ABSTRACT:

One of the fundamental functions of the immune system is the accurate enforcement self/non-self discrimination: leukocytes must decide between activation and tolerance based on the dynamic filtering of immune events. 

In the context of the T cell receptor (TCR) signaling pathway, I will discuss how the dynamics competition between positive and negative signaling feedbacks was first postulated to sharpen antigen recognition by T cells [PMID: 10358766]. Previous experimental work [PMID: 16231973], and theoretical collaboration with Paul François [PMID: 41642238] added quantitative insights and theoretical heft with the introduction of the Adaptive Kinetic Proofreading (AKPR) scheme of immune activation: this model demonstrates how the proximal activation of negative regulators  (SHP-1 phosphatase as a negative feedback of TCR) filters spurious immune activation by large quantity of self antigens, while retaining extreme sensitivity to few non-self antigens. 

I will then present results in collaboration with Nihal Altan-Bonnet’s lab (same last name, smarter PI), demonstrating how AKPR enforces similar self/non-self discrimination in the context of the innate immune response of virally-infected cells [Mukai, Jia et al., Cell, in press]: our work demonstrates how AKPR in the RIG-I, MDA-5 or PKR pathways critically filters out spurious activation of these viral sensors by self mRNA and limits auto-immune activation. Such application of AKPR in response to viral entry also accounted for the surprising high threshold of viral copies required to license successful viral replication. Our experimental results and quantitative modeling will rewrite textbooks of virology/immunology regarding the early events of viral infection. 

Finally, I will discuss how AKPR similarly applies to population-level immune dynamics [PMID: 26004178, 34157301], enabling effector T cells to sharpen the specificity of their response to antigens using the early activation and filtering of/by regulatory T cells. 

All in all, I will present a consistent theoretical framework explaining how AKPR can be the cornerstone of self/non-self discrimination across multiple spatiotemporal scales in the immune system. 

BIOSKETCH:

I have been trained in Statistical Physics and nonlinear dynamics (PhD) and in Immunology (post-doctoral studies). My field of expertise is Systems Immunology: the ImmunoDynamics group I have been heading at since 2005 (first at Memorial Sloan Kettering, and at the NCI since 2016) has been developing experimentally validated quantitative models of different aspects of the immune response. In particular, we have addressed the interplay between the robustness and variability of self/non-self discrimination, as well as the bridging of local and global cytokine regulations in the immune system. We are currently focused on developing quantitative models of leukocyte-leukocyte communications within the cytokine network and exploring applications of machine learning to the field of cancer immunology and virology.