CCR5 deficiency impairs CD4+ T-cell memory responses and antigenic sensitivity through increased ceramide synthesis

Article CCR5deficiency impairs CD4 + T-cell memory responses and antigenic sensitivity through increased ceramide synthesis Ana Martín-Leal 1,† , Raquel Blanco 1,† , Josefina Casas 2,3 , María E Sáez 4 , Elena Rodríguez-Bovolenta 5 , Itziar de Rojas 6 , Carina Drechsler 7,8,9 , Luis Miguel Real 10,11 , Gemma Fabrias 2,3 , Agustín Ruíz 6,12 , Mario Castro 13 , Wolfgang WA Schamel 7,8,14 , Balbino Alarcón 5 , Hisse M van Santen 5 & Santos Mañes 1,* Abstract CCR5is not only a coreceptor for HIV-1infection in CD4 + T cells, but also contributes to their functional fitness. Here, we show that by limiting transcription of specific ceramide synthases, CCR5 signaling reduces ceramide levels and thereby increases T-cell antigen receptor (TCR) nanoclustering in antigen-experienced mouse and human CD4 + T cells. This activity is CCR5-specific and independent of CCR5co-stimulatory activity. CCR5-deficient mice showed reduced production of high-affinity class-switched antibodies, but only after antigen rechallenge, which implies an impaired memory CD4 + T-cell response. This study identifies a CCR5function in the generation of CD4 + T-cell memory responses and establishes an antigen-independent mechanism that regulates TCR nanoclustering by altering specific lipid species. Keywords ccr5[delta]32; humoral response; membrane phase; sphingolipid; T-cell receptor Subject Category Immunology DOI 10.15252/embj.2020104749 | Received 18 February 2020 | Revised 12 May 2020 | Accepted 14 May 2020 | Published online 11 June 2020 The EMBO Journal (2020)39:e104749 See also: C Matti & DF Legler (August 2020) Introduction The C-C motif chemokine receptor 5 (CCR5) is a seven-transmembrane G protein-coupled receptor (GPCR) expressed on the surface of several innate and adaptive immune cell subtypes, including effector and memory CD4 + T lymphocytes (GonzalezMartin et al, 2012). CCR5 acts also a necessary coreceptor for infection by HIV-1. An HIV-resistant population served to identify a 32-bp deletion within the CCR5 coding region (ccr5D32), which yields a non-functional receptor (Blanpain et al, 2002). Since ccr5D32 homozygous individuals are seemingly healthy, a radical body of thought considers that CCR5 is dispensable for immune cell function. Experimental and epidemiological evidence nonetheless indicates that CCR5 has an important role in innate and acquired immune responses. CCR5 and its ligands C-C motif ligand 3 (CCL3; also termed macrophage inflammatory protein [MIP]-1a), CCL4 (MIP1b), CCL5 (regulated upon activation, normal T cell expressed and secreted [RANTES]), and CCL3L1 have been associated with exacerbation of chronic inflammatory and autoimmune diseases. Despite varying information due probably to ethnicity effects (Lee et al, 2013; Schauren et al, 2013), further complicated in admixed populations (Toson et al, 2017), epidemiological studies support the 1Department of Immunology and Oncology, Centro Nacional de Biotecnología (CNB/CSIC), Madrid, Spain 2Department of Biological Chemistry, Institute of Advanced Chemistry of Catalonia (IQAC-CSIC), Barcelona, Spain 3CIBER Liver and Digestive Diseases (CIBER-EDH), Instituto de Salud Carlos III, Madrid, Spain 4Centro Andaluz de Estudios Bioinformáticos (CAEBi), Seville, Spain 5Department of Cell Biology and Immunology, Centro de Biología Molecular Severo Ochoa (CBMSO/CSIC), Madrid, Spain 6Alzheimer Research Center, Memory Clinic of the Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain 7Signaling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany 8Department of Immunology, Faculty of Biology, University of Freiburg, Freiburg, Germany 9Institute for Pharmaceutical Sciences, University of Freiburg, Freiburg, Germany 10 Unit of Infectious Diseases and Microbiology, Hospital Universitario de Valme, Seville, Spain 11 Department of Biochemistry, Molecular Biology and Immunology, School of Medicine, Universidad de Málaga, Málaga, Spain 12 CIBER Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain 13 Interdisciplinary Group of Complex Systems, Escuela Técnica Superior de Ingeniería, Universidad Pontificia Comillas, Madrid, Spain 14 Centre for Chronic Immunodeficiency (CCI), University of Freiburg, Freiburg, Germany *Corresponding author. Tel: +34 91 585 4840; Fax: +34 91 372 0493; E-mail: [email protected] † These authors contributed equally to this work. ª2020 The Authors. Published under the terms of the CC BY 4.0license The EMBO Journal 39:e104749 |2020 1of 19 ccr5D32 allele as a marker for good prognosis for these overreactive immune diseases (Vangelista & Vento, 2017). In contrast, ccr5D32 homozygotes are prone to fatal infections by several pathogens such as influenza, West Nile, and tick-borne encephalitis viruses (Lim & Murphy, 2011; Falcon et al, 2015; Ellwanger & Chies, 2019). The mechanisms by which the ccr5D32 polymorphism affects all these pathologies have usually been linked to the capacity of CCR5 to regulate leukocyte trafficking. For example, CCR5 deficiency reduces recruitment of influenza-specific memory CD8 + T cells and accelerates macrophage accumulation in lung airways during virus rechallenge (Dawson et al, 2000; Kohlmeier et al, 2008); this could lead to acute severe pneumonitis, a fatal flu complication. CCR5 nonetheless has migration-independent functions that maximize T-cell activation by affecting immunological synapse (IS) formation (Molon et al, 2005; Floto et al, 2006; Franciszkiewicz et al, 2009) as well as T-cell transcription programs associated with cytokine production (Lillard et al, 2001; Camargo et al, 2009). CCR5 and its ligands are also critical for cell-mediated immunity to tumors and pathogens, including HIV-1 (Dolan et al, 2007; Ugurel et al, 2008; Gonza ´lezMartı ´net al, 2011; Bedognetti et al, 2013). Whereas the role of CCR5 in T-cell priming is well established, its involvement in memory responses has not been addressed in depth. Only a single report suggested CCR5 involvement in CD4 + T-cell promotion of memory CD8 + T-cell generation through a migration-dependent process (Castellino et al, 2006). It remains unknown whether CCR5 endows memory T cells with additional properties. One such property is the elevated sensitivity of effector and memory (“antigen-experienced”) CD4 + and CD8 + T cells to their cognate antigen compared to naı ¨ve cells (Kimachi et al, 1997; Kersh et al, 2003; Huang et al, 2013). This sensitivity gradient (memory >> effector >naı ¨ve) in CD8 + T cells is linked to increased valency of preformed T-cell antigen receptor (TCR) oligomers at the cell surface, termed TCR nanoclusters (Kumar et al, 2011). This antigen-independent TCR nanoclustering (Schamel et al, 2005, 2006; Lillemeier et al, 2010; Sherman et al, 2011; Schamel & Alarcon, 2013) enhances antigenic sensitivity by increasing avidity to multimeric peptide-major histocompatibility complexes (Kumar et al, 2011; Molnar et al, 2012) and by allowing cooperativity between TCR molecules (Martı ´nez-Martı ´net al, 2009; Martı ´n-Blanco et al, 2018). TCRbsubunit interaction with cholesterol (Chol) and the presence of sphingomyelins (SM) are both essential for TCR nanoclustering (Molnar et al, 2012; Beck-Garcia et al, 2015). Replacement of Chol by Chol sulfate impedes TCR nanocluster formation and reduces CD4 + CD8 + thymocyte sensitivity to weak antigenic peptides (Wang et al, 2016). Whether antigen-experienced CD4 + T-cell sensitivity is linked to TCR nanoscopic organization and the homeostatic factors that regulate TCR nanoclustering remains unexplored. Given its co-stimulatory role in CD4 + T cells, we speculated that CCR5 signals would affect the antigenic sensitivity of CD4 + memory T cells. To test this hypothesis, we analyzed the function of in vivogenerated memory CD4 + T cells in wild-type (WT) and CCR5 / mice, and the effect of CCR5 deficiency on CD4 T-cell help in the Tdependent humoral response. We found that CCR5 is necessary for the establishment of a functional CD4 memory response through a mechanism independent of its co-stimulatory role for the TCR signal. We show that CCR5 deficiency does not affect memory CD4 T-cell generation, but reduces their sensitivity to antigen. Our data demonstrate an unreported CCR5 regulatory role in memory CD4 + T-cell function by inhibiting the synthesis of ceramides, which are identified here as negative membrane regulators of TCR nanoscopic organization. Results CCR5deficiency impairs the CD4 + T-cell memory response To determine the role of CCR5 in CD4 + memory T-cell generation and/or function, we adoptively transferred congenic CD45.1 mice with lymph node/spleen cell suspensions from OT-II WT or CCR5 / mice (CD45.2) and subsequently infected them with OVA-encoding vaccinia virus; 5 weeks post-immunization, we analyzed spleen CD45.2 + donor cells from OT-II mice. CCR5 expression on OT-II cells affected neither the total number of memory CD4 + T cells (Fig 1A and B) nor the percentage of CD4 + T EM (CD44 hi ;CD62L  ; Fig 1C) or T CM (CD44 hi ; CD62L + ; Fig 1D) cells generated. OT-II WT cells nonetheless had stronger responses to antigenic restimulation than OT-II CCR5 / memory T cells, as determined by the percentage of interferon (IFN)c-producing cells after ex vivo stimulation with OVA 323–339 (Fig 1E). We also studied T cell-dependent B-cell responses in WT and CCR5 / mice after immunization with the hapten 4-hydroxy-3iodo-5-nitrophenylacetyl coupled to ovalbumin (NIP-OVA; Fig 1F). We detected no difference in the percentage or absolute number of T follicular helper (T fh ) cells (CD4 + , CD44 hi , CXCR5 + , PD1 + ) between WT and CCR5 / mice at 7 days post-immunization (Fig 1G–I). At day 30, half of the mice were boosted with the same NIP-OVA immunogen (OVA/OVA) and the other half received NIP conjugated with another carrier protein (OVA/KLH); levels of NIPspecific highand low-affinity immunoglobulins (Ig) were analyzed 15 days later. Comparison of the humoral responses between OVA/ OVAand OVA/KLH-immunized mice would assess the effect of memory CD4 + T cells specific for the first carrier protein on the humoral response to NIP. There were no differences in high/lowaffinity NIP-specific IgM production between WT and CCR5 / mice with either immunization strategy (Fig 1J and K). CCR5 deficiency markedly impaired the generation of high-affinity class-switched anti-NIP antibodies specifically in OVA/OVA-immunized mice (Fig 1J and K). Since class switching was similar in WT and CCR5 / OVA/KLH-immunized mice, our results suggest that CCR5 deficiency reduces the generation of high-affinity class-switched immunoglobulins due to deficient memory CD4 + T-cell function. The CCR5effect on antigen-experienced CD4 + T cells is cell-autonomous To test whether the in vivo memory defect associated with CCR5 deficiency was intrinsic to CD4 + T cells, we activated OT-II WT and CCR5 / spleen T cells with OVA 323–339 antigen for 3 days; after antigen removal, we cultured cells with IL-2 or IL-15. OT-II cells that differentiated in exogenous IL-2 expressed CCL3, CCL4, CCL5, and a functional CCR5 receptor, as determined by their ability to flux Ca 2+ and migrate after CCL4 stimulation (Appendix Fig S1A–D). Like CD8 + T cells (Richer et al, 2015), OT-II cells cultured with IL-15 showed a memory-like phenotype (Fig EV1); they were smaller than IL-2-cultured cells and retained CD62L with reduced 2of 19 The EMBO Journal 39:e104749 |2020 ª2020 The Authors The EMBO Journal Ana Martín-Leal et al 0 2 4 6 8 10 OVA/OVA OVA/KLH High affinity p = 0.4 BC DE FG KJ NIP-KLH NIP-OVA WT / CCR5 -/- Day 30 Day 0 Day 45 Electron microscopy. Ig ELISA. FACS TFH Day 7 A 0 2 4 6 8 10 0 20 40 60 80 100 0 2 4 6 8 10 NIP-OVA PD1-ef780 CXCR5-PeCy7 CD44-APC CD4-PB CD4+ Vα2+ cells (x106) CD4-ef450 SSC CD45.2-FITC CD44-PECy5 CD62L-APC TCM TEM Vα2-PE TCM TEM WT CCR5-/- TEM cells (%) p = 0.5 0 20 40 60 80 100 TCM cells (%) p = 0.5 ** IFNγ+ cells (%) WT CCR5-/- TFH cells (%) p = 0.3 *** * * 0 1 2 3 IgM IgG1 IgG2a IgG2b IgG3 IgA Class-switched Igs Absorbance (A.U.) IgM IgG1 IgG2a IgG2b IgG3 IgA Class-switched Igs *** IgM IgG1 IgG2a IgG2b IgG3 IgA Class-switched Igs 0 1 2 3 Absorbance (A.U.) * IgM IgG1 IgG2a IgG2b IgG3 IgA Class-switched Igs OVA/OVA OVA/KLH Low affinity WT CCR5-/- WT CCR5-/- 101102103104105 101102103104105101102103104105 100 101 102 103 104 105 101 102 103 104 105 102 103 104 101 102103104 101102103104 101 WT CCR5-/- 105 102 103 104 101 105 105105 TFH TFH WT CCR5-/- 0 2 1 3 4 TFH cells/spleen (x105) p = 0.3 HI Figure 1. CCR5deficiency impairs CD4 + T-cell memory responses. A Representative plots of splenocytes from CD45.1mice adoptively transferred with CD45.2OT-II WT or CCR5 / lymph node cell suspensions, 5weeks after infection with rVACV-OVA virus. The gating strategy used to identify the memory CD4 + T-cell subtypes is shown (n=5). B Absolute number of OT-II cells recovered in spleens of mice as in A (n=5). C, D Percentage of CD4 + T EM (C) and T CM (D) in the OT-II WT and CCR5 / populations (n=5). E IFNc-producing OT-II WT and CCR5 / memory cells isolated from mice as in (A) and restimulated ex vivo with OVA 323–339 (1lM) (n=4). F Immunization scheme for NIP-OVA and NIP-KLH in WT and CCR5 / mice. G–I Representative plots (G) and quantification of the frequency (H) and absolute number (I) of T fh cells (CD4 + CD44 + PD-1 + CXCR5 + ) in the spleen after primary immunization (day 7) with NIP-OVA (n=7). J, K ELISA analysis of high- (J) and low-affinity (K) isotype-specific anti-NIP antibodies in sera from OVA/OVAand OVA/KLH-immunized mice (day 15 post-challenge; n=5mice/group). Data representative of one experiment of two. Data information: (B–E, H–K), Data are mean SEM. *P<0.05,**P<0.01, ***P<0.001, two-tailed unpaired Student’st-test. ª2020 The Authors The EMBO Journal 39:e104749 |2020 3of 19 Ana Martín-Leal et al The EMBO Journal activation marker expression (CD25, CD69, CD44) compared to IL2-cultured T cells (Fig 2A). Findings were similar in OT-II WT and CCR5 / cells (Fig 2B), which reinforced the idea that CCR5 is not involved in CD4 + T memory cell differentiation. Restimulation of IL-2or IL-15-expanded OT-II lymphoblasts with the OVA 323–339 peptide nonetheless indicated that CCR5-expressing cells showed strong proliferation and higher IL-2 production at low antigen concentrations than CCR5-deficient cells (Fig 2C–F), indicative of an increased number of cells responding to antigenic stimulation. CCR5 might thus increase the antigenic sensitivity of antigen-experienced CD4 + T cells in a cell-autonomous manner. CCR5modulates TCR nanoclustering in antigen-experienced CD4 + T cells The high antigenic sensitivity of antigen-experienced CD8 + T cells was partially attributed to increased TCR nanoclustering (Kumar et al, 2011). To determine whether CCR5 deficiency influences TCR organization, we used electron microscopy (EM) to analyze surface replicas of OT-II WT and CCR5 / naı ¨ve cells and lymphoblasts after labeling with anti-CD3eantibody and 10 nm gold-conjugated protein A; a representative image of a IL-15-expanded WT lymphoblast is shown (Fig EV2). We found no differences in TCR CD25-PE CD62-L-FITC CCR5 -/- WT [3H]-TdR (cpm x 103) AB D C 0 10 20 30 40 * ** 0 50 100 150 200 250 * 100101102103 0 5 10 15 20 25 * ** * 0 20 40 60 80 **** IL-2 (pg/ml) F E 0 20 40 60 80 100 CD62L+ cells (%) 0 20 40 60 80 100 0 20 40 60 80 100 *** *** *** *** ** *** *** 0 50 100 150 200 250 300 FSC CD25+ cells (%) CD69+ cells (%) CD44 expression (MFI) CD69-PeCy7 CD44-APC OVA323-339 (nM) 100101102103 OVA323-339 (nM) 100101102103 OVA323-339 (nM) [3H]-TdR (cpm x 103) WT CCR5-/- WT CCR5-/- WT CCR5-/- 100101102103 OVA323-339 (nM) IL-2 (pg/ml) IL-2-derived lymphoblasts IL-15-derived lymphoblasts WT CCR5-/- WT CCR5-/- WT CCR5-/- IL-2 IL-15 IL-2 IL-15 IL-2 IL-15 Figure 2. CCR5increases the sensitivity of antigen-experienced CD4 + T cells. A, B Representative histograms and quantification of mean fluorescence intensity (MFI; A) or the percentage of cells positive for the indicated memory markers (B) in OT-II WT and CCR5 / lymphoblasts expanded in IL-2or IL-15, as specified. Data shown as mean SEM (n≥3). The gating strategy is shown in Fig EV1. C–F IL-2- (C, D) and IL-15-expanded lymphoblasts (E, F) were restimulated with indicated concentrations of OVA 323–339 ; cell proliferation (thymidine incorporation into DNA; C, E) and IL-2production (by ELISA; D, F) were measured after 72 h. Data are presented as mean SEM (n=5). Data information: *P<0.05,**P<0.01, ***P<0.001, two-way ANOVA (B) or two-tailed unpaired Student’st-test (C–F). 4of 19 The EMBO Journal 39:e104749 |2020 ª2020 The Authors The EMBO Journal Ana Martín-Leal et al nanoclusters between OT-II WT and CCR5 / naı ¨ve cells, which had a small percentage of TCR nanoclusters larger than 4 TCR in both genotypes (Fig 3A). In contrast, there was a significant increase in TCR nanocluster number and size in WT compared to CCR5 / lymphoblasts (Fig 3B and C). The number of TCR nanoclusters per cell analyzed in each condition is also indicated (Appendix Table S1). As predicted, there was a gradient in TCR nanoclustering of naı ¨ve IL-2- <IL-15-differentiated OT-II WT cells (Appendix Fig S1E), which coincided with increased antigenic sensitivity of the IL-15-expanded cells (Appendix Fig S1F and G). These findings thus reinforce the IL-15-induced memorylike phenotype versus the IL-2-induced effector-like phenotype and link TCR nanoclustering with increased sensitivity in antigenexperienced CD4 + T cells. The difference in TCR nanoclustering between WT and CCR5 / cells was nevertheless similar in IL-2and IL-15-expanded lymphoblasts, which indicates that CCR5 affects TCR nanoclustering in lymphoblasts independently of the cytokine milieu. Using a Monte Carlo simulation, we applied data from surface replicas of naı ¨ve and IL-2-expanded OT-II lymphoblasts to determine whether the experimental frequency of cluster size was due to random distribution of gold particles. In all cases, the cluster distributions observed experimentally differed significantly from pure random proximity between clusters (Appendix Fig S2). To define the differences between OT-II WT and CCR5 / cells, we used a model that accounts for receptor clustering dynamics (Castro et al, 2014), a Bayesian inference method that estimates the so-called clustering parameter, b. Based on this model, we concluded that the probability of a chance nanocluster distribution similar to that observed for naı ¨ve and activated OT-II WT and CCR5 / cells approaches 0% (Fig 3D and E). Posterior distribution analysis also showed that whereas the clustering parameter was very similar between naı ¨ve OT-II WT and CCR5 / cells (Fig 3D), there was clear separation in lymphoblasts (Fig 3E). These analyses provide a mathematical framework that validates the TCR nanoclustering differences between WT and CCR5 / cells, as determined by EM. The differences in TCR oligomerization between OT-II WT and CCR5 / lymphoblasts were also studied using blue-native gel electrophoresis (BN-PAGE) (Schamel et al, 2005; Swamy & Schamel, 2009). Cell lysis with digitonin, a detergent that disrupts TCR nanoclusters into their monomeric components, showed that WT and CCR5 / lymphoblasts expressed comparable TCR levels, as detected with anti-CD3fantibodies (Fig 3F). Cell lysis with Brij96, which preserves TCR nanoclusters, showed a notable reduction in large TCR complexes in CCR5 / compared to WT lymphoblasts (Fig 3F). Two independent techniques thus support a CCR5 role in TCR nanoscopic organization in antigen-experienced CD4 + T cells. To determine whether CCR5 controls TCR nanoclustering in in vivo-generated memory T cells, we analyzed TCR distribution in surface replicas of CD4 + memory T cells purified by negative selection from OVA/OVA-immunized WT and CCR5 / mice (Appendix Fig S3). CD4 + memory cells from CCR5 / mice showed fewer, smaller TCR nanoclusters than those from WT counterparts (Fig 3G; Appendix Table S1), which indicates that CCR5 promotes formation of large TCR nanoclusters in endogenously generated CD4 + memory T cells. CCR5-induced TCR nanoclustering is independent of its co-stimulatory activity Since CCR5 has co-stimulatory functions in CD4 + T-cell priming (Molon et al, 2005; Gonza ´lez-Martı ´net al, 2011), it is of interest to know whether defective TCR clustering in CCR5 / lymphoblasts is due to suboptimal primary activation of these cells. To address this question, we treated OT-II WT cells with the CCR5 antagonist TAK-779 at various intervals throughout culture and analyzed TCR nanoclusters in IL-2-expanded T lymphoblasts. TAK-779 addition during the priming phase (blockade of CCR5 costimulatory function) decreased the percentage of large TCR nanoclusters compared to untreated controls (Fig 4A). TAK-779 treatment did not alter TCR clustering in OT-II CCR5 / cells (Appendix Fig S4), which indicates that the TAK-779 effect on OTII cells is CCR5-specific. To avoid interference with the CCR5 co-stimulatory activity, we primed OT-II WT cells in the absence of the inhibitor and added TAK-779 only during IL-2-driven expansion of the CD4 + lymphoblasts. In these conditions, TAK-779 also reduced the percentage of large TCR nanoclusters (Fig 4B), which indicates that the CCR5 signals that control TCR organization are independent of those involved in its co-stimulatory function. We next explored whether other chemokine receptors involved in T-cell activation control TCR nanoclusters in CD4 + T cells. CXCR4 is a paradigmatic chemokine receptor that also provides costimulatory signals (Kumar et al, 2006; Smith et al, 2013). We primed OT-II WT cells in the presence of the CXCR4 antagonist AMD3100 and analyzed TCR nanoclusters in IL-2-expanded T lymphoblasts. Vehicleand AMD3100-treated cells showed similar TCR nanocluster distribution (Fig 4D), which implies that CXCR4 blockade does not interfere with TCR nanoclustering. CCR5deficiency increases ceramide levels in CD4 + T cells We analyzed CCR5 regulation of TCR nanoclustering in CD4 + T cells and found no differences between OT-II WT and CCR5 / cells in TCR/CD3 chain mRNA levels or in cell surface expression of the TCRachain (Fig EV3). These data suggest that the reduction in TCR clustering in CCR5 / cells is not due to decreased TCR expression. T-cell antigen receptor nanoclustering is dependent on plasma membrane Chol and SM (Molnar et al, 2012), two lipids also necessary for CCR5 signaling (Man ˜es et al, 2001). OT-II WT and CCR5 / lymphoblasts expressed comparable levels of total Chol and SM species (Fig 5A and B). OT-II CCR5 / lymphoblasts nonetheless showed a significant increase in most ceramide (Cer) species and their dihydroCer (dhCer) precursors (Fig 5C and D). These differences were not observed in naı ¨ve OT-II WT and CCR5 / cells (Appendix Fig S5A), indicative that the Cer increase was specific to antigen-experienced cells. The increase in Cer species in CCR5 / lymphoblasts was not linked to enhanced apoptosis compared to WT cells (Appendix Fig S5B). CCR5deficiency upregulates specific ceramide synthases in CD4 + T cells Our analysis of the mRNA levels of key enzymes involved in Cer metabolism showed no differences in ceramidases (ASAH1, ACER 2, ª2020 The Authors The EMBO Journal 39:e104749 |2020 5of 19 Ana Martín-Leal et al The EMBO Journal TCR nanoclusters Brij-96 440 880 Digitonin KDa Monomeric TCR A B C F G 0.0 0.2 0.4 0.6 0.8 1.0 Clustering parameter (b) mean=0.31mean=0.23 mean=0.03 0% in ROPE D E * Digitonin Brij-96 0 1 2 3 Multi / Monomeric TCR 0 20 40 60 80 100 1234>4 0 20 40 60 80 100 WT CCR5-/- Total gold particles (%) WT CCR5-/- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 > 15 Gold particles per cluster Naïve cells 1234>4 0 20 40 60 80 *** ** *** ** 0 20 40 60 80 Total gold particles (%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 > 15 Gold particles per cluster WT CCR5-/- IL-2-derived lymphoblasts ** * 0 20 40 60 Total gold particles (%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 > 15 Gold particles per cluster 1234>4 0 20 40 60 WT CCR5-/- IL-15-derived lymphoblasts WT CCR5-/- WT CCR5-/- WT CCR5-/- ** * 0 20 40 60 12 3 4>4 123456789101112131415> 15 Gold particles per cluster 0 20 40 60 Total gold particles (%) WT CCR5-/- WT CCR5-/- WT CCR5-/- Random Naïve cells mean=0.79 mean=0.44 mean=0.03 0% in ROPE 0.0 0.2 0.4 0.6 0.8 1.0 Clustering parameter (b) IL-2-derived lymphoblasts Figure 3. 6of 19 The EMBO Journal 39:e104749 |2020 ª2020 The Authors The EMBO Journal Ana Martín-Leal et al ACER 3) and sphingomyelinases (SMPD1–4) between OT-II WT and CCR5 / naı ¨ve cells or lymphoblasts (Fig EV4). mRNA levels of the ceramide synthases (CerS) CerS2, CerS3, and CerS4 were nonetheless upregulated in OT-II CCR5 / lymphoblasts (Fig 5E); CerS5 and CerS6 were unaltered, and the nervous system-specific CerS1 isoenzyme was not detected. CerS2, CerS3, and CerS4 levels were comparable in naı ¨ve CD4 + WT or CCR5 / cells (Fig 5E), which again associate the CCR5 transcriptional effect on these genes with activation. We sought to validate the CerS isoforms upregulated by CCR5 deficiency at the protein level. In accordance with mRNA analyses, CerS2 protein levels were significantly higher in CCR5 / than in WT lymphoblasts (Fig 5F); CerS3 and CerS4 were undetectable or only barely detectable by immunoblot. This is consistent with the fact that CerS2 has the highest expression level and the broadest substrate specificity in other cell types (Laviad et al, 2008). Chromatin immunoprecipitation (ChIP), followed by amplification of a region of the CerS2 promoter enriched in CpG islands, showed that binding of the transcriptional activation marker acetylated histone H3K9 (H3K9Ac) was higher in CCR5 / than in WT lymphoblasts (Fig 5G). Moreover, blockade of CCR5 signaling with pertussis toxin (PTx; an inhibitor of the Ga i subunit) also increased CerS2 mRNA expression (Fig 5H). To further study CCR5 transcriptional regulation of CerS, we scanned for transcription factors with putative binding sites in the CerS2, CerS3, and CerS4 promoters, which are transcriptionally upregulated in CCR5 / lymphoblasts, but not represented in the CerS6 promoter, which is not CCR5-regulated. We selected two regions; region 1 comprised 5 kb to the 50UTR, and region 2 encompassed the 50UTR to the first coding exon (Fig 5I). This bioinformatic approach identified GATA-1 and NF-IB (nuclear factor-1B) as putative transcription factors involved in the differential expression of the CerS2 isoform (Fig 5J and K). We focused on GATA-1, since it is implicated in the differentiation of some CD4 + T-cell subtypes (Sundrud et al, 2005; Fu et al, 2012). Immunofluorescence analyses showed increased nuclear levels of the phosphoSer142-GATA-1 form in OT-II CCR5 / compared to WT lymphoblasts (Fig 5L and M), which correlated with enriched GATA-1 binding to the CerS2 promoter in CD4 + CCR5 / lymphoblasts (Fig 5N). CCR5 deficiency might induce CerS2 transcription through GATA-1. Ceramide levels control TCR nanoclustering We used a synthetic biology approach to determine whether ceramide content affects TCR nanoclustering. Large unilamellar vesicles (LUV) were prepared at different molar ratios of PC, Chol, SM, and Cer (Fig 6A) and then reconstituted with a streptavidin-bindingpeptide-tagged TCR purified in its native state from murine M.mfSBP (streptavidin-binding peptide) T cells (Swamy & Schamel, 2009). The proteoliposomes were analyzed by BN-PAGE after solubilization in 0.5% Brij96 to maintain TCR nanocluster integrity or in 1% digitonin to disrupt TCR clusters. As anticipated (Molnar et al, 2012; Wang et al, 2016), TCR was monomeric in PC-containing LUV, whereas it formed nanoclusters when reconstituted in PC/ Chol/SM liposomes (Fig 6B and C). The inclusion of ceramides in these LUV (PC/Chol/SM/Cer liposomes) reduced TCR nanoclustering in a dose-dependent manner. This effect was not due to differential TCR reconstitution in Cer-containing LUV, since digitonin treatment rendered equivalent levels of monomeric TCR in each condition (Fig 6B). These data suggest that Cer membrane content impairs TCR nanoclustering. To test whether this effect also occurs in live cells, we treated OT-II WT lymphoblasts with recombinant sphingomyelinase (SMase), which hydrolyzes SM to ceramide (Kitatani et al, 2008). SMase treatment of WT OT-II blasts increased Cer levels robustly (Fig 6D), but did not compromise cell viability (Appendix Fig S6). Analysis of membrane replicas from these cells showed that SMase treatment reduced the number of high valency TCR nanoclusters compared to controls (Fig 6E; Appendix Table S1), which indicates that high Cer levels hinder TCR nanoclustering in CD4 + T cells. CerS2silencing restores TCR nanoclustering after CCR5 functional blockade To correlate increased CerS2 expression with the impaired TCR nanoclustering in OT-II CCR5 / T cells, we attempted to silence CerS2 expression by lentiviral transduction of primary lymphoblasts with short-hairpin (sh) RNA for CerS2 or control (shCtrl). In the most successful experiments, we were only able to transduce ~20% of the lymphoblasts, which did not lead to solid CerS2 mRNA silencing (Appendix Fig S7A and B). Despite the low efficiency, antigenic restimulation tended to promote stronger responses in shCerS2- ◀Figure 3. CCR5increases TCR nanoclustering in antigen-experienced CD4 + T cells. A–C Analysis of TCR nanoclustering by EM in OT-II WT and CCR5 / naïve cells (A; n=6cells/genotype; WT: 3,427, CCR5 / :3,528 particles), and IL-2- (B; WT, n=8 cells, 15,419 particles; CCR5 / ,n=6cells, 5,410 particles) or IL-15-expanded lymphoblasts (C; WT, n=8cells, 27,518 particles; CCR5 / ,n=7cells, 22,696 particles). A representative small field image at the top of each panel shows gold particle distribution in the cell surface replicas of anti-CD3e-labeled cells; at bottom, quantification (mean SEM) of gold particles in clusters of indicated size in WT (gray bars) and CCR5 / cells (red). Insets show the distribution of clusters of one, two, three, four, or more than four particles, and statistical analysis. D, E Posterior distribution in naïve (D) and IL-2-expanded lymphoblasts (E) of the clustering parameter bfor WT (gray) and CCR5 / cells (red); randomly generated distributions of receptors are shown in blue. The mean value of the bparameter is indicated for each condition. The probability of a chance distribution similar to that determined in cells is nearly 0% by the ROPE. F Comparison of TCR oligomer size using BN-PAGE and anti-CD3fimmunoblotting in day 10, IL-2-expanded WT and CCR5 / OT-II lymphoblasts lysed in buffer containing digitonin or Brij-96. The marker protein is ferritin (f1,440 and f2,880 kDa forms). The ratio of TCR nanoclusters to monomeric TCR in each lysis condition was quantified by densitometry (right; n=5). G Top, representative small field EM images showing gold particle distribution in the cell surface replicas of CD4 + T cells isolated from OVA/OVA-immunized WT and CCR5 / mice. Bottom, quantification (mean SEM) of gold particles in clusters of the indicated size (WT, gray bars; n=5cells, 14,680 particles; CCR5 / , red; n=7cells, 15,374 particles). Insets show the distribution between clusters of one, two, three, four, or more than four particles, and statistical analysis. Data information: (A–C, F, G), Data are mean SEM. *P<0.05,**P<0.01, ***P<0.001, one-tailed unpaired Student’st-test. Scale bar, 50 nm (A–C, G). ª2020 The Authors The EMBO Journal 39:e104749 |2020 7of 19 Ana Martín-Leal et al The EMBO Journal than in shCtrl-transduced cells (Appendix Fig S7C and D). The low efficiency also precluded analysis of TCR nanoclusters in membrane replicas, as transduced cells could not be distinguished from nontransduced cells. To overcome these difficulties, we used the 2B4 CD4 + T-cell line. We verified that 2B4 cells expressed CCR5 and that TAK779 treatment increased CerS2 levels and impaired TCR nanoclustering (Appendix Fig S8). The data suggest that CCR5 effects on TCR nanoclustering and CerS2 induction are not exclusive to the OT-II system and that TAK-779-treated 2B4 cells mimic the functional findings in OT-II CCR5 / lymphoblasts. 2B4 cells were transduced efficiently by lentiviruses and, after Day3 Day10 OT-II WT Antigen removal TAK-779 Electron microscopy * AB ***** Medium TAK-779 OT-II WT Electron microscopy TAK-779 Day10 Antigen removal 1234>4 0 20 40 60 80 0 20 40 60 80 Total gold particles (%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 > 15 Gold particles per cluster Medium TAK-779 Medium TAK-779 0 20 40 60 80 1234>4 123456789101112131415> 15 Gold particles per cluster Medium TAK-779 0 20 40 60 80 Total gold particles (%) Day3 Day10 OT-II WT Antigen removal AMD3100 Electron microscopy Vehicle AMD3100 C 123456789101112131415> 15 Gold particles per cluster 0 20 40 60 80 Total gold particles (%) 0 20 40 60 80 1234>4 Vehicle AMD3100 Figure 4. CCR5-induced TCR nanoclustering is specific and independent of its co-stimulatory activity. A OT-II WT cells were activated with OVA 323–339 , alone or with TAK-779. After 3days, antigen and TAK-779 were removed and lymphoblasts expanded in IL-2-containing medium. TCR nanoclustering was analyzed in anti-CD3e-labeled surface replicas of day 10 lymphoblasts. Top, representative small field EM images showing gold particle distribution in the cell surface replicas of WT CD4 + T cells alone or with TAK-779. Bottom, quantification of gold particles in clusters of the indicated size. Inset, distribution of gold particles between clusters of one, two, three, four, or more than four particles in vehicle- (gray bars; n=5cells, 11,266 particles) and TAK-779-treated cells (black; n=6cells, 5,138 particles). B OT-II WT cells were activated with OVA 323–339 , and TAK-779 was added at days 3,5, and 7after antigen removal. Analysis as above, untreated (gray bars; n=5cells, 6,400 particles) and TAK-779-treated cells (black; n=6cells, 7,153 particles). Inset shows the distribution between clusters of one, two, three, four, or more than four particles, and statistical analysis. C OT-II WT naïve cells were activated with antigen in the presence or not of the CXCR4inhibitor AMD3100. Left, representative EM images showing gold particle distribution in the cell surface replicas. Right, analysis of gold particles in clusters as above, vehicle- (gray bars; n=6cells, 12,339 particles) and AMD3100-treated cells (black; n=7cells, 17,059 particles). Inset shows the distribution between clusters of one, two, three, four, or more than four particles, and statistical analysis. Data information: (A–C), Data are mean SEM. *P<0.05,**P<0.01, ***P<0.001, one-tailed unpaired Student’st-test. Scale bar, 50 nm. 8of 19 The EMBO Journal 39:e104749 |2020 ª2020 The Authors The EMBO Journal Ana Martín-Leal et al 3 days of antibiotic selection, 100% of the cells expressed the shRNA; this led to strong silencing of CerS2 mRNA and protein in shCerS2compared to shCtrl-transduced cells (Fig 6F–H). Analysis of TCR organization showed recovery of large TCR nanoclustering in TAK-779-treated, shCerS2-transduced cells compared to controls (Fig 6I; Appendix Table S1); after restimulation with plate-bound anti-CD3eantibody in the presence of TAK-779, CD69 upregulation was higher in CerS2-deficient than in shCtrl-cells (Fig 6J). CCR5modulates TCR nanoclustering in human CD4 + T cells Finally, we tested whether CCR5 deficiency also impairs TCR organization in human CD4 + T cells. Approximately 1% of the C CerS3 CerS5 CerS2 *** mRNA expression (Rq) D 0 0.5 1.0 1.5 2.0 2.5 * *** 0 0.5 1.0 1.5 signal relative to input * G F C14:0 C16:0 C16:1 C18:0 C18:1 C20:0 C20:1 C22:0 C22:1 C24:0 C24:1 C24:2 C24:3 0 100 200 300 AB BlastsNaïve * 0 0.5 1.0 1.5 2.0 2.5 0 5 10 15 20 * CerS4 0 1 2 3 0 50 100 150 0 5 10 15 20 25 Chol (μg /106 cells) p = 0.9 SM (pmol /106 cells) * * * ******** ** * Cer (pmol /106 cells) C14:0 C16:0 C16:1 C18:0 C18:1 C20:0 C20:1 C22:0 C22:1 C24:0 C24:1 C24:2 Cer species SM species dhCer species C16:0 C18:0 C20:0 C22:0 C24:0 C24:1 dhCer (pmol /106 cells) Blasts Naïve BlastsNaïve p = 0.7 BlastsNaïve BlastsNaïve p = 0.4 CerS6 WT CCR5-/- WT CCR5-/- E CerS2 promoter -2,500 CpG +1 ChIP Y H3K9Ac WT CCR5-/- Naïve Blasts CerS2 β-actin Naïve Blasts WT CCR5-/- WT CCR5-/- CerS2 / β-actin ratio 0 5 10 15 ** BlastsNaïve p = 0.08 0 5 10 15 20 0 5 10 15 20 47 0 5 30 524 25 23 52 0 0 30 Cers2 Cers6 Cers4 Cers3 23 36 10 1 11 10 1 81 9 2 4 54 0 Exon Intron 3’UTR 5’UTR TSS -5 kb +5 kb Region 1 Region 2 promoter 15 5 Cers2 Cers6 Cers4Cers3 I GATA-1 NF-IB 0 5 10 15 * 100 200 300 pSer142-GATA-1 M L 0 ** WT CCR5-/- DAPI N GATA-1 (pSer142) CerS2 promoter -2,500 -260 ChIP Y GATA-1 WT CCR5-/- Integrated density (x103) signal relative to input WT CCR5-/- K J PTx (μg/ml) *** * 0 5 10 15 20 25 10-3 10-2 10-1 0 H CerS2 mRNA (Rq) * Figure 5. ª2020 The Authors The EMBO Journal 39:e104749 |2020 9of 19 Ana Martín-Leal et al The EMBO Journal cells were selected with puromycin (2 lg/ml) for 3 days prior to analyses. Immunofluorescence analyses OT-II 10-day WT or CCR5 / lymphoblasts were plated in poly-Llysine-coated coverslips (Nunc Lab-Tek Chamber Slide, Thermo Scientific; 50 lg/ml, overnight, 4°C). After adhesion (1 h, 37°C), cells were fixed in 4% PFA (10 min), Triton-X100-permeabilized (0.3% in PBS, 15 min), and blocked with BSA 0.5% in PBS. Samples were incubated (overnight, 4°C) with anti-mouse phosphoGATA1 pSer142 antibody (1/200), followed by anti-rabbit Ig Alexa-488 secondary antibody (1 h). Coverslips were mounted in Fluoromount-G with DAPI (Southern Biotech); images were acquired with a Zeiss LSM710 and analyzed by a blind observer with NIH ImageJ software. ChIP assay Chromatin immunoprecipitation assays were performed with the EZ-ChIP Kit (Millipore). In brief, OT-II WT or CCR5 / lymphoblasts (2 ×10 7 ) were fixed (1% PFA, 10 min, RT) and quenched (125 mM glycine, 5 min, RT). Cells were harvested (1 ×10 7 cells/ ml), lysed (15 min, 4°C), and DNA sheared by sonication (45 cycles; 30 s on/off; Bioruptor Pico, Diagenode) in aliquots (0.2 ml). Of each lysate, 1% was stored as input reference, and the remaining material was immunoprecipitated (14 h, 4°C, with rotation) with antibodies to GATA1, histone H3-Lys9, or purified IgG (control). Immune complexes were captured using Protein G Magnetic Beads (Bio-Rad) and, after washing, eluted with 100 mM NaHCO 3 , 1% SDS; protein/DNA bonds were disrupted with proteinase K (10 lg/ll, 2 h, 62°C). DNA was purified using spin columns, and Cers2 gene promoter sequences were analyzed with specific primers (Appendix Table S3). The relative quantity of amplified product in the input and ChIP samples was calculated (Mira et al, 2018). Cell migration and Ca 2+ flux assays OT-II WT or CCR5 / lymphoblasts (10 6 ) were added to the upper chamber of a Transwell (3-lm pore; Corning) and allowed to migrate toward 100 nM CCL4 for 4 h. Migrating cells were quantified by flow cytometry (Cytomics FC500). Mobilization of intracellular Ca 2+ stores after CCL4 (100 nM) stimulation was measured as reported (Go ´mez-Mouto ´net al, 2015). Mathematical and Bayesian analyses To analyze cluster size distribution, we used a standard chi-square test to compare the fraction of clusters of a given size (1, 2, 3, etc.) in each dataset. In all plots, “Random” refers to synthetic distributions of receptors generated randomly. To quantify the mechanistic relevance of cluster size between random distributions of clusters and clusters in WT and in CCR5 / CD4 + T cells, we used a Bayesian inference model on top of a mechanistic model (Castro et al, 2014). The model assumes that TCR aggregates by incorporating one receptor at a time, with on and off rates that depend on the diffusion properties of the receptor on the membrane, but not existing cluster size. That is, 1 q qþ2 q qþ3 q qþ ...  q qþn1 q qþn q qþnþ1... The “affinity” of the process is given by b=q+/q, which we also refer to as the clustering or affinity parameter. In the steady state, we can calculate analytically the fraction of clusters of a given size n: pn¼bn11bðÞ 1bNmax ðÞ with b\1;n1;2;3;...;Nmax fg: The model was fitted using the Bayesian JAGS code (Kruschke, 2014) (see Appendix Supplementary Methods). The histograms for the number of clusters of a given size n(N n ) were modeled as a multinomial distribution with the number of observations, N, given by the total count per experiment, and probabilities p n given by the formulas above. The priors for the clustering parameter bare beta distributions with shape parameters Aand Bwith non-informative uniform priors. Specifically, N n ~ Multinomial (p n,N ) b~ Beta (A,B) A~ Uniform (0, 1,000) B~ Uniform (0, 1,000) Posterior distribution of the estimated clustering parameter, b, is given with the so-called Region of Practical Equivalent (ROPE), defined as the probability of a parameter from a dataset to be explained by another dataset. ROPE quantifies the probability that the observed clustering parameter (and distribution of clusters) in the experiment can be obtained by pure random proximity. At the molecular level, the kinetic rates q + and q  can be expressed in terms of the diffusion rates of the receptors,kþ= d, the receptor size (a), the mean distance between receptors (s), and the correct receptor–receptor binding rates, k +/ , through the equations (Lauffenburger & DeLisi, 1983): qþ¼ kþ dkþ kþ dþkq¼ k dk kþ dþk)b¼kþ dkþ k dþk with kþ d¼4pD log s=aðÞ3=4k d¼2pD s2log s=aðÞ3=4ðÞ : The clustering parameter bis independent of the TCR diffusivity (as Dis canceled), so the observed TCR nanoclustering differences for WT and CCR5 / cells would be due to TCR-TCR interactions, as previously reported (Beck-Garcia et al, 2015). 16 of 19 The EMBO Journal 39:e104749 |2020 ª2020 The Authors The EMBO Journal Ana Martín-Leal et al Identification of transcription factors in CerS promoters Cers2, Cers3, Cers4, and Cers6 gene coordinates were obtained from the UCSC Genome browser (https://genome.ucsc.edu/; mouse genome version GRCm38/mm10). Known transcription factors for these genes were identified at GTRD v17.04 (http://gtrd17-04.b iouml.org/). Venn diagrams were constructed to identify common and specific transcription factors for ceramide synthase genes. Statistical analyses For comparison between two conditions, data were analyzed using parametric Student’s t-tests, paired when different treatments were applied to the same sample, or unpaired with Welch’s correction. Multiple parametric comparisons were analyzed with one-way ANOVA with Bonferroni’s post-hoc test. The chi-square test was used to analyze overall distribution of gold particles. F test was used to compare variances. All analyses were performed using Prism 6.0 or 7.0 software (GraphPad). Differences were considered significant when P<0.05. Data and code availability This study includes no data deposited in external repositories. The authors confirm that all relevant data and materials supporting the findings of this study are available on reasonable request. This excludes materials obtained from other researchers, who must provide their consent for transfer. The Bayesian JAGS code generated in the study is provided as supplementary information in the Appendix Supplementary Methods. Expanded View for this article is available online. Acknowledgements We thank D Sancho and JW Yewdell for rVACV-OVA virus, RM Peregil for technical assistance, MC Moreno and S Escudero for flow cytometry services (CNB), MT Rejas and M Guerra for EM service (CBM-SO), and C Mark for excellent editorial assistance. Fundació ACE would like to thank patients, controls, and the staff who participated in this project. This work was funded by grants from the Spanish Ministerio de Ciencia, Innovación y Universidades (SAF2017–83732-R to SM; FIS2016-78883-C2-2-P to MC; CTQ2017-85378-R; AEI/FEDER, EU), the Instituto de Salud Carlos III (ISCIII) (PI13/02434,PI16/01861 to AR), the Comunidad de Madrid (B2017/BMD-3733; IMMUNOTHERCAN-CM to SM), and the Merck-Salud Foundation (to SM). WWS and CD were supported by the Deutsche Forschungsgemeinschaft (DFG) through BIOSS-EXC294 and CIBSS-EXC 2189 (Project 390939984), SCHA976/7-1, and SFB1381. The Genome Research @Fundació ACE project (GR@ACE) is supported by the Fundación Bancaria La Caixa, Grifols SA, Fundació ACE, and ISCIII (Ministry of Health, Spain). Fundació ACE is a participating center in the Dementia Genetics Spanish Consortium (DEGESCO). Author contributions SM conceived the study. AM-L and RB designed, performed most experiments, and interpreted data. JC and GF performed lipid analyses, and CD performed liposome experiments. ER-B performed analysis of TCR chains. MC carried out mathematical simulations and Bayesian analyses. MES carried out bioinformatic analyses. IR, LMR, and AR performed ccr5D32 genotyping and provided samples. WWAS, HMS, and BA contributed ideas and technical support. SM and RB wrote the manuscript. All authors read, discussed, and edited the manuscript. Conflict of interest The authors declare that they have no conflict of interest. References Alonso A, Goñi FM (2018) The physical properties of ceramides in membranes. Annu Rev Biophys 47:633 –654 Beck-Garcia K, Beck-Garcia E, Bohler S, Zorzin C, Sezgin E, Levental I, Alarcon B, Schamel WW (2015) Nanoclusters of the resting T cell antigen receptor (TCR) localize to non-raft domains. Biochim Biophys Acta 1853:802 –809 Bedognetti D, Spivey TL, Zhao Y, Uccellini L, Tomei S, Dudley ME, Ascierto ML, De Giorgi V, Liu Q, Delogu LG et al (2013) CXCR3/CCR5pathways in metastatic melanoma patients treated with adoptive therapy and interleukin-2.Br J Cancer 109:2412 –2423 Blanpain C, Libert F, Vassart G, Parmentier M (2002) CCR5and HIV infection. Receptors Channels 8:19 –31 Camargo JF, Quiñones MP, Mummidi S, Srinivas S, Gaitan AA, Begum K, Jimenez F, VanCompernolle S, Unutmaz D, Ahuja SS et al (2009) CCR5 expression levels influence NFAT translocation, IL-2production, and subsequent signaling events during T lymphocyte activation. J Immunol 182:171 –182 Castellino F, Huang AY, Altan-Bonnet G, Stoll S, Scheinecker C, Germain RN (2006) Chemokines enhance immunity by guiding naive CD8 + T cells to sites of CD4 + T cell-dendritic cell interaction. Nature 440:890 –895 Castro BM, Silva LC, Fedorov A, de Almeida RF, Prieto M (2009) Cholesterolrich fluid membranes solubilize ceramide domains: implications for the structure and dynamics of mammalian intracellular and plasma membranes. J Biol Chem 284:22978 –22987 Castro M, van Santen HM, Ferez M, Alarcon B, Lythe G, Molina-Paris C (2014) Receptor pre-clustering and T cell responses: insights into molecular mechanisms. Front Immunol 5:132 Contento RL, Molon B, Boularan C, Pozzan T, Mañes S, Marullo S, Viola A (2008) CXCR4-CCR5: a couple modulating T cell functions. Proc Natl Acad Sci USA 105:10101 –10106 Crotty S (2011) Follicular helper CD4T cells (TFH). Annu Rev Immunol 29: 621 –663 Dawson TC, Beck MA, Kuziel WA, Henderson F, Maeda N (2000) Contrasting effects of CCR5and CCR2deficiency in the pulmonary inflammatory response to influenza A virus. Am J Pathol 156:1951 –1959 DeJarnette JB, Sommers CL, Huang K, Woodside KJ, Emmons R, Katz K, Shores EW, Love PE (1998) Specific requirement for CD3epsilon in T cell development. Proc Natl Acad Sci USA 95:14909 –14914 Dolan MJ, Kulkarni H, Camargo JF, He W, Smith A, Anaya JM, Miura T, Hecht FM, Mamtani M, Pereyra F et al (2007) CCL3L1and CCR5influence cellmediated immunity and affect HIV-AIDS pathogenesis via viral entryindependent mechanisms. Nat Immunol 8:1324 –1336 Ellwanger JH, Chies JAB (2019) Host immunogenetics in tick-borne encephalitis virus infection-The CCR5crossroad. Ticks Tick Borne Dis 10: 729 –741 Falcon A, Cuevas MT, Rodriguez-Frandsen A, Reyes N, Pozo F, Moreno S, Ledesma J, Martinez-Alarcon J, Nieto A, Casas I (2015) CCR5deficiency predisposes to fatal outcome in influenza virus infection. J Gen Virol 96: 2074 –2078 ª2020 The Authors The EMBO Journal 39:e104749 |2020 17 of 19 Ana Martín-Leal et al The EMBO Journal Floto RA, MacAry PA, Boname JM, Mien TS, Kampmann B, Hair JR, Huey OS, Houben EN, Pieters J, Day C et al (2006) Dendritic cell stimulation by mycobacterial Hsp70 is mediated through CCR5.Science 314: 454 –458 Franciszkiewicz K, Le Floc’h A, Jalil A, Vigant F, Robert T, Vergnon I, Mackiewicz A, Benihoud K, Validire P, Chouaib S et al (2009) Intratumoral induction of CD103 triggers tumor-specific CTL function and CCR5dependent T-cell retention. Cancer Res 69:6249 –6255 Fu W, Ergun A, Lu T, Hill JA, Haxhinasto S, Fassett MS, Gazit R, Adoro S, Glimcher L, Chan S et al (2012) A multiply redundant genetic switch ‘locks in’the transcriptional signature of regulatory T cells. Nat Immunol 13: 972 –980 Garcia-Arribas AB, Alonso A, Goñi FM (2016) Cholesterol interactions with ceramide and sphingomyelin. Chem Phys Lipids 199:26 –34 Gómez-Moutón C, Fischer T, Peregil RM, Jiménez-Baranda S, Stossel TP, Nakamura F, Mañes S (2015) Filamin A interaction with the CXCR4third intracellular loop regulates endocytosis and signaling of WT and WHIMlike receptors. Blood 125:1116 –1125 González-Martín A, Gómez L, Lustgarten J, Mira E, Mañes S (2011) Maximal T cell-mediated antitumor responses rely upon CCR5expression in both CD4 + and CD8 + T cells. Cancer Res 71:5455 –5466 Gonzalez-Martin A, Mira E, Mañes S (2012) CCR5in cancer immunotherapy: more than an “attractive”receptor for T cells. Oncoimmunology 1: 106 –108 Hu YS, Cang H, Lillemeier BF (2016) Superresolution imaging reveals nanometerand micrometer-scale spatial distributions of T-cell receptors in lymph nodes. Proc Natl Acad Sci USA 113:7201 –7206 Huang J, Brameshuber M, Zeng X, Xie J, Li QJ, Chien YH, Valitutti S, Davis MM (2013) A single peptide-major histocompatibility complex ligand triggers digital cytokine secretion in CD4(+) T cells. Immunity 39:846 –857 Jung Y, Riven I, Feigelson SW, Kartvelishvily E, Tohya K, Miyasaka M, Alon R, Haran G (2016) Three-dimensional localization of T-cell receptors in relation to microvilli using a combination of superresolution microscopies. Proc Natl Acad Sci USA 113:E5916 –E5924 Kersh EN, Kaech SM, Onami TM, Moran M, Wherry EJ, Miceli MC, Ahmed R (2003) TCR signal transduction in antigen-specific memory CD8T cells. J Immunol 170:5455 –5463 Kimachi K, Croft M, Grey HM (1997) The minimal number of antigen-major histocompatibility complex class II complexes required for activation of naive and primed T cells. Eur J Immunol 27:3310 –3317 Kitatani K, Idkowiak-Baldys J, Hannun YA (2008) The sphingolipid salvage pathway in ceramide metabolism and signaling. Cell Signal 20: 1010 –1018 Kohlmeier JE, Miller SC, Smith J, Lu B, Gerard C, Cookenham T, Roberts AD, Woodland DL (2008) The chemokine receptor CCR5plays a key role in the early memory CD8 + T cell response to respiratory virus infections. Immunity 29:101 –113 Kruschke JK (2014)Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan. Burlington, MA: Academic Press, Elsevier Kumar A, Humphreys TD, Kremer KN, Bramati PS, Bradfield L, Edgar CE, Hedin KE (2006) CXCR4physically associates with the T cell receptor to signal in T cells. Immunity 25:213 –224 Kumar R, Ferez M, Swamy M, Arechaga I, Rejas MT, Valpuesta JM, Schamel WW, Alarcon B, van Santen HM (2011) Increased sensitivity of antigenexperienced T cells through the enrichment of oligomeric T cell receptor complexes. Immunity 35:375 –387 Lauffenburger D, DeLisi C (1983) Cell surface receptors: physical chemistry and cellular regulation. Int Rev Cytol 84:269 –302 Laviad EL, Albee L, Pankova-Kholmyansky I, Epstein S, Park H, Merrill AH Jr, Futerman AH (2008) Characterization of ceramide synthase 2: tissue distribution, substrate specificity, and inhibition by sphingosine 1phosphate. J Biol Chem 283:5677 –5684 Lee YH, Bae SC, Song GG (2013) Association between the chemokine receptor 5delta32 polymorphism and rheumatoid arthritis: a meta-analysis. Mod Rheumatol 23:304 –310 Lillard JW Jr, Boyaka PN, Taub DD, McGhee JR (2001) RANTES potentiates antigen-specific mucosal immune responses. J Immunol 166:162 –169 Lillemeier BF, Mortelmaier MA, Forstner MB, Huppa JB, Groves JT, Davis MM (2010) TCR and Lat are expressed on separate protein islands on T cell membranes and concatenate during activation. Nat Immunol 11:90 –96 Lim JK, Murphy PM (2011) Chemokine control of West Nile virus infection. Exp Cell Res 317:569 –574 Mañes S, Lacalle RA, Gómez-Moutón C, del Real G, Mira E, Martínez A-C (2001) Membrane raft microdomains in chemokine receptor function. Semin Immunol 13:147 –157 Mañes S, Mira E, Colomer R, Montero S, Real LM, Gomez-Mouton C, JimenezBaranda S, Garzon A, Lacalle RA, Harshman K et al (2003) CCR5expression influences the progression of human breast cancer in a p53-dependent manner. J Exp Med 198:1381 –1389 Martín-Blanco N, Blanco R, Alda-Catalinas C, Bovolenta ER, Oeste CL, Palmer E, Schamel WW, Lythe G, Molina-París C, Castro M et al (2018) A window of opportunity for cooperativity in the T Cell Receptor. Nat Commun 9: 2618 Martínez-Martín N, Risueño RM, Morreale A, Zaldivar I, Fernández-Arenas E, Herranz F, Ortiz AR, Alarcón B (2009) Cooperativity between T cell receptor complexes revealed by conformational mutants of CD3epsilon. Sci Signal 2:ra43 Martinez-Muñoz L, Rodriguez-Frade JM, Barroso R, Sorzano COS, TorrenoPina JA, Santiago CA, Manzo C, Lucas P, Garcia-Cuesta EM, Gutierrez E et al (2018) Separating actin-dependent chemokine receptor nanoclustering from dimerization indicates a role for clustering in CXCR4 signaling and function. Mol Cell 70:106 –119.e10 Mellado M, Rodriguez-Frade JM, Vila-Coro AJ, Fernandez S, Martin de Ana A, Jones DR, Toran JL, Martinez A-C (2001) Chemokine receptor homoor heterodimerization activates distinct signaling pathways. EMBO J 20: 2497 –2507 Merrill AH Jr, Sullards MC, Allegood JC, Kelly S, Wang E (2005) Sphingolipidomics: high-throughput, structure-specific, and quantitative analysis of sphingolipids by liquid chromatography tandem mass spectrometry. Methods 36:207 –224 Mira E, Carmona-Rodriguez L, Perez-Villamil B, Casas J, Fernandez-Acenero MJ, Martinez-Rey D, Martin-Gonzalez P, Heras-Murillo I, Paz-Cabezas M, Tardaguila M et al (2018) SOD3improves the tumor response to chemotherapy by stabilizing endothelial HIF-2alpha. Nat Commun 9:575 Molnar E, Swamy M, Holzer M, Beck-Garcia K, Worch R, Thiele C, Guigas G, Boye K, Luescher IF, Schwille P et al (2012) Cholesterol and sphingomyelin drive ligand-independent T-cell antigen receptor nanoclustering. J Biol Chem 287:42664 –42674 Molon B, Gri G, Bettella M, Gomez-Mouton C, Lanzavecchia A, Martinez AC, Mañes S, Viola A (2005) T cell costimulation by chemokine receptors. Nat Immunol 6:465 –471 Muñoz-Olaya JM, Matabosch X, Bedia C, Egido-Gabas M, Casas J, Llebaria A, Delgado A, Fabriás G (2008) Synthesis and biological activity of a novel inhibitor of dihydroceramide desaturase. ChemMedChem 3:946 –953 Nesbeth YC, Martinez DG, Toraya S, Scarlett UK, Cubillos-Ruiz JR, Rutkowski MR, Conejo-Garcia JR (2010)CD4 + T cells elicit host immune 18 of 19 The EMBO Journal 39:e104749 |2020 ª2020 The Authors The EMBO Journal Ana Martín-Leal et al responses to MHC class II-negative ovarian cancer through CCL5 secretion and CD40-mediated licensing of dendritic cells. J Immunol 184:5654 –5662 Nie C, Sato K, Misawa N, Kitayama H, Fujino H, Hiramatsu H, Heike T, Nakahata T, Tanaka Y, Ito M et al (2009) Selective infection of CD4 + effector memory T lymphocytes leads to preferential depletion of memory T lymphocytes in R5HIV-1-infected humanized NOD/SCID/IL2Rgammanull mice. Virology 394:64 –72 Pageon SV, Tabarin T, Yamamoto Y, Ma Y, Nicovich PR, Bridgeman JS, Cohnen A, Benzing C, Gao Y, Crowther MD et al (2016) Functional role of T-cell receptor nanoclusters in signal initiation and antigen discrimination. Proc Natl Acad Sci USA 113:E5454 –E5463 Ramstedt B, Slotte JP (2002) Membrane properties of sphingomyelins. FEBS Lett 531:33 –37 Richer MJ, Pewe LL, Hancox LS, Hartwig SM, Varga SM, Harty JT (2015) Inflammatory IL-15 is required for optimal memory T cell responses. J Clin Invest 125:3477 –3490 Schamel WW, Arechaga I, Risueno RM, van Santen HM, Cabezas P, Risco C, Valpuesta JM, Alarcon B (2005) Coexistence of multivalent and monovalent TCRs explains high sensitivity and wide range of response. J Exp Med 202:493 –503 Schamel WW, Risueño RM, Minguet S, Ortiz AR, Alarcon B (2006)A conformationand avidity-based proofreading mechanism for the TCRCD3complex. Trends Immunol 27:176 –182 Schamel WW, Alarcon B (2013) Organization of the resting TCR in nanoscale oligomers. Immunol Rev 251:13 –20 Schauren JS, Marasca JA, Veit TD, Monticielo OA, Xavier RM, Brenol JC, Chies JA (2013) CCR5delta32 in systemic lupus erythematosus: implications for disease susceptibility and outcome in a Brazilian population. Lupus 22: 802 –809 Sherman E, Barr V, Manley S, Patterson G, Balagopalan L, Akpan I, Regan CK, Merrill RK, Sommers CL, Lippincott-Schwartz J et al (2011) Functional nanoscale organization of signaling molecules downstream of the T cell antigen receptor. Immunity 35:705 –720 Smith X, Schneider H, Kohler K, Liu H, Lu Y, Rudd CE (2013) The chemokine CXCL12 generates costimulatory signals in T cells to enhance phosphorylation and clustering of the adaptor protein SLP-76.Sci Signal 6:ra65 Sofi MH, Heinrichs J, Dany M, Nguyen H, Dai M, Bastian D, Schutt S, Wu Y, Daenthanasanmak A, Gencer S et al (2017) Ceramide synthesis regulates T cell activity and GVHD development. JCI Insight 2:e91701 Sot J, Ibarguren M, Busto JV, Montes LR, Goni FM, Alonso A (2008) Cholesterol displacement by ceramide in sphingomyelin-containing liquid-ordered domains, and generation of gel regions in giant lipidic vesicles. FEBS Lett 582:3230 –3236 Stiban J, Perera M (2015) Very long chain ceramides interfere with C16ceramide-induced channel formation: a plausible mechanism for regulating the initiation of intrinsic apoptosis. Biochim Biophys Acta 1848: 561 –567 Sundrud MS, Vancompernolle SE, Eger KA, Bruno TC, Subramaniam A, Mummidi S, Ahuja SK, Unutmaz D (2005) Transcription factor GATA-1 potently represses the expression of the HIV-1coreceptor CCR5in human T cells and dendritic cells. Blood 106:3440 –3448 Swamy M, Schamel WW (2009) Purification of the T cell antigen receptor and analysis by blue-native PAGE. Methods Mol Biol 514:135 –150 Toson B, Dos Santos EJ, Adelino JE, Sandrin-Garcia P, Crovella S, LouzadaJunior P, Oliveira RD, Pedroza LS, de Fatima Lobato Cunha Sauma M, de Lima CP et al (2017) CCR5Delta32 and the genetic susceptibility to rheumatoid arthritis in admixed populations: a multicentre study. Rheumatology (Oxford) 56:495 –497 Ugurel S, Schrama D, Keller G, Schadendorf D, Brocker EB, Houben R, Zapatka M, Fink W, Kaufman HL, Becker JC (2008) Impact of the CCR5gene polymorphism on the survival of metastatic melanoma patients receiving immunotherapy. Cancer Immunol Immunother 57:685 –691 Vangelista L, Vento S (2017) The expanding therapeutic perspective of CCR5 blockade. Front Immunol 8:1981 Vieira CR, Muñoz-Olaya JM, Sot J, Jimenez-Baranda S, Izquierdo-Useros N, Abad JL, Apellaniz B, Delgado R, Martinez-Picado J, Alonso A et al (2010) Dihydrosphingomyelin impairs HIV-1infection by rigidifying liquid-ordered membrane domains. Chem Biol 17:766 –775 Vinuesa CG, Linterman MA, Yu D, MacLennan IC (2016) Follicular helper T cells. Annu Rev Immunol 34:335 –368 Wang F, Beck-Garcia K, Zorzin C, Schamel WW, Davis MM (2016) Inhibition of T cell receptor signaling by cholesterol sulfate, a naturally occurring derivative of membrane cholesterol. Nat Immunol 17:844 –850 Xu Y, Phetsouphanh C, Suzuki K, Aggrawal A, Graff-Dubois S, Roche M, Bailey M, Alcantara S, Cashin K, Sivasubramaniam R et al (2017) HIV-1and SIV predominantly use CCR5expressed on a precursor population to establish infection in T follicular helper cells. Front Immunol 8:376 License: This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. ª2020 The Authors The EMBO Journal 39:e104749 |2020 19 of 19 Ana Martín-Leal et al The EMBO Journal