GRAIL (gene related to anergy in lymphocytes), is an E3 ubiquitin ligase with increased expression in anergic CD4+ T cells. The expression of GRAIL has been shown to be both necessary and sufficient for the induction of T cell (T) anergy. To date, several subsets of anergic T cells have demonstrated altered interactions with antigen-presenting cells (APC) and perturbed TCR-mediated signaling. The role of GRAIL in mediating these aspects of T cell anergy remains unclear. We used flow cytometry and confocal microscopy to examine T/APC interactions in GRAIL-expressing T cells. Increased GRAIL expression resulted in reduced T/APC conjugation efficiency as assessed by flow cytometry. Examination of single T/APC conjugates by confocal microscopy revealed altered polarization of polymerized actin and LFA-1 to the T/APC interface. When GRAIL expression was knocked down, actin polarization to the T/APC interface was restored, demonstrating that GRAIL is necessary for alteration of actin cytoskeletal rearrangement under anergizing conditions. Interestingly, proximal TCR signaling including calcium flux and phosphorylation of Vav were not disrupted by expression of GRAIL in CD4+ T cells. In contrast, interrogation of distal signaling events demonstrated significantly decreased JNK phosphorylation in GRAIL-expressing T cells. In sum, GRAIL expression in CD4+ T cells mediates alterations in the actin cytoskeleton during T/APC interactions. Moreover, in this model, our data dissociates proximal T cell signaling events from functional unresponsiveness. These data demonstrate a novel role for GRAIL in modulating T/APC interactions and provide further insight into the cell biology of anergic T cells.
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http://dx.doi.org/10.1074/jbc.M109.024497 | DOI Listing |
Cancer Med
January 2025
Medical Data Analytics Centre, The Chinese University of Hong Kong, Hong Kong SAR, China.
Purpose: Recent research (Li et al. 2021) suggests an upregulated expression and activation of H1 receptors on macrophages in the tumor microenvironment, and concomitant H1-antihistamine use is associated with improved overall survival in patients with lung and skin cancers receiving immunotherapy. Therefore, we retrospectively evaluated the impacts of H1-antihistamine use in cancer patients during immunotherapy.
View Article and Find Full Text PDFNat Cancer
January 2025
Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
Human tumors are diverse in their natural history and response to treatment, which in part results from genetic and transcriptomic heterogeneity. In clinical practice, single-site needle biopsies are used to sample this diversity, but cancer biomarkers may be confounded by spatiogenomic heterogeneity within individual tumors. Here we investigate clonally expressed genes as a solution to the sampling bias problem by analyzing multiregion whole-exome and RNA sequencing data for 450 tumor regions from 184 patients with lung adenocarcinoma in the TRACERx study.
View Article and Find Full Text PDFFront Immunol
January 2025
Department of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Background: Chronic graft-versus-host disease (cGVHD) manifests with characteristics of autoimmune disease with organs attacked by pathogenic helper T cells. Recent studies have highlighted the role of T cells in cGVHD pathogenesis. Due to limited understanding of underlying mechanisms, preventing cGVHD after allogenic hematopoietic cell transplantation (HCT) has become a major challenge.
View Article and Find Full Text PDFCell Res
January 2025
Department of Surgery, University of Cambridge, Cambridge, UK.
Cancer Cell
December 2024
Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA. Electronic address:
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.
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