Looking Beyond the Event Horizon: Modeling the Synapse

1. Looking Beyond the Event Horizon:Modeling the Synapse by Yves Konigshofer

2. Introduction • Cell-to-cell (e.g. T cell / APC) contact sometimes leads to the formation of a synapse • Over the past few years, more and more molecules have been identified that accumulate at a synapse • TCR, MHC, LFA-1, ICAM-1, CD2, CD4, CD8, CD28, etc. • Different patterns have resulted from the accumulation of these molecules • Different explanations have been given for the shapes and the significances of these patterns

3. Why Simulate Cell-to-Cell Contact? • To determine whether or not the observed accumulation patterns at a synapse are compatible with the explanations for their generation • To determine what pieces of the puzzle are currently missing but are needed to properly characterize and understand the events that shape cell-to-cell contact and lead to the formation of a synapse

4. The Representation of Cells, Molecules, and the Synapse • There are twocells that touch each other in a contact area (synapse) • These cells are represented as one or morespheres • Each sphere has a representation of the contact area • There are one or more different types of molecules • Each type of molecule is found on one particular sphere • Definable amounts of molecules of each type are found on the surfaces of the spheres • Only those molecules that are found inside of the contact area interact with each other

5. There is only onecontact area This contact area is divided into rings and sectors, which give rise to regions Most calculations are done on a per-region basis Molecules interact with other molecules that are found inside of the same region Bound molecules cannot leave the contact area while they are still bound The Contact Area

6. Release, Diffusion, and Binding • Release is calculated using the koff values for the dimers • Diffusion is calculated using the diffusion coefficient, D, for the molecule or dimer • Binding is calculated using the 2D and 3D kon values for the molecules trying to bind inside of particular regions, their concentrations inside of these regions, and the distance between the opposing membranes

7. Defining the Characteristics of Different Types of Molecules Optimal binding distances, bound and free diffusion coefficients, bound and free diffusion biases, bound and free optimal region entry heights, color, on-rates for binding, off-rates for release, confinement distances, binding times for endocytosis, transit times until exocytosis, modifiers for many of these parameters, etc.

8. Three Major Types of Binding A) One molecule is always immobile B) 2x reduction in D after binding C) 200x reduction in D after binding

9. The Event Horizon relative number of molecules degrees from the middle of the contact area

10. Modeling the Effects of LFA-1 (CD11a/CD18) Activation • LFA-1 • an integrin (aLb2) • binds ICAM-1 • essentially immobile when cells are resting • D ≈ 2.3E-15 m2/s (Kucik et al.) • mobile when cells are activated • D ≈ 2.9E-14 m2/s • its affinity for ICAM-1 changes after activation (Lollo et al. and Tominaga et al.) • activated kon ≈ 2.0E+5 M-1s-1; koff ≈ 0.1 s-1 • resting kon ≈ 3.7E+2 M-1s-1; koff ≈ 0.033 s-1

11. LFA-1 Activation and ICAM-1 Binding Resting Activated

12. Modeling Interactions Between Activated T cells and APCs • Some Assumptions • The TCR moves preferentially towards the synapse • The TCR is removed after being bound for 10 seconds • The molecules have the following diffusion coefficients: • TCR : 1.0E-14 m2/s (no reported values) • MHC : 2.0E-14 m2/s (large range of values) • LFA-1 : 2.9E-14 m2/s (stimulated cells) • ICAM-1 : 2.0E-14 m2/s (no reported values) • The cells membranes are between 40 and 80 nm apart • TCR / MHC interact optimally at 15 nm • LFA / ICAM-1 interact optimally at 40 nm

13. T cell Activation(random peptide) Large Molecule TCRMHC LFA-1 ICAM-1 TCR / MHC: kon = 1.0E+3 M-1s-1, koff = 2.0 s-1

14. T cell Activation?(random diffusion) Large Molecule TCRMHC LFA-1 ICAM-1

15. Observations and Conclusions • When molecules bind each other on opposing cells, the standard result is the formation of a ring • koff is important, kon is not • getting molecules not to bind is difficult • Diffusion coefficients of molecules need to be measured for the types of cells being simulated • Directed as opposed to random diffusion is needed for the formation of the central TCR / MHC cluster during T cell / APC interactions • Lots of parameters still need to be measured to accurately model cell-to-cell interactions

16. Acknowledgements • Chien lab • Davis lab • Cenk Sumen • Lawren Wu • Duke University • Jun Yang

18. The Simulation

19. T cell Activation(strong agonist peptide) Large Molecule TCRMHC LFA-1 ICAM-1 TCR / MHC: kon = 1.57E+3 M-1s-1, koff = 0.063 s-1 (2B4 MCC/IEk)