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

ABSTRACT: 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.

SUBMITTER: Filer A

PROVIDER: S-EPMC4373951 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Filer Andrew A Antczak Philipp P Parsonage Greg N GN Legault Holly M HM O'Toole Margot M Pearson Mark J MJ Thomas Andrew M AM Scheel-Toellner Dagmar D Raza Karim K Buckley Christopher D CD Falciani Francesco F

PloS one 20150325 3

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 ...[more]

PMID: 25807374

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Project description: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 program. We tested the hypothesis that a serum response program can be used to classify diseased tissues, and investigated the serum response program 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 RA, 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 program 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. Fibroblasts were isolated from synovium, bone marrow and skin tissue samples taken at the time of knee or hip replacement surgery from 12 rheumatoid arthritis patients meeting the 1987 ACR criteria and 6 osteoarthritis patients diagnosed on the basis of characteristic x-ray findings and the absence of features suggestive of inflammatory arthritis. Only one hip sample was present in either disease group. Fibroblasts were maintained in fibroblast medium (consisting of 81.3% RPMI 1640, 10% FCS, 0.81x MEM non-essential amino acids, 0.81mM sodium orthopyruvate, 1.62mM glutamine, 810U/ml penicillin and 81?g/ml streptomycin) at 37°C in a humidified 5% CO2 atmosphere.

Project description:Marek's disease virus (MDV), an alpha herpes virus, causes a lymphoproliferative state in chickens known as Marek's disease (MD), resulting in severe monetary losses to the poultry industry. Because lymphocytes of bursa of Fabricius and spleen are prime targets of MDV replication during the early cytolytic phase of infection, the immune response in bursa and spleen should be the foundation of late immunity induced by MDV. However, the mechanism of the MDV-mediated host immune response in lymphocytes in the early stage is poorly understood. The present study is primarily aimed at identifying the crucial genes and significant pathways involved in the immune response of chickens infected with MDV CVI988 and the very virulent RB1B (vvRB1B) strains. Using the RNA sequencing approach, we analyzed the generated transcriptomes from lymphocytes isolated from chicken bursa and spleen. Our findings validated the expression of previously characterized genes; however, they also revealed the expression of novel genes during the MDV-mediated immune response. The results showed that after challenge with CVI988 or vvRB1B strains, 634 and 313 differentially expressed genes (DEGs) were identified in splenic lymphocytes, respectively. However, 58 and 47 DEGs were observed in bursal lymphocytes infected with CVI988 and vvRB1B strains, respectively. Following MDV CVI988 or vvRB1B challenge, the bursal lymphocytes displayed changes in IL-6 and IL-4 gene expression. Surprisingly, splenic lymphocytes exhibited an overwhelming alteration in the expression of cytokines and cytokine receptors involved in immune response signaling. On the other hand, there was no distinct trend between infection with CVI988 and vvRB1B and the expression of cytokines and chemokines, such as IL-10, IFN-γ, STAT1, IRF1, CCL19, and CCL26. However, the expression profiles of IL-1β, IL-6, IL8L1, CCL4 (GGCL1), and CCL5 were significantly upregulated in splenic lymphocytes from chickens infected with CVI988 compared with those of chickens infected with vvRB1B. Because these cytokines and chemokines are considered to be associated with B cell activation and antigenic signal transduction to T cells, they may indicate differences of immune responses initiated by vaccinal and virulent strains during the early phase of infection. Collectively, our study provides valuable data on the transcriptional landscape using high-throughput sequencing to understand the different mechanism between vaccine-mediated protection and pathogenesis of virulent MDV in vivo.

Dataset Information

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

Publications

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

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