Heterogeneity of synovial molecular patterns in patients with arthritis

PLoS One. 2015 Apr 30;10(4):e0122104. doi: 10.1371/journal.pone.0122104. eCollection 2015.

Abstract

Objectives: Early 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.

Methods: Low-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).

Results: According 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.

Conclusions: Gene 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Arthritis, Rheumatoid / metabolism*
  • Arthritis, Rheumatoid / pathology
  • Female
  • Gene Expression Profiling
  • Gene Expression Regulation*
  • Humans
  • Male
  • Microfluidic Analytical Techniques
  • Middle Aged
  • Oligonucleotide Array Sequence Analysis
  • Synovial Membrane / metabolism*
  • Synovial Membrane / pathology
  • Transcriptome*

Associated data

  • GEO/GSE24742
  • GEO/GSE36700
  • GEO/GSE45867

Grants and funding

This work was supported by grants from the Région Wallonne (BioWin), and the Fondation Saint-Luc (Cliniques Universitaires Saint-Luc, Brussels, Belgium). JD is funded by a unrestricted UCB grant (« Chaire UCB sur les rhumatismes inflammatoires et systémiques »). BRL is a part-time research fellow (« Clinicien-chercheur ») of the Fonds National de la Recherche Scientifique (Communauté française de Belgique). DNAlytics provided support in the form of salaries for authors PG and TH, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.