Decision letter: Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Abstract G-protein-coupled receptors (GPCR) are present at the cell surface in different conformational and oligomeric states. However, how these states impact GPCRs biological function and therapeutic targeting remains incompletely known. Here, we investigated this issue in living cells for the CC chemokine receptor 5 (CCR5), a major receptor in inflammation and the principal entry co-receptor for Human Immunodeficiency Viruses type 1 (HIV-1). We used TIRF microscopy and a statistical method to track and classify the motion of different receptor subpopulations. We showed a diversity of ligand-free forms of CCR5 at the cell surface constituted of various oligomeric states and exhibiting transient Brownian and restricted motions. These forms were stabilized differently by distinct ligands. In particular, agonist stimulation restricted the mobility of CCR5 and led to its clustering, a feature depending on β-arrestin, while inverse agonist stimulation exhibited the opposite effect. These results suggest a link between receptor activation and immobilization. Applied to HIV-1 envelope glycoproteins gp120, our quantitative analysis revealed agonist-like properties of gp120s. Distinct gp120s influenced CCR5 dynamics differently, suggesting that they stabilize different CCR5 conformations. Then, using a dimerization-compromized mutant, we showed that dimerization (i) impacts CCR5 precoupling to G proteins, (ii) is a pre-requisite for the immobilization and clustering of receptors upon activation, and (iii) regulates receptor endocytosis, thereby impacting the fate of activated receptors. This study demonstrates that tracking the dynamic behavior of a GPCR is an efficient way to link GPCR conformations to their functions, therefore improving the development of drugs targeting specific receptor conformations. Editor's evaluation This manuscript seeks to push the frontiers of live-cell single-molecule imaging by tracking the diffusive movements of CCR5 receptors and CCR5 receptor complexes within the plasma membrane of living cells and how these motional behaviors change with physiological stimuli. The results will be important for researchers working at the interface of cell biology and biophysics on membrane-bound receptors. https://doi.org/10.7554/eLife.76281.sa0 Decision letter eLife's review process Introduction G-protein-coupled receptors (GPCRs), also known as 7TM (seven transmembrane helical) receptors, represent the largest group of cell surface receptors in humans that transduce chemical signals from the extracellular matrix into the cell. They constitute one of the primary drug target classes (Pierce et al., 2002). GPCRs exist in different subpopulations at the cell surface, in part due to differential post-translational modifications (Patwardhan et al., 2021; Scurci et al., 2021) and arrangements of receptor loops and transmembrane domains (Deupi and Kobilka, 2010). Receptor activation and G protein coupling indeed involves a series of conformational changes from an inactive to an active state (Ahn et al., 2021). Coupling to different G proteins or to other protein transducers (e.g. arrestins), as well as receptor oligomerization expand the diversity of conformational states (Seyedabadi et al., 2019; Sleno and Hébert, 2018). Molecular dynamics along with biophysical and structural studies brought to light this variety of GPCR arrangements and showed how binding of different ligands can stabilize or select different receptor conformations, which can in turn activate different signaling pathways (Ahn et al., 2021). This concept of ‘functional selectivity’ (or ‘biased agonism’) opens the possibility to develop therapies specifically targeting a selected receptor conformation, thereby increasing the effectiveness of drugs and reducing their adverse effects (Seyedabadi et al., 2019). The nature and proportion of the different forms of GPCRs vary depending on their environment. This is likely to regulate the functional properties of the receptors (Colin et al., 2018; Patwardhan et al., 2021). Few studies, however, confirmed this diversity of receptors in living cells and investigated its regulation in time and space (Calebiro et al., 2012; Gormal et al., 2020; Kasai et al., 2018; Martínez-Muñoz et al., 2018; Sungkaworn et al., 2017; Veya et al., 2015). In this study, we tracked the chemokine receptor CCR5 at the particle level to access its dynamic behavior at the plasma membrane and identify the organization and the functional properties of the various receptor forms. CCR5 is a class A GPCR expressed on the surface of hematopoietic and non-hematopoietic cells. It is a key player in the trafficking of lymphocytes and monocytes/macrophages and has been implicated in the pathophysiology of multiple diseases, including viral infections and complex disorders with an inflammatory component (Brelot and Chakrabarti, 2018; Flanagan, 2014; Vangelista and Vento, 2017). In addition, the CCL5/CCR5 axis represents a major marker of tumor development (Aldinucci et al., 2020). CCR5 binds several chemokines, including CCL3, CCL4, and CCL5. Binding of chemokines results in conformational change of the receptor, which then activates intracellular signaling pathways and leads to cell migration (Flanagan, 2014). CCR5 also binds the envelope glycoprotein of HIV-1, then acting as the major HIV-1 entry co-receptor (Alkhatib et al., 1996; Brelot and Chakrabarti, 2018). One CCR5 allosteric ligand, maraviroc (MVC), is part of the anti-HIV-1 therapeutic arsenal (Dorr et al., 2005), although emergence of MVC-resistant variants has been identified in some patients (Tilton et al., 2010). We and others showed the existence of various CCR5 populations present at the cell surface (Abrol et al., 2014; Berro et al., 2011; Colin et al., 2013; Colin et al., 2018; Fox et al., 2015; Jacquemard et al., 2021; Jin et al., 2014; Jin et al., 2018; Scurci et al., 2021). Computational analysis predicts that CCR5 can adopt an ensemble of low-energy conformations, each of which being differentially favored by distinct ligands and receptor mutations (Abrol et al., 2014). CCR5 conformations display distinct antigenic properties, which vary depending on cell types (Colin et al., 2018; Fox et al., 2015). The multiple conformations interact differently with distinct ligands (agonist, antagonist, HIV-1 envelope glycoprotein) and differ in their biological properties, HIV co-receptor functions, and abilities to serve as therapeutic targets (Abrol et al., 2014; Colin et al., 2013; Colin et al., 2018; Jacquemard et al., 2021; Jin et al., 2014; Jin et al., 2018; Scurci et al., 2021). In particular, coupling to G proteins distinguishes CCR5 populations that are differently engaged by chemokines and HIV-1 envelope. This explains why HIV-1 escapes inhibition by chemokines (Colin et al., 2013). In this context, the improved capacity of chemokine analogs to inhibit HIV infection, as compared to native chemokines, is related to their ability to target a large amount of CCR5 conformations (Jin et al., 2014). Like other receptors of this class, CCR5 forms homo- and heterodimers with other receptors, which contribute to the diversity of conformational states (Jin et al., 2018; Sohy et al., 2009). We identified three homodimeric organizations of CCR5 involving residues of transmembrane domain 5 (TM5) (Jin et al., 2018). Two dimeric states corresponded to unliganded receptors, whereas binding of the inverse agonist MVC stabilized a third state (Jin et al., 2018). CCR5 dimerization occurs in the endoplasmic reticulum, thereby regulating the receptor targeting to the cell surface (Jin et al., 2018). CCR5 dimerization also modulates ligand binding and HIV-1 entry into cells (Colin et al., 2018). MVC stabilizes CCR5 homodimerization, illustrating that CCR5 dimerization can be modulated by ligands (Jin et al., 2018), a feature shared with other chemokine receptors (Işbilir et al., 2020). Allosteric interaction within CCR2/CCR5 heterodimers is reported as well as cross-inhibition by specific antagonists (Sohy et al., 2009). This suggests that dimerization impacts therapeutic targeting. To characterize the diversity of CCR5 subpopulations at the cell surface and to investigate the impact of CCR5 dynamics on its function, we tracked CCR5 fluorescent particles by total internal reflection fluorescence (TIRF) microscopy (Calebiro et al., 2012) and quantitatively classify their motion over time using a statistical method. We described CCR5 mobility patterns both at the basal state and after ligand binding (using two agonists, the inverse agonist MVC, and HIV-1 envelope glycoproteins) and under conditions that modulate CCR5 /G protein coupling, β-arrestin binding, and dimerization. This study provides novel insights into the organization of a GPCR at the cell surface and the mechanisms regulating its signaling and fate after activation. Results Statistical classification of receptor trajectories at the cell membrane We studied CCR5 dynamics in two different models: eGFP-CCR5 and FLAG-SNAP-tagged-CCR5 (FLAG-ST-CCR5) expressing cells, in which we tracked either eGFP or receptor-bound fluorescent anti-FLAG antibodies. We used HEK 293 cell lines stably expressing a low density of eGFP-CCR5 or FLAG-ST-CCR5 at the cell surface (<0.5 particles/μm2), which is critical for single particle tracking on the surface of living cells (Calebiro et al., 2012). We chose HEK 293 cells because they do not express CCR5. Fusion of proteins to the N-terminus of CCR5 does not alter cell surface expression of the receptor or its intracellular trafficking (Boncompain et al., 2019; Jin et al., 2018). To study the dynamics of CCR5 as a single particle at the plasma membrane of living cells, we used TIRF microscopy, which restricts the observation to the first 200 nm from the coverslip. The acquisitions were carried out at 37 °C. From the movies obtained, we tracked the motion of the particles over time using the Spot tracking plugin of the ICY software (Chenouard et al., 2013; de Chaumont et al., 2012; Figure 1A–C, Videos 1–4, see Materials and methods). Figure 1 with 1 supplement see all Download asset Open asset Single particle detection of eGFP-CCR5 using TIRF microscopy and analysis with the statistical method. (A) Distribution of eGFP-CCR5 stably expressed in HEK 293 cells. Imaging was acquired at 30 Hz. The region of interest defined by the green line is used for A-C and F. Analysis of movies was performed using the ICY software and (B) the Spot detection and (C) the Spot tracking plugins. Scale bar 2 μm. (D) Single receptor tracks were partitioned into tracklets of five images each. (E) Analysis of tracks with the statistical method: tracklets were classified into confined, Brownian, and directed motion. (F) Results obtained from Matlab. (G) Pooled tracklets classification provided a global estimate of receptor dynamics and the number of motion changes along the track (transition rates). (Restricted motions: immobile and confined motions). Figure 1—source code 1 Matlab code used for simulations. https://cdn.elifesciences.org/articles/76281/elife-76281-fig1-code1-v1.zip Download elife-76281-fig1-code1-v1.zip Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg TIRF movie of a cell stably expressing eGFP-CCR5-WT acquired at 30 Hz. The region of interest was defined by the green line. Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg TIRF movie of the same cell as in Video 1 analyzed using the Icy software. Red circles correspond to the detection of bright spots using the Spot detection plugin. Video 3 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg TIRF movie of the same cell as in Videos 1 and 2 analyzed using the Icy software and the Spot tracking plugin. Colored lines correspond to the tracked spots. Video 4 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg TIRF movie of a cell stably expressing FLAG-ST-CCR5-WT and stained with M2-Cy3. Movie was acquired at 10 Hz. The method generally used to evaluate the dynamics of a particle is based on Mean Square Displacement (MSD) analysis (Qian et al., 1991). However, MSD is a global analysis of particle trajectory that does not handle possible changes in particle motion. In particular, it indicates whether the observed motion is standard Brownian motion and computes the related diffusion coefficient of the trajectory, but it cannot characterize more complex stochastic motions as the frequency of motion changes. In addition, the MSD analysis does not provide a statistical significance of classified motion. More robust analysis using the Bayesian probabilistic framework have been proposed to classify single particle trajectories (Karslake et al., 2021; Monnier et al., 2015; Türkcan and Masson, 2013). However, Bayesian inference is often associated with a high computational load and is not very robust for short trajectories. Therefore, to robustly characterize the complex stochastic motions of single receptors at the cell membrane, we chose to implement a statistical hypothesis testing method introduced in Briane et al., 2018. To mitigate the risk of tracking errors over long trajectories, and to detect potential motion changes between tracklets within each single particle trajectory, we partitioned single spot trajectories into small tracklets (with N=5 consecutive detections each; Figure 1D and Figure 1—figure supplement 1A). We first evaluated immobile objects and then used a robust statistical method to classify tracklet motion (see Materials and methods and Figure 1E–F). Briefly, for each tracklet X, we computed the statistics SX,N introduced in Briane et al., 2018 that evaluate the ratio between the maximal distance reached by the tracklet particle from the initial point and the motion standard deviation. We then used the statistics SX,N to classify each tracklet into one of the three following motion categories: confined, Brownian, or directed stochastic motion. For this, we computed S(X,N) for each tracklet and compared it to the quantiles (qα,q1-α), which are statistical reference values of Brownian motion at level α and 1-α . Quantiles of S(X,N) only depend on N and α (Briane et al., 2018), and can be evaluated independently of the characteristics of experimental trajectories. Finally, tracklets X were classified according to the associated stochastic motion: confined (if S(X,N)6 (Figure 1—figure supplement 1B), the experimental SNR being ~10, and that classification was robust to spots’ density (Figure 1—figure supplement 1C). After having implemented this statistical classification in the ICY software (processor Dynamics Classifier in the plugin Track Manager), we characterized the dynamics of CCR5 particles at the cell membrane. CCR5 particles have different motions at the plasma membrane We investigated CCR5 mobility in the basal state using the statistical method described above (Figure 1). The result of the classification of all the pooled tracklets provided a global estimate of the receptor dynamics, while the number of motion changes along the same trajectory gave us an estimate of the overall stability of the motion (Figure 1G). In the basal state, the eGFP-CCR5 particles distributed homogeneously over the entire membrane surface (Figure 1A, Videos 1–3). However, the motions of eGFP-CCR5 particles were heterogeneous (Figure 2A). Eighty percent of the pooled CCR5 tracklets were mobile with Brownian motion, while 20% were classified as restricted motion (i.e. immobile and confined; Figure 2A). We observed almost no directed trajectories (<0.5 %). Around 50% of particles (52%) exhibited Brownian motion over the entire length of the path (Figure 2B). The other half fluctuated between Brownian and restricted motion (Figure 2B). This high degree of fluctuation between motions within one trajectory suggested the existence of transient conformations of CCR5 at the plasma membrane. Similarly, the motions of FLAG-ST-CCR5 particles were heterogeneous with high degree of fluctuation between motions (Figure 2—figure supplement 1). Note that compared to eGFP-CCR5, FLAG-ST-CCR5 exhibited a higher percentage of tracklet in restricted motion (50%), which we attributed to antibody binding (Harms et al., 2012). Figure 2 with 1 supplement see all Download asset Open asset In the basal state, eGFP-CCR5 exhibits different motions at the plasma membrane. (A) Distribution of tracklets motion: restricted, Brownian, or directed (mean ± SEM, n=28,305 tracks from 19 cells, 3 independent experiments). (B) Distribution of tracklets motion changes along tracks (mean ± SEM, n=48,237 tracks from 45 cells, 7 experiments). Figure 2—source data 1 Source data for Figure 2. https://cdn.elifesciences.org/articles/76281/elife-76281-fig2-data1-v1.xlsx Download elife-76281-fig2-data1-v1.xlsx Together, these analyses revealed heterogeneity of CCR5 motion at the basal state consistent with the diversity of CCR5 forms described previously by other methods (Abrol et al., 2014; Colin et al., 2013; Fox et al., 2015; Jin et al., 2018; Scurci et al., 2021). Multiple ligands impact CCR5 mobility differently Since ligands modulate the conformation of CCR5 (Colin et al., 2018; Jacquemard et al., 2021; Jin et al., 2018), we investigated the impact of ligand binding on its spatiotemporal dynamic properties. We evaluated the effect of saturating concentration of ligands (two agonists with different efficacies and the inverse agonist MVC, i.e. a ligand with a negative efficacy) on CCR5 trajectories at the plasma membrane over time. We first incubated eGFP-CCR5-expressing cells in the presence of the native CCR5 chemokine CCL4 at a saturating concentration (>100 nM, kd = 0.4 nM; Colin et al., 2013) for the indicated time. The mobility of the receptor was then assessed immediately after addition of the ligand in a window of 1–12 min (Figure 3A). CCL4 triggered no significant change in CCR5 mobility after 10 min of stimulation (Figure 3B). However, a longer time of CCL4 stimulation (>12 min) increased the percentage of restricted CCR5 tracklets, indicating localized immobility of a small fraction of receptors (Figure 3—figure supplement 1). We also noted the formation of large and immobile spots after 12 min of stimulation (Video 5). Figure 3 with 2 supplements see all Download asset Open asset Different ligands, agonists and inverse agonist, impact eGFP-CCR5 mobility differently. eGFP-CCR5-WT expressing cells were treated or not with a saturating concentration of agonists (CCL4, 200 nM or PSC-RANTES, 20 nM) or inverse agonist (maraviroc, 10 μM) and single particle tracking analysis was performed. (A) Percentage of restricted tracklets after treatment over time (n=tracks from 10, 4, and 3 cells for PSC-RANTES, CCL4, and MVC conditions respectively, at least three independent experiments). (B) Distribution of tracklets motion after 10 min of treatment (mean ± SEM, n=40,564, 15,421, 11,213, 9828 tracks for each condition from 35, 12, 12, and 9 cells, respectively, at least three independent experiments). Unpaired t test on restricted motions only: ns, nonsignificant; (C) Distribution of tracklets motion changes along tracks after 10 min of treatment (mean ± SEM, 9828 tracks from and 9 cells for each condition respectively, at least three experiments). Unpaired t test on all restricted motions only: ns, nonsignificant; (D) Single particle detection of eGFP-CCR5-WT after 3 min of stimulation with nM) from 1 of movie Mean of the of fluorescence under large immobile spots and small mobile spots after min of stimulation (mean ± SEM, least spots from 12 cells, three experiments). (E) Percentage of restricted tracklets after stimulation with maraviroc 5 min) and nM, one (F) Distribution of tracklets motions after stimulation with maraviroc 5 min) and nM, min) (mean ± SEM, tracks from and 2 cells respectively, one Figure data 1 Source data for Figure Download Video 5 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg TIRF movie acquired at 30 of a cell stably expressing eGFP-CCR5-WT and treated by CCL4 nM) for We compared the effect of CCL4 with that of an agonist targeting a proportion of receptor conformations and a agonist et al., Jin et al., 2014). We incubated the cells in the presence of a saturating concentration of nM, = nM; Colin et al., 2013) and evaluated the motion of the receptors under the same triggered a in the number of tracklets classified as restricted motion over time (Figure 3A). after stimulation with PSC-RANTES, 50% of eGFP-CCR5 tracklets were in a restricted state under basal conditions (Figure 3B). the fraction of all Brownian trajectories while the fraction of fluctuated and all restricted trajectories increased (Figure we observed the formation of large immobile spots in cells (Figure These large spots a long (Video The of the fluorescence of the spots from the 1 of movies showed that the large spots on higher the other indicating a clustering of at least receptors large spot (Figure These results revealed a change in CCR5 mobility upon activation receptor immobilization and clustering, receptors in Video Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg TIRF movie acquired at 30 of cells stably expressing eGFP-CCR5-WT and treated by nM) for 3 agonists, the inverse agonist MVC = 1 nM) et al., not receptor mobility (Figure and the the fraction of restricted eGFP-CCR5 tracklets at the surface of cells showed a compared to cells (Figure 3B). We the of CCR5 immobility by cells with MVC MVC treatment receptor immobilization (Figure indicating that CCR5 immobilization on binding to CCR5. We observed the same effect of ligand binding (using CCL4, PSC-RANTES, and on FLAG-ST-CCR5 that our were independent of the used (Figure 3—figure supplement These results showed that distinct ligands differently stabilize CCR5 in living cells, in with our results (Colin et al., 2013; Colin et al., 2018; Jin et al., 2014; Jin et al., 2018). the amount of receptors with the of ligands suggesting a link between receptor activation and immobilization. coupling and β-arrestin CCR5 motion differently under basal state and conditions To the above we to whether the mobility of CCR5 is influenced by its coupling to which stabilizes the receptor in an activated We analyzed the of restricted CCR5 tracklets in the presence of which the receptor from proteins (Figure Figure 4 with 1 supplement see all Download asset Open asset coupling and eGFP-CCR5 mobility at basal state or after (A) Percentage of restricted tracklets in eGFP-CCR5-WT expressing HEK 293 cells or not with of for 3 (mean ± SEM, and tracks for each 12 and cells respectively, 3 independent experiments). Unpaired t (B) Percentage of restricted tracklets over time of eGFP-CCR5-WT expressed on nM) treated cells after or not with (mean ± independent experiments). (C) of restricted tracklets in eGFP-CCR5-WT expressing cells with (mean ± and tracks for each from 7 and cells, Unpaired t (D) Percentage of restricted tracklets over time of eGFP-CCR5-WT expressed on nM) treated cells after Figure data 1 Source data for Figure Download In the basal state, the fraction of restricted eGFP-CCR5 tracklets from cells with compared to cells (Figure this also a process that on CCR5 coupling to proteins (Figure supplement 1). These results suggested that a small of CCR5 is in a in its basal state, which may contribute to the transient of the motion of CCR5 at the cell After receptor immobilization be due to the of receptors in the receptor the activation and in the G protein et al., 2017). To evaluate the of coupling on receptor immobilization after we analyzed tracks of TIRF movies of cells or not with In this the fraction of restricted tracklets increased over time after stimulation in the same proportion of treatment (Figure This suggested that coupling was not in immobilization of CCR5 after several of This result is consistent with our study high interaction of with protein CCR5 (Colin et al., 2013). After stimulation by PSC-RANTES, CCR5 a involving which the receptor to and et al., Jin et al., 2014). We previously showed that β-arrestin 1 and β-arrestin 2 with CCR5 after stimulation (Jin et al., 2014). in eGFP-CCR5 cells with not impact eGFP-CCR5 motion in the basal state (Figure but eGFP-CCR5 immobilization and clustering (Figure These indicated that to CCR5 immobilization after Together, these results to the existence of a fraction of CCR5 in a transient signaling complex in the basal state, which is consistent with studies CCR5 et al., 2011; et al., They also suggested that the fate of CCR5 several after activation is independent of coupling but on β-arrestin in with receptor and after activation (Flanagan, 2014). of CCR5 after stimulation on its oligomeric state We previously showed by and a functional that a point of CCR5 in leads to a receptor, which has a dimerization capacity compared to