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Kakhki, M. P.

Publications and source records attributed to Kakhki, M. P..

2 recordsLinked to original sources

Anoctamin-2-specific T Cells Link Epstein-Barr Virus to Multiple Sclerosis

Multiple sclerosis (MS) occurs when the central nervous system (CNS) is damaged by misguided adaptive immune responses, likely caused by a combination of environmental factors in genetically susceptible individuals. A known prerequisite for disease is Epstein-Barr virus (EBV) infection, and previous studies have demonstrated elevated Epstein-Barr virus nuclear antigen 1 (EBNA1) antibodies which cross-react with the calcium-activated chloride channel anoctamin-2 (ANO2) in persons with MS (pwMS). ANO2-reactive antibodies have been associated with greater neuroaxonal damage in MS, although their exact effector function is still uncertain. Here, we demonstrate that ANO2 is also the target of IFN{gamma}-producing CD4+ T cells, which are more frequent in untreated and natalizumab-treated pwMS compared to control individuals. Immunisation of SJL/J mice with either ANO2 or EBNA1 elicited cross-reactive CD4+ T cell and antibody responses in vivo. Pre-immunisation of young mice with ANO2 worsened proteolipid protein (PLP)-induced experimental autoimmune encephalomyelitis (EAE), which in older mice included atypical clinical phenotype, immune infiltration into the brain and reduced survival. EAE exacerbation was recapitulated with the adoptive co-transfer of ANO2 and myelin antigen-specific CD4+ T cells, and ANO2-specific T cells alone could induce the cell death of ANO2-expressing glial cells in vitro. T cell clones with cross-reactivity to both EBNA1 and ANO2 antigens could be isolated from natalizumab-treated pwMS. Single cell sequencing of EBNA1 and ANO2-specific T cell receptors (TCR) from four pwMS revealed a significant overlap between their antigen-specific expanded TCR repertoires within donors and transcriptomic analysis showed cross-reactive T cells to have predominantly activated and cytotoxic phenotypes. In summary, we report the first mechanistic evidence that EBNA1 CD4+ T cells can target the MS-associated autoantigen ANO2, thereby establishing a link between EBV infection and development of autoimmune neuroinflammatory disease.

immunology↗

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

BackgroundGene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comprehensive exploration and interpretation of biological findings. To address this challenge, we developed GeneSetCluster, an R package designed to summarize and integrate GSA results. Over time, we and users as well identified limitations in the original version, such as difficulties in managing redundancies across multiple gene-sets, large computational times, and its lack of accessibility for users without programming expertise. ResultsWe present GeneSetCluster 2.0, a comprehensive upgrade that delivers methodological, computational, interpretative, and user-experience enhancements. Methodologically, GeneSetCluster 2.0 introduces a novel approach to address duplicated gene-sets and implements a seriation-based clustering algorithm that reorders results, aiding pattern identification. Computationally, the package is optimized for parallel processing, significantly reducing execution time. GeneSetCluster 2.0 enhances cluster annotations by associating clusters with relevant tissues and biological processes to improve biological interpretation, particularly for human and mouse data. To broaden accessibility, we have developed a user-friendly web application enabling non-programmers to use it. This version also ensures seamless integration between the R package, catering to users with programming expertise, and the web application for broader audiences. We evaluated the updates in a single-cell RNA public dataset. ConclusionGeneSetCluster 2.0 offers substantial improvements over its predecessor. Furthermore, by bridging the gap between bioinformaticians and clinicians in multidisciplinary teams, GeneSetCluster 2.0 facilitates collaborative research. The R package and web application, along with detailed installation and usage guides, are available on GitHub (https://github.com/TranslationalBioinformaticsUnit/GeneSetCluster2.0), and the web application can be accessed at https://translationalbio.shinyapps.io/genesetcluster/.

bioinformatics↗