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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 www.sciencedirect.com 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). www.sciencedirect.com 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 www.sciencedirect.com 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 www.sciencedirect.com 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 www.sciencedirect.com 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. References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1. Yvert G, Brem RB, Whittle J, Akey JM, Foss E, Smith EN, Mackelprang R, Kruglyak L: Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat Genet 2003, 35:57-64. 2. Brem RB, Yvert G, Clinton R, Kruglyak L: Genetic dissection of transcriptional regulation in budding yeast. Science 2002, 296:752-755. 3. Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G et al.: Genetics of gene expression surveyed in maize, mouse and man. Nature 2003, 422:297-302. 4. 147 14. 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