Memory CD4+ T cells in chronic uveitis: pathogenicity, mechanisms, and therapeutic implications
Abstract
Chronic non-infectious uveitis (NIU) is a vision-threatening inflammatory disease in which relapses often occur despite corticosteroids, conventional immunosuppression, and biologic therapy. A growing body of experimental and human evidence indicates that disease persistence is not explained solely by transient effector inflammation. Instead, autoreactive memory CD4+ T cells can survive after the initiating trigger and retain retinal antigen responsiveness, sustaining persistent inflammation and reinitiating acute flares and exacerbations. In experimental chronic autoimmune uveitis, a specific subset of CD44hiIL-7R+IL-15R+ memory CD4+ T cells is maintained in the retina and peripheral secondary lymphoid organs (SLO), while additional autoreactive memory cells can persist in bone marrow niches through signal transducer and activator of transcription 3 (STAT3)-dependent mechanisms. Human studies further show expansion of memory and T helper 17 (Th17)-associated CD4+ T cell populations in NIU; however, the dominant immune cell subsets, clonotypes, and cytokine programs vary among heterogeneous clinical entities of NIU. This review summarizes how memory CD4+ T cells are generated, maintained, and linked to ocular tissue injury, with emphasis on their effector functions, IL-7 and IL-15-dependent survival signaling, STAT3-linked persistence, trafficking, and failed control by ocular immune regulation. We further discuss memory-directed therapeutic concepts grouped as disruption of…
Read the paper
Related studies
- Diarylethenes for Memories and Switches
- Long-Term Potentiation and Memory
- Social Memory Studies: From “Collective Memory” to the Historical Sociology of Mnemonic Practices
- The Cognitive Neuroscience of Working Memory
- Memory aging and brain maintenance
- Effector and Memory CTL Differentiation
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.