Publications by authors named "Trieneke C G Timmer"
Article Synopsis
- The study aimed to understand the significance of different types of synovial tissue in rheumatoid arthritis (RA) and find potential biomarkers in the blood.
- Researchers analyzed gene expression in synovial tissue and blood samples from 17 RA patients, linking these findings to disease characteristics and inflammation levels.
- Results showed that high-inflammation synovial tissue correlated with more severe disease outcomes, but no significant differences were found in single-gene expression in blood samples between the tissue types.
Objective: Increased expression of insulin-like growth factor 2 (IGF2) by fibroblast-like synoviocytes (FLS) was associated with low inflammatory synovium of patients with rheumatoid arthritis (RA). The aim of this study was to analyse whether the differential expression of IGF2, whose expression is normally restricted to one allele, is due to activation of the normally suppressed allele.
Methods: IGF2 gene expression of RA FLS was quantified by quantitative real-time PCR.
Objective: In approximately 25% of synovial tissues from rheumatoid arthritis (RA) patients, infiltrates of T cells, B cells, and follicular dendritic cells (FDCs) are spatially organized into structures resembling lymph nodes with germinal centers. The remainder of the tissues lack FDCs and show either a diffuse or an aggregated T cell and B cell infiltrate. To gain more insight into this specific disease process, we sought to identify the genes expressed in RA tissues with ectopic lymphoid structures.
View Article and Find Full Text PDFObjective: Given the heterogeneity of gene expression patterns and cellular distribution between rheumatoid arthritis (RA) synovial tissues, we sought to determine whether this variability was also reflected at the level of the fibroblast-like synoviocyte (FLS) cultured from RA synovial tissues.
Methods: Gene expression profiles in FLS cultured from synovial tissues obtained from 19 RA patients were analyzed using complementary DNA microarrays and hierarchical cluster analysis. To validate the subclassification, we performed prediction analysis and principal components analysis.