Download Genome-scale profiling reveals a subset of genes regulated by DNA

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.
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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
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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
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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.
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& 2012 Macmillan Publishers Limited