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Systems optimisation of host cell tRNA usage and codon decoding for the improvement of bioprocessing parameters

Reference	BB/I010351/1
Principal Investigator / Supervisor	Dr Tobias von der Haar
Co-Investigators / Co-Supervisors	Dr Dominique Chu
Institution	University of Kent
Department	Sch of Biosciences
Funding type	Research
Value (£)	66,449
Status	Completed
Type	Research Grant
Start date	01/10/2010
End date	30/06/2011
Duration	9 months

Abstract

A major research challenge in bioprocessing is to understand the relationship between host cell health, product quality and product performance during downstream processing, and how these parameters can be optimised. Decoding of the genetic code occurs through multiple interacting elements including tRNAs, ribosomes and mRNA codons, and these elements and their interactions are heavily optimised to maximise the speed of translation and cell fitness, while minimising errors in the decoding process. The introduction of additional genes expressed at very high levels, such as recombinant protein (rP) genes in modern expression systems, disrupts optimisation of the decoding system. This negatively affects cell health, rP quality, and rP behaviour during purification. Conversely, a detailed understanding of the principles of optimisation during protein synthesis enables us to re-establish optimisation under conditions of rP expression, thus improving all of the parameters listed above. Although connections between optimal decoding of the genetic code and rP performance parameters are strongly suggested by the current state of academic knowledge, there are no experimental data avilable that conclusively prove such connections. We therefore propose to use BRIC Enabling Fund resources to conduct a pilot study, where we will use a highly focussed set of effective whole bioprocess-modelling exercises combined with experimental work to investigate for a representative yeast-based expression system: a) whether loss of optimisation in the tRNA dependent decoding system during high-level rP expression is a source of problems with product performance, and b) whether model-based re-optimisation of the decoding system under conditions of rP expression can improve rP quality and rP homogeneity. If this pilot study was successful, a larger study would be proposed to develop more broadly applicable strategies (eg for mammalian and insect cell expression systems).

Summary

The decoding of genes during protein synthesis is a complex process that must occur with great accuracy in order for cells and organisms to remain healthy. Accurate protein synthesis is achieved through the interplay of many different molecules, including ribosomes (the molecular machines that actually achieve protein synthesis), tRNAs (adapter molecules that transport amino acids to the ribosome), and translation factors (helper proteins that establish the correct contact between ribosomes and tRNAs). In order to achieve accurate protein synthesis it is critical that the levels of each of these elements are matched exactly to the frequency with which they are used: if cells contain too much or too little of any of these elements, protein synthesis errors occur more frequently and cellular health declines. In normal cells that only produce proteins from their own genes, the protein synthesis system and levels of the molecules described above are optimised to achieve the required low error rates and high translational speed. However, in industrial applications additional genes are often introduced into cells with the aim of producing specific proteins that are not naturally produced by them. This strategy is used in hte pharmaceutical industry to produce the latest generation drugs against cancer, multiple sclerosis and arthritis. When cells make proteins from foreign or artificial genes, the protein synthesis machinery must deal with a situation for which it has not been optimised. We predict that this will increase error rates during the production of the relevant proteins. Protein synthesis errors have negative effects for the ease with which protein-based drugs can be purified and formulated following synthesis in the host cells, and may also adversely affect the potency of the final product. A second prediction we make is that, if we understood the principles of optimisation in detail, we might develop strategies that restore optimal protein synthesis and reduce error rates. Both predictions follow logically from existing knowledge of the translational machinery, although to date they have not yet been experimetnally tested and therefore we can not be completely sure whether they are true. Because our predictions on the relationship between optimised protein synthesis and expression of foreign proteins have important consequences for our ability to make high-quality protein-based drugs, we wish to test them in a small pilot study. We will develop computational models of protein synthesis that will help us to understand the principles of optimisation in protein synthesis. We will then use thes models to suggest strategies for achieving optimisation under conditions of foreign protein synthesis in a simple yeast-based expression system. Lastly, we will test experimentally whether these strategies do indeed improve the quality of proteins, and facilitate their processing following synthesis. If this pilot study confirms our predictions, we will use this as basis for a larger study in which we develop optimisation strategies for the various protein synthesis systems used in the pharmaceutical industry.

Impact Summary

This project will have both economic and societal impacts as well as training impact. The expected results of this and any possible follow-on project will have direct impact on improved health and well being. The direct aim of the proposed project is to improve the efficiency of the production of pharmaceutical drugs. The methods we propose to develop here are directly aimed at improving industrial processes in this area and we expect that they will lead to improved cost structures in the industry. This identifies two groups of beneficiaries: (1) The UK industrial/pharmaceutical sector which benefits from this research through an increased competitiveness. (2) Users of drugs (patients) will be indirect beneficiaries through reduced costs of drugs and potentially through the resulting improvements in availability. A direct aim of this project is to transfer skills and new methods to the industrial sector. Should this pilot project be successful, then we will place great emphasis on developing knowledge transfer strategies that enable industry to apply and extend the optimisation methods we propose. Specifically, we will seek use contacts and resources of BRIC but also of the Enterprise office of the University of Kent to identify concrete industrial users of the results of this project and transfer knowledge to them. Hence our project will enhance the research capacity of private and third sector organisations. The project does not limit itself to solving specific industrial problems but to develop general solving strategies with direct impact on the competitiveness of the individual industrial users. In this sense it is a direct contribution to the knowledge economy and increases the to the general wealth and wellbeing of the UK. The results of this project also have scope for commercialisation. Specifically, we anticipate that the intellectual property generated in this project will lend itself for to commercialisation. This could take the shape of a spin-off consulting company specialising on advising industry on modelling techniques in the context of rP expression. The project will employ a postdoctoral researcher (Radu Zabet) who will through his activites receive further competence in cross and inter-disciplinary work. While Mr. Zabet is currently receiving intense training within his chose field of study (modelling transcription processes) this additional expose to a related but still different set of research problems will be an additional training opportunity that will increase his overall skill. Hence, even though this project does not have an explicit training component, it contributes to the training of highly skilled researchers and helps to develop a workforce that is crucial for the UK knowledge economy. This project is highly interdiciplinary in that it uses both wet-lab experiments and mathematical/computational modelling to achieve a tangible practical goal. The development of innovative techniques and cross-disciplinary approaches is therefor crucial to the project and as such it will improve methodology and cross disciplinary approaches.

Committee	Research Committee D (Molecules, cells and industrial biotechnology)
Research Topics	Industrial Biotechnology, Microbiology, Pharmaceuticals, Systems Biology
Research Priority	X – Research Priority information not available
Research Initiative	Bioprocessing Research Industry Club enabling (BRIC2E) [2010]
Funding Scheme	X – not Funded via a specific Funding Scheme

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