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 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.
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.
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).
1. Planning a DoE Strategy
Table 1. Factors affecting transient protein expression in tobacco including the variation ranges during DoE. Factors in bold were only included in the design for the experiments described under "A descriptive model for DsRed accumulation during transient expression using different promoter/5'UTRs" whereas factors in italics were only included in the design for "Optimizing incubation conditions and harvest schemes for the production of monoclonal antibodies in plants using transient expression".
Figure 2. DoE planning process. Factors with a significant impact on the response under investigation are selected based on available data. Then factor attributes (e.g. numeric), ranges and levels are assigned. Previous knowledge and experiments are used to define a suitable base model. The predictive power requirements are defined based on the application/purpose of the final model. The compiled data can then be transferred into appropriate DoE software.
2. Setting up a RSM in DesignExpert
Figure 3. Comparison of FDS plots. A. A DoE consisting of 90 runs produces an insufficient FDS of only 1% for the standard error of prediction, using a quadratic base model in combination with the values for the minimum detectable difference (20 μg/ml) and estimated standard deviation of the system (8 μg/ml). B. Augmentation of the DoE to a total of 210 runs achieved a 100% FDS and a flat curve indicating uniform precision of the model throughout the design space.
3. Cloning and Analysis of Expression Cassettes
Figure 4. Promoter and 5'UTR variants. The expression cassettes were generated by the stepwise exchange of the 5'UTR, resulting in four combinations with CaMV 35SS promoter, followed by the replacement of this promoter with the nos sequence yielding four additional variants and a total of eight different promoter/5'UTR combinations.
4. Plant Cultivation
5. Transient Protein Expression
6. Protein Quantitation
7. Data Analysis and Evaluation
A descriptive model for DsRed accumulation during transient expression using different promoters and 5'UTRs
DsRed fluorescence in leaf extracts was used to indicate the expression level of the recombinant protein and thus was used as the response in the DoE strategy. The minimum detectable difference we considered relevant was 20 μg/ml and the estimated standard deviation of the system was 8 μg/ml based on initial experiments. Factors included in the transient expression model were selected based on literature data 7,8 and our previous results 9. The investigated ranges were also chosen according to these data (Table 1). At least three levels were selected for all discrete numeric factors to allow the calculation of a quadratic base model. A D-optimal selection algorithm was chosen for the selection of the DoE runs to obtain the most accurate estimates for the coefficients of the regression model. The design initially suggested by DesignExpert consisted of 90 runs but the FDS was insufficient to achieve a 1% standard error of prediction (Figure 3A). D-optimal augmentation of the design to a total of 210 runs resolved this issue and resulted in a FDS of 100% with more uniform prediction accuracy across the design space indicated by the flat curve (Figure 3B).
The DsRed concentrations were determined for all 210 runs and the data were Log10 transformed. Model factors were chosen by automated backward selection from a cubic model with an alpha level of 0.100. This resulted in a significant model (Table 2) with insignificant lack-of-fit and high values for the multiple correlation coefficients (Table 2). The p-value of all model factors was <0.05 and thus no further manual manipulation of the model was required. The model contained three-factor interactions that were not part of the initial quadratic base model (Table 2). Re-evaluation of the FDS graph using all factors included in the final prediction model revealed that the FDS for the standard error of prediction had not significantly diminished by including the additional three-factor interactions (FDS = 0.99).
The model quality diagnostic tools in DesignExpert indicated that the data transformation was useful and there were no missing factors in the model because the normal plot of residuals showed linear behavior and no specific pattern was observed in the residuals vs. predicted plot (Figures 5A and 5B). There was also no trend throughout the course of the experiment to indicate a hidden time-dependent variable (Figure 5C). Instead, the model predictions were in very good agreement with the observed DsRed fluorescence (Figure 5D). We therefore assumed that the selected model was useful to predict the transient expression of DsRed in non-cotyledon tobacco leaves driven by different promoter/5'UTR combinations during a post-infiltration incubation period lasting 8 days. We also selected an artificial linear regression model without data transformation to illustrate the consequences of wrong factor selection and transformation (Figure 6). Clearly, the normal plot of residuals deviates from the expected linear behavior (Figure 6A) and there is a "v"-shaped pattern in the residuals vs. predicted plot instead of a random scatter (Figure 6B). Additionally, the residuals vs. run plot highlights two extreme values (Figure 6C), while predictions are poor for both, small and high values, deviating from the optimal model (diagonal) (Figure 6D).
The 5'UTR combinations with the CaMV 35SS promoter resulted in stronger DsRed expression than combinations with the nos promoter (Figures 5A and 5B) as previously reported 34 and the corresponding factors were found to be significant in the DoE model (Table 2). The model also predicted that leaf age was a significant factor (Table 2) with expression levels shown to be higher in young leaves (Figures 7B and 7D) which was in good agreement with our previous findings 19 and those of others 7,8. The progression of DsRed accumulation in leaves was not linear or exponential but followed a sigmoidal curve during the 8 days of post-infiltration incubation according to the model. Interestingly, there was no fixed order of the 5'UTRs in terms of corresponding DsRed expression. Hence, the "strength" of a 5'UTR was dependent on the accompanying promoter. It is unlikely that this interdependence between the promoter and 5'UTR would have been revealed using an OFAT experiment. The predictive model also indicated that certain pairs of promoter/5'UTR combinations (e.g. CaMV 35SS/CHS and nos/CHS (Figures 7A and 7B), CaMV 35SS/omega and nos/CHS or CaMV 35SS/CHS and CaMV 35SS/TL) resulted in a balanced expression levels, differing by less than 30% from a defined ratio across all leaves and incubation times >2 days (e.g. for CaMV 35SS/omega and nos/CHS the ratio was ~8.0) 20. Such balanced expression would be useful for the expression of multimeric proteins such as IgA with a defined stoichiometry 35,36.
Figure 5. Graphical evaluation of model quality. A. The normal probability plot of the internally studentized residuals indicates normal distribution because the residuals follow a line. B. Internally studentized residuals scatter around the value zero for all ranges of the predicted response (fluorescence) indicating an appropriate data transformation. C. Internally studentized residuals scatter around the value zero during the whole course of the experiment, showing the absence of hidden temporal effects. D. Predicted and actual values of the response are in good agreement as all points lie close to the diagonal (ideal model).
Figure 6. Diagnostics indicating low model quality. A. The normal probability plot of the internally studentized residuals indicates non-normal distribution because the residuals deviate from a line. B. Internally studentized residuals show a "v"-shaped distribution indicating an inappropriate data transformation. C. Internally studentized residuals scatter not around the value zero during the whole course of the experiment, but exhibit a tendency towards positive values. Additionally, two extreme values can be found. D. Predicted and actual values of the response are in poor agreement as most points deviate from the diagonal (ideal model).
Figure 7. Response surfaces for transient DsRed expression in tobacco leaves. A. nos/CHS in leaf 2; B. CaMV 35SS/CHS in leaf 2; C. CaMV 35SS/TL in leaf 6; D. CaMV 35SS/CHS in leaf 6. DsRed concentrations increased in a sigmoidal manner during the incubation period. Expression levels were lower in old leaves (e.g. leaf 2 in A and B) compared to young leaves (e.g. leaf 6 in C and D) and for nos (A) compared to the CaMV 35SS promoter (B). The 5'UTR also had a significant impact on DsRed expression (e.g. TL (C) vs. CHS (D)) but the expression strength was dependent on the accompanying promoter.
Source | Sum of squares | df | Mean square | F-value | p-value |
Model | 174.85 | 95 | 1.84 | 182.12 | < 0.0001 |
Position on leaf (A) | 0.16 | 1 | 0.16 | 16.06 | 0.0001 |
Incubation time (B) | 112.46 | 1 | 112.46 | 11128.22 | < 0.0001 |
Leaf age (C) | 15.24 | 7 | 2.18 | 215.39 | < 0.0001 |
Promoter (D) | 23.49 | 1 | 23.49 | 2324.29 | < 0.0001 |
5'UTR (E) | 0.93 | 3 | 0.31 | 30.61 | < 0.0001 |
AC | 0.24 | 7 | 0.034 | 3.38 | 0.0026 |
BC | 1.46 | 7 | 0.21 | 20.64 | < 0.0001 |
BD | 2.27 | 1 | 2.27 | 224.51 | < 0.0001 |
BE | 0.87 | 3 | 0.29 | 28.74 | < 0.0001 |
CD | 0.29 | 7 | 0.042 | 4.11 | 0.0005 |
CE | 0.43 | 21 | 0.021 | 2.04 | 0.0093 |
DE | 0.48 | 3 | 0.16 | 15.73 | < 0.0001 |
B2 | 6.34 | 1 | 6.34 | 627.29 | < 0.0001 |
BCD | 0.95 | 7 | 0.14 | 13.42 | < 0.0001 |
BCE | 0.39 | 21 | 0.019 | 1.83 | 0.0230 |
BDE | 0.16 | 3 | 0.054 | 5.37 | 0.0017 |
B2D | 1.49 | 1 | 1.49 | 147.93 | < 0.0001 |
Residual | 1.15 | 114 | 0.010 | ||
Lack-of-fit | 1.08 | 104 | 0.010 | 1.45 | 0.2669 |
Pure error | 0.072 | 10 | 7.153E-003 | ||
Correlation total | 176.01 | 209 | |||
Correlation coefficients | Value | ||||
R-Squared | 0.9935 | ||||
Adjusted R-Squared | 0.9880 | ||||
Predicted R-Squared | 0.9770 |
Table 2. Factors included in the predictive model for transient DsRed expression in tobacco leaves using different promoter/5'UTR combinations. Three-factor interactions are highlighted in bold.
Optimizing incubation conditions and harvest schemes for the production of monoclonal antibodies in plants by transient expression
The DoE approach was also used to optimize the incubation temperature, bacterial OD600nm for leaf infiltration, plant age and harvest schemes, for the simultaneous production of the monoclonal HIV-neutralizing antibody 2G12 and DsRed in tobacco 19. The harvest scheme determined which leaves were used for protein extraction, e.g. the six uppermost leaves. We therefore established a predictive model for the expression of each protein (2G12 and DsRed) in plants at different ages (young = early bud stage = harvest at 40 days after seeding; old = mature bud stage = harvest at 47 days after seeding). These four models were then evaluated and a consensus model established that included each factor found to be significant in the individual models. We then confirmed that the consensus model was still a good representation of all initial data sets (Table 3). The consensus model was then used to identify optimal incubation temperatures (~22 °C for 2G12 and ~25 °C for DsRed) and bacterial OD600nm (~1.0) for both proteins. These optimal conditions were then used to predict protein concentrations in all leaves (1-8) and leaf positions (1-4) in young and old plants. The concentration profiles were integrated with biomass data 19 to yield the absolute amount of target protein obtained from different leaves and plant ages (Figure 8A). Absolute protein amounts were then correlated with associated downstream costs allowing us to carry out a cost-benefit analysis for the processing of each leaf/plant age. This revealed that young plants were advantageous for transient expression because the proteins reached higher concentrations during shorter growth periods despite the lower overall biomass compared to old plants (Figures 8B and 8C). We also found that processing all the leaves from old plants was more expensive than discarding leaves 1-3 and increasing the number of plants per batch instead (Figure 8D). Hence, DoE-based models are suitable not only to mark the final step of an experiment, but also for combination with other data to facilitate more complex aspects of process analysis. A series of interconnected models covering the whole production process for a biopharmaceutical protein could be the ultimate goal.
Figure 8. Integration of biomass and protein concentration data resulting in absolute protein yield and process costs. A. Leaf biomass and target protein concentration did not develop in a collinear manner resulting in a biased accumulation of 2G12 in young leaves. B. The same amount of DsRed and ~65% of 2G12 was found in young plants compared to old ones despite a ~50% lower average biomass. C. This reflected the higher specific protein expression in young plants. D. The increased specific expression translated into reduced production costs for both proteins because less biomass needed to be processed requiring less greenhouse space, fewer consumables (e.g. filters), and less downstream equipment.
Plant age [dps] | 40 | 47 | ||||||
Target protein [-] | DsRed | 2G12 | DsRed | 2G12 | ||||
Model [-] | Initial | Consensus | Initial | Consensus | Initial | Consensus | Initial | Consensus |
R-squared [-] | 0.9829 | 0.9577 | 0.9321 | 0.9099 | 0.9436 | 0.9403 | 0.8826 | 0.8782 |
Adjusted R-squared [-] | 0.9711 | 0.9480 | 0.9059 | 0.8893 | 0.9362 | 0.9350 | 0.8716 | 0.8674 |
Predicted R-squared [-] | 0.9510 | 0.9336 | 0.8272 | 0.8587 | 0.9254 | 0.9282 | 0.8554 | 0.8516 |
Table 3. Comparison of correlation coefficients for initial RSM models and the final consensus model of DsRed and 2G12 expression in tobacco plants.
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'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 "temperature" is often restricted to the range 10-40 °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 ±2.0 °C so it is inappropriate to investigate temperature differences of ±1.0 °C. Practical limitations may also come into play, e.g. using >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 "split-designs" 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 < 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 "real" 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 "Design augmentation" 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 authors have nothing to disclose.
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 “Future-Pharma”, proposal number 269110 and the Fraunhofer Zukunftsstiftung (Fraunhofer Future Foundation).
Design-Expert(R) 8 | Stat-Ease, Inc. | n.a. | DoE software |
Tryptone | Carl Roth GmbH | 8952.2 | Media component |
Yeast extract | Carl Roth GmbH | 2363.2 | Media component |
Sodium chloride | Carl Roth GmbH | P029.2 | Media component |
Ampicillin | Carl Roth GmbH | K029.2 | Antibiotic |
Agar-Agar | Carl Roth GmbH | 5210.2 | Media component |
Escherichia coli K12 DH5a | Life technologies | 18263-012 | Microorganism |
pPAM | GenBank | AY027531 | Cloning/expression vector; |
NucleoSpin Plasmid | MACHEREY-NAGEL GmbH | 740588.250 | Plasmid DNA isolation kit |
NucleoSpin Gel and PCR Clean-up | MACHEREY-NAGEL GmbH | 740609.250 | Plasmid DNA purification kit |
NanoDrop 2000 | Thermo Scientific | n.a. | Spectrophotometer |
NcoI | New England Biolabs Inc. | R3193L | Restrictionendonuclease |
EcoRI | New England Biolabs Inc. | R3101L | Restrictionendonuclease |
AscI | New England Biolabs Inc. | R0558L | Restrictionendonuclease |
NEB 4 | New England Biolabs Inc. | B7004S | Restrictionendonuclease buffer |
TRIS | Carl Roth GmbH | 4855.3 | Media component |
Disodium tetraborate | Carl Roth GmbH | 4403.3 | Media component |
EDTA | Carl Roth GmbH | 8040.2 | Media component |
Agarose | Carl Roth GmbH | 6352.4 | Media component |
Bromophenol blue | Carl Roth GmbH | A512.1 | Color indicator |
Xylene cyanol | Carl Roth GmbH | A513.1 | Color indicator |
Glycerol | Carl Roth GmbH | 7530.2 | Media component |
Mini-Sub Cell GT Cell | BioRad | 170-4406 | Gel electrophoresis chamber |
Agrobacterium tumefaciens strain GV3101:pMP90RK | DSMZ | 12365 | Microorganism |
Electroporator 2510 | Eppendorf | 4307000.658 | Electroporator |
Beef extract | Carl Roth GmbH | X975.2 | Media component |
Peptone | Carl Roth GmbH | 2365.2 | Media component |
Sucrose | Carl Roth GmbH | 4621.2 | Media component |
Magnesium sulfate | Carl Roth GmbH | 0261.3 | Media component |
Carbenicillin | Carl Roth GmbH | 6344.2 | Antibiotic |
Kanamycin | Carl Roth GmbH | T832.3 | Antibiotic |
Rifampicin | Carl Roth GmbH | 4163.2 | Antibiotic |
FWD primer | Eurofins MWG Operon | n.a. | CCT CAG GAA GAG CAA TAC |
REV primer | Eurofins MWG Operon | n.a. | CCA AAG CGA GTA CAC AAC |
2720 Thermal cycler | Applied Biosystems | 4359659 | Thermocycler |
RNAfold webserver | University of Vienna | n.a. | Software |
Ferty 2 Mega | Kammlott | 5.220072 | Fertilizer |
Grodan Rockwool Cubes 10x10cm | Grodan | n.a. | Rockwool block |
Greenhouse | n.a. | n.a. | For plant cultivation |
Phytotron | Ilka Zell | n.a. | For plant cultivation |
Omnifix-F Solo | B. Braun | 6064204 | Syringe |
Murashige and Skoog salts | Duchefa | M 0222.0010 | Media component |
Glucose | Carl Roth GmbH | 6780.2 | Media component |
Acetosyringone | Sigma-Aldrich | D134406-5G | Phytohormon analogon |
BioPhotometer plus | Eppendorf | 6132 000.008 | Photometer |
Osram cool white 36 W | Osram | 4930440 | Light source |
Disodium phosphate | Carl Roth GmbH | 4984.3 | Media component |
Centrifuge 5415D | Eppendorf | 5424 000.410 | Centrifuge |
Forma -86C ULT freezer | ThermoFisher | 88400 | Freezer |
Synergy HT | BioTek | SIAFRT | Fluorescence plate reader |
Biacore T200 | GE Healthcare | n.a. | SPR device |
Protein A | Life technologies | 10-1006 | Antibody binding protein |
HEPES | Carl Roth GmbH | 9105.3 | Media component |
Tween-20 | Carl Roth GmbH | 9127.3 | Media component |
2G12 antibody | Polymun | AB002 | Reference antibody |