bioRxivImmunologyPreprint
Functional Type I and Type II interferon crosstalk restricts progenitor exhausted CD8 T cells through spatial exclusion and checkpoint enforcement
S. Liu, Heidi J. Elsaesser, René Vielman QuevedoiD, Diala Abd-RabboiD, Bruna C. BertoliD, Wenxi Xu, Melissa Yi Ran Liu, Sabelo LukheleiD, Sara LamorteiD, Tracy L. McGahaiD, David G. BrooksiD
Abstract
Type I interferon (IFN-I) and interferon-{gamma} (IFN{gamma}) are central regulators of antiviral immunity, yet how they cooperatively govern CD8 T cell fate during chronic infection remains unresolved. Here, we uncover a previously unrecognized, spatially encoded interferon circuit that actively constrains progenitor exhausted CD8 T cells (Tpex) during chronic LCMV infection. Persistent IFN-I signaling indirectly restricts Tpex expansion by enforcing their sequestration within PDL1-rich B cell niches of lymphoid tissue and by suppressing T cell-derived IFN{gamma}. Blockade of IFN-I signaling enables Tpex migration into T cell zones of splenic follicles driving IFN{gamma} production, which in turn sustains PDL1 expression on myeloid cells to re-impose local inhibitory pressure. Combined IFN-I and IFN{gamma} blockade disrupts this feedback, promoting coordinated niche redistribution of Tpex and checkpoint remodeling that drives robust Tpex expansion. Single-cell transcriptomics reveal that this layered IFN-I-IFN{gamma} interplay establishes a regulatory balance that constrains Tpex proliferation while preserving effector-like transcriptional programs in their progeny effector CD8 T cells, ultimately preventing premature terminal differentiation. Thus, interferons orchestrate the coordinated T cell-myeloid regulatory circuit that integrates tissue organization, cytokine feedback, and checkpoint control to regulate CD8 T cell exhaustion during chronic infection.
- organism: mouse
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 0
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