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Intrathymic programming of effector fates in three molecularly distinct γδ T cell subtypes

Abstract

Innate γδ T cells function in the early phase of immune responses. Although innate γδ T cells have often been studied as one homogenous population, they can be functionally classified into effector subsets on the basis of the production of signature cytokines, analogous to adaptive helper T cell subsets. However, unlike the function of adaptive T cells, γδ effector T cell function correlates with genomically encoded T cell antigen receptor (TCR) chains, which suggests that clonal TCR selection is not the main determinant of the differentiation of γδ effector cells. A high-resolution transcriptome analysis of all emergent γδ thymocyte subsets segregated on the basis of use of the TCR γ-chain or δ-chain indicated the existence of three separate subtypes of γδ effector cells in the thymus. The immature γδ subsets were distinguished by unique transcription-factor modules that program effector function.

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Figure 1: Distinct global gene-expression profiles of the γδ cell subsets defined by TCR repertoire.
Figure 2: Distinct expression of transcription factors and divergence of γδ T cell subsets.
Figure 3: Expression of transcription factors and genes encoding molecules involved in metabolic processes distinguishes γδ thymocyte subsets.
Figure 4: Convergence of the gene-expression profiles of γδ subsets after maturation.
Figure 5: Generation of mature γδ cell subsets poised for the elaboration of effector function programs.
Figure 6: Common features of αβ iNKT cells and γδ matV6 cells.

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Acknowledgements

We thank M. Mohrs (Trudeau Institute) for IL-4–GFP reporter mice; H. Birchmeier (Max-Delbrück-Center for Molecular Medicine Berlin) for Axin2 reporter mice; V. Lefebvre (Cleveland Clinic) for Sox5 reporter mice; K. Rock (University of Massachusetts Medical School) for Ctsl−/− mice; E. Huseby (University of Massachusetts Medical School) for Cd74−/− mice; S. Davis (Harvard Medical School) for the ConsolidateProbeSets module and PopulationDistances PCA program; A. Hayday (King's College, London) for anti-Vδ1 (17D1); members of the ImmGen Consortium for discussions; the ImmGen core team (M. Painter, J. Ericson and S. Davis) for help with data generation and processing; and eBioscience, Affymetrix and Expression Analysis for support of the ImmGen Project. Core resources supported by the Diabetes Endocrinology Research Center (DK32520) were used. Supported by the National Institute of Allergy and Infectious Diseases of the US National Institutes of Health (R24 AI072073 to the ImmGen group, and CA100382 to J.K.).

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K.E.S. sorted cell subsets; K.N., N.M., K.E.S. and C.C.Y. did follow-up experiments and analyzed data; G.M., T.V. and K.N. did the studies of M. tuberculosis; H.K. supervised the studies of M. tuberculosis; N.X. provided reagents and cells from a mutant strain; N.R.C. and M.B.B. generated the gene-expression profiles of NKT cell subsets; L.J.B. provided reagents and shared data used in the interpretation of some results; K.N. analyzed gene-expression data; and J.K. and K.N. designed studies and wrote the paper.

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Correspondence to Joonsoo Kang.

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Narayan, K., Sylvia, K., Malhotra, N. et al. Intrathymic programming of effector fates in three molecularly distinct γδ T cell subtypes. Nat Immunol 13, 511–518 (2012). https://doi.org/10.1038/ni.2247

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