Chikina Lab Publish in The Journal of Clinical Investigation

Dr. Maria Chikina and associates contributed to a publication regarding avenues for the treatment of autoimmunity based on selectively exploiting the exhausted phenotype of tissue-infiltrating T cells.

Abstract:

While T cells are important for the pathogenesis of systemic lupus erythematosus (SLE) and lupus nephritis, little is known about how T cells function after infiltrating the kidney. The current paradigm suggests that kidney-infiltrating T cells (KITs) are activated effector cells contributing to tissue damage and ultimately organ failure. Herein, we demonstrate that the majority of CD4+ and CD8+ KITs in 3 murine lupus models are not effector cells, as hypothesized, but rather express multiple inhibitory receptors and are highly dysfunctional, with reduced cytokine production and proliferative capacity. In other systems, this hypofunctional profile is linked directly to metabolic and specifically mitochondrial dysfunction, which we also observed in KITs. The T cell phenotype was driven by the expression of an “exhausted” transcriptional signature. Our data thus reveal that the tissue parenchyma has the capability of suppressing T cell responses and limiting damage to self. These findings suggest avenues for the treatment of autoimmunity based on selectively exploiting the exhausted phenotype of tissue-infiltrating T cells.

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Chikina Lab Publish in Nucleic Acids Research

Dr. Maria Chikina and associates contributed to a publication regarding a web-interface that provides access to a variety of tools for testing associations between epigenetic data samples.

Abstract:
Functional genomics assays produce sets of genomic regions as one of their main outputs. To biologically interpret such region-sets, researchers often use colocalization analysis, where the statistical significance of colocalization (overlap, spatial proximity) between two or more region-sets is tested. Existing colocalization analysis tools vary in the statistical methodology and analysis approaches, thus potentially providing different conclusions for the same research question. As the findings of colocalization analysis are often the basis for follow-up experiments, it is helpful to use several tools in parallel and to compare the results. We developed the Coloc-stats web service to facilitate such analyses. Coloc-stats provides a unified interface to perform colocalization analysis across various analytical methods and method-specific options (e.g. colocalization measures, resolution, null models). Coloc-stats helps the user to find a method that supports their experimental requirements and allows for a straightforward comparison across methods. Coloc-stats is implemented as a web server with a graphical user interface that assists users with configuring their colocalization analyses. Coloc-stats is freely available at https://hyperbrowser.uio.no/coloc-stats/.

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