Stromal transcriptional profiles reveal hierarchies of anatomical site, serum response and disease and identify disease specific pathways

Synovial fibroblasts in persistent inflammatory arthritis have been suggested to have parallels with cancer growth and wound healing, both of which involve a stereotypical serum response programme. We tested the hypothesis that a serum response programme can be used to classify diseased tissues, and investigated the serum response programme in fibroblasts from multiple anatomical sites and two diseases. To test our hypothesis we utilized a bioinformatics approach to explore a publicly available microarray dataset including rheumatoid arthritis (RA), osteoarthritis (OA) and normal synovial tissue, then extended those findings in a new microarray dataset representing matched synovial, bone marrow and skin fibroblasts cultured from RA and OA patients undergoing arthroplasty. The classical fibroblast serum response programme discretely classified RA, OA and normal synovial tissues. Analysis of low and high serum treated fibroblast microarray data revealed a hierarchy of control, with anatomical site the most powerful classifier followed by response to serum and then disease. In contrast to skin and bone marrow fibroblasts, exposure of synovial fibroblasts to serum led to convergence of RA and OA expression profiles. Pathway analysis revealed three inter-linked gene networks characterising OA synovial fibroblasts: Cell remodelling through insulin-like growth factors, differentiation and angiogenesis through -3 integrin, and regulation of apoptosis through CD44. We have demonstrated that Fibroblast serum response signatures define disease at the tissue level, and that an OA specific, serum dependent repression of genes involved in cell adhesion, extracellular matrix remodelling and apoptosis is a critical discriminator between cultured OA and RA synovial fibroblasts

Ingenuity Network analyses.

<p>The three major networks identified were: (A) Actin cytoskeleton remodelling by extracellular insulin-like growth factor binding proteins through gonadotropic hormones. (B) ITGB3 signalling connected to differentiation and angiogenesis. (C) Regulation of apoptosis through CD44. Yellow nodes represent genes resulting from input whereas empty nodes represent added genes by Ingenuity. Dashed and solid lines represent indirect and direct relationships respectively.</p

A synovium specific gene signature in low serum.

<p>Panel A shows a Venn diagram showing the overlap between genes differentially expressed in synovium, bone marrow and skin derived fibroblasts between RA and OA groups in the low serum state. The high degree of overlap between skin and bone marrow and the very different signature in the synovium are evident. Panel B shows the result of a two factor cluster analysis of the 296 synovium specific genes identified in Panel A. Panel C represents the number of statistically differentially expressed genes between low and high serum states within each tissue and disease. As expected OA synovium is associated with the largest number of differentially expressed genes.</p

Identification of a hierarchy of molecular signatures in arthritis derived fibroblasts.

<p>The relative similarity of the different groups of fibroblast in a principal component (PC) plot is shown. Groups defined by disease state and anatomical location are indicated by coloured symbols whereas individual samples response to serum (Hi, High serum; Lo, low serum) is shown by a solid grey line. The PCA clearly separates synovium (Syn, red and orange) from bone marrow (BoM, blue and grey) and skin (Skn, green and light blue) on the first PC. Skin and bone marrow samples are only separated on the second PC. Additionally it can be observed that all samples are separated by their serum response mainly on the first PC.</p

Differential gene expression analyses at tissue, serum and disease level.

<p>The figure shows the differential gene expression analysis performed within each level of the study. For each level of organisation we quantified the number of differentially expressed genes at an FDR < 5% followed by a 2-fold filter. This allowed us to devise a hierarchy of organisation that follows Anatomical Location then serum response and finally disease status (A). A more detailed tissue-by-tissue analysis to identify serum response genes in RA and OA fibroblasts (B) and to identify disease genes within high and low serum (C) was then performed. The arrows in panel A represent the direction of the hierarchy defined by the number of genes differentially expressed. BoM, Bone marrow.</p

Serum response in arthritis derived fibroblasts within anatomical sites.

<p>The figure describes the relationships between RA and OA derived fibroblasts in low and high serum conditions using PCA. Bone marrow (A), skin (B) and synovium (C) derived fibroblasts are represented separately.</p

Functional classification of differentially expressed genes.

<p>Each set of differentially expressed genes in response to serum was submitted to Ingenuity and searched for functional terms with predicted increased (red) or decreased (green) activity.</p