Life Science AllianceCancer BiologyPeer-reviewed journal article
Cross-Species Transcriptomic Integration Reveals a MIRO1-Mediated Macrophage-T Cell Axis in Glioma
Zehui DuiD, Menghan LiiD, Brandon Hwa-Lin BergsneideriD, Andy Po‐Yi TsaiiD, Kwang Bog ChoiD, Lily H. KimiD, John Hyunkuk ChoiiD, Gordon LiiD, Tony Wyss‐CorayiD, Michael K. LimiD, Xinnan WangiD
Abstract
Mitochondrial regulators are increasingly recognized for their influence on immune signaling within the tumor microenvironment (TME). In glioma, where immunosuppression limits therapeutic efficacy, we investigate how targeting the mitochondrial protein MIRO1 alters the TME. We combine single-nucleus RNA sequencing of murine gliomas treated in vivo with a MIRO1-binding compound and bulk RNA sequencing of human glioma resections treated with the same compound ex vivo. Cross-species transcriptomic integration reveals a MIRO1-responsive program in the TME. Among shared targets, we identify PARP11/Parp11 as a consistently upregulated gene in glioma that is downregulated following MIRO1-binding compound treatment in both human and mouse gliomas. Cell-cell communication analysis shows that a specific cluster of macrophages (MAC1), which exhibits robust Parp11 and Pdl1 (encoding PD-L1) expression, sends immunosuppressive signals to CD8+ cytotoxic T cells, and may receive prostaglandin E{square} signals from another cluster of macrophages (MAC4). Targeting MIRO1 eliminates this cell circuitry and reduces the tumor cell population. Our study provides a transcriptomic framework for understanding mitochondria-immune crosstalk and nominates MIRO1-PARP11 as a potential effector axis of brain immune dysfunction. Summary BlurbTranscriptomic integration of single-nucleus RNA sequencing of murine gliomas and bulk RNA sequencing of human gliomas reveals an immunosuppressive signaling axis in the glioma TME, mediated by MIRO1.
- organism: mouse
- organism: human
- assay: scRNA-seq
- assay: bulk RNA-seq
- research focus: cell-cell communication
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 2
- FWCI
- 2.94
- Field percentile
- top 9%
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