Rheumatoid arthritis is a systemic autoimmune disease, but disease flares typically affect only a subset of joints, distributed in a distinctive pattern for each patient. Pursuing this intriguing ...pattern, we show that arthritis recurrence is mediated by long-lived synovial resident memory T cells (TRM). In three murine models, CD8+ cells bearing TRM markers remain in previously inflamed joints during remission. These cells are bona fide TRM, exhibiting a failure to migrate between joints, preferential uptake of fatty acids, and long-term residency. Disease flares result from TRM activation by antigen, leading to CCL5-mediated recruitment of circulating effector cells. Correspondingly, TRM depletion ameliorates recurrence in a site-specific manner. Human rheumatoid arthritis joint tissues contain a comparable CD8+-predominant TRM population, which is most evident in late-stage leukocyte-poor synovium, exhibiting limited T cell receptor diversity and a pro-inflammatory transcriptomic signature. Together, these findings establish synovial TRM as a targetable mediator of disease chronicity in autoimmune arthritis.
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•Arthritic human and murine joints acquire CD8+ resident memory T cells (TRM)•TRM from rheumatoid joints display a restricted T cell receptor repertoire•Antigen-activated TRM recruit other immune cells to cause an arthritis flare•Depletion of synovial TRM blocks local disease recurrence
Rheumatoid arthritis is characterized by recurrent inflammation of some, but not all, joints. Chang et al. show that CD8+ tissue-resident memory T cells accumulate in previously inflamed joints, nucleating local disease recurrence when triggered by antigens. TRM depletion attenuates arthritis flares, defining a mechanistic basis for joint-specific memory.
Structural reliability evaluation is considered to be the solution for modern complex engineering systems possessing uncertain parameters. Reliability estimation involves probabilistic theory when ...the uncertainties are defined as random variables, whereas with limited resources, it is strenuous to estimate precise parameters in the structural model. Therefore, for such cases, imprecise parameters should be treated appropriately in the design and analysis stage for the improvement of serviceability of the system. On the other side, analyses involving multi-dimensional, computationally expensive, and highly nonlinear structures are formidable in simulation-based methods in the presence of uncertainties. An efficient uncertainty analysis procedure is presented in this paper for analysing the systems with imprecise uncertainties defined as probability-box variables. The estimated bounds of failure probability for the numerical examples from structural mechanics are compared with the traditional approaches to demonstrate the efficiency of the methodology.
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