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AB0005 Weighted Gene Co-Expression Network Analysis Reveals Link between Protein Kinase Signalling and Response To Methotrexate in New-Onset Rheumatoid Arthritis
  1. D. Plant1,
  2. S. Smith2,
  3. N. Nair2,
  4. J. Massey2,
  5. K. Hyrich2,
  6. A. Barton3,
  7. S. Verstappen2
  1. 1NIHR Manchester Musculoskeletal BRU, Central Manchester Foundation Trust
  2. 2Arthritis Research UK Centre for Genetics and Genomics and Centre for Epidemiology, Centre for Musculoskeletal Research, Manchester Academic Health Sciences Centre
  3. 3NIHR Manchester Musculoskeletal BRU, Central Manchester Foundation Trust. Arthritis Research UK Centre for Genetics and Genomics and Centre for Epidemiology, Centre for Musculoskeletal Research, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, United Kingdom

Abstract

Background Methotrexate (MTX) is recommended as the first-line therapy in rheumatoid arthritis (RA). However, only 55% of patients remain on this inexpensive drug beyond 2 years after initiation of treatment. A stratified medicine approach therefore necessitates identification of reliable biomarkers of MTX response which are currently lacking.

Objectives To identify gene co-expression networks in pre-treatment blood of new-onset RA patients that correlate with response to MTX at 6-months.

Methods Patients with RA participating in the Rheumatoid Arthritis Medication Study (RAMS), a multi-centre one-year longitudinal observational study investigating predictors of response to MTX in the UK, who were EULAR good or poor responders at 6-months were included in this analysis. Total RNA was extracted from Tempus™ tubes using the Tempus Spin RNA isolation kit according to the manufacturer's protocol. Following extraction, quantification and assessment of RNA integrity, RNA samples were labelled with biotin and amplified (Illumina TotalPrep RNA Amplification Kit) prior to being loaded onto an Illumina HumanHT-12 v4 BeadChip, which targets over 47,000 probes. Highly correlated genes were identified from the expression data and summarised with a modular eigengene using the weighted gene co-expression network analysis (WGCNA) bioconductor package. Pearson's correlation was used to assess the significance of the correlation between eigengene and improvement in disease activity, defined using EULAR response and change in CRP.

Results Thirty-two good and 43 poor responders were included in this study [76% female; mean age 60 (SD 14) years, median symptom duration 6-months (IQR 3.7, 19) at MTX start]. Seventeen co-expression modules were identified. None of the modules were correlated with EULAR response at 6 months. However, one module including 1,840 genes was correlated with change in CRP between baseline and 6-months (corr 0.37, p=0.001). Furthermore, the genes most central to the module were also the most correlated with CRP difference (cor 0.5, p=5.6e-117). The most significant gene ontology (GO) molecular function term for this module was protein kinase (61 genes, 1.50 fold enrichment, p=4.2e-04, Benjamini p=0.04). Within this GO the expression of TYK2 was found to be correlated with CRP change (p=0.005). Patients with higher TYK2 expression at baseline experienced more improvement in CRP following MTX treatment.

Conclusions These results suggest gene co-expression network analysis has the potential to reveal important insight into the biological effects of MTX therapy in RA. The assumption behind this analysis is that networks of genes are co-regulated e.g. by an environmental exposure, transcription factor of genetic variants. The next step will be to incorporate genome-wide single nucleotide polymorphism data to identify eigengene and gene expression quantitative trait loci.

Disclosure of Interest None declared

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