Advances in molecular biotechnology have resulted in the generation of numerous potential production strains. Because every strain can be screened under various process conditions, the number of potential cultivations is multiplied. Exploiting this potential without increasing the associated timelines requires a cultivation platform that offers increased throughput and flexibility to perform various bioprocess screening protocols. Currently, there is no commercially available fully automated cultivation platform that can operate multiple microbial fed-batch processes, including at-line sampling, deep freezer off-line sample storage, and complete data handling. To enable scalable high-throughput early-stage microbial bioprocess development, a commercially available microbioreactor system and a laboratory robot are combined to develop a fully automated cultivation platform. By making numerous modifications, as well as supplementation with custom-built hardware and software, fully automated milliliter-scale microbial fed-batch cultivation, sample handling, and data storage are realized. The initial results of cultivations with two different expression systems and three different process conditions are compared using 5 L scale benchmark cultivations, which provide identical rankings of expression systems and process conditions. Thus, fully automated high-throughput cultivation, including automated centralized data storage to significantly accelerate the identification of the optimal expression systems and process conditions, offers the potential for automated early-stage bioprocess development.
Tremendous advancements in cell and protein engineering methodologies and bioinformatics have led to a vast increase in bacterial production clones and recombinant protein variants to be screened and evaluated. Consequently, an urgent need exists for efficient high-throughput (HTP) screening approaches to improve the efficiency in early process development as a basis to speed-up all subsequent steps in the course of process design and engineering. In this study, we selected the BioLector micro-bioreactor (µ-bioreactor) system as an HTP cultivation platform to screen E. coli expression clones producing representative protein candidates for biopharmaceutical applications. We evaluated the extent to which generated clones and condition screening results were transferable and comparable to results from fully controlled bioreactor systems operated in fed-batch mode at moderate or high cell densities. Direct comparison of 22 different production clones showed great transferability. We observed the same growth and expression characteristics, and identical clone rankings except one host-Fab-leader combination. This outcome demonstrates the explanatory power of HTP µ-bioreactor data and the suitability of this platform as a screening tool in upstream development of microbial systems. Fast, reliable, and transferable screening data significantly reduce experiments in fully controlled bioreactor systems and accelerate process development at lower cost.
During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events.
Background: The genome-integrated T7 expression system offers significant advantages, in terms of productivity and product quality, even when expressing the gene of interest (GOI) from a single copy. Compared to plasmid-based expression systems, this system does not incur a plasmid-mediated metabolic load, and it does not vary the dosage of the GOI during the production process. However, long-term production with T7 expression system leads to a rapidly growing non-producing population, because the T7 RNA polymerase (RNAP) is prone to mutations. The present study aimed to investigate whether two σ 70 promoters, which were recognized by the Escherichia coli host RNAP, might be suitable in genome-integrated expression systems. We applied a promoter engineering strategy that allowed control of expressing the model protein, GFP, by introducing lac operators (lacO) into the constitutive T5 and A1 promoter sequences. Results: We showed that, in genome-integrated E. coli expression systems that used σ 70 promoters, the number of lacO sites must be well balanced. Promoters containing three and two lacO sites exhibited low basal expression, but resulted in a complete stop in recombinant protein production in partially induced cultures. In contrast, expression systems regulated by a single lacO site and the lac repressor element, lacI Q , on the same chromosome caused very low basal expression, were highly efficient in recombinant protein production, and enables fine-tuning of gene expression levels on a cellular level. Conclusions: Based on our results, we hypothesized that this phenomenon was associated with the autoregulation of the lac repressor protein, LacI. We reasoned that the affinity of LacI for the lacO sites of the GOI must be lower than the affinity of LacI to the lacO sites of the endogenous lac operon; otherwise, LacI autoregulation could not take place, and the lack of LacI autoregulation would lead to a disturbance in lac repressor-mediated regulation of transcription. By exploiting the mechanism of LacI autoregulation, we created a novel E. coli expression system for use in recombinant protein production, synthetic biology, and metabolic engineering applications.
Recombinant production of pharmaceutical proteins like antigen binding fragments (Fabs) in the commonly-used production host Escherichia coli presents several challenges. The predominantly-used plasmid-based expression systems exhibit the drawback of either excessive plasmid amplification or plasmid loss over prolonged cultivations. To improve production, efforts are made to establish plasmid-free expression, ensuring more stable process conditions. Another strategy to stabilize production processes is lactose induction, leading to increased soluble product formation and cell fitness, as shown in several studies performed with plasmid-based expression systems. Within this study we wanted to investigate lactose induction for a strain with a genome-integrated gene of interest for the first time. We found unusually high specific lactose uptake rates, which we could attribute to the low levels of lac-repressor protein that is usually encoded not only on the genome but additionally on pET plasmids. We further show that these unusually high lactose uptake rates are toxic to the cells, leading to increased cell leakiness and lysis. Finally, we demonstrate that in contrast to plasmid-based T7 expression systems, IPTG induction is beneficial for genome-integrated T7 expression systems concerning cell fitness and productivity.
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