Genetic and Epigenetic Factors in Etiology of Diabetes
Mellitus Type 1
abstract
AUTHORS: Karmen Stankov, MD, PhD, Damir Benc, MD, and
Dragan Draskovic, MD, PhD
Diabetes mellitus type 1 (T1D) is a complex disease resulting from the
interplay of genetic, epigenetic, and environmental factors. Recent
progress in understanding the genetic basis of T1D has resulted in
an increased recognition of childhood diabetes heterogeneity. After
the initial success of family-based linkage analyses, which uncovered
the strong linkage and association between HLA gene variants and
T1D, genome-wide association studies performed with high-density
single-nucleotide polymorphism genotyping platforms provided evidence for a number of novel loci, although fine mapping and characterization of these new regions remains to be performed. T1D is one
of the most heritable common diseases, and among autoimmune
diseases it has the largest range of concordance rates in monozygotic
twins. This fact, coupled with evidence of various epigenetic modifications of gene expression, provides convincing proof of the complex
interplay between genetic and environmental factors. In T1D, epigenetic phenomena, such as DNA methylation, histone modifications,
and microRNA dysregulation, have been associated with altered gene
expression. Increasing epidemiologic and experimental evidence supports the role of genetic and epigenetic alterations in the etiopathology of diabetes. We discuss recent results related to the role of genetic
and epigenetic factors involved in development of T1D. Pediatrics
2013;132:1112–1122
Clinical Centre of Vojvodina, Medical Faculty, University of Novi
Sad, Hajduk Veljkova 1, 21000 Novi Sad, Serbia
KEY WORDS
diabetes mellitus, Type 1, etiology, genetics, epigenetics
ABBREVIATIONS
AID—autoimmune disease
CpG—cytosine guanine dinucleotide
CTLA4—cytotoxic lymphocyte-associated protein 4
EOD—early-onset diabetes
ER—endoplasmic reticulum
GAD65—glutamic acid decarboxylase, 65-kDa isoform
GWAS—genome-wide association study
IFIH1—interferon induced with helicase C domain 1
IL—interleukin
IL2RA—interleukin-2 receptor a
LD—linkage disequilibrium
IFN-g—interferon g
miRNA—microRNA
MS—multiple sclerosis
NOD—nonobese diabetic
OR—odds ratio
PTM—posttranslational modification
RA—rheumatoid arthritis
SNP—single-nucleotide polymorphism
T1D—diabetes mellitus type 1
T1D-MVPs—T1D-specific methylation variable positions
TcR—T-cell receptor
TNF-a—tumor necrosis factor a
Treg—regulatory T cells
UBASH3A—ubiquitin-associated and SH3 domain-containing
protein A
UTR—untranslated region
Dr Stankov conceptualized the manuscript, drafted the initial
manuscript, and critically reviewed and revised the manuscript;
Dr Benc conceptualized the manuscript, drafted the initial
manuscript, and critically reviewed the manuscript; Dr
Draskovic conceptualized the manuscript and critically reviewed
the manuscript; and all authors approved the final manuscript
as submitted.
www.pediatrics.org/cgi/doi/10.1542/peds.2013-1652
doi:10.1542/peds.2013-1652
Accepted for publication Aug 15, 2013
Address correspondence to Karmen Stankov, MD, PhD, Clinical
Centre of Vojvodina, Medical Faculty, University of Novi Sad,
Hajduk Veljkova 1, 21000 Novi Sad, Serbia. E-mail:
[email protected]
(Continued on last page)
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STANKOV et al
STATE-OF-THE-ART REVIEW ARTICLE
Diabetes mellitus type 1 (T1D) is a complex disease resulting from the interplay
of genetic, epigenetic, and environmental factors.1 Worldwide, the T1D epidemic represents an increasing global
public health burden, and the incidence
of T1D among children has been rising.2
Over the last few decades, there has
been an overall increase in the incidence
of T1D of ∼3% to 5% per year, and it is
estimated that there are ∼65 000 new
cases per year in children ,15 years
old.3–6 This significant worldwide increase in the incidence of T1D suggests the importance of interactions
between genetic predisposition and
environmental factors in the multifactorial etiology of T1D.7–10 Environmental factors implicated so far include
infection with enteroviruses or endogenous retroviruses and consumption of
milk proteins, as well as the influence of
environmental pollutants, variations in
gut flora, and vitamin D exposure.2,10,11
Only 10% to 15% of newly diagnosed
patients have a positive family history of
T1D. However, increased susceptibility
to T1D can be inherited, because the
average prevalence risk in children of
a person with T1D is ∼6%, and ∼7% in
siblings, whereas 12% to 67.7% of
identical twins of patients with T1D are
at risk, compared with 0.4% of the
general population.12,13 In fact, T1D is
one of the most heritable common
diseases, with the sibling relative risk
estimated at 15 and the largest range
in concordance rates in monozygotic
twins among autoimmune diseases
(AIDs).8,13 This, coupled with evidence
of various epigenetic modifications of
gene expression, provides important
and convincing proof of the complex
interplay between genetic and environmental factors (Fig 1). In T1D,
epigenetic phenomena, such as DNA
methylation, histone modifications,
and microRNA (miRNA) dysregulation,
have been associated with altered gene
expression.14
PEDIATRICS Volume 132, Number 6, December 2013
FIGURE 1
Genetic, exogenous, and epigenetic predisposing factors and biomarkers in T1D.
Extensive familial and population genetic studies conducted over the past 30
years have provided an explanation for
nearly 80% of the heritability of T1D,
suggesting the primary role of several
risk alleles in diabetes susceptibility.15
The main susceptibility genes currently
accepted for T1D are the HLA class II
alleles, which account for up to 50% of
genetic T1D risk. Among HLA class I
genes, one of the strongest associations with T1D is reported for B*39 allele, which significantly increases the
risk of T1D.16 Multiple non-HLA loci
contribute to disease risk with smaller
effects. These include the insulin gene,
CTLA4, PTPN22, interleukin 2 receptor a
(IL2RA), the interferon induced with
helicase C domain 1 (IFIH1), the CAPSLIL7R block, the lectin CLEC16A, the Th1
transcription factor STAT4, the tyrosine
phosphatase PTPN2, and other recently
discovered loci.17,18
Common variants account for only part
of the risk associated with complex
disease phenotypes.8 The remaining
genetic susceptibility (ie, missing heritability) can be accounted for by the
influence of additional variants that
remain underrepresented in current
genotyping platforms applied in underpowered studies, which are insufficient for detecting gene–gene
(epistatic) and gene–environment
interactions.19 These biological interactions are critical for gene regulation,
signal transduction, biochemical networks, and numerous other physiologic and developmental pathways. The
concept of epistasis implies that analyses of haplotype combinations and
their interactions with environmental
factors are needed to establish the risk
profile for T1D.19 Therefore, the application of statistical and computational
methods that detect patterns of epistasis across the genome, implemented
in a system biology framework, may
help solve the missing heritability
problem.20
An important example of a 2-locus epistatic interaction is the susceptibility
conferred by HLA and PTPN22 loci: The
effect of PTPN22 coding for an R620W
variant of LYP protein, measured by
relative risk, is higher in low-risk HLA
1113
genotypes. In vivo experiments in mice
suggest that this common PTPN22
variant promotes degradation of LYP
protein, indicating a loss-of-function
allele. However, when main effects
are included and the results converted
to absolute risks, it can be seen that the
additional risk due to PTPN22 is largest
in the high-risk HLA group.21 Compared
with PTPN22, there is no evidence for
a statistical interaction with HLA class II
genotypes for INS locus variants, further supporting the hypothesis that
rs2476601/R620W is the most likely
causal variant for T1D.22
The latest advances in personalized
medical genomics that are translated to
the clinic are aimed at comprehensive
sequencing of the disease genome,
exome, epigenome, and transcriptomes.
Next-generation sequencing technology
has contributed to the precise mapping and quantification of chromatin
features, DNA modifications, and several specific steps in the cascade of
information from transcription to
translation.23,24
Increasing epidemiologic and experimental evidence supports the role of
genetic and epigenetic alterations in
the etiopathogenesis of diabetes. This
review aims to briefly discuss recent
results related to the role of genetic and
epigenetic factors in the development of
T1D.
MULTIFACTORIAL ETIOLOGY OF T1D
T1D results from deficient insulin production, as a consequence of autoimmune
T-cell–mediated destruction of pancreatic
b cells.25 The initiating events in T1D
pathogenesis that trigger the provision of b-cell antigens (the first described target antigen is insulin) to
dendritic cells and presentation to
T cells for their activation are yet to be
elucidated.26 As depicted in the Fig 2,
proinflammatory cytokines, produced
by infiltrating leukocytes and islet
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STANKOV et al
cells, including interleukin-1b (IL-1b),
tumor necrosis factor-a (TNF-a), and
interferon g (IFN-g), play a central role
in b-cell failure and the development of
diabetes.27 The destruction is caused by
infiltration of the islets of Langerhans
by dendritic cells, macrophages, and
T lymphocytes, is specific for insulinproducing b cells, and does not affect
glucagon-producing (a) or somatostatinproducing (d) cells. Prolonged exposure
to cytokines leads to a decreased capacity
of b cells to produce and release insulin in response to secretagogues
and, in the long term, destruction of
the cells by apoptosis or necrosis.25,28
T1D typically affects young children,
with onset as early as 1 year of age; most
cases are diagnosed before the patient
is 18 years old. The autoimmune process starts even earlier, as evidenced by
the presence of autoantibodies in serum against T1D-specific antigens. The 4
major autoantigens involved are insulin
itself, glutamic acid decarboxylase,
65-kDa isoform (GAD65), insulin autoantigen 2 (an intracellular phosphatase), and zinc transporter 8.29,30 The
autoantibodies, such as insulin autoantibody, glutamic acid decarboxylase
antibody, insulinoma-associated protein 2 autoantibody, and zinc transporter 8 antibody, are markers of
T-lymphocyte–mediated b-cell destruction.29 Both CD4+ (helper) and
CD8+ (cytotoxic) T-lymphocyte subsets
are important in T1D. The former recognize extracellular antigens and promote inflammation through cytokine
release, whereas the latter respond to
endogenously synthesized antigens
and directly kill target cells. By the time
symptoms leading to clinical diagnosis
appear, most of the b-cell insulin secretory capacity has been lost. Therefore, prediction and prevention of T1D
are of crucial importance, and it is
hoped that better knowledge of the
genetic underpinnings of T1D will
greatly facilitate both.31
In addition to T-cell–mediated effectors
of b-cell death, other apoptotic stimuli
also contribute to b-cell apoptosis.
Pancreatic b cells surrounded by inflammatory infiltrate are exposed to
multiple cell stress mediators, hyperglycemia, and reactive oxygen species,
and in such a cellular environment
endoplasmic reticulum (ER) stress may
also develop.25,32 The link between ER
stress and T1D mellitus has been suggested by several investigators.33,34 A
balance between ER protein load and
folding capacity is needed for the
proper folding of insulin within the ER.
Large biosynthetic load, defects in ER
folding machinery, and disturbances in
the regulation of Ca2+ can all disrupt ER
homeostasis, leading to accumulation
of misfolded proteins within the ER lumen and ER stress.34,35 ER stress activates an elaborate adaptive process
called the unfolded protein response.36
Unfolded protein response protects
against developing T1D by ensuring
that efficient function is maintained in
b cells, despite large fluctuations in ER
protein flux.37
OVERVIEW OF GENETIC FACTORS
INVOLVED IN T1D
ETIOPATHOGENESIS
Recent progress in understanding the
genetic basis of T1D has led to an increased recognition of childhood diabetes heterogeneity.8 It has become
apparent that not all diabetes presenting in childhood is type 1.38,39 Non–
type 1 diabetes mellitus can have
marked differences in treatment and
complications from T1D. Although the
monogenic forms of diabetes remain
mainly unrecognized, their molecular
diagnosis has provided insight into the
genes controlling human b-cell function, and their study has revealed the
basic mechanisms of diabetes pathogenesis.40,41 These rare forms of
monogenic diabetes have significant
STATE-OF-THE-ART REVIEW ARTICLE
FIGURE 2
Pathogenic mechanisms and environmental factors that trigger T1D onset in genetically susceptible people, resulting in b-cell apoptosis and diabetes. ROS,
reactive oxygen species.
clinical and scientific importance, because their recognition and diagnosis
enable the prevention and treatment of
complications and necessitate appropriate genetic counseling for other
family members. Therefore, it is important to correctly diagnose monogenic diabetes.41 The most common
specific inherited monogenic forms of
diabetes in children are maturity-onset
diabetes of the young, which accounts
for 10% of newly presenting non–type 1
diabetes, and syndromic multisystem
monogenic diabetes, such as Wolfram
syndrome, Alström syndrome, and
Wolcott–Rallison syndrome.42–46
PEDIATRICS Volume 132, Number 6, December 2013
Susceptibility to T1D is determined by
complex interactions between several
genetic loci and environmental factors.
Common allelic variants at the HLA
class II loci account for the major T1D
genetic risk in children and young
adults (primarily HLA-DRB1, HLA-DQA1,
and HLA-DQB1 genes).47 Alleles at the
HLA locus on chromosome 6p21, which
encode the highly polymorphic antigenpresenting proteins, explain up to
50% of the familial clustering of T1D
through a large variety of protective
and predisposing haplotypes, and the
remainder is contributed to by multiple loci, of which only 4 were known
until recently.31 Two HLA class II haplotypes, DR4–DQ8 and DR3–DQ2, are
present in ∼90% of children T1D.48
Within the major histocompatibility
complex class II haplotypes, the
DRB1*1501–DQA1*0102–DQB1*0602 allele is strongly associated with T1D
protection.49,50 The protection associated with DQA1*0102–DQB1*0602 is
dominant over the susceptibility of all
other DQ haplotypes, and this protection in children is thought to occur
even in the presence of islet cell
antibodies. 51 Early-onset diabetes
(EOD) (diagnosed before 5 years of
age) is characterized by significant
1115
differences in the frequencies of HLA
alleles and haplotypes from other T1D
age groups. None of the 40 patients
with EOD analyzed in a study by Hathout
et al52 (average age 2.6 years) had either of the protective (DRB1*1501 or
DQB1*0602) alleles. In this EOD cohort,
there was a negative correlation between GAD65 and the predisposing
haplotype DR3/DQ2. Other established
loci associated with T1D risk have
smaller effects than HLA, conferring
a relative risk ranging from odds ratio
(OR) = 2.38 (11p15.5, INS) to OR = 1.05
(17q21.2; SMARCE1).19,53–61 A comprehensive list of genetic loci variants
predisposing to T1D is provided in
Supplemental Table 1.
Since completion of the HapMap project
and the development of massively
parallel array-based genotyping technologies, the results of genome-wide
association studies (GWASs) have uncovered dozens of additional solidly
replicated loci.53–58 Several consortia
analyzed a remarkable number of individuals within cohorts of cases and
controls (with trios in 1 study), using
.200 000 markers for GWASs. The
results identified only 5 loci associated
with T1D, and in second-stage typing in
additional cohorts, 18 loci reached
genome-wide significance (P , 1027).8,56
These GWAS results are enriched by
recent examination of associations
from the largest meta-analysis to date
between T1D and 2.54 million singlenucleotide polymorphisms (SNPs), involving a combined cohort of 9934 cases
and 16 956 controls originating from 6
consortia and resulting in replication
of 3 loci. Targeted follow-up of 53 SNPs,
with fixed-effects P , .00001, in a replication set of 1120 affected case–
parent trios from the T1DGC consortium,
uncovered 3 new loci associated
with T1D that reached genome-wide
significance.59
GWASs have identified 140 regions
with polymorphic variants that have
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STANKOV et al
statistically significant association with
AID susceptibility, including T1D.62,63 Numerous examples of susceptibility
variants shared by multiple AIDs are
summarized in Fig 3. It is estimated
that at least 44% of SNPs associated
with 1 AID are associated with another,
providing an example of biological
pleiotropy (a genetic variant or gene
that has a direct biological influence
on .1 phenotypic trait).64 The common loss-of-function allele of PTPN22
decreases the risk of Crohn disease
but increases the risk of rheumatoid
arthritis (RA) and T1D.65 Another association analysis was performed
with multiple SNPs in an allele-specific
fashion, to compare genetic variation
profiles of multiple sclerosis (MS), ankylosing spondylitis, autoimmune thyroid disease, RA, Crohn disease, and
T1D with 5 non-AIDs. This approach
resulted in identification of specific
SNPs and genes with opposite risk
profiles in 2 classes of diseases: RA and
ankylosing spondylitis form one such
class, and MS and autoimmune thyroid
disease another.63 The HLA-related
genes associated with T1D that reside
in HLA class II DR and DQ loci are shared
by other AIDs, such as RA, MS, systemic
lupus erythematosus, and celiac disease. Analysis of celiac disease and T1D
risk loci in a large sample of patients
with RA and controls yielded significant
evidence for association of the TAGAP
locus, which is associated with risk of
celiac disease and protection against
T1D.66,67 According to recent crossphenotype meta-analysis, which statistically supports the hypothesis that
each independent SNP has multiple
phenotypic associations, 2 gene clusters were associated with T1D and
other AIDs.68 This analysis was extended to protein–protein interactions,
revealing that variant-encoded proteins
within groups were more likely to
interact with each other than proteins
encoded by variants in other groups,
providing more evidence for biological
pleiotropy in AIDs.68 The immunochip is
a cost-effective approach in AIDs that
share several common loci, including
T1D. To genotype common and rare
variants, to map remaining heritability,
and to fine-map established loci, an
array of 200 000 known SNPs has been
developed. However, it will probably be
necessary to supplement it with novel
SNPs identified from recent resequencing
data.8
To understand T1D pathogenesis using
GWAS data, it is important to apply an
integrative network- or pathway-based
approach. Data from pilot 1000 Genome
Project studies and other sequencing
data showed that next-generation deep
sequencing in candidate regions has
been successful in discovering the 4
protective rare variants in the IFIH1
gene.58,60 This combined approach of
next-generation sequencing technologies and GWASs strengthened the hypothesis of disease pathogenesis involving
virus–genetic interplay and raised
type 1 interferon levels as a cofactor
in b-cell destruction.53
Another interesting insight into T1D
pathogenesis was provided by the
mapping of T1D-associated linkage
disequilibrium (LD) block with the IL-2–
IL-21 ligand gene and variant in the
a subunit of the IL-2 receptor gene
IL2RA. The protein encoded by IL2RA is
upregulated in effector T cells, and the
protein expressed from the protective
allele is higher in CD4+ memory cells,
as an example of loss-of-function of
activation of the adaptive immune response that is attenuated in T1D. Decreased expression of IL-2 cytokine
from the risk allele in nonobese diabetic (NOD) mice results in suppression of T regulatory (Treg) cells, indicating
complex and cell-specific regulation of
self-tolerance.19
Initiation of an immune response by
recognition of a specific antigen by
transmembrane T-cell receptors (TcRs)
may result in autoimmune attack,
STATE-OF-THE-ART REVIEW ARTICLE
FIGURE 3
Overlapping susceptibility loci associated with AIDs identified by GWAS. IBD, inflammatory bowel disease; JIA, juvenile idiopathic arthritis.
further potentiated by gene variants
that impair antigen presentation or
T-cell signaling during thymic selftolerance. Such variants, which downregulate the TcR activation response,
are mapped in PTPN22, UBASH3A, and
CTLA4 loci. Like the PTPN22 R620W variant, which may interfere with proper
tolerance induction in the thymus
through lower secretion of IL-2 and
IL-10 by T cells on TcR activation, a
PEDIATRICS Volume 132, Number 6, December 2013
T1D-associated A17T CTLA4 variant similarly affects T-cell activation.69 UBASH3A
is specifically expressed in lymphocytes,
with a risk allele associated with gain
of function through overexpression of
protein in lymphoblastoid lines of
patients with T1D.2,56 These insights into
alterations of genetic pathways associated with risk variants in T1D represent
targets for translational medicine
efforts, exemplified by prevention and
reversal of diabetes in mice by IL-2
supplementation and by discovery of
small-molecule inhibitors of the PTPN22
protein product Lyp.70,71
Functional insights into the role of T1D
susceptibility loci revealed that candidate genes are involved in functions
related to T-cell–mediated adaptive
immune response and tolerance
mechanisms and also to innate immunity involved in recognition of b-cell
1117
antigens.8 Post-GWAS fine mapping and
functional characterization of T1D risk
loci remain to be performed.25 One of
the most important challenges in postGWAS functional characterization is to
distinguish loss-of-function from gainof-function mutations and mechanisms
that cause small changes in gene dosage with disease-specific phenotypic
consequences. Many SNPs identified in
GWASs are only tag SNPs that are in LD
with the causative variant, in addition to
being located in non–protein-coding
regions of the genome, such as gene
regulatory elements that do not directly
affect protein structure. Therefore, it is
particularly important to perform systematic mapping of transcription
factor– and miRNA binding sites, chromatin modifications, and expression
quantitative trait loci, because these
variations may be phenotypically relevant.72,73 The existing linkage and GWAS
results for loci predisposing to T1D
may be improved by identification of
the remaining variants associated with
higher risk. It is estimated that SNPs
validated to date explain a fraction of
10% to 40% of the total heritability in
the majority of complex-trait diseases.
Many more additional variants may be
identified by increasing the sample size
and power of analysis, combining
GWAS information with gene expression profiles for construction of gene
networks derived from integrated
genomics and proteomics data, and
taking into account gene–gene interactions (epistasis) as well as epigenetic and environmental factors.58,59
EPIGENETIC FACTORS IN T1D
ETIOLOGY
Epigenetics may be defined as the study of
the stable inheritance of gene expression
that occurs without modification of the
DNA sequence, by mechanisms that include DNA methylation, a variety of noncoding RNAs, and enzymatically catalyzed
posttranslational histone modifications.74
1118
STANKOV et al
Recent progress in understanding genetic susceptibility in T1D is in a complex
dynamic with other hypotheses proposed
to explain the increasing incidence of T1D,
including infections; socioeconomic, lifestyle, and dietary factors; pollutants;
prenatal environment; gut microbiome
variability; and epigenetic factors such as
DNA methylation and histone modification.74–76 b-cell development, maintenance, metabolism, and regeneration can
all be influenced by epigenetic mechanisms. Also, immune responses, including the activation of T cells and
induction of Treg cells, rely on appropriate
epigenetic regulation. Furthermore, insulin and glucose metabolism influence
the epigenome of tissues such as the
liver, and potentially the pancreas, which
could contribute to T1D associated pathologies. The hope is that advances in
T1D epigenomics will provide new understanding of the pathogenesis, potential treatment, or even prevention of
T1D.77
EPIGENETIC CONTROL OF GENE
TRANSCRIPTION IN DIABETES
DNA methylation is a tissue-specific
epigenetic mechanism of cellular response to stress essential for regulating the expression of genes. DNA
methyltransferases catalyze the covalent addition of a methyl group to the
C5 position in cytosine guanine dinucleotides (CpG), usually found in the
promoter regions of genes, thus inducing transcriptional gene silencing.
In AIDs, including T1D, defects in CpG
methylation, resulting from decreased
DNA methyltransferase activity, are
associated with dysregulated gene expression, chromosomal instability, and
increased susceptibility to disease development. Several nutritional factors,
including diets deficient in methionine,
folic acid, and choline, are associated
with global hypomethylation.18 DNA
tissue-specific hypomethylation in
hepatocytes is observed in animal
models as a result of diabetes-induced
perturbations of methyl group and
homocysteine metabolism, leading to
functional methyl deficiency.78 In addition, a recent study of DNA methylation
patterns of 7 CpGs proximal to the
transcription start site in the INS gene
promoter revealed that patients with
T1D have a reduced methylation of CpG19, CpG-135, and CpG-234 (P = 2 3 10216)
and increased methylation of CpG-180
compared with controls.79 A novel
method, the highly sensitive methylationspecific quantitative polymerase chain
reaction assay, was successfully applied
in the detection of circulating b-cell DNA
in peripheral blood from diabetic mice
and holds great promise for future
monitoring of b-cell death and the prediction of diabetes onset.80 Another
study, an epigenome-wide association
study, involving 27 458 different CpG sites
within 14 475 promoters in the monocytes of 15 T1D-discordant monozygotic
twin pairs, identified 132 different CpG
sites where differences significantly
correlated with diabetic state.81 This
study included identification of T1Dspecific methylation variable positions
in patients before the appearance of T1D
autoantibodies, thus demonstrating that
T1D-specific methylation variable positions antedate clinical disease, including
epigenetic changes in HLA class II gene,
HLA-DQB1, or in GAD2, which encodes
GAD65, a major T1D autoantigen involved
in T1D etiology.69
Several classes of small RNA can regulate genes by targeting transcripts in
the cytoplasm, such as miRNAs, small
interfering RNAs, repeat-associated
RNAs, and germline-specific RNAs.82
The human genome encodes numerous
noncoding molecules, including .1600
miRNA precursors, generating up to
2237 mature miRNAs. Binding of miRNAs
to 39 untranslated regions (UTRs) of
target mRNA is consistent with a role
for miRNAs as key negative regulators
of gene expression, involved in the
STATE-OF-THE-ART REVIEW ARTICLE
control of cellular processes and disease pathogenesis.83 The ability of
noncoding RNA molecules to associate
with 39 UTRs of target mRNAs and to
repress their translation has been
suggested to be necessary for proper
b-cell development and function. During
the initial stages of T1D, b cells are
submitted to diverse types of stress
stimuli, causing dysfunction and death
that may be associated with modifications in miRNA localization, potentially resulting in b-cell dysfunction.83
Mature miRNAs can be released by
cells into the extracellular compartment. Based on an experimental model,
increased levels of circulating miR-375
can be used as a marker of b-cell death
and a potential predictor of diabetes,
before the onset of hyperglycemia.84 In
serum samples from children with
new-onset T1D and age-matched
healthy controls, residual b-cell function and glycemic control after 3
months of clinical disease were associated with miR-25 overexpression,
present soon after diagnosis, providing
evidence for the role of miRNAs as
clinically applicable biomarkers in
T1D.85 The expression levels of miR-326
are significantly higher in peripheral
blood lymphocytes from T1D antibody–
positive patients than in patients with
T1D without autoantibodies.86 Analysis
of miRNA expression in Treg cells in
patients with T1D revealed that miR-342
and miR-19 were downregulated,
whereas miR-510 showed significantly
higher expression.87 In light of these
results, measurement of specific miRNA
levels may be useful for identifying
people at risk for developing T1D, possibly preventing the development of the
disease.28 Lentiviral transgenesis has
been used to generate NOD mice in
which the Slc11a1 T1D candidate gene
was silenced by RNA interference. Silencing reduced the frequency of T1D,
thus demonstrating a role for Slc11a1 in
modifying susceptibility to T1D.88
PROFILES OF EPIGENETIC HISTONE
POSTTRANSLATIONAL
MODIFICATIONS IN DIABETES
The chromatin status of gene promoters
determines gene expression modulation
as a response to various stimuli. Within
the identified network in lymphocytes,
posttranslational modifications (PTMs)
at the N-terminal amino acids of histones, including acetylation, methylation, phosphorylation, or ubiquitination,
might affect the etiology of T1D and its
complications.89 Recent studies have
shown that diabetic stimuli, including
hyperglycemia, can alter the levels of
key histone PTMs, including H3K9Ac,
H3K9me2, H3K9me3, and H3K9me1, at
the promoters of several genes related
to diabetes pathogenesis. CLTA4, a
known T1D susceptibility gene, is also
overexpressed in the lymphocytes of
patients with T1D compared with controls, in addition to displaying differential H3K9me2 methylation.89 Recent
data from key histone PTM profiling,
obtained by chromatin immunoprecipitation linked to microarrays, showed
marked variations in H3K9Ac levels at
upstream regions of HLA-DRB1 and
HLA-DQB1 within the T1D locus in
monocytes of patients with T1D relative to controls.90
In summary, T1D is a polygenic AID with
a complex origin, caused by the interplay of genetic, epigenetic, and
environmental factors. Our current
knowledge of T1D etiopathogenesis has
been significantly improved by GWAS
and recent epigenome-wide association study analyses aimed at identifying
epigenetic mechanisms with the potential to reveal specific networks that
may be involved in gene–environment
interactions. Advances in epigenetic
research may enable the identification
and potential use of circulating miRNAs
as epigenetic blood biomarkers for the
detection of b-cell death and diabetes
prediction.
ACKNOWLEDGMENTS
Supported by the Ministry of Education
and Science, Republic of Serbia, project
no. 41012. The authors are grateful to Dr
Edward Petri for editing assistance.
REFERENCES
1. Hakonarson H, Grant S. Genome-wide association studies (GWAS): impact on elucidating the aetiology of diabetes. Diabetes
Metab Res Rev. 2011;27(7):685–696
2. Forlenza GP, Rewers M. The epidemic of
type 1 diabetes: what is it telling us? Curr
Opin Endocrinol Diabetes Obes. 2011;18(4):
248–251
3. Borchers AT, Uibo R, Gershwin ME. The
geoepidemiology of type 1 diabetes. Autoimmun Rev. 2010;9(5):A355–A365
PEDIATRICS Volume 132, Number 6, December 2013
4. Jarosz-Chobot P, Polanska J, Szadkowska
A, et al. Rapid increase in the incidence
of type 1 diabetes in Polish children
from 1989 to 2004, and predictions for
2010 to 2025. Diabetologia. 2011;54(3):
508–515
5. Bruno G, Maule M, Merletti F, et al; RIDI
Study Group. Age-period-cohort analysis of
1990–2003 incidence time trends of childhood diabetes in Italy: the RIDI study. Diabetes. 2010;59(9):2281–2287
6. Ehehalt S, Dietz K, Willasch AM, Neu A; Baden–
Württemberg Diabetes Incidence Registry
(DIARY) Group. Epidemiological perspectives
on type 1 diabetes in childhood and adolescence in Germany: 20 years of the Baden–
Württemberg Diabetes Incidence Registry
(DIARY). Diabetes Care. 2010;33(2):338–340
7. Diaz-Horta O, Baj A, Maccari G, Salvatoni A,
Toniolo A. Enteroviruses and causality of
type 1 diabetes: how close are we? Pediatr
Diabetes. 2012;13(1):92–99
1119
8. Polychronakos C, Li Q. Understanding type
1 diabetes through genetics: advances and
prospects. Nat Rev Genet. 2011;12(11):781–
792
9. Patterson CC, Dahlquist GG, Gyürüs E, Green
A, Soltész G; EURODIAB Study Group. Incidence trends for childhood type 1 diabetes in Europe during 1989–2003 and
predicted new cases 2005–20: a multicentre prospective registration study. Lancet. 2009;373(9680):2027–2033
10. Thomas IH, Pietropaolo M. Type 1 diabetes:
a genetic Pandora’s box? Pediatr Diabetes.
2010;11(8):511–512
11. Sugihara S. Genetic susceptibility of childhood
type 1 diabetes mellitus in Japan. Pediatr
Endocrinol Rev. 2012;10(Suppl 1):62–71
12. Cernea S, Dobreanu M, Raz I. Prevention of
type 1 diabetes: today and tomorrow. Diabetes Metab Res Rev. 2010;26(8):602–605
13. Huber A, Menconi F, Corathers S, Jacobson
EM, Tomer Y. Joint genetic susceptibility to
type 1 diabetes and autoimmune thyroiditis: from epidemiology to mechanisms.
Endocr Rev. 2008;29(6):697–725
14. Dang MN, Buzzetti R, Pozzilli P. Epigenetics
in autoimmune diseases with focus on type
1 diabetes. Diabetes Metab Res Rev. 2013;
29(1):8–18
15. Groop L, Pociot F. Genetics of diabetes:
Are we missing the genes or the disease?
Mol Cell Endocrinol. 2013. doi:10.1016/
j.mce.2013.04.002
16. Nejentsev S, Howson JM, Walker NM,
et al; Wellcome Trust Case Control Consortium. Localization of type 1 diabetes
susceptibility to the MHC class I genes
HLA-B and HLA-A. Nature. 2007;450(7171):
887–892
17. Nepom GT, Buckner JH. A functional
framework for interpretation of genetic
associations in T1D. Curr Opin Immunol.
2012;24(5):516–521
18. Morahan G. Insights into type 1 diabetes
provided by genetic analyses. Curr Opin
Endocrinol Diabetes Obes. 2012;19(4):263–
270
19. Pociot F, Akolkar B, Concannon P, et al.
Genetics of type 1 diabetes: what’s next?
Diabetes. 2010;59(7):1561–1571
20. Moore JH, Asselbergs FW, Williams SM.
Bioinformatics challenges for genome-wide
association studies. Bioinformatics. 2010;
26(4):445–455
21. Clayton DG. Prediction and interaction in
complex disease genetics: experience in
type 1 diabetes. PLoS Genet. 2009;5(7):
e1000540
22. Smyth DJ, Cooper JD, Howson JM, et al.
PTPN22 Trp620 explains the association of
chromosome 1p13 with type 1 diabetes and
1120
STANKOV et al
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
shows a statistical interaction with HLA
class II genotypes. Diabetes. 2008;57(6):
1730–1737
Eichler EE, Flint J, Gibson G, et al. Missing
heritability and strategies for finding the
underlying causes of complex disease. Nat
Rev Genet. 2010;11(6):446–450
Soon WW, Hariharan M, Snyder MP. Highthroughput sequencing for biology and
medicine. Mol Syst Biol. 2013;9:640
Thomas HE, Kay TW. Intracellular pathways
of pancreatic b-cell apoptosis in type 1
diabetes. Diabetes Metab Res Rev. 2011;27
(8):790–796
Culina S, Brezar V, Mallone R. Insulin and
type 1 diabetes: immune connections. Eur J
Endocrinol. 2013;168(2):R19–R31
Roggli E, Britan A, Gattesco S, et al. Involvement of microRNAs in the cytotoxic
effects exerted by proinflammatory cytokines on pancreatic beta-cells. Diabetes.
2010;59(4):978–986
Guay C, Roggli E, Nesca V, Jacovetti C,
Regazzi R. Diabetes mellitus, a microRNArelated disease? Transl Res. 2011;157(4):
253–264
Nokoff N, Rewers M. Pathogenesis of type 1
diabetes: lessons from natural history
studies of high-risk individuals. Ann N Y
Acad Sci. 2013;1281:1–15
Wenzlau JM, Juhl K, Yu L, et al. The cation
efflux transporter ZnT8 (Slc30A8) is a major autoantigen in human type 1 diabetes.
Proc Natl Acad Sci USA. 2007;104(43):
17040–17045
Ounissi-Benkalha H, Polychronakos C. The
molecular genetics of type 1 diabetes: new
genes and emerging mechanisms. Trends
Mol Med. 2008;14(6):268–275
Padgett LE, Broniowska KA, Hansen PA,
Corbett JA, Tse HM. The role of reactive
oxygen species and proinflammatory cytokines in type 1 diabetes pathogenesis. Ann
N Y Acad Sci. 2013;1281:16–35
Zhong J, Rao X, Xu JF, Yang P, Wang CY. The
role of endoplasmic reticulum stress in
autoimmune-mediated beta-cell destruction
in type 1 diabetes. Exp Diabetes Res. 2012.
doi:10.1155/2012/238980
Eizirik DL, Miani M, Cardozo AK. Signalling
danger: endoplasmic reticulum stress and
the unfolded protein response in pancreatic islet inflammation. Diabetologia. 2013;
56(2):234–241
Stankov K. Genetic predisposition for type
1 diabetes mellitus: the role of endoplasmic reticulum stress in human disease
etiopathogenesis. J Med Biochem. 2010;29
(3):139–149
Ron D, Walter P. Signal integration in the
endoplasmic reticulum unfolded protein
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
response. Nat Rev Mol Cell Biol. 2007;8(7):
519–529
Marciniak SJ, Ron D. Endoplasmic reticulum stress signaling in disease. Physiol
Rev. 2006;86(4):1133–1149
Melki I, Lambot K, Jonard L, et al. Mutation
in the SLC29A3 gene: a new cause of
a monogenic, autoinflammatory condition.
Pediatrics. 2013;131(4). Available at: www.
pediatrics.org/cgi/content/full/131/4/e1308
Boettcher C, Brosig B, Zimmer KP, Wudy SA.
The subtle signs of Wolfram (DIDMOAD)
syndrome: not all juvenile diabetes is type
1 diabetes. J Pediatr Endocrinol Metab.
2011;24(1–2):71–74
Torres JM, Cox NJ, Philipson LH. Genome
wide association studies for diabetes:
perspective on results and challenges.
Pediatr Diabetes. 2013;14(2):90–96
Hattersley A, Bruining J, Shield J, Njolstad P,
Donaghue KC. The diagnosis and management of monogenic diabetes in children
and adolescents. Pediatr Diabetes. 2009;10
(Suppl 12):33–42
Kumar S. Wolfram syndrome: important
implications for pediatricians and pediatric
endocrinologists. Pediatr Diabetes. 2010;11
(1):28–37
Zalloua PA, Azar ST, Delépine M, et al. WFS1
mutations are frequent monogenic causes
of juvenile-onset diabetes mellitus in Lebanon. Hum Mol Genet. 2008;17(24):4012–
4021
Girard D, Petrovsky N. Alström syndrome:
insights into the pathogenesis of metabolic
disorders. Nat Rev Endocrinol. 2011;7(2):
77–88
Senée V, Vattem KM, Delépine M, et al.
Wolcott–Rallison syndrome: clinical, genetic, and functional study of EIF2AK3
mutations and suggestion of genetic
heterogeneity. Diabetes. 2004;53(7):1876–
1883
Julier C, Nicolino M. Wolcott–Rallison syndrome. Orphanet J Rare Dis. 2010;5:29
Grant SFA, Hakonarson H, Schwartz S. Can
the genetics of type 1 and type 2 diabetes
shed light on the genetics of latent autoimmune diabetes in adults? Endocr Rev.
2010;31(2):183–193
Gillespie KM. Type 1 diabetes: pathogenesis
and prevention. CMAJ. 2006;175(2):165–170
Erlich H, Valdes AM, Noble J, et al; Type 1
Diabetes Genetics Consortium. HLA DR-DQ
haplotypes and genotypes and type 1 diabetes risk: analysis of the Type 1 Diabetes
Genetics Consortium families. Diabetes.
2008;57(4):1084–1092
Sanjeevi CB, Landin-Olsson M, Kockum I,
Dahlquist G, Lernmark A. Effects of the
second HLA-DQ haplotype on the
STATE-OF-THE-ART REVIEW ARTICLE
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
association with childhood insulindependent diabetes mellitus. Tissue Antigens. 1995;45(2):148–152
Pugliese A, Gianani R, Moromisato R, et al.
HLA-DQB1*0602 is associated with dominant protection from diabetes even among
islet cell antibody–positive first-degree
relatives of patients with IDDM. Diabetes.
1995;44(6):608–613
Hathout EH, Hartwick N, Fagoaga OR, et al.
Clinical, autoimmune, and HLA characteristics of children diagnosed with type 1
diabetes before 5 years of age. Pediatrics.
2003;111(4 pt 1):860–863
Todd JA, Walker NM, Cooper JD, et al; Genetics of Type 1 Diabetes in Finland; Wellcome Trust Case Control Consortium.
Robust associations of four new chromosome regions from genome-wide analyses
of type 1 diabetes. Nat Genet. 2007;39(7):
857–864
Wellcome Trust Case Control Consortium.
Genome-wide association study of 14,000
cases of seven common diseases and 3,000
shared controls. Nature. 2007;447(7145):
661–678
Grant SF, Qu HQ, Bradfield JP, et al; DCCT/
EDIC Research Group. Follow-up analysis of
genome-wide association data identifies
novel loci for type 1 diabetes. Diabetes.
2009;58(1):290–295
Barrett JC, Clayton DG, Concannon P, et al;
Type 1 Diabetes Genetics Consortium.
Genome-wide association study and metaanalysis find that over 40 loci affect risk of
type 1 diabetes. Nat Genet. 2009;41(6):703–
707
Mohlke KL, Scott LJ. What will diabetes
genomes tell us? Curr Diab Rep. 2012;12(6):
643–650
Hu X, Daly M. What have we learned from
six years of GWAS in autoimmune diseases,
and what is next? Curr Opin Immunol. 2012;
24(5):571–575
Bradfield JP, Qu HQ, Wang K, et al. A
genome-wide meta-analysis of six type 1
diabetes cohorts identifies multiple associated loci. PLoS Genet. 2011;7(9):e1002293
Nejentsev S, Walker N, Riches D, Egholm M,
Todd JA. Rare variants of IFIH1, a gene
implicated in antiviral responses, protect
against type 1 diabetes. Science. 2009;324
(5925):387–389
Senée V, Chelala C, Duchatelet S, et al.
Mutations in GLIS3 are responsible for
a rare syndrome with neonatal diabetes
mellitus and congenital hypothyroidism.
Nat Genet. 2006;38(6):682–687
Fung EY, Smyth DJ, Howson JM, et al. Analysis of 17 autoimmune disease–associated
variants in type 1 diabetes identifies 6q23/
PEDIATRICS Volume 132, Number 6, December 2013
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
TNFAIP3 as a susceptibility locus. Genes
Immun. 2009;10(2):188–191
Sirota M, Schaub MA, Batzoglou S,
Robinson WH, Butte AJ. Autoimmune disease classification by inverse association
with SNP alleles. PLoS Genet. 2009;5(12):
e1000792
Solovieff N, Cotsapas C, Lee PH, Purcell SM,
Smoller JW. Pleiotropy in complex traits:
challenges and strategies. Nat Rev Genet.
2013;14(7):483–495
Zhernakova A, van Diemen CC, Wijmenga C.
Detecting shared pathogenesis from the
shared genetics of immune-related diseases. Nat Rev Genet. 2009;10(1):43–55
Richard-Miceli C, Criswell LA. Emerging patterns of genetic overlap across autoimmune
disorders. Genome Med. 2012;4(1):6
Eyre S, Hinks A, Bowes J, et al; Yorkshire
Early Arthritis Consortium; Biologics in RA
Control Consortium. Overlapping genetic
susceptibility variants between three autoimmune disorders: rheumatoid arthritis,
type 1 diabetes and coeliac disease. Arthritis Res Ther. 2010;12(5):R175
Cotsapas C, Voight BF, Rossin E, et al; FOCiS
Network of Consortia. Pervasive sharing of
genetic effects in autoimmune disease.
PLoS Genet. 2011;7(8):e1002254
Herold KC, Vignali DA, Cooke A, Bluestone
JA. Type 1 diabetes: translating mechanistic
observations into effective clinical outcomes. Nat Rev Immunol. 2013;13(4):243–
256
Grinberg-Bleyer Y, Baeyens A, You S, et al. IL2 reverses established type 1 diabetes in
NOD mice by a local effect on pancreatic
regulatory T cells. J Exp Med. 2010;207(9):
1871–1878
Stanford SM, Krishnamurthy D, Falk MD,
et al. Discovery of a novel series of inhibitors of lymphoid tyrosine phosphatase
with activity in human T cells. J Med Chem.
2011;54(6):1640–1654
Lehner B. Genotype to phenotype: lessons
from model organisms for human genetics.
Nat Rev Genet. 2013;14(3):168–178
Freedman ML, Monteiro AN, Gayther SA,
et al. Principles for the post-GWAS functional characterization of cancer risk loci.
Nat Genet. 2011;43(6):513–518
Jiménez-Chillarón JC, Díaz R, Martínez
D, et al. The role of nutrition on epigenetic modifications and their implications on health. Biochimie. 2012;94(11):
2242–2263
Lam DW, LeRoith D. The worldwide diabetes
epidemic. Curr Opin Endocrinol Diabetes
Obes. 2012;19(2):93–96
Boerner BP, Sarvetnick NE. Type 1 diabetes:
role of intestinal microbiome in humans
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
and mice. Ann N Y Acad Sci. 2011;1243:103–
118
MacFarlane AJ, Strom A, Scott FW. Epigenetics: deciphering how environmental
factors may modify autoimmune type 1
diabetes. Mamm Genome. 2009;20(9–10):
624–632
Williams KT, Garrow TA, Schalinske KL. Type
I diabetes leads to tissue-specific DNA
hypomethylation in male rats. J Nutr. 2008;
138(11):2064–2069
Fradin D, Le Fur S, Mille C, et al. Association
of the CpG methylation pattern of the
proximal insulin gene promoter with type 1
diabetes. PLoS ONE. 2012;7(5):e36278
Husseiny MI, Kuroda A, Kaye AN, Nair I,
Kandeel F, Ferreri K. Development of
a quantitative methylation-specific polymerase chain reaction method for monitoring beta cell death in type 1 diabetes.
PLoS ONE. 2012;7(10):e47942
Rakyan VK, Beyan H, Down TA, et al. Identification of type 1 diabetes–associated
DNA methylation variable positions that
precede disease diagnosis. PLoS Genet.
2011;7(9):e1002300
Castel SE, Martienssen RA. RNA interference in the nucleus: roles for small
RNAs in transcription, epigenetics and beyond. Nat Rev Genet. 2013;14(2):100–112
Guay C, Jacovetti C, Nesca V, Motterle A,
Tugay K, Regazzi R. Emerging roles of noncoding RNAs in pancreatic b-cell function
and dysfunction. Diabetes Obes Metab.
2012;14(suppl 3):12–21
Erener S, Mojibian M, Fox JK, Denroche
HC, Kieffer TJ. Circulating miR-375 as
a biomarker of b-cell death and diabetes
in mice. Endocrinology. 2013;154(2):603–
608
Nielsen LB, Wang C, Sørensen K, et al. Circulating levels of microRNA from children
with newly diagnosed type 1 diabetes and
healthy controls: evidence that miR-25
associates to residual beta-cell function
and glycaemic control during disease progression. Exp Diabetes Res. 2012;2012:896362
Sebastiani G, Grieco FA, Spagnuolo I, Galleri
L, Cataldo D, Dotta F. Increased expression
of microRNA miR-326 in type 1 diabetic
patients with ongoing islet autoimmunity.
Diabetes Metab Res Rev. 2011;27(8):862–
866
Hezova R, Slaby O, Faltejskova P, et al.
microRNA-342, microRNA-191 and microRNA-510
are differentially expressed in T regulatory
cells of type 1 diabetic patients. Cell Immunol.
2010;260(2):70–74
Kissler S, Stern P, Takahashi K, Hunter K,
Peterson LB, Wicker LS. In vivo RNA interference demonstrates a role for Nramp1 in
1121
modifying susceptibility to type 1 diabetes.
Nat Genet. 2006;38(4):479–483
89. Miao F, Smith DD, Zhang L, Min A, Feng W,
Natarajan R. Lymphocytes from patients
with type 1 diabetes display a distinct
profile of chromatin histone H3 lysine 9
dimethylation: an epigenetic study in diabetes. Diabetes. 2008;57(12):3189–3198
90. Miao F, Chen Z, Zhang L, et al. Profiles of
epigenetic histone post-translational modifications at type 1 diabetes susceptible
genes. J Biol Chem. 2012;287(20):16335–16345
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PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2013 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
FUNDING: Supported by the Ministry of Education and Science, Republic of Serbia, project no. III41012.
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.
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