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. 2012 Oct 26;161(1):486–496. doi: 10.1104/pp.112.204404

Genome-Wide Analysis of Stowaway-Like MITEs in Wheat Reveals High Sequence Conservation, Gene Association, and Genomic Diversification1,[C],[W]

Beery Yaakov 1,2, Smadar Ben-David 1,2, Khalil Kashkush 1,*
PMCID: PMC3532278  PMID: 23104862

Abstract

The diversity and evolution of wheat (Triticum-Aegilops group) genomes is determined, in part, by the activity of transposable elements that constitute a large fraction of the genome (up to 90%). In this study, we retrieved sequences from publicly available wheat databases, including a 454-pyrosequencing database, and analyzed 18,217 insertions of 18 Stowaway-like miniature inverted-repeat transposable element (MITE) families previously characterized in wheat that together account for approximately 1.3 Mb of sequence. All 18 families showed high conservation in length, sequence, and target site preference. Furthermore, approximately 55% of the elements were inserted in transcribed regions, into or near known wheat genes. Notably, we observed significant correlation between the mean length of the MITEs and their copy number. In addition, the genomic composition of nine MITE families was studied by real-time quantitative polymerase chain reaction analysis in 40 accessions of Triticum spp. and Aegilops spp., including diploids, tetraploids, and hexaploids. The quantitative polymerase chain reaction data showed massive and significant intraspecific and interspecific variation as well as genome-specific proliferation and nonadditive quantities in the polyploids. We also observed significant differences in the methylation status of the insertion sites among MITE families. Our data thus suggest a possible role for MITEs in generating genome diversification and in the establishment of nascent polyploid species in wheat.

Wheat (Triticum-Aegilops group) likely originated from a common ancestor some 4 million years ago and has since undergone multiple polyploidization events. As such, this organism has been the subject of substantial research into genomic evolution and diversification. Beginning with three ancestral diploid species, two major allopolyploidization events subsequently occurred, resulting in the appearance of tetraploid (pasta) wheat (Triticum turgidum ssp. durum; 2n = 4x = 28; genome AABB) around 0.5 million years ago and hexaploid (bread) wheat (Triticum aestivum; 2n = 6x = 42; genome AABBDD) around 10,000 years ago (Feldman and Levy, 2005). Bread wheat harbors three distinct, yet related, genomes, namely the Au genome originating from Triticum urartu, the B (or S) genome originating from a section of Sitopsis species, most probably Aegilops speltoides or Aegilops searsii, and the D genome originating from Aegilops tauschii (Petersen et al., 2006). The availability of several diploid ancestors of wheat and their polyploid species as research models allows for the tracking of those evolutionary changes that enabled diversification of the different genomes as well as their differentiation within the polyploid species. Past studies on phylogenetic relationships between members of the Triticum-Aegilops group employed nuclear (Mori et al., 1995; Sasanuma et al., 1996; Wang et al., 2000a; Huang et al., 2002; Kudryavtsev et al., 2004; Sallares and Brown, 2004) or organellar (Wang et al., 2000b; Haider and Nabulsi, 2008) DNA markers to cluster divergent species. At the same time, molecular markers have been developed to study wheat phylogeny resulting from polymorphism in transposable element (TE) insertions (Queen et al., 2004; Kalendar et al., 2011; Baruch and Kashkush, 2012), including miniature inverted-repeat transposable elements (MITEs; Yaakov et al., 2012).

TEs are sequences of DNA that multiply independently of the cell cycle, with some sequences, termed retrotransposons, relying on transcription to “copy and paste” themselves into new sites in the genome. A second group of sequences, termed DNA transposons, employ a recombination-like mechanism to the same end (Wicker et al., 2007). The host genome combats the potential deleterious effects of TE activity by inhibiting their transcription and transposition through epigenetic mechanisms, such as cytosine methylation, chromatin modification, and RNA interference. MITEs are nonautonomous DNA elements (i.e. sequences that rely on transposases expressed by autonomous elements for their transposition) and are ubiquitous to eukaryotic genomes. MITEs are very short in length, containing up to a few hundred base pairs, and present structural similarity, conserved terminal repeats, and high copy numbers in some species (Wicker et al., 2001; Jiang et al., 2004; Isidore et al., 2005; Miller et al., 2006; Cloutier et al., 2007; Choulet et al., 2010). Moreover, MITEs have been shown to be active in rice (Oryza sativa; Jiang et al., 2003; Kikuchi et al., 2003; Nakazaki et al., 2003; Shan et al., 2005; Naito et al., 2006, 2009).

Although several Stowaway-like MITE families have been characterized in wheat, their structural similarity, level of activation, epigenetic regulation, and association with wheat genes are poorly understood. In this study, we performed a detailed analysis of thousands of MITE insertions belonging to 18 Stowaway-like elements as found in publicly available wheat sequences, including the 454-pyrosequencing draft of a hexaploid wheat ‘Chinese Spring’ genome. As was reported for MITEs in other fully sequenced plant genomes, we noted high sequence conservation, most notably TA-dinucleotide target site preference, and significant association with transcribed regions in wheat. We also noticed a significant correlation between the average length of a MITE family and its copy number in hexaploid wheat. Furthermore, we assessed the genomic composition of nine MITE families using real-time quantitative PCR (qPCR) in 40 different accessions of wheat, including Triticum spp. and Aegilops spp., and predicted their copy numbers, based on the number of elements for each family, estimated bioinformatically in the hexaploid, employing information from the 454-pyrosequencing database. The qPCR data revealed Triticum spp. or Aegilops spp. element specificity as well as deviations from expected additive values in the polyploid species.

RESULTS

In Silico Analysis of MITEs

Retrieval of Stowaway-Like MITE Families from the Wheat 454-Pyrosequencing Database

The availability of a 454 sequence draft for hexaploid wheat facilitated a genome-wide analysis of 18 characterized Stowaway-like MITE families (publicly available at the Triticeae Repeat Sequence Database; http://wheat.pw.usda.gov/ITMI/Repeats/), given their short length (Table I). Overall, 18,217 MITE insertions were retrieved in silico, using the MITE analysis kit (MAK) software (kindly provided by Guojun Yang, University of Toronto; Yang and Hall, 2003; Janicki et al., 2011). The publicly available sequence of each of the 18 MITEs was used as a query in the MAK program to perform BLASTN against the draft 454-pyrosequencing database. Use of the MAK software also retrieved 100 bp of flanking sequence (5′ and 3′ flanking sequences) and indicated terminal duplications for each hit. As a negative control, we included the publicly available sequence of a rice-unique MITE family, termed mPing (Jiang et al., 2003), as a BLASTN query against the draft 454 wheat sequence database. As expected, no sequences were retrieved from the wheat genomic database in this case. It is important to mention that because of the unassembled (5× coverage) reads and because of the quality of the sequence information, we used the following criteria in our analysis. (1) Output sequences from BLASTN with the same identifier number in the 454-pyrosequencing database were removed from the analysis, because in some cases the MAK software-generated output file included sequences from both positive and negative strands. We noticed this phenomenon for MITE families that contain short internal sequences (such as Athos; Table I), meaning that both positive and negative stands can pass the E-value used in the BLAST analysis. (2) The 454-pyrosequencing reads that contain nearly intact elements that significantly align with the query transposon sequence were included in our analysis. It is important to note that we considered the elements that were truncated at one of the terminal sequences as being nearly intact elements. (3) Duplicated hits in the MAK output file, resulting from duplicate reads in the 454-pyrosequencing database, were removed manually by BLAST-based sequence alignment of the flanking sequences of all output MAK file sequences to each other and subsequent exclusion of similar sequences (see “Materials and Methods”). Thus, the number of retrieved elements could be an underestimation and might not represent the true copy number of each family in hexaploid wheat. With this in mind, we noted a massive difference in the number of retrieved insertions in each family, from 14 insertions for Phoebus and up to 2,604 and 4,855 insertions for Hades and Athos, respectively (Table I). When considered together, the retrieved MITE sequences account for approximately 1.3 Mb (calculated based on copy number and average element size) of the approximately 17,000 Mb that constitutes the wheat genome.

Table I. In silico analysis of 18,217 Stowaway-like MITEs.
TE Copy No. Element Size TIR Size Target Site Preference TE BLAST Hitsa Flanking BLAST Hitsb
EST mRNA Total EST mRNA Total
bp % %
Athos 4,855 85 41 TA 38 129 167 (3.4) 1,488 1,071 2,559 (52.7)
Hades 2,604 96 22 TA 25 26 89 (3.4) 1,278 556 1,834 (70.4)
Thalos 2,031 162 61 TA 300 78 378 (18.6) 1,125 266 1,391 (68.4)
Icarus 1,663 112 28 TA 115 27 142 (8.5) 895 239 1,134 (68.1)
Xados 1,391 116 30 TA 79 10 41 (2.9) 595 271 866 (62.2)
Minos 1,132 236 25 TA 10 0 29 (2.5) 300 232 532 (46.9)
Pan 1,048 127 58 AC 37 4 51 (4.8) 782 219 1,001 (95.5)
Aison 775 219 45 TA 21 8 3 (0.3) 393 202 595 (76.7)
Eos 615 354 52 CTTAG 3 0 10 (1.6) 470 39 509 (82.7)
Stolos 538 259 21 TA 2 0 2 (0.3) 149 52 201 (37.3)
Oleus 489 150 30 TA 12 4 16 (3.2) 86 78 164 (33.5)
Antonio 415 108 25 TA 6 3 9 (2.1) 157 99 256 (61.6)
Minimus 335 55 26 TA 2 0 2 (0.5) 157 76 233 (69.5)
Fortuna 169 353 30 TA 2 0 2 (1.1) 118 49 167 (98.8)
Tantalos 112 257 30 TA 1 0 1 (0.8) 23 11 34 (30.3)
Polyphemus 16 241 73 TA 0 0 0 (0) 16 3 19 (118.7)
Jason 15 260 51 TA 0 1 1 (6.6) 0 1 1 (6.6)
Phoebus 14 319 15 CG 0 0 0 (0) 9 2 11 (78.5)
Total 18,217 – – – – – 943 (5.1) – – 11,507 (63.1)
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a

Number (and percentage of the overall number of TEs) of TEs containing EST hits.

b

Number (and percentage of the overall number of TEs) of TE-flanking sequences (within 100 bp downstream and/or upstream of a TE) containing EST hits.

High Level of Conservation of Stowaway-Like MITE Families in Wheat

Detailed analysis using Galaxy software (see “Materials and Methods”) of each MITE family showed a high level of conservation in average element length (Supplemental Fig. S1). For all MITE families, we noted very low variation in the length of the different members of the same family, as the sd varied between 3.9 and 9.5 bases (Supplemental Fig. S1). This is despite the fact that truncated elements (nearly intact elements; see above) were included in the analysis. In addition, we observed high sequence conservation for the 18 MITE families, as revealed by multiple sequence alignment. The level of sequence similarity ranged from 61% for high-copy-number families, such as Athos, Hades, and Thalos, and up to 99% for families with low copy numbers, such as Jason, Phoebus, and Polyphemus. Interestingly, sequence conservation at terminal inverted repeat (TIR) regions was very high for all MITE families (over 95%). It is important to note that our analysis was unbiased toward highly conserved elements, because an E value of e−3 was used to retrieve sequences with the MAK software. Recall that we retrieved no mPing elements in the wheat database using the same E value, indicating that no artifacts were obtained in our analysis.

The MAK software also retrieves short duplicated target site sequences based on the analysis of both flanking sequences of a MITE element. We described target site preferences using the short duplicated output sequences created by MAK as an input file in the WebLogo 3.0 package (Crooks et al., 2004). Briefly, WebLogo calculates the relative frequency of each nucleotide at a given position and their relative abundance at different positions (see “Materials and Methods”). The observed logos with significant probabilities of certain nucleotides indicate target site preference. This analysis revealed that the 18 MITE families possess notable target site preference (Table I; Fig. 1). In most cases, the target site preference was the TA dinucleotide, in agreement with the literature on the preference of Stowaway-like MITEs (for review, see Jiang et al., 2004).

Figure 1.

Figure 1.

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Target site preference of MITE insertions, as analyzed by WebLogo 3.0. Analysis was performed based on the sequence of target site duplications retrieved from wheat databases by MAK software. The name of each MITE family is indicated on top of each logo. [See online article for color version of this figure.]

Annotation of MITEs and Flanking Sequences

Because MITEs are nonautonomous, namely lacking sequences that code for transposases and a promoter, it is assumed that they are not transcribed. However, when performing BLAST with MITE sequences against Triticum spp. and Aegilops spp. EST and mRNA databases from the National Center for Biotechnology Information (NCBI), we identified 943 unique chimeric transcripts (653 unique ESTs and 290 unique transcripts containing mRNA characteristics) that contained MITE sequences (Table I). As these 943 transcripts are unique, we assumed them to contain different MITE insertions and thus concluded that approximately 5.1% (943 of 18,217) of the retrieved MITEs underwent transcription, most probably from adjacent promoters. We then tested the locations of the additional MITE insertions (18,217 – 934 = 17,283 elements) by annotating MITE-flanking sequences that were retrieved by MAK, together with the MITEs (see “Materials and Methods”). However, because of the short read length (approximately 388 bp on average) of the 454-pyrosequencing database sequences, we were only able to retrieve short flanking sequences (approximately 100 bp from each side of the element). Surprisingly, we found that approximately 63% of the MITE insertions (11,507 of the 18,217 elements) are located adjacent (within 100 bp) to unique transcribed sequences (Table I). Detailed analysis led to the identification of 76 MITE insertions within introns or near well-characterized wheat genes (Supplemental Table S1). Specifically, 20 MITE insertions (26.3%) were found in the introns of 11 genes (Supplemental Fig. S2), 24 insertions (31.5%) were found upstream of the 5′ untranslated region of a gene, and 32 insertions (42.1%) were found downstream of the 3′ untranslated region of a gene (Supplemental Table S2). The MITE-containing genes included those involved in disease resistance, transport, cell division, DNA repair, transcription, and other roles as well as a glutenin precursor.

Massive Variation in MITE Composition in Triticum and Aegilops Species

To evaluate the contribution of MITEs to genomic diversification among wheat species, we performed qPCR on genomic DNA from 40 accessions of Triticum spp. and Aegilops spp. These included 10 different species (37 diploid and three polyploid; Supplemental Table S3), of which 19 contain B genomes, 10 contain D genomes, eight contain A genomes, two contain AB genomes (tetraploids), and one contains an ABD genome (hexaploid; Supplemental Table S3). Of the 18 MITEs analyzed (Table I), only nine allowed efficient primer design for real-time PCR analysis (an example of quality control for qPCR experiments is shown in Supplemental Fig. S3). By visualizing the PCR products on 1.5% agarose gels, PCR amplification quality was further validated (Supplemental Fig. S4). It is important to note that two different pairs of primers were designed for some MITE families so as to ensure reproducibility of the qPCR results. The absolute copy number of each MITE in each genome was calculated based on an estimated copy number in T. aestivum (cv Chinese Spring wheat) derived from the 454 database (Table I; see “Materials and Methods”). Thus, the copy number of any genome is the ratio of its relative quantity to the relative quantity of T. aestivum, multiplied by the estimated copy number for cv Chinese Spring wheat. Additional validation of the relative quantification of MITEs in different wheat species was derived from our 454-pyrosequencing analysis of transposon display (TD) products (Yaakov and Kashkush, 2012). TD allows the amplification of multiple TE insertions using a TE-specific primer together with an adaptor primer. We performed 454-pyrosequencing of TD products of one MITE family called Minos in four wheat species: A. tauschii, A. sharonensis, T. monococcum, and T. durum (Yaakov and Kashkush, 2012). The results show that the relative quantities of copy numbers, as provided by both qPCR analysis (this study) and 454-pyrosequencing of TD products (Yaakov and Kashkush, 2012), in the four wheat species are very similar (Supplemental Fig. S5).

The qPCR results further demonstrate the massive proliferation of some MITEs in the A genome (Fig. 2), as all accessions of T. urartu and species containing the A genome (i.e. T. urartu, T. monococcum, T. dicoccoides, T. durum, and T. aestivum) showed some base level of MITE copy number, with most showing high levels. Furthermore, two MITE families (Minos and Fortuna) were specifically amplified in this genome (i.e. Triticum spp.-specific amplification; Fig. 2, A and C). The other A genome species, T. monococcum aegilopoides (genome Am), showed similar copy numbers to T. urartu (genome Au) for four of the nine MITEs (Aison, Oleus, Icarus, and Polyphemus; Fig. 2, B, D, E, and G).

Figure 2.

Figure 2.

Figure 2.

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Copy numbers of MITE families Minos (A), Aison (B), Fortuna (C), Oleus (D), Icarus (E), Phoebus (F), Polyphemus (G), Stolos (H), and Eos (I) in various wheat accessions, based on qPCR and the 454-pyrosequencing database. The accession names and plant identifiers or U.S. Department of Agriculture inventory numbers (Supplemental Table S3), as well as respective species names and genome composition, are indicated. sd is indicated based on three replicates. [See online article for color version of this figure.]

In considering Aegilops spp., only A. speltoides showed species-specific proliferation (for Aison; Fig. 2B). In addition, copy numbers in A. speltoides and A. searsii were clearly distinguishable from one another (Fig. 2, B, C, and E–G) in five of nine MITEs (Aison, Fortuna, Icarus, Phoebus, and Polyphemus), while A. sharonensis (accession TH02) and A. longissima (accession TL05) were similar to A. searsii and A. tauschii (and different from A. speltoides) in three MITEs (Aison, Icarus, and Polyphemus; Fig. 2, B, E, and G), yet they differed from A. searsii and A. tauschii in one MITE (Fortuna; Fig. 2C).

As expected, none of the analyzed MITEs had low copy numbers in the polyploid species. In addition, of the nine MITEs analyzed, only two (Minos and Fortuna) showed a shift from the expected additive values of the parental species (Fig. 2, A and C) in the polyploid species (reflected as an increase in the tetraploid level and a reduction in the hexaploid level in Fortuna and vice versa for Minos) that could not be explained by any combination of accessions of the parental species. Interestingly, these two elements are the only ones specific to Triticum spp. Note that the nonadditive values that were observed in these two cases, of the nine cases considered, were derived from the available wheat accessions analyzed in this study.

The combination of MITE copy numbers from A genomes and B genomes, as compared with the tetraploid genomes, was best explained when T. urartu was combined with A. speltoides for two elements (Aison and Icarus) or with A. searsii for one element (Stolos). For the remaining MITEs, this difference could be explained by combining either both or neither of these species.

Examination of the intraspecific coefficient of variation of MITE copy numbers in different accessions of each species revealed that A. speltoides presents the most variation in MITE copy number, specifically showing high and significant variation in three elements (Oleus, Eos, and Stolos). T. urartu accessions showed high and significant variation in two elements (Minos and Icarus). For example, the copy number of Minos in TMU38 was approximately 5-fold higher than in the other T. urartu accessions (Fig. 2A). This value was obtained in experiments repeated three times, using three replicates in each experiment. Similarly, the copy number of Aison in accession 6008 was approximately 8-fold more than in TS47 (Fig. 2B). Furthermore, significant variations were observed in A. searsii accessions for Aison (Fig. 2B), with some accessions including one or more copies (such as TE16 and TE44), while others included over 170 insertions (such as 599124 and 599149). In addition, the coefficient of variation was higher between species (interspecific variation) than within a species (intraspecific variation) for six of nine elements considered (Fortuna, Minos, Aison, Icarus, Phoebus, and Stolos).

Cytosine Methylation of MITEs in Hexaploid Wheat

To assess the involvement of epigenetic regulation in the activity of MITEs in natural allohexaploid wheat, we performed transposon methylation display (TMD) on 13 MITE families (Eos, Fortuna, Oleus, Minos, Thalos, Aison, Antonio, Hades, Jason, Phoebus, Polyphemus, Tantalos, and Xados). TMD allows for analysis of the methylation status of MITE-flanking CCGG sites in a genome-wide manner (Khasdan et al., 2010; Kraitshtein et al., 2010; Yaakov and Kashkush, 2011). Genomic DNA was restricted with either of two methylation-sensitive enzymes (HpaII or MspI), ligated to adaptors, and amplified with radiolabeled primers specific to the adaptor and transposon sequences. The resulting polyacrylamide gel band patterns were analyzed by comparing the ratio of amplicons that exist in only one restriction digest (e.g. bands generated with HpaII only indicate hemimethylation of the outer cytosine [i.e. CNG methylation], whereas bands generated with MspI only indicate methylation of the inner cytosine [i.e. CG methylation], in CCGG TE-flanking sites) to those found in both restriction digestions (monomorphic bands). An example of a TMD gel is presented in Supplemental Figure S6.

Using TMD, we analyzed between 60 and 115 CCGG sites flanking each of the 13 MITE elements (Table II). For each element, we calculated the number of unmethylated CCGG sites (monomorphic bands generated upon digestion with HpaII and MspI; Supplemental Fig. S6) and the number of methylated CCGG sites (polymorphic bands generated upon digestion with HpaII and MspI, where a MspI-unique band indicates CG methylation and a HpaII-unique band indicates CNG methylation; Table II; Supplemental Fig. S6). The TMD results showed that different levels of cytosine methylation are observed at CCGG sites flanking the different MITE elements in T. aestivum (Table II). Methylation levels ranged from 52.9% for Hades-flanking CCGG sites to 87.2% for Thalos-flanking CCGG sites. This indicates that different MITE elements exist in different methylation environments. It is important to mention that for most elements, CNG hemimethylation was predominant, except for Thalos-flanking CCGG sites, where CG methylation was predominant (Table II). Thalos, however, resides in relatively heavily methylated sites (87.2% methylated flanking CCGG sites). These data thus support our previous conclusion that Thalos might be the least active MITE in wheat (Yaakov and Kashkush, 2011), while Hades and Minos might be the most active (Yaakov and Kashkush, 2012).

Table II. Analysis of the methylation status of CCGG sites flanking 13 Stowaway-like MITEs in T. aestivum, as revealed by TMD.

MITE Family No. of Methylated CCGG Sitesa Monomorphic Bandsb Totalc
CNG CG
%
Hades 29 16 40 85 (52.9)
Thalos 25 70 14 109 (87.2)
Xados 25 20 19 64 (70.3)
Minos 18 18 24 60 (60)
Aison 48 13 13 74 (82.4)
Eos 16 21 27 64 (57.8)
Oleus 64 18 33 115 (71.3)
Antonio 39 15 14 68 (79.4)
Fortuna 35 23 34 92 (63)
Tantalos 42 20 18 80 (77.5)
Polyphemus 45 30 33 108 (69.4)
Jason 60 16 20 96 (79.2)
Phoebus 49 32 11 92 (88)
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a

Bands present only in samples digested by HpaII or only in samples digested by MspI are considered as being methylated in CNG and CG contexts, respectively (see “Materials and Methods”).

b

Monomorphic bands from the HpaII and MspI digestions indicate nonmethylated CCGG sites.

c

The total number of bands indicates the number of analyzed CCGG sites (both methylated and nonmethylated). The level of methylated CCGG sites is also indicated. It is important to note that the number of CCGG sites flanking MITE insertions is not directly correlated with the number of MITE insertions, as some insertions have several CCGG sites that can be analyzed by TMD (see “Materials and Methods”).

DISCUSSION

The evolution of genomes, as reflected in both the diversification of related species and the differentiation of homeologous chromosomes in allopolyploids, is realized by various rapid (revolutionary) and slow (evolutionary) mechanisms, including the activation of TEs (Chantret et al., 2004; Kazazian, 2004; Feldman and Levy, 2005). A detailed mechanism describing the impact of transposons on genomic evolution, however, has yet to be presented. Furthermore, any mechanistic description of TE-mediated genomic evolution would necessarily have to take into account the epigenetic changes induced by transposition as well as the influence of such changes on chromosomal structure and gene expression (Slotkin and Martienssen, 2007). Thus, a genome-wide examination of genetic and epigenetic variation of Stowaway-like MITEs between related species and their combined polyploid species might provide a mechanistic perspective on this important category of transposons.

In this study, we retrieved and analyzed the sequences of over 18,000 MITE insertions belonging to 18 Stowaway-like families in wheat. As expected for MITEs, based on genome-wide studies in rice (Jiang et al., 2004), all 18 families were short in length (ranging from 55 to 354 bp), presented high sequence conservation, and displayed a clear preference for TA dinucleotides as a target site (Table I). In addition, we found that wheat MITEs might exist in strong association with genes or transcribed regions. Indeed, the strong association of MITEs and wheat genes was reported previously, based on analysis of a subset of bacterial artificial chromosome (BAC) sequences (Sabot et al., 2005; Choulet et al., 2010). Furthermore, massive copy number variation was seen among the 18 MITE families, with values ranging from 14 copies up to 4,855 (Table I). In addition, genome-specific proliferation of MITEs may contribute to genomic diversification in diploid species and, possibly, to the differentiation of subgenomes in allopolyploid species, an event that might aid in their diploidization. Moreover, we noticed that the relatively short members of MITE families (namely, those less than 150 bp in length) had the highest copy numbers, while long elements (measuring over 200 bp in length) had the lowest copy numbers. This negative correlation was found to be statistically significant (Fig. 3). Finally, we also found that the methylation levels of CCGG sites surrounding each family differed substantially among MITE families (ranging from 52.9% to 88%), indicative of different levels of regulation among these elements.

Figure 3.

Figure 3.

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Correlation between the copy number of each MITE family and average length, as calculated for elements retrieved from the 454-pyrosequencing database. The r2 and P values are indicated. Error bars represent sd for MITE length. [See online article for color version of this figure.]

Genome-Specific Proliferation of MITEs

TEs assume a central role in the formation and maintenance of structural elements of the genome, including telomeres and centromeres. TEs can affect the structure of the genome and the regulation of genes by inducing changes in DNA methylation and heterochromatin and by the production of small RNAs (Nakayashiki, 2011). The tendency of TEs to cause mutations, both genetic and epigenetic, has supposedly been coopted by the host genome to increase genetic variability, as TEs are known to be active during stress, in gametes, and in early development (Levin and Moran, 2011). An analogous mechanism may be acting on genomes undergoing “genomic stress,” such as new polyploids, or over large expanses of time, following reproductive isolation of a species. With this in mind, we calculated the relative quantities of nine MITE families using qPCR for 40 accessions of 10 species of wheat. We then translated the PCR data into absolute copy numbers based on the observed copy number of MITE families in hexaploid wheat, with each experiment being repeated at least three times using different primer pairs (sd is indicated in each figure). The results demonstrated specific proliferation of two MITE families (Minos and Fortuna; Fig. 2, A and C, respectively) in the A genome and one in the B genome of A. speltoides (Aison; Fig. 2B). Furthermore, differences revealed by qPCR between intraspecific and interspecific copy number variations of MITEs in the diploid wheat genomes suggest that MITEs play a role in the diversification of genomes during speciation. We specifically focused on MITE content in A. speltoides and A. searsii, the two best candidates for contributing the B genome to wheat. MITE content was clearly distinguishable between the two species (Fig. 2, B, C, and E–G) in five of nine MITEs (Aison, Fortuna, Icarus, Phoebus, and Polyphemus). We also found that A. sharonensis (accession TH02) and A. longissima (accession TL05) were similar to A. searsii and A. tauschii (and different from A. speltoides) in three MITEs (Aison, Icarus, and Polyphemus; Fig. 2, B, E, and G). These data, together with the finding that Aison (Fig. 2B) specifically proliferated in A. speltoides, support A. speltoides as being the choice candidate for donating the B genome to wheat. Our data, however, do show that the diploid donor of the B genome underwent massive genomic changes after the formation of the allotetraploid. In addition, we showed a nonadditive change in the polyploid species, as compared with their progenitors, in two MITEs that displayed specific proliferation in the A genome (Fortuna and Minos), suggesting that T. urartu is the true donor (Feldman and Levy, 2005). This result, in concert with known genetic and epigenetic changes that occur following polyploidization, including transcriptional (Kashkush et al., 2002, 2003) and transpositional (Kraitshtein et al., 2010; Yaakov and Kashkush, 2012) activation of transposons, implies that TEs respond to hybridization, resulting in the differentiation and diploidization of the subgenomes.

Correlation between Element Length and Copy Number

In a recent study of rice, we showed a possible connection between the copy numbers of TEs and the methylation levels of flanking CCGG sites, where a negative correlation was seen in different rice strains for a MITE family termed mPing (Baruch and Kashkush, 2012). The nature of the connection between the methylation of TE insertion sites and TE copy number could be explained by a difference in the genomic context of the initial insertion of an element. Whereas high-copy-number MITEs were inserted into euchromatic regions, where they are able to easily proliferate, low-copy-number MITEs were inserted into heterochromatic regions, where element transposition is hindered by the silenced chromatin. Here, we assessed the nature of this connection in detail for 13 MITE families in a natural hexaploid wheat ‘Chinese Spring’. The overall methylation level of all MITE sites was high, as reported previously (Yaakov and Kashkush, 2011), yet we found no correlation with copy numbers. We did, however, note a significant negative correlation between mean element lengths (as calculated for all elements retrieved from the 454 database; Supplemental Table S1) and their copy numbers (P = 0.0297, r2 = 0.28; Fig. 3). This result suggests three possible reasons for the success of short-sequence MITEs: (1) short-sequence MITEs can evade the epigenetic silencing mechanisms imposed on larger elements; (2) short-sequence MITEs are less likely to be eliminated by recombinational mechanisms; and (3) the chances of short-sequence MITEs to transpose is higher due to the proximity of the TIRs to one another.

In summary, this study has demonstrated that wheat MITEs may have retained their activity throughout evolution. As such, MITEs might play a prominent role in the diversification of the wheat genome, specifically in the stabilization of nascent polyploid species in nature, and could provide new insight into the origin of the B genome.

MATERIALS AND METHODS

Plant Material and DNA Isolation

In this study, 40 accessions of Triticum spp. and Aegilops spp., including 10 different diploid, tetraploid, and hexaploid species, were used (Supplemental Table S3). This includes 34 accessions of four diploid species (Triticum urartu, Aegilops speltoides, Aegilops searsii, and Aegilops tauschii; for details, see Supplemental Table S3). DNA was isolated from young leaves (4 weeks post germination) using the DNeasy plant kit (Qiagen).

In Silico Analysis

MITEs and flanking sequences were retrieved from the cv Chinese Spring 454-pyrosequencing database (5× coverage; kindly provided by members of the Chinese Spring Sequencing Consortium; http://www.cerealsdb.uk.net), where over 95% of the genome is represented by at least one read using the MITE analysis kit (Yang and Hall, 2003) below an E value of e−3, an end mismatch tolerance of 20 nucleotides, and a 100-nucleotide flanking size for retrieved members, and from the NCBI using the BLAST 2.0 package (http://www.ncbi.nlm.nih.gov/BLAST/) on the publicly available Triticum spp. and Aegilops spp. BAC sequences. All analyses included the rice (Oryza sativa)-specific MITE, mPing, as a negative control (Jiang et al., 2003). MAK uses BLASTN to search input MITE sequences against a nucleotide database to retrieve high-scoring pairs, according to a defined E value and nucleotide mismatches at the ends of the sequence, as well as retrieving target site duplications and a defined number of nucleotides flanking the high-scoring pairs. Preparation and statistical analysis of the 454-pyrosequencing reads were achieved using Galaxy (Blankenberg et al., 2010; Goecks et al., 2010). For the calculation of average read lengths and MITE lengths in the 454 database, we used Compute Sequence Length, which calculates the lengths of nucleotide sequences in a FASTA file, and Summary Statistics, which calculates the summation, mean, sd, and various percentiles of a series of numbers (in this case, sequence lengths) in Galaxy. Levels of sequence conservation in each MITE family and analysis of target site preference for each MITE family were performed using MAFFT for multiple sequence alignment (Katoh et al., 2009) and the publicly available online WebLogo 3.0 package (Crooks et al., 2004). The WebLogo 3.0 software creates logos for each MITE family sequence and for target site preferences (for examples, see Fig. 1), where the height of symbols within the stack indicates the relative frequency of each nucleotide at that position, while the width of the stack is proportional to the fraction of valid nucleotides at that position, such that an abundance of short sequences yields thin stacks at the end. It is important to mention that because the 454-pyrosequencing database is not assembled, it includes many redundant sequences. In addition, redundant sequences can be produced as a result of the analysis of both the NCBI and 454-pyrosequencing databases. Redundant MITE-containing sequences were removed manually by comparing a subset of sequences with the database and manually calculating redundancy (the number of sequences with an E value equal to or lower than the query sequence against itself, divided by the total number of sequences analyzed). Copy number was then corrected using this factor.

Annotation of MITE sequences and their flanking sequences was performed against the EST and mRNA databases at PlantGDB (http://www.plantgdb.org/prj/ESTCluster/) and NCBI (http://www.ncbi.nlm.nih.gov/nucest/), respectively. The annotation was performed using BLAST+, stand-alone version 2.2.24. Redundant transcripts and hits below an E value of e−10 were removed from the analysis. The 5′ and 3′ MITE flanking sequences from the 454-pyrosequencing database, as well as the MITEs themselves, were used separately as query against the above-mentioned EST databases. Furthermore, publicly available BAC sequences that contain MITE sequences were analyzed for the association of MITEs with wheat genes (i.e. located in an intron, 1 kb downstream or upstream from a given gene). Statistical analysis of the correlation between the overall methylation status of MITEs or their average length and MITE copy number was performed with JMP version 5 (SAS Institute).

Real-Time qPCR

Primers for previously annotated MITE consensus sequences were designed using Primer Express software, version 2.0 (Applied Biosystems; Supplemental Table S4). Each template for qPCR analysis was run in triplicate reactions, each consisting of 7.5 µL of KAPA SYBR FAST Universal 2× qPCR Master Mix (KAPA Biosystems), 5 µL of DNA template (0.24 ng µL), 1 µL of forward primer (10 µm), 1 µL of reverse primer (10 µm), 0.3 µL of ROX low (serving as a passive reference dye), and 0.2 µL of Ultra Pure Water (Biological Industries). The thermal profile employed with the 7500 Fast Real-Time PCR system (Applied Biosystems) consisted of 20 s at 95°C, then 40 cycles of 3 s at 95°C and 30 s at 60°C. The relative quantity (RQ) of each MITE was measured in comparison with the VRN1 gene and with TQ27 as reference, as described previously (Kraitshtein et al., 2010), based on the following equation: ΔΔCt(test sample) = [Ct(target) – Ct(VRN1)]test sample – [Ct(target) – Ct(VRN1)]TQ27, that is, RQ = (2 × primer efficiency)–ΔΔCt, where Ct denotes the cycle at which the PCR amplification reaches a certain level of fluorescence (Livak and Schmittgen, 2001). The relative quantity for each sample was then normalized to its ploidy level, as tetraploids and hexaploids have twice and three times as many VRN1 genes, as compared with diploids, respectively. Reproducibility of the results was evaluated for each sample by running three technical replicates of each reaction. To distinguish specific from nonspecific PCR products, a melting curve was generated immediately after amplification consisting of a 15-s incubation at 95°C and a 1-min incubation at 60°C, after which time the temperature was increased by increments of 0.1°C s−1 until 95°C was reached. A single specific product was detected using either the target or reference gene as template. The copy number of each accession or species was calculated by multiplying the ratio of its relative quantity to that of Triticum aestivum (accession TAA01) with the copy number (CN) of T. aestivum retrieved from the 454 database for each MITE: (RQsample ÷ RQTAA01) × CNT. aestivum. All primer efficiencies were derived from standard curves with an adequate slope (between −3.0 and −3.6) and r2 > 0.98 (for an example, see Supplemental Fig. S3). Fold amplification at each cycle was calculated according to PCR efficiency, which was deduced by the software from the slope of the regression line (y) according to the following equation: E = [(10−1/y) − 1] × 100. For primers with 100% efficiency, fold amplification equals 2.

Site-Specific PCR

PCR was prepared using 12 µL of Ultra Pure Water (Biological Industries), 2 µL of 10× Taq DNA polymerase buffer (EURX), 2 µL of 25 mm MgCl2 (EURX), 0.8 µL of 2.5 mm deoxyribonucleotide triphosphates, 0.2 µL of Taq DNA polymerase (5 units µL−1; EURX), 1 µL of each qPCR primer (50 ng µL−1), and 1 µL of template genomic DNA (approximately 50 ng µL−1). The PCR conditions employed were 94°C for 3 min, repeat (94°C for 1 min, 60°C for 1 min, 72°C for 1 min) 30 times, and 72°C for 3 min. PCR products (approximately 10 µL) were separated on 1.5% agarose gels and stained with ethidium bromide (Amresco), along with a DNA standard (100-bp ladder; Fermentas). Primer sequences are available upon request.

TMD

TMD reactions were performed according to a previously published protocol (Kashkush and Khasdan, 2007). Briefly, DNA was cleaved with two isoschizomers, HpaII and MspI, both able to recognize CCGG sites, with HpaII being sensitive to methylation of either cytosine (except when the external cytosine is hemimethylated [i.e. when methylation of only one DNA strand occurs]) and MspI being affected only when the external cytosine is methylated. Thus, the different types of CCGG site methylation resulted in different isoschizomer-generated cleavage patterns and the appearance of polymorphic PCR fragments. Gel-based and sequence analyses of the TMD products revealed that each TMD band contains a chimeric (TE/flanking DNA) sequence. Note that in some cases, TE internal sequences might also be amplified, thus enabling analysis of the methylation status of CCGG sites within that transposon.

Primers were designed for 13 of the 18 MITEs (some MITEs did not allow efficient primer design, and some were excluded as they contained a terminal CCGG site). These primers (Supplemental Table S5) were used together with an adapter primer containing four additional selective nucleotides (TCAG) (Kashkush and Khasdan, 2007) to amplify fragments of DNA resulting from the HpaII and MspI digestions. Levels of methylation were calculated by dividing the number of polymorphic bands from the HpaII and MspI digestions (indicating methylated CCGG sites) by the total number of bands. Note that monomorphic bands in both HpaII and MspI digestions, indicative of nonmethylated CCGG sites, were scored only once. It is important to mention that the calculated number of methylation levels might be underestimated, as the TMD assay does not detect cases where both cytosines are methylated, since both isoschizomers do not cleave the site. As such, no PCR products are seen in such instances.

Supplemental Data

The following materials are available in the online version of this article.

Acknowledgments

We thank Dr. Guojun Yang (University of Toronto) for providing the updated stand-alone MAK software, Moshe Feldman (Weizmann Institute) and Hakan Ozkan (University of Cukurova) for providing seed material, and Mike Bevan (John Innes Center), Neil Hall (Liverpool University), and Keith Edwards (Bristol University) for providing access to the 454 database and for their permission to publish the data.

Glossary

MITE

miniature inverted-repeat transposable element

TE

transposable element

qPCR

quantitative PCR

MAK

MITE analysis kit

TIR

terminal inverted repeat

NCBI

National Center for Biotechnology Information

TD

transposon display

TMD

transposon methylation display

BAC

bacterial artificial chromosome

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