1. ¹Rheumatology Research and Advanced Therapeutics, Department of Rheumatology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
2. ²Department of Internal Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
3. ³Novartis Institutes for BioMedical Research, Basel, Switzerland

Correspondence: Fons AJ van de Loo, Rheumatology Research and Advanced Therapeutics, Department of Rheumatology, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands. E-mail: a.vandeloo@reuma.umcn.nl

Current address: Molecular Signaling and Cell Death Unit, Department of Molecular Biomedical Research, VIB-University of Ghent, Ghent, Belgium

Received 13 March 2009; Accepted 15 July 2009; Published online 18 August 2009.

Abstract

The promoter regions of genes that are differentially regulated in the synovial membrane during the course of rheumatoid arthritis (RA) represent attractive candidates for application in transcriptionally targeted gene therapy. In this study, we applied an unbiased computational approach to define proximal-promoters from a gene expression profiling study of murine experimental arthritis. Synovium expression profiles from progressing stages of collagen-induced arthritis (CIA) were classified into six distinct groups using k-means clustering. Using an algorithm based on local over-representation and comparative genomics, we identified putatively functional transcription factor–binding sites (TFBS) in TATA-dependent proximal-promoters. Applying a filter based on spacing between TATA box and transcription start site (TSS) combined with the presence of over-represented nuclear factor B (NFB), AP-1, or CCAAT/enhancer-binding protein (C/EBP) sites, 382 candidate murine and human promoters were reduced to 66, corresponding to 45 genes. In vitro, 9 out of 10 computationally defined promoter regions conferred cytokine-inducible expression in murine cells and human synovial fibroblasts. Under these conditions, the serum amyloid A3 (Saa3) promoter showed the strongest transcriptional induction and strength. We applied this promoter for driving therapeutically efficacious levels of the interleukin-1 receptor antagonist (Il1rn) in a disease-regulated fashion. These results demonstrate the value of bioinformatics for guiding the selection of endogenous promoters for transcriptionally targeted gene therapy.