Many scientific research projects are based on the investigation of either transiently or stably expressed proteins. Achieving detectable and reliable amounts of recombinant protein may be challenging, especially in heterologous expression systems or while expressing highly regulated proteins.

In this white paper, we summarize several different technologies that can be applied to maximize the success of your protein expression experiments.

Gene optimization

After you have chosen your favorite expression system (e.g., mammalian, bacterial, or many other systems) there are several ways to obtain the DNA starting material for your expression experiments. Gene synthesis offers the utmost flexibility in realizing individual sequence requirements like functional motifs, cloning sites, and detection tags.

Since
codon usage is diverse in different organisms, a
good starting point is to optimize the DNA sequence for expression in your host and obtain your gene by de novo synthesis. By using our GeneOptimizerTM algorithm, you not only adapt the gene to the codon usage of your host system, but you also remove elements that potentially inhibit expression (e.g., killer motifs, splice sites, and RNA secondary structures).

Overall, the GeneOptimizer algorithm takes more than 50 parameters into account in order to determine the optimal gene sequence for more reliable and higher- level protein expression without altering the protein sequence. Figure 1 shows an example of increased expression by optimized gene sequences in different host cells [1].

Figure 1. Gene optimization effects are not restricted to a distinct mammalian cell system. Western blot analyses of ZNRD1 protein transfected using three independent plasmid preparations (PP) into HEK 293T and CHO-K1 cells, or two independent plasmid preparations into Sf9 cells. Right: the fold increase in expression of the optimized gene [1].

Vector optimization

The coding sequence of a gene is not the only factor that needs to be considered when optimizing a construct for expression. It is also recommended to optimize the surrounding noncoding DNA elements. Tuning the expression level by choosing the optimal promoter and terminator combination could also be an essential part of an expression project, as high expression levels of foreign protein driven by a strong promoter or insufficient termination by a weak terminator can lead to growth inhibition of the host.

The copy number of the vector, which is determined by the origin of replication, also has a significant influence on the expression level of a foreign protein [2]. There is a broad range of commercially available, predesigned vectors optimized for various expression systems. You can use our Vector Selection Tool available at thermofsher.com/vectors to see if one fits your research purposes.

In some cases you might need a vector that is not commercially available. The GeneArtTM ElementsTM Vector Construction service provides you with individually designed vectors, serving your personal experimental needs. Example applications that might require tailored vectors are gene therapy where the on/off regulation of a gene could be a major goal [3] or DNA vaccines where the presence of CpG motifs in plasmid DNA [4] plays an important role.

Cell culture and transfection optimization

The choice of expression system is of further importance for getting optimal and reliable protein expression. Options include stable cell line expression systems and transient expression systems.

Factors that can be optimized in transient expression
systems include cell density, the expression host, and transfection efficiency. For expression in mammalian cells, we have developed the Expi293TM Expression System that optimizes all three of these factors.

In collaboration with 22 labs, expression levels of 98 different proteins were tested using the Expi293 Expression System (Figure 2). The following results were obtained:

• 87 per cent of proteins demonstrated increased expression in the Expi293 system compared with the user’s current system
• 4.6x average increase for all proteins (n = 98)
• 4.0x average increase for mAbs (n = 54); highest level was 826 mg/L
• 5.3x average increase for non-mAbs (n = 44); highest level was 790 mg/L

Figure 2. External collaborator results.

The expression levels of 98 proteins were examined by 22 labs using the Expi293 Expression System. An average increase of 4.6-fold was observed.

Conclusions

We have summarized various factors influencing protein expression. These factors, while not comprehensive, are of considerable importance for achieving reliable and high-level protein expression for your research.

Please see the following resources for additional information on the technologies presented here.

Resources:

thermofisher.com/genesynthesis
thermofisher.com/expi293
thermo sher.com/elementsvc

References:

• Fath S, Bauer AP, Liss M et al. (2011) Multiparameter RNA and codon optimization: a standardized tool to assess and enhance autologous mammalian gene expression. PLoS One 6:e17596.
• Da Silva NA, Baily JE (1991) In uence of plasmid origin and promoter strength in fermentations of recombinant yeast. Biotechnol Bioeng 37:318–324.
• Goverdhana S, Puntel M, Xiong W et al. (2005) Regulatable gene expression systems for gene therapy applications: progress and future challenges. Mol Ther 12:189–211.
• Chen YS, Hsiao YS, Lin HH et al. (2006) CpG-modi ed plasmid DNA encoding agellin improves immunogenicity and provides protection against Burkholderia pseudomallei infection in BALB/c mice. Infect Immun 74:1699–1705.

Authors: Michael Liss is senior manager of R&D; Geoffrey Cassell is market development manager at GeneArt Cloning and Gene Synthesis; Axel Trefzer is R&D team lead; and Arnd Dakesreiter is senior product manager Gene Synthesis, all from Thermo Fisher Scientific.

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The growing number of government-led initiatives concerning protein engineering is anticipated to create high growth potential. The report noted that Protein Engineering Network of Centres of Excellence (PENCE) contributed approximately $1.0 million for proteomics projects and hosted conferences on proteomics in Canada as an effort to broaden research associated with this technology.

“The market is driven by presence of regulatory authorities consistently striving to reduce time and cost involved in the drug discovery process,” according to the latest protein market analysis by Grand View Research Inc., “One such example is the launch of critical path initiative by U.S. FDA, for incorporation of advanced technologies, such as protein engineering, in drug discovery processes.”

This, the research firm said, facilitates prediction of probable adverse reactions and toxicity and improves the efficacy of target molecules early on, resulting in lowered drug attrition rates in the later stages.

“This is anticipated to help control overall expenditure,” according to Grand View Research. “Rise in R&D expenditure by countries indicates the urgent need for adoption of these tools in all drug discovery and development processes. “

The rising number of government initiatives aimed at enhancing protein engineering capabilities is anticipated to present the market with high growth potential. This has resulted in a significant rise in a number of research activities & programs and fund allotment for R&D.

Valued at $ 823.0 million in 2016 the market is expected to grow 15.9 per cent throughout the forecast period – predominantly driven by increasing preference for protein therapeutics over nonprotein drugs.

The high preference is presumed to be a consequence of positive clinical outcomes associated with these drugs, according to Grand View.

“Protein engineering is broadly used to circumvent weaknesses associated with drugs, and it possesses the potential to enhance affinity and efficacy of molecules for wide range applications, such as cardiac repair,” the research firm said.  This has led to an unprecedented demand and, consequentially, a significant increase in adoption of these tools for development of advanced therapeutics, thereby resulting in rapid market progression.”

For instance, the popularity of recombinant Monoclonal Antibodies (mAbs) is a result of extensive use of protein engineering tools.

The humanization and chimerization of mAbs and additional approaches to improve them in vivo activity led to an increase in a number of alternatives for treatment of various cancers, transplant rejections, autoimmune diseases, and other conditions, the firm said.

The engineered molecules exhibit enhanced efficacy, reduced immunogenicity, greater safety, and improved delivery. Humulin (human insulin), the first protein therapeutic developed through recombinant DNA technology, was approved by U.S.FDA in 1982.

The ability to significantly modify the functioning of a particular drug molecule through protein engineering, such as modification of substrate-specificity of human butyrylcholinesterase, exemplifies the high potential of protein engineering on drug metabolism and medicine, the Grand View Research report said.

Other key findings suggest:

• • A hybrid approach is predicted to observe exponential growth as a consequence of technological superiority and ability to overcome the shortcomings associated with the aforementioned technologies
  • Instrument accounted for the largest share in the product segment in 2016. The largest share captured by this segment is believed to be a consequence of high preference for automated technology by the researchers in order to facilitate faster molecule development process
  • Software and services are expected to emerge as the fastest growing segment owing to high inclination of companies to outsourcing of services to achieve overall cost-efficiency
  • Monoclonal antibodies held the largest share in the protein-type segment due to the development of these antibodies on a large scale through an extensive adoption of these engineering tools.
  • North America held a substantial share, which can be attributed to extensive research activities that are likely to provide high-potential growth platform through consistent usage in this region

The research also projected that the market in Asia-Pacific will exhibit exponential growth over the coming years owing to rising awareness pertaining to benefits of protein engineering and increasing disposable income.

“The competition is marked by players employing strategies such as mergers & acquisitions and distribution agreements resulting in significant growth in their market share,” the report said.

Over the last three decades, significant effort has been devoted to the development and engineering of proteins, including monoclonal antibodies (mAbs), as pharmaceutical products. Similar to small-molecule generics, numerous companies are now beginning to offer generic alternatives to the innovator protein therapeutics, commonly referred to as biosimilars. Regardless of the type of molecule, the main attributes important for similarity are identity, quality and purity, potency¹ and method of action. In the small molecule drug manufacturing space, these quality attributes are relatively straightforward to test. However, in the biotherapeutic space, defining similarity is significantly more complicated. The complexity of the task is, in part, due to the heterogeneous nature of these drugs.²

Regardless of the size of the drug, the journey to the market is very similar. Innovator molecules must undergo extensive animal and clinical trials to prove safety, efficacy and effectiveness. It is generally intended for biosimilars to circumvent the extensive trials required of an innovator drug and their cost to be minimized through a shorter drug application process. However, their time to market is very critical because of the competitive landscape surrounding drugs with expiring patent protection.

Undoubtedly, the biosimilar industry is becoming increasingly more competitive as the number of potential manufacturers continues to grow and as the patents for several protein-based biologic drugs are set to expire in the coming years, with relevant examples being listed in Table 1.³

The race to get to market first is a critical one to win. Therefore, it is advantageous when developing biosimilar products to be sharply focused on how to get to market faster without compromising the safety and efficacy of the drug.

To prove that a biosimilar drug is comparable to the innovator molecule, it is subjected to a battery of analytical tests, which may include primary sequence confirmation by intact mass analysis and peptide mapping experiments, elucidation of higher order structure, analyses of product impurities (host-cell proteins) and characterization of post-translation modifications, including glycosylation patterns.^4,5 These analyses were previously performed on the innovator molecule during its development and manufacturing. However, companies producing biosimilar products are not required to perform exactly the same analyses as those used by the innovator company.¹ This allows the biosimilar companies to utilize new technologies which can decrease the time invested in each test, while increasing the analytical measurement sensitivity, which ultimately will provide a “high definition” picture of the minor differences that occur and need to be well characterized and tracked for bioactivity and safety.

A pertinent example of a test that may slow a drug’s entrance onto the market is glycan profiling. Traditionally, this is one of the more complicated and time-consuming product quality attributes to characterize. Yet, glycosylation, the enzyme-mediated attachment of saccharides to a protein backbone, can influence a number of physicochemical properties of a therapeutic glycoprotein, including its serum half-life, effector functions like complement-dependent cytotoxicity (CDC) and antibody-dependent cell cytotoxicity (ADCC).⁶ Glycosylation may also induce immunogenic responses if non-human epitopes, like N-glycolylneuraminic acids⁷ or α-linked galactose pairs⁸ are present at sufficient abundance levels. It is, therefore, important to characterize and control glycosylation during the development and manufacturing of therapeutic glycoproteins.

In this article, we present a case study that demonstrates the advantages that new analytical approaches for glycan profiling can have on facilitating the comparability testing of an innovator versus biosimilar monoclonal antibody.

The technological advances that enabled this comparison were made in both sample preparation and in analytical workflow including informatics. These advances enabled fast sample processing combined with highly automated data processing, annotation and reporting. Traditional glycan sample preparation protocols for LCMS are time consuming and often require overnight PNGase F digestions followed by extended labeling times and sample purification procedures. This methodology can be quite labour intensive and lengthy and when high throughput is required, for example during clone selection, such procedures can hinder the overall progress of bringing a product to market. This is clearly not advantageous for companies developing biosimilar products.

To address these time-consuming areas, a new higher throughput platform was developed to streamline and simplify N-linked glycan sample preparation (Figure 1)⁹.

In this approach, biotherapeutic samples are denatured with 1% RapiGest® SF surfactant at a temperature of at least 90o C for 5 minutes and then cooled to room temperature. The N-linked glycans are then released using Rapid PNGase F through a 5-minute incubation at 55O C. A short incubation is essential to preserving a form of released N-glycans, known as glycosylamines, which can be derivatized with a highly active amine labeling reagent, RapiFluor-MS (RFMS). Complete labeling is achieved in 5 minutes. Subsequently, the samples are purified by μ-elution solid phase extraction (SPE) and analyzed by hydrophilic interaction chromatography (HILIC). Beyond trimming the turn-around time of N-linked glycan sample preparation to less than an hour, RFMS uniquely facilitates both fluorescence (FLR) and mass spectrometric (MS) detection.

As depicted in Figure 2, RFMS provides significantly enhanced MS signal intensities while maintaining sensitive FLR detection, making it possible to detect, characterize, and quantify even the low abundance glycans that typically are missed when using conventional labels such as 2-aminobenzamide.

To further streamline the task of Nlinked glycan profiling, a dedicated glycan analysis workflow was developed within the Waters UNIFI Scientific Information System to automate the analytical process using combined FLR and MS data from a single injection (FLR and MS detectors are in tandem). One of the key features of this workflow is the ability to perform glycan library searches based on the calibrated HILIC retention time in Glucose Units (GU) and accurate mass values. For this, each glycan component is matched and assigned by the glycan entries from the scientific library that have close GU values and matched m/z values within the analytical standard deviation set by the user. In addition, the integrated FLR peaks are used to quantify the relative amount of each glycan.

In the comparability study presented here, the RFMS Glycan GU Scientific Library was used. This library was co-developed with the National Institute for Bioprocessing Research and Training (NIBRT) in Dublin, Ireland and has experimental GU values for over 160 N-glycans derived from nine different glycoproteins.

Combining the RFMS sample preparation platform with the above glycan analysis workflow provides a high throughput, yet powerful, approach to glycan analysis. The capabilities of this approach can be seen in a case study that compares the N-linked glycans of the innovator mAb infliximab (Remicade®), produced in an SP2/0 mouse cell line, to those from a biosimilar equivalent (Inflectra®) produced by a Chinese hamster ovary (CHO) cell line. Three different lots of the innovator drug and one biosimilar sample were studied.

For the innovator molecule, 23 mass confirmed N-linked glycans were identified, and 21 of these were observed from the biosimilar mAb. The majority of the glycans were neutral complex type, though eight glycans with N-glycolylneuraminic acid were also observed. Six glycans with α-linked galactose pairs were also identified.

To initially evaluate the N-linked glycan profiles, fluorescence traces were stacked, as shown in Figure 3.

The trace for the biosimilar mAb is shown in red, and the innovator sample traces are shown in blue, black, and purple. The glycan profiles of the innovator mAb and biosimilar appeared to be quite comparable, though some differences could be observed for low abundance species. This was confirmed by summary plots that compare normalized abundance levels (Figure 4).

Such a plot made it possible to visualize an increased relative amount of a glycan with N-glycolylneuraminic acid (Figure 4a) and a decreased relative amount of a glycan with α-linked galactose on the biosimilar (Figure 4b). Derivatizing the glycans from these samples with RFMS was critical to achieving suitable MS sensitivity to mass confirm these lower abundance glycans with immunogenic properties.

Proving bioequivalency is critical for the development and approval processes of biosimilars. As many potential biosimilars are glycoproteins, there is an analytical burden to demonstrate the comparability of glycosylation. The noted advances in glycan profiling technology can help biosimilar drug manufacturers overcome this hurdle faster. In particular, HILIC-FLR-MS can be used as a powerful analytical tool for resolving and identifying critical low abundance glycans. Moreover, advances in sample preparation, such as the RapiFluor-MS™ kit, combined with bioinformatics platforms, such as UNIFI, can help biosimilar companies quickly detail their glycan profiles and to more efficiently access deeper levels of information on their candidate biologics. When combined, these techniques afford a competitive advantage for biosimilar development.

References:
1. US Food and Drug Administration. What are biosimilar drugs? http://www.fda.gov/Drugs/ResourcesForYou/Consumers/BuyingUsingMedicineSafely/UnderstandingBiosimilarDrugs/ucm167991.htm (accessed November
14, 2016).
2. Consalvo, AP, Bio E. Pharmaceutical Manufactoring. Five Questions Large Molecule CMOs Want to Be Asked. http://www.pharmamanufacturing.com/articles/2016/five-questionslarge-molecule-cmos-want-to-be-asked/.
3. Drug Store News. Generic Drug Report 2016. https://www.drugstorenews.com/sites/drugstorenews.Com/files/GenericReport_2016.pdf.
4. Xie, H, Chakraborty A, Ahn J, Yu YQ, Dakshinamoorthy DP, Gilar M, Chen W, Skilton SJ, Mazzeo JR. Rapid Comparison of a Candidate Biosimilar to an Innovator Monoclonal Antibody with Advanced Liquid Chromatography and Mass Spectrometry Technologies. MAbs 2010; 2: 379-394.
5. Fang, J, Doneanu CE, Alley WR, Yu YQ, Beck A, Chen W. Advanced Assessment of the Physicochemical Characteristics of Remicade® and Inflectra® by Sensitive LC/MS Techniques. mAbs 2016; 8: 1021-1034.
6. Shields, RL, Lai J, Keck R, O’Connell LY, Hong K, Meng YG, Weikert SH, Presta LG. Lack of Fucose on Human IgG1 N-linked Oligosaccharide Improves Binding to Human Fc Gamma RIII and Antibody-dependent Cellular Toxicity. J Biol Chem 2002; 277: 26733-26740.
7. Noguchi, A, Mukuria CJ, Suzuki E, Naiki M. Immunogenicity of N-glycolylneuraminic Acid-containing Carbohydrate Chains of Recombinant Human Erythropoietin Expressed in Chinese Hamster Ovary Cells. J Biochem 1995; 117: 59-62.
8. Chung, CH, Mirakhur B, Chan E, Le QT, Berlin J, Morse M, Murphy BA, Satinover SM, Hosen J, Mauro D, Slebos RJ, Zhou Q, Gold D, Hatley T, Hicklin DJ, Platts-Mills TA. Cetuximab-induced Anaphylaxis and IgE Specific for Galactose-alpha-1,3-galactose. N Engl J Med 2008; 358: 1109-1117.
9. Lauber, MA, Yu YQ, Brousmiche DW, Hua Z, Koza SM, Magnelli P, Guthrie E, Taron CH, Fountain KJ. Rapid Preparation of Released N-glycans for HILIC Analysis Using a Labeling Reagent that Facilitates Sensitive Fluorescence and ESI-MS Detection. Anal Chem 2015; 87: 5401-5409.

Proteins sometimes can run amok. Specifically, all the good stuff (the useful genetic and biological material) they contain can get distorted. Mutations in specific amino acids can cause long strands of proteins to curl in on themselves (like a ball of wool a cat has played with) and refuse to break apart. These strands, known as amyloid fibrils, can be extremely toxic and are usually harmful. They attach to organs like the brain and pancreas, preventing them from functioning as they should. They are responsible for diseases as seemingly different as diabetes and Alzheimer’s, to name just a couple.

Developing effective medications to treat these diseases, and cause the fibrils to dissolve typically involves biochemists in a lengthy and expensive process of trial and error.

McGill researchers, led by Prof. Jérôme Waldispühl of the School of Computer Science, have created a suite of computer programs that should speed up the process of drug discovery for diseases of this kind. The programs are designed to scan the fibrils (or misfolded proteins) looking for weak spots. The idea is to then design helpful genetic mutations to dissolve the bonds that hold the fibrils together – a bit like finding the right strand of wool to tug on to unravel a whole knotted ball. It’s potentially a gargantuan task, because looking for the mutations that will prove useful in drug development involves exploring millions of possible structural combinations of genetic material.

But for the Fibrilizer, as McGill has dubbed its suite of computer tools, a name that hints at the super heroic nature of the programs they have developed, the task is of a very different order.

“Within the space of a week, by using our programs and a supercomputer, we were able to look at billions of possible ways to weaken the bonds within these toxic protein strands,” says Mohamed Smaoui, a McGill postdoctoral fellow and the first author on three recent papers on the research. “We narrowed it down to just 30 to 50 possibilities that can now be explored further. Typically biochemists can spend months or years in the lab trying to pinpoint these promising mutations.”

Supercomputing to the rescue

The researchers tested their program on a medical compound that scientists have been trying to improve for the last couple decades. The compound is administered as part of a drug that is used by diabetes patients to boost the performance of insulin and is sold under the name Symlin. The synthetic compound is based on a version of the protein amylin, yet is known to be toxic to the pancreas over the long-term, creating amyloid fibrils. The McGill team were able to use Fibrilizer to pinpoint a limited number of possible genetic modifications to the compound that would act to reduce its toxicity.

Jérôme Waldispühl, the lead researcher on the papers, believes that computational research of this kind will play an increasingly important role in drug discovery in the future.

“Computers are transforming the way that drugs are being developed,” says Waldispühl. “Amyloid research has accelerated in the last 10 years. But computers may prove to be the key to finding better medications for a whole range of systemic and neurodegenerative diseases, from arthritis to Parkinson’s. Without supercomputers and programs of this kind, it would take much longer and be much more expensive to do this kind of research and come up with these possible solutions to the problem.”

The research was funded by the Canadian Institutes of Health Research (CIHR) System Biology Training program at McGill University, the Fonds de recherche Nature et technologies Quebec, and the Natural Science and Engineering Research Council of Canada (NSERC).