The authors are grateful to Dr. Thomas Rademacher for providing the pPAM plant expression vector and Ibrahim Al Amedi for cultivating the tobacco plants used in this study. We would like to thank Dr. Richard M. Twyman for his assistance with editing the manuscript. This work was in part funded by the European Research Council Advanced Grant &#8220;Future-Pharma&#8221;, proposal number 269110 and the Fraunhofer Zukunftsstiftung (Fraunhofer&#160;Future Foundation).

<ol>
	<li>Fischer, R., Emans, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+farming+of+pharmaceutical+proteins.">Molecular farming of pharmaceutical proteins.</a> Transgenic research. 9, 277-299 (2000).</li><li>Commandeur, U., Twyman, R. M., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+biosafety+of+molecular+farming+in+plants.">The biosafety of molecular farming in plants.</a> AgBiotechNet. 5, 9 (2003).</li><li>Shoji, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+plant-based+system+for+rapid+production+of+influenza+vaccine+antigens.">A plant-based system for rapid production of influenza vaccine antigens.</a> Influenza Other Resp. 6, 204-210 (2012).</li><li>Goodin, M. M., Zaitlin, D., Naidu, R. A., Lommel, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nicotiana+benthamiana:+Its+history+and+future+as+a+model+for+plant-pathogen+interactions.">Nicotiana benthamiana: Its history and future as a model for plant-pathogen interactions.</a> Mol Plant Microbe In. 21, 1015-1026 (2008).</li><li>Berg, R. H., Beachy, R. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescent+protein+applications+in+plants.">Fluorescent protein applications in plants.</a> Method Cell Biol. 85, 153 (2008).</li><li>Chung, S. M., Vaidya, M., Tzfira, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agrobacterium+is+not+alone:+gene+transfer+to+plants+by+viruses+and+other+bacteria.">Agrobacterium is not alone: gene transfer to plants by viruses and other bacteria.</a> Trends in plant science. 11, 1-4 (2006).</li><li>Sheludko, Y. V., Sindarovska, Y. R., Gerasymenko, I. M., Bannikova, M. A., Kuchuk, N. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+several+Nicotiana+species+as+hosts+for+high-scale+Agrobacterium-mediated+transient+expression.">Comparison of several Nicotiana species as hosts for high-scale Agrobacterium-mediated transient expression.</a> Biotechnology and Bioengineering. 96, 608-614 (2007).</li><li>Wydro, M., Kozubek, E., Lehmann, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+transient+Agrobacterium-mediated+gene+expression+system+in+leaves+of+Nicotiana+benthamiana.">Optimization of transient Agrobacterium-mediated gene expression system in leaves of Nicotiana benthamiana.</a> Acta Biochimica Polonica. 53, 289-298 (2006).</li><li>Buyel, J. F., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Processing+heterogeneous+biomass:+Overcoming+the+hurdles+in+model+building.">Processing heterogeneous biomass: Overcoming the hurdles in model building.</a> Bioengineered. 4, (2013).</li><li>Fischer, R., Schillberg, S., Hellwig, S., Twyman, R. M., Drossard, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GMP+issues+for+recombinant+plant-derived+pharmaceutical+proteins.">GMP issues for recombinant plant-derived pharmaceutical proteins.</a> Biotechnol Adv. 30, 434-439 (2012).</li><li>Daniel, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One-at-a-time+plans.">One-at-a-time plans.</a> Journal of the American Statistical Association. 68, 353-360 (1973).</li><li>Czitrom, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One-Factor-at-a-Time+versus+Designed+Experiments+The+American+Statistician.">One-Factor-at-a-Time versus Designed Experiments The American Statistician.</a> 53, 6 (1999).</li><li>Anderson, M. J., Kraber, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Keys+to+successful+designed+experiments.">Keys to successful designed experiments.</a> ASQ - The global voice of quality. 6, 6 (1999).</li><li>Montgomery, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Design and Analysis of Experiments. , (2007).</li><li>Myers, R. H., Montgomery, D. C., Anderson-Cook, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Response Surface Methodology: Process and Product Optimization Using Designed Experiments. , (2009).</li><li>Piepel, G. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Programs+for+generating+extreme+vertices+and+centroids+of+linearly+constrained+experimental+regions.">Programs for generating extreme vertices and centroids of linearly constrained experimental regions.</a> J Qual Technol. 20, 15 (1988).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> FDA. , (2009).</li><li>Shivhare, M., McCreath, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Practical+Considerations+for+DoE+Implementation+in+Quality+By+Design.">Practical Considerations for DoE Implementation in Quality By Design.</a> BioProcess International. 8, 9 (2010).</li><li>Buyel, J. F., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+models+for+transient+protein+expression+in+tobacco+(Nicotiana+tabacum+L.)+can+optimize+process+time,+yield,+and+downstream+costs.">Predictive models for transient protein expression in tobacco (Nicotiana tabacum L.) can optimize process time, yield, and downstream costs.</a> Biotechnology and bioengineering. 109, 2575-2588 (2012).</li><li>Buyel, J. F., Kaever, T., Buyel, J. J., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+models+for+the+accumulation+of+a+fluorescent+marker+protein+in+tobacco+leaves+according+to+the+promoter/5'UTR+combination.">Predictive models for the accumulation of a fluorescent marker protein in tobacco leaves according to the promoter/5'UTR combination.</a> Biotechnology and bioengineering. 110, 471-482 (2013).</li><li>Anderson, M. J., Whitcomb, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> DOE Simplified: Practical Tools for Effective Experimentation. , (2000).</li><li>Anderson, M. J., Whitcomb, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Response Surface Methods Simplified. , (2005).</li><li>De Gryze, S., Langhans, I., Vandebroek, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+the+correct+intervals+for+prediction:+A+tutorial+on+tolerance+intervals+for+ordinary+least-squares+regression.">Using the correct intervals for prediction: A tutorial on tolerance intervals for ordinary least-squares regression.</a> Chemometr Intell Lab. 87, 147-154 (2007).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Plasmid DNA purification User manual. , (2012).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PCR clean-up Gel extraction User manual. , (2012).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Quick Ligation Protocol. 4, (2009).</li><li>Inoue, H., Nojima, H., Okayama, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-Efficiency+Transformation+of+Escherichia-Coli+with+Plasmids.">High-Efficiency Transformation of Escherichia-Coli with Plasmids.</a> Gene. 96, 23-28 (1990).</li><li>Main, G. D., Reynolds, S., Gartland, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electroporation+protocols+for+Agrobacterium.">Electroporation protocols for Agrobacterium.</a> Methods in Molecular Biology. 44, 405-412 (1995).</li><li>Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubock, R., Hofacker, I. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Vienna+RNA+websuite.">The Vienna RNA websuite.</a> Nucleic acids research. 36, 70-74 (2008).</li><li>Howell, S., Kenmore, M., Kirkland, M., Badley, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-density+immobilization+of+an+antibody+fragment+to+a+carboxymethylated+dextran-linked+biosensor+surface.">High-density immobilization of an antibody fragment to a carboxymethylated dextran-linked biosensor surface.</a> J Mol Recognit. 11, 200-203 (1998).</li><li>Newcombe, A. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+a+biosensor+assay+to+quantify+polyclonal+IgG+in+ovine+serum+used+for+the+production+of+biotherapeutic+antibody+fragments.">Evaluation of a biosensor assay to quantify polyclonal IgG in ovine serum used for the production of biotherapeutic antibody fragments.</a> Process Biochem. 41, 842-847 (2006).</li><li>Peixoto, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hierarchical+Variable+Selection+in+Polynomial+Regression-Models.">Hierarchical Variable Selection in Polynomial Regression-Models.</a> Am Stat. 41, 311-313 (1987).</li><li>Peixoto, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Property+of+Well-Formulated+Polynomial+Regression-Models.">A Property of Well-Formulated Polynomial Regression-Models.</a> Am Stat. 44, 26-30 (1990).</li><li>Sanders, P. R., Winter, J. A., Barnason, A. R., Rogers, S. G., Fraley, R. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+cauliflower+mosaic+virus+35S+and+nopaline+synthase+promoters+in+transgenic+plants.">Comparison of cauliflower mosaic virus 35S and nopaline synthase promoters in transgenic plants.</a> Nucleic acids research. 15, 1543-1558 (1987).</li><li>Ma, J. K. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generation+and+Assembly+of+Secretory+Antibodies+in+Plants.">Generation and Assembly of Secretory Antibodies in Plants.</a> Science. 268, 716-719 (1995).</li><li>Wycoff, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Secretory+IgA+antibodies+from+plants.">Secretory IgA antibodies from plants.</a> Curr Pharm Design. 11, 2429-2437 (2005).</li><li>Pace, C. N., Vajdos, F., Fee, L., Grimsley, G., Gray, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+measure+and+predict+the+molar+absorption+coefficient+of+a+protein.">How to measure and predict the molar absorption coefficient of a protein.</a> Protein Sci. 4, 2411-2423 (1995).</li></ol>

The publication fee was partially sponsored by the companies Statease, Inc. (USA) and Statcon (Germany), which were not involved in the involved in the preparation of the manuscript or responsible for any of its contents.

Every experiment requires careful planning because resources are often scarce and expensive. This is particularly true for DoE strategies because errors during the planning phase (e.g. selecting a base model that does not cover all significant factor interactions) can substantially diminish the predictive power of the resulting models and thus devalue the entire experiment. However, these errors can easily be avoided by following basic procedures.
Considerations during DoE planning
First, it is important to select a response that is suitable to evaluate the outcome (positive or negative) for each DoE run. For example: the concentration of a target protein determined by UV absorption 37 is useful to assess the activity of a certain promoter/5&#39;UTR combination. However, functional evaluation of a protein (e.g. by fluorescence in the case of DsRed or binding to Protein A in the case of 2G12) is even better because it also includes a protein quality aspect. Responses can also be values calculated from two or more separate parameters, such as the power number NP in fermentation experiments (calculated from power input, rotational speed and stirrer diameter). The response should yield values with high precision (i.e. low standard deviation) and accuracy (i.e. zero or few interfering parameters) to improve the model quality. Suitable detection devices and methods should therefore be optimized prior the experiment if necessary.
Factors affecting the response must be identified before the DoE is implemented because the RSM is used to determine the precise impact of the factors and not to select them. The selection of factors with a significant influence on the response can be achieved by studying literature data but full or fractional factorial DoE strategies are more effective. These screening experiments should also be used to determine a range for each factor within which meaningful results can be achieved, e.g. in biological systems the factor &#34;temperature&#34; is often restricted to the range 10-40 &#176;C. Discrete levels should then be defined within these ranges for numeric factors that are technically difficult to control, e.g. phytotron temperatures have a tolerance of &#177;2.0 &#176;C so it is inappropriate to investigate temperature differences of &#177;1.0 &#176;C. Practical limitations may also come into play, e.g. using &#62;25 different concentrations of a particular compound. However, the number of levels must be at least one greater than the anticipated polynomial order of that factor in the base model, e.g. if the polynomial order of temperature is n = 2, then at least three temperature levels must be investigated. Using base models of high polynomial order (cubic or above) is an option if the polynomial order of a factor is uncertain. However, this can significantly increase the number of runs required for the DoE.
For complex systems with many factors and high polynomial orders this can result in designs which cannot be calculated effectively. In such cases it may be advisable to split the problem into two or more designs investigating different subsets of factors. However, the significant factors omitted from such &#34;split-designs&#34; must be carefully adjusted to fixed values to prevent any distortion of the results. Each split-design should be repeated at different settings for the omitted factors to reveal interactions between them and the factors included in the design. It is beneficial to identify the factors with the greatest impact on the response and include them in all designs.
Critical aspects during DoE evaluation
The FDS should be the first parameter assessed in a newly-calculated DoE. The operator must confirm that the DoE provides the necessary coverage of the design space for the required minimal detectable difference and estimated standard deviation of the response. If this is not the case, the design should be augmented with a suitable number of runs and replicates until the requirements are met. The procedure to calculate DoE runs can be initialized several times to select designs with a minimal loss of optimality if the balance between categoric factors is enabled.
Data transformation should be carried out if response values span more than one order of magnitude. A Log10 transformation is often appropriate but the Box-Cox plot will suggest the transformation that will yield the most stable model. The transformation suggested in this plot may change once the final model has been selected and should therefore be reevaluated. Only significant factors (p-value &#60; 0.05) should be included in the response surface model in order to guarantee its predictive power. Exceptions can be made to maintain the model hierarchy 32,33 or to include factors that are known to have an effect based on previous results. In the latter case, it is advisable to investigate why such important factors were not found to be significant in the current DoE. The lack-of-fit parameter can be used in combination with the R-squared value (Tables 2 and 3) for the rapid detection of poor models/fits, whereas the adjusted R-squared indicates whether too many/insignificant factors have been selected and the predicted R-squared is an indicator for the predictive power of the model. However, the residual plots provided in the diagnostics section of DesignExpert are even more important to assess the model quality because they allow the detection of trends in the dataset indicating the presence of hidden factors as well as the identification of extreme values. The latter have an above average impact on the fit and distort the model. Such values can originate from flawed data (e.g. permuted values) that must be corrected, or from failed experiments that must be repeated. If an extreme value is found to be &#34;real&#34; in such repetition experiments it is advisable to add several runs in the proximity of this value to improve the model quality within this region of the design space.
Corrections to designs which are imperfect or too small
Regardless of why additional runs may be required (e.g. missing data, failed runs, genuine extreme values and regions, unanticipated high order factor interactions), these runs should be added to the existing design in an ordered manner by using the &#34;Design augmentation&#34; tool to introduce the new runs in a separate block. Doing so will facilitate the use of the previous dataset while selecting the most meaningful additional runs. The result will be a model that fits to the data and allows the reliable prediction of future results.

The production of biopharmaceutical proteins in plants is advantageous because plants are inexpensive to grow, the platform can be scaled up just by growing more plants, and human pathogens are unable to replicate 1,2. Transient expression strategies based for example on the infiltration of leaves with Agrobacterium tumefaciens provides additional benefits because the time between the point of DNA delivery and the delivery of a purified product is reduced from years to less than 2 months 3. Transient expression is also used for functional analysis, e.g. to test genes for their ability to complement loss-of-function mutants or to investigate protein interactions 4-6. However, transient expression levels tend to show greater batch-to-batch variation than expression levels in transgenic plants 7-9. This reduces the likelihood that biopharmaceutical manufacturing processes based on transient expression will be approved in the context of good manufacturing practice (GMP) because reproducibility is a critical quality attribute and is subject to risk assessment 10. Such variation can also mask any interactions that researchers intend to investigate. Therefore, we set out to identify the major factors that affect transient expression levels in plants and to build a high-quality quantitative predictive model.
The one-factor-at-a-time (OFAT) approach is often used to characterize the impact (effect) of certain parameters (factors) on the outcome (response) of an experiment 11. But this is suboptimal because the individual tests (runs) during an investigation (experiment) will be aligned like pearls on a string through the potential area spanned by the factors that are tested (design space). The coverage of the design space and hence the degree of information derived from the experiment is low, as shown in Figure 1A 12. Furthermore, interdependencies among different factors (factor interactions) can remain concealed resulting in poor models and/or the prediction of false optima, as shown in Figure 1B 13.
The drawbacks described above can be avoided by using a design of experiments (DoE) approach in which the runs of an experiment are scattered more evenly throughout the design space, meaning that more than one factor is varied between two runs 14. There are specialized designs for mixtures, screening factors (factorial designs) and the quantitation of factor impacts on responses (response surface methods, RSMs) 15. Furthermore, RSMs can be realized as central-composite designs but can also be achieved effectively by using specialized software that can apply different criteria for the selection of runs. For example, the so called D-optimality criterion will select runs so to minimize the error in the coefficients of the resulting model, whereas the IV-optimality criterion selects runs that achieve the lowest prediction variance throughout the design space 15,16. The RSM we describe here allows the precise quantitation of transient protein expression in plants, but it can easily be transferred to any system involving several (~5-8) numeric factors (e.g. temperature, time, concentration) and a few (~2-4) categoric factors (e.g. promoter, color) in which a mechanistic description is unavailable or too complex to model.
The DoE approach originated in the agricultural sciences but has spread to other areas because it is transferable to any situation where it is useful to reduce the number of runs necessary to obtain reliable data and generate descriptive models for complex processes. This in turn has led to the inclusion of DoE in the "Guidance for Industry, Q8(R2) Pharmaceutical Development" published by the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) 17. DoE is now used widely in scientific research and industry 18. However, care must be taken during the planning and execution of the experiment because selecting an improper polynomial degree for the multiple-linear-regression model (base model) can introduce a need for additional runs to model all factor effects correctly. Furthermore, corrupted or missing data generate incorrect models and flawed predictions, and may even prevent any model building attempt as described in the protocol and discussion sections 18. In the protocol section, we will initially set out the most important planning steps for a RSM-based experiment and then explain the design based on the DoE software DesignExpert v8.1. But similar designs can be built with other software including JMP, Modde, and STATISTICA. The experimental procedures are followed by instructions for data analysis and evaluation.
<img alt="3D plot and contour map analysis of factors A, B, C; data visualization in experimental design." fo:content-width="5in" fo:src="/files/ftp_upload/51216/51216fig1highres.jpg" src="/files/ftp_upload/51216/51216fig1.jpg" data-altupdatedat="1747911167000" data-alt="Figure 1"> 
Figure 1. Comparison of OFAT and DoE. A. Sequential variation of one factor at a time (OFAT) in an experiment (black, red and blue circles) achieves a low coverage of the design space (hatched regions). In contrast, the variation of more than one factor at a time using the design of experiments (DoE) strategy (green circles) enhances the coverage and thus the precision of the resulting models. B. The biased design space coverage means that OFAT experiments (black circles) can also fail to identify optimal operating regions (red) and predict sub-optimal solutions (large black circle), whereas DoE strategies (black stars) are more likely to identify preferable conditions (large black star).

<table><tbody><tr><td>Design-Expert(R) 8</td><td>Stat-Ease, Inc.</td><td>n.a.</td><td>DoE software</td></tr><tr><td>Tryptone</td><td>Carl Roth GmbH</td><td>8952.2</td><td>Media component</td></tr><tr><td>Yeast extract</td><td>Carl Roth GmbH</td><td>2363.2</td><td>Media component</td></tr><tr><td>Sodium chloride</td><td>Carl Roth GmbH</td><td>P029.2</td><td>Media component</td></tr><tr><td>Ampicillin</td><td>Carl Roth GmbH</td><td>K029.2</td><td>Antibiotic</td></tr><tr><td>Agar-Agar</td><td>Carl Roth GmbH</td><td>5210.2</td><td>Media component</td></tr><tr><td>&lt;i&gt;Escherichia coli&lt;/i&gt; K12 DH5a</td><td>Life Technologies</td><td>18263-012</td><td>Microorganism</td></tr><tr><td>pPAM</td><td>GenBank</td><td>AY027531</td><td>Cloning/expression vector;&amp;nbsp;</td></tr><tr><td>NucleoSpin Plasmid&amp;nbsp;</td><td>MACHEREY-NAGEL GmbH</td><td>740588.250</td><td>Plasmid DNA isolation kit</td></tr><tr><td>NucleoSpin Gel and PCR Clean-up</td><td>MACHEREY-NAGEL GmbH</td><td>740609.250</td><td>Plasmid DNA purification kit</td></tr><tr><td>NanoDrop 2000</td><td>Thermo Scientific</td><td>n.a.</td><td>Spectrophotometer</td></tr><tr><td>NcoI</td><td>New England Biolabs Inc.</td><td>R3193L</td><td>Restrictionendonuclease</td></tr><tr><td>EcoRI</td><td>New England Biolabs Inc.</td><td>R3101L</td><td>Restrictionendonuclease</td></tr><tr><td>AscI</td><td>New England Biolabs Inc.</td><td>R0558L</td><td>Restrictionendonuclease</td></tr><tr><td>NEB 4</td><td>New England Biolabs Inc.</td><td>B7004S</td><td>Restrictionendonuclease buffer</td></tr><tr><td>TRIS</td><td>Carl Roth GmbH</td><td>4855.3</td><td>Media component</td></tr><tr><td>Disodium tetraborate</td><td>Carl Roth GmbH</td><td>4403.3</td><td>Media component</td></tr><tr><td>EDTA</td><td>Carl Roth GmbH</td><td>8040.2</td><td>Media component</td></tr><tr><td>Agarose</td><td>Carl Roth GmbH</td><td>6352.4</td><td>Media component</td></tr><tr><td>Bromophenol blue</td><td>Carl Roth GmbH</td><td>A512.1</td><td>Color indicator</td></tr><tr><td>Xylene cyanol</td><td>Carl Roth GmbH</td><td>A513.1</td><td>Color indicator</td></tr><tr><td>Glycerol</td><td>Carl Roth GmbH</td><td>7530.2</td><td>Media component</td></tr><tr><td>Mini-Sub Cell GT Cell</td><td>BioRad</td><td>170-4406</td><td>Gel electrophoresis chamber</td></tr><tr><td>&lt;i&gt;Agrobacterium tumefaciens&lt;/i&gt; strain GV3101:pMP90RK</td><td>DSMZ</td><td>12365</td><td>Microorganism</td></tr><tr><td>Electroporator 2510</td><td>Eppendorf</td><td>4307000.658</td><td>Electroporator</td></tr><tr><td>Beef extract</td><td>Carl Roth GmbH</td><td>X975.2</td><td>Media component</td></tr><tr><td>Peptone</td><td>Carl Roth GmbH</td><td>2365.2</td><td>Media component</td></tr><tr><td>Sucrose</td><td>Carl Roth GmbH</td><td>4621.2</td><td>Media component</td></tr><tr><td>Magnesium sulfate</td><td>Carl Roth GmbH</td><td>0261.3</td><td>Media component</td></tr><tr><td>Carbenicillin</td><td>Carl Roth GmbH</td><td>6344.2</td><td>Antibiotic</td></tr><tr><td>Kanamycin</td><td>Carl Roth GmbH</td><td>T832.3</td><td>Antibiotic</td></tr><tr><td>Rifampicin</td><td>Carl Roth GmbH</td><td>4163.2</td><td>Antibiotic</td></tr><tr><td>FWD primer</td><td>Eurofins MWG Operon</td><td>n.a.</td><td>CCT CAG GAA GAG CAA TAC</td></tr><tr><td>REV primer</td><td>Eurofins MWG Operon</td><td>n.a.</td><td>CCA AAG CGA GTA CAC AAC</td></tr><tr><td>2720 Thermal cycler</td><td>Applied Biosystems</td><td>4359659</td><td>Thermocycler</td></tr><tr><td>RNAfold webserver</td><td>University of Vienna</td><td>n.a.</td><td>Software</td></tr><tr><td>Ferty 2 Mega</td><td>Kammlott</td><td>5.220072</td><td>Fertilizer</td></tr><tr><td>Grodan Rockwool Cubes 10 x10 cm</td><td>Grodan</td><td>n.a.</td><td>Rockwool block</td></tr><tr><td>Greenhouse</td><td>n.a.</td><td>n.a.</td><td>For plant cultivation</td></tr><tr><td>Phytotron</td><td>Ilka Zell</td><td>n.a.</td><td>For plant cultivation</td></tr><tr><td>Omnifix-F Solo</td><td>B. Braun</td><td>6064204</td><td>Syringe</td></tr><tr><td>Murashige and Skoog salts</td><td>Duchefa</td><td>M 0222.0010</td><td>Media component</td></tr><tr><td>Glucose</td><td>Carl Roth GmbH</td><td>6780.2</td><td>Media component</td></tr><tr><td>Acetosyringone</td><td>Sigma-Aldrich</td><td>D134406-5G</td><td>Phytohormon analogon</td></tr><tr><td>&amp;nbsp;BioPhotometer plus</td><td>Eppendorf</td><td>6132 000.008</td><td>Photometer</td></tr><tr><td>Osram cool white 36 W</td><td>Osram</td><td>4930440</td><td>Light source</td></tr><tr><td>Disodium phosphate</td><td>Carl Roth GmbH</td><td>4984.3</td><td>Media component</td></tr><tr><td>Centrifuge 5415D</td><td>Eppendorf</td><td>5424 000.410</td><td>Centrifuge</td></tr><tr><td>Forma -86C ULT freezer</td><td>ThermoFisher</td><td>88400</td><td>Freezer</td></tr><tr><td>Synergy HT</td><td>BioTek</td><td>SIAFRT</td><td>Fluorescence plate reader</td></tr><tr><td>Biacore T200</td><td>GE Healthcare</td><td>n.a.</td><td>SPR device</td></tr><tr><td>Protein A</td><td>Life Technologies</td><td>10-1006</td><td>Antibody binding protein</td></tr><tr><td>HEPES</td><td>Carl Roth GmbH</td><td>9105.3</td><td>Media component</td></tr><tr><td>Tween-20</td><td>Carl Roth GmbH</td><td>9127.3</td><td>Media component</td></tr><tr><td>2G12 antibody</td><td>Polymun</td><td>AB002</td><td>Reference antibody</td></tr></tbody></table>

characterization of complex systems using the design of experiments approach: transient protein expression in tobacco as a case study

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.

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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

We describe a design of experiments approach that can be used to determine and model the influence of transgene regulatory elements, plant growth and development parameters, and incubation conditions on the transient expression of monoclonal antibodies and reporter proteins in plants.

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the ...

characterization-complex-systems-using-design-experiments-approach

Nanyang Technological University

Research

JoVE Journal

Bioengineering

16.4K Views.  RWTH Aachen University.  We beschrijven een experimenteel ontwerp aanpak die kan worden bepaald en modelleren invloed van transgene regulerende elementen plantengroei en ontwikkeling parameters en incubatieomstandigheden de tijdelijke expressie van monoklonale antilichamen en reporter eiwitten in planten. 

Video: Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

A Robotic Platform for High-throughput Protoplast Isolation and Transformation

Over the last decade there has been a resurgence in the use of plant protoplasts that range from model species to crop species, for analysis of signal transduction pathways, transcriptional regulatory networks, gene expression, genome-editing, and gene-silencing. Furthermore, significant progress has been made in the regeneration of plants from protoplasts, which has generated even more interest in the use of these systems for plant genomics. In this work, a protocol has been developed for automation of protoplast isolation and transformation from a &#39;Bright Yellow&#39; 2 (BY-2) tobacco suspension culture using a robotic platform. The transformation procedures were validated using an orange fluorescent protein (OFP) reporter gene (pporRFP) under the control of the Cauliflower mosaic virus 35S promoter (35S). OFP expression in protoplasts was confirmed by epifluorescence microscopy. Analyses also included protoplast production efficiency methods using propidium iodide. Finally, low-cost food-grade enzymes were used for the protoplast isolation procedure, circumventing the need for lab-grade enzymes that are cost-prohibitive in high-throughput automated protoplast isolation and analysis. Based on the protocol developed in this work, the complete procedure from protoplast isolation to transformation can be conducted in under 4 hr, without any input from the operator. While the protocol developed in this work was validated with the BY-2 cell culture, the procedures and methods should be translatable to any plant suspension culture/protoplast system, which should enable acceleration of crop genomics research.

A high-throughput, automated, tobacco protoplast production and transformation methodology is described. The robotic system enables massively parallel gene expression and discovery in the model BY-2 system that should be translatable to non-model crops.

Over the last decade there has been a resurgence in the use of plant protoplasts that range from model species to crop species, for analysis of signal ...

Genetics

Transient Gene Expression in Tobacco using Gibson Assembly and the Gene Gun

In order to target a single protein to multiple subcellular organelles, plants typically duplicate the relevant genes, and express each gene separately using complex regulatory strategies including differential promoters and/or signal sequences. Metabolic engineers and synthetic biologists interested in targeting enzymes to a particular organelle are faced with a challenge: For a protein that is to be localized to more than one organelle, the engineer must clone the same gene multiple times. This work presents a solution to this strategy: harnessing alternative splicing of mRNA. This technology takes advantage of established chloroplast and peroxisome targeting sequences and combines them into a single mRNA that is alternatively spliced. Some splice variants are sent to the chloroplast, some to the peroxisome, and some to the cytosol. Here the system is designed for multiple-organelle targeting with alternative splicing. In this work, GFP was expected to be expressed in the chloroplast, cytosol, and peroxisome by a series of rationally designed 5&#8217; mRNA tags. These tags have the potential to reduce the amount of cloning required when heterologous genes need to be expressed in multiple subcellular organelles. The constructs were designed in previous work11, and were cloned using Gibson assembly, a ligation independent cloning method that does not require restriction enzymes. The resultant plasmids were introduced into Nicotiana benthamiana epidermal leaf cells with a modified Gene Gun protocol. Finally, transformed leaves were observed with confocal microscopy.

This work describes a novel method for selectively targeting subcellular organelles in plants, assayed using the BioRad Gene Gun.

In order to target a single protein to multiple subcellular organelles, plants typically duplicate the relevant genes, and express each gene separately ...

Environment

Controllable Ion Channel Expression through Inducible Transient Transfection

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be investigated through electrophysiological analysis, biochemical characterization, mutational studies, and their effects on cellular processes. Transient transfections offer a simple protocol in which the protein becomes available for analysis within a few hours to days. Although this method presents a relatively straightforward and time efficient protocol, one of the critical components is calibrating the expression of the gene of interest to physiological relevant levels or levels that are suitable for analysis. To this end, many different approaches that offer the ability to control the expression of the gene of interest have emerged. Several stable cell transfection protocols provide a way to permanently introduce a gene of interest into the cellular genome under the regulation of a tetracycline-controlled transcriptional activation. While this technique produces reliable expression levels, each gene of interest requires a few weeks of skilled work including calibration of a killing curve, selection of cell colonies, and overall more resources. Here we present a protocol that uses transient transfection of the Transient Receptor Potential cation channel subfamily V member 1 (TRPV1) gene in an inducible system as an efficient way to express a protein in a controlled manner which is essential in ion channel analysis. We demonstrate that using this technique, we are able to perform calcium imaging, whole cell, and single channel analysis with controlled channel levels required for each type of data collection with a single transfection. Overall, this provides a replicable technique that can be used to study ion channels structure and function.

Studying ion channels through a heterologously expressing system has become a core technique in biomedical research. In this manuscript, we present a time efficient method to achieve tightly controlled ion channel expression by performing transient transfection under the control of an inducible promoter.

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be ...

A Modified Yeast-one Hybrid System for Heteromeric Protein Complex-DNA Interaction Studies

Over the years, the yeast one-hybrid assay has proven to be an important technique for the identification and validation of physical interactions between proteins such as transcription factors (TFs) and their DNA target. The method presented here utilizes the underlying concept of the Y1H but is modified further to study and validate protein complexes binding to their target DNA. Hence, it is referred to as the modified yeast one-hybrid (Y1.5H) assay. This assay is cost effective and can be easily performed in a regular laboratory setting. Albeit using a heterologous system, the described method could be a valuable tool to test and validate the heteromeric protein complex binding to their DNA target(s) for functional genomics in any system of study, especially plant genomics.

The modified yeast one-hybrid assay described here is an extension of the classical yeast one-hybrid (Y1H) assay to study and validate the heteromeric protein complex-DNA interaction in a heterologous system for any functional genomics study.

Over the years, the yeast one-hybrid assay has proven to be an important technique for the identification and validation of physical interactions between ...

Biochemistry

Transient Expression in Nicotiana Benthamiana Leaves for Triterpene Production at a Preparative Scale

The triterpenes are one of the largest and most structurally diverse families of plant natural products. Many triterpene derivatives have been shown to possess medicinally relevant biological activity. However, thus far this potential has not translated into a plethora of triterpene-derived drugs in the clinic. This is arguably (at least partially) a consequence of limited practical synthetic access to this class of compound, a problem that can stifle the exploration of structure-activity relationships and development of lead candidates by traditional medicinal chemistry workflows. Despite their immense diversity, triterpenes are all derived from a single linear precursor, 2,3-oxidosqualene. Transient heterologous expression of biosynthetic enzymes in N. benthamiana can divert endogenous supplies of 2,3-oxidosqualene towards the production of new high-value triterpene products that are not naturally produced by this host. Agro-infiltration is an efficient and simple means of achieving transient expression in N. benthamiana. The process involves infiltration of plant leaves with a suspension of Agrobacterium tumefaciens carrying the expression construct(s) of interest. Co-infiltration of an additional A. tumefaciens strain carrying an expression construct encoding an enzyme that boosts precursor supply significantly increases yields. After a period of five days, the infiltrated leaf material can be harvested and processed to extract and isolate the resulting triterpene product(s). This is a process that is linearly and reliably scalable, simply by increasing the number of plants used in the experiment. Herein is described a protocol for rapid preparative-scale production of triterpenes utilizing this plant-based platform. The protocol utilizes an easily replicable vacuum infiltration apparatus, which allows the simultaneous infiltration of up to four plants, enabling batch-wise infiltration of hundreds of plants in a short period of time.

Transient Expression in Nicotiana Benthamiana Leaves for Triterpene Production at a Preparative Scale

Transient heterologous expression of biosynthetic enzymes in Nicotiana benthamiana leaves can divert endogenous supplies of 2,3-oxidosqualene towards the production of new high-value triterpene products. Herein is described a detailed protocol for rapid (5 days) preparative-scale production of triterpenes and analogs utilizing this powerful plant-based platform.

The triterpenes are one of the largest and most structurally diverse families of plant natural products. Many triterpene derivatives have been shown to ...

Rapid Characterization of Genetic Parts with Cell-Free Systems

Characterizing and cataloging genetic parts are critical to the design of useful genetic circuits. Having well-characterized parts allows for the fine-tuning of genetic circuits, such that their function results in predictable outcomes. With the growth of synthetic biology as a field, there has been an explosion of genetic circuits that have been implemented in microbes to execute functions pertaining to sensing, metabolic alteration, and cellular computing. Here, we show a rapid and cost-effective method for characterizing genetic parts. Our method utilizes cell-free lysate, prepared in-house as a medium to evaluate parts via the expression of a reporter protein. Template DNA is prepared by PCR amplification using inexpensive primers to add variant parts to the reporter gene, and the template is added to the reaction as linear DNA without cloning. Parts that can be added in this way include promoters, operators, ribosome binding sites, insulators, and terminators. This approach, combined with the incorporation of an acoustic liquid handler and 384-well plates, allows the user to carry out high-throughput evaluations of genetic parts in a single day. By comparison, cell-based screening approaches require time-consuming cloning and have longer testing times due to overnight culture and culture density normalization steps. Further, working in cell-free lysate allows the user to exact tighter control over the expression conditions through the addition of exogenous components and DNA at precise concentrations. Results obtained from cell-free screening can be used directly in applications of cell-free systems or, in some cases, as a way to predict function in whole cells.

Well-characterized genetic parts are necessary for the design of novel genetic circuits. Here we describe a cost-effective, high-throughput method for rapidly characterizing genetic parts. Our method reduces cost and time by combining cell-free lysates, linear DNA to avoid cloning, and acoustic liquid handling to increase throughput and reduce reaction volumes.

Characterizing and cataloging genetic parts are critical to the design of useful genetic circuits. Having well-characterized parts allows for the ...

Efficient Agroinfiltration of Plants for High-level Transient Expression of Recombinant Proteins

Mammalian cell culture is the major platform for commercial production of human vaccines and therapeutic proteins. However, it cannot meet the increasing worldwide demand for pharmaceuticals due to its limited scalability and high cost. Plants have shown to be one of the most promising alternative pharmaceutical production platforms that are robust, scalable, low-cost and safe. The recent development of virus-based vectors has allowed rapid and high-level transient expression of recombinant proteins in plants. To further optimize the utility of the transient expression system, we demonstrate a simple, efficient and scalable methodology to introduce target-gene containing Agrobacterium into plant tissue in this study. Our results indicate that agroinfiltration with both syringe and vacuum methods have resulted in the efficient introduction of Agrobacterium into leaves and robust production of two fluorescent proteins; GFP and DsRed. Furthermore, we demonstrate the unique advantages offered by both methods. Syringe infiltration is simple and does not need expensive equipment. It also allows the flexibility to either infiltrate the entire leave with one target gene, or to introduce genes of multiple targets on one leaf. Thus, it can be used for laboratory scale expression of recombinant proteins as well as for comparing different proteins or vectors for yield or expression kinetics. The simplicity of syringe infiltration also suggests its utility in high school and college education for the subject of biotechnology. In contrast, vacuum infiltration is more robust and can be scaled-up for commercial manufacture of pharmaceutical proteins. It also offers the advantage of being able to agroinfiltrate plant species that are not amenable for syringe infiltration such as lettuce and Arabidopsis. Overall, the combination of syringe and vacuum agroinfiltration provides researchers and educators a simple, efficient, and robust methodology for transient protein expression. It will greatly facilitate the development of pharmaceutical proteins and promote science education.

Plants offer a novel system for the production of pharmaceutical proteins on a commercial scale that is more scalable, cost-efficient and safe than current expression paradigms. In this study, we report a simple and convenient, yet scalable approach to introduce target-gene containing Agrobacterium tumefaciens into plants for protein transient expression.

Mammalian cell culture is the major platform for commercial production of human vaccines and therapeutic proteins. However, it cannot meet the increasing ...

Biology

Protein-protein Interactions Visualized by Bimolecular Fluorescence Complementation in Tobacco Protoplasts and Leaves

Many proteins interact transiently with other proteins or are integrated into multi-protein complexes to perform their biological function. Bimolecular fluorescence complementation (BiFC) is an in vivo method to monitor such interactions in plant cells. In the presented protocol the investigated candidate proteins are fused to complementary halves of fluorescent proteins and the respective constructs are introduced into plant cells via agrobacterium-mediated transformation. Subsequently, the proteins are transiently expressed in tobacco leaves and the restored fluorescent signals can be detected with a confocal laser scanning microscope in the intact cells. This allows not only visualization of the interaction itself, but also the subcellular localization of the protein complexes can be determined. For this purpose, marker genes containing a fluorescent tag can be coexpressed along with the BiFC constructs, thus visualizing cellular structures such as the endoplasmic reticulum, mitochondria, the Golgi apparatus or the plasma membrane. The fluorescent signal can be monitored either directly in epidermal leaf cells or in single protoplasts, which can be easily isolated from the transformed tobacco leaves. BiFC is ideally suited to study protein-protein interactions in their natural surroundings within the living cell. However, it has to be considered that the expression has to be driven by strong promoters and that the interaction partners are modified due to fusion of the relatively large fluorescence tags, which might interfere with the interaction mechanism. Nevertheless, BiFC is an excellent complementary approach to other commonly applied methods investigating protein-protein interactions, such as coimmunoprecipitation, in vitro pull-down assays or yeast-two-hybrid experiments.

Formation of protein complexes in vivo can be visualized by bimolecular fluorescence complementation. Interaction partners are fused to complementary parts of fluorescent tags and transiently expressed in tobacco leaves, resulting in a reconstituted fluorescent signal upon close proximity of the two proteins.

Many proteins interact transiently with other proteins or are integrated into multi-protein complexes to perform their biological function. Bimolecular ...

A Comparative Analysis of Recombinant Protein Expression in Different Biofactories: Bacteria, Insect Cells and Plant Systems

Plant-based systems are considered a valuable platform for the production of recombinant proteins as a result of their well-documented potential for the flexible, low-cost production of high-quality, bioactive products.
In this study, we compared the expression of a target human recombinant protein in traditional fermenter-based cell cultures (bacterial and insect) with plant-based expression systems, both transient and stable.
For each platform, we described the set-up, optimization and length of the production process, the final product quality and the yields and we evaluated provisional production costs, specific for the selected target recombinant protein.
Overall, our results indicate that bacteria are unsuitable for the production of the target protein due to its accumulation within insoluble inclusion bodies. On the other hand, plant-based systems are versatile platforms that allow the production of the selected protein at lower-costs than Baculovirus/insect cell system. In particular, stable transgenic lines displayed the highest-yield of the final product and transient expressing plants the fastest process development. However, not all recombinant proteins may benefit from plant-based systems but the best production platform should be determined empirically with a case-by-case approach, as described here.

In this study the expression of a target human recombinant protein in different production platforms was compared. We focused on traditional fermenter-based cultures and on plants, describing the set-up of each system and highlighting, on the basis of the reported results, the inherent limits and advantages for each platform.

Plant-based systems are considered a valuable platform for the production of recombinant proteins as a result of their well-documented potential for the ...

Imaging Spatial Reorganization of a MAPK Signaling Pathway Using the Tobacco Transient Expression System

Visualization of dynamic signaling events in live cells has been a challenge. We expanded an established transient expression system, the biomolecular fluorescent complementation (BiFC) assay in tobacco epidermal cells, from testing protein-protein interaction to monitoring spatial distribution of signal transduction in plant cells. In this protocol, we used the BiFC assay to show that the interaction and the signaling between the Arabidopsis MAPKKK YODA and MAPK6 occur at the plasma membrane. When the scaffold protein BASL was co-expressed, the YODA-MAPK interaction redistributed and spatially co-polarized with CFP-BASL. This modified tobacco expression system allows for quick examination of signaling localization and dynamic changes (less than 4 days) and can accommodate multiple of fluorescent protein colors (at least three). We also presented detailed methods to quantify protein distribution (asymmetric spatial localization, or &#34;polarization&#34;) in tobacco cells. This advanced tobacco expression system has a potential to be used widely for quick testing of dynamic signaling events in live plant cells.

At the subcellular level, signaling events are dynamically modulated by developmental and environmental cues. Here we describe a protocol that employs the tobacco transient expression system to monitor dynamic protein-protein interaction and to disclose spatial organization of signal transduction in plant cells.

Visualization of dynamic signaling events in live cells has been a challenge. We expanded an established transient expression system, the biomolecular ...

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

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