibody coverage. This was confirmed by 2D Gel-Western Blot analysis. Furthermore, a LC-MS/MS method with non-denaturing digestion and a long gradient chromatographic separation coupled with data dependent acquisition (DDA) on a Thermo/QE-HF-X mass spectrometer was developed as an orthogonal method to help identify the specific types of HCPs in this CFB product. Because of the high sensitivity, selectivity and adaptability of the new developed LC-MS/MS method, significantly more species of HCP contaminants were able to be identified. Even though high levels of HCPs were observed in the harvest bulk of this CFB product, the development of multiple processes and analytical control strategies may greatly mitigate potential risks and reduce HCPs contaminants to a very low level. No high-risk HCP was identified and the total amount of HCPs was very low in the CFB final product.

There is an unmet need for delivery platforms that realize the full potential of next-generation nucleic acid therapeutics. The in vivo usefulness of current delivery systems is limited by numerous weaknesses, including poor targeting specificity, inefficient access to target cell cytoplasm, immune activation, off-target effects, small therapeutic windows, limited genetic encoding and cargo capacity, and manufacturing challenges. Here we characterize the safety and efficacy of a delivery platform comprising engineered live, tissue-targeting, non-pathogenic bacteria (Escherichia coli SVC1) for intracellular cargo delivery. SVC1 bacteria are engineered to specifically bind to epithelial cells via a surface-expressed targeting ligand, to allow escape of their cargo from the phagosome, and to have minimal immunogenicity. We describe SVC1's ability to deliver short hairpin RNA (shRNA), localized SVC1 administration to various tissues, and its minimal immunogenicity. To validate the therapeutic potential of SVC1, we used it to deliver influenza-targeting antiviral shRNAs to respiratory tissues in vivo. These data are the first to establish the safety and efficacy of this bacteria-based delivery platform for use in multiple tissue types and as an antiviral in the mammalian respiratory tract. We expect that this optimized delivery platform will enable a variety of advanced therapeutic approaches.