Genes and Immunity (2017), 1–8 © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved 1466-4879/17 www.nature.com/gene ORIGINAL ARTICLE Differential transcriptome of tolerogenic versus inflammatory dendritic cells points to modulated T1D genetic risk and enriched immune regulation T Nikolic1,5, NJC Woittiez1,5, A van der Slik1, S Laban1, A Joosten1, C Gysemans2, C Mathieu2, JJ Zwaginga1, B Koeleman3 and BO Roep1,4 Tolerogenic dendritic cells (tolDCs) are assessed as immunomodulatory adjuvants to regulate autoimmunity. The underlying gene expression endorsing their regulatory features remains ill-defined. Using deep mRNA sequencing, we compared transcriptomes of 1,25-dihydroxyvitaminD3/dexametasone-modulated tolDCs with that of non-modulated mature inflammatory DCs (mDCs). Differentially expressed genes controlled cellular interactions, metabolic pathways and endorse tolDCs with the capacity to regulate cell activation through nutrient and signal deprivation, collectively gearing tolDCs into tolerogenic immune regulators. Gene expression differences correlated with protein expression, designating low CD86 and high CD52 on the cell surface as superior discriminators between tolDCs and mDCs. Of 37 candidate genes conferring risk to developing type 1 diabetes (T1D), 11 genes differentially expressed in tolDCs and mDCs regulated immune response and antigen-presenting activity. Differential-expressed transcripts of candidate risk loci for T1D suggest a role of these ‘risk genes’ in immune regulation, which targeting may modulate the genetic contribution to autoimmunity. Genes and Immunity advance online publication, 10 August 2017; doi:10.1038/gene.2017.18 INTRODUCTION capacity to inhibit effector T-cell responses, while inducing 10 A healthy immune system maintains a delicate balance between antigen-specific regulatory T cells. These combined VD3/Dex- surveillance of harmful entities such as pathogens or transformed modulated tolDCs showed a unique protein expression pattern, 10 cells, and maintenance of self tolerance. Dendritic cells contribute compared with tolDCs generated with either modulator alone. to both operating modes of the immune system by employing However, the mechanisms that control tolDC induction and different functional entities; mature inflammatory dendritic cells immunomodulating functions remain largely unknown. Therefore, (mDC) promote adaptive immunity leading to active clearance of we performed next-generation deep mRNA sequencing of VD3/ pathogens or tumors, whereas tolerogenic dendritic cells (tolDCs) Dex-modulated tolDCs and non-modulated inflammatory mDCs to dampen immune reactions and induce specific tolerance to self- reveal transcriptional networks supporting tolerogenic function. In antigens. addition, we focused on the expression pattern of 37 genes In the pathogenesis of autoimmune diseases such as type 1 conferring genetic risk for T1D in an attempt to shed light on the diabetes (T1D) or rheumatoid arthritis, investigations have been contribution of these genes to autoimmunity versus tolerogenic primarily directed toward defining autoreactive responses leading pathways and tissue protection. to tissue destruction. Therapies suppressing such immune responses nonspecifically show reduced autoreactivity but only temporarily or in subgroups of patients,1,2 while ablating necessary immune defense to pathogens and tumors. Several RESULTS immune intervention strategies are currently explored as a Global gene expression segregates tolDC and mDC therapy to repair an impaired immune tolerance causing To analyse molecular networks that differentiate VD3/Dex- autoimmunity. Recently, promising approaches were introduced modulated tolDCs from non-modulated inflammatory mDCs, we to induce tissue-specific immune tolerance by antigen performed next-generation deep mRNA sequencing of mature administration.3–5 Improved induction of tissue-specific protection tolDCs compared with mDCs that were generated from mono- may be achieved using antigen-loaded tolDCs and this approach cytes of four healthy donors. Detected reads were further mapped is current being clinically tested for a number of autoimmune and to human transcriptome and genes with a reliable mapping score inflammatory diseases.6–8 Several protocols have been reported (430%, Po0.00001) were further transformed into reads per that generate tolDCs.9 Using combined modulation with 1,25- kilobase per million mapped reads (RPKMs; Figure 1).11 Genes dihydroxyvitamin D3, the active form of vitamin D3 (VD3) and were excluded from the analyses if the average gene expression in dexamethasone (Dex), we generated tolDCs with a superior the four donors was lower than 1 RPKM in both tolDCs and mDCs. 1Department of Immunohematology and Blood Transfusion, Leiden University Medical Center (LUMC), Leiden, The Netherlands; 2Department of Clinical and Experimental Endocrinology, University of Leuven, Leuven, Belgium; 3Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands and 4Department of Diabetes Immunology, Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA, USA. Correspondence: Dr T Nikolic, Department of Immunohematology and Blood Transfusion, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands. E-mail: [email protected] 5These authors contributed equally to this work. Received 21 January 2017; revised 11 May 2017; accepted 22 June 2017 Differential transcriptome of tolDCs T Nikolic et al 2 The remaining 12 754 genes were used in a principal Total RNA component analysis that clearly segregated tolDC from mDC samples (Figure 2a). Comparisons of individual gene expression Fragmentation between tolDC and mDC identified 4527 genes with ⩾ 2 fold different expression in each donor. These differentially expressed genes clustered into two groups of similar size: 2142 RNA reads genes higher expressed and 2385 genes lower expressed in Quality control tolDC compared with mDC (Figure 2b). Taking into account Reverse transcription the variation in expression between donors, a total of 3121 genes demonstrated significantly different expression between tolDC and mDC (one-way analysis of variance, qo0.01). cDNA reads These genes designated as differential tolDC/mDC transcriptome were considered for subsequent Gene Ontology (GO) analyses. Reads mapping to reference When aligned with GO terms, the differential transcribed genome genes were mainly associated with cell activation and in particular with T-cell activation (Figure 2c). Further GO terms represented in the differential gene set were biological Quantification of gene processes involved in immune regulation and response to external expression and data analysis stimuli. Figure 1. Flowchart showing the steps taken in the mRNA-sequencing analysis. Total mRNA from mature tolDC and mDC samples of four Gene pathways differentiating tolDCs from mDCs donors was isolated, fragmented and mRNA reads quantified. Raw reads Molecular pathways represented by the differential tolDC/mDC were mapped to human genome (Hg19) and normalized to RPKM. fi transcriptome were investigated using Partek Pathway analysis Genes with a mapping ef ciency of 30% and higher and average RPKM software that simultaneously considers qualitative and quantita- of > 1 across all samples were selected for further analyses (n = 4). tive changes in a given data set (Table 1). We identified 22 150 100 50 0 PC#2 21.3% -50 -100 tolDC mDC -150 -150 -100 -50 0 50 100 150 PC#1 53.7% regulation of cytokine production - immune effector process granulocyte migration - defense responses - IL-10 production negative regulation of - protein transport cell activation regulation of inflammatory response cell activation response to wounding response to molecule of bacterial origin leukocyte activation involved in immune response regulation of protein kinase activity hemopoiesis T cell activation -2.020.00 2.02 Figure 2. Principal component analysis (PCA) and unsupervised clustering segregate tolDCs from mDCs. (a) Genes that fulfilled the mapping and expression threshold were used for PCA analysis. Green circles represent tolDC samples and red circles represent mDC samples. The line connects tolDC and mDC samples of the same donor. (b) Expression data were filtered for genes that show ⩾ 2-fold (log2) differential expression between tolDC and mDC. In total, 4527 genes were selected and used for unsupervised clustering, separating tolDC (green columns) from mDC (red columns). The bar shows the color scaling of the higher (yellow) and lower (blue) expressed genes between compared samples. (c) Genes that show low variance between donors (3121 genes; qo0.01, Partek analysis of variance) were further filtered and used for GO terms analysis using ClueGO software. The pie chart depicts GO terms that were significantly (Po0.001) represented by the selected genes. Colors represent different terms, which are sorted based on the frequency of genes presented in the differential transcriptome. Genes and Immunity (2017), 1 – 8 © 2017 Macmillan Publishers Limited, part of Springer Nature. Differential transcriptome of tolDCs T Nikolic et al 3 pathways that were ⩾ 2-fold different in tolDCs compared with Table 1. Differentially expressed pathways as determined using mDCs (Po0.001, Figure 3). Three differentially expressed path- Partek pathway-ANOVA analysis software ways related to cellular interactions were twofold lower in tolDCs Pathway name Fold change P-value (Po0.0002) and lysosome degradation was threefold