ABSTRACT: Background ; Rheumatoid arthritis (RA) is a chronic inflammatory disease, characterized by joint destruction and perpetuated by the synovial membrane (SM). In the inflamed SM, activated synovial fibroblasts (SFB) form the major cell type promoting development and progression of the disease by an abnormal expression/secretion of pro-inflammatory cytokines, tissue-degrading enzymes resulting in a predominant degradation of the extra-cellular matrix (ECM), and collagens causing joint fibrosis. We developed a new procedure, based on human knowledge and formal concept analysis (FCA), to simulate and analyze the temporal behaviour of regulatory and signaling networks. It was applied to a regulatory network (containing 18 genes from 5 functional groups) representing ECM formation and destruction in TGFβ - and TNFα -stimulated SFB. Results ; For the modelling of SFB-controlled ECM turnover in rheumatic diseases, Boolean network architecture was used as well as extensive literature information and revision by experimental gene expression data from stimulated SFB. In course of revision, the additional experimental information resulted in different biologically reasonable changes, yielding two Boolean networks that describe TGFβ and TNFα effects, respectively. The final simulations were further analyzed by the attribute exploration algorithm of FCA, integrating again the observed time series in a more fine-grained and automated manner. The generated temporal rules clearly reveal subtle regulatory relationships between different genes, co-expression patterns and converse gene expression regulation in rheumatic diseases. Conclusion ; The developed Boolean network based method for the dynamical analysis of regulatory and signaling networks represents a reliable systems biological solution for the improved understanding of complex regulatory pathways and the interactions among different genes in disease. The resulting knowledge base can be used for further analysis of the ECM system in human fibroblasts and may be queried to predict the functional consequences of observed (e.g. in diseases as RA) or hypothetical (e.g. for therapeutic purposes) gene expression disturbances. Experiment Overall Design: Patients and tissue samples: Experiment Overall Design: Synovial membrane samples were obtained within 10 min following tissue excision upon joint replacement/synovectomy from RA and OA patients (n = 3 each). After removal, tissue samples were frozen and stored at -70°C. Informed patient consent was acquired and the study was approved by the ethics committees of the respective universities. RA patients were classified according to the American College of Rheumatology (ACR) criteria, OA patients according to the respective criteria for osteoarthritis. The preparation of primary semi-transformed synovial fibroblasts from RA and OA patients was performed as previously described (Zimmermann et al., Arthritis Res. 2001;3(1):72-6). Briefly, the tissue samples were minced and digested with trypsin/collagenase P. The resulting single cell suspension was cultured for seven days. Non-adherent cells were removed by medium exchange. SFB were then negatively purified using Dynabeads® M 450 CD14 and subsequently cultured over 4 passages in DMEM containing 100 μg/ml gentamycin, 100 μg/ml penicillin/streptomycin, 20 mM HEPES, and 10% FCS. Experiment Overall Design: Cell stimulation and isolation of total RNA : Experiment Overall Design: At the end of the fourth passage, the SFB were stimulated by 10 ng/ml TGFβ or TNFα in serum-free DMEM for 0, 1, 2, 4, and 12 h. At the end of each time point, medium was removed and the cells were digested with trypsin/versene (0.25%). Following centrifugation and washing with PBS, the cells were lysed with RLT buffer (Qiagen) and frozen at 70°C. Total RNA was isolated using the RNeasy Kit (Qiagen) according to the supplier's recommendation. Experiment Overall Design: Microarray data analysis: Experiment Overall Design: RNA probes were labelled according to the supplier's instructions (Affymetrix®, Santa Clara, CA, USA). Analysis of gene expression was performed using U133 Plus 2.0 RNA microarrays. Hybridization and washing was performed according to the supplier's instructions. Microarrays were analyzed by laser scanning (Hewlett-Packard Gene Scanner). Background-corrected signal intensities were determined using the MAS 5.0 software (Affymetrix®) and normalized among arrays to facilitate comparisons between different patients. For this purpose, arrays were grouped according to patient class the respective stimulus (TGFβ and TNFα, n=6 each). The arrays in each group were normalized using quantile normalization. Experiment Overall Design: See publication for further details.
