TAILIEUCHUNG - Báo cáo y học: " Identification of arthritis-related gene clusters by microarray analysis of two independent mouse models for rheumatoid arthritis"
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học General Psychiatry cung cấp cho các bạn kiến thức về ngành y đề tài: Identification of arthritis-related gene clusters by microarray analysis of two independent mouse models for rheumatoid arthritis. | Available online http content 8 4 R100 Research article Identification of arthritis-related gene clusters by microarray analysis of two independent mouse models for rheumatoid arthritis Noriyuki Fujikado Shinobu Saijo and Yoichiro Iwakura Center for Experimental Medicine Institute of Medical Science University of Tokyo 4-6-1 Shirokanedai Minato-ku Tokyo 108-8639 Japan Corresponding author Yoichiro Iwakura iwakura@ Received 25 Jan 2006 Revisions requested 16 Feb 2006 Revisions received 11 May 2006 Accepted 2 Jun 2006 Published 28 Jun 2006 Arthritis Research Therapy 2006 8 R100 doi ar1985 This article is online at http content 8 4 R100 2006 Fujikado et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Open Access Abstract Rheumatoid arthritis RA is an autoimmune disease affecting approximately 1 of the population worldwide. Previously we showed that human T-cell leukemia virus type I-transgenic mice and interleukin-1 receptor antagonist-knockout mice develop autoimmunity and joint-specific inflammation that resembles human RA. To identify genes involved in the pathogenesis of arthritis we analyzed the gene expression profiles of these animal models by using high-density oligonucleotide arrays. We found 1 467 genes that were differentially expressed from the normal control mice by greater than threefold in one of these animal models. The gene expression profiles of the two models correlated well. We extracted 554 genes whose expression significantly changed in both models assuming that pathogenically important genes at the effector phase would change in both models. Then each of these commonly changed genes was mapped into the whole genome in a scale of .
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