Reliable analysis of complex human diseases such as arthritis will require graspable knowledge of the functional interactions between key components of cells (such as T cells, macrophages, neutrophils, osteoclasts, chondrocytes and synovial cells), tissues (synovium, bone and cartilage) and systems (mobile joints in animal models such as rodents and Zebra fish), as well as the interactions that change in the disease state (clinical material and diagnosis) (Fig. ). This information resides neither in the genome nor in individual gene(s)/protein(s), but it seems to lie at the level of protein interactions within the context of subcellular, cellular, tissue, organ and system structure.
An integrative system biology approach to functional genomics in arthritis. 2D-MALDI-TOFF, 2D-matrix assisted laser desorption ionizartion-time of flight; OA, osteoarthritis; RA, rheumatoid arthritis; PCR, polymerase chain reaction; Wt, Wild type.
A system biology approach to functional genomics in arthritis is shown in Fig. . The scheme shows the role and involvement of various cell types, tissues and organs, and the use of animal models to understand the pathophysiology of arthritis. Understanding expression and functions of 'uncharacterized genes' in target cells and various (normal and disease) tissues requires the use of different cell types in the complex interaction and interplay. The synovium can be classified and analyzed as normal and hypertrophic, and the latter can be subdivided as cartilage invasive and noninvasive in different forms of arthritis [7
]. The subchondral bone has been impacted significantly in these diseases, as observed by the remodeling and thickening in OA. The combined role of all five cell types (T cells, macrophages, neutrophils, osteoclasts and chondrocytes) is important to understand the pathogenesis of arthritis [8
]. They may be acting as complex traits fine tuning the disease process.
Mouse and Zebra fish models (knockin/knockout) have been proven to mimic symptoms observed in man, as shown for type II collagen and endothelin, respectively [9
]. For example, endothelin and its receptor were found to be differentially expressed in normal and human OA-affected cartilage (Amin, Attur and Dave, unpublished data, 2003). A mutation of sucker
that encodes a Zebra fish endothelin 1 showed distortion of the ventral cartilage, the pharyngeal segments and craniofacial development in endothelin receptor-deficient mice [10
]. Functional genomics requires an integrated team of experts including biochemists, cell biologists, structural biologists, physiologists and geneticists to create a unified whole due to the unknown nature of genes to be analyzed and the type of expertise regained. The structure–function relationship of differentially expressed genes in normal and diseased tissue can be analyzed in cells to organ cultures, as recently described for a type II IL-1β decoy receptor [12
At least four technologies have been extensively used for gene mining and functional genomics. Figure also shows various approaches that can be applied selectively or simultaneously to various cell types, organs, and animal models and human subjects to understand the structure–function relationship of genes in arthritis. These include gene expression arrays, real-time PCR, proteomics, high-throughput DNA sequencing, single nucleotide polymorphism and haplotyping analysis, and 2D-matrix assisted laser desorption ionization-time of flight (2D MALD-TOF) [13
Gene and protein mining technologies such as gene expression array, proteomics, single nucleotide polymorphism, haplotyping and linkage disequilibrium, and microsatellites generate a significant amount of correlative data that requires annotating using various bioinformatic platforms. Although computer-intensive disciplines and bioinformatics allow clustering analysis for gene expression arrays and provide insight into the 'correlation' among genes and biological phenomena, they have limitations in revealing the 'causality' of regulatory relationships and/or predicting ab initio gene structure, gene function and protein folds from the raw sequence data.
The key to bioinformatics is integration, interpretability between various data platforms and the ability to visualize retrieved complex data in a way that aids their interpretation. Integrating various incompatible bioinformatics platforms is essential. Such efforts are currently under way by the Interoperable Informatics Infrastructure Consortium, a computer hardware 14-member organization. In summary, bioinformatics facilitates deriving hypotheses allowing us to enter the network structure, followed by identifying structure–function relationships using other tools.