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Sep 2019

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In vitro Assessment of Pathogen Effector Binding to Host Proteins by Surface Plasmon Resonance
利用表面等离子体共振技术评价病原菌与宿主蛋白的结合   

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Abstract

The mechanisms of virulence and immunity are often governed by molecular interactions between pathogens and host proteins. The study of these interactions has major implications on understanding virulence activities, and how the host immune system recognizes the presence of pathogens to initiate an immune response. Frequently, the association between pathogen molecules and host proteins are assessed using qualitative techniques. As small differences in binding affinity can have a major biological effect, in vitro techniques that can quantitatively compare the binding between different proteins are required. However, these techniques can be manually intensive and often require large amounts of purified proteins. Here we present a simplified Surface Plasmon Resonance (SPR) protocol that allows a reproducible side-by-side quantitative comparison of the binding between different proteins, even in cases where the binding affinity cannot be confidently calculated. We used this method to assess the binding of virulence proteins (termed effectors) from the blast fungus Magnaporthe oryzae, to a domain of a host immune receptor. This approach represents a rapid and quantitative way to study how pathogen molecules bind to host proteins, requires only limited quantities of proteins, and is highly reproducible. Although this method requires the use of an SPR instrument, these can often be accessed through shared scientific services at many institutions. Thus, this technique can be implemented in any study that aims to understand host-pathogen interactions, irrespective of the expertise of the investigator.

Keywords: Surface Plasmon Resonance (表面等离子体共振), Protein-Protein interactions (蛋白质相互作用), Pathogens (病原体), Effectors (效应物), NLR (NLR), Binding (结合), Affinity (亲和力), Kinetics (动力学)

Background

As part of their colonization process, pathogens can deploy an array of molecules, termed effectors, inside the host cell (Win et al., 2012). Frequently, these effectors bind to host targets to modify them, and/or re-direct their activities, which ultimately allows pathogens to overcome host immune defenses and subvert host cell pathways in their favor (Dodds and Rathjen, 2010). On the other hand, plants and animals have evolved a set of diverse intracellular immune receptors (NLRs) that detect the presence of pathogen effectors (Jones et al., 2016). This can be by direct binding (Kourelis and van der Hoorn, 2018). This recognition event triggers host immune signaling and restricts pathogen growth. Therefore, understanding the association between effectors and their host targets, as well as between effectors and immune receptors, have major implications in the study of host-pathogen interactions.

The biology of pathogen effectors and their mechanisms of action can be investigated by a combination of methods including cell biology, molecular biology and biophysics (Varden et al., 2017). The biochemical study of the interactions between effectors and their targets often employs qualitative techniques such as Yeast-Two-Hybrid (Y2H) (Mukhtar et al., 2011; Weßling et al., 2014) and co-immunoprecipitation (co-IP) (Fujisaki et al., 2015; Dagdas et al., 2016). However, small differences in binding between effectors and host proteins can have major impacts in function (De la Concepcion et al., 2018). Therefore, techniques that can quantitatively determine the binding between an effector and a given host protein are increasingly being required.

Isothermal Titration Calorimetry (ITC) has been commonly used as the gold standard to measure interactions between pathogen effectors and host virulence targets (Dagdas et al., 2016; Maqbool et al., 2016). This technique has also been used to investigate the binding between effectors and immune receptors, and how this is translated into immune recognition (Zhang et al., 2017). However, in many cases, multiple allelic variants of both effectors and host proteins are involved in the virulence/immunity process (Zess et al., 2019), increasing the number of combinations to test and the labor-intensity of this approach. This, together with the requirement of relatively large amounts of purified proteins, can make the study of interactions by ITC impractical in some cases.

Surface Plasmon Resonance (SPR) has several advantages over ITC. First, the microfluidic nature of the technique allows the use of very small volumes of proteins at often nanomolar concentration, reducing the amount of purified protein required for the experiments compared with ITC. Also, as SPR is a high-throughput and automatable technique, multiple interactions and their respective controls can be tested at the same time under the same conditions, increasing the robustness and reproducibility of the data.

We have successfully used SPR to understand how direct binding of a domain from the rice NLR immune receptor Pik to an effector from the rice blast fungus (Magnaporthe oryzae), leads to immune recognition (Maqbool et al., 2015; De la Concepcion et al., 2018 and 2019). The rice blast effector AVR-PikD is recognized by the rice NLR Pikp via direct binding to the integrated Heavy Metal Associated (HMA) domain of the plant receptor (Maqbool et al., 2015; De la Concepcion et al., 2018). However, polymorphic variants of AVR-Pik that escape immune recognition are present in nature (Kanzaki et al., 2012; Bialas et al., 2018). Although these natural variants can also bind to the integrated HMA domain, they do so with lower affinity, resulting in lack of immune response (De la Concepcion et al., 2018 and 2019).

Although SPR can be used to calculate binding affinities and kinetics, this was not possible for some Pik-HMA/AVR-Pik combinations due to weak binding (De la Concepcion et al., 2018 and 2019). However, the SPR protocol presented here ranks the binding of different AVR-Pik variants to different alleles and mutants of the integrated HMA domain of the Pik receptor in the absence of precise quantification of binding affinities (expressed as equilibrium dissociation or KD values), allowing us to overcome these issues with respect to biological function (De la Concepcion et al., 2018 and 2019). Therefore, this method presents a quick way to screen and quantitatively rank interactions, which will be informative to understanding the biological implications of the interactions.

Although the protocol presented here has been optimized for the interaction between two proteins where one partner is immobilized on a Nitrilotriacetic Acid (NTA) chip through a hexa-histidine tag, it is equally applicable if any other form of capture is used. For example, effector-DNA interactions can be easily tested by immobilizing biotinylated DNA to a streptavidin (SA) chip (Stevenson et al., 2013).

Materials and Reagents

  1. MF-MilliporeTM Membrane Filter, 0.22 µm pore size (Merck, catalog number: GSWP04700 )
  2. Proteins of interest
    The proteins to be tested must be purified and concentrated prior dilution into the running buffer. One of the proteins whose interaction wants to be tested must contain a histidine tag while the other requires no tag.
    Notes:
    1. Protein concentrations must be adjusted in every case: In this protocol we provide a particular example to which working protein concentrations have been optimized. As starting point, we recommend using two different protein concentrations (e.g., 50 nM and 500 nM). A range of dilutions could be tested in subsequent experiments.
    2. Consider which protein is to be tagged: Consider which protein will be immobilized on to the chip surface (ligand), and which one will be flowed over (analyte). Many factors can affect the choice for immobilization like purity, amount available, stoichiometry and theoretical response. Ideally both proteins could be produced tagged and untagged and both orientations tested. Whether the hexa-histidine tag is placed in the N-terminus or C-terminus of the protein should be also considered (e.g., if the proteins are predicted to bind near to the C-terminus, proteins should be attached to the chip in the N-terminus). It is also crucial that the untagged protein does not bind directly to the chip.
  3. HEPES (Melford, catalog number: H75030-1000.0 )
  4. NaCl (Merck, catalog number: 1064041000 )
  5. Tween® 20 (Merck, catalog number: P9416-100ML )
  6. NiCl2·6H2O (Merck, catalog number: 203866-5G )
  7. EDTA (VWR Chemicals, catalog number: 20302.26 0)
  8. NaOH (Merck, catalog number: 221465 )
  9. Running buffer (see Recipes)
  10. 0.5 mM NiCl2 (see Recipes)
  11. Regeneration solution (see Recipes)

Equipment

  1. BiacoreTM T200 SPR instrument (GE Healthcare, catalog number: 28975001 )
    Note: This method can be used with different instruments: Although the method presented here has been implemented using a BiacoreTM T200 SPR instrument, it could be adapted to run with any SPR instrument.
  2. BiacoreTM Sensor Chip Series S NTA (GE Healthcare, catalog number: 28994951 )
    Note: Different brands of NTA chips can be used: We use GE Healthcare NTA chips. However, this method can use any compatible NTA chip.
  3. Direct Detect® Infrared Spectrometer (Merck, catalog number: DDHW00010-WW )
    Note: Different methods to measure the concentration of the proteins can be used: As some of the proteins we used in the example do not contain aromatic residues, we used a Direct Detect® Infrared Spectrometer to determine concentrations. However, other standard methods such as Bradford or absorbance at 280 nm can be used. Also, the concentration of the His-tagged proteins does not necessarily have to be known as their concentrations can be assessed by the response on binding to the chip.

Software

  1. BiacoreTM T200 evaluation software (GE Healthcare)
  2. R studio (R Core Development Team, 2018) with ggplot2 package (Wickham, 2016)

Procedure

For all experiments, dock an NTA chip into the SPR instrument and prime it with running buffer. Prior to docking, the NTA chip needs to equilibrate at room temperature for 10-30 min to prevent condensation. Once docked there are four flow cells available with a Biacore T200. In this experiment, use two of these flow cells, with flow cell 1 as the reference (FCref) without immobilize-d ligand and flow cell 2 as the test cell (FCtest). For all experiments, load the tubes with the appropriate amount of solution and placed in the rack as detailed in the BiacoreTM T200 Control Software.
Note: Leave the reference cell blank: In this protocol, the reference flow cell is blank and the analyte injected over both cells, this will reveal any non-specific binding of the analyte to the chip and will be subsequently subtracted from the final result. Ideally very little or no binding of the analyte to the chip surface should occur.

  1. Preparation of the ligand protein
    The method uses a hexa-histidine tagged protein (Magnaporthe oryzae AVR-Pik, C-terminally tagged in our example), which is captured to the surface on a standard NTA chip (Figure 1). We purified the proteins as described in Maqbool et al. (2015) and De la Concepcion et al. (2018 and 2019).


    Figure 1. Cartoon representation and example result of the four steps of the binding experiment. (1) Activation of the NTA chip with nickel. (2) Binding of the his-tagged protein ligand to the chip. (3) Binding of the protein analyte. (4) Regeneration of the chip with EDTA to remove everything bound to start the cycle again. Steps 1 and 2 are only carried out on Flow cell 2 whereas steps 3 and 4 are carried out over both flow cells. The sensorgram shown represent a typical trace of the subtracted FC2-1 sensorgram.

    1. Preparation of stock dilution of His-tagged (ligand) protein
      The concentration of the protein that will be immobilized on the chip is measured and diluted in buffer (20 mM HEPES, 150 mM NaCl, pH 7.5) to obtain 2 ml of stock solution at 2 μM.
      Note: The stock solution can vary depending on the experiment: The amount of stock solution we prepare in our example is larger than needed. This is because the proteins used in this example can be produce in large amounts, are stable, and can be kept on ice for a few days. If the protein to be tested is not produced in enough quantities and/or is not stable, we recommend preparing a fresh stock solution with a lower volume each time before running.
    2. Preparation of working solution of ligand protein
      From the stock solution, we take 25 μl and dilute it with 975 μl of running buffer to obtain a 50 nM dilution. The volume of the ligand solution will vary depending on the number of cycles that are set up, and will be indicated by the BiacoreTM T200 SPR control software.

  2. Manual run to estimate the amount of protein immobilized onto the chip
    Once the proteins that will serve as ligand are prepared, test the binding to the chip by performing a manual run. In our experiments, we aimed to use a final capture level of ligand of 250 ± 50 Response Units (RU) for each ligand to be tested.
    Note: Ligand RUs value: In this experiment, we aim for a ligand immobilization level of ~250 RU as the value that gives a response sufficiently large enough to be measured even for weak binders. This value was selected by using the Rmax calculation in the data analysis section and this theoretical response obtained is related to the molecular weight of the two proteins and the amount of the His-tagged protein immobilized on the chip. Therefore, we first recommend determining the optimal level of immobilization for each ligand. If the level of binding of the ligand differs too much from the desired RU, it can be corrected by reducing or increasing the concentration of ligand in the working solution. If multiple His-tagged protein are to be tested, the concentration to use to capture on the chip will need to be optimized for each protein.

    1. Select flow cells
      Carry out the experiments using flow cells 1 and 2. The ligand is captured in flow cell 2 and the analyte is injected over both flow cells. The final result (sensorgram) is a subtraction of the response from flow cell 2 min cell 1 (FC2-1).
    2. Activation of the chip
      Inject 30 µl of 0.5 mM NiCl2 with a flow rate of 30 μl/min to activate the chip. This step only applies to flow cell 2.
    3. Immobilization of the ligand
      Inject 30 µl of C-terminally His-tagged AVR-Pik in flow cell 2 with a flow rate of 30 μl/min. Binding to the Ni2+ on the surface of the chip is recorded as (RU). An additional check should be carried out without activation with NiCl2 to ensure that the protein does not non-specific binding to the chip surface.
    4. Regeneration
      Regenerate the sensor chip by removing anything bound to the chip, with an injection of 30 μl of 0.35 M EDTA with a flow rate of 30 μl/min over both the flow cells.

  3. Preparation of the protein analyte
    Once the manual run is completed and the binding of each ligand to the chip is around 250 ± 50 RUs, prepare the working dilutions of the protein analyte. In our case, we produced purified Pik-HMA domain as described in De la Concepcion et al. (2018 and 2019).
    1. Preparation of stock dilution of analyte protein
      After measuring the concentration of the protein, dilute it in buffer (20 mM HEPES, 150 mM NaCl, pH 7.5) to obtain 2 ml of stock solution at 2 μM as described above for the protein ligand.
    2. Preparation of working solution of analyte protein
      Make serial dilutions of the protein stock to obtain final concentrations of 4 nM, 40 nM and 100 nM. The total volume depends on the number of cycles and will be indicated by the BiacoreTM T200 SPR control software.
      Note: Adjust working concentration of analyte: The working concentration of the analyte will vary in each experimental case depending the strength of interaction between proteins. We recommend starting with concentrations of 50 nM and 500 nM to find the appropriate concentrations to use. Ideally a top concentration should be used where the binding is saturated for the strongest interaction to be tested.

  4. Set up cycle parameters and Rmax run
    For the experiment, dock the chip in the instrument and prime it with running buffer. We carried out the experiment at 25 °C but the samples are stored at 4 °C. We used a flow rate of 30 μl/min.
    Note: Adjusting experimental temperature and flow rate: The optimal temperature and flow rate can vary depending on the proteins to be tested. The parameters used by this protocol are standard, but some proteins might require a different temperature and/or a higher contact time (achieved by a lower flow rate) for a successful interaction. Likewise, the time that the protein stock can be stored at 4 °C is protein-dependent. As a standard, we do not recommend freeze-thaw the protein aliquots (working with small, single-use protein aliquots is preferred).
    Each experimental cycle consists of 4 steps described below and represented in Figure 1. This cycle can be repeated multiple times in an automated fashion using different concentrations of analyte tested over different ligands.

  1. Chip activation
    As a first step, inject a solution of 0.5 mM NiCl2 with a flow rate of 30 μl/min over Flow cell 2 to activate the chip.
  2. Ligand immobilization
    After chip activation, inject the His tagged protein (ligand) to be tested (in our case C-terminally tagged AVR-Pik effector) over the flow cell 2 (FCtest). Sixty seconds are used as the injection time to achieve a desired response at this concentration. After the ligand has been immobilized buffer is flowed over FC1 and 2 to ensure any non-specific his tagged protein is removed and a stable baseline should be achieved prior to analyte injection.
  3. Analyte injection
    Once the ligand is bound to the chip surface, inject the test analyte at a given concentration (or buffer-only control) over both flow cells (FCref and FCtest). A contact time of 120 s is used as a standard to make sure the maximum concentration is (ideally) reaching the steady state. After injection of the protein, the system switches back to buffer only flow and the bound protein will start to dissociate. Generally, 120 s of buffer only is used to see this dissociation. This region of the sensorgram can be used to evaluate differences between analyte proteins as generally tighter interactions take longer to dissociate. 
  4. Chip regeneration
    After the analyte has been injected in the flow cell, pass a regeneration solution of 0.35 M EDTA over both flow cells (FCref and FCtest) at a flow rate of 30 µl/min. This strips the Ni2+ from the chip, and the proteins bound to it. After this step, the response should return to similar levels to those prior to Step D1.
    For each analyte, we set 3 replicates of each cycle at working concentration of 4 nM, 40 nM and 100 nM. In addition, two start up cycles were carried out using buffer only as the analyte. The total running time of 11 cycles is around 4 h for each ligand to be tested and once initiated it does not require further user intervention. Multiple runs involving different ligands and analytes can be stack together in a single run.
    When the experiment is completed, the NTA chip can be removed from the instrument and stored in buffer at 4 °C until next use. The chip can be re-used multiple times for different experiments. Each time a chip is re-used the capture of nickel and his-tagged protein is checked. As long as this is what is expected the chip can be used again. On the rare occasions a chip fails it is obvious as the nickel and his tagged protein is no longer captured.

Data analysis

To compare the results between multiple cycles, the data must be normalized by correcting the different capture levels and according to the molecular weight of the different proteins. To do this, calculate the theoretical maximal binding at saturation of the analyte (Rmax) value for each run (Buckle, 2001; Majka and Speck, 2007). This value is measured in Response Units (RU), which is how binding events are recorded in SPR. This is calculated following the equation:



Mw is the molecular mass of the protein bound to the chip (ligand) and the protein flowed over the chip (analyte). This is corrected with the stoichiometry of the binding between the proteins and the amount of ligand immobilized on to the chip surface measured as Response Units (RU).
Note: The stoichiometry of the binding will affect to the final result: For example, if the binding stoichiometry is 2:1 analyte: ligand, the final %Rmax could be higher than 100% compared to assuming a 1:1 binding. If the stoichiometry is not known by other techniques, we recommend assuming a 1:1 binding and correct if the final result indicates otherwise.

Once we establish the Rmax for each run, we can express the level of binding as the percentage of Rmax calculated as follows:



Where RUmax is the binding response measured immediately after the end of the injection of the analyte and expressed in Response Units (RU).

In the case of the results for the binding between Pikm-HMA and AVR-PikD (1:1 binding stoichiometry) presented in De la Concepcion et al. (2018 and 2019), calculations were as follows (Table 1):

Table 1. Example of binding values obtained for RMax calculation extracted from De la Concepcion et al. (2018)


For visualization, export the SPR data and plot it using R v3.4.3 (https://www.r-project.org/) and the function ggplot2 (Wickham, 2009). SPR box plot graphs as presented in De la Concepcion et al. (2019) can be generated as follows:

  1. Preparation of dataset
    We first prepare the dataset to generate the graph. We assigned a sample name and a position in the graph corresponding to the effector used as ligand (1, 2, 3 and 4 for AVR-PikD, AVR-PikE, AVR-PikA and AVR-PikC, respectively). We annotated the analyte concentrations (4, 40 and 100 nM) for each dataset as we generated their respective graphs separate. Rmax corresponds to the value calculated as presented above. Data belonging to each biological replicate is ranked with 1, 2 and 3, respectively. And the two analytes to be compared (Wild-type Pikp-HMA and Pikp-HMANK-KE in this case) are classified as A or B in HMA.
    Sample
    Effector
    Conc
    Rmax
    Replica HMA
    NameA
    1
    40
    67
    1A
    NameB
    1
    40
    66.6
    1B
  2. Generation of the box plots
    1. We attached the dataset and defined the factors in the X and Y axis as:

      > graph <- ggplot(dataset, aes(x=factor(Effector), y=Rmax))

    2. Then we defined the colour for each analyte and for the different biological replicas:

      > colori2=c("#8faadc", "#c9a9ff")
      > colori3=c("#ff85ff", "#00b4b5", "#f8f300")

    3. The aestethic parameters for the graph are defined as follows:

      Graph + geom_boxplot(aes (x=factor(Effector), y=Rmax, fill=factor(HMA)), position=position_dodge(width=0.9), lwd=0.3, fatten= 4, outlier.shape = 21, outlier.size = 0.7, outlier.stroke=0) + scale_fill_manual(values=colori2) + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent"), rect = element_rect(fill = "transparent")) + scale_y_continuous(breaks=seq(0,70,10), limits=c(0,70)) + geom_point(aes (x=factor(Effector), y=Rmax, fill=factor(HMA), colour=factor(Replica)), position=position_jitterdodge(jitter.width=0.7, jitter.height=0, dodge.width=0.9), size=0.7, shape=16) + scale_colour_manual(values = colori3)

    4. As a final step, we produced the graph image with the following command:

      ggsave('P-NK Rmax 35x3.png', width = 3.5, height = 3, dpi = 300, bg = "transparent")

    5. The generated figure will then look similar to the graph presented below (Figure 2):


      Figure 2. Example graph generated to represent Rmax results. Side-by-side comparison box plot graph for the binding of each Analyte (HMA A or B) to the different Ligands (Effector). The position of the values for each ligand are distributed on the X axis while the calculated %Rmax value in represented on the Y axis. The centre line represents the median, the box limits are the upper and lower quartiles, the whiskers extend to the largest value within Q1 - 1.5 × the interquartile range (IQR) and the smallest value within Q3 + 1.5 × IQR. All the data points are represented as dots with distinct colours for each biological replicate (with three technical replicates within each biological replicate). The graph has been generated using the results presented in De la Concepcion et al. (2019).

Recipes

Prepare all solutions using ultrapure water and analytical grade reagents. Filter all solutions with a 0.22 µm pore size filter prior use. Solutions can be stored at room temperature.
Note: Running buffer should be modified according to the protein investigated: The buffer presented in this protocol has been optimized for a particular subset of proteins. Running buffers should be based on the buffer condition were the protein of interest is stable. It is also important to include any component necessary for the interaction, e.g., presence of metals in the solution. As a good starting point we recommend using a standard concentration of 150 mM NaCl.

  1. Running buffer
    20 mM HEPES pH 7.5
    860 mM NaCl
    0.1% Tween® 20
  2. 0.5 mM NiCl2
    1. Prepare 10 ml of 100 mM NiCl2 stock solution by dissolving 0.24 g of NiCl2·6H2O in water
    2. 0.5 mM NiCl2 working solution is prepared before use by diluting 50 μl of stock solution into 10 ml of water
  3. Regeneration solution
    1. 0.35 M EDTA prepared by dissolving 6.5 g EDTA in 50 ml of water
    2. pH must be adjusted to 8.0 with NaOH for EDTA solubilization

Acknowledgments

This protocol has been developed and implemented in the John Innes Centre Biophysical Analysis Platform. We thank Julia Mundy (julia.e.mundy@gmail.com) for art work in Figure 1.
  Research in the Banfield lab is supported by the BBSRC (grants BB/J004553, BB/P012574, BB/M02198X), the ERC (proposals 743165, 669926) and the John Innes Foundation. J.C. was supported by the John Innes Foundation/John Innes Centre/The Sainsbury Laboratory/Earlham Institute Rotation Program. This protocol is derived from De la Concepcion et al. (2018 and 2019).

Competing interests

The authors declare no competing interests.

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  19. Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer International Publishing.
  20. Win, J., Chaparro-Garcia, A., Belhaj, K., Saunders, D. G., Yoshida, K., Dong, S., Schornack, S., Zipfel, C., Robatzek, S., Hogenhout, S. A. and Kamoun, S. (2012). Effector biology of plant-associated organisms: concepts and perspectives. Cold Spring Harb Symp Quant Biol 77: 235-247.
  21. Zess, E. K., Jensen, C., Cruz-Mireles, N., De la Concepcion, J. C., Sklenar, J., Stephani, M., Imre, R., Roitinger, E., Hughes, R., Belhaj, K., Mechtler, K., Menke, F. L. H., Bozkurt, T., Banfield, M. J., Kamoun, S., Maqbool, A. and Dagdas, Y. F. (2019). N-terminal beta-strand underpins biochemical specialization of an ATG8 isoform. PLoS Biol 17(7): e3000373. 
  22. Zhang, Z. M., Ma, K. W., Gao, L., Hu, Z., Schwizer, S., Ma, W. and Song, J. (2017). Mechanism of host substrate acetylation by a YopJ family effector. Nat Plants 3: 17115.

简介

[摘要] 病原菌与宿主蛋白之间的分子相互作用是毒力和免疫机制的基础。对这些相互作用的研究对于理解毒力活动以及宿主免疫系统如何识别病原体的存在来启动免疫反应具有重要意义。通常,病原体分子和宿主蛋白之间的联系是用定性技术来评估的。由于结合亲和力的微小差异可能产生重大的生物学效应,因此需要能够定量比较不同蛋白质之间结合的体外技术。然而,这些技术可能是人工密集型的,并且通常需要大量纯化的蛋白质。在这里,我们提出了一个简化的表面等离子体共振(SPR)协议,它允许重复的并排定量比较不同蛋白质之间的结合,即使在结合亲和力无法可靠计算的情况下。我们用这种方法来评估稻瘟病菌的毒力蛋白(称为效应器)与宿主免疫受体的结合。这种方法代表了一种快速定量的方法来研究病原体分子如何与宿主蛋白质结合,只需要有限数量的蛋白质,并且具有高度的可重复性。虽然这种方法需要使用SPR仪器,但这些仪器通常可以通过许多机构的共享科学服务获得。因此,这项技术可以应用于任何旨在了解宿主-病原体相互作用的研究中,而不考虑研究者的专业知识。

[背景] 作为其定植过程的一部分,病原体可以在宿主细胞内部署一系列称为效应器的分子(Win等人,2012)。通常,这些效应器与宿主靶点结合以修改它们,和/或重新引导它们的活动,这最终允许病原体克服宿主免疫防御,并破坏有利于它们的宿主细胞通路(Dodds和Rathjen,2010)。另一方面,植物和动物已经进化出一系列不同的细胞内免疫受体(NLR),它们可以检测病原体效应器的存在(Jones等人,2016)。这可以通过直接绑定来实现(Kourelis和van der Hoorn,2018年)。这种识别事件触发宿主免疫信号并限制病原体的生长。因此,了解效应器与其宿主靶点之间以及效应器与免疫受体之间的关联,对于研究宿主-病原体相互作用具有重要意义。

病原体效应器的生物学及其作用机制可通过结合细胞生物学、分子生物学和生物物理学等方法进行研究(Varden等人,2017)。对效应器与其靶标之间相互作用的生化研究通常采用定性技术,如酵母双杂交(Y2H)(Mukhtar等人,2011;Weßling等人,2014)和免疫共沉淀(co-IP)(Fujisaki et al.,2015;Dagdas et al.,2016)。然而,效应器和宿主蛋白质之间结合的微小差异可能对功能产生重大影响(De la Concepcion等人,2018年)。因此,越来越需要能够定量确定效应器与宿主蛋白之间结合的技术。

恒温滴定量热法(ITC)通常被用作测量病原体效应器和宿主毒性靶点之间相互作用的金标准(Dagdas等人,2016年;Maqbool等人,2016年)。该技术还用于研究效应器和免疫受体之间的结合,以及如何将其转化为免疫识别(Zhang等人,2017)。然而,在许多情况下,效应器和宿主蛋白的多个等位基因变体参与了毒力/免疫过程(Zess等人,2019),增加了要测试的组合数量和这种方法的劳动强度。这一点,再加上相对大量纯化蛋白质的需求,使得ITC研究相互作用在某些情况下不切实际。

表面等离子体共振(SPR)比ITC有许多优点。首先,该技术的微流控特性允许使用非常小体积的蛋白质(通常为纳摩尔浓度),与ITC相比,实验所需的纯化蛋白质量减少。此外,由于SPR是一种高通量和自动化的技术,可以在相同的条件下同时测试多个交互作用及其各自的控制,从而提高了数据的稳健性和再现性。

我们已经成功地使用SPR来了解水稻NLR免疫受体Pik的结构域与稻瘟病菌(Magnaporthe oryzae)的效应器直接结合如何导致免疫识别(Maqbool等人,2015年;De la Concepcion等人,2018年和2019年)。稻瘟病效应因子AVR-PikD通过直接结合到植物受体的整合重金属相关(HMA)域被水稻NLR-Pikp识别(Maqbool等人,2015;De la Concepcion等人,2018)。然而,自然界中存在逃避免疫识别的AVR-Pik多态性变体(Kanzaki等人,2012年;Bialas等人,2018年)。尽管这些天然变体也能与整合的HMA结构域结合,但它们的亲和力较低,导致缺乏免疫应答(De la Concepcion等人,2018年和2019年)。

尽管SPR可用于计算结合亲和力和动力学,但由于弱结合,这对于某些Pik HMA/AVR-Pik组合是不可能的(De la Concepcion等人,2018年和2019年)。然而,在没有精确量化结合亲和力(以平衡离解或KD值表示)的情况下,本文提出的SPR协议对不同AVR-Pik变体与Pik受体整合HMA域的不同等位基因和突变体的结合进行排序,使我们能够克服生物功能方面的这些问题(De la Concepcion等人,2018年和2019年)。因此,这种方法提供了一种快速筛选和定量排序相互作用的方法,这将有助于理解相互作用的生物学意义。

虽然本文提出的方案已经针对两种蛋白质之间的相互作用进行了优化,其中一个伴侣通过六组氨酸标签固定在氮三乙酸(NTA)芯片上,但如果使用任何其他形式的捕获,它同样适用。例如,通过将生物素化的DNA固定到链霉亲和素(SA)芯片上,可以很容易地测试效应器与DNA的相互作用(Stevenson等人,2013年)。

关键字:表面等离子体共振, 蛋白质相互作用, 病原体, 效应物, NLR, 结合, 亲和力, 动力学

材料和试剂


 


1.     MF MilliporeTM膜过滤器,0.22µm孔径(默克公司,目录号:GSWP04700)


2.     感兴趣的蛋白质


待测蛋白质必须经过纯化和浓缩,然后稀释到流动缓冲液中。其中一种需要检测其相互作用的蛋白质必须含有组氨酸标签,而另一种则不需要组氨酸标签。


笔记:


a。在任何情况下都必须调整蛋白质浓度:在这个方案中,我们提供了一个工作蛋白浓度得到优化的具体例子。作为起点,我们建议使用两种不同的蛋白质浓度(例如,50nm和500nm)。一系列稀释液可以在随后的实验中进行测试。


b。考虑要标记哪个蛋白质:考虑哪种蛋白质将被固定在芯片表面(配体),哪一种会流过(分析物)。许多因素会影响固定化的选择,如纯度、可用量、化学计量和理论反应。理想情况下,这两种蛋白质都可以被标记和未标记,并且两种方向都可以被检测。还应考虑是否将六组氨酸标签放置在蛋白质的N-端还是C-端(例如,如果预测蛋白质会在C-端附近结合,则应将蛋白质连接到N-端的芯片上)。同样重要的是,未标记的蛋白质不能直接与芯片结合。


3.     HEPES(梅尔福德,目录号:H75030-1000.0)


4.     氯化钠(默克公司,产品目录号:1064041000)


5.     吐温®20(默克,产品目录号:P9416-100ML)


6.     NiCl26H2O(默克公司,产品目录号:203866-5G)·


7.     EDTA(VWR化学品,目录号:20302.260)


8.     NaOH(默克公司,产品目录号:221465)


9.     运行缓冲区(见配方)


10.  0.5 mM NiCl2(见配方)


11.  再生溶液(见配方)


 


设备


 


1.     BiacoreTM T200 SPR仪器(GE Healthcare,目录号:28975001)


注:此方法可用于不同的仪器:虽然本文介绍的方法是使用BiacoreTM T200 SPR仪器实现的,但它可以适用于任何SPR仪器。


2.     BiacoreTM传感器芯片系列S NTA(GE Healthcare,目录号:28994951)


注:可以使用不同品牌的NTA芯片:我们使用GE Healthcare NTA芯片。然而,这种方法可以使用任何兼容的NTA芯片。


3.     Direct Detect®红外光谱仪(默克公司,目录号:DDHW00010-WW)


注:可以使用不同的方法来测量蛋白质的浓度:由于我们在示例中使用的一些蛋白质不含芳香族残基,因此我们使用Direct Detect®红外光谱仪测定浓度。但是,也可以使用其他标准方法,如布拉德福德或280 nm处的吸光度。此外,His标记蛋白的浓度不一定要知道,因为它们的浓度可以通过结合到芯片上的反应来评估。


 


软件


 


1.     BiacoreTM T200评估软件(GE Healthcare)


2.     R studio(R核心开发团队,2018年),采用ggplot2软件包(Wickham,2016年)


 


程序


 


对于所有的实验,将NTA芯片连接到SPR仪器中,并用运行缓冲区对其进行预处理。对接前,NTA芯片需要在室温下平衡10-30分钟,以防止冷凝。一旦对接,有四个流动单元可与Biacore T200一起使用。在本实验中,使用两个这样的流动细胞,流动细胞1作为参考(FCref),没有固定化-d配体和流动细胞2作为测试细胞(FCtest)。对于所有实验,在试管中装入适当量的溶液,并按照BiacoreTM T200控制软件中的详细说明放置在机架中。


注:将参考单元格留空: 在本方案中,参考流池为空白,分析物注入两个池中,这将显示分析物与芯片的任何非特异性结合,随后将从最终结果中减去。理想情况下,分析物与芯片表面的结合很少或没有。


 


A、 配体蛋白的制备


该方法使用六组氨酸标记的蛋白质(Magnaporthe oryzae AVR-Pik,在我们的例子中为C-末端标记),它被捕获到标准NTA芯片的表面(图1)。我们纯化了Maqbool等人描述的蛋白质。(2015)和De la Concepcion等人。(2018年和2019年)。


 






图1。结合实验的四个步骤的动画表现和实例结果。(1) 用镍激活NTA芯片。(2) 他标记的蛋白质配体和芯片的结合。(3) 蛋白质分析物的结合。(4) 用EDTA再生芯片,去除一切束缚,重新开始循环。步骤1和2仅在流动池2上执行,而步骤3和4在两个流动单元上执行。所示的传感器图代表减去的FC2-1传感器图的典型轨迹。


 


1.     His标记(配体)蛋白稀释液的制备


测量将固定在芯片上的蛋白质浓度,并在缓冲液(20 mM HEPES,150 mM NaCl,pH 7.5)中稀释,以获得2 ml 2μM的储备溶液。


注:储备溶液可能因实验而异: 在我们的例子中,我们准备的储备溶液的数量比需要的大。这是因为在这个例子中使用的蛋白质可以大量生产,是稳定的,并且可以在冰上保存几天。如果待测蛋白质产量不足和/或不稳定,我们建议每次运行前准备一份体积较小的新鲜储备溶液。


2.     配体蛋白工作液的制备


从储备液中取25μl,用975μl流动缓冲液稀释,得到50nm的稀释度。配体溶液的体积将根据设置的循环次数而变化,并由BiacoreTM T200 SPR控制软件指示。


 


B、 手动运行以估计固定在芯片上的蛋白质量


一旦准备好作为配体的蛋白质,通过手动操作来测试与芯片的结合。在我们的实验中,我们的目标是对每个要测试的配体使用250±50个响应单元(RU)的最终捕获水平。


注:配体RUs值:在本实验中,我们的目标是配体固定化水平为~250ru,该值足以产生足够大的响应,即使对于较弱的粘合剂也可以测量。这个值是通过数据分析部分的Rmax计算选择的,得到的理论响应和这两种蛋白质的分子量和芯片上固定的His标记蛋白的量有关。因此,我们首先建议确定每个配体的最佳固定化水平。如果配体的结合水平与期望的RU相差太大,可以通过降低或增加工作溶液中配体的浓度来校正。如果要测试多个His标记的蛋白质,则需要针对每个蛋白质优化在芯片上捕获的浓度。


 


1.     选择流动单元


使用流动池1和2进行实验。配体被捕获在流动池2中,分析物被注入两个流动池中。最后的结果(传感器图)是从流动池2 min cell 1(FC2-1)减去响应。


2.     芯片激活


以30μl/0μl的流速注入30μl/l的芯片。此步骤仅适用于流动池2。


3.     配体的固定化


以30μl/min的流速将30µl C-末端His标记的AVR-Pik注入流动池2中。与芯片表面的Ni2+结合的记录为(RU)。另外一项检查应该在不使用NiCl2激活的情况下进行,以确保蛋白质不会非特异性结合到芯片表面。


4.     再生


在两个流动池中注入30μl 0.35 M EDTA,流速为30μl/min,去除芯片上的任何物质,使传感器芯片再生。


 


C、 蛋白质分析物的制备一旦手动操作完成且每个配体与芯片的结合量约为250±50 RUs,则制备蛋白质分析物的工作稀释液。在我们的例子中,我们生产了纯化的Pik-HMA结构域,如De-la-Concepcion等人所述。(2018年和2019年)。


1.     分析蛋白原液稀释液的制备


测量蛋白质浓度后,在缓冲液(20 mM HEPES,150 mM NaCl,pH 7.5)中稀释,以获得2 ml储备溶液,如上文所述,用于蛋白质配体。


2.     分析蛋白工作液的制备


连续稀释蛋白质储备,以获得4nm、40nm和100nm的最终浓度。总容积取决于循环次数,并由BiacoreTM T200 SPR控制软件指示。


注:调整分析物工作浓度:分析物的工作浓度在每种实验情况下都会有所不同,这取决于蛋白质之间相互作用的强度。我们建议从50nm和500nm的浓度开始寻找合适的浓度。理想情况下,应在结合饱和的情况下使用最高浓度,以测试最强的相互作用。


 


D、 设置循环参数和Rmax运行


在实验中,将芯片固定在仪器中,并用运行缓冲区对其进行预处理。我们在25°C下进行实验,但样品在4°C下储存。我们使用30μl/min的流速。


注:调整实验温度和流量:最佳温度和流速可根据待测蛋白质的不同而变化。该协议使用的参数是标准的,但有些蛋白质可能需要不同的温度和/或更高的接触时间(通过较低的流速实现)才能成功地相互作用。同样,蛋白质储备可在4℃下储存的时间取决于蛋白质。作为标准,我们不建议冻融蛋白质小份(最好使用一次性小份蛋白质)。


每个实验循环包括4个步骤,如下所述,如图1所示。这个循环可以自动重复多次,使用不同浓度的分析物在不同的配体上测试。


 


1.     芯片激活


第一步,在流动池2上注入流速为30μl/min的0.5 mM NiCl2溶液以激活芯片。


2.     配体固定化


芯片激活后,将His标记的蛋白质(配体)注射到流动细胞2(FCtest)上进行测试(在我们的例子中,C-末端标记AVR-Pik效应器)。60秒作为注射时间,以在该浓度下获得所需的响应。在配体固定化后,缓冲液流过FC1和FC2,以确保任何非特异性的his标记蛋白被去除,并在分析物注射前达到稳定的基线。


3.     分析物注入


一旦配体与芯片表面结合,在两个流动池(FCref和FCtest)上注入给定浓度的测试分析物(或仅限缓冲液控制)。以120 s的接触时间为标准,以确保最大浓度(理想情况下)达到稳定状态。注射蛋白质后,系统切换回缓冲液流,结合蛋白将开始解离。一般来说,120秒的缓冲液只能用来观察这种分离。由于通常更紧密的相互作用需要更长的时间才能分离,因此感测图的这一区域可用于评估分析物蛋白质之间的差异。


4.     切屑再生


将分析物注入流动池后,以30µl/min的流速将0.35 M EDTA的再生溶液通过两个流动池(FCref和FCtest)。这将从芯片中剥离Ni2+和与之结合的蛋白质。在该步骤之后,响应应返回到与步骤D1之前的水平相似的水平。


对于每个分析物,我们在4 nM、40 nM和100 nM的工作浓度下设置每个循环的3个重复。此外,仅使用缓冲液作为分析物进行两次启动循环。11个循环的总运行时间约为4小时,每个配体被测试,一旦启动,不需要进一步的用户干预。涉及不同配体和分析物的多个运行可以在一次运行中堆叠在一起。


实验完成后,NTA芯片可以从仪器中取出,并在4℃的缓冲液中储存,直到下次使用。该芯片可以多次重复使用,用于不同的实验。每次芯片被重复使用时,镍和他的标记蛋白的捕获量都会被检查。只要这是预期的,芯片可以再次使用。在极少数情况下芯片失效,很明显镍和他的标记蛋白不再被捕获。


 


数据分析


 


为了比较多个循环之间的结果,必须根据不同蛋白质的分子量,通过校正不同的捕获水平对数据进行标准化。为此,计算每次运行分析物饱和时的理论最大结合(Rmax)值(Buckle,2001;Majka和Speck,2007)。这个值以响应单元(RU)度量,这就是绑定事件在SPR中的记录方式。其计算公式如下:


 






 


Mw是与芯片(配体)结合的蛋白质分子质量,蛋白质流过芯片(分析物)。通过蛋白质之间结合的化学计量学和固定在芯片表面的配体数量作为响应单位(RU)来纠正这一点。


注: 例如,如果结合化学计量比为2:1分析物:配体,则与假设1:1结合相比,最终%Rmax可能高于100%。如果化学计量学不为其他技术所知,我们建议假设为1:1结合,如果最终结果表明不是这样,则进行校正。


 


一旦我们为每次运行建立了Rmax,我们可以将绑定级别表示为Rmax的百分比,计算如下:


 






 


式中,RUmax是在分析物注射结束后立即测量的结合反应,用响应单位(RU)表示。


 


关于Pikm-HMA和AVR-PikD(1:1结合化学计量学)之间结合的结果,见De la Concepcion等人。(2018年和2019年),计算如下(表1):


 


表1。从De la Concepcion et al.中提取的RMax计算的结合值示例。(2018年)


配体


分子量(配体)


分析物


MW(分析物)


浓度分析物(nM)


钌捕获(配体)


最大值


钌捕获(分析物)


%RMax最大


AvrD公司


11786.3


皮公里


8536.9


0


299.58


217.0


-1.54


-0.7


AvrD公司


11786.3


皮公里


8536.9


4


301.43


218.3


35.89


16.4


AvrD公司


11786.3


皮公里


8536.9


40


301.17


218.1


146.33


67.1


AvrD公司


11786.3


皮公里


8536.9


100


301.26


218.2


158.59


72.7


 


为了可视化,导出SPR数据并使用Rv3.4.3绘制它(https://www.r-project.org/)以及函数ggplot2(Wickham,2009)。SPR盒图如De la Concepcion等人所示。(2019年)可生成如下:


1.     数据集准备


我们首先准备数据集以生成图形。我们指定了一个样本名称和一个位置,对应于作为配体的效应器(分别为1、2、3和4代表AVR-PikD、AVR-PikE、AVR-PikA和AVR-PikC)。当我们分别生成各自的图表时,我们标注了每个数据集的分析物浓度(4、40和100 nM)。Rmax对应于上述计算值。属于每个生物复制的数据分别按1、2和3排序。两种待比较的分析物(本例为野生型Pikp-HMA和Pikp-HMANK-KE)在HMA中被划分为A或B。


样本效应器Conc Rmax Replica HMA


名称1 40 67 1 A


名称B 140 66.6 1 B


2.     盒形图的生成


a、 我们附加了数据集,并将X和Y轴上的因子定义为:


 


>图形<-ggplot(数据集,aes(x=因子(效应器),y=Rmax))


 


b、 然后我们定义了每种分析物和不同生物复制品的颜色:


 


>colori2=c(“#8faadc”,“#c9a9ff”)


>颜色3=c(“#ff85ff”、“#00b4b5”、“#f8f300”)


 


c、 图形的AESTERIC参数定义如下:


 


图形+几何框线图(aes(x=系数(效应器),y=Rmax,填充=系数(HMA)),位置=位置减淡(宽度=0.9),lwd=0.3,脂肪=4,离群值.shape=21,离群值.size=0.7,异常值。笔划=0)+scale_fill_manual(值=colori2)+主题_bw()+主题(panel.grid.major面板=元素_blank(),面板.网格.次要=元素_blank(),面板.背景=元素矩形(fill=“transparent”),rect=元素矩形(fill=”transparent“)+缩放连续(breaks=seq(0,70,10),limits=c(0,70))+几何点(aes(x=因子(效应器),y=Rmax,fill=factor(HMA),colour=因子(replicate)),position=位置抖动(抖动。宽度=0.7条,抖动。高度=0, dodge.width=0.9),尺寸=0.7,形状=16)+秤颜色手册(值=颜色3)


 


d、 最后,我们使用以下命令生成图形图像:


 


ggsave('P-NK Rmax 35x3.png',宽度=3.5,高度=3,dpi=300,bg=“透明”)


 


e、 生成的图形将与下图类似(图2):


 






图2。生成的Rmax表示结果。每个分析物(HMA A或B)与不同配体(效应器)结合的并排比较框图。每个配体的值的位置分布在X轴上,而计算的%Rmax值在Y轴上表示。中心线代表中值,框限为上下四分位数,晶须延伸至Q1-1.5×四分位间距(IQR)内的最大值,最小值在Q3+1.5×IQR内。所有的数据点都表示为每个生物复制的不同颜色的点(每个生物复制中有三个技术复制)。该图是根据De la Concepcion等人提出的结果生成的。(2019年)。


食谱


 


使用超纯水和分析级试剂制备所有溶液。使用前用0.22µm孔径的过滤器过滤所有溶液。溶液可在室温下储存。


注: 该方案中的缓冲液已经针对特定的蛋白质子集进行了优化。运行缓冲液时,应根据缓冲条件,对感兴趣的蛋白质进行稳定处理。还必须包括相互作用所需的任何成分,例如溶液中是否存在金属。作为一个好的起点,我们建议使用标准浓度为150 mM的NaCl。


 


1.     运行缓冲器


20毫米HEPES pH值7.5


860毫米氯化钠


0.1%吐温®20


2.     0.5毫米NiCl2


a、 在水中溶解0.24 g NiCl2·6H2O,制备10 ml 100 mM NiCl2储备溶液


b、 使用前,将50μl储备液稀释到10 ml水中,制备0.5 mM NiCl2工作溶液


3.     再生液


a、 将6.5 g EDTA溶解于50 ml水中制备0.35 M EDTA


b、 必须用NaOH将pH值调节至8.0,以溶解EDTA


 


致谢


 


该协议已在johninnes中心生物物理分析平台上开发和实现。我们感谢朱莉娅·蒙迪(Julia.e。mundy@gmail.com网站)对于图1中的艺术作品。


班菲尔德实验室的研究得到了BBSRC(拨款BB/J004553、BB/P012574、BB/M02198X)、ERC(提案743165669926)和约翰·因内斯基金会的支持。J、 C.得到了约翰·因内斯基金会/约翰·因内斯中心/塞恩斯伯里实验室/厄勒姆研究所轮换项目的支持。本方案来源于De la Concepcion等人。(2018年和2019年)。


 


相互竞争的利益


 


作者声明没有利益冲突。


 


工具书类


 


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引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Franceschetti, M., Banfield, M. J., Stevenson, C. E. and De la Concepcion, J. (2020). In vitro Assessment of Pathogen Effector Binding to Host Proteins by Surface Plasmon Resonance. Bio-protocol 10(13): e3676. DOI: 10.21769/BioProtoc.3676.
  2. De la Concepcion, J. C., Franceschetti, M., MacLean, D., Terauchi, R., Kamoun, S. and Banfield, M. J. (2019). Protein engineering expands the effector recognition profile of a rice NLR immune receptor. eLife 8: e47713.
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