Genome-Scale Profiling Reveals a Subset of Genes Regulated by DNA

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 cytokines, thus remaining ‘poised’ for commitment.5,14 Although Shortly after birth, there are rapid phenotypic and functional these observations have contributed to our understanding of the changes in both innate and adaptive immunity. This critical period mechanisms that govern T-cell plasticity and lineage commitment, of early immune programming is not only important for they have largely been observed under highly polarizing 15–18 establishing normal patterns of immunity, but also represents a experimental conditions, and studied for a restricted period of heightened susceptibility to various immune disorders. number of important cytokine gene loci. Therefore, a more In the adaptive immune compartment, there is a developmental complete picture of the epigenetic processes that govern normal transition as ‘less mature’ T-cells emerging from the thymus T-cell development is warranted, which has only recently been undergo maturation in the periphery.1 This transition becomes possible with the advent of genome-wide technologies. apparent over the first year of life;2 however, the molecular In the current study, we investigated the role of DNA processes that drive this are poorly understood. Understanding methylation and its association with patterns of gene expression these processes is critical, as disruption in these pathways may under two scenarios: (1) during the steady-state development of alter the normal course of T-cell development, and potentially naive CD4 þ T-cells shortly after birth; (2) following the activation program susceptibility may lead to a range of allergic and of T-cells during entry into the cell cycle. Our data provide insights autoimmune diseases.3,4 into the molecular pathways of early T-cell programming, and Epigenetic modifications are likely to mediate early develop- characterize developmental pathways potentially susceptible to mental changes in T-cells, because these modifications are known disruption through early-life environmental exposures. 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 RESULTS 8–11 active or repressive states of transcription at key cytokine loci. Combined genome-wide DNA methylation and gene expression In differentiated CD4 þ T-cells, these mechanisms are responsible analysis reveals a dynamic genomic program during steady-state for the somatic heritability of differentiated T-cell states, and T-cell development are described in close association with the acquisition of Neonatal CD4 þ T-cells are phenotypically and functionally effector phenotypes, and specialized patterns of cytokine unique compared with later ages. To investigate the epigenetic 11–13 gene expression. In undifferentiated (naive) T-cells, DNA differences between these cell types, we compared DNA methylation marks maintain the plasticity of CD4 þ T-cells, methylation and gene expression in neonatal CD4 þ cells with because these cells express low levels of a broad range of their 12-month counterparts under two conditions. The

1Cancer, 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] 3Equal 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, mono- 1471 probes (31.9%) had no associated gene annotation and were nuclear cells collected from the same infants at birth and located either in intergenic regions or in regions occupied by two 12months were cultured with and without anti-CD3 and IL-2 for or more refseq transcripts. A total of 3224 CpGs (70%) showed 24 h, in a 371 incubator maintained at 5% CO2. After this time, increased methylation between neonatal and 12-month CD4 þ media supernatants were reserved for cytokine analysis and cells, and 1383 CpGs (30%) showed reduced methylation, CD4 þ cells were purified by magnetic bead sorting. DNA and indicative of dynamic changes in DNA methylation during early total RNA were co-harvested for microarray analysis. Studies of development. normal development were conducted in resting (unstimulated) Unsupervised sample clustering based on the 4607 differentially cells, and studies of T-cell activation were conducted in anti-CD3- methylated probes correctly discriminated neonatal from treated cultures. 12-month samples (Supplementary Figure 1). We performed To validate the experimental approach, we first needed to ontology enrichment analysis on the gene-associated probes determine whether T-cells rested in culture for 24 h undergo any and identified terms associated with gene expression, RNA non-physiological changes in DNA methylation that may compli- polymerase II activity and transcription. Alongside these, we also cate data interpretation. To address this, we performed a small observed a host of developmental terms, including cell and tissue pilot experiment detailed in Materials and methods (Figure 1b). morphogenesis, mesenchymal differentiation, skeletal muscle, and We compared freshly thawed T cells with cultured Tcells genome- olfactory and neuronal development (Supplementary Table 1). wide, using exploratory techniques and probe-wise tests for Examples of genes associated with these terms include the differential methylation (adjusted P-valueo0.05 and b-fold myosins (MYO1D, MYOIC), myosin light-chain kinases (MYLK), change40.1). We found no changes in DNA methylation profiles olfactory receptor family members (ORS1E2, OR4D2) and neuronal between fresh and cultured CD4 þ cells, for neonatal or adult peptides (NRP2, NRTN). Epigenetic changes at these loci are likely samples (Figure 1c). This demonstrated that short-term cell culture to reflect the developmental control of gene expression during does not distort the physiological patterns of genomic lineage commitment.19,20 Several immunological terms were also methylation. enriched in the list of differentially methylated genes, and these To gain a broad picture of the extent to which neonatal CD4 þ included antigen processing and presentation, immune response, cells are developmentally different from their 12-month counter- leukocyte activation, protein kinase signaling, TGFb signaling and parts, we compared unstimulated cultures. Probe-wise compar- MAPK signaling (Table 1). isons of DNA methylation between neonatal and 12-month CD4 þ In the gene expression data set, we observed 986 probes that cells identified a total of 4607 differentially methylated CpG sites varied significantly between unstimulated neonatal and 12-month (adjusted P valueo0.01 and b-fold change40.10), of which 3136 T cells (adjusted P-valueo0.01, b-fold change42). This consti- sites mapped to 1826 unique genes (Figure 2a). The remaining tuted 287 (29%) upregulated probes and 699 (71%)

& 2012 Macmillan Publishers Limited 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 factors, with the exception of numerous HLA genes of class 1 and (Figure 2b). Ontology terms associated with these genes included 2, interferon-inducible proteins, the interleukin 17a receptor, translation, cell cycle and chromosome organization (Supple- interleukin 1 receptor-like 2, interleukin-4-induced protein, mentary Table 2). Interestingly, the bulk of ontology terms were numerous immunoglobulin molecules, lymphocyte-specific pro- related to cell cycle control and development, and no terms tein 1, protein kinase C alpha, protein kinase regulator molecules, associated with immune function were observed. tumor necrosis factor and TNF superfamily receptor molecules We validated several of the differentially methylated probes (Supplementary Table 3). Ontology analysis of these genes using Sequenom EpiTYPER technology (Sequenom, San Diego, CA, identified TGF-b signaling, MAPK signaling, protein kinase signal- USA). PCR amplicons were designed to interrogate the probe ing and JNK signaling pathways (Supplementary Table 4). binding sites as well as several adjacent CpGs for IL21R, HLA-A, Our next goal was to obtain more detailed information on the HLA-DMB, TGFB and PRKCA. Sample clustering according to distribution of differentially methylated and expressed regions methylation levels assessed using EpiTYPER correctly discrimi- across the T-cell genome. We focused on the 4607 CpG sites nated birth from 12-month samples, corroborating observations differentially methylated between neonatal and 12-month CD4 þ from the Illumina HumanMethylation450 (HM450, Illumina Inc., T-cells by plotting these against annotated genomic regions in San Diego, CA, USA) platform (Figure 2c). The concordance the HM450 manifest. As shown in Figure 3b, we found the between the methylation platforms was high for all CpGs differentially methylated sites were evenly distributed across CpG interrogated (R ¼ 0.958, Po0.001) (Figure 2d). islands, shores, shelves, regulatory regions, gene bodies and To broadly visualize the relationship between changes in DNA untranslated regions. We therefore restricted the analysis to methylation and changes in gene expression, the two data sets include only the 1188 differentially methylated probes associated were merged by ENTREZ ID. As shown in Figure 3a, only a subset with measurable changes in gene expression, and found evidence of genes appear to be coordinately regulated, as indicated by an of enrichment for these CpG sites in reprogramming differentially inverse relationship between gene expression and DNA methyla- methylated regions (R-DMR; Figure 3c). To determine whether this tion (Figure 3a, points in red). A total of 1188 CpG loci displayed association was significant, we employed a robust permutations- this particular methylation pattern, which equates to a coordi- based gene set procedure (see Materials and methods for details) nated change in methylation and gene expression in 599 unique and found that, between neonatal and 12-month samples, genes. A smaller portion of these genes were directly related to methylation marks that affect gene expression were enriched in immune function, with the majority consisting of various R-DMRs (false discovery rateo0.001, 1000 permutations). In total developmental genes, transmembrane proteins and transcription there were 87 probes with membership in the R-DMR category,

Genes and Immunity (2012) 388 – 398 & 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

Antigen processing and presentation 3.60E À 03 FCER1G Fc fragment of IgE, high affinity I, receptor for; gamma polypeptide Antigen processing and presentation 3.60E À 03 B2M Beta-2-microglobulin Antigen processing and presentation 3.60E À 03 IFI30 Interferon, gamma-inducible protein 30 Antigen processing and presentation 3.60E À 03 HLA-A Major histocompatibility complex, class I, A Antigen processing and presentation 3.60E À 03 HLA-B, HLA-C Major histocompatibility complex, class I, C; major histocompatibility complex, class I, B Antigen processing and presentation 3.60E À 03 HLA-E Major histocompatibility complex, class I, E Antigen processing and presentation 3.60E À 03 HLA-F Major histocompatibility complex, class I, F Antigen processing and presentation 3.60E À 03 HLA-G Major histocompatibility complex, class I, G Antigen processing and presentation 3.60E À 03 HLA-DMA Major histocompatibility complex, class II, DM alpha Antigen processing and presentation 3.60E À 03 HLA-DRA Major histocompatibility complex, class II, DR alpha Antigen processing and presentation 3.60E À 03 TRPC4AP Transient receptor potential cation channel, subfamily C, member 4–associated protein Antigen processing and presentation 3.60E À 03 TAP2 Transporter 2, ATP-binding cassette, subfamily B (MDR/TAP) Leukocyte activation 6.0E À 3 BCL11A B-cell CLL/lymphoma 11A (zinc-finger protein) Leukocyte activation 6.0E À 3 BCL11B B-cell CLL/lymphoma 11B (zinc-finger protein) Leukocyte activation 6.0E À 3 BCL2 B-cell CLL/lymphoma 2 Leukocyte activation 6.0E À 3 CD93 CD93 molecule Leukocyte activation 6.0E À 3 FYN FYN oncogene related to SRC, FGR, YES Leukocyte activation 6.0E À 3 GIMAP5 GTPase, IMAP family member 5 Leukocyte activation 6.0E À 3 NCK2 NCK adaptor protein 2 Leukocyte activation 6.0E À 3 BST2 NPC-A-7; bone marrow stromal cell antigen 2 Leukocyte activation 6.0E À 3 RAB27A RAB27A, member RAS oncogene family Leukocyte activation 6.0E À 3 SMAD3 SMAD family member 3 Leukocyte activation 6.0E À 3 SOX4 SRY (sex determining region Y)-box 4 Leukocyte activation 6.0E À 3 CXCL12 Chemokine (C-X-C motif) ligand 12 (stromal cell–derived factor 1) Leukocyte activation 6.0E À 3 CXCR4 Chemokine (C-X-C motif) receptor 4 Leukocyte activation 6.0E À 3 CX3CL1 Chemokine (C-X3-C motif) ligand 1 Leukocyte activation 6.0E À 3 CCND3 Cyclin D3 Leukocyte activation 6.0E À 3 DDOST Dolichyl-diphosphooligosaccharide-protein glycosyltransferase Leukocyte activation 6.0E À 3 EOMES Eomesodermin homolog (Xenopus laevis) Leukocyte activation 6.0E À 3 FLT3 fms-related tyrosine kinase 3 Leukocyte activation 6.0E À 3 FOXP1 Forkhead box P1 Leukocyte activation 6.0E À 3 HDAC4 Histone deacetylase 4 Leukocyte activation 6.0E À 3 ITPKB Inositol 1,4,5-trisphosphate 3-kinase B Leukocyte activation 6.0E À 3 ITGB1 Integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12) Leukocyte activation 6.0E À 3 ITIH1 Inter-alpha (globulin) inhibitor H1 Leukocyte activation 6.0E À 3 IRF1 Interferon regulatory factor 1 Leukocyte activation 6.0E À 3 IL21R Interleukin 21 receptor Leukocyte activation 6.0E À 3 IL8 Interleukin 8 Leukocyte activation 6.0E À 3 LIG4 Ligase IV, DNA, ATP-dependent Leukocyte activation 6.0E À 3 LAT Linker for activation of T cells Leukocyte activation 6.0E À 3 LCP2 Lymphocyte cytosolic protein 2 (SH2-domain containing leukocyte protein of 76kDa) Leukocyte activation 6.0E À 3 LAX1 Lymphocyte transmembrane adaptor 1 Leukocyte activation 6.0E À 3 HLA-DMA Major histocompatibility complex, class II, DM alpha Leukocyte activation 6.0E À 3 NCR1 Natural cytotoxicity triggering receptor 1 Leukocyte activation 6.0E À 3 NTRK1 Neurotrophic tyrosine kinase, receptor, type 1 Leukocyte activation 6.0E À 3 NHEJ1 Non-homologous end-joining factor 1 Leukocyte activation 6.0E À 3 PIK3R1 Phosphoinositide-3-kinase, regulatory subunit 1 (alpha) Leukocyte activation 6.0E À 3 STAT5A Signal transducer and activator of transcription 5A Leukocyte activation 6.0E À 3 SLAMF1 Signaling lymphocytic activation molecule family member 1 Leukocyte activation 6.0E À 3 SBNO2 Strawberry notch homolog 2 (Drosophila) Leukocyte activation 6.0E À 3 STXBP2 Syntaxin binding protein 2 Leukocyte activation 6.0E À 3 TLR1 Toll-like receptor 1 Leukocyte activation 6.0E À 3 TLR3 Toll-like receptor 3 Leukocyte activation 6.0E À 3 TLR6 Toll-like receptor 6 Leukocyte activation 6.0E À 3 TREML2 Triggering receptor expressed on myeloid cells-like 2 pseudogene; triggering receptor expressed on myeloid cells-like 2 Leukocyte activation 6.0E À 3 ZAP70 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 differentially methylated regions were mostly localized to the responsive to transforming growth factor B signaling (SMAD3, shores of CpG islands,21 and increased in methylation from birth SMAD7), fibroblast growth factor signaling (FGF20), as well as to 12 months, concurrent with the reduced gene expression various receptor molecules utilized in the brain, heart or olfactory relative to neonatal levels (Figure 4). Taken together, the data system (Table 2). Consistent with previous studies, these support a model whereby epigenetic changes in R-DMRs support

& 2012 Macmillan Publishers Limited 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 the top 18 developmentally regulated activation genes. A total of and consolidate tissue-specific gene expression. 228 interrogated CpG sites correspond to these 18 genes in the methylation data set and we compared the levels of methylation Gene expression is dynamic but DNA methylation is stable in for these probes between neonatal and 12-month samples. As activated T cells shown in Figure 5c, we found no evidence to support a role for We next sought to investigate the relationship between gene DNA methylation in driving this age-dependent shift in functional expression and DNA methylation in rapidly dividing activated gene expression in response to T-cell receptor activation. T-helper cells. Neonatal and 12-month mononuclear cells were We next sought to obtain more detailed information on the treated with anti-CD3 for 24 h, after which CD4 þ cells were methylation status at specific cytokine loci and how this might purified and DNA and total RNA were harvested. The CD3 change in early life. According to the literature, neonatal CD4 þ antibody engages the T-cell receptor on the surface of CD4 þ cells cells are ‘less-mature’ recent thymic emigrant phenotypes.1,22 and drives a program of T-cell clonal expansion and cytokine gene These have been described as poor at secreting IL-2 under expression. Comparisons between anti-CD3-treated and untreated activation conditions and biased toward IL-4 production under CD4 þ cells identified a core set of 634 inducible genes expressed non-polarizing conditions compared with later ages.1,22 This has in response to T-cell activation independently of age (false led to speculation that specific cytokine loci may be subject to discovery rateo0.05, logFc41). This included 497 (78%) genes distinct epigenetic regulation in neonatal T cells compared with upregulated and 137 (22%) genes downregulated at 24 h. later ages. To test for the potential involvement of DNA Ontology analysis of this gene list revealed a clear signal for methylation changes at these cytokine loci, the HM450 data set an adaptive immune response involving the induction of IL-2 was filtered for probes interrogating IL4, IL13, IL2 and IFNG pathways, mobilization of a cell proliferative response and genes.1,22,23 Clustering and heatmap visualization did not suggest induction of a number of cytokine genes, including IL10, IL6 and any age-dependent or treatment-dependent effects on IFNg, indicating robust stimulation (Supplementary Table 5). methylation at these loci (Figure 6). 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 DISCUSSION P valueo0.05 and b-fold change40.1), suggesting that the Neonatal T cells are immunologically unique in both phenotype inducible gene response is largely independent of changes in and function compared to later ages.24 Rapid developmental the DNA methylome (Figure 5a). changes occur in T cells during early life in association with We compared the genes induced by anti-CD3 treatment changes in gene expression, and these processes are potentially between neonatal and 12-month samples and found age- mediated by corresponding genomic changes in DNA dependent differences in the two responses. A total of 18 genes methylation. Understanding the role of DNA methylation in this were substantially developmentally regulated (adjusted P-value context is an area of interest, as it may represent a mechanism by o0.01, b-fold change42), including a subset of cytokines IL5, IL9, which susceptibility to a range of immunological disorders could IL13 and IL22, upregulated specifically in the 12-month samples be programmed into the developing T-cell compartment. In this (Figure 5b). Direct measurement of these cytokines in cellular study, we sought to extend the current knowledge of DNA supernatants collected from neonatal and 12-month cultures methylation events associated with early T-cell development, with confirmed these observations (Figure 5b). In order to test the a particular view to identifying specific epigenetic modifications potential for DNA methylation differences to underscore this age- that have functionally relevant consequences for gene expression. dependent shift in the inducible gene expression profile, the By combining DNA and RNA data genome-wide, several novel HM450 data set was filtered by shared ENTREZ ID to include only insights were revealed.

Genes and Immunity (2012) 388 – 398 & 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 Description CHR Ref gene Relation to DMR group CpG island

ANXA7 Annexin A7 10 5’UTR N_Shore R-DMR CDH2 Cadherin 2, type 1, N-cadherin (neuronal) 18 Body N_Shore R-DMR CHD6 Chromodomain helicase DNA binding protein 6 20 TSS1500 S_Shore R-DMR CPE Carboxypeptidase E 4 Body S_Shore R-DMR ELL3 Elongation factor RNA polymerase II-like 3 15 Body Island R-DMR ENC1 Ectodermal-neural cortex (with BTB-like domain) 5 5’UTR N_Shore R-DMR EXOSC2 Exosome component 2 9 TSS1500 N_Shore R-DMR FAM124A Family with sequence similarity 124A 13 Body S_Shore R-DMR FAM38A Family with sequence similarity 38, member A 16 Body N_Shore R-DMR FAM38B Family with sequence similarity 38, member B 18 Body N_Shore R-DMR FGF20 Fibroblast growth factor 20 8 TSS1500 S_Shore R-DMR FOSL2 FOS-like antigen 2 8 1st exon; 5’UTR S_Shore R-DMR FOXK1 Forkhead box K1 2 Body S_Shore R-DMR GABBR1 Gamma-aminobutyric acid (GABA) B receptor, 1 7 Body NA R-DMR HIST1H2BD Histone cluster 1, H2bd 6 3’UTR S_Shelf R-DMR IGF2BP1 Insulin-like growth factor 2 mRNA binding protein 1 17 Body S_Shore R-DMR IGSF9B Immunoglobulin superfamily, member 9B 11 Body NA R-DMR IQCE IQ motif containing E 7 3’UTR NA R-DMR KDM2B Lysine (K)-specific demethylase 2B 12 Body N_Shore R-DMR LPPR2 Lipid phosphate phosphatase-related protein type 2 19 Body N_Shore R-DMR MAGI3 Membrane-associated guanylate kinase, WW and PDZ domain containing 3 1 Body S_Shore R-DMR MAP3K9 Mitogen-activated protein kinase kinase kinase 9 14 TSS1500 S_Shore R-DMR MEGF10 Multiple EGF-like-domains 10 5 5’UTR S_Shore R-DMR NR3C1 Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) 5 Body N_Shore R-DMR OSBPL3 Oxysterol binding protein-like 3 7 5’UTR N_Shore R-DMR PLEC1 Plectin 1 8 Body;TSS1500 S_Shore R-DMR PPM1L Protein phosphatase 1 (formerly 2C)-like 3 Body S_Shore R-DMR PPP1R12C Protein phosphatase 1, regulatory (inhibitor) subunit 12C 19 TSS1500 S_Shore R-DMR PRR16 Proline rich 16 5 5’UTR S_Shore R-DMR RAI1 Retinoic acid induced 1 17 5’UTR S_Shore R-DMR RIPK4 Receptor-interacting serine-threonine kinase 4 21 Body Island R-DMR RNF165 Ring-finger protein 165 18 Body S_Shore R-DMR SCD5 Stearoyl-CoA desaturase 5 4 Body N_Shore R-DMR SEMA4C Sema domain, immunoglobulin domain (Ig), transmembrane 2 5’UTR N_Shore R-DMR domain (TM) and short cytoplasmic domain, (semaphorin) 4C SMAD3 SMAD family member 3 15 TSS1500 N_Shore R-DMR SMAD7 SMAD family member 7 18 Body N_Shore R-DMR SPEG SPEG complex locus 2 Body S_Shore R-DMR SPHK2 Sphingosine kinase 2 19 3’UTR Island R-DMR STK10 Serine/threonine kinase 10 5 TSS1500 S_Shore R-DMR TBC1D16 TBC1 domain family, member 16 17 Body S_Shore R-DMR TBX2 T-box 2 17 Body N_Shore R-DMR TMEFF2 Transmembrane protein with EGF-like and 2 Body N_Shore R-DMR two follistatin-like domains 2 TOM1L1 Target of myb1 (chicken)-like 1 17 Body S_Shore R-DMR TPM1 tropomyosin 1 (alpha) 15 TSS1500 N_Shore R-DMR ZC3HAV1L Zinc-finger CCCH-type, antiviral 1-like 7 Body N_Shore 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 measurable effects on baseline gene expression. Other notable co-receptor and we profiled neonatal and 12-month CD4 þ cells. immune genes in this category include the IL21 receptor and Under the steady-state condition, we observed widespread TGFb, the former being a class of common gamma-chain receptors changes in both methylation and gene expression in neonatal that signal through the JAK–STAT pathway to regulate prolifera- versus 12-month CD4 þ cells. The majority of these changes tion and growth, the latter having a key role in establishing are likely to reflect the transition of recent thymic emigrant regulatory T-cell populations and Th17 cell types. These networks into naive CD4 þ T-cells in the periphery,1,25 and, to a lesser of epigenetically regulated immune genes may be of future extent, may also reflect quantitative differences in other effector, interest as candidate pathways are potentially subject to memory and regulatory subtypes. Collectively, the data modification by environmental exposures. A more complete provide some interesting insights into the developmental understanding of the control of developmental processes that processes that occur during T-cell maturation and turnover in occur during early-life maturation of the T-cell compartment may the periphery. The bulk of methylation changes occurred more yield insights relevant to a range of autoimmune and allergic frequently in developmental genes, although we also report for disorders.26,27 the first time substantial epigenetic restructuring around the HLA Integration of DNA methylation and gene expression data on a locus in T cells, with similar changes observed in mitogen- genome-wide scale revealed that a direct relationship between activated and protein kinase pathways. Integration of gene DNA methylation and gene expression is often difficult to infer. expression data revealed these epigenetic changes have Indeed, only a subset of probes on the methylation array

& 2012 Macmillan Publishers Limited 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 The use of soluble anti-CD3 in the presence of Fc receptor-bearing direction based on the current dogma of DNA methylation being accessory cells to activate T cells has been shown to provide largely inhibitory to gene expression. Therefore, our findings optimal stimulation,32 although memory cells may have a different challenge this prevailing view, and this is supported by a recent requirement for this second signal than naive cells.33,34 Therefore, similar finding in CD4 þ cells, which supports the notion that not it was possible that our interpretation of the data was based on all methylation marks are transcriptionally repressive in these the co-stimulation pathway. However, a study comparing the cells.28 This highlights the importance of the spatial context in changes in methylation in CpG islands after full-scale activation of which methylation events occur. Therefore, future studies in this CD4 þ cells using plate-bound anti-CD3 with no requirement for area should seek to develop novel bioinformatics approaches to co-stimulation also reported that DNA methylation is essentially 35 unravel the complex biological relationship between DNA stable in T-cell blasts induced by strong activating stimuli. These methylation and gene expression. findings are in sharp contrast to epigenetic studies of T cells Although these approaches are still in their infancy, our data maintained under highly polarizing conditions,12 and therefore it suggest this may yield a more informative understanding of gene is likely that signals further downstream of T-cell receptor regulatory networks. An example of this is the finding that R-DMRs activation alter the methylation status at key cytokine regions as represent the bulk of differential methylation in the network of naive T cells fully differentiate into specialized phenotypes. developmental genes that appear to be overtly under the control Our data did not support a role for changes in DNA methylation of DNA methylation. R-DMRs were originally identified in induced in mediating the age-dependent changes in cytokine gene pluripotent stem cells (iPS) as key regions in which tissue expression observed following T-cell activation. However, we did differentiation is specified as cells mature away from a stem not address the potential for cell-type-specific differences in the cell-like phenotype.29 In comparisons between neonatal and diversity of the T-helper pool between neonatal and 12-month 12-month CD4 þ cells, this observation was only apparent after CD4 þ cells. Therefore the data may reflect quantitative differ- removing DNA methylation marks not clearly associated with ences in T-cell subsets with age, and future studies seek to address changes in gene expression. The latter finding therefore provides this. Furthermore, the results of this study were derived from clues to suggest that post-thymic maturation of T cells in the pooled RNA and DNA samples and therefore provide robust periphery involves a developmental network of epigenetically measures of group averages;36 however, data at the individual regulated genes that specify somatic T-cell phenotypes and level were not available. control tissue-specific gene expression. This notion is reinforced in To summarize, this study demonstrates a role for DNA the ontology analysis, in which DNA methylation changes were methylation in the control of gene expression during the period associated with pluripotency genes and transcripts expressed in of early T-cell development. The results provide baseline informa- differentiated cell types other than T cells. We observed epigenetic tion about molecular pathways that drive the normal course of changes in transcripts expressed in the brain, heart and olfactory immune development. These are potentially modifiable by early systems, all of which have well-documented interactions with life events and exposures, and therefore represent plausible immunity.19,20 Several HLA gene transcripts that are normally pathways of disease susceptibility. To extend this work, it will be silenced in adult T cells appear to be unrestrained in neonatal important to demonstrate that disruption in the developing T-cell cells, suggesting the latter may be closer to a stem cell phenotype. epigenome alters the normal pattern of T-cell responses providing This reasoning is in line with in vitro human data30 and studies in the next link between the genes, the environment and immune mice31 that suggest neonatal T-cell responses tend to be more disease. 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 MATERIALS AND METHODS altered in rapidly proliferating T cells. This is not entirely Volunteers unexpected, and suggests that replication of DNA methylation Sixty subjects were selected from existing bio-banked specimens for this marks during clonal expansion maintains a chromatin state study. These subjects were recruited in the last trimester of pregnancy permissive to the induction of hundreds of genes in progeny through the Princess Margaret Hospital for Children under approval from cells, and retains the ability for T-cell sub-lineage specification.5 the Institutional Ethics Committee. All volunteers were non-smokers and

Genes and Immunity (2012) 388 – 398 & 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 Cell culture blood was collected at birth and peripheral blood was collected from Cryopreserved stocks of PBMC or CBMC were thawed and seeded at infants at 12-month follow-up clinical visits using standardized procedures 2.5 Â 105 cells per well in 96-well round-bottom polystyrene plates with 20 37 documented previously. Mononuclear cells were separated by density replicate wells (5 Â 106 cells) per condition (treated, untreated). Cells were centrifugation, enumerated and cryopreserved viably. cultured in AIM-V media plus b-mercaptoethanol (4 Â 10 À 5 mol l À 1), alone

& 2012 Macmillan Publishers Limited 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 hybridization to Illumina Human Methylation450 Beadchips. Raw data files antibody (0.5 mgmlÀ 1) (Miltenyi, North Ryde, NSW, Australia) with were exported from Genome Studio (Illumina, San Diego, CA, USA) into the recombinant human IL-2 (10 Units) (Sigma Aldrich, Castle Hill, NSW, R statistical environment (http://cran.r-project.org/index.html). Data quality Australia). The activation protocol here depends on co-stimulation was assessed using the methylumi package39–41 to assess signal-to-noise provided by accessory cells that constitutively express CD80 and CD8638 ratios, and identify outlying samples and batch effects. All samples passed and has been shown to provide a highly effective proliferative signal.32 The QC. Probes on the X and Y chromosomes were removed to eliminate optimal stimulation protocol was determined in forerunner experiments gender bias. The lumi package42 was used to calculate the log2 ratio for provided in Supplementary Figure 2. Following 24 h in culture, replicate methylated probe intensity to unmethylated probe intensity, the M value. wells were combined and CD4 þ T-cells were isolated by positive selection These probes underwent colour adjustment, background correction and using magnetic Dynabeads (Life Technologies, Mulgrave, VIC, Australia) to quantile normalization. Any poor-performing probes were filtered out of 85–95% purity (as determined by flow cytometry). Cell supernatants were the final data set, defined as those with a detection P-value call 40.01 for frozen for cytokine analysis 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 Nucleic acid purification and QC unmethylated probes given by B ¼ M/(U þ M þ 100) and were used as a measure of effect size. 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 Affymetrix human gene 1.0ST data acquisition and processing purity were determined by spectrophotometry using the Nanodrop Sixty individuals were used in the Affymetrix array experiment. Pooled RNA (Thermo Scientific, Scoresby, VIC, Australia). All samples had a light samples were converted to single-stranded fragmented DNA using the WT absorbance 260/280 ratio of X1.8. Integrity of RNA was measured on the sense target labeling protocol according to the manufacturer’s instructions Agilent 2100 Bioanalyzer (Agilent Technologies, Mulgrave, VIC, Australia) (Affymetrix, Santa Clara, CA, USA). Converted DNA products were sent to using the RIN method. All RINs were between 7.2 and 10. the Australian Neuromuscular Research Institute for hybridization washing and scanning. The quality of the microarray data was assessed using QC Illumina human methylation 450k data acquisition and processing metrics in the Expression Console software (Affymetrix), with the average positive versus negative AUC being 0.8695 (n ¼ 80, ±0.017) for all DNA (1 mg) was bisulfite converted using the Methyl Easy bisulphite microarray experiments. The microarray data were preprocessed with the modification kit (Human Genetic Signatures, Sydney, NSW, Australia), PLIER algorithm (gcbg background subtraction, quantile normalization, according to the manufacturer’s instructions. Conversion efficiency was iterPLIER summarization).41–43 Data were variance stabilized by adding 16 assessed by bisulfite-specific PCR. Forty-eight individuals were chipped in to all data points, followed by log2 transformation in the R environment the methylation array study and the remaining twelve were reserved for (http://cran.r-project.org/). 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 Statistical analysis and bioinformatics to provide an accurate estimate of group methylation values.36 Pooled The data underwent unsupervised hierarchical clustering analysis with the DNA samples were sent to Service XS (Leiden, The Netherlands) for Euclidean distance and complete linkage algorithm, and a heatmap with

Genes and Immunity (2012) 388 – 398 & 2012 Macmillan Publishers Limited Epigenetic programming of CD4 þ T-cell phenotypes during early life D Martino et al 397 39 associated dendrogram was created using gplots. For differential 5 Zhou L, Chong MMW, Littman DR. Plasticity of CD4( þ ) T cell lineage differ- analysis, a linear model was fitted for all comparisons using the limma entiation. Immunity 2009; 30: 646–655. 42 package. The P values derived from the moderated t-statistics were 6 Wilson CB, Rowell E, Sekimata M. Epigenetic control of T-helper-cell differentia- adjusted to control the false discovery rate using the Benjamini–Hotchberg tion. Nat Rev Immunol 2009; 9: 91–105. 43 method. For combined gene expression and DNA methylation analysis, 7 Murphy KM, Stockinger B. Effector T cell plasticity: flexibility in the face of change in methylation was defined by M values from contrasts between changing circumstances. 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Supplementary Information accompanies the paper on Genes and Immunity website (http://www.nature.com/gene)

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