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Genes and Immunity (2012) 13, 388–398 & 2012 Macmillan Publishers Limited All rights reserved 1466-4879/12 www.nature.com/gene ORIGINAL ARTICLE Genome-scale profiling reveals a subset of genes regulated by DNA methylation that program somatic T-cell phenotypes in humans D Martino1,2, J Maksimovic1, J-HE Joo1, SL Prescott2,3 and R Saffery1,3 The aim of this study was to investigate the dynamics and relationship between DNA methylation and gene expression during early T-cell development. Mononuclear cells were collected at birth and at 12 months from 60 infants and were either activated with anti-CD3 for 24 h or cultured in media alone, and the CD4 þ T-cell subset purified. DNA and RNA were co-harvested and DNA methylation was measured in 450 000 CpG sites in parallel with expression measurements taken from 25 000 genes. In unstimulated cells, we found that a subset of 1188 differentially methylated loci were associated with a change in expression in 599 genes (adjusted P valueo0.01, b-fold 40.1). These genes were enriched in reprogramming regions of the genome known to control pluripotency. In contrast, over 630 genes were induced following low-level T-cell activation, but this was not associated with any significant change in DNA methylation. We conclude that DNA methylation is dynamic during early T-cell development, and has a role in the consolidation of T-cell-specific gene expression. During the early phase of clonal expansion, DNA methylation is stable and therefore appears to be of limited importance in short-term T-cell responsiveness. Genes and Immunity (2012) 13, 388–398; doi:10.1038/gene.2012.7; published online 12 April 2012 Keywords: T-cell epigenetics; immune epigenetics; DNA methylation; gene expression; T-cell development; reprogramming differentially methylated region INTRODUCTION Shortly after birth, there are rapid phenotypic and functional changes in both innate and adaptive immunity. This critical period of early immune programming is not only important for establishing normal patterns of immunity, but also represents a period of heightened susceptibility to various immune disorders. In the adaptive immune compartment, there is a developmental transition as ‘less mature’ T-cells emerging from the thymus undergo maturation in the periphery.1 This transition becomes apparent over the first year of life;2 however, the molecular processes that drive this are poorly understood. Understanding these processes is critical, as disruption in these pathways may alter the normal course of T-cell development, and potentially program susceptibility may lead to a range of allergic and autoimmune diseases.3,4 Epigenetic modifications are likely to mediate early developmental changes in T-cells, because these modifications are known to have a well-defined role in determining both the diversity and plasticity of T-helper cell phenotypes.5–7 Variability in DNA methylation levels and histone modification profiles establishes active or repressive states of transcription at key cytokine loci.8–11 In differentiated CD4 þ T-cells, these mechanisms are responsible for the somatic heritability of differentiated T-cell states, and are described in close association with the acquisition of effector phenotypes, and specialized patterns of cytokine gene expression.11–13 In undifferentiated (naive) T-cells, DNA methylation marks maintain the plasticity of CD4 þ T-cells, because these cells express low levels of a broad range of cytokines, thus remaining ‘poised’ for commitment.5,14 Although these observations have contributed to our understanding of the mechanisms that govern T-cell plasticity and lineage commitment, they have largely been observed under highly polarizing experimental conditions,15–18 and studied for a restricted number of important cytokine gene loci. Therefore, a more complete picture of the epigenetic processes that govern normal T-cell development is warranted, which has only recently been possible with the advent of genome-wide technologies. In the current study, we investigated the role of DNA methylation and its association with patterns of gene expression under two scenarios: (1) during the steady-state development of naive CD4 þ T-cells shortly after birth; (2) following the activation of T-cells during entry into the cell cycle. Our data provide insights into the molecular pathways of early T-cell programming, and characterize developmental pathways potentially susceptible to disruption through early-life environmental exposures. RESULTS Combined genome-wide DNA methylation and gene expression analysis reveals a dynamic genomic program during steady-state T-cell development Neonatal CD4 þ T-cells are phenotypically and functionally unique compared with later ages. To investigate the epigenetic differences between these cell types, we compared DNA methylation and gene expression in neonatal CD4 þ cells with their 12-month counterparts under two conditions. The 1 Cancer, Disease and Developmental Epigenetics, Murdoch Children’s Research Institute, Royal Melbourne Hospital, Parkville, Victoria, Australia and 2School of Paediatrics and Child Health, University of Western Australia, Perth, Western Australia, Australia. Correspondence: Dr R Saffery, Cancer, Disease and Developmental Epigenetics, Murdoch Children’s Research Institute, Royal Melbourne Hospital, Flemington Road, Parkville, Victoria 3052, Australia. E-mail: [email protected] 3 Equal senior authors. Received 24 January 2012; revised 28 February 2012; accepted 29 February 2012; published online 12 April 2012 Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 389 Figure 1. Experimental methodology and validation of in vitro protocol. (a) Experimental approach used in this study. (b) Experimental design for methylation comparisons between fresh and 24-h cultured CD4 þ cells. (c) Matrix scatterplot of fresh versus 24-h cord blood and adult blood samples. The figure shows scatterplot comparisons between all samples. MDS, multidimensional scaling plot. experimental strategy is outlined in Figure 1a. Briefly, mononuclear cells collected from the same infants at birth and 12months were cultured with and without anti-CD3 and IL-2 for 24 h, in a 371 incubator maintained at 5% CO2. After this time, media supernatants were reserved for cytokine analysis and CD4 þ cells were purified by magnetic bead sorting. DNA and total RNA were co-harvested for microarray analysis. Studies of normal development were conducted in resting (unstimulated) cells, and studies of T-cell activation were conducted in anti-CD3treated cultures. To validate the experimental approach, we first needed to determine whether T-cells rested in culture for 24 h undergo any non-physiological changes in DNA methylation that may complicate data interpretation. To address this, we performed a small pilot experiment detailed in Materials and methods (Figure 1b). We compared freshly thawed T cells with cultured Tcells genomewide, using exploratory techniques and probe-wise tests for differential methylation (adjusted P-valueo0.05 and b-fold change40.1). We found no changes in DNA methylation profiles between fresh and cultured CD4 þ cells, for neonatal or adult samples (Figure 1c). This demonstrated that short-term cell culture does not distort the physiological patterns of genomic methylation. To gain a broad picture of the extent to which neonatal CD4 þ cells are developmentally different from their 12-month counterparts, we compared unstimulated cultures. Probe-wise comparisons of DNA methylation between neonatal and 12-month CD4 þ cells identified a total of 4607 differentially methylated CpG sites (adjusted P valueo0.01 and b-fold change40.10), of which 3136 sites mapped to 1826 unique genes (Figure 2a). The remaining & 2012 Macmillan Publishers Limited 1471 probes (31.9%) had no associated gene annotation and were located either in intergenic regions or in regions occupied by two or more refseq transcripts. A total of 3224 CpGs (70%) showed increased methylation between neonatal and 12-month CD4 þ cells, and 1383 CpGs (30%) showed reduced methylation, indicative of dynamic changes in DNA methylation during early development. Unsupervised sample clustering based on the 4607 differentially methylated probes correctly discriminated neonatal from 12-month samples (Supplementary Figure 1). We performed ontology enrichment analysis on the gene-associated probes and identified terms associated with gene expression, RNA polymerase II activity and transcription. Alongside these, we also observed a host of developmental terms, including cell and tissue morphogenesis, mesenchymal differentiation, skeletal muscle, and olfactory and neuronal development (Supplementary Table 1). Examples of genes associated with these terms include the myosins (MYO1D, MYOIC), myosin light-chain kinases (MYLK), olfactory receptor family members (ORS1E2, OR4D2) and neuronal peptides (NRP2, NRTN). Epigenetic changes at these loci are likely to reflect the developmental control of gene expression during lineage commitment.19,20 Several immunological terms were also enriched in the list of differentially methylated genes, and these included antigen processing and presentation, immune response, leukocyte activation, protein kinase signaling, TGFb signaling and MAPK signaling (Table 1). In the gene expression data set, we observed 986 probes that varied significantly between unstimulated neonatal and 12-month T cells (adjusted P-valueo0.01, b-fold change42). This constituted 287 (29%) upregulated probes and 699 (71%) Genes and Immunity (2012) 388 – 398 Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 390 Figure 2. Changes in DNA methylation and gene expression in CD4 þ cells from birth to 12 months. (a) Scatterplot of differentially methylated CpGs in birth versus 12-month CD4 þ cells. Data are representative of the average beta methylation values of birth and 12-month samples. Red gates show a minimum beta value change of 10%. Significant (false discovery rate (FDR) P value o0.01) probes are shown in blue. (b) Scatterplot of differentially expressed genes in birth versus 12-month CD4 þ cells. Data are average log2 expression of birth samples and 12-month samples. Significant (FDRo0.01, FC42) probes are shown in blue. (c) Sequenom EpiTYPER validation of candidate genes. Data are represented as a cluster heatmap. Rows represent genes and columns represent samples. Cells are colorized according to level of methylation (blue ¼ hypermethylated, yellow ¼ hypomethylated). Samples have correctly clustered according to age. (d) Correlation between specific CpG sites measured by Infinium array and Epityper. down-regulated probes associated with 789 unique genes (Figure 2b). Ontology terms associated with these genes included translation, cell cycle and chromosome organization (Supplementary Table 2). Interestingly, the bulk of ontology terms were related to cell cycle control and development, and no terms associated with immune function were observed. We validated several of the differentially methylated probes using Sequenom EpiTYPER technology (Sequenom, San Diego, CA, USA). PCR amplicons were designed to interrogate the probe binding sites as well as several adjacent CpGs for IL21R, HLA-A, HLA-DMB, TGFB and PRKCA. Sample clustering according to methylation levels assessed using EpiTYPER correctly discriminated birth from 12-month samples, corroborating observations from the Illumina HumanMethylation450 (HM450, Illumina Inc., San Diego, CA, USA) platform (Figure 2c). The concordance between the methylation platforms was high for all CpGs interrogated (R ¼ 0.958, Po0.001) (Figure 2d). To broadly visualize the relationship between changes in DNA methylation and changes in gene expression, the two data sets were merged by ENTREZ ID. As shown in Figure 3a, only a subset of genes appear to be coordinately regulated, as indicated by an inverse relationship between gene expression and DNA methylation (Figure 3a, points in red). A total of 1188 CpG loci displayed this particular methylation pattern, which equates to a coordinated change in methylation and gene expression in 599 unique genes. A smaller portion of these genes were directly related to immune function, with the majority consisting of various developmental genes, transmembrane proteins and transcription Genes and Immunity (2012) 388 – 398 factors, with the exception of numerous HLA genes of class 1 and 2, interferon-inducible proteins, the interleukin 17a receptor, interleukin 1 receptor-like 2, interleukin-4-induced protein, numerous immunoglobulin molecules, lymphocyte-specific protein 1, protein kinase C alpha, protein kinase regulator molecules, tumor necrosis factor and TNF superfamily receptor molecules (Supplementary Table 3). Ontology analysis of these genes identified TGF-b signaling, MAPK signaling, protein kinase signaling and JNK signaling pathways (Supplementary Table 4). Our next goal was to obtain more detailed information on the distribution of differentially methylated and expressed regions across the T-cell genome. We focused on the 4607 CpG sites differentially methylated between neonatal and 12-month CD4 þ T-cells by plotting these against annotated genomic regions in the HM450 manifest. As shown in Figure 3b, we found the differentially methylated sites were evenly distributed across CpG islands, shores, shelves, regulatory regions, gene bodies and untranslated regions. We therefore restricted the analysis to include only the 1188 differentially methylated probes associated with measurable changes in gene expression, and found evidence of enrichment for these CpG sites in reprogramming differentially methylated regions (R-DMR; Figure 3c). To determine whether this association was significant, we employed a robust permutationsbased gene set procedure (see Materials and methods for details) and found that, between neonatal and 12-month samples, methylation marks that affect gene expression were enriched in R-DMRs (false discovery rateo0.001, 1000 permutations). In total there were 87 probes with membership in the R-DMR category, & 2012 Macmillan Publishers Limited Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 391 Table 1. Immune genes differentially methylated from birth to 12 months GO term P value* Symbol Description Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide Beta-2-microglobulin Interferon, gamma-inducible protein 30 Major histocompatibility complex, class I, A Major histocompatibility complex, class I, C; major histocompatibility complex, class I, B Major histocompatibility complex, class I, E Major histocompatibility complex, class I, F Major histocompatibility complex, class I, G Major histocompatibility complex, class II, DM alpha Major histocompatibility complex, class II, DR alpha Transient receptor potential cation channel, subfamily C, member 4–associated protein Transporter 2, ATP-binding cassette, subfamily B (MDR/TAP) Antigen Antigen Antigen Antigen Antigen processing processing processing processing processing and and and and and presentation presentation presentation presentation presentation 3.60E 03 3.60E 03 3.60E 03 3.60E 03 3.60E 03 FCER1G B2M IFI30 HLA-A HLA-B, HLA-C Antigen Antigen Antigen Antigen Antigen Antigen processing processing processing processing processing processing and and and and and and presentation presentation presentation presentation presentation presentation 3.60E 03 3.60E 03 3.60E 03 3.60E 03 3.60E 03 3.60E 03 HLA-E HLA-F HLA-G HLA-DMA HLA-DRA TRPC4AP Antigen processing and presentation 3.60E 03 TAP2 Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation activation 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 BCL11A BCL11B BCL2 CD93 FYN GIMAP5 NCK2 BST2 RAB27A SMAD3 SOX4 CXCL12 CXCR4 CX3CL1 CCND3 DDOST EOMES FLT3 FOXP1 HDAC4 ITPKB ITGB1 Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte activation activation activation activation activation activation activation 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 ITIH1 IRF1 IL21R IL8 LIG4 LAT LCP2 Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte Leukocyte activation activation activation activation activation activation activation activation activation activation activation activation activation activation 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 6.0E 3 LAX1 HLA-DMA NCR1 NTRK1 NHEJ1 PIK3R1 STAT5A SLAMF1 SBNO2 STXBP2 TLR1 TLR3 TLR6 TREML2 Leukocyte activation 6.0E 3 ZAP70 B-cell CLL/lymphoma 11A (zinc-finger protein) B-cell CLL/lymphoma 11B (zinc-finger protein) B-cell CLL/lymphoma 2 CD93 molecule FYN oncogene related to SRC, FGR, YES GTPase, IMAP family member 5 NCK adaptor protein 2 NPC-A-7; bone marrow stromal cell antigen 2 RAB27A, member RAS oncogene family SMAD family member 3 SRY (sex determining region Y)-box 4 Chemokine (C-X-C motif) ligand 12 (stromal cell–derived factor 1) Chemokine (C-X-C motif) receptor 4 Chemokine (C-X3-C motif) ligand 1 Cyclin D3 Dolichyl-diphosphooligosaccharide-protein glycosyltransferase Eomesodermin homolog (Xenopus laevis) fms-related tyrosine kinase 3 Forkhead box P1 Histone deacetylase 4 Inositol 1,4,5-trisphosphate 3-kinase B Integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12) Inter-alpha (globulin) inhibitor H1 Interferon regulatory factor 1 Interleukin 21 receptor Interleukin 8 Ligase IV, DNA, ATP-dependent Linker for activation of T cells Lymphocyte cytosolic protein 2 (SH2-domain containing leukocyte protein of 76kDa) Lymphocyte transmembrane adaptor 1 Major histocompatibility complex, class II, DM alpha Natural cytotoxicity triggering receptor 1 Neurotrophic tyrosine kinase, receptor, type 1 Non-homologous end-joining factor 1 Phosphoinositide-3-kinase, regulatory subunit 1 (alpha) Signal transducer and activator of transcription 5A Signaling lymphocytic activation molecule family member 1 Strawberry notch homolog 2 (Drosophila) Syntaxin binding protein 2 Toll-like receptor 1 Toll-like receptor 3 Toll-like receptor 6 Triggering receptor expressed on myeloid cells-like 2 pseudogene; triggering receptor expressed on myeloid cells-like 2 Zeta-chain (T-cell receptor) associated protein kinase 70kDa *P values derived from the modified Fisher exact test for enrichment analysis and adjusted for multiple testing by Benjamini–Hochberg method. Owing to size limitations, only two ontology terms are displayed here. and these localized to 44 unique genes, including genes responsive to transforming growth factor B signaling (SMAD3, SMAD7), fibroblast growth factor signaling (FGF20), as well as various receptor molecules utilized in the brain, heart or olfactory system (Table 2). Consistent with previous studies, these & 2012 Macmillan Publishers Limited differentially methylated regions were mostly localized to the shores of CpG islands,21 and increased in methylation from birth to 12 months, concurrent with the reduced gene expression relative to neonatal levels (Figure 4). Taken together, the data support a model whereby epigenetic changes in R-DMRs support Genes and Immunity (2012) 388 – 398 Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 392 Figure 3. The relationship between DNA methylation and gene expression in resting CD4 þ cells from birth to 12 months. (a) Scatterplot of change in DNA methylation versus change in gene expression. Gates are set at methylation±10%, and gene expression±1-fold change. Points in red indicate genes under epigenetic regulation (by DNA methylation). (b) Boxplot of differentially methylated probes, stratified by annotated genomic region. The top panel shows 4607 differentially methylated probes are distributed relatively evenly across known genomic regions. The lower panel shows significant differential methylation at R-DMRs of the 1188 coordinately regulated genes. The width of the boxplot reflects the number of observations in each category. UTR, untranslated region; TSS200, 200 bp within transcriptional start site; TSS1500, 1.5 kb within transcriptional start site; N shore, north shore; N shelf, north shelf; S shore, south shore; S shelf, south shelf. the transition away from a more pluripotent neonatal phenotype and consolidate tissue-specific gene expression. Gene expression is dynamic but DNA methylation is stable in activated T cells We next sought to investigate the relationship between gene expression and DNA methylation in rapidly dividing activated T-helper cells. Neonatal and 12-month mononuclear cells were treated with anti-CD3 for 24 h, after which CD4 þ cells were purified and DNA and total RNA were harvested. The CD3 antibody engages the T-cell receptor on the surface of CD4 þ cells and drives a program of T-cell clonal expansion and cytokine gene expression. Comparisons between anti-CD3-treated and untreated CD4 þ cells identified a core set of 634 inducible genes expressed in response to T-cell activation independently of age (false discovery rateo0.05, logFc41). This included 497 (78%) genes upregulated and 137 (22%) genes downregulated at 24 h. Ontology analysis of this gene list revealed a clear signal for an adaptive immune response involving the induction of IL-2 pathways, mobilization of a cell proliferative response and induction of a number of cytokine genes, including IL10, IL6 and IFNg, indicating robust stimulation (Supplementary Table 5). Parallel analysis of DNA methylation in anti-CD3-treated samples versus untreated samples failed to identify any variably methylated HM450 probes between these samples (adjusted P valueo0.05 and b-fold change40.1), suggesting that the inducible gene response is largely independent of changes in the DNA methylome (Figure 5a). We compared the genes induced by anti-CD3 treatment between neonatal and 12-month samples and found agedependent differences in the two responses. A total of 18 genes were substantially developmentally regulated (adjusted P-value o0.01, b-fold change42), including a subset of cytokines IL5, IL9, IL13 and IL22, upregulated specifically in the 12-month samples (Figure 5b). Direct measurement of these cytokines in cellular supernatants collected from neonatal and 12-month cultures confirmed these observations (Figure 5b). In order to test the potential for DNA methylation differences to underscore this agedependent shift in the inducible gene expression profile, the HM450 data set was filtered by shared ENTREZ ID to include only Genes and Immunity (2012) 388 – 398 the top 18 developmentally regulated activation genes. A total of 228 interrogated CpG sites correspond to these 18 genes in the methylation data set and we compared the levels of methylation for these probes between neonatal and 12-month samples. As shown in Figure 5c, we found no evidence to support a role for DNA methylation in driving this age-dependent shift in functional gene expression in response to T-cell receptor activation. We next sought to obtain more detailed information on the methylation status at specific cytokine loci and how this might change in early life. According to the literature, neonatal CD4 þ cells are ‘less-mature’ recent thymic emigrant phenotypes.1,22 These have been described as poor at secreting IL-2 under activation conditions and biased toward IL-4 production under non-polarizing conditions compared with later ages.1,22 This has led to speculation that specific cytokine loci may be subject to distinct epigenetic regulation in neonatal T cells compared with later ages. To test for the potential involvement of DNA methylation changes at these cytokine loci, the HM450 data set was filtered for probes interrogating IL4, IL13, IL2 and IFNG genes.1,22,23 Clustering and heatmap visualization did not suggest any age-dependent or treatment-dependent effects on methylation at these loci (Figure 6). DISCUSSION Neonatal T cells are immunologically unique in both phenotype and function compared to later ages.24 Rapid developmental changes occur in T cells during early life in association with changes in gene expression, and these processes are potentially mediated by corresponding genomic changes in DNA methylation. Understanding the role of DNA methylation in this context is an area of interest, as it may represent a mechanism by which susceptibility to a range of immunological disorders could be programmed into the developing T-cell compartment. In this study, we sought to extend the current knowledge of DNA methylation events associated with early T-cell development, with a particular view to identifying specific epigenetic modifications that have functionally relevant consequences for gene expression. By combining DNA and RNA data genome-wide, several novel insights were revealed. & 2012 Macmillan Publishers Limited Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 393 Table 2. Reprogramming differentially methylated regions involved in CD4 þ T-cell maturation Symbol ANXA7 CDH2 CHD6 CPE ELL3 ENC1 EXOSC2 FAM124A FAM38A FAM38B FGF20 FOSL2 FOXK1 GABBR1 HIST1H2BD IGF2BP1 IGSF9B IQCE KDM2B LPPR2 MAGI3 MAP3K9 MEGF10 NR3C1 OSBPL3 PLEC1 PPM1L PPP1R12C PRR16 RAI1 RIPK4 RNF165 SCD5 SEMA4C SMAD3 SMAD7 SPEG SPHK2 STK10 TBC1D16 TBX2 TMEFF2 TOM1L1 TPM1 ZC3HAV1L Description CHR Ref gene group Relation to CpG island DMR Annexin A7 Cadherin 2, type 1, N-cadherin (neuronal) Chromodomain helicase DNA binding protein 6 Carboxypeptidase E Elongation factor RNA polymerase II-like 3 Ectodermal-neural cortex (with BTB-like domain) Exosome component 2 Family with sequence similarity 124A Family with sequence similarity 38, member A Family with sequence similarity 38, member B Fibroblast growth factor 20 FOS-like antigen 2 Forkhead box K1 Gamma-aminobutyric acid (GABA) B receptor, 1 Histone cluster 1, H2bd Insulin-like growth factor 2 mRNA binding protein 1 Immunoglobulin superfamily, member 9B IQ motif containing E Lysine (K)-specific demethylase 2B Lipid phosphate phosphatase-related protein type 2 Membrane-associated guanylate kinase, WW and PDZ domain containing 3 Mitogen-activated protein kinase kinase kinase 9 Multiple EGF-like-domains 10 Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) Oxysterol binding protein-like 3 Plectin 1 Protein phosphatase 1 (formerly 2C)-like Protein phosphatase 1, regulatory (inhibitor) subunit 12C Proline rich 16 Retinoic acid induced 1 Receptor-interacting serine-threonine kinase 4 Ring-finger protein 165 Stearoyl-CoA desaturase 5 Sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4C SMAD family member 3 SMAD family member 7 SPEG complex locus Sphingosine kinase 2 Serine/threonine kinase 10 TBC1 domain family, member 16 T-box 2 Transmembrane protein with EGF-like and two follistatin-like domains 2 Target of myb1 (chicken)-like 1 tropomyosin 1 (alpha) Zinc-finger CCCH-type, antiviral 1-like 10 18 20 4 15 5 9 13 16 18 8 8 2 7 6 17 11 7 12 19 1 14 5 5 7 8 3 19 5 17 21 18 4 2 5’UTR Body TSS1500 Body Body 5’UTR TSS1500 Body Body Body TSS1500 1st exon; 5’UTR Body Body 3’UTR Body Body 3’UTR Body Body Body TSS1500 5’UTR Body 5’UTR Body;TSS1500 Body TSS1500 5’UTR 5’UTR Body Body Body 5’UTR N_Shore N_Shore S_Shore S_Shore Island N_Shore N_Shore S_Shore N_Shore N_Shore S_Shore S_Shore S_Shore NA S_Shelf S_Shore NA NA N_Shore N_Shore S_Shore S_Shore S_Shore N_Shore N_Shore S_Shore S_Shore S_Shore S_Shore S_Shore Island S_Shore N_Shore N_Shore R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR 15 18 2 19 5 17 17 2 TSS1500 Body Body 3’UTR TSS1500 Body Body Body N_Shore N_Shore S_Shore Island S_Shore S_Shore N_Shore N_Shore R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR R-DMR 17 15 7 Body TSS1500 Body S_Shore N_Shore N_Shore R-DMR R-DMR R-DMR Abbreviations: CHR, chromosome location; R-DMR, reprogramming differentially methylated regions; TSS1500, 1500 bases from transcription start site; UTR, untranslated region. Our experimental strategy enriches cells expressing the CD4 þ co-receptor and we profiled neonatal and 12-month CD4 þ cells. Under the steady-state condition, we observed widespread changes in both methylation and gene expression in neonatal versus 12-month CD4 þ cells. The majority of these changes are likely to reflect the transition of recent thymic emigrant into naive CD4 þ T-cells in the periphery,1,25 and, to a lesser extent, may also reflect quantitative differences in other effector, memory and regulatory subtypes. Collectively, the data provide some interesting insights into the developmental processes that occur during T-cell maturation and turnover in the periphery. The bulk of methylation changes occurred more frequently in developmental genes, although we also report for the first time substantial epigenetic restructuring around the HLA locus in T cells, with similar changes observed in mitogenactivated and protein kinase pathways. Integration of gene expression data revealed these epigenetic changes have & 2012 Macmillan Publishers Limited measurable effects on baseline gene expression. Other notable immune genes in this category include the IL21 receptor and TGFb, the former being a class of common gamma-chain receptors that signal through the JAK–STAT pathway to regulate proliferation and growth, the latter having a key role in establishing regulatory T-cell populations and Th17 cell types. These networks of epigenetically regulated immune genes may be of future interest as candidate pathways are potentially subject to modification by environmental exposures. A more complete understanding of the control of developmental processes that occur during early-life maturation of the T-cell compartment may yield insights relevant to a range of autoimmune and allergic disorders.26,27 Integration of DNA methylation and gene expression data on a genome-wide scale revealed that a direct relationship between DNA methylation and gene expression is often difficult to infer. Indeed, only a subset of probes on the methylation array Genes and Immunity (2012) 388 – 398 Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 394 Figure 4. Coordinate changes in both DNA methylation and gene expression for R-DMRs in CD4 þ cells. Both the DNA methylation and gene expression data sets were filtered via shared ENTREZ ID to include only probes found in R-DMR genomic regions. Methylation and expression were visualized on a heatmap. In both data sets, samples clustered according to age as expected. These loci show reduced methylation at birth and are associated with increased gene expression relative to 12 months. correlated with gene expression measurements in the expected direction based on the current dogma of DNA methylation being largely inhibitory to gene expression. Therefore, our findings challenge this prevailing view, and this is supported by a recent similar finding in CD4 þ cells, which supports the notion that not all methylation marks are transcriptionally repressive in these cells.28 This highlights the importance of the spatial context in which methylation events occur. Therefore, future studies in this area should seek to develop novel bioinformatics approaches to unravel the complex biological relationship between DNA methylation and gene expression. Although these approaches are still in their infancy, our data suggest this may yield a more informative understanding of gene regulatory networks. An example of this is the finding that R-DMRs represent the bulk of differential methylation in the network of developmental genes that appear to be overtly under the control of DNA methylation. R-DMRs were originally identified in induced pluripotent stem cells (iPS) as key regions in which tissue differentiation is specified as cells mature away from a stem cell-like phenotype.29 In comparisons between neonatal and 12-month CD4 þ cells, this observation was only apparent after removing DNA methylation marks not clearly associated with changes in gene expression. The latter finding therefore provides clues to suggest that post-thymic maturation of T cells in the periphery involves a developmental network of epigenetically regulated genes that specify somatic T-cell phenotypes and control tissue-specific gene expression. This notion is reinforced in the ontology analysis, in which DNA methylation changes were associated with pluripotency genes and transcripts expressed in differentiated cell types other than T cells. We observed epigenetic changes in transcripts expressed in the brain, heart and olfactory systems, all of which have well-documented interactions with immunity.19,20 Several HLA gene transcripts that are normally silenced in adult T cells appear to be unrestrained in neonatal cells, suggesting the latter may be closer to a stem cell phenotype. This reasoning is in line with in vitro human data30 and studies in mice31 that suggest neonatal T-cell responses tend to be more promiscuous toward low-affinity T-cell receptor/MHC-peptide interactions compared with naive T cells of later ages. In studies of activated T cells, DNA methylation marks were not altered in rapidly proliferating T cells. This is not entirely unexpected, and suggests that replication of DNA methylation marks during clonal expansion maintains a chromatin state permissive to the induction of hundreds of genes in progeny cells, and retains the ability for T-cell sub-lineage specification.5 Genes and Immunity (2012) 388 – 398 The use of soluble anti-CD3 in the presence of Fc receptor-bearing accessory cells to activate T cells has been shown to provide optimal stimulation,32 although memory cells may have a different requirement for this second signal than naive cells.33,34 Therefore, it was possible that our interpretation of the data was based on the co-stimulation pathway. However, a study comparing the changes in methylation in CpG islands after full-scale activation of CD4 þ cells using plate-bound anti-CD3 with no requirement for co-stimulation also reported that DNA methylation is essentially 35 stable in T-cell blasts induced by strong activating stimuli. These findings are in sharp contrast to epigenetic studies of T cells maintained under highly polarizing conditions,12 and therefore it is likely that signals further downstream of T-cell receptor activation alter the methylation status at key cytokine regions as naive T cells fully differentiate into specialized phenotypes. Our data did not support a role for changes in DNA methylation in mediating the age-dependent changes in cytokine gene expression observed following T-cell activation. However, we did not address the potential for cell-type-specific differences in the diversity of the T-helper pool between neonatal and 12-month CD4 þ cells. Therefore the data may reflect quantitative differences in T-cell subsets with age, and future studies seek to address this. Furthermore, the results of this study were derived from pooled RNA and DNA samples and therefore provide robust measures of group averages;36 however, data at the individual level were not available. To summarize, this study demonstrates a role for DNA methylation in the control of gene expression during the period of early T-cell development. The results provide baseline information about molecular pathways that drive the normal course of immune development. These are potentially modifiable by early life events and exposures, and therefore represent plausible pathways of disease susceptibility. To extend this work, it will be important to demonstrate that disruption in the developing T-cell epigenome alters the normal pattern of T-cell responses providing the next link between the genes, the environment and immune disease. MATERIALS AND METHODS Volunteers Sixty subjects were selected from existing bio-banked specimens for this study. These subjects were recruited in the last trimester of pregnancy through the Princess Margaret Hospital for Children under approval from the Institutional Ethics Committee. All volunteers were non-smokers and & 2012 Macmillan Publishers Limited Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 395 Figure 5. The relationship between DNA methylation and gene expression in activated CD4 þ T cells. (a) Differential methylation of activated versus non-activated CD4 þ cells plotted against gene expression. Activated cells show large-scale changes in gene expression with no significant changes in DNA methylation. (b) Top 15 most differentially expressed genes in activated cord blood T cells versus activated 12-month T cells. The right panel shows cytokine production in cellular supernatants taken from these cells. Cytokine production from birth to 12 months agreed with gene expression data. Statistical analysis by Mann–Whitney U test. (c) Comparison between gene expression profiles for top 15 developmentally expressed activation genes, and corresponding CpG measurements. Methylation status at these genes was independent of age (right panel). free of any pregnancy complications or congenital abnormalities. Cord blood was collected at birth and peripheral blood was collected from infants at 12-month follow-up clinical visits using standardized procedures documented previously.37 Mononuclear cells were separated by density centrifugation, enumerated and cryopreserved viably. & 2012 Macmillan Publishers Limited Cell culture Cryopreserved stocks of PBMC or CBMC were thawed and seeded at 2.5 105 cells per well in 96-well round-bottom polystyrene plates with 20 replicate wells (5 106 cells) per condition (treated, untreated). Cells were cultured in AIM-V media plus b-mercaptoethanol (4 10 5 mol l 1), alone Genes and Immunity (2012) 388 – 398 Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 396 Figure 6. Heatmap visualization of DNA methylation for specific cytokine genes. Rows represent Illumina array probes for specific cytokine genes, columns represent samples. Cells are colorized according to level of methylation (blue ¼ hypomethylated, yellow ¼ hypermethylated). Rows and columns are clustered according to Euclidean distance (unstim ¼ media only, stim ¼ anti-CD3/IL2). or in the presence of optimal levels of soluble anti-CD3 monoclonal antibody (0.5 mg ml 1) (Miltenyi, North Ryde, NSW, Australia) with recombinant human IL-2 (10 Units) (Sigma Aldrich, Castle Hill, NSW, Australia). The activation protocol here depends on co-stimulation provided by accessory cells that constitutively express CD80 and CD8638 and has been shown to provide a highly effective proliferative signal.32 The optimal stimulation protocol was determined in forerunner experiments provided in Supplementary Figure 2. Following 24 h in culture, replicate wells were combined and CD4 þ T-cells were isolated by positive selection using magnetic Dynabeads (Life Technologies, Mulgrave, VIC, Australia) to 85–95% purity (as determined by flow cytometry). Cell supernatants were frozen for cytokine analysis Nucleic acid purification and QC DNA and total RNA were co-purified from CD4 þ cells using a column extraction method (All-prep kits, Qiagen, Doncaster, VIC, Australia) according to the manufacturer’s instructions. Nucleic acid quantity and purity were determined by spectrophotometry using the Nanodrop (Thermo Scientific, Scoresby, VIC, Australia). All samples had a light absorbance 260/280 ratio of X1.8. Integrity of RNA was measured on the Agilent 2100 Bioanalyzer (Agilent Technologies, Mulgrave, VIC, Australia) using the RIN method. All RINs were between 7.2 and 10. Illumina human methylation 450k data acquisition and processing DNA (1 mg) was bisulfite converted using the Methyl Easy bisulphite modification kit (Human Genetic Signatures, Sydney, NSW, Australia), according to the manufacturer’s instructions. Conversion efficiency was assessed by bisulfite-specific PCR. Forty-eight individuals were chipped in the methylation array study and the remaining twelve were reserved for validation. Equimolar amounts of DNA from two individuals were pooled on each array. This allowed us to survey a large number of individuals, reducing the variability attributable to genetic effects, and has been shown to provide an accurate estimate of group methylation values.36 Pooled DNA samples were sent to Service XS (Leiden, The Netherlands) for Genes and Immunity (2012) 388 – 398 hybridization to Illumina Human Methylation450 Beadchips. Raw data files were exported from Genome Studio (Illumina, San Diego, CA, USA) into the R statistical environment (http://cran.r-project.org/index.html). Data quality was assessed using the methylumi package39–41 to assess signal-to-noise ratios, and identify outlying samples and batch effects. All samples passed QC. Probes on the X and Y chromosomes were removed to eliminate gender bias. The lumi package42 was used to calculate the log2 ratio for methylated probe intensity to unmethylated probe intensity, the M value. These probes underwent colour adjustment, background correction and quantile normalization. Any poor-performing probes were filtered out of the final data set, defined as those with a detection P-value call 40.01 for all samples. This reduced the size of the final data set to 462 172. b-Values were derived from intensities as defined by the ratio of methylated to unmethylated probes given by B ¼ M/(U þ M þ 100) and were used as a measure of effect size. Affymetrix human gene 1.0ST data acquisition and processing Sixty individuals were used in the Affymetrix array experiment. Pooled RNA samples were converted to single-stranded fragmented DNA using the WT sense target labeling protocol according to the manufacturer’s instructions (Affymetrix, Santa Clara, CA, USA). Converted DNA products were sent to the Australian Neuromuscular Research Institute for hybridization washing and scanning. The quality of the microarray data was assessed using QC metrics in the Expression Console software (Affymetrix), with the average positive versus negative AUC being 0.8695 (n ¼ 80, ±0.017) for all microarray experiments. The microarray data were preprocessed with the PLIER algorithm (gcbg background subtraction, quantile normalization, iterPLIER summarization).41–43 Data were variance stabilized by adding 16 to all data points, followed by log2 transformation in the R environment (http://cran.r-project.org/). Statistical analysis and bioinformatics The data underwent unsupervised hierarchical clustering analysis with the Euclidean distance and complete linkage algorithm, and a heatmap with & 2012 Macmillan Publishers Limited Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 397 associated dendrogram was created using gplots.39 For differential analysis, a linear model was fitted for all comparisons using the limma package.42 The P values derived from the moderated t-statistics were adjusted to control the false discovery rate using the Benjamini–Hotchberg method.43 For combined gene expression and DNA methylation analysis, change in methylation was defined by M values from contrasts between 12-month and matched birth samples, and the M values were plotted against the average log2 fold change from the same comparisons (12 months—neonatal) in the gene expression data set. To identify differentially expressed pathways, the GSA gene sets test was performed on the methylation data set.44 Gene sets were populated with probe ids using the annotated regions provided in the Illumina Human Methylation450 manifest file. The data set was filtered to include 1188 differentially methylated probes and a two-class paired comparison of gene sets was performed using a minimum of 1000 permutations to estimate P values, and a false discovery rate cutoff of 0.01 was specified. Ontology enrichment was performed using the DAVID bioinformatics tool under the default settings.45 Sequenom Massarray target validation Target validation was performed using the Sequenom EpiTYPER (Sequenom). Amplicons were designed using the Sequenom EpiDesigner software (http://www.epidesigner.com/). Amplification conditions were as follows: 95 1C for 5 min, 56 1C for 1 min 30 s and 72 1C for 1 min 30 s for 40 cycles, 72 1C for 7 min. Primer sequences are provided in Supplementary Table 6. Cytokine protein measurements Cytokine production (IL-5, IL-10, IL-13, IL-17, TNF-a and IFN-g) to anti-CD3 was monitored in cell culture supernatants, and was quantified with Luminex Xmap multiplexing technology (Luminex Corp, Austin, TX, USA). The limits of detection were 3–10 000 pg ml 1 for all cytokines, and all data are shown as increases above unstimulated controls. Pilot experiment PBMC (n ¼ 2) and CBMC (n ¼ 2) derived from unrelated donors were thawed from frozen stocks and resuspended to a concentration of 1 106 cells ml 1 in AIM-V media plus b-mercaptoethanol (4 10 5 mol l 1). Cells were rested in 96-well polystyrene plates for 24 h in a 371 incubator. CD4 þ T cells were purified by flow cytometry and DNA was recovered. DNA samples were bisulfite converted and hybridized to HM450K arrays. The data were processed as described above and comparisons were made between cell types and time points using clustering and tests for differential expression outlined in the Statistical Analysis section. DATA ARCHIVING Microarray data described in this manuscript have been submitted to the GEO public repository and are freely available under the following accession number: DNA methylation data— GSE34639. CONFLICT OF INTEREST The authors declare no conflict of interest. ACKNOWLEDGEMENTS We wish to thank Dr Alicia Oshlack and Dr Lavinia Gordon for advice on data analysis. REFERENCES 1 Fink PJ, Hendricks DW. Post-thymic maturation: young T cells assert their individuality. Nat Rev Immunol 2011; 11: 544–549. 2 Zaghouani H, Hoeman CM, Adkins B. Neonatal immunity: faulty T-helpers and the shortcomings of dendritic cells. Trends Immunol 2009; 30: 585–591. 3 Williams M, Georas S. Gene expression patterns and susceptibility to allergic responses. 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