(485ao) Proteomics-Guided Assessment of the Cellular Responses of Prokaryotic Cells Over-Producing Recombinant Glycosylated Proteins

A major goal of recombinant protein therapeutics is to increase overall production with the aim to alleviate demand associated with high/multiple doses. There are a number of choices of expression systems that can be used to express recombinant proteins, ranging from mammalian hosts to bacteria. Of these, bacterial hosts are particularly attractive, since these organisms tend to grow rapidly and on relatively inexpensive media. They have a wealth of biological information, and are readily genetically manipulatable. However, in the pharmaceutical industry, over 75% of proteins with human therapeutic importance are post-translationally modified (either released or in clinical and preclinical development). Since this modification is often essential for biological activity, alternative expression systems such as yeasts, filamentous fungi, plants, mammalian and insect cells are chosen.

Glycosylation is an important and the most common post-translational modification (PTM) which occurs to proteins. The process of covalently attaching glycan groups to polypeptide chains has several implications for the role of the protein final product including functional structure, half-life and cellular locality. Presently, the preferred cellular host for therapeutic protein production, and in particular antibodies, are CHO (Chinese Hamster Ovary) cells.

Between 1998 and 2003 several studies reviewed the then recent findings that prokaryotes, archaea and bacteria, performed glycosylation of their proteins. Long considered to be restricted to eukaryotes, this discovery led to scarce but exciting information of how widespread and important this modification was across all three domains of life. In 2002, a glycosylation pathway (pgl), characterised in Campylobacter jejuni, was transformed into Escherichia coli together with a vector (pET24b-AcrA) harbouring a glycosylation target from the same source organism. These elegant studies allowed for the prospect of using E.coli as a host for the production of human therapeutic proteins.

Increasing the titres of glycoprotein produced in host cell line fermentation processes is a challenge. The most popular strategy to date has been to introduce the gene encoding the therapeutic protein into the host cell and subsequently screen for increased production by: a) perturbing the growth conditions, b) varying vector characteristics, e.g. promoter/enhancer regions, codon usage etc. and, c) random mutagenesis of genes or incorporation of homologous genes. These approaches have seen some success, but what they neglect to do is appreciate that the gene(s) is part of a larger and more complex system. This study focuses on the use of high-throughput quantitative proteomics to ascertain the metabolic capacity of bacterial host cells to glycosylate an exemplar protein. In order to enhance production of this protein, it is vital to understand cell metabolism during growth as well as the production of the modified protein.

In this work, the glycosylation pathway operon and glycosylation target protein AcrA (from C jejuni)) are expressed in E.coli CLM24 cells on separate vectors (12 modification genes on pACYC vector and AcrA gene on arabinose inducible pEC vector). In vitro chemical tagging of proteome samples using 8-plex isobaric tags for relative and absolute quantitation (iTRAQ) is used to scrutinise the proteome in search of the metabolic and regulatory effects of: a) introducing this pathway into a non-native organism, b) the effects of inducing expression of AcrA c) the effects of inducing the expression of a mutant AcrA protein, in which the N-glycosylation consensus sequence has been eliminated. Methods to analyse the glycosylated product are further enhanced by developing MRM (multiple reaction monitoring) using a 3-dimensional ion trap tandem mass spectrometer coupled to a nanoflow liquid chromatography

Over 300 proteins were reliably identified and quantified, and cluster analysis revealed significantly (> ?b 1.5 fold) differentially regulated proteins. Six metabolic networks were highlighted as having been significantly altered. Twenty proteins belonging to energy production and conversion, as well as carbohydrate metabolism and transport were up-regulated. Eight protein translation-related proteins were significantly reduced in expression. Using a probability-based approach called Mixture Model on Graphs (MMG) assisted the metabolic analysis of this large data set. These results have been interpreted with the ultimate aim of increasing production of glycosylated proteins in E.coli through forward engineering.