DBP4: Spatiotemporal modeling of T cell signaling

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A. Collaborating Investigator(s): Christoph Wülfing,1 Peter Cullen,1 Paul Verkade,1 Robert F. Murphy,2 Deva Ramanan,2 James Faeder3

B. Institutions: 1University of Bristol, 2Carnegie Mellon University, 3Pitt

C. Funding Status: European Res Council PCIG11-GA-2012-321554 (Wülfing) 8/12 – 7/16; Wellcome Trust 102387/Z/13/Z (Wülfing) 8/14 – 9/17; Wellcome Trust 201254/Z/16/Z (Wülfing) 5/16 – 4/19; Medical Res Council GW4 BioMed DTP (Wuelfing) 9/16 – 8/19; Wellcome Trust Senior Investigator Award 104568/Z/14/Z (Cullen).

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Fig VII.4. Frame from movie showing 4D distributions of cofilin (red), MRLC (green) and WAVE2 (blue) (from Roybal et al (2016). At this timepoint, cofilin and WAVE2 have translocated to the synapse but MRLC is lagging.

D. Driving relationship between TR&D and DBP. This project drives Aims 2 and 3 of TR&D4 and Aim 1 of TR&D3. During the current grant period, this DBP has resulted in the addition of important new capabilities to CellOrganizer. The first is a new class of generative models for proteins that are not contained in organelles, and in which the ability to compare protein models constructed from different images/movies is achieved through registration to a common event (in this case, formation of the immunological synapse). The second is the ability to perform causal modeling of spatiotemporal relationships between sensors. This will be further extended in the proposed renewal, and new capabilities will be developed, as described in detail below.

E. Innovations: DBP4 has led to new insight into actin dynamics at the T cell synapse in the tuning of T cell activation by costimulation; will provide a new paradigm for learning models of signaling pathways.

F. Methods and Procedures: The goal of DBP4 in the original MMBioS proposal was to build models enabling the understanding of the way in which various molecules in the T cell signaling system influence each other in time and space. Such mutual influence is critical in the tuning of T cell activation. While antigen recognition is mediated by the T cell receptors (TCR), the outcome of T cell activation is greatly influenced by how the TCR signal is amplified or suppressed with critical roles in autoimmune disease and the immune response to cancer. The specific aims were: (1) To construct models of the relationship between the spatiotemporal distributions of potential signaling molecules; (2) To construct biochemical cell simulations that capture the sequence of events involved in T cell signaling. The first aim consisted of two subaims, both of which have been accomplished. The first built on initial work aimed at creating registered maps of the spatiotemporal distribution of signaling intermediates just before and after formation of a synapse between a T cell and an antigen presenting cell. Many complications were encountered with aligning and morphing many different proteins to a common template so that they could be compared. These were all overcome, the pipeline has been created as a new functionality in CellOrganizer, and complete maps have been obtained for 9 proteins (actin and 8 actin regulators) under two conditions, a strong T cell stimulus where the TCR signal is amplified through costimulation as compared to a stimulus where the dominant costimulatory receptor, CD28, is blocked. Resolving actin regulation by costimulation, WAVE2 and cofilin showed the most significant changes (Fig VII.4) when comparing maps, a result that would not have been possible without the quantitative comparisons made possible by the computational pipeline. Cell biological reconstitution experiments confirmed their roles. This work has led to a major paper recently published in Science Signaling.83 The 2nd subaim was to learn the relationships between the spatiotemporal patterns of different proteins. We adopted a more systematic approach to construct a generative model that captures the relationships between different sensors in different regions of the cell. A manuscript describing this work has recently been submitted. These methods are described in TR&D4 along with the proposed further developments. Work on the second aim was delayed while the maps were refined, but will be completed by the end of the 5th year.

 

The dramatic success of the first aim has led us to propose significant evolution in the project over the following five years. We propose to add an investigation of the attenuation of T cell signaling by inhibitory receptors84 as enabled by collaborations recently established at the University of Bristol. This is of great scientific importance as inhibitory receptors both limit autoimmune disease and suppress the immune response to cancer.85-89 We also propose to complement our cell-wide subcellular signaling distributions with imaging of vesicles and proteomic data. The majority of the cellular pool of inhibitory receptors resides in vesicles, incapable of engaging their ligands.90,91 Vesicular trafficking to the plasma membrane thus is of great importance in inhibitory receptor function. In addition, as mechanisms of inhibitory receptor trafficking and signaling are largely unresolved, proteomic approaches to complement the imaging data will provide the necessary genome-wide coverage for unbiased functional discovery. Dr. Wülfing has recruited two biological collaborators from the University of Bristol, Dr. Peter Cullen, a leader in vesicular trafficking and proteomics, and Dr. Paul Verkade, a leader in electron microscopy (EM). The goal is to construct accurate spatiotemporal simulations of T cell signaling that consider both costimulatory and inhibitory receptor signaling. The specific aims are:

Aim 1. To construct a predictive model of the biochemical interactions among molecules involved in T cell signaling and to test and refine that model using experimental manipulations. This aim will drive Aim 2 of TR&D4 and Aim 1 of TR&D3. We will begin by constructing a tentative reaction network of the molecules for which we have detailed maps, use initial estimates for rate constants, carry out reaction-diffusion simulations, compare the resulting molecule distributions to our maps, adjust the rate estimates, and iterate. Information from other sources will be added to provide initial values or constraints. Dr. Wülfing’s group will provide movies of additional signaling molecules as prompted by the models. To allow incorporation of vesicle-based inhibitory receptors, Dr. Wülfing’s group will provide TIRF data on inhibitory receptor insertion into the plasma membrane and imaging data on the distribution of groups of vesicles. Dr. Verkade will complement these data by immuno-EM to quantify inhibitory receptor localization. Dr. Cullen’s group will collect proteomics data to characterize vesicles containing specific immunoregulatory receptors through the identification of their luminal contents. This will allow separation of vesicles into classes with distinct compositions. The information that specific groups of molecules are trafficked in distinct vesicles will be used to further constrain the proposed reaction network. Taking advantage of Dr. Cullen’s expertise in the regulation of vesicular trafficking, specific vesicle populations will then be perturbed and the information used to further refine the network. Dr. Wülfing’s group has already generated first constructs to manipulate the localization of key signaling intermediates and inhibitory receptors, and will collect movies for signaling molecules after expression of these constructs. It is important to note that there is previous work on modeling and simulating T cell signaling for an experimental system in which antigen is presented on a flat surface to T cells,92 a form of T cell activation that imposes substantially different biophysical constraints on T cell signaling than the more physiological interactions with APC that we analyze. We will compare both models.

 

Aim 2. To explore the relationship of synapse geometry to signaling pathways. 2 This aim will drive aim 3 of TR&D4. Dr. Verkade’s group will collect large sets of electron microscope images of synapse geometry at various times during signaling under various conditions. Models of this geometry will be created and used to improve the simulations above. They will also collect immune-EM images for specific molecules so that the distributions between fluorescence and EM can be registered. Dr. Wülfing’s group will provide the samples for the EM analysis for various conditions and constructs.

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