Dataset Information

Immune cell transcript modules reveal leukocyte heterogeneity in synovial biopsies of seronegative spondylarthropathy patients.

ABSTRACT:

Background

The objective of this study was to identify leukocyte cell types found within the synovia of patients with seronegative spondylarthropathies (SpA), such as ankylosing spondylitis (AS), using transcription based analyses.

Methods

Leukocyte transcriptional profiles obtained from the NCBI's gene expression omnibus and prediction analysis of microarrays (PAM) was used to identify 25-gene leukocyte metagenes. Subsequently, transcriptional profiles from murine and clinical models of AS and SpA were interrogated to characterize the local infiltration of leukocytes in SpA synovia.

Results

Analysis of a proteoglycan-induced murine model of AS reveals infiltration of dendritic cells, CD4+ T cells, monocytes, and natural killer cells to the spine. In human SpA and AS patients, transcriptional analysis of synovial biopsies revealed local infiltration of dendritic cells and CD4+ T cells.

Conclusions

We identified leukocyte cell types that infiltrated the synovial of SpA patients. Our results imply a role for dendritic cells and CD4+ T cells in the local inflammation that underlies pathogenesis in patients with SpA.

SUBMITTER: Hallett RM

PROVIDER: S-EPMC4320502 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Publications

Immune cell transcript modules reveal leukocyte heterogeneity in synovial biopsies of seronegative spondylarthropathy patients.

Hallett Robin M RM Chew Tracy T

BMC musculoskeletal disorders 20141219

<h4>Background</h4>The objective of this study was to identify leukocyte cell types found within the synovia of patients with seronegative spondylarthropathies (SpA), such as ankylosing spondylitis (AS), using transcription based analyses.<h4>Methods</h4>Leukocyte transcriptional profiles obtained from the NCBI's gene expression omnibus and prediction analysis of microarrays (PAM) was used to identify 25-gene leukocyte metagenes. Subsequently, transcriptional profiles from murine and clinical mo ...[more]

PMID: 25526985

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Project description:ObjectivesEarly diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms.MethodsLow-density assays (cDNA microarray and microfluidics qPCR) were designed, based on the results of the high-density microarray data. Knee synovial biopsies were obtained from patients with RA, spondyloarthropathies (SA) or osteoarthritis (OA) (n = 39), and also from patients with initial undifferentiated arthritis (UA) (n = 49).ResultsAccording to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation. Strikingly, disease activity (DAS28-CRP) has a significant influence on gene expression patterns in RA samples. Using the low-density assays, samples from patients with OA are easily discriminated from RA and SA samples. However, overlapping molecular patterns are found, in particular between RA and SA biopsies. Therefore, prediction of the clinical diagnosis based on gene expression data results in a diagnostic accuracy of 56.8%, which is increased up to 98.6% by the addition of specific clinical symptoms in the prediction algorithm. Similar observations are made in initial UA samples, in which overlapping molecular patterns also impact the accuracy of the diagnostic algorithm. When clinical symptoms are added, the diagnostic accuracy is strongly improved.ConclusionsGene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions. Therefore, an accurate diagnosis in patients with UA requires a combination of gene expression and clinical data.

Project description:Rheumatoid arthritis (RA) is an inflammatory joint disorder that results in progressive joint damage when insufficiently treated. In order to prevent joint destruction and functional disability in RA, early diagnosis and initiation of appropriate treatment with Disease-Modifying Antirheumatic Drugs (DMARDs) is needed. However, in daily clinical practice, patients may initially display symptoms of arthritis that do not fulfil the classification criteria for a definite diagnosis of RA, or any other joint disease, a situation called “Undifferentiated Arthritis” (UA). Out of the patients with UA, 30 to 50% usually develop RA, and early identification of these remains a challenge. At the present time, although several risk factors associated with the development of RA have been identified (6-9), a model that reliably predicts the probability of evolution of UA into RA in individual patients is lacking. In order to better identify early RA patients, an American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) collaboration recently developed new classification criteria. Although these criteria are more sensitive, the risk of over-diagnosis is an important issue to consider, especially in very early disease. In this context, the present study explores the feasibility of a molecular diagnosis of arthritis, based on the identification of disease-specific transcriptomic profiles in synovial biopsies from patients with arthritis according to the underlying condition. In a previous study, we performed global analyses of gene expression in synovial biopsies from patients with RA, Systemic Lupus Erythematosus (SLE) and Osteoarthritis (OA), using high-density oligonucleotide spotted microarrays. We found that the gene expression profiles are strikingly different according to the underlying condition. Thus, the majority of the genes up-regulated in SLE are type I Interferon-inducible genes, as compared with the up-regulation genes involved in T cell and B cell activation in RA, and in extracellular matrix homeostasis in OA. Based on these results, similar analyses were performed in synovial biopsies from patients with seronegative arthritis (SA) and microcrystalline arthritis (MIC), in order to identify disease-specific molecular signatures.

Project description:OBJECTIVE:Currently, there are no reliable biomarkers for predicting therapeutic response in patients with rheumatoid arthritis (RA). The synovium may unlock critical information for determining efficacy, since a reduction in the numbers of sublining synovial macrophages remains the most reproducible biomarker. Thus, a clinically actionable method for the collection of synovial tissue, which can be analyzed using high-throughput strategies, must become a reality. This study was undertaken to assess the feasibility of utilizing synovial biopsies as a precision medicine-based approach for patients with RA. METHODS:Rheumatologists at 6 US academic sites were trained in minimally invasive ultrasound-guided synovial tissue biopsy. Biopsy specimens obtained from patients with RA and synovial tissue from patients with osteoarthritis (OA) were subjected to histologic analysis, fluorescence-activated cell sorting, and RNA sequencing (RNA-seq). An optimized protocol for digesting synovial tissue was developed to generate high-quality RNA-seq libraries from isolated macrophage populations. Associations were determined between macrophage transcriptional profiles and clinical parameters in RA patients. RESULTS:Patients with RA reported minimal adverse effects in response to synovial biopsy. Comparable RNA quality was observed from synovial tissue and isolated macrophages between patients with RA and patients with OA. Whole tissue samples from patients with RA demonstrated a high degree of transcriptional heterogeneity. In contrast, the transcriptional profile of isolated RA synovial macrophages highlighted different subpopulations of patients and identified 6 novel transcriptional modules that were associated with disease activity and therapy. CONCLUSION:Performance of synovial tissue biopsies by rheumatologists in the US is feasible and generates high-quality samples for research. Through the use of cutting-edge technologies to analyze synovial biopsy specimens in conjunction with corresponding clinical information, a precision medicine-based approach for patients with RA is attainable.

Dataset Information

Immune cell transcript modules reveal leukocyte heterogeneity in synovial biopsies of seronegative spondylarthropathy patients.

Background

Methods

Results

Conclusions

Publications

Immune cell transcript modules reveal leukocyte heterogeneity in synovial biopsies of seronegative spondylarthropathy patients.

Similar Datasets

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