Escherishia coli cells are able to add sugars to proteins using genes from Campylobacter jenuni. Here we learn that an inverse metabolic engineering approach can improve the efficiency of the process and potentially increase the toolbox of this biopharmaceutical workhorse
When choosing a host cell to make a therapeutic protein, the product means everything. Relatively simple proteins, such as insulin, can be produced in E. coli. This is ideal because E. coli has fast growth, simple growth requirements and is very well understood in terms of its genetics and metabolism. However, E. coli does fail if proteins have complex structural necessities and also if they require a post-translational modification (PTM).
A common PTM in human therapeutic proteins is glycosylation, the process of adding sugar groups (glycans) to the polypeptide backbone.1 These glycans affect protein structure and function, including half-life, tissue targeting and pharmacokinetics. Furthermore, the wrong type of glycosylation (incorrect section of the protein or different glycan type or structure) can lead to immunogenic responses in patients. For glycosylated proteins, the preferred hosts are Chinese Hamster Ovary (CHO) cells. Effectively immortalised cancer cell lines, CHO cells are the leading industry production factories for complex human therapeutics.2
Not only are CHO cells able to modify proteins in a desirable manner, but also process improvements have meant high yielding cells are commonplace (grams per litre of product). Here, process control is crucial, as different growth conditions can affect glycosylation patterns and efficiency. Although research is being undertaken to better understand this phenomenon, the task is huge considering the hundreds of genes involved in glycosylation, the lack of metabolic understanding and the genetic heterogeneity in CHO cell lines used worldwide.
This is where E. coli cells could offer an advantage for the production of specific proteins that require glycosylation. Not only is it much cheaper to cultivate E. coli cells, but also their relatively simple cell chassis means glycosylation heterogeneity is less of a concern.3-5 However, to see an ideal glycosylating E. coli host cell in the biopharmaceutical industry, two major developments are required. Firstly, the glycan structure must be engineered to resemble human sugar patterns. Secondly, host cell improvements are needed to increase overall yields. Currently, the glycans are not the correct type and the yields are poor (few milligrams per litre).
To see an ideal glycosylating E.coli two developments are required: engineered glycan structure and increased yields
In this study, the goal was to meet the second challenge using inverse metabolic engineering. The idea was to uncover which native genetic elements in E. coli
could be tuned to increase the amount of glycosylated recombinant protein. An inverse metabolic engineering approach was designed based on previous successful applications demonstrated by Ryan Gill’s group in University of Colorado, Boulder. In this case, researchers designed a screening strategy to look for better performing cells. Four different sized libraries of genetic elements were created and used to make thousands of E. coli
clones, each one containing a different insert. The library was screen for increased amount of cellular glycan using a sugar-specific lectin bound to a chemiluminescent target. Although the screen did not differentiate between protein-bound and free glycan, many bright glowing colonies were observed implying higher levels of the glycan. Labelled as candidates for improved glycosylation efficiency, the E. coli
clones were sequenced to find the genetic elements they contained. It is this ‘working backward’ strategy to reveal how and why a genetically manipulated cell has the desirable phenotype, which explains the ‘inverse’ part of the metabolic engineering
To hone in on the most likely improved host cell lines, the sequenced genetic elements were mapped onto the E. coli chromosome. Candidates for forward engineering verification were chosen where three or more libraries showed the same genes overlapping, and this produced five potential host cells. These were forward engineered using an alternative vector to allow more precise control of gene expression. Total glycoprotein yield and glycosylation efficiencies were calculated using a targeted mass spectrometry approach with an isotopic internal standard. Almost a seven-fold increase in glycoprotein production was calculated and verified using western blots, as well as a 1.6 fold increase in glycosylation efficiency. Furthermore, the effects of the genes were tested in E. coli cells expressing an IgG antibody fragment, and a 75% improvement in glycosylation efficiency was observed.
This study has contributed to the overall aim of increasing the E. coli toolbox for biopharmaceuticals production, by improving glycosylation efficiency. Further advances in scale-up and downstream processing will be required to achieve the higher overall yields seen in aglycosylated recombinant protein production in bacteria. Although the host cell system would realistically be used for relatively smaller and less complex glycoproteins than currently manufactured in CHO cells, they have the potential to take the pressure of mammalian cell lines as demand for protein therapeutics rise and inspire the development of novel drug candidates, as well as pave the way for constructing optimised ‘synthetic cells’ for biopharmaceutical manufacture.
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4. Schwarz, F.; Huang, W.; Li, C.; Schulz, B.; Lizak, C.; Palumbo, A.; Numao, S.; Neri, D.; Aebi, M.; Wang, L., A combined method for producing homogeneous glycoproteins with eukaryotic N-glycosylation. Nat. Chem. Biol. 2010, 6, (4), 264-266.
5. Pandhal, J.; Wright, P. C., N-Linked glycoengineering for human therapeutic proteins in bacteria. Biotechnol. Lett. 2010, 32, (9), 1189-1198.