Download Genetic and epigenetic dissection of cis regulatory

Genetic and epigenetic dissection of cis regulatory variation
Xu Zhang1, Eric J Richards2 and Justin O Borevitz1
Divergence in gene expression is of interest because it
generates molecular markers for phenotypic variation,
potentially including the causes underlying this variation.
Alteration of gene expression patterns can have a direct
genetic (or epigenetic) basis in cis regulatory polymorphism or
can be indirectly regulated by trans-acting factors. Expression
mapping studies have begun to reveal the local (suggesting cis)
and distant (usually trans) patterns of inheritance of genetic
variation that underlies transcriptional polymorphism. The
molecular basis that contributes to transcriptional divergence
is, however, largely unknown especially for genes under
selection that might influence phenotype. Additional genomewide empirical data from many related organisms are required
to dissect cis-, trans-, and cis x trans- dependent sources of
variation in gene expression to provide a better understanding
of the evolution of transcriptional regulatory networks.
Addresses
1
Department of Ecology and Evolution, The University of Chicago,
Chicago, Illinois 60637, USA
2
Department of Biology, Washington University, St. Louis,
Missouri 63130, USA
Corresponding author: Borevitz, Justin O ([email protected])
Current Opinion in Plant Biology 2007, 10:142–148
This review comes from a themed issue on
Genome studies and molecular genetics
Edited by Stefan Jansson and Edward S Buckler
Available online 14th February 2007
1369-5266/$ – see front matter
# 2007 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.pbi.2007.02.002
Introduction
Phenotypic variation provides the raw material for selection, both in natural settings and in artificial breeding
programs. Alternative gene expression programs are
direct responses of individual genotypes to environmental inputs. Such responses might be polymorphic among
strains, revealing molecular markers for diverse phenotypic responses to environmental stimuli. In the past five
years, genetic studies of gene expression have treated
transcript abundance itself as a quantitative trait and have
mapped it to local-acting or distant-acting expression
quantitative trait loci (eQTLs) [1–4]. These studies of
segregating population also suggest significant epistatic
interactions among the eQTLs that contribute to transcriptional variation [5]. Mechanistically, the effect of
eQTLs on gene expression can be in cis or in trans. transacting loci represent regulatory factors that affect both
Current Opinion in Plant Biology 2007, 10:142–148
alleles of the expressed gene, whereas cis-acting loci
represent genetic polymorphisms in regulatory elements
of the expressed gene itself, which lead to steady-state
differences in allele-specific expression (ASE) levels. In
addition, local chromatin status, mediated through epigenetic modification, can potentially affect gene expression
in cis (at the gene itself) or in trans (by regulating loci
indirectly). The emerging field of epigenomics tries to
assess the correlation of epigenotypes with differential
gene expression [6,7] and ultimately phenotypic variation. Building an understanding of the genetic and epigenetic architecture that underlies the variation of gene
expression traits addresses a core problem in genetics. It
also impacts many applications in plant breeding, including
the mechanism of hybrid vigor (heterosis) and the
performance and stability of cultivars under different
environmental conditions across generations.
Microarrays are currently used as a major tool for
exploring the natural diversity at both genetic and transcriptional levels [8]. Single nucleotide polymorphism
(SNP) genotyping arrays are a common choice to accurately capture the genetic variation among individuals
when SNPs are known. Whole-genome tiling arrays have
begun to gain popularity for transcriptional studies owing
to their complete coverage of sequence without any a
priori knowledge of transcribed regions [8]. In this
review, we describe the application of a SNP–tiling array,
a microarray platform that combines SNP genotyping and
whole-genome tiling, in profiling multiple genomic traits
including ASE and DNA methylation.
Natural variation in gene expression
Variation in transcript abundance among individuals has a
genetic component. A simple assumption is that gene
expression traits are inherited in an additive fashion,
where hybrids show intermediate values between
parental expression levels. Molecularly, such dosage
effects fit with either intermediate levels of trans regulatory factors present in hybrids or differing cis regulatory
elements affecting the expressed gene. When trans-acting
repressors or activators are brought together in new combinations, gene expression cascades might be altered,
resulting in dominance inheritance in the hybrids. Thus,
transcript measurements from F1 hybrids and their inbred
parents allow direct testing of non-additivity in transcription inheritance. A few studies have looked globally at the
distribution of dominance effects in gene expression with
the hope of revealing the underlying basis of hybrid vigor.
Studies of intraspecific or interspecific hybrids of Drosophila [9,10,11], Arabidopsis [12,13] and maize [14–16]
generally show that nearly one quarter of gene expression
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Genetic and epigenetic dissection of cis regulatory variation Zhang, Richards and Borevitz
is non-additive, implying a significant contribution from
trans factors in transcription regulatory networks. This is
consistent with ASE studies of local regulatory variation
in yeast [4].
Allelic imbalance in expression levels at heterozygous loci
directly demonstrates cis regulatory polymorphism. In
hybrids, both parental alleles have the same cellular context and are equally exposed to trans-acting factors. Thus,
difference in expression level between two parental alleles
is directly due to cis-acting variation. ASE assays in F1
hybrids use a transcribed SNP to distinguish the relative
amount of two alleles in the reverse transcription (RT)PCR products of the studied gene [17]. This approach is
distinct from genomic linkage analysis in that the detection
of cis effects is direct [18] rather than suggestive, as is the
case when local regulatory variations are mapped [4]. Such
studies have demonstrated that ASE is common in humans
[19,20], Drosophila [21] and maize [15]. This approach
has also been applied to rule out parental imprinting in
143
reciprocal F1 hybrids in Drosophila [22] and to reveal the
variability of X inactivation in human cell lines [23]. This
popular gene by gene approach to ASE is, however, tedious
to apply at the genome level. The use of whole-genome
SNP-tiling arrays to simultaneously test all genes for ASE is
now feasible (Figure 1).
Parental genotypes can affect gene expression in their
progeny. In particular, maternal effects are expected to
have great influence on early development. Parental
genotypes also affect the gene expression of progeny in
an allele-specific manner through imprinting, or epigenetic silencing, of one parental allele. Imprinting can be
distinguished from (but might depend on) maternal
effects, which act in trans to regulate both alleles in
the progeny. The extent to which gene expression is
affected by maternal effects and genomic imprinting can
be revealed by the analysis of reciprocal hybrids. Variation
in ASE between reciprocal hybrids suggests imprinting
because ASE tracks with the parental genotype rather
Figure 1
Detecting cis regulatory variation and imprinting using AtSNPtile. The allele intensities for a given SNP are shown from four replicates of two
parental lines, Columbia (Col) and Vancouver (Van), and their reciprocal F1 hybrids. For each line, intensity for Col allele and Van allele are
represented on the x-axis and y-axis, respectively. s is strand corrected allele intensity, calculated by CRLMM [53]. (a) Genomic DNA
hybridization reveals heterozygous genotypes at the SNP base in hybrids (blue, green) and homozygous in parents (red, black). (b–f) With RNA,
the SNP base will read out the relative contribution of expression by each allele in the hybrid. Possible results for hybrids are: (b) bi-allele
expression (no evidence for ASE), (c,d) Col or Van ASE (cis regulatory variation), or (e,f) paternal or maternal ASE (maternal or paternal imprinting).
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Current Opinion in Plant Biology 2007, 10:142–148
144 Genome studies and molecular genetics
than the actual chromosomal allele when cis regulatory
variation is the cause.
Maternal effects again are detected when the total transcript levels for a gene differ between reciprocal hybrids
while the ratios of two alleles are similar. Genes that are
controlled by maternal effects [9] have been shown to
be regulated mainly by trans effects in a recent study of
ASE in Drosophila reciprocal hybrids, although wholegenome scans were not performed [22].
Natural variation in the epigenome
Until recently, epigenetic regulation of global gene expression has been difficult to assess. Within the nucleus,
chromatin regions are committed to either transcriptioncompetent or transcription-silenced states, as mediated by
epigenetic marks [24]. These chromatin effects might
underlie Regions of IncreaseD Gene Expression (RIDGE)
[25,26]. Epigenetically related transcriptional variation can
have an immediate phenotypic consequence, as demonstrated by the recently reported tomato epigenetic allele
(epiallele) that affects fruit ripening [27] and by the classic
study of the Linaria Lcyc epiallele, which alters floral
asymmetry [28]. The stable inheritance of such epigenetic
variation over generations in plants further implies
the potential evolutionary role of epigenetic marks in
influencing transcription patterns. Unlike sequence polymorphism, epigenetic modification can be modulated by
environmental stimuli, as demonstrated by the vernalization reaction of Arabidopsis [29], although the epigenetic
mark is reset at each generation in this case. Recently
Shindo et al. [30] showed that the degree of epigenetic
silencing of the floral repressor FLOWERING LOCUS C
was directly correlated with the degree of vernalization or
cold requirement across wild accessions. A fundamental
question is the extent to which epigenetic modification
patterns vary across genotypes and environments, and the
role that these modifications play in controlling phenotypic
variation.
Cytosine methylation is the best-understood eukaryotic
epigenetic modification, relatively easy to assay, and an
accurate proxy for other epigenetic marks, such as histone
methylation and acetylation [31,32]. The majority of CpG
dinucleotides in mammalian genomes are heavily methylated outside of CpG islands, which are often located
within promoter regions. It was thought that cytosine
methylation in plants would be restricted to isolated
patches of heterochromatin; however, an unexpectedly
high proportion of genic methylation has recently been
reported in Arabidopsis [6,7,33] and is suggested to
modulate transcriptional elongation [7]. In mammals,
correct establishment of DNA methylaton in early embryogenesis is crucial for embryo viability [34]. DNA methylation in plant also exhibits temporally and spatially
specific dynamics [35], although evidence for its role in
plant development is indirect [36]. In Arabidopsis,
Current Opinion in Plant Biology 2007, 10:142–148
mutations in genes that are responsible for maintenance
and de novo DNA methylation both cause a suite of
developmental defects [37,38] and global changes in
chromatin and gene expression level [6,7].
Microarray-based profiling of cytosine methylation
promises to provide an insight into global cytosine methylation pattern and its correlation with transcriptional
control. Bisulfite genomic sequencing is often used to
characterize cytosine methylation patterns. Bisulfite treatment converts unmethylated cytosine to uracil, which is
read as thymine by subsequent PCR and sequencing.
Although precise, this method is laborious to apply at the
whole-genome level across individuals and environmental conditions but is the basis for the human epigenome
project (www.epigenome.org). Two alternative approaches are methyl-sensitive/insensitive restriction enzyme
digestion and chromatin immuno-precipitation (ChIP).
The first approach digests target DNA with isoschizomers
that have differential cytosine methylation sensitivity. A
frequently used isoschizomer pair, HpaII and MspI, both
cut at CCGG sites but have different sensitivity to
methylation at the internal cytosine (Figure 2). The
second approach involves the immuno-precipitation of
target DNA fragments using antibodies against 5-methyl
cytosine. When combined with microarray technology,
these two approaches have been used successfully in
global DNA methylation profiling [39–42]. The first
high-density genomic methylation map for Arabidopsis
was recently published using ChIP followed by hybridization to a whole-genome tiling array [6].
eQTL and mQTL mapping
Understanding the genetic architecture of variation in
gene expression is now a popular method for attempts to
dissect complex traits into component gene expression
pathways [43]. The hypothesis that these intermediate
traits are themselves more simply inherited is only beginning to be tested [44]. eQTL studies suggest multi-genic
inheritance for most expression polymorphisms [45].
Compared with often simply inherited cis eQTLs, trans
eQTLs may have relatively small effects and are pleiotropic by controlling groups of correlated genes [43],
which limits their detection by gene-wise linkage analysis
[46]. Nevertheless, trans eQTLs might be responsible for
important regulatory variation underlying phenotypic
diversity, including variation that is dependent on
environmental or developmental factors.
In some cases, however, ASE might be environment- or
genotype-dependent. This cis regulatory variation is due to
epistatic interactions with trans factors [5]. For example,
promoter variation (cis) is differentially regulated by
alternative expression of upstream regulatory transcription
factors. Joint mapping of cis x trans-dependent variation in
gene expression can be achieved by eQTL mapping of
ASE traits in recombinant inbred intercrosses (RIX) F1
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Genetic and epigenetic dissection of cis regulatory variation Zhang, Richards and Borevitz
145
Figure 2
Detecting CCGG methylation difference by enzyme methylome approach. The genomic region shown here contains six CCGG sites, three of
them (red asterisk) methylated at internal cytosine (CCmGG). After enzyme digestion, the DNA fragments are subjected to random labeling using
random octamers. The resulting amplification products are 50 bp in size and hybridized to a tiling array. (a) HpaII cuts CCGG sites except
three methylated sites. Probes spanning the methylated CCGG sites have normal intensity on array. (b) MspI cuts all CCGG sites. Intensities of
probe spanning the methylated sites are significantly reduced.
lines [47]. RIX F1 lines are F1s derived from a set of
recombinant inbred lines and essentially represent a set
of isogenic F2 lines. Because they contain heterozygous
regions, both ASE and dominance can be treated as quantitative traits in these lines. Thus, markers can be scanned
across the genome to detect the interacting trans factors. In
this way, allele-specific expression QTL (aseQTL)
identify trans loci that can distinguish and regulate alternative promoters (cis). In addition, the effects of maternal
genotype on ASE, i.e. imprinting, can be mapped when
the maternal genotype is also included as a cofactor; thus,
the loci that regulate imprinting polymorphism can be
revealed (Figure 3). Another experimental design involves
profiling global ASE from F1s that are derived from many
divergence inbred accessions. Here again, ASE is the
quantitative trait and common variation is mapped by
linkage disequilibrium association when the F1s are
derived from the haplotype map reference panel.
As with transcript abundance, DNA methylation is
quantitative in nature. To date, however, only one study
has dissected the genetic factors that underlie epigenetic
variation [48]. The same quantitative genetics questions
are important for DNA methylation QTL (mQTL). For
example, what is the heritability of methylation patterns? How many genes control DNA methylation and
what are their effect sizes? What is the distribution of
dominance deviations, i.e. how often do heterozygous
genotypes have homozygous methylation patterns? And
how do mQTL depend on developmental stage and
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environmental setting? In addition, comparison of the
eQTLs and mQTLs that affect a gene locus might reveal
the overlap of genetic and epigenetic effects on gene
expression. Genetic networks can be constructed that
include mQTL targets at eQTL where epigenetic marks
are responsible for variation in transcription. Alternatively, eQTL targets might map to mQTL where
expression variation controls epigenetic patterns on
the genome.
Arabidopsis SNP-tiling array (AtSNPtile)
Can all of the studies mentioned above be integrated
using a single technological platform? Whole-genome
tiling arrays are versatile tools that can be used to read
out various forms of genomic data [8]. RNA profiling on
tiling arrays identifies expression variation without biased
observations at previously known or predicted genes. De
novo identification of alternative transcription fragments
can reveal novel alternative splicing and/or previously
unknown genes [8,49]. ChIP-chip and methylome
analysis can be used to detect binding sites and epigenetically modified sites genome wide [50,51]. In addition,
comparative genomic hybridization can detect copy number and single feature polymorphisms (SFPs) [52]. SNP
arrays contain multiple probes that are specifically
designed to assay known SNPs, where genotype calls
are made by contrasting intensities from probes of different alleles. The 100k and 500k SNP arrays are now
popular in work on the human genome study. Several
robust statistical methods have just been released [53].
Current Opinion in Plant Biology 2007, 10:142–148
146 Genome studies and molecular genetics
Figure 3
Distribution of ASE in RIX F1 lines. The allele scale is shown in bins on the x-axis as homozygous Van/Van = 1, Col/Col = 1, and midparent
(expected heterozygous state) Col/Van = Van/Col = 0. The percentage of RIX lines with given ASE phenotype is shown on the y-axis. (a) No
evidence for cis regulatory difference between two alleles. (b) Fixed cis regulatory difference between two alleles. (c) cis regulatory difference is a
quantitative trait and depends on trans loci and/or maternal genotype. (d) cis regulatory variation is caused by imprinting. The labels above each
bar represent corresponding marker genotypes at the test locus. The maternal allele is listed first. Any mixture of these four possibilities is
possible for each gene.
The Nordborg (University of Southern California) and
Borevitz groups have recently developed a second generation SNP-tiling array for Arabidopsis thaliana. AtSNPtile1
arrays are publicly available and contain probes for each
allele and each strand of 250 000 known non-singleton
SNPs, as well as 1.7 million unique 25mer tiling probes
covering the non-repetitive part of the genome at 35 bp
resolution. A single array is used to hybridize RNA or
genomic DNA derived from one of many different
hybrids and inbred accessions. DNA-based SFPs are
detected and can be masked prior to transcription profiling, thereby reducing the confounding effects of transcript variation and hybridization variation in RNA signal.
This is particularly important when profiling transcripts in
species that have high genetic diversity [54]. In addition,
among the 250 000 common SNPs, around 56 000 are
annotated as transcribed. This allows most genes to be
assayed redundantly for ASE. Because both SNP alleles
are represented on the array, both the transcript levels and
the genotype of the message can be jointly assayed
(Figure 1). The raw probe intensity at SFPs can also
indicate ASE [55]. However, contrasting SNP alleles will
be more powerful and more comprehensive with this
dual-use array reagent. Even with 35 bp resolution, small
exons of length less than 70 bp are likely missed as
estimating the expression level of an exon with two or less
probes is unreliable due to possible noise and probe
effect. In addition, it has been argued that 25-mer oligos,
although highly specific, are less sensitive than longer
oligos [56], meaning that not every probe gives a reliable
signal.
Current Opinion in Plant Biology 2007, 10:142–148
AtSNPtile also contains probes to cover centrally all 130
000 CCGG sites for methylation analysis, in which
enzyme treatments (i.e. HpaII and MspI) are contrasted
after DNA hybridization. Given the Arabidopsis genome
size of 120 Mb, this resolution is equivalent to surveying
potential methylation sites at an average density exceeding one site per 1 kb. Importantly, specific CCGG sites
are individually profiled for potential genetic, environmental, or genotype x environmental effects on regulation. The anti-5-methylcytosine ChIP-chip approach also
has a resolution of 1 kb [6] and selects for regions of
highly methylated DNA. Specific sites are not tested in
this case, however, so the dynamics of individual methylation sites cannot be assessed. The approach outlined in
Figure 2 shows how methylation variation can be revealed
at specific CCGG sites using the alternative enzyme
methylome strategy. An additional advantage of the
enzyme methylome approach, which involves whole-genome labeling, is that genetic variation at SNPs and indels
can be simultaneously assayed. Because genetic effects
are independent of enzyme treatment, the two hybridizations are essentially technical replicates.
Conclusions and outlook
Genetic studies in the past few years have provided
insights and uncovered some unexpected patterns of
natural variation in transcriptional regulation. Additional
empirical data are essential to generalize the conclusions
and to test new hypotheses. With the advance of microarray technology, studies of transcriptional variation will
extend from transcriptional profiling to surveying
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Genetic and epigenetic dissection of cis regulatory variation Zhang, Richards and Borevitz
multiple gene expression traits, dissection of the underlying genetics, and assessment of the epigenetic and
environmental dependence of this variation.
Acknowledgements
We thank James Ronald, Trisha Wittkopp and Ed Buckler for discussion
and comments, and National Institutes of Health (NIH) grant
RO1GM073822 to JB for development of AtSNPtile1.
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