Molecular mechanism for differential recognition of membrane phosphatidylserine by the immune regulatory receptor Tim4

Abstract

Recognition of phosphatidylserine (PS) lipids exposed on the extracellular leaflet of plasma membranes is implicated in both apoptotic cell removal and immune regulation. The PS receptor T cell immunoglobulin and mucin-domain-containing molecule 4 (Tim4) regulates T-cell immunity via phagocytosis of both apoptotic (high PS exposure) and nonapoptotic (intermediate PS exposure) activated T cells. The latter population must be removed at lower efficiency to sensitively control immune tolerance and memory cell population size, but the molecular basis for how Tim4 achieves this sensitivity is unknown. Using a combination of interfacial X-ray scattering, molecular dynamics simulations, and membrane binding assays, we demonstrate how Tim4 recognizes PS in the context of a lipid bilayer. Our data reveal that in addition to the known Ca(2+)-coordinated, single-PS binding pocket, Tim4 has four weaker sites of potential ionic interactions with PS lipids. This organization makes Tim4 sensitive to PS surface concentration in a manner capable of supporting differential recognition on the basis of PS exposure level. The structurally homologous, but functionally distinct, Tim1 and Tim3 are significantly less sensitive to PS surface density, likely reflecting the differences in immunological function between the Tim proteins. These results establish the potential for lipid membrane parameters, such as PS surface density, to play a critical role in facilitating selective recognition of PS-exposing cells. Furthermore, our multidisciplinary approach overcomes the difficulties associated with characterizing dynamic protein/membrane systems to reveal the molecular mechanisms underlying Tim4's recognition properties, and thereby provides an approach capable of providing atomic-level detail to uncover the nuances of protein/membrane interactions.

Keywords: PS recognition; differential membrane recognition.

Fig. 1.

Tim4 binding is sensitive to PS surface density. (A) POPS and POPC lipids. (B) Comparison of 170 nM Tim4 binding titration for lipid vesicle systems of 7:3 (circles) and 9:1 (squares) POPC:POPS. Total lipid concentration at any given point is 3× higher for 10% versus 30%, so that the total PS concentration is matched. Lines through data points are the fits to a single-site binding model. Addition of 10 mM EGTA to 90 μM [PS] in the 30% system is shown as x. (C) Surface titration of PS mole fraction at constant total lipid concentration of 600 μM for 170 nM Tim4. Line through points is fit to a Hill binding model. For all data points, error bars denote 1 SD for measurements done at least in triplicate.

Fig. 2.

X-ray reflectivity reveals membrane-bound Tim4 orientation. (A) Representative X-ray reflectivity data sets and fits for 7:3 SOPC:SOPS alone (light gray squares) or with 1 μM Tim4 (dark gray circles). Lines through data are fits based on a model-dependent, iterative fitting procedure. Data are normalized by Fresnel reflectivity of an ideal air–water interface. (B) Best fit, 1D electron density profiles corresponding to fits of reflectivity data in A (dashed light gray: 7:3 SOPC:SOPS alone; solid black: with 1μM Tim4). The cartoon overlay shows the approximate orientation of Tim4 that was used to calculate the profile. (C) Representative X-ray reflectivity data sets and fits for 7:3 SOPC:SOPS with 1 μM Tim4 after injection of EGTA to 8 mM final concentration to remove bound protein layer (dark gray circles). Also shown is pure SOPC with 1 μM Tim4 (light gray squares). Lines through points are model-dependent fits, as in A. (D) The 1D electron density profiles corresponding to the best fits from C showing the absence of a significant protein layer either after EGTA injection (solid black) or with pure SOPC + 1 μM Tim4 (dashed light gray). (E) Scale model of Tim4 best-fit orientation from X-ray reflectivity measurements. Lipid is shown as a two-box model with tail group in gray and head group in burgundy. Hydrophobic residues W97 and F98 (gray) are shown penetrating the tail region. The PS head group analog from the Tim4 crystal structure [3BIB (23)] is shown as sticks, with the coordinating Ca²⁺ shown as a yellow sphere. (F) Structural diagram of four peripheral basic residues within close proximity of the membrane surface.

Fig. 3.

Dynamic model of membrane-bound Tim4. (A) Depiction of simulated lipid bilayer system with Tim4 docked according to parameters derived from X-ray fitting. Centrally bound PS is shown as spheres, as is the coordinating Ca²⁺ ion (yellow). Also shown is the definition of bilayer surface depth and protein rotational angles. (B) Trajectories of θ (blue) and Φ (red) angles, as defined in A, over the course of the MD simulation. Overlaying dashed lines depict best-fit orientation (black) from X-ray reflectivity and 95% confidence window (gray). (C) Surface depth distribution of the center of mass of W97 (Left) relative to the start of the tail region (dashed light gray line). (Right) Center-of-mass, surface depth distribution of the Ca²⁺-bound, central PS-phosphate (horizontal orange bars) relative to the distribution of all other phosphates in the leaflet (burgundy line). All distributions are normalized to total number of sampled frames. (D) Center-of-mass, surface depth distributions of side chain nitrogens (cationic sites) for the four residues identified in Fig. 2F (horizontal blue bars). These are shown relative to distributions of the two different species of anionic binding partners: phosphates (orange line; includes both PC and PS) and carboxyl moieties of PS head groups (burgundy line includes only PS). The anion distributions are normalized to the total frames of the phosphate to show the relative abundance of phosphates (100% of lipids) versus carboxyl moieties (30% of lipids). The dashed black line denotes phosphate-dominated regions (above the line) versus carboxyl-dominated regions (below the line) of potential anionic interaction.

Fig. 4.

Alanine mutants verify functional relevance of four peripheral basic residues. (A) Lipid vesicle titrations of 7:3 POPC:POPS for Tim4 mutants. Open shapes represent binding data, whereas filled shapes correspond to 90 μM [PS] + 10 mM EGTA for mutants (all points are overlapping at ∼0% binding), and the X is the corresponding WT EGTA control. Lines through data are fits to a single-site binding model. (B) PS mole fraction titrations at 600 μM total lipid with 170 nM protein for each alanine mutant. Lines through the data are fits to a Hill model. All data points are mean of measurements done at least in triplicate, with error bars showing 1 SD. (C) Apparent change in free energy for the Tim4 mutants calculated from binding titration data in A or Fig. S2 (for N39A). *P < 0.0001; N.S., statistically nonsignificant differences (N39A P value = 0.7228). (D) Bar plot of relative change in Hill coefficient, as determined by fitting to data in C or Fig. S2 (for N39A). *Statistically significant changes (P values relative to WT: R48A and K41A, P < 0.0001; R27A, P = 0.002; K102, P = 0.0195; and P values relative to R48A: R27A, P = 0.0013; K41A, P = 0.0044). N.S., statistically nonsignificant differences (N39A P value = 0.9628). All error bars denote 1 SD.

Fig. 5.

The Tim proteins have different sensitivities to PS surface density. (A) PS mole fraction titrations at constant total lipid of 600 μM with 170 nM Tim1 (black squares), Tim4 (dark grey circles), or Tim3 (light grey triangles). Each data point is the mean of at least three measurements with error bars denoting 1 SD. Lines through data points are fits to a Hill binding model (Tim1-black; Tim4-dark grey; Tim3-light grey). (B) Comparison of best-fit Hill coefficients between Tim4, Tim1, and Tim3. Error bars signify 1 SD. N.S., differences that do not reach statistical significance (Tim1 relative to Tim3: P = 0.4001); *P < 0.0001.