1. Cell Biology
  2. Plant Biology

Regulation of immune receptor kinase plasma membrane nanoscale organization by a plant peptide hormone and its receptors

  1. Julien Gronnier  Is a corresponding author
  2. Christina M Franck
  3. Martin Stegmann
  4. Thomas A DeFalco
  5. Alicia Abarca
  6. Michelle von Arx
  7. Kai Dünser
  8. Wenwei Lin
  9. Zhenbiao Yang
  10. Jürgen Kleine-Vehn
  11. Christoph Ringli
  12. Cyril Zipfel  Is a corresponding author
  1. Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Switzerland
  2. The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, United Kingdom
  3. Department of Applied Genetics and Cell Biology, University of Natural Resources and Life Sciences Vienna, Austria
  4. FAFU-UCR Joint Center for Horticultural Biology and Metabolomics Center, Haixia, Institute of Science and Technology, Fujian Agriculture and Forestry University, China
https://doi.org/10.7554/eLife.74162
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Cite this article
  1. Julien Gronnier
  2. Christina M Franck
  3. Martin Stegmann
  4. Thomas A DeFalco
  5. Alicia Abarca
  6. Michelle von Arx
  7. Kai Dünser
  8. Wenwei Lin
  9. Zhenbiao Yang
  10. Jürgen Kleine-Vehn
  11. Christoph Ringli
  12. Cyril Zipfel
(2022)
Regulation of immune receptor kinase plasma membrane nanoscale organization by a plant peptide hormone and its receptors
eLife 11:e74162.
https://doi.org/10.7554/eLife.74162
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Abstract

Spatial partitioning is a propensity of biological systems orchestrating cell activities in space and time. The dynamic regulation of plasma membrane nano-environments has recently emerged as a key fundamental aspect of plant signaling, but the molecular components governing it are still mostly unclear. The receptor kinase FERONIA (FER) controls ligand-induced complex formation of the immune receptor kinase FLAGELLIN SENSING 2 (FLS2) with its co-receptor BRASSINOSTEROID-INSENSITIVE 1-ASSOCIATED KINASE 1 (BAK1), and perception of the endogenous peptide hormone RAPID ALKALANIZATION FACTOR 23 (RALF23) by FER inhibits immunity. Here, we show that FER regulates the plasma membrane nanoscale organization of FLS2 and BAK1. Our study demonstrates that akin to FER, leucine-rich repeat (LRR) extensin proteins (LRXs) contribute to RALF23 responsiveness and regulate BAK1 nanoscale organization and immune signaling. Furthermore, RALF23 perception leads to rapid modification of FLS2 and BAK1 nanoscale organization, and its inhibitory activity on immune signaling relies on FER kinase activity. Our results suggest that perception of RALF peptides by FER and LRXs actively modulates plasma membrane nanoscale organization to regulate cell surface signaling by other ligand-binding receptor kinases.

Editor's evaluation

In elegant quantitative live-cell imaging and biochemical experiments, the authors show how activity of the plant immune signaling complex FLS2-BAK1 is affected by nanoscale mobility behaviors mediated through peptide signaling and the receptor kinase FERONIA (FER). Additionally, they are able to define separable roles for FER domains in different biological activities. The details of this work advance our understanding of plant immunity, but also provide generalizable concepts about the roles of nanoscale organization in signaling.

https://doi.org/10.7554/eLife.74162.sa0

Introduction

Multicellular organisms evolved sophisticated surveillance systems to monitor changes in their environment. In plants, receptor kinases (RKs) and receptor proteins (RPs) are the main ligand-binding cell-surface receptors perceiving self, non-self, and modified-self molecules (Hohmann et al., 2017). For example, recognition of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) initiates signaling events, leading to pattern-triggered immunity (PTI) (DeFalco and Zipfel, 2021). The Arabidopsis thaliana (hereafter Arabidopsis) leucine-rich repeat receptor kinases (LRR-RKs) FLS2 and EFR recognize the bacterial PAMPs flagellin (or its derived epitope flg22) and elongation factor-Tu (or its derived epitope elf18), respectively (Gómez-Gómez and Boller, 2000; Zipfel et al., 2006). Both FLS2 and EFR form ligand-induced complexes with the co-receptor BAK1 (a LRR-RK also referred as SERK3) to initiate immune signaling, such as the production of apoplastic reactive oxygen species (ROS), and calcium influx (Chinchilla et al., 2007; Heese et al., 2007; Schulze et al., 2010; Roux, 2011; Sun et al., 2013; Thor et al., 2020).

We previously showed that the Catharanthus roseus RECEPTOR-LIKE PROTEIN KINASE 1-LIKE (CrRLK1L) FERONIA (FER) and the GPI-anchored protein LORELEI-LIKE GPI-ANCHORED PROTEIN 1 (LLG1) are required for flg22-induced FLS2-BAK1 complex formation (Stegmann et al., 2017; Xiao et al., 2019). Notably, the endogenous peptide hormone RALF23 is perceived by a LLG1-FER heterocomplex, which leads to inhibition of flg22-induced FLS2-BAK1 complex formation (Stegmann et al., 2017; Xiao et al., 2019). As such, although FER and LLG1 are positive regulator of PTI, RALF23 is a negative regulator. How these components regulate FLS2-BAK1 complex formation remains however unclear.

Several members of the CrLKL1L family are involved in RALFs perception (Haruta et al., 2014; Ge et al., 2017; Gonneau et al., 2018; Liu, 2021). Among them, FER plays a pivotal role in the perception of several Arabidopsis RALF peptides (Haruta et al., 2014; Stegmann et al., 2017; Gonneau et al., 2018; Zhao et al., 2018; Abarca et al., 2021; Liu, 2021). In addition, cell wall-associated LEUCINE-RICH REPEAT-EXTENSINs (LRXs) proteins are also involved in CrRLK1L-regulated pathways and were shown to bind RALFs with high affinity (Mecchia, 2017; Zhao et al., 2018; Dünser, 2019; Herger et al., 2020; Moussu, 2020). Structural and biochemical analyses indicate that RALF binding by CrRLK1L/LLG complexes and LRXs are mutually exclusive and mechanistically distinct from each other (Xiao et al., 2019; Moussu, 2020). While CrRLK1Ls and LRXs have emerged as important RALF-regulated signaling modules, it is still unknown whether LRXs are also involved in RALF23-mediated regulation of immune signaling.

Plasma membrane lipids and proteins dynamically organize into diverse membrane domains giving rise to fluid molecular patchworks (Gronnier et al., 2018; Ballweg et al., 2020; Jaillais and Ott, 2020). These domains are proposed to provide dedicated biochemical and biophysical environments to ensure acute, specific, and robust signaling events (Gronnier et al., 2019; Jacobson et al., 2019). For instance, FLS2 localizes in discrete and static structures proposed to specify immune signaling (Bücherl et al., 2017). The cell wall is thought to impose physical constraints on the plasma membrane, limiting the diffusion of its constituents (Feraru, 2011; Martinière, 2012). Indeed, alteration of cell wall integrity leads to aberrant protein motions at the plasma membrane (Martinière, 2012; McKenna, 2019). Notably, perturbation of the cell wall affects FLS2 nanoscale organization (McKenna, 2019). Despite its utmost importance, it remains largely unknown how the cell wall and its integrity modulate the organization of the plasma membrane. Interestingly, both CrRLK1Ls and LRXs are proposed cell wall integrity sensors and conserved modules regulating growth, reproduction, and immunity (Franck et al., 2018; Herger et al., 2019). However, their mode of action and potential links between cell wall integrity sensing and RALF perception are still poorly understood.

Here, we show that FER regulates the plasma membrane nanoscale organization of FLS2 and BAK1. Similarly, we show that LRXs contribute to RALF23 responsiveness and regulate BAK1 nanoscale organization and immune signaling. Importantly, our work reveals an unexpected uncoupling of FER and LRX modes of action in growth and immunity. We demonstrate that RALF23 perception leads to rapid modulation of FLS2 and BAK1 nanoscale organization and that its inhibitory activity on immune signaling requires FER kinase activity. We propose that the regulation of the plasma membrane nanoscale organization by RALF23 receptors underscores their role in the formation of protein complexes and initiation of immune signaling.

Results and discussion

FER regulates membrane nanoscale organization of FLS2 and BAK1

We combined variable angle total internal reflection fluorescence microscopy (VA-TIRFM) and single-particle tracking to analyze the lateral mobility of FLS2-GFP in transgenic Arabidopsis lines. Two lines expressing FLS2-GFP under the control of its native promoter were crossed with FER knock-out alleles fer-2 and fer-4. In line with previous reports (Bücherl et al., 2017; Tran et al., 2020), we observed that FLS2-GFP localized to laterally stable foci in wild-type (WT) (Figure 1—video 1). Consistently, FLS2-GFP single-particle trajectories exhibited a confined mobility behavior (Figure 1—figure supplement 1, Figure 1—video 1). Comparative analysis of the diffusion coefficient (D), which describes the diffusion properties of detected single particles (Kusumi et al., 1993), showed that FLS2-GFP was more mobile in fer mutants than in WT (Figure 1—figure supplement 1, Figure 1—figure supplement 2, and Figure 1—video 1). To analyze FLS2-GFP organization, we reconstructed images using a temporal averaging of FLS2-GFP fluorescence observed across VA-TIRFM time series. Furthermore, individual image sections were subjected to kymograph analysis. Using this approach, we found that FLS2-GFP fluorescence was maintained into well-defined and static structures in WT, while it appeared more disperse and more mobile in both fer mutants (Figure 1A and B, Figure 1—figure supplement 2). To substantiate these observations, we used the previously established spatial clustering index (SCI), which describes protein lateral organization (Gronnier et al., 2017; Tran et al., 2020). As expected, SCI of FLS2-GFP was lower in fer-4 than in WT (Figure 1C), indicating disturbance in FLS2-GFP lateral organization.

Figure 1 with 5 supplements see all
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FER regulates the nanoscale organization of FLS2-GFP and BAK1-mCherry.

(A, E) FLS2-GFP and BAK1-mCherry nanodomain organization. Pictures are maximum projection of 20 variable angle total internal reflection fluorescence microscopy (VA-TIRFM) images obtained at 5 frames per second for FLS2-GFP (A) and 10 VA-TIRFM images obtained at 2.5 frames per second for BAK1-mCherry (E) in Col-0 and fer-4 cotyledon epidermal cells. (B, F) Representative kymograph showing lateral organization of FLS2-GFP (B) and BAK1-mCherry (F) overtime in Col-0 and fer-4. (C, G) Quantification of FLS2-GFP (C) and BAK1-mCherry (G) spatial clustering index. Graphs are notched box plots, scattered data points show measurements, colors indicate independent experiments, n = 16 cells for Col-0/pFLS2::FLS2-GFP; n = 31 cells for fer-4/pFLS2::FLS2-GFP, n = 23 cells for Col-0/pBAK1::BAK1-mCherry, n = 18 cells for fer-4/pBAK1::BAK1-mCherry. p-Values report two-tailed nonparametric Mann–Whitney test. (D, H) Graphical illustrations summarizing our observations for FLS2-GFP (D) and BAK1-mCherry (H) nanoscale dynamics.

Figure 1—source data 1

Source data points for the graphs in Figure 1C and G.

https://cdn.elifesciences.org/articles/74162/elife-74162-fig1-data1-v2.xlsx

In Medicago truncatula and yeast, alteration of nanodomain localization has been linked to impaired protein accumulation at the plasma membrane due to increased protein endocytosis (Grossmann et al., 2008; Liang et al., 2018). To inquire for a potential defect in FLS2 plasma membrane accumulation, we observed subcellular localization of FLS2-GFP using confocal microscopy. The analysis revealed a decrease in FLS2-GFP accumulation in fer mutants (Figure 1—figure supplement 3). Whether the proposed role of FER in regulating endocytosis (Yu et al., 2020) accounts for this defect is unknown. Altogether, these results show that FER is genetically required to control FLS2-GFP nanoscale organization and accumulation at the plasma membrane.

To further characterize the impact of FER loss of function in RK organization, we analyzed the behavior of BAK1-mCherry at the plasma membrane. Fluorescence recovery after photobleaching experiments previously suggested that the vast majority of BAK1 molecules are mobile (Hutten et al., 2017). Consistent with this result, BAK1-mCherry was more mobile than FLS2-GFP in the WT (Figure 1—video 2). Given that BAK1 is a common co-receptor for multiple LRR-RK signaling pathways (Hohmann et al., 2017), we hypothesized that BAK1 might dynamically associate with various pre-formed signaling platforms, such as FLS2 nanodomains (Figure 1, Bücherl et al., 2017). Under our experimental conditions, we were not able to perform high-quality single-particle tracking analysis for BAK1-mCherry (Figure 1—video 2, see Materials and methods section). However, visual inspection of particles behavior suggested that BAK1-mCherry was less mobile in fer-4 than in WT (Figure 1—video 2). Accordingly, reconstructed VA-TIRFM images and kymographs showed that BAK1-mCherry fluorescence was more structured and static in fer-4 than in WT (Figure 1F). Furthermore, we observed an increase of BAK1-mCherry SCI in fer-4 (Figure 1G). Confocal microscopy analysis did not reveal significant differences in BAK1-mCherry plasma membrane accumulation between fer-4 and WT (Figure 2—figure supplement 1). Altogether, these data show that loss of FER perturbs FLS2 and BAK1 nanoscale organization, albeit in an opposite manner (Figure 1D and H). Previous reports have similarly shown that altering the composition of the cell wall can lead to opposed effects on the mobility of different proteins. For instance, inhibition of cellulose synthesis increases the mobility of HYPERSENSITIVE-INDUCED REACTION 1 (Daněk et al., 2020) but limits the mobility of LOW-TEMPERATURE-INDUCED PROTEIN 6B (Martinière, 2012; Daněk et al., 2020). Modification of pectin methyl esterification status of the cell wall increases the mobility of FLS2 (McKenna, 2019) but decreases the mobility of FLOTILIN 2 (Daněk et al., 2020). Collectively, these observations suggest that various membrane environments are differentially regulated by the cell wall and the proposed cell wall integrity sensor FER.

LRX3, LRX4, and LRX5 regulate BAK1 nanoscale organization and PTI signaling

LRXs are dimeric, cell wall-localized, high-affinity RALF-binding proteins suggested to monitor cell wall integrity in growth and reproduction (Baumberger et al., 2001; Mecchia, 2017; Dünser, 2019; Herger et al., 2019; Herger et al., 2020; Moussu, 2020). Their extensin domain confers cell wall anchoring, and their LRR domain mediates RALF binding (Herger et al., 2019; Moussu, 2020). Among the Arabidopsis 11-member LRX family, LRX3, LRX4, and LRX5 are the most expressed in vegetative tissues, and the lrx3 lrx4 lrx5 triple mutant (hereafter lrx3/4/5) shows stunted growth and salt hypersensitivity phenotypes reminiscent of fer-4 (Zhao et al., 2018; Dünser, 2019). Therefore, we hypothesized that LRXs also regulate immune signaling. Indeed, co-immunoprecipitation experiments showed that lrx3/4/5 was defective in flg22-induced FLS2-BAK1 complex formation (Figure 2A). Consistently, flg22-induced ROS production was reduced in lrx3/4/5 similar to the levels observed in fer-4 (Figure 2B). In addition, lrx3/4/5 was impaired in elf18-induced ROS production (Figure 2C), suggesting that, as for FLS2-BAK1 complex formation, LRX3/4/5 are required for complex formation between EFR and BAK1. Thus, we conclude that LRX3/4/5 are positive regulators of PTI signaling.

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LRX3, LRX4, and LRX5 regulate pattern-triggered immunity (PTI) and BAK1-mCherry organization.

(A) flg22-induced FLS2-BAK1 complex formation. Immunoprecipitation of FLS2 in Arabidopsis Col-0 and lr3/4/5 seedlings either untreated or treated with 100 nM flg22 for 10 min. Blot stained with Coomassie brilliant blue (CBB) is presented to show equal loading. Western blots were probed with α-FLS2, α-BAK1, or α-FER antibodies. Numbers indicate quantification of BAK1 bands normalized based on the corresponding intensities of FLS2 bands and relative to the control Col-0. Similar results were obtained in at least three independent experiments. (B, C) Reactive oxygen species (ROS) production after elicitation with 100 nM elf18 (B) or 100 nM flg22 (C). Values are means of total photon counts over 40 min. Red crosses and red horizontal lines denote mean and SEM, n = 32. Conditions that do not share a letter are significantly different in Dunn’s multiple comparison test (p<0.0001). (D) BAK1-mCherry nanodomain organization. Pictures are maximum projection images (10 variable angle total internal reflection fluorescence microscopy [VA-TIRFM] images obtained at 2.5 frames per second) of BAK1-mCherry in Col-0, fer-4, and lrx3/4/5 cotyledon epidermal cells. (E) Representative kymograph showing lateral organization of BAK1-mCherry overtime in Col-0, fer-4, and lrx3/4/5. (F) Quantification of BAK1-mCherry spatial clustering index. Graphs are notched box plots, scattered data points show measurements, colors indicate independent experiments, n = 26 cells for Col-0/pBAK1::BAK1-mCherry, n = 31 cells for fer-4/pBAK1::BAK1-mCherry, n = 28 cells for lrx3/4/5/pBAK1::BAK1-mCherry. Conditions that do not share a letter are significantly different in Dunn’s multiple comparison test (p<0.0001). (G) Graphical illustration summarizing our observations for BAK1-mCherry nanoscale dynamics in lrx3/4/5.

Figure 2—source data 1

Source data points for the graphs in Figure 2B, C and F.

https://cdn.elifesciences.org/articles/74162/elife-74162-fig2-data1-v2.xlsx
Figure 2—source data 2

Source blots images for the co-immunoprecipitation (co-IP) in Figure 2A.

https://cdn.elifesciences.org/articles/74162/elife-74162-fig2-data2-v2.pdf

We then asked whether, similar to FER, LRX3/4/5 regulate plasma membrane nanoscale organization. We crossed lines expressing FLS2-GFP and BAK1-mCherry under the control of their respective native promoter with the lrx3/4/5 mutant. However, despite several attempts, we could not retrieve homozygous lrx3/4/5 lines expressing FLS2-GFP. Nonetheless, VA-TIRFM and confocal imaging showed that, like in fer-4, BAK1-mCherry was more organized and more static in lrx3/4/5 (Figure 2D and E, Figure 2—video 1), and that BAK1-mCherry plasma membrane localization was not affected by the loss of LRX3/4/5 (Figure 2—figure supplement 1). Thus, like in fer mutants, perturbation in PTI signaling observed in lrx3/4/5 correlates with alterations of plasma membrane RK organization.

LRX3, LRX4, and LRX5 have been proposed to sequester RALF peptides to prevent internalization of FER and inhibition of its function (Zhao et al., 2018). Following this logic, defects in PTI observed in lrx3/4/5 could be explained by a depletion of FER at the plasma membrane. However, our confocal microscopy analysis and western blotting with anti-FER antibodies indicated that FER accumulation and plasma membrane localization were not affected in lrx3/4/5 (Figure 2—figure supplement 2). Furthermore, VA-TIRFM revealed that FER-GFP transiently accumulated in dynamic foci, independently of LRX3/4/5 (Figure 2—figure supplement 3, Figure 2—video 2). Together, these results suggest that LRX3/4/5 do not prevent RALF association with FER to modulate PTI. Moreover, our results suggest that active monitoring by the proposed cell wall integrity sensors FER and LRXs regulates plasma membrane nanoscale dynamics of RKs.

The ability of LRX3/4/5 to associate with RALF23 in planta (Zhao et al., 2018) prompted us to test whether LRX3/4/5 are required for RALF23 responsiveness. Indeed, LRX3, LRX4, and LRX5 were required for RALF23-induced inhibition of elf18-triggered ROS production (Figure 2—figure supplement 4A). Similarly, we observed a decrease in RALF23-induced seedlings growth inhibition in lrx3/4/5 compared to WT (Figure 2—figure supplement 4B). Altogether, these data show that LRX3/4/5 contribute to RALF23 responsiveness (Figure 2—figure supplement 4C), and that LRXs and FER have analogous functions in regulating PTI.

We next asked whether FER and LRX3/4/5 form a complex. For this, we made use of a truncated version of LRX4 lacking its extensin domain (LRX4ΔE), previously used to assess protein complex formation (Dünser, 2019; Herger et al., 2020). Consistent with previous reports based on transient expression in Nicotiana benthamiana (Dünser, 2019; Herger et al., 2020), co-immunoprecipitation experiments with stable transgenic Arabidopsis showed that FER was constitutively associated with LRX4ΔE-FLAG, and that RALF23 treatment did not modulate this interaction (Figure 2—figure supplement 5). This suggests that direct monitoring of the cell wall mediated by a possible FER-LRX complex (Dünser, 2019; Herger et al., 2019) is not regulated by RALF23. In agreement with structural and biochemical analyses of RALF-binding by CrRLK1Ls/LLGs and LRXs (Moussu, 2020), FER-LLG1 and LRX3/4/5 may form distinct RALF23 receptor complexes. Similar to their roles in pollen tube and root hair growth and integrity (Ge et al., 2017; Mecchia, 2017; Moussu, 2020; Dünser, 2019; Herger et al., 2020), future investigations are thus needed to understand the exact molecular link between RALF-binding LRXs and CrRLK1s.

Functional dichotomy of FER and LRXs in regulating growth and immunity

In line with previous reports, our data show that FER and LRXs can form a complex (Dünser, 2019; Herger et al., 2019, Figure 2—figure supplement 5). Moreover, they are known to associate with the cell wall (Baumberger et al., 2001; Feng, 2018) and are proposed to cooperatively relay its properties (Dünser, 2019; Herger et al., 2019). We thus asked if direct cell wall sensing underlies FER and LRXs function in PTI. In the context of growth and cell expansion, plants overexpressing LRX4ΔE are phenotypically reminiscent of lrx3/4/5 and fer-4 mutants (Dünser, 2019). This dominant negative effect is proposed to be caused by competition of the overexpressed truncated LRX4ΔE with endogenous LRXs and consequent loss of cell wall anchoring (Dünser, 2019). Similarly, overexpression of LRX1ΔE inhibits root hair elongation, phenocopying LRX1/LRX2 loss of function (Herger et al., 2020). By contrast, we observed that LRX4ΔE overexpression did not affect flg22-induced interaction between FLS2 and BAK1 (Figure 3—figure supplement 1A). In good agreement with this notion, overexpression of LRX4ΔE did not affect flg22- nor elf18-induced ROS production (Figure 3—figure supplement 1B and C). To corroborate these results, we tested inhibition of root growth triggered by flg22 treatment. Consistent with the positive role of FER and LRX3/4/5 in PTI, we observed that fer-4 and lrx3/4/5 were hyposensitive to flg22 treatment (Figure 3—figure supplement 1D). By contrast, overexpression of LRX4ΔE did not affect inhibition of root growth by flg22 (Figure 3—figure supplement 1D). In addition, we observed that LRX4ΔE overexpression did not impact RALF23 responsiveness (Figure 3—figure supplement 1E). Altogether, these data suggest that the function of LRX3/4/5 in PTI is distinct from their role during growth.

The ectodomain of FER contains two malectin-like domains, malA and malB (Figure 3A), which share homology with malectin, a carbohydrate-binding protein from Xenopus laevis (Boisson-Dernier et al., 2011). Despite lacking the canonical carbohydrate-binding site of malectin (Moussu, 2018; Xiao et al., 2019), malA and malB were proposed to bind pectin (Feng, 2018; Lin et al., 2021), and FER-mediated cell wall sensing regulates pavement cell and root hair morphogenesis (Duan, 2010; Lin et al., 2021). To investigate if direct cell wall sensing underlies FER’s function in regulating PTI, we used transgenic lines expressing a FER truncated mutant, lacking the malA domain, C-terminally fused to YFP (FER∆malA-YFP) in the fer-4 mutant background (Figure 3B). We observed that FER∆malA-YFP did not complement the cell shape and root hair elongation defects of fer-4 (Figure 3C and D), emphasizing the importance of malA in FER-regulated cell morphogenesis. In contrast, immunoprecipitation assays showed that FER∆malA-YFP fully complemented flg22-induced complex formation between endogenous FLS2 and BAK1 (Figure 3E) as well as ROS production in response to flg22 and elf18 (Figure 3F and G). Altogether, these data suggest that malA-mediated cell wall sensing underlies specific function(s) of FER in regulating growth and cell morphology, but is dispensable for FER’s role in PTI. Interestingly, we observed that expression of FER∆malA-YFP restored inhibition of growth triggered by RALF23, suggesting that malB is sufficient for RALF responsiveness (Figure 3H), as suggested by its physical interaction with RALF23 (Xiao et al., 2019). While we cannot formally exclude the implication of pectin-binding by malB in regulating immunity, the contrasted context-dependent functionality of FER∆malA-YFP suggests that FER’s function in PTI is primarily mediated by RALF perception. Altogether, our data indicate molecular and functional dichotomy of FER and LRXs in regulating growth and immunity.

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FER malectin A domain regulates cell morphogenesis not pattern-triggered immunity (PTI).

(A) Graphical representation of RALF23 perception by FER-LLG1 complex. (B) Morphology of 4-week-old Arabidopsis plants; scale bar indicates 5 cm. (C, D) Confocal microscopy pictures of 5-day-old seedlings cotyledon (C) and root (D) epidermal cells stained with propidium iodide. 3–4 seedlings per genotypes were observed per experiment. For each seedling, we observed the center of both cotyledons, and at the initiation site of root hairs. Similar results were obtained in at least three independent experiments. (E) Flg22-induced FLS2-BAK1 complex formation. Immunoprecipitation of FLS2 in Arabidopsis Col-0, fer-4, and fer-4/p35S::FER∆MalA-YFP seedlings that were either untreated or treated with 100 nM flg22 for 10 min. Blot stained with Coomassie brilliant blue (CBB) is presented to show equal loading. Western blots were probed with α-FLS2, α-BAK1, or α-FER antibodies. Numbers indicate quantification of BAK1 bands normalized based on the corresponding intensities of FLS2 bands and relative to the control Col-0 + flg22. Similar results were obtained in at least three independent experiments. (F, G) Reactive oxygen species (ROS) production after elicitation with 100 nM flg22 (F) or 100 nM elf18 (G). Values are means of total photon counts over 40 min, n = 8. Red crosses and red horizontal lines denote mean and SEM, respectively. Conditions that do not share a letter are significantly different in Dunn’s multiple comparison test (p<0.0001). (H) Fresh weight of 12-day-old seedlings grown in the absence (mock) or presence of 1 µM of RALF23 peptide. Fresh weight is expressed as relative to the control mock treatment for each genotype. Similar results were obtained in at least three independent experiments. Conditions that do not share a letter are significantly different in Dunn’s multiple comparison test (p<0.001).

Figure 3—source data 1

Source data points for the graphs in Figure 3F–H.

https://cdn.elifesciences.org/articles/74162/elife-74162-fig3-data1-v2.xlsx
Figure 3—source data 2

Source blots images for the co-immunoprecipitation (co-IP) in Figure 3E.

https://cdn.elifesciences.org/articles/74162/elife-74162-fig3-data2-v2.pdf

RALF23 alters FLS2 and BAK1 organization and function through active FER signaling

We next asked whether RALF23 activity is mediated by active FER signaling. We used a kinase-dead mutant (FERK565R) C-terminally fused to GFP, expressed in fer knock-out backgrounds (Chakravorty et al., 2018), and selected lines showing comparable accumulation to endogenous FER in WT (Figure 4—figure supplements 1 and 2). Interestingly, we observed that FERK565R-GFP complemented fer’s defect in FLS2-BAK1 complex formation (Figure 4—figure supplement 1A) and PAMP-induced ROS production (Figure 4—figure supplement 1B and C). In contrast, we observed that inhibition of FLS2-BAK1 complex formation by RALF23 depended on FER kinase activity (Figure 4—figure supplement 2B). Similarly, inhibition of elf18-induced ROS production and seedling growth inhibition by RALF23 depended on FER kinase activity (Figure 4—figure supplement 2C). Overall, these data show that inhibition by RALF23 is mediated by active FER signaling while FER’s positive role in immune signaling is kinase activity-independent.

We next asked whether inhibition of FLS2-BAK1 complex formation by RALF23 correlates with a modulation of FLS2 or BAK1 nanoscale organization. VA-TIRFM imaging showed an increase of FLS2-GFP mobility and an alteration of FLS2-GFP nanodomain organization within minutes of RALF23 treatment (Figure 4—figure supplements 3 and 4, Figure 4—videos 1–3, imaging performed 2–30 min post treatment; Figure 4—figure supplement 5). In addition, we observed that RALF23 treatment stabilized BAK1-mCherry nanoscale organization (Figure 4, Figure 4—figure supplement 6, Figure 1—video 2). These data suggest that RALF23 perception leads to rapid modification of FLS2 and BAK1 membrane organization and thereby potentially inhibits their association. In addition, these data, based on short-term RALF23 treatment, demonstrate that the aforementioned defects in FLS2 and BAK1 organization observed in fer and lrx3/4/5 mutant plants are not caused by their pleiotropic growth defects.

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RALF23 perception regulates BAK1-mCherry organization.

(A) BAK1-mCherry nanodomain organization (pBAK1::BAK1-mCherry). Pictures are maximum projection images (10 variable angle total internal reflection fluorescence microscopy [VA-TIRFM] images obtained at 2.5 frames per second) of BAK1-mCherry in Col-0 and fer-4 cotyledon epidermal cells with or without 1 µM RALF23 treatment (2–30 min). (B) Representative kymograph showing lateral organization of BAK1-mCherry overtime in Col-0 and fer-4 with or without 1 µM RALF23 treatment. (C) Quantification of BAK1-mCherry spatial clustering index. Graphs are notched box plots, scattered data points show measurements, colors indicate independent experiments, n = 21 and n = 23 cells for Col-0/pBAK1::BAK1-mCherry with and without RALF23, respectively, n = 20 and n = 21 cells for fer-4/pBAK1::BAK1-mCherry with and without RALF23, respectively. Conditions that do not share a letter are significantly different in Dunn’s multiple comparison test (p<0.0001). (D) Graphical illustration summarizing our observations for BAK1-mCherry nanoscale dynamics upon RALF23 treatment.

Figure 4—source data 1

Source data points for the graph in Figure 4C.

https://cdn.elifesciences.org/articles/74162/elife-74162-fig4-data1-v2.xlsx

Our study unravels the regulation of FLS2 and BAK1 nanoscale organization by the RALF receptors FER and LRX3/4/5 (Figure 4—figure supplement 6). The function of RALF receptors in other processes might similarly rely on the regulation of RK nanoscale dynamics, and the identification of the corresponding regulated RKs is an exciting prospect for future investigation. Further work will be required to decipher how FLS2 and BAK1 associate in a ligand-dependent manner within the plasma membrane and to understand how FER and LRXs control this process. While FER associate with LLG1 to perceive RALF peptides, whether perception of these peptides by LRXs involves additional unknown components remains open. For both FER-LLG1 and LRXs, it will be important in the future to identify the components mediating RALF23 signaling and modification of FLS2 and BAK1 nanoscale dynamics. Because FER-LLG1 and LRX3/4/5 – components of distinct RALFs receptor complexes – are genetically required to control FLS2 and BAK1 nanoscale dynamics, we hypothesize that perception of additional RALF peptides may be involved in regulating this process (Figure 4—figure supplement 6). Plants have evolved coordinated RK protein-protein interaction networks to process extracellular signals into specific responses (Smakowska-Luzan et al., 2018), and thus may have co-evolved mechanisms to regulate these interactions in both space and time. Our results suggest that perception of endogenous peptides by distinct receptor complexes actively modulates the plasma membrane nanoscale organization to regulate cell surface signaling by other RKs.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Arabidopsis thaliana)Col-0/pFLS2::FLS2-GFP#1Göhre et al., 2008See Materials and methods
Genetic reagent (A. thaliana)Col-0/pFLS2::FLS2-GFP#2This paperSee Materials and methods
Genetic reagent (A. thaliana)fer-2/pFLS2::FLS2-GFP#1Stegmann et al., 2017See Materials and methods
Genetic reagent (A. thaliana)fer-4/pFLS2::FLS2-GFP#2This paperSee Materials and methods
Genetic reagent (A. thaliana)fer-4Duan, 2010See Materials and methods
Genetic reagent (A. thaliana)fer-4/pFER::FER-GFPDuan, 2010See Materials and methods
Genetic reagent (A. thaliana)fer-4/pFER::FERKD-GFPChakravorty et al., 2018See Materials and methods
Genetic reagent (A. thaliana)lrx3/4/5Dünser, 2019See Materials and methods
Genetic reagent (A. thaliana)p35S::LRX4ΔE-CitrineDünser, 2019See Materials and methods
Genetic reagent (A. thaliana)p35S::LRX4ΔE-FLAGDünser, 2019See Materials and methods
Genetic reagent (A. thaliana)Col-0/pBAK1::BAK1-mCherryBücherl et al., 2013See Materials and methods
Genetic reagent (A. thaliana)fer-4/ pBAK1::BAK1-mCherryThis paperSee Materials and methods
Genetic reagent (A. thaliana)lrx3/4/5/ pBAK1::BAK1-mCherryThis paperSee Materials and methods
Genetic reagent (A. thaliana)lrx3/4/5/ pFER::FER-GFPThis paperSee Materials and methods
Antibodyanti-FLAG-HRPSigma-AldrichA8592WB (1:4000 dilution)
AntibodyMonoclonal rabbit anti-FLS2Chinchilla et al., 2007WB (1:1000 dilution)
AntibodyPolyclonal rabbit anti-BAK1Roux, 2011WB (1:5000 dilution)
AntibodyPolyclonal rabbit anti-BAK1 pS612Perraki, 2018WB (1:3000 dilution)
AntibodyPolyclonal rabbit anti-FERXiao et al., 2019WB (1:2000 dilution)
AntibodyAnti-rabbit IgG-HRP TrueblotRockland18-8816-31WB (1:10,000 dilution)
Peptide, recombinant proteinFlg22Synthesized by EZBiolab(purity >95%)See Materials and methods
Peptide, recombinant proteinElf18Synthesized by EZBiolab(purity >95%)See Materials and methods
Peptide, recombinant proteinRALF23Synthesized by EZBiolab(purity >95%)See Materials and methods
Chemical compound, drugGFP-Trap agarose beadsChromoTekSee Materials and methods
Chemical compound, drugM2 anti-Flag affinity gelSigma-AldrichA2220-5MLSee Materials and methods
Chemical compound, drugAnti-rabbit Trueblot agarose beadseBioscienceSML1656See Materials and methods
Software, algorithmFijihttps://imagej.net/FijiSee Materials and methods

Plant materials and growth

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A. thaliana ecotype Columbia (Col-0) was used as WT control. The fer-4, fer-4/pFER::FER-GFP (Duan, 2010), fer-4/pFER::FERKD-GFP (Chakravorty et al., 2018), fer-4/p35S::FER∆MalA-GFP (Lin, 2018), Col-0/pFLS2::FLS2-GFP#1 (Göhre et al., 2008), Col-0/pFLS2::FLS2-GFP#2 (this study), fer-2/pFLS2::FLS2-GFP (Stegmann et al., 2017), lrx3/4/5, p35S::LRX4ΔE-Citrine and p35S::LRX4ΔE-FLAG (Dünser, 2019) lines were previously published. Col-0/pFLS2::FLS2-GFP (Göhre et al., 2008) was crossed with fer-4 to obtain fer-4/pFLS2::FLS2-GFP. Col-0/pBAK1::BAK1-mCherry (Bücherl et al., 2013) was crossed with fer-4 and lrx3/4/5 to obtain fer-4/pBAK1::BAK1-mCherry and lrx3/4/5/pBAK1::BAK1-mCherry. fer-4/pFER::FER-GFP was crossed with lrx3/4/5 to obtain fer-4/lrx3/4/5;pFER::FER-GFP. For the VA-TIRFM imaging, we initially used a line expressing pFLS2::FLS2-GFP in fer-2 we previously generated (Stegmann et al., 2017). Despite that both alleles are well-characterized null allele of FER, for consistent and direct comparison of our biochemical, physiological, and imaging experiments, we also crossed another Col-0/pFLS2::FLS2-GFP with fer-4. To further facilitate the single-particle tracking analysis, we choose a Col-0/pFLS2::FLS2-GFP line expressing less FLS2-GFP. For ROS burst assays, plants were grown in individual pots at 20–21°C with a 10 hr photoperiod in environmentally controlled growth rooms. For seedling-based assays, seeds were surface-sterilized using chlorine gas for 5 hr and grown at 22°C and a 16 hr photoperiod on Murashige and Skoog (MS) medium supplemented with vitamins, 1% sucrose and 0.8% agar.

Synthetic peptides and chemicals

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The flg22 (QRLSTGSRINSAKDDAAGLQIA), elf18 (SKEKFERTKPHVNVGTIG), and RALF23 (ATTKYISYGALRRNTVPCSRRGASYYNCRRGAQANPYSRGCSAITRCRR) peptides were synthesized by EZBiolab (USA) with a purity of >95%. All peptides were dissolved in sterile purified water.

ROS burst measurement

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ROS burst measurements were performed as previously documented (Kadota et al., 2014). At least eight leaf discs (4 mm in diameter) per individual genotype were collected in 96-well plates containing sterile water and incubated overnight. The next day the water was replaced by a solution containing 17 μg/mL luminol (Sigma-Aldrich), 20 μg/mL horseradish peroxidase (HRP, Sigma-Aldrich), and the peptides in the appropriate concentration. Luminescence was measured for the indicated time period using a charge-coupled device camera (Photek Ltd., East Sussex, UK). The effect of RALF23 on elf18-triggered ROS production was performed as previously described (Stegmann et al., 2017). 8–10 leaf discs per treatment and/or genotype were collected in 96-well plates containing water and incubated overnight. The following day the water was replaced by 75 µL of 2 mM MES-KOH pH 5.8 to mimic the apoplastic pH. Leaf discs were incubated further for 4–5 hr before adding 75 μL of a solution containing 40 μg/mL HRP, 1 μM L-O12 (Wako Chemicals, Germany), and 2× elicitor RALF peptide solution (final concentration 20 μg/mL HRP, 0.5 µM L-O12, 1× elicitors). ROS production is displayed as the integration of total photon counts.

Root growth inhibition assay

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Three-day-old Col-0, fer-4, lrx3/4/5, and 35S::LRR4-Cit seedlings (n = 9–12) were transferred for additional 3 days to 3 mL liquid ½ MS medium containing different concentrations (100 nM, 300 nM, or 1 µM) of flg22 or the appropriate amount of solvent. The seedlings were then placed on solid MS plates before scanning. Root length was measured using ImageJ.

Live-cell imaging

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For confocal microscopy and VA-TIRF microscopy experiments, surface-sterilized seeds were individually placed in line on square Petri dishes containing 1/2 MS 1% sucrose, 0.8% phytoagar, stratified 2 days in the dark at 4°C, then placed in a growth chamber at 22°C and a 16 hr photoperiod for 5 days. Seedlings were mounted between a glass slide and a coverslip in liquid 1/2 MS, 1% sucrose medium. For VA-TIRF microscopy experiments, 2–4 seedlings were sequentially imaged for each genotype and/or condition. To test the effect of RALF23 on FLS2-GFP dynamics and nanodomain organization, seedlings were preincubated in 2 mM MES-KOH pH 5.8 for 3–4 hr prior treatment. Seedlings were imaged 2–30 min after treatment.

Confocal laser scanning microscopy (CLSM)

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Confocal microscopy was performed using a Leica SP5 CLSM system (Leica, Wetzlar, Germany) equipped with Argon, DPSS, He-Ne lasers, hybrid detectors, and using a 63 × 1.2 NA oil immersion objective. GFP was excited using 488 nm argon laser, and emission wavelengths were collected between 495 and 550 nm. mCherry was excited using 561 nm He/Ne laser, and emission wavelengths were collected between 570 and 640 nm. Propidium iodide was imaged using 488 nm and 500–550 nm excitation and emission wavelengths, respectively. In order to obtain quantitative data, experiments were performed using strictly identical confocal acquisition parameters (e.g., laser power, gain, zoom factor, resolution, and emission wavelengths reception), with detector settings optimized for low background and no pixel saturation. Pseudo-color images were obtained using look-up-table (LUT) of Fiji software (Schindelin et al., 2012).

Total internal reflection fluorescence (TIRF) microscopy

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TIRF microscopy was performed using an inverted Leica GSD equipped with a ×160 objective (NA = 1.43, oil immersion), and an Andor iXon Ultra 897 EMCCD camera. Images were acquired by illuminating samples with a 488 nm solid-state diode laser set at 15 mW using a cube filter with an excitation filter 488/10 and an emission filter 535/50 for FLS2-GFP and FER-GFP. Optimum critical angle was determined as giving the best signal-to-noise for our sample and was kept fixed for each experiment. Images time series were recorded at 20 frames per second (50 ms exposure time) for Figure 1—figure supplement 2 and Figure 4—figure supplements 3 and 4; 5 frames per second for Figure 1A–C and Figure 2—figure supplement 3. To observe BAK1-mCherry, we could only use a 532 nm solid-state diode laser (ca. 40% of maximum excitation for mCherry) using a cube filter with an excitation filter 532/10 and an emission filter 600/100. To obtain a sufficient signal-to-noise ratio, image time series were recorded at 2.5 frames per second (Figures 1, 2 and 4). Due to apparent high mobility of BAK1 and relatively slow acquisition rate, we could not asses with confidence the identity of fluorescent particles from one time frame to another and therefore did not perform particle tracking analysis of BAK1-mCherry. VA-TIRFM images were subjected to background subtraction (30 rolling pixel radius) and smoothing. Kymographs were generated using Orthogonal views in Fiji (Schindelin et al., 2012).

Single-particle tracking analysis

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To analyze single-particle tracking experiments, we used the plugin TrackMate 2.7.4 (Tinevez et al., 2017) in Fiji (Schindelin et al., 2012). Single particles were segmented frame-by-frame by applying a Laplacian of Gaussian (LoG) filter and estimated particle size of 0.4 μm. Individual single particles were localized with sub-pixel resolution using a built-in quadratic fitting scheme. Then, single-particle trajectories were reconstructed using a simple linear assignment problem (Jaqaman et al., 2008) with a maximal linking distance of 0.4 μm and without gap closing. Thresholds were empirically determined for optimal single-particle detection and linking. Only tracks with at least 10 successive points (tracked for 500 ms) were selected for further analysis. Diffusion coefficients of individual particles were determined using TraJClassifier (Wagner et al., 2017). For each particle, the slope of the first four time points of their mean square displacement (MSD) plot was used to calculate their diffusion coefficient according to the following equation: MSD = (x – x0)2 + (y – y0)2 and D = MSD/4t, where x0 and y0 are the initial coordinates, and x and y are the coordinates at any given time, and t is the time frame.

Quantification of SCI

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Genotype and/or treatment-dependent variation in fluorescence intensity of FLS2-GFP and fluorescence pattern of FLS2-GFP and BAK1-mCherry compromised the use of a unique set of parameters to compute nanodomain size and density across the different experiments. To uniformly quantify differences in membrane organization of both FLS2 and BAK1 across all experiments, we used the SCI that was shown to be largely insensitive to variation in fluorescence intensity (Gronnier et al., 2017). Quantifications were performed as previously described (Gronnier et al., 2017). Briefly, fluorescence intensity was plotted along an 8-µm-long line on maximum projection of VA-TIRFM images. Three plots were randomly recorded per cell and at least eight cells per condition per experiment were analyzed. For each line plot, the SCI was calculated by dividing the mean of the 5% highest values by the mean of 5% lowest values. Because the absence of correlation between fluorescence intensity and SCI was assessed on confocal microscopy images and for a single protein (Gronnier et al., 2017), we tested whether this was also the case in our experimental conditions. Indeed, we consistently observed poor to no correlation between variation in fluorescence intensity and values of SCI (Figure 4—figure supplement 7).

Co-immunoprecipitation experiments

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20–30 seedlings per plate were grown in wells of a 6-well plate for 2 weeks, transferred to 2 mM MES-KOH, pH 5.8, and incubated overnight. The next day flg22 (final concentration 100 nM) and/or RALF23 (final concentration 1 µM) were added and incubated for 10 min. Seedlings were then frozen in liquid N2 and subjected to protein extraction. To analyze FLS2-BAK1 receptor complex formation, proteins were isolated in 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 10% glycerol, 5 mM dithiothreitol, 1% protease inhibitor cocktail (Sigma-Aldrich), 2 mM Na2MoO4, 2.5 mM NaF, 1.5 mM activated Na3VO4, 1 mM phenylmethanesulfonyl fluoride, and 0.5% IGEPAL. For immunoprecipitations, α-rabbit Trueblot agarose beads (eBioscience) coupled with α-FLS2 antibodies (Chinchilla et al., 2007) or GFP-Trap agarose beads (ChromoTek) were used and incubated with the crude extract for 3–4 hr at 4°C. Subsequently, beads were washed three times with wash buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM phenylmethanesulfonyl fluoride, 0,1% IGEPAL) before adding Laemmli sample buffer and incubating for 10 min at 95°C. Analysis was carried out by SDS-PAGE and immunoblotting. To test the association between Flag-LRX4 and FER, total protein from 60 to 90 seedlings per treatment per genotype was extracted as previously described. For immunoprecipitations, M2 anti-Flag affinity gel (Sigma A2220-5ML) was used and incubated with the crude extract for 2–3 hr at 4°C. Subsequently, beads were washed three times with wash buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM phenylmethanesulfonyl fluoride, 0.1% IGEPAL) before adding Laemmli sample buffer and incubating for 10 min at 95°C. Analysis was carried out by SDS-PAGE and immunoblotting. The replicates of the co-immunoprecipitation are presented in Figure 4—figure supplement 8.

Immunoblotting

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Protein samples were separated in 10% bisacrylamide gels at 150 V for approximately 2 hr and transferred into activated PVDF membranes at 100 V for 90 min. Immunoblotting was performed with antibodies diluted in blocking solution (5% fat-free milk in TBS with 0.1% [v/v] Tween-20). Antibodies used in this study were α-BAK1 (1:5000; Roux, 2011), α-FLS2 (1:1000; Chinchilla et al., 2007), α-FER (1:2000; Xiao et al., 2019), α-BAK1 pS612 (1:3000; Perraki, 2018), α-FLAG-HRP (Sigma-Aldrich, A8592, dilution 1:4000), and α -GFP (sc-9996, Santa Cruz, used at 1:5000). Blots were developed with Pierce ECL/ECL Femto Western Blotting Substrate (Thermo Scientific). The following secondary antibodies were used: anti-rabbit IgG-HRP Trueblot (Rockland, 18-8816-31, dilution 1:10,000) for detection of FLS2-BAK1 co-immunoprecipitation or anti-rabbit IgG (whole molecule)–HRP (A0545, Sigma, dilution 1:10,000) for all other western blots.

Statistical analysis

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Statistical analyses were carried out using Prism 6.0 software (GraphPad). As mentioned in the figure legends, statistical significances were assessed using nonparametric Kruskal–Wallis bilateral tests combined with post-hoc Dunn’s multiple pairwise comparisons or using a two-way nonparametric Student’s t-test Mann–Whitney test.

Accession numbers

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FER (AT3G51550), LRX3 (AT4G13340), LRX4 (AT3G24480), LRX5 (AT4G18670), RALF23 (AT3G16570), FLS2 (AT5G46330), BAK1 (AT4G33430).

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

The following previously published data sets were used

References

Decision letter

  1. Dominique C Bergmann
    Reviewing Editor; Stanford University, United States
  2. Jonathan A Cooper
    Senior Editor; Fred Hutchinson Cancer Research Center, United States
  3. Jan Petrášek
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Regulation of immune receptor kinases plasma membrane nanoscale landscape by a plant peptide hormone and its receptors" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Jonathan Cooper as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Jan Petrášek (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

From the written reviews and discussion, there was much enthusiasm for the work, but two general concerns emerged: (1) replicated data were not fully analyzed and reported and (2) the ability of a broad audience to engage with and appreciate the detailed work would be enhanced by modifications to the text. From the individual reviews appended below you will find specific examples of places where modifying text or presentation would have the largest impact.

1) That "similar results were found among three replicates" is mentioned in nearly every figure, but these replicates are not shown. After a healthy discussion among the reviewers about what was essential to show, all three reviewers concurred that in the cases of single particle tracking and the quantitative imaging of mobility (reported, for example, in Figures 1C, G; 2F; 4C), the data from the three replicates should be analyzed and the differences between experiments reported. This is in part because this paper has the potential to serve as a standard bearer for this technique.

In the case of Western blots, showing a single representative blot was fine, but the band intensities should be quantified using standard densitometry scans. We also encourage the authors to include the replicate, uncropped, blots in a data supplement. For the confocal images of leaves in Figure 3C-D, the single set of images is also fine, but additional details about what constitutes a replicate (see comment from Rev 1) is needed. For other phenotypic measurements like seedling fresh weight, reporting the single replicate is fine.

2) A weak experimental point is the examination of protein accumulation on the plasma membrane – which is used to show that the nanodomain results in the stabilization of FLS on the membrane, preventing them from being endocytosed. Here single channel images are used to determine the amount of protein based on fluorescent intensity without further controls (e.g. molecular, biochemical, internal signal controls, to confirm the overall amount of protein in the different lines). Additionally, it is not shown whether fer mutants have altered endocytosis in general. Please either include supporting experiments or modify the text to indicate the limitations of the current study.

3) The manuscript is very detailed as it described the nanoscale localization of receptor kinases, and the jargon and extensive use of abbreviations makes it challenging for readers outside the plant immune signaling world. Additional discussion and presentation of these findings in an integrative model would help to make the details illustrative of general signaling rules and would broaden the impact of this work. In addition, the phrasing of the abstract that includes mention of a "plasma membrane nanoscale landscape" may lead one to expect that paper will focus on the "landscape" of the PM and the structural changes in the PM provoked by the activity of individual receptor kinases. However, the main focus in on the complex dynamics of individual receptor protein kinases with the PM – no less interesting, but not completely aligned with the abstract.

4) Many of the experimental details or choices are not fully explained; for example, FER effects on cell wall integrity are mostly known from root hair work, but the experiments here focus on cotyledon epidermal cells. Please add some rationale for experimental design choices, be clear about what a "sample" or "replicate" entails, and mention potential limitations of the current work.

Reviewer #1:

How does a protein identified as having a role in many different and seemingly independent plant responses act in specific ways in those responses? An example in plants is the receptor kinase FERONIA (FER) that has been implicated in numerous processes including cell wall integrity signaling and response to biotic and abiotic stresses. Using a series of cell biological and biochemical approaches, the authors show, using the intensively studied FLS2-BAK1 immune receptor complex and its previous identified ligands and signaling potentiators or inhibitors, that different domains and activities of FER have discrete roles in different cellular events. They further show that FER and LRX proteins may affect the mobility of plasma membrane proteins in their nanodomains, though precisely how loss of FER or LRX leads to changes in nanodomain properties is not entirely clear. This detailed work adds interesting and important information about how individual proteins and cellular milieus can alter signaling. Additional discussion and presentation of these findings is an integrative model would help to make the details illustrative of general signaling rules and would broaden the impact of this work outside of the plant immunity niche.

From the set-up of this paper, I imagined that the authors would show a unified way in which FER and/or LRXs affected a cellular feature (e.g. nanodomain formation) and this would account for multiple activities. The results of their experiments, however, showed that they can separate activities of FER (e.g. malectin A domain being involved in cell morphogenesis but not PTI whereas FER kinase activity is not required for PTI). I am left not quite knowing how direct FER activity is on FLS2-BAK1 behaviors. I was expecting it to be indirect through some nanodomain organization, but I failed to find this thread followed through. The direct effects of FER are also interesting results, but as an outsider to the PTI and cell wall integrity fields, I find it challenging to synthesize the results into a general overall model. I don't think the authors need more experiments, but I do think a figure that summarizes their updated concept of how FER works would make this paper accessible and appreciated by a wider audience.

Reviewer #2:

This paper provides insight into the molecular mechanisms which mediate a plants immune response. Specifically looking at the dynamic organization of nano-domains of immune receptors, where the role of hormone peptides, co-receptors and the cell wall upon this domain are examined. This thus provides insights into overall plant physiology and into the finer details of membrane dynamic organization and signaling hubs. The authors use a range of experimental approaches (genetic, biochemical, live imaging) to show that FER regulates the formation of the FLS2-BAK1 complex, that additional receptors also regulate the stabilization these complexes on membrane as 'nano-domains'. Furthermore, they show that a domain of FER that regulates cell shape is dispensable for the immune response and RALF23 (a ligand for FER) also stabilizes BAK into nanodomains.

The story presented shows a logical progression and is based upon the results presented.

The major strength of the paper is the methods used. For example, high resolution imaging of the cell surface is utilized to directly visualize the dynamics of these proteins, and this is coupled with single particle tracking analysis, thus providing an accurate picture of these domains and their dynamics. Genetic lines are used to specifically examine the functionality of the proteins and their domains.

However, in order to improve the manuscript, I believe the authors should provide further details about the methods used and analysis details. For example, at present many of the figures are presented with statistical analysis to compare experimental conditions, but in the figure legends they state 'similar results were obtained in three independent experiments'. To present the data as robustly as possible, I suggest that the authors should show and perform statistical analysis on these similar experiments. Thus, providing the readers with a chance to evaluate how robust the effect is and to understand how variable the experiments were.

A weak experimental point is the examination of protein accumulation on the plasma membrane – which is used to show that the nanodomain results in the stabilization of FLS on the membrane, preventing them from being endocytosed. Here single channel images are used to determine the amount of protein based on fluorescent intensity without further controls (e.g. molecular, biochemical, internal signal controls, to confirm the overall amount of protein in the different lines). Additionally, it is not clarified if the fer mutants used have altered endocytosis in general.

The legends/methods need further details. For example, in figure 1 supplemental 1, it is not clear what the data points are; are they individual tracks, or cells from the same plant? Thus, it is important to clarify further how the analyses was conducted (ie, what the data points plotted are, further details on Ns/repeats).

I am not sure 'propensity' is the appropriate word, perhaps 'property', line 23.

In order to appeal to non-specialists, and to aid the readers comprehension, the authors should consider introducing less abbreviations and focusing on only the critical ones. For example, just in the 1st introduction paragraph (lines 41-55) there are 9, thus at present, it is quite hard to follow the text.

The authors should change the word static to describe the FLS foci on the cell surface (line 108). The foci are not static as they appear and disappear over time, thus they should consider using the terminology, 'laterally stable foci' or something similar.

As much of the analysis of live imaging relies on trackMate, and while the authors detail the settings used, there is no information about how the threshold values was selected. This is important as for example, during the videos, there is bleaching during acquisition which could result in the shortening of tracks. Furthermore, while I understand that is visually easy to show these results with kymographs, the authors should include a histogram of the foci spot lifetimes (as they have already tracked the spots) to more robustly depict the data.

In general, while the videos with tracking are a great addition to the manuscript, at present the fact that the tracks remain after the spots have disappeared is distracting and makes it hard to see the dynamics of the foci. It should be simple enough to change the videos with trackMate (it is just a case of changing the track display mode to 'show local tracks' and play with the 'show track depth' option), which would greatly improve the usefulness of the tracking videos.

The western blots should be quantified to show the results are robust and reproducible. And there are some signals which appear to be saturated.

While I understand the focus is on the FLS BAK dynamics, I think it would be interesting to show how specific this interaction is for mediating the formation of the nanodomains. For example, by examining another receptor or cargo in the mutant lines it would tell us if FER is a general nanodomain scaffold protein.

Line 114 – authors should state how many frames were combined to create the average projections.

There are no scale bars on the kymographs, so it's impossible to know the duration of imaging/tracks/nanodomains.

Line 123 – I think the authors mean formation/composition and not localization.

Line 145 – reference to figure needs updating.

Line 188 – should be mobile rather than labile.

For the figures showing a single track as a model, it would be good in include a scale bar to allow the reader to understand the scale of these diffusions/domains.

Line 200 – 'deleted' should be mutated, truncated or altered.

206 – should be '…can directly monitor the cell wall.'

219 – co-jointly should be rewritten to say, '..and together they relay..'.

Figure 3 – it would be good to quantify these effects to show how reproducible they are. Maybe for cotyledon – a line profile across the image to show the cell is more wavey? And a density for root hairs over a certain length?

If possible, it would be a great addition to the paper to show that dual dynamics of FLS and BAK in the different experimental conditions.

Line 869 – reference to figure needs updating.

Reviewer #3:

The manuscript of Gronnier et al. (23-09-2021-RA-eLife-74162) brings an original set of data describing the role of FER receptor kinase in the control of PM organization and dynamics of plant immune receptor kinases FLS2 and its co-receptor BAK1. Using an advanced fluorescence microscopy approach combined with biochemistry and molecular biology, authors show how the perception of plant peptide hormone RALF23 triggers a specific changes in the distribution of receptor kinases FLS2 and BAK1 in cells at the surface of cotyledons (epidermis of 5-day-old young seedlings). Moreover, these changes are shown to be regulated also by other other receptors of FER, i.e. LRX3, LRX4 and LRX5. The formation of the immune receptor kinase complexes with downstream signalling are therefore suggested to be under the control of RALF23 peptide hormone through the action of both FER and LRXs. Moreover, the data allowed authors to conclude that the effects on root hair growth and immune response are uncoupled in the case of both FER and LRXs receptor pathways and that the kinase activity of FER is needed for the inhibitory effect of RALF23 on the immune response, while the role of FER kinase in the pattern recognition receptors-triggered immunity is kinase-independent. The manuscript is very detailed, bringing the description of the nanoscale localization of receptor kinases; however, the biology behind the observed effects is still a point for future research. Results are basically supported by data, although I have some comments on their presentation.

Strengths

This work combines in a very effective way advanced fluorescence microscopy, biochemistry and molecular genetics, this all in a well-established model of Arabidopsis thaliana. I really appreciate the level of microscopy details that were possible to perform in a quantitative way. For sure, the microscopy approach shown here is very important for any future work in this field and in numerous technical aspects, it truly paves the road for other researchers.

It is very important that there are more elements of the pathway analyzed in one experimental/observation setup. Without considering both FER and LRXs and evaluating them separately, it would not be possible to conclude on the extent of the changes in the nanoscale organization of RKs involved in the RALF23-controlled pathway. Of course, such work is very technology-demanding and time-consuming.

Weaknesses

I feel that this report needs more attention to the biology itself. For the broader community, it would be perfect to understand in what process the mechanism described here is crucial. Therefore, I feel that authors would much improve this manuscript if they would be able to defend why they use epidermal lobed cells in 5-day-old seedlings. I know that there might be plenty of technical reasons, previous work, etc., but biologists would ask about it; considering that effects on cell growth are shown in root hairs, while all immune responses are studied in cotyledon epidermal lobed cells. The introduction on why it is actually so important to study described processes in cotyledons would help.

Perhaps I am wrong, but the "plasma membrane nanoscale landscape", as mentioned in the last sentence of the abstract, is related to the nanoscale organization of receptor kinases studied here, not the "landscape" of the PM itself. Of course, PM is extremely dynamic, but this manuscript is not focused on the understanding of PM structural changes provoked by the activity of individual receptor kinases. It is rather focused on surprisingly complex dynamics of individual receptor protein kinases with the PM. This I feel needs to be presented in a clearer form.

Statistics is provided for the majority of analyses. However, authors mention in numerous cases (at least in 17 analyses) that "similar results were obtained in three independent experiments". I think that in the case of quantifications of microscopy images, it would be perfect to understand how observed differences in the dynamics of receptor kinases are robust when analyzed in these three mentioned biological repetitions. It would also be informative to include some rationale on the selection of cells for the analysis, e.g. was the size the criterion or something else?

For a broader community of readers, it might be perhaps better to introduce a bit what is that „peptide hormone". I know that authors are very deeply involved in the RALF23 and often simply call this molecule „peptide". However, in the title of this manuscript, the term “peptide hormone" is used, but, the word "hormone" is not used in the manuscript at all. For broader community, this is a bit difficult to follow.

I think that for sure the biological implication of this work would be enhanced if data from biological repetitions mentioned in the text would be involved.

Kymographic analyses are not described in methods nor in captions. Axes of kymograms shown in the manuscript are not annotated; therefore it is not clear how actually dynamic the processes are. Time scale would help here.

In the Figure 1, suppl. Figure 1 the caption is not mentioning the statistics used in this analysis.

Line 877 – subscript should be used for numbers in chemical formulas.

VA-TIRFM is mentioned by authors as the main microscopy method used in this contribution. I hope I got it right, therefore, the abbreviation TIRFM in all main and supplementary captions should be changed to VA-TIRFM, as well as in the description of the microscopy itself (lines 807 and 862).

Line 869 – the reference to the suppl. image is not correct, it should not be Sup Figure 15, but Figure Suppl 6.

The quality of language is very good, however, there are some subtle grammar issues, e.g. on line 136 – „BAK1 might dynamically associates with", I found also some typos etc (line 831, the sentence should begin with a capital letter). I did not have time to find all of them, I encourage authors to check it again.

In vivo advanced fluorescence GSD microscopy is used here and I appreciate a lot this technique and how it is implemented. It would be perhaps good to discuss how far individual markers characterize the mobility of the structure where it is located (PM, cell wall, cytoskeleton, etc.) and how far this technique might be taken as the characterization of the mobility of the particular molecule within the particular structure.

https://doi.org/10.7554/eLife.74162.sa1

Author response

Essential revisions:

From the written reviews and discussion, there was much enthusiasm for the work, but two general concerns emerged: (1) replicated data were not fully analyzed and reported and (2) the ability of a broad audience to engage with and appreciate the detailed work would be enhanced by modifications to the text. From the individual reviews appended below you will find specific examples of places where modifying text or presentation would have the largest impact.

1) That "similar results were found among three replicates" is mentioned in nearly every figure, but these replicates are not shown. After a healthy discussion among the reviewers about what was essential to show, all three reviewers concurred that in the cases of single particle tracking and the quantitative imaging of mobility (reported, for example, in Figures 1C, G; 2F; 4C), the data from the three replicates should be analyzed and the differences between experiments reported. This is in part because this paper has the potential to serve as a standard bearer for this technique.

We now present data of individual replicate experiment as well as their statistical analysis.

In the case of Western blots, showing a single representative blot was fine, but the band intensities should be quantified using standard densitometry scans. We also encourage the authors to include the replicate, uncropped, blots in a data supplement. For the confocal images of leaves in Figure 3C-D, the single set of images is also fine, but additional details about what constitutes a replicate (see comment from Rev 1) is needed. For other phenotypic measurements like seedling fresh weight, reporting the single replicate is fine.

We have quantified the bands intensities (values shown below each line) of the western blots. We present replicate experiments, and corresponding uncropped blots in supplementary data. We also clarified what constitutes a replicate for Figure 3C and D.

2) A weak experimental point is the examination of protein accumulation on the plasma membrane – which is used to show that the nanodomain results in the stabilization of FLS on the membrane, preventing them from being endocytosed. Here single channel images are used to determine the amount of protein based on fluorescent intensity without further controls (e.g. molecular, biochemical, internal signal controls, to confirm the overall amount of protein in the different lines). Additionally, it is not shown whether fer mutants have altered endocytosis in general. Please either include supporting experiments or modify the text to indicate the limitations of the current study.

We think that the use of single channel images to analyze the amount of tagged proteins is well-suited to quantify protein accumulation at the plasma membrane. Indeed, it has previously been used to robustly quantify FLS2 plasma membrane accumulation (e.g. Göhre et al., Curr. Biol. 2008; Smith et al., PLoS Pathog. 2014; Wang et al., New Phytologist 2020), and was shown to reveal defect in protein accumulation that could otherwise only be detected by tedious biochemically purification of the plasma membrane (e.g. Collins et al., Plant Physiol. 2020). Furthermore, VA-TIRM experiments consistently showed a decrease in FLS2-GFP accumulation at the plasma membrane (Figure 1; Figure 1 – supplemental figure 2 and Figure 4 – supplemental figure 4). Similar to previous studies, we observed that compromised in nanodomain organization correlates with a defect in plasma membrane accumulation, but we do not claim that these observations demonstrate causality. As suggested by Reviewer 2, we now also discuss the potential involvement of the proposed regulation of endocytosis by FER (Yu et al., Development 2020).

3) The manuscript is very detailed as it described the nanoscale localization of receptor kinases, and the jargon and extensive use of abbreviations makes it challenging for readers outside the plant immune signaling world. Additional discussion and presentation of these findings in an integrative model would help to make the details illustrative of general signaling rules and would broaden the impact of this work. In addition, the phrasing of the abstract that includes mention of a "plasma membrane nanoscale landscape" may lead one to expect that paper will focus on the "landscape" of the PM and the structural changes in the PM provoked by the activity of individual receptor kinases. However, the main focus in on the complex dynamics of individual receptor protein kinases with the PM – no less interesting, but not completely aligned with the abstract.

We reduced the number of abbreviations. We now further discuss our findings and present a model. We agree that the word ‘landscape’ would imply a broader investigation of the properties of the plasma membrane, and therefore replaced landscape by organization in the abstract and in the title.

4) Many of the experimental details or choices are not fully explained; for example, FER effects on cell wall integrity are mostly known from root hair work, but the experiments here focus on cotyledon epidermal cells. Please add some rationale for experimental design choices, be clear about what a "sample" or "replicate" entails, and mention potential limitations of the current work.

We now further explain experimental details, and clarified the meaning of sample and replicate in the figure legends and in the method section.

FERONIA function in cell wall integrity is well described in cotyledon epidermal cells and was linked to cell morphogenesis (e.g. Lin et al., bioRxiv 2018; Lin et al., Curr. Biol. 2021) and mechano-sensing (Malivert et al., PLoS Biol. 2021; Tang et al., Curr. Biol. 2021). We recapitulated observations related to cell morphogenesis in our laboratory conditions (Figure 3 C,D) to provide a clear comparative analysis with the immune signaling outputs.

Reviewer #1:

How does a protein identified as having a role in many different and seemingly independent plant responses act in specific ways in those responses? An example in plants is the receptor kinase FERONIA (FER) that has been implicated in numerous processes including cell wall integrity signaling and response to biotic and abiotic stresses. Using a series of cell biological and biochemical approaches, the authors show, using the intensively studied FLS2-BAK1 immune receptor complex and its previous identified ligands and signaling potentiators or inhibitors, that different domains and activities of FER have discrete roles in different cellular events. They further show that FER and LRX proteins may affect the mobility of plasma membrane proteins in their nanodomains, though precisely how loss of FER or LRX leads to changes in nanodomain properties is not entirely clear. This detailed work adds interesting and important information about how individual proteins and cellular milieus can alter signaling. Additional discussion and presentation of these findings is an integrative model would help to make the details illustrative of general signaling rules and would broaden the impact of this work outside of the plant immunity niche.

From the set-up of this paper, I imagined that the authors would show a unified way in which FER and/or LRXs affected a cellular feature (e.g. nanodomain formation) and this would account for multiple activities. The results of their experiments, however, showed that they can separate activities of FER (e.g. malectin A domain being involved in cell morphogenesis but not PTI whereas FER kinase activity is not required for PTI). I am left not quite knowing how direct FER activity is on FLS2-BAK1 behaviors. I was expecting it to be indirect through some nanodomain organization, but I failed to find this thread followed through. The direct effects of FER are also interesting results, but as an outsider to the PTI and cell wall integrity fields, I find it challenging to synthesize the results into a general overall model. I don't think the authors need more experiments, but I do think a figure that summarizes their updated concept of how FER works would make this paper accessible and appreciated by a wider audience.

We thank the reviewer for her/his positive comments. We now further discuss our findings and present a model illustrating main findings and potential future directions.

Reviewer #2:

[…] In order to improve the manuscript, I believe the authors should provide further details about the methods used and analysis details. For example, at present many of the figures are presented with statistical analysis to compare experimental conditions, but in the figure legends they state 'similar results were obtained in three independent experiments'. To present the data as robustly as possible, I suggest that the authors should show and perform statistical analysis on these similar experiments. Thus, providing the readers with a chance to evaluate how robust the effect is and to understand how variable the experiments were.

The manuscript now includes the data and statistical analysis of the independent experiments.

A weak experimental point is the examination of protein accumulation on the plasma membrane – which is used to show that the nanodomain results in the stabilization of FLS on the membrane, preventing them from being endocytosed. Here single channel images are used to determine the amount of protein based on fluorescent intensity without further controls (e.g. molecular, biochemical, internal signal controls, to confirm the overall amount of protein in the different lines). Additionally, it is not clarified if the fer mutants used have altered endocytosis in general.

Please see our answer to editorial comment #2.

The legends/methods need further details. For example, in figure 1 supplemental 1, it is not clear what the data points are; are they individual tracks, or cells from the same plant? Thus, it is important to clarify further how the analyses was conducted (ie, what the data points plotted are, further details on Ns/repeats).

We now further detail the legends regarding sample size and to what correspond the data points for each graph.

I am not sure 'propensity' is the appropriate word, perhaps 'property', line 23.

We think that propensity better reflect the dynamic aspect of spatial partitioning.

In order to appeal to non-specialists, and to aid the readers comprehension, the authors should consider introducing less abbreviations and focusing on only the critical ones. For example, just in the 1st introduction paragraph (lines 41-55) there are 9, thus at present, it is quite hard to follow the text.

We have reduced the number of abbreviations and present a table summarizing them.

The authors should change the word static to describe the FLS foci on the cell surface (line 108). The foci are not static as they appear and disappear over time, thus they should consider using the terminology, 'laterally stable foci' or something similar.

FLS2 foci are now described as laterally stable foci.

As much of the analysis of live imaging relies on trackMate, and while the authors detail the settings used, there is no information about how the threshold values was selected. This is important as for example, during the videos, there is bleaching during acquisition which could result in the shortening of tracks. Furthermore, while I understand that is visually easy to show these results with kymographs, the authors should include a histogram of the foci spot lifetimes (as they have already tracked the spots) to more robustly depict the data.

The thresholds were empirically determined for optimal single particle detection and linking. We do not consider foci spot lifetimes as a relevant metric to describe the lateral organization/dynamics of particles, which is the focus of our study.

In general, while the videos with tracking are a great addition to the manuscript, at present the fact that the tracks remain after the spots have disappeared is distracting and makes it hard to see the dynamics of the foci. It should be simple enough to change the videos with trackMate (it is just a case of changing the track display mode to 'show local tracks' and play with the 'show track depth' option), which would greatly improve the usefulness of the tracking videos.

To the contrary, we think that full tracks better reflect the behavior of the single particles.

The western blots should be quantified to show the results are robust and reproducible. And there are some signals which appear to be saturated.

We now provide quantification of the western blots.

While I understand the focus is on the FLS BAK dynamics, I think it would be interesting to show how specific this interaction is for mediating the formation of the nanodomains. For example, by examining another receptor or cargo in the mutant lines it would tell us if FER is a general nanodomain scaffold protein.

We share the interest of the reviewer, and are actively working to determine whether the FER function described in the present manuscript applies to for additional RK-mediated signaling pathways. However, such detailed investigation is far reaching and beyond the scope of our current manuscript.

Line 114 – authors should state how many frames were combined to create the average projections.

This information is now indicated in each figure legend.

There are no scale bars on the kymographs, so it's impossible to know the duration of imaging/tracks/nanodomains.

We now provide scale bars for the kymographs.

Line 123 – I think the authors mean formation/composition and not localization.

We changed localization to organization. Thank you.

Line 145 – reference to figure needs updating.

This is now corrected. Thank you.

Line 188 – should be mobile rather than labile.

We assumed that the reviewer refers to L118, and changed labile to mobile.

For the figures showing a single track as a model, it would be good in include a scale bar to allow the reader to understand the scale of these diffusions/domains.

These models correspond to graphical illustration summarizing our observations but not to experimental single particle trajectories. We prefer not to include a scale bar, as it may be confusing.

Line 200 – 'deleted' should be mutated, truncated or altered.

This is now corrected. Thank you.

206 – should be '…can directly monitor the cell wall.'

We are here unsure which sentence the reviewer refers to.

219 – co-jointly should be rewritten to say, '..and together they relay..'.

We are here unsure which sentence the reviewer refers to.

Figure 3 – it would be good to quantify these effects to show how reproducible they are. Maybe for cotyledon – a line profile across the image to show the cell is more wavey? And a density for root hairs over a certain length?

We think that these observations are already very clear and are certainly reproductible.

If possible, it would be a great addition to the paper to show that dual dynamics of FLS and BAK in the different experimental conditions.

This remains technically challenging and would require further extensive work.

Line 869 – reference to figure needs updating.

This is corrected, thank you.

Reviewer #3:

[…] I feel that this report needs more attention to the biology itself. For the broader community, it would be perfect to understand in what process the mechanism described here is crucial. Therefore, I feel that authors would much improve this manuscript if they would be able to defend why they use epidermal lobed cells in 5-day-old seedlings. I know that there might be plenty of technical reasons, previous work, etc., but biologists would ask about it; considering that effects on cell growth are shown in root hairs, while all immune responses are studied in cotyledon epidermal lobed cells. The introduction on why it is actually so important to study described processes in cotyledons would help.

Please see our answer to the editorial comment #4

Perhaps I am wrong, but the "plasma membrane nanoscale landscape", as mentioned in the last sentence of the abstract, is related to the nanoscale organization of receptor kinases studied here, not the "landscape" of the PM itself. Of course, PM is extremely dynamic, but this manuscript is not focused on the understanding of PM structural changes provoked by the activity of individual receptor kinases. It is rather focused on surprisingly complex dynamics of individual receptor protein kinases with the PM. This I feel needs to be presented in a clearer form.

We agree and have replaced landscape by organization in the abstract.

Statistics is provided for the majority of analyses. However, authors mention in numerous cases (at least in 17 analyses) that "similar results were obtained in three independent experiments". I think that in the case of quantifications of microscopy images, it would be perfect to understand how observed differences in the dynamics of receptor kinases are robust when analyzed in these three mentioned biological repetitions. It would also be informative to include some rationale on the selection of cells for the analysis, e.g. was the size the criterion or something else?

We thank the reviewer for her/his proposition and now provide data from individual experiments and their statistical analysis. Concerning the observation by VA-TIRFM: after placing the center of cotyledons of 5-day-old seedlings (entire seedlings were mounted between cover slips and slides) at the center of the objective lens, we screened, by moving in x and y, using pre-defined and fixed laser angle and power, for cells surfaces in distance range of the evanescent wave without additional a priori selection criterions.

For a broader community of readers, it might be perhaps better to introduce a bit what is that „peptide hormone". I know that authors are very deeply involved in the RALF23 and often simply call this molecule „peptide". However, in the title of this manuscript, the term “peptide hormone" is used, but, the word "hormone" is not used in the manuscript at all. For broader community, this is a bit difficult to follow.

I think that for sure the biological implication of this work would be enhanced if data from biological repetitions mentioned in the text would be involved.

We now introduce RALF23 as a peptide hormone in the introduction. Thank you.

Kymographic analyses are not described in methods nor in captions. Axes of kymograms shown in the manuscript are not annotated; therefore it is not clear how actually dynamic the processes are. Time scale would help here.

We have added scale bars for all kymographs. The kymographs were generated using “orthogonal views” (Image/Stacks/Orthogonal views). This information has been added to the material and methods section. Thank you.

In the Figure 1, suppl. Figure 1 the caption is not mentioning the statistics used in this analysis.

This is now implemented. Thank you.

Line 877 – subscript should be used for numbers in chemical formulas.

This is now corrected. Thank you.

VA-TIRFM is mentioned by authors as the main microscopy method used in this contribution. I hope I got it right, therefore, the abbreviation TIRFM in all main and supplementary captions should be changed to VA-TIRFM, as well as in the description of the microscopy itself (lines 807 and 862).

This is now corrected; we replaced TIRFM by VA-TIRFM throughout the text.

Line 869 – the reference to the suppl. image is not correct, it should not be Sup Figure 15, but Figure Suppl 6.

This is now corrected. Thank you.

The quality of language is very good, however, there are some subtle grammar issues, e.g. on line 136 – „BAK1 might dynamically associates with", I found also some typos etc (line 831, the sentence should begin with a capital letter). I did not have time to find all of them, I encourage authors to check it again.

We have now checked and corrected the manuscript for grammar issues. Thank you.

In vivo advanced fluorescence GSD microscopy is used here and I appreciate a lot this technique and how it is implemented. It would be perhaps good to discuss how far individual markers characterize the mobility of the structure where it is located (PM, cell wall, cytoskeleton, etc.) and how far this technique might be taken as the characterization of the mobility of the particular molecule within the particular structure.

Thank you for your interest. We haven’t performed such a comparative analysis and are therefore unfortunately not in the position of evaluating it limits.

https://doi.org/10.7554/eLife.74162.sa2

Article and author information

Author details

  1. Julien Gronnier

    1. Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich, Switzerland
    2. The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
    Present address
    University of Tübingen, Center for Plant Molecular Biology (ZMBP), Tübingen, Germany
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Investigation, Supervision, Visualization, Writing – original draft, Writing – review and editing
    For correspondence
    julien.gronnier@zmbp.uni-tuebingen.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1429-0542

  2. Christina M Franck

    Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich, Switzerland
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  3. Martin Stegmann

    The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
    Present address
    Phytopathology, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  4. Thomas A DeFalco

    1. Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich, Switzerland
    2. The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  5. Alicia Abarca

    Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich, Switzerland
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3569-851X

  6. Michelle von Arx

    Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich, Switzerland
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  7. Kai Dünser

    Department of Applied Genetics and Cell Biology, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  8. Wenwei Lin

    FAFU-UCR Joint Center for Horticultural Biology and Metabolomics Center, Haixia, Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, China
    Contribution
    Resources
    Competing interests
    No competing interests declared

  9. Zhenbiao Yang

    FAFU-UCR Joint Center for Horticultural Biology and Metabolomics Center, Haixia, Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, China
    Contribution
    Resources
    Competing interests
    No competing interests declared

  10. Jürgen Kleine-Vehn

    Department of Applied Genetics and Cell Biology, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
    Contribution
    Conceptualization, Funding acquisition, Project administration, Writing – review and editing
    Competing interests
    Senior editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4354-3756

  11. Christoph Ringli

    Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich, Switzerland
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared

  12. Cyril Zipfel

    1. Institute of Plant and Microbial Biology and Zurich-Basel Plant Science Center, University of Zurich, Zurich, Switzerland
    2. The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review and editing
    For correspondence
    cyril.zipfel@botinst.uzh.ch
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4935-8583

Funding

Gatsby Charitable Foundation

  • Cyril Zipfel

University of Zurich

  • Cyril Zipfel

H2020 European Research Council (309858)

  • Cyril Zipfel

H2020 European Research Council (773153)

  • Cyril Zipfel

European Molecular Biology Organization (LTF 438-2018)

  • Julien Gronnier

European Molecular Biology Organization (LTF 512-2019)

  • Christina M Franck

European Molecular Biology Organization (LTF 100-2017)

  • Thomas A DeFalco

H2020 European Research Council (639678)

  • Jürgen Kleine-Vehn

Swiss National Science Foundation (31003A_182625)

  • Cyril Zipfel

Swiss National Science Foundation (31003A_166577/1)

  • Christoph Ringli

Austrian Science Fund (P 33044)

  • Jürgen Kleine-Vehn

Deutsche Forschungsgemeinschaft (STE 2448/1)

  • Martin Stegmann

Austrian Academy of Sciences (Doctoral fellowship)

  • Kai Dünser

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank all present and past members of the Zipfel laboratory for fruitful discussions and comments on the manuscript. We thank the members of the Grossniklaus, Ringli, Sanchez-Rodriguez, and Keller laboratories for sharing results and comments during our stimulating CCWI meetings. We thank Vera Gorelova, Yvon Jaillais, Alexandre Martinière, and Birgit Kemmerling for comments on the manuscript. This research was funded by the Gatsby Charitable Foundation (CZ), the University of Zürich (CZ), the European Research Council under the Grant Agreements 309858 and 773153 (grants PHOSPHinnATE and IMMUNO-PEPTALK to CZ) and 639678 (grant AuxinER to JK-V), the Swiss National Science Foundation (grant no. 31003A_182625 to CZ and 31003A_166577/1 to CR), and the Austrian science fund (FWF; P33044 to JK-V). JG, CMF, and TAD were supported by Long-Term Fellowships from the European Molecular Biology Organization (EMBO) (numbers 438-2018, 512-2019, and 100-2017, respectively), while MS was supported by a postdoctoral fellowship (STE 2448/1) from the Deutsche Forschungsgemeinschaft (DFG) and KD by a doctoral fellowship from the Austrian Academy of Sciences (ÖAW). We thank Sarah Assman, Sacco de Vries, Silke Robatzek, and Nana Keinath for kindly providing segregating lines of fer-4/pFER::FERK565R-GFP, Col-0/pBAK1::BAK1-mCherry, Col-0/pFLS2::FLS2-GFP, and fer-2/pFER::FERK565R-GFP, respectively.

Senior Editor

  1. Jonathan A Cooper, Fred Hutchinson Cancer Research Center, United States

Reviewing Editor

  1. Dominique C Bergmann, Stanford University, United States

Reviewer

  1. Jan Petrášek

Publication history

  1. Preprint posted: July 21, 2020 (view preprint)
  2. Received: September 23, 2021
  3. Accepted: January 5, 2022
  4. Accepted Manuscript published: January 6, 2022 (version 1)
  5. Version of Record published: January 25, 2022 (version 2)

Copyright

© 2022, Gronnier et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Julien Gronnier
  2. Christina M Franck
  3. Martin Stegmann
  4. Thomas A DeFalco
  5. Alicia Abarca
  6. Michelle von Arx
  7. Kai Dünser
  8. Wenwei Lin
  9. Zhenbiao Yang
  10. Jürgen Kleine-Vehn
  11. Christoph Ringli
  12. Cyril Zipfel
(2022)
Regulation of immune receptor kinase plasma membrane nanoscale organization by a plant peptide hormone and its receptors
eLife 11:e74162.
https://doi.org/10.7554/eLife.74162

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    Ignacio Bravo-Plaza, Victor G Tagua ... Miguel A Peñalva
    Research Article

    Uso1/p115 and RAB1 tether ER-derived vesicles to the Golgi. Uso1/p115 contains a globular-head-domain (GHD), a coiled-coil (CC) mediating dimerization/tethering and a C-terminal region (CTR) interacting with golgins. Uso1/p115 is recruited to vesicles by RAB1. Genetic studies placed Uso1 paradoxically acting upstream of, or in conjunction with RAB1 (Sapperstein et al., 1996). We selected two missense mutations in uso1 resulting in E6K and G540S in the GHD that rescued lethality of rab1-deficient Aspergillus nidulans. The mutations are phenotypically additive, their combination suppressing the complete absence of RAB1, which emphasizes the key physiological role of the GHD. In living hyphae Uso1 recurs on puncta (60 sec half-life) colocalizing partially with the Golgi markers RAB1, Sed5 and GeaA/Gea1/Gea2, and totally with the retrograde cargo receptor Rer1, consistent with Uso1 dwelling in a very early Golgi compartment from which ER residents reaching the Golgi recycled back to the ER. Localization of Uso1, but not of Uso1E6K/G540S, to puncta is abolished by compromising RAB1 function, indicating that E6K/G540S creates interactions bypassing RAB1. That Uso1 delocalization correlates with a decrease in the number of Gea1 cisternae supports that Uso1-and-Rer1-containing puncta are where the protein exerts its physiological role. In S-tag-coprecipitation experiments Uso1 is an associate of the Sed5/Bos1/Bet1/Sec22 SNARE complex zippering vesicles with the Golgi, with Uso1E6K/G540S showing stronger association. Using purified proteins, we show that Bos1 and Bet1 bind the Uso1 GHD directly. However, Bet1 is a strong E6K/G540S-independent binder, whereas Bos1 is weaker but becomes as strong as Bet1 when the GHD carries E6K/G540S. G540S alone markedly increases GHD binding to Bos1, whereas E6K causes a weaker effect, correlating with their phenotypic contributions. AlphaFold2 predicts that G540S increases binding of the GHD to the Bos1 Habc domain. In contrast, E6K lies in an N-terminal, potentially alpha-helical, region that sensitive genetic tests indicate as required for full Uso1 function. Remarkably, this region is at the end of the GHD basket opposite to the end predicted to interact with Bos1. We show that unlike dimeric full-length and CTR∆ Uso1 proteins, the GHD lacking the CC/CTR dimerization domain, whether originating from bacteria or Aspergillus extracts and irrespective of whether it carries or not E6K/G540S, would appear to be monomeric. With the finding that overexpression of E6K/G540S and wild-type GHD complement uso1∆, our data indicate that the GHD monomer is capable of providing, at least partially, the essential Uso1 functions, and that long-range tethering activity is dispensable. Rather, these findings strongly suggest that the essential role of Uso1 involves the regulation of SNAREs.

    1. Cell Biology

    Global analysis of contact-dependent human-to-mouse intercellular mRNA and lncRNA transfer in cell culture

    Sandipan Dasgupta, Daniella Y Dayagi ... Jeffrey E Gerst
    Research Article

    Full-length mRNAs transfer between adjacent mammalian cells via direct cell-to-cell connections called tunneling nanotubes (TNTs). However, the extent of mRNA transfer at the transcriptome-wide level (the 'transferome') is unknown. Here, we analyzed the transferome in an in vitro human-mouse cell co-culture model using RNA-sequencing. We found that mRNA transfer is non-selective, prevalent across the human transcriptome, and that the amount of transfer to mouse embryonic fibroblasts (MEFs) strongly correlates with the endogenous level of gene expression in donor human breast cancer cells. Typically, <1% of endogenous mRNAs undergo transfer. Non-selective, expression-dependent RNA transfer was further validated using synthetic reporters. RNA transfer appears contact-dependent via TNTs, as exemplified for several mRNAs. Notably, significant differential changes in the native MEF transcriptome were observed in response to co-culture, including the upregulation of multiple cancer and cancer-associated fibroblast-related genes and pathways. Together, these results lead us to suggest that TNT-mediated RNA transfer could be a phenomenon of physiological importance under both normal and pathogenic conditions.

    1. Cell Biology

    Insights into cargo sorting by SNX32 and its role in neurite outgrowth

    Jini Sugatha, Amulya Priya ... Sunando Datta
    Research Article Updated

    Sorting nexins (SNX) are a family of proteins containing the Phox homology domain, which shows a preferential endo-membrane association and regulates cargo sorting processes. Here, we established that SNX32, an SNX-BAR (Bin/Amphiphysin/Rvs) sub-family member associates with SNX4 via its BAR domain and the residues A226, Q259, E256, R366 of SNX32, and Y258, S448 of SNX4 that lie at the interface of these two SNX proteins mediate this association. SNX32, via its PX domain, interacts with the transferrin receptor (TfR) and Cation-Independent Mannose-6-Phosphate Receptor (CIMPR), and the conserved F131 in its PX domain is important in stabilizing these interactions. Silencing of SNX32 leads to a defect in intracellular trafficking of TfR and CIMPR. Further, using SILAC-based differential proteomics of the wild-type and the mutant SNX32, impaired in cargo binding, we identified Basigin (BSG), an immunoglobulin superfamily member, as a potential interactor of SNX32 in SHSY5Y cells. We then demonstrated that SNX32 binds to BSG through its PX domain and facilitates its trafficking to the cell surface. In neuroglial cell lines, silencing of SNX32 leads to defects in neuronal differentiation. Moreover, abrogation in lactate transport in the SNX32-depleted cells led us to propose that SNX32 may contribute to maintaining the neuroglial coordination via its role in BSG trafficking and the associated monocarboxylate transporter activity. Taken together, our study showed that SNX32 mediates the trafficking of specific cargo molecules along distinct pathways.