AB0108 USING RNA SEQUENCING TO IDENTIFY GENE EXPRESSION SIGNATURES ASSOCIATED WITH RESPONSE TO ABATACEPT IN PATIENTS WITH RHEUMATOID ARTHRITIS

Background:Rheumatoid arthritis (RA) is a common chronic autoimmune disease. Abatacept (CTLA4-immunoglobulin) is one of the biological disease-modifying antirheumatic drug (bDMARD) for RA patients with indequate response to methotrexate. Recently, Yokoyama-Kokuryo et al. compared gene expression levels between abatacept responders and non-responders in RA patients using a microarray and found that type I IFN score and expression levels of nine genes may be used as a biomarker to predict response to abatacept. However, little study used RNA sequencing (RNA-seq) to identify whole blood gene expression signatures to predict therapeutic response to abatacept.Objectives:The aim of this study is to identify gene expression signatures to predict therapeutic responses to abatacept in RA patients using RNA-seq.Methods:This study is a single-center, prospective study. We used a PAX gene Blood RNA kit to collect whole blood at baseline and 4 weeks after abatacept treatment from RA patients. We also measured DAS28, physician global assessment, HAQ, ESR, CRP at baseline and 12 week to calculate EULAR response at 12 week. Patients with good EULAR response were defined as responders and those with moderate or no EULAR response were defined as non-responders.Results:We finally conducted RNA-seq for whole blood from 7 RA patients initiating abatacept therapy. Of the 7 RA patients, one was non-responder and 6 were responders. We first use DESeq2 to analyze the differentially expressed genes of non-responder and responder before taking the drug. We used hierarchical clustering and PCA to evaluate the overall similarity of the samples, and group the patient data, and find that the nonresponder can be distinguished from responders. Subsequently, we analyzed the differentially expressed genes of the two groups of non-responder and responder patients before taking the drug. Before treatment, we found that 72 genes had a higher expression in the non-responder, and 23 genes had a higher expression level in responders. Figure 1 showed the top 20 DEG Heatmap between the non-responder and responders.Using these two sets of genes for GO analysis, we found that most of the pathways in the non-responder are related to immune response and cytokine production, and most of the pathways in the responders are related to antigen processing and MHC class II.Figure 1.Top 20 DEG Heatmap between non-responder and respondersConclusion:The study showed that most of the pathways in RA patients with no EULAR response to abatacept are related to immune response and cytokine production; while most of the pathways in RA patients with moderate/good response to abatacept are related to antigen processing and MHC class II.References:[1]Yokoyama-Kokuyo W, Yamazaki H, Takeuchi T, et al. Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis. Arthritis Research & Therapy. 2020;22:46.Disclosure of Interests:Hsin-Hua Chen Grant/research support from: This is an investigator-sponsored trial with Bristol-Myers Squibb who provides funding support., Wen-Cheng Chao: None declared, Jing-Rong Wang: None declared, Tai-Ming Ko: None declared

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OP0021 IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES IN EARLY RHEUMATOID ARTHRITIS PATIENTS RESPONDING TO TOCILIZUMAB

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3668 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 12.2-12

Author(s):

I. Muller ◽

M. Verhoeven ◽

H. Gosselt ◽

M. Lin ◽

T. De Jong ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

T Cells ◽

Gene Ontology ◽

Regulatory T Cells ◽

Early Rheumatoid Arthritis ◽

Whole Blood ◽

Differentially Expressed ◽

Rna Seq ◽

Double Blind

Background:Tocilizumab (TCZ) is a monoclonal antibody that binds to the interleukin 6 receptor (IL-6R), inhibiting IL-6R signal transduction to downstream inflammatory mediators. TCZ has shown to be effective as monotherapy in early rheumatoid arthritis (RA) patients (1). However, approximately one third of patients inadequately respond to therapy and the biological mechanisms underlying lack of efficacy for TCZ remain elusive (1). Here we report gene expression differences, in both whole blood and peripheral blood mononuclear cells (PBMC) RNA samples between early RA patients, categorized by clinical TCZ response (reaching DAS28 < 3.2 at 6 months). These findings could lead to identification of predictive biomarkers for TCZ response and improve RA treatment strategies.Objectives:To identify potential baseline gene expression markers for TCZ response in early RA patients using an RNA-sequencing approach.Methods:Two cohorts of RA patients were included and blood was collected at baseline, before initiating TCZ treatment (8 mg/kg every 4 weeks, intravenously). DAS28-ESR scores were calculated at baseline and clinical response to TCZ was defined as DAS28 < 3.2 at 6 months of treatment. In the first cohort (n=21 patients, previously treated with DMARDs), RNA-sequencing (RNA-seq) was performed on baseline whole blood PAXgene RNA (Illumina TruSeq mRNA Stranded) and differential gene expression (DGE) profiles were measured between responders (n=14) and non-responders (n=7). For external replication, in a second cohort (n=95 therapy-naïve patients receiving TCZ monotherapy), RNA-seq was conducted on baseline PBMC RNA (SMARTer Stranded Total RNA-Seq Kit, Takara Bio) from the 2-year, multicenter, double-blind, placebo-controlled, randomized U-Act-Early trial (ClinicalTrials.gov identifier: NCT01034137) and DGE was analyzed between 84 responders and 11 non-responders.Results:Whole blood DGE analysis showed two significantly higher expressed genes in TCZ non-responders (False Discovery Rate, FDR < 0.05): urotensin 2 (UTS2) and caveolin-1 (CAV1). Subsequent analysis of U-Act-Early PBMC DGE showed nine differentially expressed genes (FDR < 0.05) of which expression in clinical TCZ non-responders was significantly higher for eight genes (MTCOP12, ZNF774, UTS2, SLC4A1, FECH, IFIT1B, AHSP, and SPTB) and significantly lower for one gene (TND2P28M). Both analyses were corrected for baseline DAS28-ESR, age and gender. Expression of UTS2, with a proposed function in regulatory T-cells (2), was significantly higher in TCZ non-responders in both cohorts. Furthermore, gene ontology enrichment analysis revealed no distinct gene ontology or IL-6 related pathway(s) that were significantly different between TCZ-responders and non-responders.Conclusion:Several genes are differentially expressed at baseline between responders and non-responders to TCZ therapy at 6 months. Most notably, UTS2 expression is significantly higher in TCZ non-responders in both whole blood as well as PBMC cohorts. UTS2 could be a promising target for further analyses as a potential predictive biomarker for TCZ response in RA patients in combination with clinical parameters (3).References:[1]Bijlsma JWJ, Welsing PMJ, Woodworth TG, et al. Early rheumatoid arthritis treated with tocilizumab, methotrexate, or their combination (U-Act-Early): a multicentre, randomised, double-blind, double-dummy, strategy trial. Lancet. 2016;388(10042):343-55.[2]Bhairavabhotla R, Kim YC, Glass DD, et al. Transcriptome profiling of human FoxP3+ regulatory T cells. Human Immunology. 2016;77(2):201-13.[3]Gosselt HR, Verhoeven MMA, Bulatovic-Calasan M, et al. Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis. Journal of Personalized Medicine. 2021;11(1).Disclosure of Interests:None declared

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Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

10.1101/647719 ◽

2019 ◽

Author(s):

William A Figgett ◽

Katherine Monaghan ◽

Milica Ng ◽

Monther Alhamdoosh ◽

Eugene Maraskovsky ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Systemic Lupus Erythematosus ◽

Clinical Trials ◽

Lupus Erythematosus ◽

Whole Blood ◽

Rna Seq ◽

Systemic Lupus ◽

Disease Heterogeneity ◽

Healthy Donors

ABSTRACTObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.MethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.ResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.ConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

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OP0332 The genomic architecture of systemic lupus erythematosus (SLE) by RNA-SEQ: distinct disease susceptibility, activity and severity signatures and extensive genetic effects on whole blood gene expression

10.1136/annrheumdis-2017-eular.6802 ◽

2017 ◽

Author(s):

G Bertsias ◽

N Panousis ◽

I Gergianaki ◽

M Tektonidou ◽

M Trachana ◽

...

Keyword(s):

Gene Expression ◽

Systemic Lupus Erythematosus ◽

Lupus Erythematosus ◽

Whole Blood ◽

Disease Susceptibility ◽

Rna Seq ◽

Systemic Lupus ◽

Blood Gene Expression ◽

Genomic Architecture ◽

Distinct Disease

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Cytokine production from stimulated whole blood cultures in rheumatoid arthritis patients treated with various TNF blocking agents

European Cytokine Network ◽

10.1684/ecn.2009.0150 ◽

2009 ◽

Vol 20 (2) ◽

pp. 88-93 ◽

Cited By ~ 12

Author(s):

Calin Popa ◽

Pilar Barrera ◽

Leo A.B. Joosten ◽

Piet L.C.M. van Riel ◽

Bart-Jan Kullberg ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Whole Blood ◽

Cytokine Production ◽

Blood Cultures ◽

Blocking Agents

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Immune-related gene expression deficit of leukemia stem cells (LSC) in AML.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.7011 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 7011-7011

Author(s):

Kamal Chamoun ◽

Christopher Brent Benton ◽

Ahmed AlRawi ◽

Rodrigo Jacamo ◽

Patrick Williams ◽

...

Keyword(s):

Gene Expression ◽

Antigen Presentation ◽

Mhc Class Ii ◽

Antigen Processing ◽

Differentially Expressed Gene ◽

Enrichment Analysis ◽

Class Ii ◽

Differentially Expressed ◽

Pathway Gene ◽

Presentation Pathway

7011 Background: AML LSC are believed to be responsible for residual and resistant leukemic disease leading to relapse. Understanding differences between bulk AML and the LSC subpopulation may allow the identification of novel LSC targets, especially for the most adverse risk AML where few patients are cured. Targeting LSC may be needed to eradicate AML, and immune-based therapies provide an approach for eliminating LSC. The transcriptional landscape of immune-related genes in LSC is not well understood. Methods: Samples were collected at diagnosis from 12 patients with high-risk AML prior to therapy. Bulk (CD45-dim blasts) and LSC (Lin-CD34+CD38-CD123+) AML marrow cells were FACS-sorted and analyzed using whole genome RNA-sequencing. Transcriptomes were analyzed using AltAnalyze software to identify differentially expressed genes in bulk AML cells and in AML LSC populations. These genes were further assessed by gene enrichment analysis using data from Gene Ontology (GO) and the Cancer Genome Atlas Project (CGAP). Results: Sixty-eight genes were identified with greater than 3-fold differential expression between bulk AML and LSC. GO enrichment analysis demonstrated more than 10-fold enrichment of genes involved in the molecular functions, biologic processes, and cell components related to the antigen presentation pathway, with the comparative down-regulation occurring in LSC. Among the top differentially expressed gene clusters, both the MHC class II and interferon-gamma signaling/response pathway gene expression was blunted in LSC. Additional expression analysis revealed that 42% of a CGAP-curated list of 201 antigen-processing and -presentation genes had significantly decreased expression in the LSC subpopulation compared to bulk AML. Conclusions: LSC from primary AML patient samples are characterized by reduction in expression of MHC class II receptor and antigen presentation genes compared to bulk AML. These results suggest that impairment in the presentation and/or processing of tumor associated antigens by MHC class II on LSC, along with tonic sponging of immune response cells and diversion away from LSC by bulk AML, may contribute to LSC evasion of immune surveillance and response.

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Distinctive gene expression signatures in rheumatoid arthritis synovial tissue fibroblast cells: correlates with disease activity

Genes and Immunity ◽

10.1038/sj.gene.6364400 ◽

2007 ◽

Vol 8 (6) ◽

pp. 480-491 ◽

Cited By ~ 61

Author(s):

C L Galligan ◽

E Baig ◽

V Bykerk ◽

E C Keystone ◽

E N Fish

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Disease Activity ◽

Synovial Tissue ◽

Rheumatoid Arthritis Synovial Tissue ◽

Fibroblast Cells ◽

Gene Expression Signatures

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Genomic dissection of Systemic Lupus Erythematosus: Distinct Susceptibility, Activity and Severity Signatures

10.1101/255109 ◽

2018 ◽

Author(s):

Nikolaos I. Panousis ◽

George Bertsias ◽

Halit Ongen ◽

Irini Gergianaki ◽

Maria Tektonidou ◽

...

Keyword(s):

Gene Expression ◽

Systemic Lupus Erythematosus ◽

Disease Activity ◽

Lupus Erythematosus ◽

Whole Blood ◽

Personalized Therapy ◽

Flare Activity ◽

Rna Seq ◽

Healthy Individuals ◽

Systemic Lupus

AbstractRecent genetic and genomics approaches have yielded novel insights in the pathogenesis of Systemic Lupus Erythematosus (SLE) but the diagnosis, monitoring and treatment still remain largely empirical1,2. We reasoned that molecular characterization of SLE by whole blood transcriptomics may facilitate early diagnosis and personalized therapy. To this end, we analyzed genotypes and RNA-seq in 142 patients and 58 matched healthy individuals to define the global transcriptional signature of SLE. By controlling for the estimated proportions of circulating immune cell types, we show that the Interferon (IFN) and p53 pathways are robustly expressed. We also report cell-specific, disease-dependent regulation of gene expression and define a core/susceptibility and a flare/activity disease expression signature, with oxidative phosphorylation, ribosome regulation and cell cycle pathways being enriched in lupus flares. Using these data, we define a novel index of disease activity/severity by combining the validated Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)1 with a new variable derived from principal component analysis (PCA) of RNA-seq data. We also delineate unique signatures across disease endo-phenotypes whereby active nephritis exhibits the most extensive changes in transcriptome, including prominent drugable signatures such as granulocyte and plasmablast/plasma cell activation. The substantial differences in gene expression between SLE and healthy individuals enables the classification of disease versus healthy status with median sensitivity and specificity of 83% and 100%, respectively. We explored the genetic regulation of blood transcriptome in SLE and found 3142 cis-expression quantitative trait loci (eQTLs). By integration of SLE genome-wide association study (GWAS) signals and eQTLs from 44 tissues from the Genotype-Tissue Expression (GTEx) consortium, we demonstrate that the genetic causality of SLE arises from multiple tissues with the top causal tissue being the liver, followed by brain basal ganglia, adrenal gland and whole blood. Collectively, our study defines distinct susceptibility and activity/severity signatures in SLE that may facilitate diagnosis, monitoring, and personalized therapy.

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Single Cell RNA-Seq Analysis Identifies Differentially Expressed Genes of Treg Cell in Early Treatment-Naive Rheumatoid Arthritis By Arsenic Trioxide

Frontiers in Pharmacology ◽

10.3389/fphar.2021.656124 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chunling Li ◽

Tianshu Chu ◽

Zhiyi Zhang ◽

Yue Zhang

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Amino Acids ◽

Single Cell ◽

Signaling Pathway ◽

Early Treatment ◽

Cell Apoptosis ◽

Treg Cells ◽

Rna Seq ◽

Treatment Naïve

Objective: Early treatment-naïve rheumatoid arthritis (RA) has defective regulatory T (Treg) cells and increased inflammation response. In this study, we aim to illustrate the regulation of Treg cells in pathogenesis of early rheumatoid arthritis by arsenic trioxide (As2O3).Methods: We studied the effects of As2O3 on gene expression in early treatment-naïve RA Treg cells with single cell RNA-seq (scRNA-seq). Treg cells were sorted from peripheral blood mononuclear cells (PBMCs) and purified by fluorescence-activated cell sorting (FACS) and cultured with or without As2O3 (at 0.1 µM) for 24 h. Total RNA was isolated and sequenced, and functional analysis was performed against the Gene Ontology (GO) database. Results for selected genes were confirmed with RT-qPCR.Results: As2O3 exerts no significant effect on CD4+ T-cell apoptosis under physical condition, and selectively modulate CD4+ T cells toward Treg cells not Th17 cells under special polarizing stimulators. As2O3 increased the expression of 200 and reduced that of 272 genes with fold change (FC) 2.0 or greater. Several genes associated with inflammation, Treg-cell activation and differentiation as well as glucose and amino acids metabolism were among the most strongly affected genes. GO function analysis identified top ten ranked significant biological process (BPs), molecular functions (MFs), and cell components (CCs) in treatment and nontreatment Treg cells. In GO analysis, genes involved in the immunoregulation, cell apoptosis and cycle, inflammation, and cellular metabolism were enriched among the significantly affected genes. The KEGG pathway enrichment analysis identified the forkhead box O (FoxO) signal pathway, apoptosis, cytokine–cytokine receptor interaction, cell cycle, nuclear factor-kappa B (NF-κB) signaling pathway, tumor necrosis factor α (TNF-α), p53 signaling pathway, and phosphatidylinositol 3′-kinase (PI3K)-Akt signaling pathway were involved in the pathogenesis of early treatment-naïve RA.Conclusion: This is the first study investigating the genome-wide effects of As2O3 on the gene expression of treatment-naïve Treg cells. In addition to clear anti-inflammatory and immunoregulation effects, As2O3 affect amino acids and glucose metabolism in Treg cells, an observation that might be particularly important in the metabolic phenotype of treatment-naïve RA.

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