Functional genomics approaches in arthritis.

Abstract

The post-genomic era of functional genomics and target validation will allow us to narrow the bridge between clinically correlative data and causative data for complex diseases, such as arthritis, for which the etiological agent remains elusive. The availability of human and other annotated genome sequences, and parallel developments of new technologies that allow analysis of minute amounts of human and animal cells (peripheral blood cells and infiltrating cells) and tissues (synovium and cartilage) under different pathophysiological conditions, has facilitated high-throughput gene mining approaches that can generate vast amounts of clinically correlative data. Characterizing some of the correlative/causative genes will require reverting to the hypothesis-driven, low throughput method of complementary experimental biology using genomic approaches as a tool. This will include in silico gene expression arrays, genome-wide scans, comparative genomics using various animal models (such as rodents and zebrafish), bioinformatics and a team of well trained translational scientists and physicians. For the first time, the "genomic tools" will allow us to analyze small amounts of surgical samples (such as needle biopsies) and clinical samples in the context of the whole genome. Preliminary genomic analysis in osteoarthritis has already resurrected the debate on the semantic issues in the definition of inflammation. Further analyses will not only facilitate the development of unbiased hypotheses at the molecular level, but also assist us in the identification and characterization of novel targets and disease markers for pharmacological intervention, gene therapy, and diagnosis.