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Killer-cell immunoglobulin-like receptors define a potent effector program in human γδ T cells
Mahya Razmi, Yeganeh Almasi, Marilee Larrivée, Jonathan B. Angel, Alexandre Blais, Zakia Djaoud
Mahya Razmi, Yeganeh Almasi, Marilee Larrivée, Jonathan B. Angel, Alexandre Blais, Zakia Djaoud
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Research Article Hematology Immunology

Killer-cell immunoglobulin-like receptors define a potent effector program in human γδ T cells

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Abstract

Human γδ T cells are a rare but functionally diverse lymphocyte subset critical for tumor surveillance and antimicrobial immunity. Although they express NK cell–associated receptors such as killer-cell immunoglobulin-like receptors (KIRs), the relevance of KIR expression on γδ T cells remains largely unexplored. Using flow cytometry, ATAC-seq, and RNA-seq, we identified KIR expression as a marker that distinguished 2 functionally and molecularly distinct γδ T cell subsets. KIR+ γδ T cells exhibited an advanced, memory-like differentiation state characterized by heightened cytotoxicity, stable epigenetic remodeling, and a predominant IFN-γ–producing profile. In contrast, KIR– γδ T cells maintained a naive-like phenotype and preferentially produced IL-17 upon polarization. Notably, KIR+ γδ T cells were consistently observed across individuals but were significantly enriched in cytomegalovirus (CMV)-seropositive donors, suggesting that chronic antigenic stimulation could promote the emergence of KIR+ effector γδ T cells. These findings reveal a functional dichotomy in human γδ T cells defined by KIR expression, linking IFN-γ–driven cytotoxicity with KIR+ cells and IL-17 production with KIR– cells. This insight advances our understanding of γδ T cell heterogeneity and has implications for viral immunity, immune memory, and the development of γδ T cell–based immunotherapies.

Authors

Mahya Razmi, Yeganeh Almasi, Marilee Larrivée, Jonathan B. Angel, Alexandre Blais, Zakia Djaoud

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Usage data is cumulative from April 2026 through June 2026.

Usage JCI PMC
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Figure 202 0
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Citation downloads 242 0
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Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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