Engineering in Life Sciences最新文献

The utilization of Streptomyces as a microbial chassis for developing innovative drugs and medicinal compounds showcases its capability to produce bioactive natural substances. Recent focus on the clustered regularly interspaced short palindromic repeat (CRISPR) technology highlights its potential in genome editing. However, applying CRISPR technology in certain microbial strains, particularly Streptomyces, encounters specific challenges. These challenges include achieving efficient gene expression and maintaining genetic stability, which are critical for successful genome editing. To overcome these obstacles, an innovative approach has been developed that combines several key elements: activation-induced cytidine deaminase (AID), nuclease-deficient cas9 variants (dCas9), and Petromyzon marinus cytidine deaminase 1 (PmCDA1). In this study, this novel strategy was employed to engineer a Streptomyces coelicolor strain. The target gene was actVA-ORF4 (SCO5079), which is involved in actinorhodin production. The engineering process involved introducing a specific construct [pGM1190-dcas9-pmCDA-UGI-AAV-actVA-ORF4 (SCO5079)] to create a CrA10 mutant strain. The resulting CrA10 mutant strain did not produce actinorhodin. This outcome highlights the potential of this combined approach in the genetic manipulation of Streptomyces. The failure of the CrA10 mutant to produce actinorhodin conclusively demonstrates the success of gene editing at the targeted site, affirming the effectiveness of this method for precise genetic modifications in Streptomyces.

Within this interdisciplinary study, we demonstrate the applicability of a 6D printer for soft tissue engineering models. For this purpose, a special plant was constructed, combining the technical requirements for 6D printing with the biological necessities, especially for soft tissue. Therefore, a commercial 6D robot arm was combined with a sterilizable housing (including a high-efficiency particulate air (HEPA) filter and ultraviolet radiation (UVC) lamps) and a custom-made printhead and printbed. Both components allow cooling and heating, which is desirable for working with viable cells. In addition, a spraying unit was installed that allows the distribution of fine droplets of a liquid. Advanced geometries on uneven or angled surfaces can be created with the use of all six axes. Based on often used bioinks in the field of soft tissue engineering (gellan gum, collagen, and gelatin methacryloyl) with very different material properties, we could demonstrate the flexibility of the printing system. Furthermore, cell-containing constructs using primary human adipose-derived stem cells (ASCs) could be produced in an automated manner. In addition to cell survival, the ability to differentiate along the adipogenic lineage could also be demonstrated as a representative of soft tissue engineering.

Saccharomyces cerevisiae is a commonly used microorganism in the biotechnological industry. For the industrial heterologous production of compounds, it is of great advantage to work with growth-controllable yeast strains. In our work, we utilized the natural pheromone system of S. cerevisiae and generated a set of different strains possessing an α-pheromone controllable growth behavior. Naturally, the α-factor pheromone is involved in communication between haploid S. cerevisiae cells. Perception of the pheromone initiates several cellular changes, enabling the cells to prepare for an upcoming mating event. We exploited this natural pheromone response system and developed two different plasmid-based modules, in which the target genes, MET15 and FAR1, are under control of the α-factor sensitive FIG1 promoter for a controlled expression in S. cerevisiae. Whereas expression of MET15 led to a growth induction, FAR1 expression inhibited growth. The utilization of low copy number or high copy number plasmids for target gene expression and different concentrations of α-factor allow a finely adjustable control of yeast growth rate.

Cell engineering strategies typically rely on energy-consuming overexpression of genes or radical gene-knock out. Both strategies are not particularly convenient for the generation of slightly modulated phenotypes, as needed in biosimilar development of for example differentially fucosylated monoclonal antibodies (mAbs). Recently, transiently transfected small noncoding microRNAs (miRNAs), known to be regulators of entire gene networks, have emerged as potent fucosylation modulators in Chinese hamster ovary (CHO) production cells. Here, we demonstrate the applicability of stable miRNA overexpression in CHO production cells to adjust the fucosylation pattern of mAbs as a model phenotype. For this purpose, we applied a miRNA chaining strategy to achieve adjustability of fucosylation in stable cell pools. In addition, we were able to implement recently developed artificial miRNAs (amiRNAs) based on native miRNA sequences into a stable CHO expression system to even further fine-tune fucosylation regulation. Our results demonstrate the potential of miRNAs as a versatile tool to control mAb fucosylation in CHO production cells without adverse side effects on important process parameters.

Shake flask cultivation, a cornerstone in bioprocess research encounters limitations in supplying sufficient oxygen and exchanging gases, restricting its accuracy in assessing microbial growth and metabolic activity. In this communication, we introduce an innovative gas supply apparatus that harnesses the rotational motion of a shaking incubator to facilitate continuous air delivery, effectively overcoming these limitations. We measured the mass transfer coefficient (k_La) and conducted batch cultures of Corynebacterium glutamicum H36LsGAD using various working volumes to assess its performance. Results demonstrated that the gas supply apparatus significantly outperforms conventional silicone stoppers regarding oxygen delivery, with k_La values of 2531.7 h⁻¹ compared to 20.25 h⁻¹ at 230 rpm. Moreover, in batch cultures, the gas supply apparatus enabled substantial improvements in microbial growth, maintaining exponential growth even at larger working volumes. Compared to the existing system, an increase in final cell mass by a factor of 3.4-fold was observed when utilizing 20% of the flask's volume, and a remarkable 9-fold increase was achieved when using 60%. Furthermore, the gas supply apparatus ensured consistent oxygen supply and efficient gas exchange within the flask, overcoming challenges associated with low working volumes. This approach offers a simple yet effective solution to enhance gas transfer in shake flask cultivation, bridging the gap between laboratory-scale experiments and industrial fermenters. Its broad applicability holds promise for advancing research in bioprocess optimization and scale-up endeavors.

Bioreactor scale-up and scale-down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail-safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale-up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale-up studies involving CHO cell-generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small- and large-scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale-sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large-scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling.