Gene Modules Correlated with Disease Activity and Abatacept Treatment Identified with Weighted Gene Co-Expression Network Analysis of CD4+ T Cell Subsets of RA

Shuji Sumitomo¹, Yasuo Nagafuchi¹, Yumi Tsuchida¹, Haruka Tsuchiya¹, Mineto Ota¹, Kazuyoshi ishigaki², Shinichiro Nakachi¹, Rika Kato¹, Keiichi Sakurai¹, Norio Hanata¹, Shoko Tateishi³, Hiroko Kanda³, Akari Suzuki⁴, Yuta Kochi⁴, Keishi Fujio¹ and Kazuhiko Yamamoto¹, ¹Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, ²Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, The Institute of Physical and Chemical Research (RIKEN), Yokohama, Japan, ³Department of Immunotherapy Management, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, ⁴Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

Meeting: 2016 ACR/ARHP Annual Meeting

Date of first publication: September 28, 2016

Keywords: Abatacept, bioinformatics and rheumatoid arthritis (RA), Gene Expression

Session Information

Date: Monday, November 14, 2016

Title: Rheumatoid Arthritis – Human Etiology and Pathogenesis - Poster II

Session Type: ACR Poster Session B

Session Time: 9:00AM-11:00AM

Background/Purpose: Although there are several reports of transcriptome analysis of peripheral blood mononuclear cells (PBMC) in RA, analysis of detailed CD4⁺ subset and the effect of abatacept on their expression has not been reported so far. We analyzed the transcriptome of detailed CD4⁺ T cell subsets including them after abatacept treatment, and examined the difference among CD4⁺T cell subsets and identified gene sets that are closely associated disease activity and abatacept treatment.

Methods: PBMC were taken from RA patients (n = 10) fulfilling 2010 ACR/EULAR criteria, and healthy control (HC) (n = 10). Samples were repeatedly taken from three RA patients 6 months after abatacept treatment. Seven CD4⁺ T cell subsets (Naive, CD25⁺ regulatory T cell, follicular helper T cell, helper T cell subsets (Th1, Th17, Th17.1, Th2)) were sorted and total RNA was extracted. Libraries for RNA-sequence were prepared using TruSeq Stranded mRNA Library Prep kit (Illumina), and paired-end sequencing was performed using HiSeq 2500 (Illumina). 149 samples except for 12 outliers were analyzed (4 outliers because of different FACS gating strategy, 8 outliers detected with robust PCA). R version 3.2.3 was used for analysis. Knowledge-based pathway analysis were performed using Ingenuity Pathway Analysis (QIAGEN).

Results: Overview of expression using principal component analysis (PCA) revealed that the samples form RA and HC form distinct groups. Moreover, administration of abatacept exert a large shift toward the expression pattern of HC. Most of differentially expressed gene (DEG) upregulated in RA (n = 1,776) were downregulated with abatacept treatment (n = 1,349). Inversely, most of DEG downregulated in RA (n = 1,860) were upregulated with abatacept treatment (n = 1,294). Knowledge-based network analysis revealed canonical pathway and upstream analysis associated with RA CD4⁺ subsets and administration of abatacept. While the difference among CD4⁺ T cell subsets was not remarkable, abatacept treatment largely changed the direction of network including canonical pathway and upstream analysis. Weighted gene co-expression network analysis (WGCNA) revealed the association between gene set (module) and clinical traits. One module was detected that consist of 227 genes and highly correlated with DAS28-CRP (Spearman’s rho=0.46, p=4×10^-9) and abatacept administration (Spearman’s rho=-0.91, p=5×10^-57). Expression of this module differentiate the sample before abatacept treatment and after treatment. Abatacept treatment suppress this module expression and JAK3 and ZAP70 were included in top 30 gene of this module. Pathway analysis of this module revealed that abatacept treatment suppress the network under the TCR signal pathway.

Conclusion: Administration of abatacept exerts a great change on gene expression of general CD4⁺ subsets. WGCNA identified a gene module that is closely associated with disease activity and abatacept treatment, and the network under the TCR signal pathway was supposed to be suppressed with abatacept administration.

Disclosure: S. Sumitomo, None; Y. Nagafuchi, None; Y. Tsuchida, None; H. Tsuchiya, None; M. Ota, None; K. ishigaki, None; S. Nakachi, None; R. Kato, None; K. Sakurai, None; N. Hanata, None; S. Tateishi, None; H. Kanda, None; A. Suzuki, Takeda, 2; Y. Kochi, Takeda, 2; K. Fujio, Bristol-Myers Squibb, 2,Chugai, 2,Takeda, 2,Daiichi-Sankyo, 7,MitsubishiTanabe, 7,Pfizer Inc, 7,Santen, 7,Eisai, 7,Taisho Toyama, 7,UCB., 7,Janssen Pharmaceutica Product, L.P., 7; K. Yamamoto, Bristol-Myers Squibb, 2,Takeda, 2,Chugai, 2,AbbVie, 7,Astellas, 7,Daiichi-Sankyo, 7,MitsubishiTanabe, 7,Pfizer Inc, 7,Sanofi, 7,Santen, 7,Teijin, 7,Boehringer Ingelheim, 7,Eisai, 7,Ono, 7,Taisho Toyama, 7,UCB., 7,ImmunoFuture, 7,Asahi Kasei, 7,Janssen Pharmaceutica Product, L.P., 7.

To cite this abstract in AMA style:

Sumitomo S, Nagafuchi Y, Tsuchida Y, Tsuchiya H, Ota M, ishigaki K, Nakachi S, Kato R, Sakurai K, Hanata N, Tateishi S, Kanda H, Suzuki A, Kochi Y, Fujio K, Yamamoto K. Gene Modules Correlated with Disease Activity and Abatacept Treatment Identified with Weighted Gene Co-Expression Network Analysis of CD4+ T Cell Subsets of RA [abstract]. Arthritis Rheumatol. 2016; 68 (suppl 10). https://acrabstracts.org/abstract/gene-modules-correlated-with-disease-activity-and-abatacept-treatment-identified-with-weighted-gene-co-expression-network-analysis-of-cd4-t-cell-subsets-of-ra/. Accessed .

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