Genomics and epigenomics of the human glycome
Vlatka Zoldoš & Mislav Novokmet & Ivona Bečeheli &
Gordan Lauc
Received: 2 March 2012 /Revised: 11 May 2012 /Accepted: 14 May 2012 /Published online: 31 May 2012
# Springer Science+Business Media, LLC 2012
Abstract The majority of all proteins are glycosylated and
glycans have numerous important structural, functional and
regulatory roles in various physiological processes. While
structure of the polypeptide part of a glycoprotein is defined
by the sequence of nucleotides in the corresponding gene,
structure of a glycan part results from dynamic interactions
between hundreds of genes, their protein products and
environmental factors. The composition of the glycome
attached to an individual protein, or to a complex mixture of
proteins, like human plasma, is stable within an individual, but
very variable between individuals. This variability stems from
numerous common genetic polymorphisms reflecting in
changes in the complex biosynthetic pathway of glycans, but
also from the interaction with the environment. Environment
can affect glycan biosynthesis at the level of substrate
availability, regulation of enzyme activity and/or hormonal
signals, but also through gene-environment interactions.
Epigenetics provides a molecular basis how the environment
can modify phenotype of an individual. The epigenetic
information (DNA methylation pattern and histone code)
is especially vulnerable to environmental effects in the
early intrauterine and neo-natal development and many
common late-onset diseases take root already at that time. The
evidences showing the link between epigenetics and
glycosylation are accumulating. Recent progress in highthroughput
glycomics, genomics and epigenomics enabled
first epidemiological and genome-wide association studies of
the glycome, which are presented in this mini-review.
Keywords Glycosylation . Glycome . Genome-wide
association study . Epigenetics . Gene-environment
interactions
Genetics of protein glycosylation is very complex
According to the central dogma of molecular biology, function
of each protein is determined by its structure, which is defined
by the nucleotide sequence in the corresponding gene.
However, in the case of glycan moieties of glycoproteins,
there are several additional layers of complexity between
genes and the final glycan structure. The final structure of
each glycan is therefore not encoded directly in the genome
(with one or two genes) but many genes determine the
structure of proteins (enzymes and other proteins) that
participate in the complex biosynthetic pathway of glycan
synthesis. Genetic polymorphisms in the participating genes,
regulation of gene expression, posttranslational modifications,
and the activity of the corresponding proteins all work
together to determine final structure of a glycan. Additional
mechanisms, including altered intracellular localization,
competition with endogenous acceptor substrates and
variable access to monosaccharide donor substrates can
also affect the final outcome [1].
Glycan structures are generated by concerted action of
hundreds of glycosidases and glycosyltransferases [2],
V. Zoldoš (*)
University of Zagreb, Faculty of Science,
Horvatovac 102a,
Zagreb, Croatia
e-mail: vzoldos@biol.pmf.hr
M. Novokmet :I. Bečeheli :G. Lauc
Genos Ltd, Glycobiology Laboratory,
Planinska 1,
Zagreb, Croatia
G. Lauc (*)
University of Zagreb, Faculty of Pharmacy and Biochemistry,
A. Kovačića 1,
Zagreb, Croatia
e-mail: glauc@pharma.hr
Glycoconj J (2013) 30:41–50
DOI 10.1007/s10719-012-9397-y
which are probably only a tip of the iceberg of the
proteins involved in the glycosylation process. Recent
study in Drosophila revealed that only 13 % out of 109
genes found to be needed for neural-specific glycosylation
were previously known glyco-genes [3]. However,
the remaining 87 % of genes needed for neural-specific
glycosylation probably also participate in other types of
cellular processes and cannot be claimed to be new “glycogenes”.
Also the same genes are probably needed for other
types of glycosylation not covered in this study. Therefore
direct extrapolation of this ratio is not possible, but it is
highly probable that at least two or three times more genes
than currently listed 600 glyco-genes are required for protein
glycosylation. Therefore, at least 5 % of the human genome
seems to be involved in protein glycosylation, suggesting that
this biological process is the most complex biosynthetic
pathway in multicellular organisms.
Since protein structure is defined by the sequence of
nucleotides in the corresponding gene, genetic polymorphisms
affect protein structure in a known and mostly predictable way.
With glycans this is frequently not the case because
polymorphisms in different enzymes and other proteins
involved in protein glycosylation pathways can interact in
a very complex manner. For example, a polymorphism in a
given glycosyltransferase could lead to 10 % decrease in
the activity of this enzyme due to lower affinity of an
enzyme for the activated sugar nucleotide. Consequently,
glycans will bear slightly lower amount of this specific
monosaccharide and the composition of the entire glycome
will be altered. However, if the same individual also has a
polymorphism in other genes in the same biosynthetic
pathway or in the transmembrane transporter of the
activated sugar nucleotide (which decreases its concentration
in the Golgi), the combined effect of decreased enzyme
activity and decreased substrate concentration could be
very prominent. Alternatively, the same individual can
have a polymorphism in a proton transporter and altered
pH in Golgi, which could actually increase the activity
of that specific glycosyltransferase (or decrease activity
of a competing glycosyltransferase) and effectively erase
the effect of the original polymorphism on the glycome
composition.
Therefore, complex epistatic interactions among multiple
gene loci determine the final glycan structure. This is nicely
illustrated by AB0 system of blood group determination,
which arises from the existence of the three allelic
variants (A, B, and 0) of a single glycosyltransferase
gene at the same locus (AB0 locus); allele A is coding
for N-acetyl-galactosaminyltransferase, allele B is coding for
galactosyltransferase and allele 0 is a non-functional truncated
variant [4]. The two enzymes add different sugars to a
five-monosaccharide glycan precursor (H-antigen); N-acetylgalactosaminyltransferase
adds N-acetylgalactosamine and
galactosytransferase adds galactose to H-antigen to produce
A- or B- antigens, respectively. Another enzyme, fucosyltransferase
is responsible for the production of the H antigen,
and this enzyme is encoded by a gene at different locus (H). In
the human population, there are approximately 15 % of
recessive homozygotes for this gene (hh genotype) that cannot
produce the H antigen (Bombay phenotype). Therefore,
recessive homozygotes for the gene H will appear as type 0
blood group regardless of their genotype at the AB0 locus.
Two persons, each carrier of one of the two different mutations
(the carrier of the recessive allele h and the carrier of the allele
0) will show the same external phenotype following the blood
test—there will be no coagulation with antibodies against A or
B antigens in both persons, even if their biochemical
phenotypes are different (the absence of H antigen in carriers
of mutation in H gene, and the presence of H-antigen in
carriers of deletion in AB0 gene). This example of the
interaction between only two different glyco-genes illustrates
a complexity in production of glycoprotein structures and its
repercussions on the glycome composition and eventually on
the biochemical and external phenotype.
Genome-wide association studies initiated
a new revolution in genetics
Until about 5 years ago two major designs were used to study
the role of human genetic variation in common complex
diseases: genetic linkage studies and candidate-gene
approaches based on a priori hypotheses of biological
plausibility. In the studies of genetic linkage scarce set
of genetic markers were usually used and they have only been
successful in identifying rare genetic mutations with high
penetrance and large effects in extended pedigrees affected
by monogenic (“Mendelian”) disorders. These studies were
generally underpowered to detect more common genetic
variants of smaller effect sizes in the samples drafted from the
general population [5]. On the other hand, the majority of
candidate gene studies used sample sizes of up to several
hundred cases and controls, what led to results that were
generally not replicated in subsequent studies [6, 7].
However, a major technological breakthrough occurred
when HapMap project outcome was combined with the
advances in genotyping technologies. In the course of
the HapMap project, more than 3 million single nucleotide
polymorphysms (SNPs) were identified. Taking into account
recombination hotspots in the genome and their linkage
disequilibrium (LD) patterns, the selection of several hundred
thousand of particularly useful SNP markers (haplotype
tagging) was made. This has resulted in the development of high
density SNP arrays with hundreds of thousands of SNP markers
spanning across the entire genome, which made hypothesis-free
genome-wide association studies (GWAS) possible [8].
42 Glycoconj J (2013) 30:41–50
Further on, issues such as population stratification,
relatedness of the subjects in the sample and multiple testing
had to be dealt with. To address those problems new
advanced, statistical and computational methods were
developed with agreement on a very stringent genome
wide statistical significance level of approximately p05×10−7
when a set of about 300,000 SNPs is used. By accepting those
criteria, false-positive results that plagued the field for more
than a decade were resolved [9, 10]. The studies were initially
conducted in reasonably small sample sizes of up to 2,000
cases and controls. Initial successes soon led to formation of
very large consortia and pooling of samples among several
studies in genome wide association study meta-analyses. At
the moment large consortia such as GIANT (which studies
anthropometric traits) or DIAGRAM (which studies type 2
diabetes) are incorporating about a hundred thousand of cases
and controls. Hundreds of bio-medically relevant quantitative
traits and common genetic variants underlying complex
human diseases have been identified and widely replicated
across populations. Genetic networks influencing a wide
range of common complex diseases have begun to emerge,
including type 1 and 2 diabetes, ischaemic heart disease,
stroke, rheumatoid arthritis, breast/prostate/colorectal/lung
cancer, and inflammatory bowel disease; and a range
of endophenotype underlying these diseases such as
obesity, LDL cholesterol and fasting glucose. Emerged
variants typically showed small effect sizes (odd ratios
of 1.1 to 1.4) and many have been located in regions
outside the known genes, presumably in distant regulatory
regions. They individually only explain a very small
percentage of the overall variance in disease or traits
and thus cannot be used, at this point in time, for a
reliable genetic profiling of disease risk in individual subjects.
However, rather large number of new aetiological hypotheses
has been generated from those studies. A great number of
GWAS accomplished in the last few years have formed a
robust base of new knowledge from which functional studies
can now rise to explore new disease pathways [11–13]. All of
this should eventually result in the improved prediction of
disease risk and will, with new class of drugs developed due to
knowledge generated by these studies, contribute to the
advance in highly personalized era of medicine.
Genes and environment integrate in the process
of glycan biosynthesis
An important source of complexity and variability of the
glycome is the interaction with the environment. Biosynthesis
of glycans requires many hexosamine building blocks and
their availability significantly affects structure of glycans and
composition of the glycome [14]. Altered pH in Golgi [15,
16], oxygen concentration [17] and many other external
factors also affect protein glycosylation. The expression level
and activity of all enzymes in the complex pathway of glycan
biosynthesis is also not pre-defined, but changes in response
to various internal and external factors.
It is well established that the epigenome is a key mediator
of gene-environment interactions [18]. While environmental
factors, such as diet or chemical compounds, influence
enzymatic processes only while they are directly present,
their prolonged effects can be achieved through the epigenetic
cell memory. The epigenomic program (DNA methylation
pattern and histone code) is being established during early
stages of intrauterine and neonatal development, and is
particularly sensitive to environmental influences (dietary,
chemical, behavioural, etc.) in that period [19]. For example,
specific diet (such as exposure to famine, or intake of food rich
in folate or betain) or exposure to certain environmental
factors (such as cigarette smoke, aflatoxin B1 or UVradiation)
in early life was reported to be critical for the establishment of
the DNA methylation pattern with life-long effects for the
individual [20, 21]. The epigenetic principle explains how
environmental exposures can have long-lasting influence on
biology and health [22, 23] without changing the gene
sequence.
In response to environmental and extrinsic stimuli,
epigenetic mechanisms (DNA methylation, RNA-mediated
silencing and chromatin modifications) regulate gene
expression [18] and the alteration in the genome expression
program is inherited through cell divisions. Thus, the mitotic
stability of epigenetic information is responsible for the link of
the early experience with the future phenotype outcome
(health). Indeed, the vast majority of late-onset common
diseases often have epigenetic roots, mostly during early
individual development [24, 25]. This is well established for
many cancers, most notably for lung cancer [24], diabetes [26]
and other multifactorial diseases. In addition, significant
environmentally induced stochastic epigenetic changes
occur during lifetime of an individual and increase the
risk of age-associated diseases, but it is difficult to
unravel the relative contribution of genetic (mutations and
polymorphisms) and epigenetic alterations in a changed
phenotype.
The evidences showing the link between epigenetics and
glycosylation are accumulating, but, until now, they mostly
reported aberrant glycosylation in cancer. It has been shown
that a change in cytosine methylation within promoter of
certain glyco-genes is responsible for the expression of
cancer-associated carbohydrate antigens in gastrointestinal,
colon, pancreatic, and breast cancer [27–30]. Other examples
of epigenetic regulation of glyco-genes include: FUT7 in
leukocytes [27], FUT4, GDP-fucose transporter and FX genes
involved in regulation of global fucosylation [31], and the
transcription factor HNF1A, a master regulator of plasma
protein fucosylation [32]. Early studies on DNA methylation
Glycoconj J (2013) 30:41–50 43
and expression levels of HNF1A in hepatocellular carcinomas
(HCC) different in origin (HBV, HCV and alcohol induced
HCC) showed that tumors induced by chronic alcohol intake
differ from other two types of HCCs (manuscript in
preparation). This finding indicates that environmental
factors have strong influence on epigenetic regulation of
the gene involved in glycosylation. Revolutionary finding of
meiotically stable epialleles in humans has recently emerged.
Even still scarce, there are evidences of epialleles passing to
offspring and even grand-offspring through germline cells,
putting the transgenerational epigenetic inheritance [33–35]
in the focus of the epigenetic community. The importance of
this phenomenon lies in the fact that environment and the
individual experiences can shape biology across the lifecycle
and even within lineages through time. It is tempting to
speculate that epigenetic regulation of glycosylation is an
essential evolutionary mechanism, which enables quick
adaptation of complex organisms to environmental changes and
help them avoid rapidly evolving pathogenic microorganisms.
Glycome variability in a human population
The combined effects of all genetic, epigenetic and environmental
factors are reflected in high variability of glycome
composition in a human population. For example, the smallest
N-glycan in human plasma (A2, bi-antennary glycan with
structure GlcNAc2Man3GlcNAc2) was reported to represent
between 0.03 % and 0.83 % of the total plasma N-glycome
[36]. Median value for this glycan was 0.16, thus both the
lowest and the highest observed level were very different from
“normal” values. GWAS of this glycan trait identified a
number of SNPs in FUT8 gene, which explained between 1 %
and 6 % of the variance [37, 38]. Heritability for this glycan
was estimated to be approximately 50 % [36], thus the
majority of heritable polymorphisms that affect this glycan are
still not identified. The same is true for environmental factors;
all measured variables like age, gender, BMI, body mass,
biochemical parameters, and/or smoking, etc., cumulatively
explained only 5 % of the variance [39], thus main non-heritable
factors, which affect synthesis of this glycan remain to be
identified.
The variability of the composition of the plasma glycome
derives from both variability in the composition of the
plasma proteome and the variability in the glycosylation
process. However, when glycome composition of the isolated
IgG was analysed, the variability was even greater than in the
total plasma glycome. A2 glycan was found to represent
between 0.1 % and 9.4 % of the IgG glycome, what is nearly
a 100-fold difference [40] (the median value was 0.7 %). The
average ratio between maximal and minimal observed values
for all glycans in the IgG glycome was 19.7, what is nearly
three times higher than the analogous ratio in the plasma
glycome [36, 40]. Therefore, assuming that variability in
glycosylation of other proteins will be similar to the variability
observed in IgG, it appears that the large variability observed
in the composition of the plasma glycome might actually be an
averaged sum of even larger variability in the composition of
glycomes of individual proteins present in the human plasma.
This may not be a universal rule, but for some proteins at
least, this variability in glycosylation has significant functional
consequences. For example, effector functions of IgG are
largely regulated by glycosylation [41]. The activity of various
membrane receptors and transporters is also by large regulated
by their glycosylation [42, 43]. Recently, it was reported
that in a large set of experimentally determined mouse
N-glycosylation sites, the evolutionary rate of glycosylated
asparagines was significantly lower than that of nonglycosylated
asparagines of the same proteins [44]. This
widespread and strong functional constraint on glycosylation,
unlike what has been observed for phosphorylation sites,
implies an overall functional importance of N-glycosylation.
Genome wide association studies of the human glycome
Three genome wide association studies (GWAS) of
glycosylation-related traits were published until now [37,
38, 45]. The first GWAS of the glycome was performed on
2.705 individuals from Croatian islands Vis and Korčula
and Orkney islands in Scotland. The composition of the
desialylated total plasma glycome was determined by HPLC
analysis that separated the total glycome into 13 HPLC
peaks. Two further traits were derived from the original
variables to calculate the percentage of glycan structures
containing core (FUC-C) or antennary (FUC-A) fucose.
Genome-wide significant associations were found for
five HPLC peaks, as well as FUC-A [37]. Two of the
identified genes were known glycosyltransferase genes,
FUT6 and FUT8. They associated with glycan structures
that are known substrates or products of the corresponding
glycosyltransferases, thus molecular mechanisms behind
these associations were clear. The third identified gene was a
transcription factor HNF1A, which was previously not known
to affect protein glycosylation. The subsequent RNAi and
ChIP studies have clearly demonstrated that HNF1A acts as
a master regulator of plasma protein antennary fucosylation by
promoting both de novo and the salvage pathways of GDPfucose
synthesis, stimulating the expression of FUT3, FUT5
and FUT6, and supressing the expression of FUT8 [37].
The second published GWAS of the glycome was extension
of the first study, which both encompassed more individuals
(3,533) and more detailed glycome analysis [38]. All findings
from the first study were confirmed. This study revealed that
HNF1A associates not only with fucosylation but also with
glycan branching. Three new genes found to associate with
44 Glycoconj J (2013) 30:41–50
plasma glycome composition were MGAT5, B3GAT1 and
SLC9A9. MGAT5 was found to associate with the proportion
of highly branched glycans, what is in accordance with the
known biological function of this gene. The second identified
gene, B3GAT1, is a member of the glucuronyltransferase gene
family. Product of this gene adds glucuronic acid to the
terminal N-acetyllactosamine (Lac) disaccharide to form the
HNK-1 epitope precursor [46, 47]. The HNK-1 epitope is
expressed on a subset of human lymphocytes, but it was not
previously reported to exist on plasma proteins. To explain the
association of B3GAT1 with the composition of the plasma
glycome more detailed structural analysis was performed,
which confirmed the existence of glucuronic acid on a subset
of N-glycans released from human plasma glycoproteins. The
third identified gene, SLC9A9, codes for a proton pump,
which affects pH in the endosomal compartment [48]. This
gene was not previously linked to glycosylation, but it is
known that changes in Golgi pH can impair protein sialylation
[16], thus the association between SLC9A9 and tetrasialylated
glycans makes biological sense.
Recent large RNAi study identified 109 genes, which are
needed for neural-specific glycosylation in Drosophila [3]
only 14 among them were previously known glyco-genes,
while others were involved in various other cellular
functions. Two GWAS studies described above identified four
glycosyltransferases and two genes that were previously not
linked to glycosylation. The difference in the ratio of glycogenes
and other genes identified using these two approaches is
striking, and it may be explained by the fact that RNAi library
approach has focused on neural specific glycosylation, while
GWAS studies analysed composition of the total plasma
glycome. Plasma glycans originate from different cell types
and since glycosylation is known to be cell type-specific, the
regulation of glycosylation must also be cell type-specific. By
analysing pooled glycans from different proteins from
different cells, these cell type-specific effects are blurred.
Therefore, GWAS of glycomes of individual proteins will be
needed to identify genes that regulate cell type-specific
glycosylation.
Glycosyltransferases in genome wide association studies
Thousands of GWAS studies conducted between 2007 and
2012 identified over 3,000 SNPs, which were associated
with various traits with genome-wide significance. Many
of these polymorphisms were located within or near to
known glycosyltransferase genes (Table 1). Allelic variants
in glycosyltransferases were found to associate with basic
biochemical parameters like cholesterol and triglycerides,
enzymatic activity (alkaline phosphatase, gamma glutamyltransferase),
and even some general human traits
like height and obesity. Molecular mechanisms behind these
associations are generally not known. For the association
between polymorphisms in glycosyltransferases and the
activity of plasma enzymes it could be speculated that
proper glycosylation is needed for their secretion or
enzymatic activity, but for quantitative traits like red
blood count, obesity, or height, any speculation about the
molecular mechanism would be completely unjustified.
Allelic variants in glycosyltransferase genes were also
found to associate with some diseases (Table 1). For example,
a comprehensive study of genes that mediate breast
cancer metastasis to the brain identified ST6GALNAC5
(α2,6-sialyltransferase) as the key gene, which specifically
mediates brain metastasis [49]. Normally restricted to the
brain, the expression of ST6GALNAC5 in breast cancer cells
enhances their adhesion to brain endothelial cells and their
passage through the blood–brain barrier. This co-option of a
brain sialyltransferase highlights the role of the cell-surface
glycosylation in organ-specific metastatic interactions.
Another example is multiple sclerosis where MGAT5
was reported as the most significant top hit in a recent
GWAS for variants regulating disease severity [50]. MGAT5 is
known to affect membrane half-life of numerous proteins [42,
51], thus its association with disease is not unexpected, but
exact molecular mechanism still has to be revealed.
Future prospects of genomics and epigenomics
of glycosylation
The majority of all proteins are glycosylated. For some of
them the exact structure of a glycan part is perhaps not
important and a large number of different glycans could
perform the same task equally well. However, on many
glycoproteins, a glycan part has important structural, functional
and regulatory role and the alternative glycosylation is
important in many physiological processes [41]. Consequently,
differences in glycosylation are probably important element in
development and progression of virtually all complex diseases.
Numerous glyco-biomarkers have been identified [52–55], but
molecular mechanisms linking differences in glycosylation and
disease pathology are generally not known. This is not
surprising, since very little is known about the role of
glycans on individual glycoproteins. Perhaps the only
exception is IgG where the importance of different glycoforms
for effector functions has been clearly demonstrated (for a
review see [41]).
In general, two types of effects can be expected from
common polymorphisms in glycosylation: (i) general effects
and (ii) effects confined to a specific glycoprotein, a subset of
glycoproteins or a tissue. A polymorphism in a gene will result
in less efficient (or less expressed) specific glycosyltransferase
and consequently all glycoproteins, which are substrates for
that glycosyltransferase, will be affected. At the glycome
Glycoconj J (2013) 30:41–50 45
level, there will be a change in the composition, which will
reflect the decreased activity of the affected glycosyltransferase.
Since various proteins will be affected, this pleiotropic
effect will also vary significantly between individuals. Alternatively,
a polymorphism might have an effect only on a
specific glycoprotein expressed in a specific tissue. In that
case, the consequences can be very specific because they
would reflect decreased activity of a single enzyme, altered
receptor specificity or intracellular localization. An interesting
example is the miss-localisation of GLUT receptors in pancreatic
islets of mice fed with high-fat diet [56]. Specific
genetic polymorphisms or epigenetic silencing of a specific
gene might have similar consequences and consequently
make an individual more prone to diabetes.
Genomic studies of the human glycome are still very
scarce, but proof of principle studies of association between
glycome composition and both genetic polymorphisms [37,
38] and epigenetic marks [32] have recently been published.
The main obstacle is the need to reliably quantify glycans in
thousands of individuals, but with progress in highthroughput
technology this is becoming feasible. Further
genomic and epigenomic studies, which will contribute to
the understanding of the role of glycosylation in both normal
physiology and disease, are expected soon.
Acknowledgments The work in author’s laboratory is supported by
the Croatian Ministry of Science, Education and Sport grant #309-
0061194-2023 (to GL) and #119-1191196-1224 (to VZ), and by the
European Commission GlycoBioM (contract #259869) and HighGlycan
(contract #278535) grants.
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Table 1 Published genome-wide significant associations of diseases or quantitative traits with genetic polymorphisms in glycosyltransferases
Gene Gene name Disease (trait) Ref.
ABO (A3GALNT, A3GALT1) Transferase A, alpha 1-3-N-acetylgalactosaminyltransferase;
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ST6GAL1 CMP-N-acetylneuraminate-β-galactosamine-α-2,
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GLT25D2 Glycosyltransferase 25 family member 2 Height [79]
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