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Biology subjects

Risch, I.

Publications and source records attributed to Risch, I..

2 recordsLinked to original sources

Refinement of the Pathogenic T Cell Receptor Motif Suggests Type 17 CD8+ T Cells Initiate HLA-B*27-associated Autoimmunity

Human leukocyte antigen B*27 (HLA-B*27) is a major risk factor for autoimmune diseases such as axial spondyloarthritis (axSpA), acute anterior uveitis (AAU), and psoriatic arthritis (PsA). HLA-B*27 presents bacterial and self-antigens to disease-associated CD8+ T cells, however, how this leads to a pathogenic IL-17-mediated disease remains unclear. We performed mutational experiments in an established disease-associated TCR and demonstrated that the pathogenic motif encompasses a wider range of TCR sequences than previously appreciated. We next leveraged computational pairing of alpha/beta TCR sequencing (TCR-seq) to demonstrate that the broadened pathogenic motif distinguishes axSpA and AAU patients from healthy controls. Paired single-cell RNA sequencing and single-cell TCR sequencing datasets demonstrated that the pathogenic cells have a distinct transcriptional signature. We find a coupling of pathogenic TCRs to this transcriptional signature in public datasets from axSpA and PsA joint fluid and AAU ocular fluid. Finally, we detect the pathogenic CD8+ T cells in the gut and demonstrate that they express a Type 17 program. Our findings suggest that HLA-B*27+ axSpA, AAU, and PsA are initiated by HLA-B*27-restricted pathogenic CD8+ T cells that undergo Type 17 differentiation in response to intestinal antigens.

immunology↗

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.

systems biology↗