Impaired lymph node stromal cell function during the earliest phases of rheumatoid arthritis.

BACKGROUND: Systemic autoimmunity can be present years before clinical onset of rheumatoid arthritis (RA). Adaptive immunity is initiated in lymphoid tissue where lymph node stromal cells (LNSCs) regulate immune responses through their intimate connection with leucocytes. We postulate that malfunctioning of LNSCs creates a microenvironment in which normal immune responses are not properly controlled, possibly leading to autoimmune disease. In this study we established an experimental model for studying the functional capacities of human LNSCs during RA development. METHODS: Twenty-four patients with RA, 23 individuals positive for autoantibodies but without clinical disease (RA risk group) and 14 seronegative healthy control subjects underwent ultrasound-guided inguinal lymph node (LN) biopsy. Human LNSCs were isolated and expanded in vitro for functional analyses. In analogous co-cultures consisting of LNSCs and peripheral blood mononuclear cells, αCD3/αCD28-induced T-cell proliferation was measured using carboxyfluorescein diacetate succinimidyl ester dilution. RESULTS: Fibroblast-like cells expanded from the LN biopsy comprised of fibroblastic reticular cells (gp38+CD31-) and double-negative (gp38-CD31-) cells. Cultured LNSCs stably expressed characteristic adhesion molecules and cytokines. Basal expression of C-X-C motif chemokine ligand 12 (CXCL12) was lower in LNSCs from RA risk individuals than in those from healthy control subjects. Key LN chemokines C-C motif chemokine ligand (CCL19), CCL21 and CXCL13 were induced in LNSCs upon stimulation with tumour necrosis factor-α and lymphotoxin α1β2, but to a lesser extent in LNSCs from patients with RA. The effect of human LNSCs on T-cell proliferation was ratio-dependent and altered in RA LNSCs. CONCLUSIONS: Overall, we developed an experimental model to facilitate research on the role of LNSCs during the earliest phases of RA. Using this innovative model, we show, for the first time to our knowledge, that the LN stromal environment is changed during the earliest phases of RA, probably contributing to deregulated immune responses early in disease pathogenesis

Cutaneous non-allergic complications in tattoos: An overview of the literature

Tattooing is creating a permanent design by placing exogenous pigment particles and additives into the dermis. An adverse reaction may occur due to the act of tattooing and subsequent application of aftercare products. Numerous articles report the wide spectrum of adverse reactions related to tattooing, ranging from superficial infections and vasculitis to Koebner triggered autoimmune diseases. These reactions have different time of onset of symptoms, appearing immediately after placement of the tattoo until several years later. In this article we will give an overview of cutaneous non-allergic adverse reactions of tattoos

Neo-Epitopes--Fragments of Cartilage and Connective Tissue Degradation in Early Rheumatoid Arthritis and Unclassified Arthritis.

OBJECTIVE:Tissue destruction in rheumatoid arthritis (RA) is predominantly mediated by matrix metalloproteinases (MMPs), thereby generating protein fragments. Previous studies have revealed that these fragments include MMP-mediated collagen type I, II, and III degradation, citrullinated and MMP-degraded vimentin and MMP degraded C-reactive protein. We evaluated if biomarkers measuring serum levels of specific sequences of the mentioned fragments would provide further information of diagnostic and/or prognostic processes in early arthritis. METHODS:Ninety-two early arthritis patients (arthritis duration<1 year, DMARD naïve) were enrolled. Patients either fulfilled the ACR/EULAR2010 criteria for RA (n = 60) or had unclassified arthritis (UA) (n = 32). Patients fulfilling the RA criteria after 2 years follow-up were classified into non-erosive (n = 25), or erosive disease (n = 13). Concentrations of the biomarkers: C1M, C2M, C3M, VICM and CRPM were measured in baseline serum. RESULTS:C1M, C3M and CRPM were able to discriminate between the UA and RA baseline diagnosis in 92 patients with an AUROC of 0.64 (95%CI 0.517 to 0.762), 0.73 (95%CI 0.622 to 0.838) and 0.68 (95%CI 0.570 to 0.795). C2M showed a potential for discrimination between non-erosive and erosive disease in 38 patients with an AUROC of 0.75 (95%CI 0.597 to 0.910). All of the applied biomarkers correlated with one or more of the disease activity parameters: DAS28, ESR, CRP, SJC66, TJC68 and/or HAQ. CONCLUSION:This is the first study evaluating the applied biomarkers at this early stage of arthritis. C1M, C3M, CRPM might be the best diagnostic marker, whereas high levels of C2M indicated progression of disease at follow-up in early RA patients

Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Pharmacokinetic Modeling: Initial Experience in Patients With Early Arthritis

Objective. Analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using pharmacokinetic modeling (PKM) provides quantitative measures that mirror microvessel integrity and can be used as an objective marker of the level of synovial inflammation. The aim of this study was to investigate the PKM parameters K-trans, (k)ep, and v(e) in a prospective cohort of disease- modifying antirheumatic drug (DMARD)-naive patients with early arthritis, and to validate the results by assessing their correlation with the number of synovial endothelial cells (ECs). Methods. Forty-seven patients with early arthritis (arthritis duration <1 year, DMARD naive; comprising 14 patients with rheumatoid arthritis, 22 with unclassified arthritis, 6 with spondyloarthritis [SpA], and 5 with other arthritides) were included. At baseline, DCE-MRI was performed on an inflamed knee joint of each patient. These images were used to calculate the K trans (volume transfer constant between the plasma and extracellular extravascular space [EES]), the k(ep) (transfer constant between the EES and plasma), and the v(e) (fractional volume of the EES). Second, markers of disease activity were collected. Finally, vascularity was evaluated by immunohistochemical analysis of synovial tissue samples obtained from the inflamed knee joints, using antibodies to detect von Willebrand factor (vWF), a marker of ECs. Results. The 3 PKM parameters differed significantly between diagnostic groups at baseline, with the highest K-trans value being observed in patients with SpA (median 0.050/minute, interquartile range [IQR] 0.041-0.069). Furthermore, the K-trans, k(ep), and v(e) values correlated significantly with markers of disease activity. Finally, the PKM parameters K-trans and k(ep), but not v(e), correlated significantly with synovial expression of vWF (r = 0.647, P = 0.004 for K-trans; r = 0.614, P = 0.007 for k(ep); r = 0.398, P = 0.102 for ve). Conclusion. These results suggest that the K-trans, k(ep), and v(e) can be used to detect synovial inflammation in patients with early arthritis, and these PKM parameters may be helpful in differential diagnosis. This approach may also be useful in translational research analyzing tissue microcirculation and angiogenesi

Area under the receiver operating characteristic (AUROC) for discriminating between RA and UA baseline diagnosis.

<p>Area under the receiver operating characteristic (AUROC) for discriminating between RA and UA baseline diagnosis.</p

Baseline characteristics of early arthritis patients.

<p>Baseline characteristics of early arthritis patients.</p

Area under the receiver operating characteristic (AUROC) for discriminating between UA patients that progress to RA after 2 years of follow-up and those that remain UA after 2 years of follow-up.

<p>Area under the receiver operating characteristic (AUROC) for discriminating between UA patients that progress to RA after 2 years of follow-up and those that remain UA after 2 years of follow-up.</p

Area under the receiver operating characteristic (AUROC).

<p>Area under the receiver operating characteristic (AUROC).</p

Correlations between baseline biomarker concentration and other parameters for disease activity in the overall population (UA+RA).

<p>Correlations between baseline biomarker concentration and other parameters for disease activity in the overall population (UA+RA).</p