Background: Non-infectious uveitis (NIU) is a severe intra ocular inflammation, which frequently requires prompt systemic immunosuppressive therapy (IMT) to halt the development of vision-threatening complications. IMT is considered when NIU cannot be treated with corticosteroids alone, which is unpredictable in advance. Previous studies have linked blood cell subsets to glucocorticoid sensitivity, which suggests that the composition of blood leukocytes may early identify patients that will require IMT.Objective: To map the blood leukocyte composition of NIU and identify cell subsets that stratify patients that required IMT during follow-up.Methods: We performed controlled flow cytometry experiments measuring a total of 37 protein markers in the blood of 30 IMT free patients with active non-infectious anterior, intermediate, and posterior uveitis, and compared these to 15 age and sex matched healthy controls. Results from manual gating were validated by automatic unsupervised gating using FlowSOM.Results: Patients with uveitis displayed lower relative frequencies of Natural Killer cells and higher relative frequencies of memory T cells, in particular the CCR6+ lineages. These results were confirmed by automatic gating by unsupervised clustering using FlowSOM. We observed considerable heterogeneity in memory T cell subsets and abundance of CXCR3-CCR6+ (Th17) cells between the uveitis subtypes. Importantly, regardless of the uveitis subtype, patients that eventually required IMT in the course of the study follow-up exhibited increased CCR6+ T cell abundance before commencing therapy.Conclusion: High-dimensional immunoprofiling in NIU patients shows that clinically distinct forms of human NIU exhibit shared as well as unique immune cell perturbations in the peripheral blood and link CCR6+ T cell abundance to systemic immunomodulatory treatment.
Objective
To identify novel serum proteins involved in the pathogenesis of PsA as compared with healthy controls, psoriasis (Pso) and AS, and to explore which proteins best correlated to major clinical features of the disease.
Methods
A high-throughput serum biomarker platform (Olink) was used to assess the level of 951 unique proteins in serum of patients with PsA (n = 20), Pso (n = 18) and AS (n = 19), as well as healthy controls (HC, n = 20). Pso and PsA were matched for Psoriasis Area and Severity Index (PASI) and other clinical parameters.
Results
We found 68 differentially expressed proteins (DEPs) in PsA as compared with HC. Of those DEPs, 48 proteins (71%) were also dysregulated in Pso and/or AS. Strikingly, there were no DEPs when comparing PsA with Pso directly. On the contrary, hierarchical cluster analysis and multidimensional scaling revealed that HC clustered distinctly from all patients, and that PsA and Pso grouped together. The number of swollen joints had the strongest positive correlation to ICAM-1 (r = 0.81, P < 0.001) and CCL18 (0.76, P < 0.001). PASI score was best correlated to PI3 (r = 0.54, P < 0.001) and IL-17 receptor A (r = –0.51, P < 0.01). There were more proteins correlated to PASI score when analysing Pso and PsA patients separately, as compared with analysing Pso and PsA patients pooled together.
Conclusion
PsA and Pso patients share a serum proteomic signature, which supports the concept of a single psoriatic spectrum of disease. Future studies should target skin and synovial tissues to uncover differences in local factors driving arthritis development in Pso.
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