CXCR6 orchestrates brain CD8+ T cell residency and limits mouse Alzheimer's disease pathology.

Neurodegenerative diseases, including Alzheimer's disease (AD), are characterized by innate immune-mediated inflammation, but functional and mechanistic effects of the adaptive immune system remain unclear. Here we identify brain-resident CD8+ T cells that coexpress CXCR6 and PD-1 and are in proximity to plaque-associated microglia in human and mouse AD brains. We also establish that CD8+ T cells restrict AD pathologies, including ß-amyloid deposition and cognitive decline. Ligand-receptor interaction analysis identifies CXCL16-CXCR6 intercellular communication between microglia and CD8+ T cells. Further, Cxcr6 deficiency impairs accumulation, tissue residency programming and clonal expansion of brain PD-1+CD8+ T cells. Ablation of Cxcr6 or CD8+ T cells ultimately increases proinflammatory cytokine production from microglia, with CXCR6 orchestrating brain CD8+ T cell-microglia colocalization. Collectively, our study reveals protective roles for brain CD8+ T cells and CXCR6 in mouse AD pathogenesis and highlights that microenvironment-specific, intercellular communication orchestrates tissue homeostasis and protection from neuroinflammation.

CRISPR screens unveil signal hubs for nutrient licensing of T cell immunity.

Nutrients are emerging regulators of adaptive immunity1. Selective nutrients interplay with immunological signals to activate mechanistic target of rapamycin complex 1 (mTORC1), a key driver of cell metabolism2-4, but how these environmental signals are integrated for immune regulation remains unclear. Here we use genome-wide CRISPR screening combined with protein-protein interaction networks to identify regulatory modules that mediate immune receptor- and nutrient-dependent signalling to mTORC1 in mouse regulatory T (Treg) cells. SEC31A is identified to promote mTORC1 activation by interacting with the GATOR2 component SEC13 to protect it from SKP1-dependent proteasomal degradation. Accordingly, loss of SEC31A impairs T cell priming and Treg suppressive function in mice. In addition, the SWI/SNF complex restricts expression of the amino acid sensor CASTOR1, thereby enhancing mTORC1 activation. Moreover, we reveal that the CCDC101-associated SAGA complex is a potent inhibitor of mTORC1, which limits the expression of glucose and amino acid transporters and maintains T cell quiescence in vivo. Specific deletion of Ccdc101 in mouse Treg cells results in uncontrolled inflammation but improved antitumour immunity. Collectively, our results establish epigenetic and post-translational mechanisms that underpin how nutrient transporters, sensors and transducers interplay with immune signals for three-tiered regulation of mTORC1 activity and identify their pivotal roles in licensing T cell immunity and immune tolerance.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data.

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data.

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

NetBID2 provides comprehensive hidden driver analysis.

Many signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .

Integrome signatures of lentiviral gene therapy for SCID-X1 patients.

Lentiviral vector (LV)-based gene therapy holds promise for a broad range of diseases. Analyzing more than 280,000 vector integration sites (VISs) in 273 samples from 10 patients with X-linked severe combined immunodeficiency (SCID-X1), we discovered shared LV integrome signatures in 9 of 10 patients in relation to the genomics, epigenomics, and 3D structure of the human genome. VISs were enriched in the nuclear subcompartment A1 and integrated into super-enhancers close to nuclear pore complexes. These signatures were validated in T cells transduced with an LV encoding a CD19-specific chimeric antigen receptor. Intriguingly, the one patient whose VISs deviated from the identified integrome signatures had a distinct clinical course. Comparison of LV and gamma retrovirus integromes regarding their 3D genome signatures identified differences that might explain the lower risk of insertional mutagenesis in LV-based gene therapy. Our findings suggest that LV integrome signatures, shaped by common features such as genome organization, may affect the efficacy of LV-based cellular therapies.

PTEN directs developmental and metabolic signaling for innate-like T cell fate and tissue homeostasis.

Phosphatase and tensin homologue (PTEN) is frequently mutated in human cancer, but its roles in lymphopoiesis and tissue homeostasis remain poorly defined. Here we show that PTEN orchestrates a two-step developmental process linking antigen receptor and IL-23-Stat3 signalling to type-17 innate-like T cell generation. Loss of PTEN leads to pronounced accumulation of mature IL-17-producing innate-like T cells in the thymus. IL-23 is essential for their accumulation, and ablation of IL-23 or IL-17 signalling rectifies the reduced survival of female PTEN-haploinsufficient mice that model human patients with PTEN mutations. Single-cell transcriptome and network analyses revealed the dynamic regulation of PTEN, mTOR and metabolic activities that accompanied type-17 cell programming. Furthermore, deletion of mTORC1 or mTORC2 blocks PTEN loss-driven type-17 cell accumulation, and this is further shaped by the Foxo1 and Stat3 pathways. Collectively, our study establishes developmental and metabolic signalling networks underpinning type-17 cell fate decisions and their functional effects at coordinating PTEN-dependent tissue homeostasis.