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Unfolded protein responses in T cell immunity.

Front Immunol

January 2025

Shanghai Frontiers Science Center for Drug Target Identification and Delivery, and the Engineering Research Center of Cell and Therapeutic Antibody of the Ministry of Education, School of Pharmaceutical Sciences, Shanghai Jiao Tong University, Shanghai, China.

Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) are integral to T cell biology, influencing immune responses and associated diseases. This review explores the interplay between the UPR and T cell immunity, highlighting the role of these cellular processes in T cell activation, differentiation, and function. The UPR, mediated by IRE1, PERK, and ATF6, is crucial for maintaining ER homeostasis and supporting T cell survival under stress.

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Purpose: The co-inhibitory receptor B and T Lymphocyte Attenuator (BTLA) negatively regulates B and T cell activation. We have previously shown an altered BTLA expression by regulatory T cells and an impaired capacity of BTLA to inhibit CD4 T cell activation in lupus patients. In this study, we analyzed BTLA expression and function in the NZB/W lupus-mouse model and examined the therapeutic potential of BTLA targeting.

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Tissue-resident memory (T) T cells have emerged as key players in cancer immunosurveillance, and their presence has been linked to a favorable clinical outcome in solid cancer patients. Liver metastases exhibit a highly immunosuppressive tumor microenvironment, however, the role and clinical impact of T cell infiltration in colorectal cancer remain elusive. The expression of several tissue residency and activation biomarkers has been investigated on tumor-infiltrating lymphocytes isolated from 26 patients' colorectal cancer liver metastases (CRC liver metastases) and compared to 16 peripheral blood samples of patients with CRC liver metastases.

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Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms.

Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD.

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