The CD4 and CD3δε Cytosolic Juxtamembrane Regions Are Proximal within a Compact TCR–CD3–pMHC–CD4 Macrocomplex

Caleb R. Glassman, Heather L. Parrish, Neha R. Deshpande and Michael S. Kuhns
J Immunol June 1, 2016, 196 (11) 4713-4722; DOI: https://doi.org/10.4049/jimmunol.1502110

Abstract

TCRs relay information about peptides embedded within MHC molecules (pMHC) to the ITAMs of the associated CD3γε, CD3δε, and CD3ζζ signaling modules. CD4 then recruits the Src kinase p56Lck (Lck) to the TCR–CD3 complex to phosphorylate the ITAMs, initiate intracellular signaling, and drive CD4+ T cell fate decisions. Whereas the six ITAMs of CD3ζζ are key determinants of T cell development, activation, and the execution of effector functions, multiple models predict that CD4 recruits Lck proximal to the four ITAMs of the CD3 heterodimers. We tested these models by placing FRET probes at the cytosolic juxtamembrane regions of CD4 and the CD3 subunits to evaluate their relationship upon pMHC engagement in mouse cell lines. The data are consistent with a compact assembly in which CD4 is proximal to CD3δε, CD3ζζ resides behind the TCR, and CD3γε is offset from CD3δε. These results advance our understanding of the architecture of the TCR–CD3–pMHC–CD4 macrocomplex and point to regions of high CD4–Lck + ITAM concentrations therein. The findings thus have implications for TCR signaling, as phosphorylation of the CD3 ITAMs by CD4-associated Lck is important for CD4+ T cell fate decisions.

Introduction

Individual peptides embedded within class II MHC molecules (pMHC) create composite surfaces that encode essential information for T cell–mediated immunity. Thymocytes and CD4+ T cells survey the libraries of pMHC expressed on thymic epithelial cells and APCs, respectively, with clonotypic TCRs that relay mechanical information about TCR–pMHC interactions across the membrane to the ITAMs of the associated CD3 signaling modules (CD3δε, CD3γε, and CD3ζζ) (1–5). This information is then converted to chemical signals when CD4, which also binds class II MHC, recruits the Src kinase p56Lck (Lck) to the TCR–CD3 complex to phosphorylate the ITAMs (6, 7). In this way, pMHC engagement by both TCR and CD4 acts as a coincidence detector to drive the generation, maintenance, and function of a pMHC-specific TCR repertoire. There are 10 ITAMs within a TCR–CD3 complex, with CD3δ, CD3γ, and CD3ε having one each and CD3ζ having three. The quantity and quality of ITAM phosphorylation determine cell fate decisions, with fewer than seven functional ITAMs per complex leading to a breakdown in central tolerance (8, 9). Thus, assembly of a TCR–CD3–pMHC–CD4 macrocomplex is key to CD4+ T cell development and effector functions.

The sensitivity and specificity of this macromolecular machinery have inspired considerable interest in how it works. Although TCR–CD3 cross-linking with mAbs can activate T cells, not all mAbs can induce this outcome (10); furthermore, not all TCR–pMHC interactions of seemingly sufficient affinity activate T cells if they dock in a noncanonical topology (11). Thus, an ordered relationship of signaling molecules appears to be important for T cell activation, particularly when receptors are confronted with their natural ligands (1, 2, 12, 13).

In support of this idea, two crystallography studies indicate that a V-like arch is formed when CD4 and the TCR simultaneously bind a pMHC. One structure included the D1 and D2 domains of CD4 in complex with MHC but without a TCR, whereas the other constituted a ternary complex of a TCR, pMHC, and the four extracellular domains of a CD4 molecule that was affinity maturated for binding to MHC (14, 15). Both structures suggest that the TCR–pMHC axis constitutes one arm of the arch, whereas CD4 constitutes the other and the CD4–MHC binding site forms the apex.

Because TCR–pMHC interactions occur via a canonical docking orientation, the implication is that a geometrically constrained TCR–CD3–CD4–pMHC architecture is functionally mandated to achieve a specific relationship between Lck and the ITAMs for proper signaling. Mutagenesis and nuclear magnetic resonance data place the ectodomains of CD3δε and CD3γε on one side of the TCR, with CD3δε and CD3γε positioned inside the arch formed with CD4, whereas electron microscopy data indicate that these heterodimers are nested near the base of the TCR (1, 16–20). Owing to the turn of the TCRα transmembrane helix and ∼140° offset of the positively charged residues that interact with CD3ζζ and CD3δε, CD3ζζ should reside on the opposite side of the TCRα subunit from CD3δε (21). In addition, because the CD3 heterodimers have short, rigid connecting peptides and the existing structural data for activating immune receptor transmembrane domains indicate a minimal crossing angle, the CD3 transmembrane domains are likely to emerge into the cytoplasm in a spatial orientation that roughly mimics their ectodomain (22, 23). Consequently, Lck is predicted to be closest to the four ITAMs of the CD3 heterodimers upon pMHC engagement (1, 2, 12, 16). A priori, such a model is surprising because the six ITAMs within the CD3ζζ signaling module (three per subunit) clearly play an important role in T cell development and activation (6, 8, 9). Testing this model is thus necessary to gain better insights into the architecture and function of the TCR–CD3–pMHC–CD4 macrocomplex.

In this study, we used Förster resonance energy transfer (FRET) to experimentally assess the spatial relationship between the cytoplasmic juxtamembrane (JM) regions of CD4 and the CD3γ, CD3δ, CD3ε, and CD3ζ subunits upon concurrent CD4 and TCR engagement of agonist pMHC. Replacing the intracellular domains with FRET probes ensured that any proximity measurements were dependent upon binding of CD4 and the TCR to pMHC, but independent of signaling or postsignaling interactions that may tether CD4 to the TCR–CD3 complex (7). Such a system allowed us to make predictions about the native spatial relationships between kinase and substrate in the context of a living cell. The data indicate that CD4 adopts an ordered spatial arrangement with respect to the TCR–CD3 complex only when both the TCR and CD4 bind an agonist pMHC. Therein, the JM region of CD4 is positioned closer to that of CD3δ than CD3γ and closer to the CD3ε subunits than the CD3ζζ subunits. The implications of these data for the architecture of the TCR–CD3–pMHC–CD4 macrocomplex, and its function in early signaling events, are discussed.

Materials and Methods

Cell lines and constructs

M12 and 58α−β− T cell hybridoma lines were generated by retroviral transduction and drug selection, as previously described (3, 24–26). The 5c.c7 TCR, which is specific for the moth cytochrome c peptide (MCC) (88–103) in I-EK (27, 28), was used in this study in complex with C-terminally truncated CD3ε (aa: 1–139), CD3δ (aa: 1–132), CD3γ (aa: 1–143), and CD3ζ (aa: 1–57) subunits (CD3xT) or full-length CD3 subunits where indicated in the text (3, 17). When a CD3T subunit was expressed as a fusion with monomeric enhanced GFP (mEGFP), we used a GGGSAAAG linker. C-terminally truncated versions of CD4 (CD4T aa: 1–421), with or without the Δbind mutation (aa: 68–73 KGVLIR to DGDSDS), fused to mCherry via an AAAG linker were used in this study (26, 29). Glycine-based linkers were used for our fusion proteins because flexibility is required to allow equivalent probability of dipole alignment between FRET pairs, and even a single glycine in a dipeptide linker can allow a strand to double back upon itself (30–34). The AAA segment of each linker represents the reading frame of a NotI restriction enzyme cut site used for cloning. This was chosen because Ala has been used with Gly in flexible linkers (35). Furthermore, poly-Ala peptides have been reported to assume similar backbone conformation diversity to that of poly-Gly (36), provided the AAA stretch is not flanked by Glu and Lys residues, which it was not in our study (37). Cell lines were assessed for TCR–CD3 and CD4 surface expression by flow cytometry.

Murine stem cell virus–based retroviral expression vectors pP2 (IRES-puromycin resistance) and pZ4 (IRES-zeocin resistance) were used for the generation of mouse cell lines (16). We took advantage of the 2A cleavage system to generate polycistronic constructs that reduced the number of constructs needed per cell line (8).

Experimental M12 FRET lines were generated using constructs encoding CD3εT(G)-T2A-5c.c7α, CD3ζT(G)-T2A-5c.c7β, and CD3δT(G)-T2A-CD3γT(G) along with a construct encoding CD4T fused at the JM region to mCherry (CD4TmCh) or CD4TΔbind.mCh. The CD3xT(G) indicates that constructs were built encoding only the truncated CD3 subunit, or a fusion of a truncated CD3 subunit to mEGFP (G), to generate cell lines expressing a single CD3–mEGFP species. For cell lines without a CD3ζ–mEGFP fusion, CD3ζ was fused to the biotin acceptor peptide, AP-3. This is a short tag that was considered irrelevant for these studies but is connected by the same linker as mEGFP. M12 control lines consisted of C-terminally truncated CD28 [(CD28T) aa: 1–179] or PD-1 [(PD-1T) aa: 1–199] fused to mEGFP and mCherry via an AAAG linker and were cotransduced with 2.5 equivalents of full-length CD3 subunits lacking CD3ζ to account for viral load.

To generate CD3εTG::CD4TmCh or CD3ζTG::CD4TmCh 58α−β− cell lines for FRET comparisons, parental cells were transduced with vectors encoding CD3εTG-T2A-5c.c7α and CD3ζT-T2A-5c.c7β, or CD3εT-T2A-5c.c7α and CD3ζTG-T2A-5c.c7β along with a CD4 construct and a construct encoding all full-length versions of the CD3 subunits (8, 16). To generate 58α−β− cell lines comparing CD3δT fused to mEGFP (CD3δTG)::CD4TmCh and CD3γTG::CD4TmCh FRET, cells were transduced with individual constructs encoding 5c.c7α, 5c.c7β, CD3xT-mEGFP, CD4TmCherry, and all full-length CD3 subunits.

The 58α−β− control cell lines were transduced with individual constructs encoding 5c.c7α, 5c.c7β, PD-1T (aa: 1–199) fused to mEGFP and mCherry via an AAAG linker, and all full-length CD3 subunits. Further information is available upon request.

Soluble proteins for bilayers and immobile surfaces

Production of soluble pMHC and ICAM-1 was performed with a baculovirus expression as described elsewhere (3, 26).

Lipid bilayers

Bilayers were prepared with a lipid mixture consisting of 97.5 mol % POPC, 1 mol % DGS NiNTA, 1 mol % biotin-CAP PE, and 0.5 mol % DOPE-PEG5000 and were extruded to generate unilaminar vesicles (Avanti Polar Lipids) (26, 38). Liposomes were injected onto a cleaned glass coverslip, and bilayer mobility was assessed by photoablation recovery of streptavidin conjugated to APC, as previously described (26, 38). For TCR engagement experiments, each well received 0.05 μg MCC:I-EK and 0.08 μg ICAM-1 to produce an agonist pMHC density of ∼60 mol/μm2 (17, 26, 38).

Peptide–MHC surfaces

Biotinylated poly-l-lysine–coated coverslips were incubated with 5 μg/ml streptavidin, washed, and incubated with 400 μl PBS + 2% BSA + 0.01% sodium azide containing a total pMHC–biotin concentration of 5 μg/ml (Hb:I-Ek and MCC:I-Ek) and 0.5 μg/ml biotinylated anti-H2-Dd (3, 39).

Microscopy

Total internal reflection fluorescence microscopy (TIRFM) was performed at 37°C, 5% CO2, and 50% relative humidity. Cells were adhered to the glass coverslip, lipid bilayers, or immobile surface for 20 min and then imaged for 20–30 min following adhesion. TIRF images were acquired using a Marianas workstation built on a Zeiss Axio Observer Z1 [Intelligent Imaging Innovations (3I)] with a Zeiss fluorescent microscope using a ×63 Zeiss TIRF objective coupled to a Zeiss motorized TIRF slider (numerical aperture 1.46). TIRFM was performed with a Laser Stack (3I) containing 50-mW 488-nm and 561-nm solid-state lasers set at 20% power output. Photoablation of mCherry was performed with a Vector high-speed point scanner (3I) at 100% 561-nm laser output within a 6.45-μm2 region of interest (ROI). Images were collected at 500-ms intervals [Photometrics Evolve EMCCD; 1 pixel = 0.25 μm (H) × 0.25 μm (V) at ×63].

Image analysis

Median mEGFP intensity and mCherry intensity for the ROI targeted for mCherry ablation were background subtracted using SlideBook6 (3I) and exported. Data were processed in MATLAB (MathWorks), and FRET was calculated as FRET efficiency value (FRETE) = 1 − (Q/DQ), where Q (quenched) is the mEGFP intensity prior to mCherry ablation and DQ (dequenched) is the mEGFP intensity following ablation, as previously reported (3, 26). Efficiency of mCherry ablation was calculated as the ratio of postbleach to prebleach mCherry intensity, Abl = mCh (postbleach)/mCh (prebleach). Subsets designated All cells in the figures had mCherry ablation below 12.5% prebleach intensity, as described previously (3, 26). This resulted in the analysis of populations with an average ablation below 10% prebleach intensity (not shown). Subsets designated Matched subset included events with ablation below 12.5% of prebleach mCherry intensity in which analyzed populations were matched for mCherry intensity prior to photobleaching and GFP intensity following photobleaching to account for differential donor quenching in experimental conditions as well as GFP/mCherry ratio.

Flow-based fluorophore-linked immunosorbent assay

DDM (1% n-dodecyl-b-d-maltoside) lysates from CD3xTG cell lines were subject to immunoprecipitation with 6.0-μm polystyrene beads (Polysciences) coated with anti-TCRβ mAbs (H57-597; BioLegend). After washing, the beads were then probed with anti-TCRα (α-Vα11 RR8-1 allophycocyanin; eBioscience) and anti-CD3ε (145-2C11 PE; BD) with fluorescently labeled mAbs. Analysis was then performed by flow cytometry to assess coprecipitation of TCRα, CD3ε, and CD3xTG (mEGFP) with TCRβ, similarly to previously described protocols (29, 40, 41).

Statistical analysis

All statistical analyses were performed with Prism 6 (GraphPad Software). As the data presented in this article are nonparametric, the Mann–Whitney U test and Kruskal–Wallis test with Dunn’s multiple comparisons were used where appropriate.

Results

Proximal association of CD4 and CD3δ upon TCR and CD4 engagement of agonist pMHC

The existing experimental data suggest that the formation of a TCR–CD3–pMHC–CD4 macrocomplex positions Lck closest to the ITAMs of CD3δε (1). Consequently, we transduced B cell lymphoma M12 cells, which do not express endogenous TCR–CD3 subunits, to express the 5c.c7 TCR, C-terminally truncated CD3γ, CD3ε, and CD3ζ subunits (CD3γT, CD3εT, and CD3ζT) that lack ITAMs and cannot signal (3, 24), and CD3δTG as the FRET donor. We also expressed CD4T fused to mCherry as a FRET acceptor. FRET was measured by donor recovery after acceptor photobleaching via TIRFM to determine a relative FRETE for molecules on the cell membrane, as previously described (3, 26). This experimental approach allowed us to reduce the question to one of spatial proximity driven purely by pMHC engagement via the TCR and CD4 in the absence of competition with endogenous subunits, signaling, and signaling feedback.

CD3δTG::CD4TmCh FRETE was first measured for M12 cells adhered to glass coverslips to establish a baseline signal in the absence of pMHC engagement. Coverslips coated with immobile agonist pMHC were then used to determine if concurrent TCR and CD4 engagement of agonist pMHC increased FRET between CD3δ and CD4 (Fig. 1A) (26). Because we have previously established that disulfide-bonded CD28 homodimers and PD-1 monomers serve as positive and negative controls, respectively, for FRET in M12 cells, we analyzed CD3δTG::CD4TmCh FRETE along with these controls (3, 26). No difference in FRETE was observed between the PD-1TG::PD-1TmCh negative control and our experimental CD3δTG::CD4TmCh cells imaged on glass coverslips. This finding is consistent with data suggesting that CD4 and TCR do not preassociate in an unengaged state (42). Adherence of CD3δTG::CD4TmCh cells to glass coverslips functionalized with agonist pMHC resulted in a significant increase in CD3δTG::CD4TmCh FRETE, compared with cells on uncoated coverslips or the negative control cells, indicating that concurrent TCR and CD4 engagement of agonist pMHC positions CD3δ and CD4 in a close spatial relationship (Fig. 1B).

FIGURE 1.
FIGURE 1.

FRET between CD4 and CD3δ is dependent on concurrent TCR and CD4 engagement of pMHC on immobile surfaces. (A) Representative TIRF images and intensity traces showing donor recovery after acceptor photobleaching. Experimental M12 cells expressing the 5c.c7 TCR, truncated CD3 subunits and the fluorescently tagged proteins are shown adhered to glass coverslips (left: unengaged) or coverslips functionalized with MCC:I-Ek (right: TCR + CD4 engagement), as labeled and described in the text. The white box represents the region targeted for photoablation and analysis. Scale bar, 5 μm. (B) pMHC engagement increases FRETE between CD4 and CD3δ at a population level. FRETE measurements of experimental M12 cells were compared for cells on glass coverslips (unengaged) or MCC:I-Ek functionalized coverslips, as labeled and described in the text. ****p < 0.0001, Kruskal–Wallis with Dunn’s multiple comparisons posttest. (C) FRETE between CD3δ and CD4 is dependent on CD4 D1 domain interactions with MHC. FRETE was measured for the indicated cell lines adhered to MCC:I-Ek functionalized coverslips, as labeled. ****p < 0.0001, Mann–Whitney. (D–G) The reduced FRETE seen with CD4TΔbind is not due to decreased accumulation of CD4 at the contact interface. Boxed region represents matched subsets from (C) for (D) mEGFP/mCherry ratio, (E) mEGFP intensity, and (F) mCherry intensity. (G) Analysis of FRETE for matched subsets as shown in (D) through (F) and described in Materials and Methods. ****p < 0.0001, Mann–Whitney. (H) The decreased FRETE observed with CD4TΔbind is dependent on MHC engagement (ns p > 0.05, Mann–Whitney). Experiments were performed and analyzed as described in Materials and Methods. Images were acquired at 500-ms intervals with a 25-ms exposure, 50-camera intensification. Circles represent individual cells, N = number of cells analyzed, and black bars represent median values. Data are representative of at least two experiments.

Although the CD3δTG::CD4TmCh FRETE signal was lower than that of the positive control cells, it is unclear if this is due to differences in distance between FRET donor and acceptor, the frequency of productive FRET pairs, or both. In the case of CD3δ::CD4 FRET, coordinate binding of CD4 and TCR to an agonist pMHC creates a transient mEGFP and mCherry pairing for a subset of TCR and CD4 molecules that engage pMHC on an immobile surface. This happens because CD4 and the TCR have been shown by superresolution imaging to inhabit distinct membrane domains (42). Consequently, the number of TCRs within a membrane domain that are available to engage the same pMHC as a CD4 molecule in a distinct membrane domain appears to be physically limited to those that are located at the boundary of each membrane domain (42). In contrast, disulfide-bonded CD28 homodimers will speciate into CD28TG::CD28TmCh, CD28TG::CD28TG, and CD28TmCh::CD28TmCh pairs within the same membrane domain based on the relative expression of FRET constructs. These distinctions are important because FRETE measurements are based on changes in the fluorescent intensity of all FRET donors within an ROI after acceptor ablation of all donor molecules, whether or not they are paired with an acceptor. Owing to the complications outlined above, and differences in expression profiles, comparisons between the CD3δTG::CD4TmCh FRETE and CD28 provide limited quantitative information.

Given the challenges associated with comparing FRET between distinct molecular species, we also approached the problem by asking if mutating the D1 domain region of CD4, which binds class II MHC in crystal structures (14, 15), would impair the CD3δTG::CD4TmCh FRETE signal observed on pMHC-coated coverslips. To this end, we used a CD4TΔbind mutant that impairs T cell hybridoma activation (26, 29). In this situation, CD3δTG::CD4TΔbind.mCh FRETE was significantly lower than CD3δTG::CD4TmCh FRETE at the bulk population level (Fig. 1C). One caveat to this approach is that CD4–MHC interactions influenced accumulation of CD4 at the contact interface (Fig. 1D–F). However, this difference in donor and acceptor concentrations is unlikely to be responsible for the difference in FRET efficiency, as intensity- and ratio-matched subsets showed a similar reduction in FRETE for the CD3δTG::CD4TΔbind.mCh compared with the FRETE measured in CD3δTG::CD4TmCh cells (Fig. 1D–G). No difference was observed between the two cell lines on glass coverslips (Fig. 1H), again demonstrating pMHC dependence for the observed FRET. Altogether, these data strongly suggest that the CD3δTG::CD4TmCh FRET results from concurrent TCR and CD4 engagement.

We next used mobile lipid bilayers presenting MCC:I-Ek and ICAM-1 to assess whether these differences in FRET would be observed between the CD3δTG::CD4TΔbind.mCh and CD3δTG::CD4TmCh FRETE cells under more physiological conditions (Fig. 2A). Again, we saw a decrease in FRET efficiency with the Δbind mutation, consistent with the results obtained on immobile surfaces (Fig. 2B).

FIGURE 2.
FIGURE 2.

FRET between CD4 and CD3δ is dependent on concurrent TCR and CD4 engagement of pMHC on lipid bilayers. (A) Representative images showing donor recovery after acceptor photobleaching of experimental M12 cells adhered to supported lipid bilayers presenting MCC:I-Ek and ICAM-1 (LBL), as labeled and described in the text. Images are as described in Fig. 1. (B) CD4 D1 domain interactions with MHC are critical for observed FRET. Population analysis of matched subsets, as described in Materials and Methods. Circles represent individual cells, N = number of cells analyzed, and black bars represent median values. Data are representative of at least two experiments. ****p < 0.0001, Mann–Whitney.

Taken together, the data suggest that the CD4 and CD3δ JM regions assume a close spatial proximity that depends upon the D1 domain of CD4 binding class II MHC, but does not require signaling. However, because TCR–CD3 complexes and CD4 accumulate at the binding interface, these data alone do not demonstrate an ordered spatial relationship. The observed CDδTG::CD4TmCh FRET could be explained by random FRET interactions occurring in trans owing to increased molecular concentrations (i.e., clustering) rather than a defined orientation enforced by specific interactions.

Coordinate TCR–pMHC and CD4–MHC interactions create an ordered CD4–CD3 spatial relationship

If CD3δTG::CD4TmCh FRET results from random clustering, then at any given point CD4 should, on average, be equidistant to all CD3 subunits. Alternatively, if FRET is occurring as a consequence of the formation of an ordered structure, then differences in FRETE should be observed between CD4 and the CD3 subunits based on the relative distances of their defined spatial relationships. To test these possibilities, we generated M12 cells expressing CD4TmCh and TCR–CD3 complexes containing CD3δTG, CD3γTG, CD3εTG, or CD3ζTG. Each CD3 subunit was fused to mEGFP via a common flexible linker (GGGSAAAG) that should allow the probe to extend beyond the short intracellular domains of the TCR (∼5–9 aa) and other truncated CD3 subunits (∼5 aa) and allow for an equivalent probability of dipole alignment. Because the CD3 intracellular domains, which do not play a role in complex assembly (3, 16, 17, 21), are largely absent in this system, they should not inadvertently have an impact on the rotation or position of the CD3-associated mEGFP. This design strategy should make FRET a function of the spatial position of the donor within the TCR–CD3 complex relative to the position of a CD4-associated acceptor when both the TCR and the CD4 engage a pMHC. Consequently, any differences in FRETE between compared lines should reflect the distance of the particular CD3 JM regions relative to CD4 if an ordered macrocomplex is formed. In our experiments, CD3δTG and CD3γTG were compared directly because each is present only once in a single TCR–CD3 complex, and CD3εTG and CD3ζTG were compared directly because there are two copies per complex.

Cell surface expression of TCR–CD3 complexes was confirmed by flow cytometry (not shown) as well as by TIRFM (Fig. 3). Because the TCR trafficks to the cell surface only in association with the CD3 subunits, these data strongly suggest that the FRET probes do not negatively impact complex assembly. To further confirm that fusing mEGFP to the individual CD3 subunits did not differentially impact complex assembly, we assessed the composition of the TCR–CD3 complexes using a flow-based fluorophore-linked immunosorbent assay. Appropriate complex assembly involves the incorporation of two CD3ε per TCR–CD3 complex, independent of the mEGFP-tagged CD3 subunit, whereas mEGFP will be present according to the copy number of the tagged subunit: one for CD3δ and CD3γ, or two for CD3ε and CD3ζ. Comparison of anti-CD3ε intensities with mEGFP levels revealed proportional signal (i.e., diagonal) for the CD3δΤG versus CD3γΤG and CD3εΤG versus CD3ζΤG cell lines, with CD3εTG and CD3ζTG shifted toward brighter mEGFP signal (Supplemental Fig. 1A). This observation was confirmed by analysis of beads with matched TCRα intensities (i.e., equivalent αβTCR load). In matched samples, the ratio of mEGFP signal relative to CD3ε staining was similar between the CD3δΤG and CD3γΤG lines and between the CD3εTG and CD3ζTG lines (Supplemental Fig. 1B). The slightly lower ratio of CD3ζTG compared with CD3εTG is expected owing to the presence of partially assembled complexes, because it is well established that CD3ζ is the last subunit to join the complex and partially assembled complexes lacking CD3ζ can be found in whole-cell lysates (21, 43). These data establish that the mEGFP probes do not differentially affect complex assembly.

FIGURE 3.
FIGURE 3.

CD4 adopts an ordered arrangement with respect to the CD3 subunits. (A and B) Agonist pMHC engagement results in differential FRETE between CD4 and the CD3 subunits. Experimental M12 cells were adhered to supported lipid bilayers presenting MCC:I-Ek and ICAM-I (LBL), as labeled and described in the text. (C and D) Differences in FRETE depend on ligand engagement. Experimental M12 cells were adhered to glass coverslips (unengaged). (E and F) FRET between CD4 and (E) CD3γ or (F) CD3ζ subunits is dependent on CD4 D1 domain–MHC interactions. Experimental M12 cells were adhered to supported lipid bilayers presenting MCC:I-Ek and ICAM-I (LBL), as labeled and described in the text. Circles represent individual cells, N = number of cells analyzed, and black bars represent median values. Data are representative of at least two experiments per condition. Subset analysis and subset criteria are as detailed in Materials and Methods. ****p < 0.0001, Mann–Whitney.

Significantly higher FRETE was measured for CD3δTG::CD4TmCh than CD3γTG::CD4TmCh for ratio- and intensity-matched subsets on bilayers (Fig. 3A). Likewise, CD3εTG::CD4TmCh was significantly higher than CD3ζTG::CD4TmCh for ratio- and intensity-matched subsets on bilayers (Fig. 3B). These data are best explained by the formation of an ordered macrocomplex upon coordinate TCR and CD4 binding to pMHC. Of note, no difference in FRETE was observed on glass coverslips for the relevant comparisons (Fig. 3C, 3D).

We next asked if FRET between the distal CD3γ and CD3ζ subunits was dependent on CD4 engagement of MHC. Analysis of matched subsets revealed that the FRET efficiencies of CD3γTG::CD4TmCh and CD3ζTG::CD4TmCh were greater than their CD4TΔbind.mCh counterparts (Fig. 3E, 3F). These data indicate that the observed FRET was dependent on CD4 engagement.

As a second approach to resolving random versus ordered FRET between CD4 and the TCR–CD3 complex, we measured FRET on immobile surfaces coated with agonist pMHC diluted into nonstimulatory pMHC. These surfaces prevent the cells from gathering agonist pMHC into a high local concentration, so at low agonist concentrations any measured FRET should be as a consequence of concurrent TCR–CD3 and CD4 association with an agonist ligand surrounded by null ligands. Indeed, our streptavidin capture system allows for the possibility of pMHC molecules being presented in orientations that are not accessible to TCR, CD4, or both, further reducing the actual availability of agonist pMHC for coordinate TCR and CD4 engagement. Because our cells express the 5c.c7 TCR that recognizes the MCC (88–103) presented in I-Ek (MCC:I-Ek) as an agonist pMHC, we diluted MCC:I-Ek into a null pMHC complex comprising the mouse hemoglobin d allele peptide (64–76) presented in I-Ek (HB:I-Ek). This null ligand has previously been shown to lack co-agonist activity for the 5c.c7 TCR (44).

Analysis of FRETE for a titration of agonist into null pMHC revealed dose-dependent FRET. CD3δTG::CD4TmCh FRETE was observed to be higher than CD3γTG::CD4TmCh FRETE, whereas CD3εTG::CD4TmCh FRETE was higher than CD3ζTG::CD4TmCh FRETE across the titration range (Fig. 4A, 4B), including at low ligand densities (Fig. 4C, 4D). Collectively, these data are best explained by nonrandom FRET between CD4 and the TCR–CD3 complex upon coordinate TCR and CD4 binding of agonist pMHC.

FIGURE 4.
FIGURE 4.

FRET between CD4 and CD3 subunits depends on agonist pMHC concentration. Experimental M12 cells were adhered to functionalized coverslips presenting agonist (MCC:I-Ek) diluted into null (HB:I-EK) pMHC, as labeled. The total amount of pMHC on the surface was constant. FRETE was measured, and analysis was performed as described in Materials and Methods. (A and B) Dose-dependent FRET between CD4 and the CD3 subunits persists over a range of agonist pMHC concentrations. CD3δTG::CD4TmCh and CD3γTG::CD4TmCh did not differ on completely null surfaces [N(δ) = 69, N(γ) = 40, ns p > 0.05] but did for all other ligand concentrations tested [4% N(δ) = 113, N(γ) = 101, *p < 0.05; 12.5% N(δ) = 96, N(γ) = 75, **p < 0.01; 100% N(δ) = 86, N(γ) = 56, ****p < 0.0001]. Similarly, CD3εTG::CD4TmCh and CD3ζTG::CD4TmCh did not differ on completely null surfaces [N(ε) = 44, N(ζ) = 54, ns p > 0.05] but did for all other ligand concentrations tested [4% N(ε) = 71, N(ζ) = 79, *p < 0.05; 12.5% N(ε) = 52, N(ζ) = 86, **p < 0.01; 100% N(ε) = 55, N(ζ) = 66, ***p < 0.001]. Cells were intensity and ratio matched (matched subset as described in Materials and Methods) at each titration point, and medians were compared using a Mann–Whitney U test. Each data point in the graph represents the mean ±SEM. (C and D) Differences between subunits persist at low ligand densities. Comparisons made on 4% agonist surfaces for matched subsets, as described in Materials and Methods. Circles represent individual cells, N = number of cells analyzed, and black bars represent median values. Data are representative of at least two experiments. *p < 0.05, Mann–Whitney.

The same spatial relationship occurs in T cell membranes

Finally, we measured FRETE between CD4 and each CD3 subunit in 58α−β− T cell hybridomas to verify that the spatial proximity of these molecules demonstrated the same hierarchy when measured in a T cell membrane environment in the presence of full-length, signaling-competent CD3 subunits (26). First, we compared CD3δTG::CD4TmCh FRETE on lipid bilayers with negative control 58α−β− (PD-1TG::PD-1TmCh) cells that expressed TCR–CD3 complexes and were able to adhere to the bilayers (Fig. 5A). CD3δTG::CD4TmCh FRETE was observed to be higher than the negative control (Fig. 5B). This signal was dependent on CD4 binding to MHC, as the CD4TΔbind mutant reduced this signal (Fig. 5C, 5D). Importantly, CD3δTG::CD4TmCh FRETE was higher than CD3γTG::CD4TmCh FRETE, and CD3εTG::CD4TmCh FRETE was higher than CD3ζTG::CD4TmCh FRETE (Fig. 5E, 5F). The magnitude of these differences is smaller than that observed in the M12 system. This is likely due to the presence of untagged subunits or the potential for signaling and signaling feedback. Altogether, these data indicate that the spatial relationships observed in M12 cells in the absence of CD3 ITAMs holds in T cell hybridomas expressing a full complement of ITAMs.

FIGURE 5.
FIGURE 5.

CD3 subunit–dependent FRET differences are maintained in 58α−β− T cell hybridomas. (A) Representative TIRF images of donor recovery after acceptor photobleaching for PD-1TG:: PD-1TmCh (left) and CD3δTG::CD4mCh (right) 58α−β− T cell hybridomas adhered to supported lipid bilayers (LBL) presenting MCC:I-Ek and ICAM-1, as described in the text. Images were acquired as described in Fig. 1 and Materials and Methods. (B) CD3δTG::CD4TmCh FRETE is greater than PD-1TG:: PD-1TmCh FRETE on MCC–ICAM lipid bilayers in the presence of full-length CD3 subunits. (C) FRET between CD4T and CD3δT depends on the CD4 D1 domain interacting with class II MHC. (D) The CD4TΔbind mutation does not affect FRET efficiency in unengaged cells. (E and F) CD4 adopts a specific orientation with respect to the TCR–CD3 complex in a T cell membrane. FRETE measurements were performed and analyzed as described in Materials and Methods. Circles represent individual cells, n = number of cells analyzed, and black bars represent median values. Data are representative of at least two experiments. *p < 0.05, ****p < 0.0001, Mann–Whitney.

Discussion

In this article, we show that CD4 adopts an ordered spatial relationship with the TCR–CD3 complex when the TCR and CD4 bind to agonist pMHC. The results are important for our understanding of the architecture of this macrocomplex and provide a conceptual framework for further interrogating its function.

Overall, the data shed light on the subunit arrangement within the TCR–CD3–pMHC–CD4 macrocomplex. Specifically, they indicate that the CD4 JM region resides closer to the JM region of CD3δ than to CD3γ and closer to the JM region of the CD3 heterodimers than to CD3ζζ. TCRα is known to interact with CD3δε and CD3ζζ via positively charged residues that are offset by ∼140° in its transmembrane domain (21). This finding suggests that the CD3ζζ JM regions are similarly displaced (i.e., ∼140°) relative to the JM regions of CD3δε when associated with TCRα (23). Within this context, the simplest interpretation of our data is that the JM regions of CD3δε are between the JM regions of CD4 and TCRα, with the CD3ζζ JM regions sitting behind TCRα. Because CD3γε interacts via transmembrane charge interactions with TCRβ, its JM regions would be offset at a distance from CD3δε (21).

Three distinct arrangements of the TCR–CD3 complex subunits have been proposed and are considered in this article within the TCR–CD3–pMHC–CD4 macrocomplex. Our FRET data are inconsistent with a model in which CD3γε is positioned in line with CD4 such that CD3δε is situated adjacent to CD3γε, but offset at a greater distance from CD4 (12). Slight adjustments in the placement of the CD3 heterodimers might reconcile these differences for this model; however, previous proximity analysis of the CD3 JM regions does not support a juxtaposed placement of CD3δ and CD3γ (17). Furthermore, because of the 140° offset of the TCRα transmembrane domain charge residues that interact with CD3δε and CD3ζζ, too much adjustment would place CD4 closer to CD3ζζ than either CD3ε. The data presented in this article are compatible with models in which CD3δε and CD3γε are either situated on opposite sides of the TCR (22, 45) or reside on one side of the TCR such that the subunits are ordered δ:ε:ε:γ (1, 2, 16, 17). This last model is most consistent with other experimental data, as well as recent nuclear magnetic resonance data, so we currently consider it the best approximation of the subunit organization of the TCR–CD3 complex (1, 19).

When integrating this model with the ternary arched crystal structure of TCR–pMHC–CD4 (Supplemental Fig. 2), what is most striking is that, by the general approximations allowed by such modeling, the JM regions of CD3γ and either CD3ζζ subunit are >100Å away from CD4 (Supplemental Fig. 2D). The same is true if the CD3 heterodimers are positioned on opposite sides of the TCR (Supplemental Fig. 2E). This observation is important because such estimated distances between CD4 and CD3γ or CD3ζ place them at or beyond the outer limits of detection by FRET with these probes (34, 46). That our CD3γTG::CD4TmCh and CD3ζTG::CD4TmCh FRET are dependent upon the CD4 D1 domain suggests that the V-like arch identified in the crystal structures might be an initial, but not the final, arrangement of the macrocomplex.

Such a conclusion is consistent with multiple studies that suggest CD4 makes contacts with class II MHC beyond those observed in the crystal structures, and may even contact the TCR–CD3 complex. For example, mutagenesis of class II MHC or the D1 and D2 domains of CD4 suggest more extensive CD4–MHC interactions than are revealed by the existing structures (47–49). Furthermore, domain swap and mutagenesis analysis of the membrane proximal D3 and D4 domains of CD4 suggest additional contacts with the TCR–CD3 complex (50, 51).

In consideration of these data, it has been proposed that CD4 might preassociate with the TCR or dock loosely along a composite surface formed by the TCR–CD3 complex to form more avid interactions than the 200 μM–2 mM affinity estimates for CD4 binding to MHC in isolation (1, 44). Although preassociation is not supported by our data, or by superresolution imaging, such interactions could occur at a low frequency (42). Docking of CD4 along the composite surface created by TCR–CD3–pMHC would be consistent with the data presented in this article and could help explain the evolutionary advantage of having weak CD4–MHC interactions in the absence of the TCR–CD3 complex. For example, CD4 binding to MHC via the D1 domain could provide sufficient interactions to facilitate preliminary nucleation of CD4 and TCR, yet be weak enough to allow for CD4 to pivot toward a more avid interaction along the TCR–CD3 composite interface. In this way, CD4 binding would be coordinated with TCR affinity, consistent with a kinetic proofreading model for TCR signaling (52). It could also help explain CD4’s Lck-independent function by providing a structural contribution to TCR–pMHC interactions that facilitates signaling (1, 53). Also of note, interactions between the SH2 domain of Lck and phosphorylated ITAMs would not contribute to the close association observed in this study because the clasp domain of CD4 that interacts with Lck was replaced by a FRET probe (7, 40). Such interactions, however, could normally extend a continuous contact interface of a compact macrocomplex inside the cell that would be relevant for a recently proposed Lck come and stay/signal duration model (40). Additional work is needed on this long-standing problem, but the data presented in this study are suggestive of a compact TCR–CD3–pMHC–CD4 macrocomplex that is consistent with the existing mutagenesis data.

Regardless of how CD4 and the TCR–CD3 complex achieve a close spatial relationship upon binding pMHC, the data presented in this study provide evidence for models in which CD4 brings Lck in closer proximity to the CD3 heterodimers than CD3ζζ. Indeed, the data indicate that the cytosolic JM regions of CD4 most closely approach the CD3δε ITAMs, suggesting that CD4-associated Lck and these ITAMs are likely to reside in the highest local concentrations upon TCR and CD4 binding of agonist pMHC. Given that experimental evidence places the cytosolic JM regions of the two CD3ε subunits within ∼40 Å upon assembly into the TCR–CD3 complex, and unobstructed from each other (17), the data suggest that CD4 will also bring Lck proximal to the ITAMs of the CD3γε heterodimer, particularly if the kinase domain can reach beyond its immediate locale when extended in an open and active conformation. How Lck could access the CD3ζζ ITAMs is less obvious, particularly within the context of the V-like arch stabilized by ∼200 μM–2 mM interactions. However, if CD4 does dock along a composite TCR–CD3–pMHC surface, then interactions between CD4-associated Lck and the CD3ζζ ITAMs are more conceivable, given that each CD3ζ subunit has long intracellular domains with three ITAMs that might somehow reach toward Lck. Such a docking model would also be consistent with how CD8 has been proposed to function (54, 55).

In closing, the canonical docking of TCR on pMHC is thought to be functionally mandated to achieve a specific relationship between Lck and the ITAMs for positive selection and T cell activation (1, 2, 12, 13). A variety of data have predicted that CD4-associated Lck and the ITAMs of the CD3 heterodimers are most closely associated upon coordinate CD4 and TCR binding to pMHC, but this prediction has not been experimentally tested (16–19). The data in this article provide evidence for a highly ordered, compact TCR–CD3–pMHC–CD4 macrocomplex in which the cytosolic JM regions of CD4 are most closely associated with the JM regions of the CD3 heterodimers, and in particular the CD3δε subunits. Although it would be technically challenging to temporally resolve the consequences of this apposition by ITAM phosphorylation analysis, these data provide a conceptual framework from which to devise experiments aimed at testing whether the CD3δε and CD3γε ITAMs represent the ignition point for signaling under low pMHC density conditions that initiate immunological synapse formation and signal potentiation.

Disclosures

The authors have no financial conflicts of interest.

Acknowledgments

We thank Mark S. Lee and David I. Duron for thoughtful discussions, critical feedback, and technical assistance; Dominik Schenten for critically reading the manuscript; members of the Kuhns, Nikolich-Zugich, Frelinger, Wu, and Schenten laboratories for critical feedback; Hemant B. Badgandi for contributing novel constructs; and the University of Arizona Cancer Center/Arizona Research Labs Cytometry Core Facility for flow cytometry.

Footnotes

  • This work was supported by The University of Arizona College of Medicine (M.S.K.), the BIO5 Institute (M.S.K.), National Institutes of Health/National Institute of Allergy and Infectious Diseases Grant R01AI101053 (M.S.K.), and Cancer Center Support Grant CCSG-CA 023074 for flow cytometry. M.S.K is a Pew Scholar in the Biomedical Sciences, supported by The Pew Charitable Trusts.

  • The online version of this article contains supplemental material.

  • Abbreviations used in this article:

    CD28T
    C-terminally truncated CD28
    CD4T
    C-terminally truncated version of CD4
    CD3δTG
    CD3δT fused to mEGFP
    CD4TmCh
    CD4T fused at the JM region to mCherry
    FRET
    Förster resonance energy transfer
    FRETE
    FRET efficiency value
    JM
    juxtamembrane
    Lck
    Src kinase p56Lck
    MCC
    moth cytochrome c peptide
    mEGFP
    monomeric enhanced GFP
    PD-1T
    C-terminally truncated PD-1
    pMHC
    peptide embedded within class II MHC molecule
    ROI
    region of interest
    TIRFM
    total internal reflection fluorescence microscopy.

  • Received September 28, 2015.
  • Accepted March 29, 2016.

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