ABSTRACT
Rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) are inflammatory diseases with different bone remodeling patterns. Fibroblast-like synoviocytes (FLS) are cells involved in the transition from an acute and reparable phase to a chronic and persistent stage in these diseases. The distinction of joint phenotypes involves inflammatory cytokines such as tumor necrosis factor alpha (TNF-α), interleukin (IL)-17, and IL-22 directly or through key signaling pathways such as Wnt. To evaluate the role of FLS as the source of Wnt antagonists (sFRP3/FRZB and Dkk1) in the synovia, levels of TNF- α, IL-17, IL-22, Dkk1, and sFRP3 were measured by ELISA directly in the synovial fluid of patients with RA, PsA, or AS. Dkk1 and sFRP3 were also measured in the FLS culture supernatants after different inflammatory stimulus. sFRP3 and Dkk1 are constitutively expressed by FLS. IL-22 and sFRP3 were positively correlated (r=0.76; P<0.01) in synovial fluid. The stimulation of FLS with IL-22, but not TNF-alpha and IL-17, increased the production of sFRP3. No stimulus altered the basal expression of Dkk1. These results showed, for the first time, the ability of IL-22 to increase the expression of sFRP3/FRZB by human FLS in both in vitro and ex vivo models. This finding linked IL-22 to local inhibition of Wnt signaling and possibly to blockade of osteogenesis. Furthermore, FLS presented as a source of this inhibitor in synovial fluid, assigning to this cell a bone injury mechanism.
Subject(s)
Interleukins/metabolism , Synoviocytes , Adult , Cells, Cultured , Female , Fibroblasts , Humans , Male , Middle Aged , Synovial Membrane , Tumor Necrosis Factor-alpha , Interleukin-22ABSTRACT
Rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) are inflammatory diseases with different bone remodeling patterns. Fibroblast-like synoviocytes (FLS) are cells involved in the transition from an acute and reparable phase to a chronic and persistent stage in these diseases. The distinction of joint phenotypes involves inflammatory cytokines such as tumor necrosis factor alpha (TNF-α), interleukin (IL)-17, and IL-22 directly or through key signaling pathways such as Wnt. To evaluate the role of FLS as the source of Wnt antagonists (sFRP3/FRZB and Dkk1) in the synovia, levels of TNF- α, IL-17, IL-22, Dkk1, and sFRP3 were measured by ELISA directly in the synovial fluid of patients with RA, PsA, or AS. Dkk1 and sFRP3 were also measured in the FLS culture supernatants after different inflammatory stimulus. sFRP3 and Dkk1 are constitutively expressed by FLS. IL-22 and sFRP3 were positively correlated (r=0.76; P<0.01) in synovial fluid. The stimulation of FLS with IL-22, but not TNF-alpha and IL-17, increased the production of sFRP3. No stimulus altered the basal expression of Dkk1. These results showed, for the first time, the ability of IL-22 to increase the expression of sFRP3/FRZB by human FLS in both in vitro and ex vivo models. This finding linked IL-22 to local inhibition of Wnt signaling and possibly to blockade of osteogenesis. Furthermore, FLS presented as a source of this inhibitor in synovial fluid, assigning to this cell a bone injury mechanism.
Subject(s)
Humans , Male , Female , Adult , Middle Aged , Interleukins/metabolism , Synoviocytes , Synovial Membrane , Cells, Cultured , Tumor Necrosis Factor-alpha , FibroblastsABSTRACT
In many species, low levels of polymorphism prevent the assembly of linkage maps that are used to identify genetic markers related to the expression of quantitative trait loci (QTLs). This study compared two methods of locating QTLs in association studies that do not require a previous estimation of linkage maps. Method I (MI) was a Bayesian multiple marker regression and Method II (MII) combined multiple QTL mapping and "moving away from markers". In this method, markers are not directly regressed to the phenotype, but are used as pivots to search for QTLs along the genome. To compare methods, we simulated 300 individuals from an F2 progeny with two levels of marker loss (20 and 80%). A total of 165 markers and seven QTLs were spread along 11 chromosomes (roughly emulating the genetic structure of the common bean, Phaseolus vulgaris). A real data example with 186 progenies of a F2:4 generation of the species was analyzed using 59 markers (17 simple sequence repeats, 31 amplified fragment length polymorphisms, and 11 sequence-related amplified polymorphisms). MII was more precise than MI for both levels of marker loss. For real data, MII detected 17 candidate positions for QTLs, whereas MI did not detect any. MII is a powerful method that requires further studies with actual data and other designs such as crossover, and genome-wide studies.
