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2.
Nat Immunol ; 24(12): 2121-2134, 2023 Dec.
Article in English | MEDLINE | ID: mdl-37945821

ABSTRACT

The T cell antigen receptor (TCR) contains ten immunoreceptor tyrosine-based activation motif (ITAM) signaling sequences distributed within six CD3 subunits; however, the reason for such structural complexity and multiplicity is unclear. Here we evaluated the effect of inactivating the three CD3ζ chain ITAMs on TCR signaling and T cell effector responses using a conditional 'switch' mouse model. Unexpectedly, we found that T cells expressing TCRs containing inactivated (non-signaling) CD3ζ ITAMs (6F-CD3ζ) exhibited reduced ability to discriminate between low- and high-affinity ligands, resulting in enhanced signaling and cytokine responses to low-affinity ligands because of a previously undetected inhibitory function of CD3ζ ITAMs. Also, 6F-CD3ζ TCRs were refractory to antagonism, as predicted by a new in silico adaptive kinetic proofreading model that revises the role of ITAM multiplicity in TCR signaling. Finally, T cells expressing 6F-CD3ζ displayed enhanced cytolytic activity against solid tumors expressing low-affinity ligands, identifying a new counterintuitive approach to TCR-mediated cancer immunotherapy.


Subject(s)
Immunoreceptor Tyrosine-Based Activation Motif , Receptors, Antigen, T-Cell , Animals , Mice , CD3 Complex , Ligands , Peptides , T-Lymphocytes
3.
Science ; 376(6595): 880-884, 2022 05 20.
Article in English | MEDLINE | ID: mdl-35587980

ABSTRACT

Systems immunology lacks a framework with which to derive theoretical understanding from high-dimensional datasets. We combined a robotic platform with machine learning to experimentally measure and theoretically model CD8+ T cell activation. High-dimensional cytokine dynamics could be compressed onto a low-dimensional latent space in an antigen-specific manner (so-called "antigen encoding"). We used antigen encoding to model and reconstruct patterns of T cell immune activation. The model delineated six classes of antigens eliciting distinct T cell responses. We generalized antigen encoding to multiple immune settings, including drug perturbations and activation of chimeric antigen receptor T cells. Such universal antigen encoding for T cell activation may enable further modeling of immune responses and their rational manipulation to optimize immunotherapies.


Subject(s)
Antigens , CD8-Positive T-Lymphocytes , Cytokines , Lymphocyte Activation , Models, Immunological , Antigens/immunology , CD8-Positive T-Lymphocytes/immunology , Humans , Immunotherapy , Machine Learning , Receptors, Antigen, T-Cell/metabolism
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