Building a Systems Immunopeptidomics Framework to Challenge the Current T Cell Activation Paradigm

About This Grant

PROJECT SUMMARY The adaptive immune system, a cornerstone of vertebrate immunity for over 450 million years, relies heavily on T cells to monitor and respond to peptide fragments on cell surfaces. Traditionally, T cells have been thought to activate only in response to foreign or abnormal peptides (nonself), while viewing the myriad of normal peptides present on cell surfaces as mere tolerogens (self). However, contrary to the prevailing dogma, our proposal hypothesizes that the large repertoire of self-peptides presented across the entire organism—the self- immunopeptidome—is not merely tolerated but is rather a complex entity that plays a central role in regulating T cell responses, either enhancing or inhibiting them. This concept has profound implications across multiple fields of medicine, including cancer immunotherapy, autoimmune disorders, infectious diseases, vaccine design, neurodegenerative conditions, and aging-related diseases. If self-peptides are indeed central regulators of T cell responses, this discovery would challenge existing paradigms and necessitate a complete rethinking of immune system dynamics, which could lead to the development of novel therapeutic strategies and improve clinical outcomes. To investigate this hypothesis, we will develop an innovative systems immunopeptidomics framework that integrates next-generation mass spectrometry, advanced robotics, artificial intelligence, mathematical modeling, and in vivo T cell studies. This interdisciplinary approach is unparalleled in its comprehensiveness and potential for discovery. First, we aim to elucidate the composition of the self-immunopeptidome across different tissues and cell types using mass spectrometry-based immunopeptidomics to discover regulatory self-peptides at the organism level. Utilizing a novel robotic platform (IMMUNOtron), we will systematically assess the effects of combining regulatory self-peptides and nonself-peptides on T cell activation in vitro. Machine learning techniques will then be employed to model TCR signaling and predict how regulatory self-peptides modulate T cell responses. In vivo studies will involve testing the impact of regulatory self-peptides on anti-tumor responses using the NINJA mouse model, which allows for genetic manipulation of peptide presentation. Moreover, we propose a novel application in CAR T cell therapy by incorporating regulatory self-peptides to enhance cancer targeting while mitigating off-target effects. This innovative approach could significantly improve the efficacy and safety of CAR-T cell treatments, making it a potential game-changer in cancer immunotherapy. The project's novelty stems from its paradigm-shifting hypothesis and its use of cutting-edge technology and interdisciplinary methods. By recognizing self-peptides as active participants in immune responses across tissues, this research could pave the way for more effective and personalized medical treatments, ultimately contributing to the establishment of a Human Immunopeptidome Project. The insights gained from this project have the potential to redefine immune regulation, enhance our understanding of disease susceptibility and resistance, and drive the development of tailored therapeutic strategies that leverage the regulatory power of self-peptides.