Heterogeneity of synovial molecular patterns in patients with arthritis.
OBJECTIVE
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.
Pôle de pathologies rhumatismales, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain and Department of Rheumatology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
Bernard R
BR
Lauwerys
Machine Learning Group, ICTEAM Institute, Université catholique de Louvain, Place Sainte-Barbe 2, B-1348, Louvain-la-Neuve, Belgium.
Daniel
D
Hernández-Lobato
DNAlytics, Louvain-la-Neuve, Belgium.
Pierre
P
Gramme
Pôle de pathologies rhumatismales, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain and Department of Rheumatology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
Julie
J
Ducreux
Machine Learning Group, ICTEAM Institute, Université catholique de Louvain, Place Sainte-Barbe 2, B-1348, Louvain-la-Neuve, Belgium.
Adrien
A
Dessy
Pôle de pathologies rhumatismales, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain and Department of Rheumatology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
Isabelle
I
Focant
Centre de Technologies Moléculaires Appliquées, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium.
Jérôme
J
Ambroise
Centre de Technologies Moléculaires Appliquées, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium.
Bertrand
B
Bearzatto
Pôle de pathologies rhumatismales, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain and Department of Rheumatology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
Adrien
A
Nzeusseu Toukap
Institut de Duve, Université catholique de Louvain, Brussels, Belgium.
Benoît J
BJ
Van den Eynde
Rheumatology Department, Ghent University Hospital, Ghent, Belgium.
Dirk
D
Elewaut
Centre de Technologies Moléculaires Appliquées, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium.
Jean-Luc
JL
Gala
Pôle de pathologies rhumatismales, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain and Department of Rheumatology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
Patrick
P
Durez
Pôle de pathologies rhumatismales, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain and Department of Rheumatology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
Frédéric A
FA
Houssiau
DNAlytics, Louvain-la-Neuve, Belgium.
Thibault
T
Helleputte
Machine Learning Group, ICTEAM Institute, Université catholique de Louvain, Place Sainte-Barbe 2, B-1348, Louvain-la-Neuve, Belgium.
Pierre
P
Dupont
D000328:Adult; D000368:Aged; D001172:Arthritis, Rheumatoid / Q000378:metabolism* / Q000473:pathology; D005260:Female; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D008297:Male; D046210:Microfluidic Analytical Techniques; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D013583:Synovial Membrane / Q000378:metabolism* / Q000473:pathology; D059467:Transcriptome