bioRxivImmunology
IL-15 priming alters IFN-γ regulation in murine NK cells
M. Cimpean, M. P. Keppel, A. Gainullina, C. Fan, N. C. Schedler, A. Swain, A. Kolicheski, H. Shapiro, H. A. Young, T. Wang, M. Artyomov, M. A. Cooper
Abstract
Natural killer (NK) effector functions can be triggered by inflammatory cytokines and engagement of activating receptors. NK cell production of IFN-{gamma}, an important immunoregulatory cytokine, exhibits activation-specific IFN-{gamma} regulation. Resting murine NK cells exhibit activation-specific metabolic requirements for IFN-{gamma} production, which are reversed for activating receptor-mediated stimulation following IL-15 priming. While both cytokine and activating receptor stimulation leads to similar IFN-{gamma} protein production, only cytokine stimulation upregulates Ifng transcript, suggesting that protein production is translationally regulated after receptor stimulation. Based on these differences in IFN-{gamma} regulation, we hypothesized that ex vivo IL-15 priming of murine NK cells allows a switch to IFN-{gamma} transcription upon activating receptor engagement. Transcriptional analysis of primed NK cells compared to naive cells or cells cultured with low-dose IL-15 demonstrated that primed cells strongly upregulated Ifng transcript following activating receptor stimulation. This was not due to chromatin accessibility changes in the Ifng locus or changes in ITAM signaling, but was associated with a distinct transcriptional signature induced by ITAM stimulation of primed compared to naive NK cells. Transcriptional analyses identified a common signature of c-Myc (Myc) targets associated with Ifng transcription. While Myc marked NK cells capable of Ifng transcription, Myc itself was not required for Ifng transcription using a genetic model of Myc deletion. This work highlights altered regulatory networks in IL-15 primed cells, resulting in distinct gene expression patterns and IFN-{gamma} regulation in response to activating receptor stimulation.
- organism: mouse
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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