Flow cytometric analysis of E. coli on agar plates: implications for recombinant protein production

Optimisation of recombinant TNFα production in Escherichia coli using GFP fusions and flow cytometry

Escherichia coli is commonly used industrially to manufacture recombinant proteins for biopharmaceutical applications, as well as in academic and industrial settings for R&D purposes. Optimisation of recombinant protein production remains problematic as many proteins are difficult to make, and process conditions must be optimised for each individual protein. An approach to accelerate process development is the use of a green fluorescent protein (GFP) fusions, which can be used to rapidly and simply measure the quantity and folding state of the protein of interest. In this study, we used GFP fusions to optimise production of recombinant human protein tumour necrosis factor (rhTNFα) using a T7 expression system. Flow cytometry was used to measure fluorescence and cell viability on a single cell level to determine culture heterogeneity. Fluorescence measurements were found to be comparable to data generated by subcellular fractionation and SDS-PAGE, a far more time-intensive technique. We compared production of rhTNFα-GFP with that of GFP alone to determine the impact of rhTNFα on expression levels. Optimised shakeflask conditions were then transferred to fed-batch high cell density bioreactor cultures. Finally, the expression of GFP from a paraBAD expression vector was compared to the T7 system. We highlight the utility of GFP fusions and flow cytometry for rapid process development.

Process Intensification at the expression system level for the production of 1-phosphate aldolase in antibiotic-free E. coli fed-batch cultures

To successfully design expression systems for industrial biotechnology and biopharmaceutical applications; plasmid stability, efficient synthesis of the desired product and the use of selection markers acceptable to regulatory bodies are of utmost importance. In this work we demonstrate the application of a set of IPTG-inducible protein expression systems -- harboring different features namely, antibiotic vs auxotrophy marker; two-plasmids vs single plasmid expression system; expression levels of the repressor protein (LacI) and the auxotrophic marker (glyA) -- in high-cell density cultures to evaluate their suitability in bioprocess conditions that resemble industrial settings. Results revealed that the first generation of engineered strain showed a 50% reduction in the production of the model recombinant protein fuculose-1-phosphate aldolase (FucA) compared to the reference system from QIAGEN. The over-transcription of glyA was found to be a major factor responsible for the metabolic burden. The second- and third-generation of expression systems presented an increase in FucA production and advantageous features. In particular, the third-generation expression system is antibiotic-free, autotrophy-selection based and single-plasmid and, is capable to produce FucA at similar levels compared to the original commercial expression system. These new tools open new avenues for high-yield and robust expression of recombinant proteins in E. coli.

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.