Heterogeneity of synovial molecular patterns in patients with arthritis.
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...
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doaj-e5d8c3b13a874ed2b9a29176f3ab8f062020-11-25T00:20:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01104e012210410.1371/journal.pone.0122104Heterogeneity of synovial molecular patterns in patients with arthritis.Bernard R LauwerysDaniel Hernández-LobatoPierre GrammeJulie DucreuxAdrien DessyIsabelle FocantJérôme AmbroiseBertrand BearzattoAdrien Nzeusseu ToukapBenoît J Van den EyndeDirk ElewautJean-Luc GalaPatrick DurezFrédéric A HoussiauThibault HelleputtePierre DupontEarly 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.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).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.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.http://europepmc.org/articles/PMC4415786?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bernard R Lauwerys Daniel Hernández-Lobato Pierre Gramme Julie Ducreux Adrien Dessy Isabelle Focant Jérôme Ambroise Bertrand Bearzatto Adrien Nzeusseu Toukap Benoît J Van den Eynde Dirk Elewaut Jean-Luc Gala Patrick Durez Frédéric A Houssiau Thibault Helleputte Pierre Dupont |
spellingShingle |
Bernard R Lauwerys Daniel Hernández-Lobato Pierre Gramme Julie Ducreux Adrien Dessy Isabelle Focant Jérôme Ambroise Bertrand Bearzatto Adrien Nzeusseu Toukap Benoît J Van den Eynde Dirk Elewaut Jean-Luc Gala Patrick Durez Frédéric A Houssiau Thibault Helleputte Pierre Dupont Heterogeneity of synovial molecular patterns in patients with arthritis. PLoS ONE |
author_facet |
Bernard R Lauwerys Daniel Hernández-Lobato Pierre Gramme Julie Ducreux Adrien Dessy Isabelle Focant Jérôme Ambroise Bertrand Bearzatto Adrien Nzeusseu Toukap Benoît J Van den Eynde Dirk Elewaut Jean-Luc Gala Patrick Durez Frédéric A Houssiau Thibault Helleputte Pierre Dupont |
author_sort |
Bernard R Lauwerys |
title |
Heterogeneity of synovial molecular patterns in patients with arthritis. |
title_short |
Heterogeneity of synovial molecular patterns in patients with arthritis. |
title_full |
Heterogeneity of synovial molecular patterns in patients with arthritis. |
title_fullStr |
Heterogeneity of synovial molecular patterns in patients with arthritis. |
title_full_unstemmed |
Heterogeneity of synovial molecular patterns in patients with arthritis. |
title_sort |
heterogeneity of synovial molecular patterns in patients with arthritis. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2015-01-01 |
description |
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.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).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.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. |
url |
http://europepmc.org/articles/PMC4415786?pdf=render |
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