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Osteoarthritis Cartilage. 2018 Nov;26(11):1531-1538. doi: 10.1016/j.joca.2018.07.012. Epub 2018 Aug 3.

Identification of transcription factors responsible for dysregulated networks in human osteoarthritis cartilage by global gene expression analysis.

Author information

1
Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, USA.
2
Department of Molecular Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, USA.
3
Department of Molecular Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, USA; Department of Orthopaedic Surgery, Chiba University Hospital 1-8-1 Inohana, Chuo-ku, Chiba, Japan.
4
Department of Orthopaedic Surgery, Chiba University Hospital 1-8-1 Inohana, Chuo-ku, Chiba, Japan.
5
Department of Molecular Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, USA. Electronic address: mlotz@scripps.edu.

Abstract

OBJECTIVE:

Osteoarthritis (OA) is the most prevalent joint disease. As disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized for their importance in mediating the abnormal phenotype of cells in OA-affected joints. Here, we generated a genome-wide molecular profile of OA to elucidate regulatory mechanisms of OA pathogenesis and to identify possible therapeutic targets using integrative analysis of mRNA-sequencing data obtained from human knee cartilage.

DESIGN:

RNA-sequencing (RNA-seq) was performed on 18 normal and 20 OA human knee cartilage tissues. RNA-seq datasets were analysed to identify genes, pathways and regulatory networks that were dysregulated in OA.

RESULTS:

RNA-seq data analysis revealed 1332 differentially expressed (DE) genes between OA and non-OA samples, including known and novel transcription factors (TFs). Pathway analysis identified 15 significantly perturbed pathways in OA with ECM-related, PI3K-Akt, HIF-1, FoxO and circadian rhythm pathways being the most significantly dysregulated. We selected DE TFs that are enriched for regulating DE genes in OA and prioritized these TFs by creating a cartilage-specific interaction subnetwork. This analysis revealed eight TFs, including JUN, Early growth response (EGR)1, JUND, FOSL2, MYC, KLF4, RELA, and FOS that both target large numbers of dysregulated genes in OA and are themselves suppressed in OA.

CONCLUSIONS:

We identified a novel subnetwork of dysregulated TFs that represent new mediators of abnormal gene expression and promising therapeutic targets in OA.

KEYWORDS:

Cartilage; Gene expression; Transcription factors

PMID:
30081074
PMCID:
PMC6245598
[Available on 2019-11-01]
DOI:
10.1016/j.joca.2018.07.012

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