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      MSH1-Induced Non-Genetic Variation Provides a Source of Phenotypic Diversity in Sorghum bicolor

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          Abstract

          MutS Homolog 1 ( MSH1) encodes a plant-specific protein that functions in mitochondria and chloroplasts. We showed previously that disruption or suppression of the MSH1 gene results in a process of developmental reprogramming that is heritable and non-genetic in subsequent generations. In Arabidopsis, this developmental reprogramming process is accompanied by striking changes in gene expression of organellar and stress response genes. This developmentally reprogrammed state, when used in crossing, results in a range of variation for plant growth potential. Here we investigate the implications of MSH1 modulation in a crop species. We found that MSH1-mediated phenotypic variation in Sorghum bicolor is heritable and potentially valuable for crop breeding. We observed phenotypic variation for grain yield, plant height, flowering time, panicle architecture, and above-ground biomass. Focusing on grain yield and plant height, we found some lines that appeared to respond to selection. Based on amenability of this system to implementation in a range of crops, and the scope of phenotypic variation that is derived, our results suggest that MSH1 suppression provides a novel approach for breeding in crops.

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          Assessing the Impact of Transgenerational Epigenetic Variation on Complex Traits

          Introduction Continuous trait variation in natural and experimental populations is usually attributed to the actions and interactions of numerous DNA sequence polymorphisms and environmental factors [1]. These so-called complex traits encompass many of the prevalent diseases in humans (e.g. diabetes, cancer) as well as many agriculturally and evolutionarily important traits (e.g. yield, drought resistance, or flowering time in plants). The heritable basis of complex traits is classically thought to rest solely on the transmission from parents to offspring of multiple DNA sequence variants that are stable and causative [1]. However, accumulating evidence suggests that this view may be too restrictive, insofar as chromatin variation (such as differential DNA methylation) can also be propagated across generations with phenotypic consequences, independent of DNA sequence changes [2]–[7]. Indeed, examples of spontaneous, single-locus DNA methylation variants (epialleles) have been reported to influence a range of characters, such as flower shape or fruit pigmentation in plants [8],[9] and tail shape or coat color in the mouse [10],[11]. By extension, these observations raise the possibility that the genome-wide segregation of multiple epialleles could provide a so far unexplored basis of variation for many commonly studied complex traits [12]. In the flowering plant Arabidopsis thaliana, recent large-scale DNA methylation profiling has revealed a substantial degree of differences between natural accessions [13],[14]. As these accessions also differ in their DNA sequences, experimental populations derived from them, such as backcrosses, F2-intercrosses or Recombinant Inbred Lines (RILs) could potentially segregate two independent sources of heritable phenotypic variation, which are difficult to disentangle from each other [12]. As a consequence of this confounding issue, there has been little effort to date to quantify the impact of epigenetic factors on complex traits and to assess their role in the creation and maintenance of phenotypic diversity in experimental or natural settings [2],[6]. To overcome this problem as much as possible, we established a population of epigenetic Recombinant Inbred Lines (epiRILs) in Arabidopsis. This population was derived from two near-isogenic parental lines, one wild type (wt) and the other mutant for the DDM1 gene. DDM1 encodes an ATPase chromatin remodeler that is primarily involved in the maintenance of DNA methylation and silencing of repeat elements [15]–[18]. Thus, ddm1 mutant plants exhibit a ∼70% reduction of DNA methylation overall, as well as a widespread over-accumulation of transcripts corresponding to transposable elements (TEs) [15],[16],[18]. Despite this, few TEs appear to show increased transposition in ddm1 [19],[20], perhaps as a result of many TEs still being targeted by the RNAi-dependent DNA methylation machinery in this mutant background [21]. Consistent with these molecular properties, ddm1 plants exhibit only mild phenotypic alterations, except after repeated selfing, in which case the severity and the number of aberrant phenotypes tend to increase [22]. Genetic analysis has shown that many of these phenotypes segregate independently of the ddm1 mutation and are conditioned by recessive or dominant alleles of single loci. Furthermore, molecular characterization of five of these alleles indicated that they arose through TE-mediated gene disruption in one case [19] and through late onset epigenetic alteration of gene expression in the other cases, often in the context of genes that are tightly associated with TE sequences [19], [22]–[27]. Based on these observations, and given a constant environment, variation in complex traits between epiRILs is expected to result from the stable inheritance of multiple epigenetic differences (epialleles) induced by ddm1 and/or from a small number of DNA sequence differences that might also be present between epiRILs, notably as a result of ddm1-induced mobilization of some TEs. Here, we describe the phenotypic analysis of the epiRIL population, which revealed a high degree of heritability for flowering time and plant height. We also show that the epiRILs differ by numerous parental epialleles across the genome, which demonstrates that DNA methylation differences can be stably inherited over at least eight generations in the absence of extensive DNA sequence polymorphisms and with no selection. These findings provide a first indication of the potential impact of epigenetic variation on complex traits. Results Construction of the Col-wt EpiRILs The epiRIL population was initiated using two closely related parents of the same accession (Columbia, Col), one homozygous for the wild type DDM1 allele (Col-wt), and the other for the ddm1-2 mutant allele (Col-ddm1, 4th generation). Therefore, these two parents should differ extensively in their DNA methylation profiles [18], but only marginally in their DNA sequence, namely at the DDM1 locus itself and at a few other sites, such as those affected by ddm1-induced mobilization of transposable elements (see Materials and Methods and below). A single F1 plant was backcrossed as female parent to the Col-wt parental line. From the backcross progeny, we selected over 500 individuals of DDM1/DDM1 genotype, from which a final population of 505 Col-wt epigenetic Recombinant Inbred Lines (Col-wt epiRILs) were derived through six rounds of propagation by single seed descent and no selection bias (Figure 1; Materials and Methods). The Col-wt epiRILs should therefore have highly similar genomes, but markedly distinct epigenomes, if the many DNA methylation variants induced by ddm1 are stably inherited. 10.1371/journal.pgen.1000530.g001 Figure 1 Construction of the Col-wt epiRILs. Grey bars represent the A. thaliana genome, and triangles represent DNA methylation. Except at the DDM1 locus (black and white squares) located on chromosome 5, the two parents (Col-wt and Col-ddm1) are near isogenic; they differ however in their levels of DNA methylation. An F1 individual was backcrossed to the Col-wt parental line, and 509 DDM/DDM1 BC1 individuals were selfed. After three more selfing (BC1-S4), three independent sublines were established and selfed once to obtain the Col-wt epiRIL population (See Materials and Methods). The Col-wt EpiRILs Show Heritable Variation for Two Quantitative Traits Phenotypic analysis of the Col-wt epiRILs was performed for two quantitative traits, flowering time and plant height at maturity (Table S1). As illustrated in Figure 2 and Figure 3, larger phenotypic variation was observed among the Col-wt epiRILs, than among the Col-wt or Col-ddm1 parental lines (see also Tables S2, S3, S4). Increased phenotypic variation of this kind is indicative of a component of segregational variance that typically arises in the construction of Recombinant Inbred Lines obtained from parents that differ by numerous DNA sequence polymorphisms [1], except that in the present design the two parents are expected to be nearly isogenic. 10.1371/journal.pgen.1000530.g002 Figure 2 Phenotypic distributions. Top panels: density histograms of raw phenotypic values for flowering time and plant height for the Col-ddm1 parental line, the Col-wt epiRILs, and the Col-wt parental line. The units on the x-axis are given in days and cm for these traits, respectively; the y-axis shows the density. Bottom panels: box-whisker plots for the three populations (a: Col-wt parental line; b: Col-ddm1 parental line; c: Col-wt epiRILs) with sample median; the whiskers mark off ±3 standard deviations from the mean; outlier data points are represented by open circles. A total of 16 individual Col-wt epiRIL plants were outliers (>3SD) for flowering time and 52 for plant height. These outliers mainly belong to a few Col-wt epiRILs lines (3 and 8 for flowering time and plant height, respectively) and were removed for subsequent heritability analysis. 10.1371/journal.pgen.1000530.g003 Figure 3 Comparison of phenotypic means and variances. (A) Flowering time. (B) Plant height. The different populations are color-coded as indicated in the top left panel. * p-value (pB ) 3SD) were removed from the analyses. (C,D) For the two traits, density histograms (red) of Col-wt epiRILs line means (‘genetic’ values) are superimposed over a density histogram of the total phenotypic variation (grey histogram with blue density line). By visual inspection, the distribution of the line means is continuous, suggestive of ‘polygenic’ variation for these traits. (E) Bivariate plot and least-squares fit (black line) of Col-wt epiRILs line means between plant height (x-axis) and flowering time (y-axis) reveals a negligible ‘genetic’ correlation, suggesting that these two traits have a largely distinct heritable basis; * p-value 48 days, Figure 2) that are present in Col-wt epiRIL population. While FWA methylation and expression were indistinguishable from wt in all of the non-outlier lines, hypomethylation was observed in the three late flowering outlier lines and was associated with high-level expression in seedlings, where the gene is normally not expressed (Figure 6). Moreover, FWA hypomethylation and transcript accumulation in these outlier lines were much more pronounced than in the Col-ddm1 parental line and were similar to those of a previously described, ddm1-induced late flowering line (Figure 6; [23],[24]). Thus, while the FWA allele of the Col-ddm1 parent was efficiently remethylated and resilenced upon restoration of DDM1 function, further hypomethylation and reactivation occurred instead in rare cases, leading to overtly late flowering Col-wt epiRILs. These results confirm that epiallelic variation at FWA has a major effect on flowering time, but indicate also that it is rare in the Col-wt epiRIL population, concerning phenotypic outliers that were removed from the quantitative genetics analysis. We conclude therefore that epiallelic variation at FWA contributes little to the continuous variation in flowering time observed in the Col-wt epiRIL population. 10.1371/journal.pgen.1000530.g006 Figure 6 DNA methylation and expression analysis of the FWA locus. (A) McrBC-QPCR analysis of DNA methylation. (B) RT–QPCR analysis of transcript levels. Results are expressed as % of expression relative to the average of three control genes (see Materials and Methods). Mobilization of Transposable Elements Occurs in the ddm1 Parental Line and the Col-wt EpiRILs Apart from epialleles, DNA sequence variants caused by ddm1-induced transposon mobilization could also segregate among the epiRILs. To test this possibility, we carried out Southern blot analysis of the insertion profile of CACTA and MULE transposons, which are the two TE families for which ddm1-induced mobility has been documented [19],[20]. Little transposition was detected for any of the three MULE copies in either the three Col-ddm1 individuals or the eight Col-wt epiRILs that were analyzed (Figure 7A). In contrast, several transposition events could be detected for CACTA in the individuals of the Col-ddm1 parental line as well in the Col-wt epiRILs. More specifically, excision events were observed for three of the five CACTA copies that are present in wt Columbia, as indicated by the disappearance of the corresponding hybridizing fragments (Figure 7B, white asterisks). In addition, new insertions were detected, in the form of new hybridizing fragments (Figure 7B, black asterisks). The observation of continuing CACTA mobilization in the Col-wt epiRILs is consistent with previous results indicating that CACTA copies remain transpositionnally active following restoration of wild type DDM1 function through backcrosses [28]. This highly mobile transposon family may therefore contribute to the heritable variation observed among the Col-wt epiRILs, although no obvious association between specific CACTA insertion differences and flowering time variation could be detected based on our limited sampling (Figure 7B). 10.1371/journal.pgen.1000530.g007 Figure 7 Southern blot analysis of TE mobilization. (A,B) Southern blot analysis of two TE families (MULE and CACTA, respectively) in three individuals of each parental line (Col-wt and Col-ddm1), as well as in four early- and four late-flowering Col-wt epiRILs (F9). Genomic DNA was digested using HindIII and hybridized after gel electrophoresis and transfer to a nylon membrane using previously described probes [19],[20]. Question marks indicate possible new insertion sites for MULE. For CACTA, white and black stars designate excision events and new insertions, respectively. The five CACTA copies present in wt Columbia [42] are indicated on the right. Discussion Using a population of “epigenetic” Recombinant Inbred Lines (epiRILs) in the flowering plant Arabidopsis thaliana, we have demonstrated that multiple DNA methylation changes induced across the genome can be stably inherited over at least eight generations in the absence of selection, and that these changes were associated with substantial heritable variation in two complex traits. Furthermore, we show that epiallelic variation at the FWA locus has a major effect on flowering time but is rare in our epiRIL population, indicating that other loci are involved in the continuous variation for that trait in this population. In practical terms, our findings pave the way for the identification of causative epigenetic quantitative trait loci (phQTL epi ; [12]) in the Col-wt epiRIL population using whole genome DNA methylation profiling and classical linkage mapping methods, without the confounding effect of widespread DNA sequence polymorphisms [12]. By combining bisulphite methodology to interrogate the methylation status of individual cytosines with next generation sequencing [30],[31], it may now be possible to identify simultaneously the epigenetic variants segregating in the Col-wt epiRIL population and the inevitable rare DNA sequence variants also present in this population, notably as a result of ddm1induced transposable element mobilization (Figure 7). Alternatively, epigenotyping and genotyping could be carried out independently, using immunoprecipitation of methylated DNA (MeDIP) followed by hybridization to whole genome tiling arrays and next generation sequencing, respectively. The heritability values (around 30%) obtained in our study are similar to those considered in classical breeding programs for the improvement of agronomic traits. If QTL mapping of the Col-wt epiRILs were to confirm that heritability is largely due to variations in DNA methylation states, the view that DNA sequence variation is the sole basis of the heritability of complex traits may need to be revised substantially. In addition, QTL mapping will provide valuable insights into how epigenetic variation can modulate the rate of DNA sequence change in a population, notably through TE mobilization. In the context of evolutionary biology, the existence of an additional mechanism for the creation of heritable variation in complex traits could explain the faster than expected adaptation to environmental change that is often observed in natural populations [32]. There is indeed mounting evidence that epigenetic alterations (epimutations) can arise at high frequency, in response to environmental challenges or ‘genomic shocks’ [5],[33],[34]. Furthermore, our findings provide clear evidence that many epigenetic variants can be stably inherited over numerous generations in the absence of selection ([21]; this study). Such stability could thus provide populations with sufficient time to explore the adaptive landscape [35], and for neutral mutations to accumulate over the new epialleles, in a process that could ultimately lead to genetic assimilation [36]. On the other hand, the observation that about one half of DNA hypomethylation variants induced by ddm1 systematically regain wt DNA methylation over two to five generations ([21]; Figure 5) illustrates the potentially transient nature of many epialleles. However, analysis of FWA indicates that even in the case of these so-called remethylatable alleles, stable transmission of hypomethylated (and reactivated) states can occur at low frequency (Figure 6). Indeed, our findings are consistent with previous observations of sporadic occurrence of stable, phenotypic FWA hypomethylated epialleles (fwa) in ddm1 mutant lines [23]. Furthermore, comparison of FWA methylation and expression levels between the Col-ddm1 parental line and fwa as well as Col-wt epiRIL late flowering outliers suggests that stable transmission of hypomethylated/reactivated FWA can only occur when specific thresholds of hypomethylation/reactivation are reached (Figure 6A). Finally, although no naturally hypomethylated FWA epiallele has been recovered in a survey of 96 Arabidopsis accessions [13], it is tempting to speculate, on the basis of our observations at this locus, that the varying stability of epialleles could underlie the variable penetrance of disease-causing alleles that segregate in pedigrees, as well as the variable onset of many heritable diseases in response to developmental or environmental cues [37]. In summary, our study provides important new evidence that epigenetic variation can contribute significantly to complex traits, and lays the foundation for identifying causative loci. The conditions that promote the occurrence of epialleles and their transgenerational stability in natural settings will need to be further elucidated in order for epigenetics to be fruitfully incorporated into the quantitative genetic analysis of experimental and natural populations [12]. Materials and Methods Construction of the Col-wt EpiRILs and Col-wt Control Lines The recessive ddm1-2 mutation was isolated in a screen for marked decrease in DNA methylation of centromeric repeats in EMS-mutagenized seeds of the Columbia (Col) accession [16]. The Col-wt and Col-ddm1 parental lines were both derived from a ddm1/DDM1 plant stock that had been maintained in the heterozygous state by repeated backcrossing to a wild type Columbia line over six generations to remove EMS-induced mutations unlinked to ddm1 (a kind gift from Eric Richards, Washington University, Saint Louis, MO, USA). Homozygous DDM1/DDM1 and ddm1/ddm1 progeny were subsequently selfed for four generations. In ddm1/ddm1 plants, this generated genome-wide DNA hypomethylation as well as mobilization of some transposable elements ([16], [18]–[20]; Figure 7). A single plant of each genotype (Col-wt and Col-ddm1) was then used for the initial Col-wt epiRIL cross (Figure 1). Unlike in classical RIL construction, the two parents were thus near isogenic, being derived from siblings that underwent four generations of selfing, but differed extensively in their levels and patterns of DNA methylation. The Col-ddm1 parent that was used to initiate the Col-wt epiRIL cross looked normal and did not display any of the developmental epimutant phenotypes that have been reported in advanced ddm1 lines, such as superman [27], fwa [23],[24], ball [22],[25], or bonsai [26]. A single F1 individual was backcrossed to the Col-wt parental line (Figure 1). The BC1 progeny was screened by PCR-based genotyping (Text S1): of the 1140 BC1 individuals genotyped, 577 were ddm1/DDM1, 521 were DDM1/DDM1, and 42 were ddm1/ddm1. This last genotype was indicative of low-level contamination of the backcross progeny with seeds produced by self-pollination of the female F1 parent. Indeed, subtracting 42 and 84 potential self-pollination contaminants from the DDM1/DDM1 and ddm1/DDM1genotypic classes, respectively, gives a corrected total of 479 DDM1/DDM1 and 493 ddm1/DDM1 individuals, close to the 1∶1 ratio expected for the backcross. Only the DDM1/DDM1 individuals were considered for the construction of the Col-wt epiRILs (Figure 1), and our calculations show that this amount of contamination (42 out of 521 or 8% of DDM1/DDM1 BC1 individuals) has a negligible effect on the expected epigenotype frequencies in subsequent generations (Text S1). In total, 509 out of the 521 DDM1/DDM1 BC1 individuals were selfed and one seedling per line was randomly retained from four seeds sown. This process was repeated at each of the following generations (single seed descent (SSD) approach) and ensured that seedlings could be recovered in most instances with no selection bias. Under the assumption of epiallelic stability, each of the DDM1/DDM1 BC1 founders should have inherited from the female F1 parent, on average, 50% of the transmissible DNA methylation alterations that were present in the ddm1/ddm1 grandparent (Figure 1). This should lead, after repeated selfing, to the inheritance of an average of 25% of these alterations in each Col-wt epiRIL, except of course for the 8% of Col-wt epiRILs expected to derive from self-pollination of the female F1 parent, which should have each inherited instead 50% of these alterations on average. Four Col-wt epiRILs were lost during propagation and each of the remaining 505 Col-wt epiRILs was subdivided into three sublines at the F6 generation (Figure 1) to obtain 3×505 BC1-S5 (F7) plants. These were again selfed, and two BC1-S6 (F8) individuals per subline were retained for the phenotypic and quantitative genetics analyses. Since the ddm1 mutation is recessive, it follows that the sublines obtained at BC1-S6 had been free of the conditioning ddm1 mutant allele effect for a total of 8 generations. We also established 24 Col-wt control lines, starting from 24 full-sib individuals of the Col-wt parental line (hence of the same genetic background as the Col-wt epiRILs). These control lines were propagated by repeated SSD, and subdivided into three sublines before phenotypic analyses, using the same method as described above with the Col-wt epiRILs (Figure 1). Experimental Conditions and Phenotype Measurements The Col-wt epiRILs (N = 3030), the Col-wt control lines (N = 144), the Col-wt (N = 200) and Col-ddm1 (N = 200) parental populations were grown simultaneously in two replicate climate-controlled greenhouses under long day conditions (day: 16 h - 20°C/22°C, night: 8 h - 16°C/18°C) with complement of artificial light (105 µE/m2/s) when necessary. For the Col-wt epiRILs, one of the two BC1-S6 plants for each subline was grown in each greenhouse (i.e. 3×505 Col-wt epiRIL plants in each greenhouse). Within each greenhouse, the Col-wt epiRIL plants were randomized over 28 tables (3×1 m2). In addition, two or three plants from each parental line were systematically placed on each table. Finally, the positions of Col-wt epiRILs and parental lines were randomized within tables. Plants were grown in individual pots (7×7×7 cm3) filled with a 90∶10 mix of peat and volcanic sand, and topped with a thin layer of granulated cork. About 15 seeds were sown per pot and seedlings were thinned out to retain a single plant that appeared representative of the whole family. Plants were supplemented twice with a nutritive solution during the reproductive phase. Of the planned design, >99% of plants were available for trait measurements. Flowering time (i.e. number of days between sowing and opening of the first flower) was recorded during plant growth. When plants ceased flowering, they were harvested and stored in herbaria. Plant height was then measured on the dried plants. Statistical Analysis Phenotypic means and variances were calculated for the Col-wt and Col-ddm1 parental lines, the Col-wt epiRILs and the Col-wt control lines. The corresponding 95% confidence intervals were obtained empirically from 3000 non-parametric bootstrap draws. For the Col-wt epiRIL and Col-wt control populations, in which individual plants were phenotypically more similar than plants taken at random, a stratified bootstrap approach was implemented where each line was taken as an independent stratum. In this way, the boostrap estimates are consistent with the stochastic structure of the data and should therefore be unbiased [38],[39]. This resulted in slightly more conservative confidence intervals compared to analytical estimates. This re-sampling strategy was further employed to test for differences in means and variances of the traits between selected sample pairs (i.e. Col-wt epiRIL vs. Col-wt, Col-wt vs. Col-ddm1, Col-wt epiRIL vs. Col-ddm1, etc), yielding a bootstrapped t statistic (tB ) and F statistic (FB ) and their corresponding p-values (pB ), see Tables S2, S3, S4. To test for mean differences we considered the null hypothesis against its alternative . Differences in variances were assessed by testing the null hypothesis against the alternative , where the subscripts distinguish the two different samples in the comparison. To decompose the different sources of phenotypic variation in the Col-wt epiRILs, a linear mixed model was fitted. This model took the following form: , where P is the vector of Col-wt epiRIL phenotypic values, I represents the design matrix for the fixed-effects intercepts β for each of the two greenhouses, E is a vector of micro-environmental values (Text S1) with fixed effect α, L2 is the design matrix for the random Line-effect vector b2, L2 , 3 is the design matrix for the random nested Subline-effect vector b2,3, and ε is the residual error matrix. From the resulting estimates, the variance associated with the Line-effect should be directly interpreted as the portion of total phenotypic variance that is due to epigenetic differences between the lines [40], whereas the Subline-effect estimates the variance due to new DNA sequence mutations or epimutations that may have accumulated independently in the different sublines, gene×environment interactions and maternal effects. All data points exceeding three standard deviations were excluded from the analyses. The p-values associated with each of these effects were obtained from hypothesis testing using the likelihood ratio test , where is the likelihood of the full model and is the likelihood of the reduced model (the full model without the variable of interest). The is distributed as a chi-square random variable with the number of degrees of freedom equal to the difference in the number of parameters between the full and the reduced model. The 95% confidence intervals surrounding the parameter estimates were computed from 5000 parametric bootstrap samples. All analyses were performed in R [41]. Analysis of DNA Methylation, Transcription, and TE Mobilization DNA and RNA were extracted from seedlings and young rosette leaves, respectively, using DNeasy and RNeasy Qiagen kits, respectively. McrBC (New England Biolabs) digestion was performed on 200 ng of genomic DNA. Quantitative PCR was performed using an ABI 7900 machine and Eurogentec SYBR green I MasterMix Plus on equal amounts of digested and undigested DNA samples. Results were expressed as percentage of loss of molecules after McrBC digestion. Reverse transcription was performed on 1 ug of total RNA using oligodT and Superscript II (Invitrogen). Quantitative PCR was performed as above. Results were expressed as percentage of expression relative to the mean value obtained for three genes (At2g36060; At4g29130; At5g13440) that show invariant expression over hundreds of publicly available microarray experiments. Southern blot analysis of TE mobilization was performed as previously described, using 1 µg of genomic DNA [19],[20]. Supporting Information Figure S1 DNA methylation levels measured by McrBC-QPCR. Methylation levels were measured for 14 sequences chosen across the genome. (A) Col-wt and Col-ddm1. (B) Example of segregation of differential DNA methylation among the 22 Col-wt epiRILs tested at the F9 generation (BC1-S7). C) Example of loci with non-segregating, wt level DNA methylation among these 22 Col-wt epiRILs. (0.15 MB PDF) Table S1 Raw phenotypic data. (0.37 MB XLS) Table S2 Estimated population means and variances. (0.01 MB PDF) Table S3 Means comparison. (0.01 MB PDF) Table S4 Variance comparison. (0.01 MB PDF) Table S5 Linear mixed model results. (0.01 MB PDF) Text S1 Supporting materials and methods. (0.26 MB PDF)
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            Epigenetic variation creates potential for evolution of plant phenotypic plasticity.

            Heritable variation in plant phenotypes, and thus potential for evolutionary change, can in principle not only be caused by variation in DNA sequence, but also by underlying epigenetic variation. However, the potential scope of such phenotypic effects and their evolutionary significance are largely unexplored. Here, we conducted a glasshouse experiment in which we tested the response of a large number of epigenetic recombinant inbred lines (epiRILs) of Arabidopsis thaliana--lines that are nearly isogenic but highly variable at the level of DNA methylation--to drought and increased nutrient conditions. We found significant heritable variation among epiRILs both in the means of several ecologically important plant traits and in their plasticities to drought and nutrients. Significant selection gradients, that is, fitness correlations, of several mean traits and plasticities suggest that selection could act on this epigenetically based phenotypic variation. Our study provides evidence that variation in DNA methylation can cause substantial heritable variation of ecologically important plant traits, including root allocation, drought tolerance and nutrient plasticity, and that rapid evolution based on epigenetic variation alone should thus be possible. © 2012 The Authors New Phytologist © 2012 New Phytologist Trust.
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              Plants regenerated from tissue culture contain stable epigenome changes in rice

              Introduction Rice is one of the world's most important food crops, and genetic modifications are extensively used for various purposes such as to increase yield and tolerate harsh environments. Tissue culture has been heavily used for decades for transformation procedures to generate transgenic crops such as rice and maize (Rao et al., 2009). A previous study has reported that Arabidopsis cell suspension culture has a different epigenomic profile compared to wild-type plants, such that certain transposable elements (TEs) become hypomethylated and certain genes become hypermethylated (Tanurdzic et al., 2008). This raised the question of how tissue culture processes affect the epigenome of regenerated plants derived from tissue culture. Changes in the epigenome have been proposed to be a source of somaclonal variation (i.e., phenotypic variation among regenerated plants) for decades (Kaeppler and Phillips, 1993; Kaeppler et al., 2000; Thorpe, 2006; Rhee et al., 2010; Miguel and Marum, 2011; Neelakandan and Wang, 2012). Indeed, some evidence suggesting changes in the epigenome of regenerated plants have been reported at several specific loci or by methods such as methylation sensitive restrictive enzyme digestion (Neelakandan and Wang, 2012). However, the extent of methylation changes on a genome-wide level has not been previously assessed. Because, unlike most crops, Arabidopsis is almost exclusively transformed via Agrobacterium-mediated floral dip methods that do not utilize tissue culture (Clough and Bent, 1998), Arabidopsis is not a good model for the study of the effect of plant regeneration on the epigenome. The study of the model plant rice, however, may have practical implications for other crop species that are transformed using similar tissue culture methods. The rice genome is DNA methylated in all three cytosine contexts (CG, CHG, CHH, where H=A, T, or C), with high levels of CG and CHG methylation and very low levels of CHH methylation (Feng et al., 2010; Zemach et al., 2010). Whole genome bisulfite sequencing (BS-seq) enables measurement of DNA methylation at single nucleotide resolution and thus allows one to distinguish DNA methylation in different cytosine contexts (Cokus et al., 2008; Lister et al., 2008). To investigate the effect that tissue culture processes have on regenerated rice epigenomes, we generated genome-wide, single-nucleotide maps of DNA methylation in several regenerated rice lines that had been transformed with various transgenes, callus, and rice regenerated from tissue culture without transformation. We observed that the tissue culture procedure induced stable changes in DNA methylation in regenerated plants, such that all regenerated lines had ectopic losses of DNA methylation. We found that loss of DNA methylation occurred stochastically, affecting individual plants somewhat differently, was associated with loss of small RNAs, and changes were enriched at promoters of genes. Loss of DNA methylation at promoters was associated with altered expression of particular genes. Results We performed deep BS-seq to map DNA methylation in nine regenerated rice lines in the Nipponbare ecotype background that were transformed by various transgenes and were at various stages of inbreeding after transformation: rice blast resistance lines PiZ-t, PiZ-t-839 (a non-functional PiZ-t), Pi9, and an RNAi line for flowering time regulator Spin1 (Zhou et al., 2006; Vega-Sanchez et al., 2008; Table 1). For the PiZ-t line, both transgenic and non-transgenic T2 and T4 plants were available by genetic segregation of the PiZ-t transgene (Table 1). For comparison, we profiled an untransformed wild-type line, which was used to generate all the regenerated lines, WT2003 (sample 1). WT2003 was also inbred 5–7 generations to produce WT2007 (sample 2), and WT2007 was inbred 5–7 additional generations to produce WT2011 (sample 3). We obtained an average genome coverage of 15× and error rates were low at 1.5%, 1.2%, 0.8%, for CG, CHG, CHH methylation, respectively, indicating high quality data (Table 1). 10.7554/eLife.00354.003 Table 1. BS-Seq samples analyzed in this study DOI: http://dx.doi.org/10.7554/eLife.00354.003 Sample Description Raw reads Uniquely mapping reads Coverage (X) CG error rate CHG error rate CHH error rate 1 WT2003 231568902 100572780 13.5178 0.0176 0.0122 0.0099 2 WT2007 203541357 104376988 14.0292 0.0107 0.0087 0.0082 3 WT2011 187803109 84301904 11.3309 0.0158 0.0095 0.0065 4 T2-PiZt-11-R 229650259 118710094 15.9557 0.0139 0.0099 0.0069 5 T2-PiZt-11-S 263329602 136471411 18.3429 0.0101 0.0096 0.0076 6 T4-PiZt-11-R 270670871 131056700 17.6151 0.0117 0.0100 0.0074 7 T4-PiZt-11-S 252150298 128467721 17.2672 0.0096 0.0076 0.0074 8 T6-PiZt-11-R 237280137 121966745 16.3934 0.0105 0.0096 0.0064 9 T6-Pi9-R 204752699 86995742 11.6930 0.0106 0.0093 0.0050 10 T6-Spin1i-1-R 215451022 90468236 12.1597 0.0113 0.0088 0.0061 11 T2-PiZt-839-8-R (non functional PiZt) 238730281 117471332 15.7892 0.0129 0.0079 0.0056 12 T2-PiZt-839-8-S (non functional PiZt) 211006119 106172872 14.2705 0.0178 0.0129 0.0095 13 WT Callus 1 217121522 96145279 12.9228 0.0185 0.0178 0.0070 14 WT Callus 2 199261493 82617643 11.1045 0.0232 0.0222 0.0084 15 WT regenerated from tissue culture 1 218008835 116367626 15.6408 0.0170 0.0155 0.0078 16 WT regenerated from tissue culture 2 225202113 97905142 13.1593 0.0262 0.0206 0.0093 17 WT regenerated from tissue culture 3 252306428 106544735 14.3205 0.0194 0.0160 0.0073 18 WT2011 (replicate) 253971827 118140062 15.8790 0.0172 0.0148 0.0086 Number of raw sequencing reads, number of uniquely mapping reads (post-removal of identical reads), genome coverage (rice genome size = 372 Mb), and error rates are listed. DNA methylation levels of the chloroplast genome were used to estimate error rates. Samples 1–12 and samples 13–18 were prepared separately. “R” and “S” correspond to plants that either contain the transgene (R) or plants in which the transgene was segregated away (S). We observed strong losses of DNA methylation at certain sites in the genome in the regenerated plants but not in wild-type plants (Figure 1A). To further characterize these sites, we defined differentially methylated regions (DMRs) in CG contexts by applying stringent thresholds (see ‘Materials and methods'). We found that all regenerated plants tested were significantly enriched with CG hypomethylation DMRs (Figure 1B). On average, we identified 1344 CG hypomethylation DMRs in the regenerated plants, whose sizes ranged from 100 to 3200 bp (Figure 1C), whereas on average we identified only eight CG hypomethylation DMRs in the inbred wild-type lines (Figure 1—source data 1). Importantly, we observed hypomethylation even in the T2/T4 non-transgenic plants in which the transgenes had been segregated away (samples 5, 7 and 12), suggesting that loss of DNA methylation is likely due to the tissue culture or transformation process, but not due to the fact that the plants contain transgenes. While loss of DNA methylation in different regenerated lines did not always occur at the same sites (Figure 1D), there were significant overlaps of hypomethylation DMRs among regenerated lines (Figure 1E). This suggests that certain sites in the genome are susceptible to loss of DNA methylation in regenerated plants. 10.7554/eLife.00354.004 Figure 1. Aberrant loss of DNA methylation in regenerated rice. (A) Genome browser views of fractional CG methylation levels. Sample numbers correspond to those listed in Table 1. Regenerated samples of the same line are grouped together in red boxes. (B) Genome coverage of identified CG hypermethylation and hypomethylation DMRs. DMRs were defined relative to sample 1 (wild type). (C) Distribution of sizes of CG hypomethylation DMRs in regenerated plants. (D) Heat map representation of hierarchical clustering based on CG methylation levels within DMRs. Rows represent all 3610 CG-DMRs identified and columns represent the samples. (E) Overlap of CG-DMRs between samples. The bottom triangle represents the percent overlap of elements listed in the x-axis with those listed in the y-axis. The upper triangle on the other hand represents the percent overlap of elements listed in the y-axis with those listed in the x-axis. DOI: http://dx.doi.org/10.7554/eLife.00354.004 We next investigated the stability of DNA methylation losses across generations. To test this, we analyzed a line for which we had plants in T2, T4, and T6 generations (samples 4, 6, 8). 84% of sites that lost CG methylation in the T2 did not recover methylation in the T4 and T6 generations (Figure 2). This suggests that most sites do not regain DNA methylation over several subsequent generations during the process of inbreeding. Approximately 10% of sites recovered methylation in T4, and this methylation was maintained in T6. In addition, 4.4% of sites recovered methylation in T6 but not in T4. This suggests that certain sites are able to regain methylation over generations. Approximately 2% of sites regained methylation in T4, but methylation was lost again in T6, suggesting that a small fraction of sites are epigenetically unstable and continue to switch states. Our results suggest that most of the DNA hypomethylation in regenerated plants was stable over generations. 10.7554/eLife.00354.006 Figure 2. Stability of loss of DNA methylation over generations. Methylation status of sample 4 (T2) DMRs in T4 and T6 generations are indicated. Loss: less than half of respective wild-type CG methylation levels. Gain: more than half of respective wild-type CG methylation levels. DOI: http://dx.doi.org/10.7554/eLife.00354.006 Loss of DNA methylation in regenerated plants also occurred in non-CG contexts (Figure 1—source data 1). Loss of CG methylation was generally associated with loss of CHG methylation and to a lesser extent with loss of CHH methylation (Figure 3A,B). Small interfering RNAs of 24-nt in length (24-nt siRNAs) are associated with DNA methylation, and are required to guide CHH methylation to particular sites (Law and Jacobsen, 2010). We performed small RNA sequencing (smRNA-seq) on seven randomly chosen regenerated plants along with wild type (Table 2). We examined the distribution of 24-nt siRNAs over CHH hypomethylation DMRs and found that siRNAs are enriched over these sites in wild type, but eliminated in regenerated plants (Figure 3C). Hence loss of DNA methylation is associated with loss of 24 nt siRNAs. Moreover, these siRNA alterations independently confirm our findings showing loss of epigenetic marks at these loci. 10.7554/eLife.00354.007 Figure 3. Loss of DNA methylation occurs in all three cytosine contexts. (A) Average distributions of DNA methylation in wild type (faded) and regenerated plants (solid) were plotted over defined CG hypomethylation DMRs in the indicated samples. Flanking regions are the same lengths as the middle region. (B) Heat map of DNA methylation levels within all defined hypomethylation DMRs (CG + CHG + CHH). (C) Average distribution of smRNA-seq reads in wild type (black) and regenerated plants (red) over defined CHH hypomethylation DMRs in indicated samples. Flanking regions are the same lengths as the middle region. DOI: http://dx.doi.org/10.7554/eLife.00354.007 10.7554/eLife.00354.008 Table 2. smRNA-seq samples analyzed in this study DOI: http://dx.doi.org/10.7554/eLife.00354.008 Sample Description Raw reads Uniquely mapping reads 1 WT2003 22030663 3186666 2 WT2007 17069498 2598780 3 WT2011 14860767 2399713 4 T2-PiZt-11-R 22024881 3965317 5 T2-PiZt-11-S 17641623 3127938 6 T4-PiZt-11-R 18999415 3090933 7 T4-PiZt-11-S 22115074 4258752 8 T6-PiZt-11-R 12995193 2044615 9 T6-Pi9-R 16700524 3114923 10 T6-Spin1i-1-R 17275813 2973100 Number of raw sequencing reads and number of uniquely mapping reads are listed. We next examined the genomic characteristics of sites that lost DNA methylation in regenerated plants. We tested the extent of overlap between 3597 CG DMRs, 1875 CHG DMRs, and 2298 CHH DMRs defined in the regenerated lines within gene bodies, gene promoters, downstream regions of genes, gene coding sequences, gene introns, and TE genes. Although loss of DNA methylation occurred at a variety of sites, we found the most significant enrichments of DMRs at the promoters of genes (Figure 4A). These genes were not significantly associated with any particular biological processes (data not shown). Rather, they appeared to be a random set of genes involved in different processes (Figure 4—source data 1). Recent studies in Arabidopsis have shown that spontaneous changes in methylation over generations predominantly occurred in gene bodies (Becker et al., 2011; Schmitz et al., 2011). It is possible that hypomethylation observed in regenerated plants occurs through an accelerated process of whatever mechanism causes spontaneous methylation changes over generations. Alternatively, since the DNA methylation changes we observed in regenerated plants was enriched in gene promoters, and was primarily in the direction of methylation loss, it could be a distinct phenomenon from the spontaneously occurring methylation changes in wild type. 10.7554/eLife.00354.009 Figure 4. Loss of DNA methylation at promoters may impact gene expression. (A) Overlap of hypomethylation DMRs with indicated genomic elements. Observed overlap (dark bars) is compared to randomized regions of similar number and size distribution as the DMRs (light bars). Gene body: transcribed region of protein coding genes. Gene promoter: TSS to 2 kb upstream of TSS. 3' downstream of gene TTS (transcription termination site): TTS to 2 kb downstream of TTS. CDS: Coding sequence. TE: Transposable element. Error bars represent standard deviation. *Significant enrichment, p<0.01. (B) Percentages of genes with CG hypomethylation DMRs near TSSs that have significantly altered expression levels (fourfold up/down regulation, FDR<0.01). Genes with zero mRNA-seq reads in both wild type and regenerated samples were removed from the analyses. An average of 11.3 genes were deregulated. (C) Genome browser views of DNA methylation and gene expression levels. DOI: http://dx.doi.org/10.7554/eLife.00354.009 While the losses of DNA methylation in regenerated plants occurred within a relatively small proportion of the rice genome, they were concentrated near protein-coding gene promoters and therefore in regions of the genome that are more prone to alter gene expression. We therefore examined the impact of hypomethylation on gene expression by performing mRNA-seq on the same seven randomly chosen regenerated plants as well as on wild-type plants (Table 3). We found that loss of DNA methylation at promoters was associated with higher expression levels of certain genes (Figure 4B,C, Figure 4—source data 1, Figure 4—figure supplement 1, 2). Notably, the closer the hypomethylation was to the gene transcription start site, the more likely the gene tended to be misregulated (Figure 4B). Furthermore, the expression of these genes was much more frequently increased, rather than decreased, suggesting that the misexpression of these genes is likely a direct consequence of losses of DNA methylation (Figure 4B). Hence loss of DNA methylation in regenerated plants is associated with deregulated transcription of certain protein-coding genes. 10.7554/eLife.00354.015 Table 3. mRNA-seq samples analyzed in this study DOI: http://dx.doi.org/10.7554/eLife.00354.015 Sample Description Raw reads Uniquely mapping reads 2 WT2007 44029089 29461162 3 WT2011 33997755 22657098 4 T2-PiZt-11-R 42550136 27839598 5 T2-PiZt-11-S 43173764 28688381 6 T4-PiZt-11-R 46624891 35826861 7 T4-PiZt-11-S 31729173 22667633 8 T6-PiZt-11-R 46624532 35335627 9 T6-Pi9-R 38978541 30623633 10 T6-Spin1i-1-R 42280235 32485204 Number of raw sequencing reads and number of uniquely mapping reads are listed. We further sought to determine whether it was the tissue culture process or the transformation process that induced loss of DNA methylation in regenerated plants. To test this, we performed BS-seq on callus and three individual plants regenerated from untransformed callus, all of which were derived from a single parent plant (WT2011; Table 1). We were not able to perform BS-seq on individual calli because calli at the stage of transformation did not yield enough genomic DNA. Instead, we pooled multiple calli, and sequenced two separate batches. We found a strong loss of DNA methylation in plants regenerated from untransformed callus (Figure 5A). Loss of DNA methylation in callus was much more modest, though significant (Figure 5A). This relatively weak loss of DNA methylation may be because individual calli lose DNA methylation at different sites (despite being derived from the same parent plant), and pooling multiple calli diluted the loss of DNA methylation. Consistent with this notion, individual plants regenerated from untransformed callus showed differences in sites that lost DNA methylation (Figure 5B). Furthermore, when examining methylation levels of these samples at CG hypomethylation DMRs that were common in all regenerated plants, we found significant losses of DNA methylation at these sites in callus (Figure 5C,D), indicating that the methylation losses observed in callus were at largely the same sites as those observed in regenerated plants. Like in the regenerated lines, the losses of DNA methylation in the non-transformed regenerated plants occurred stochastically, affecting DNA methylation in each plant somewhat differently (Figure 5A–D). In summary, the loss of DNA methylation in regenerated plants is likely caused by the tissue culture step, and not due to the transformation process. 10.7554/eLife.00354.016 Figure 5. Tissue culture step induces loss of DNA methylation. (A) Genome coverage of identified CG hypermethylation and hypomethylation DMRs. DMRs were defined relative to sample 18 (wild type). (B) Heat map of CG methylation levels within all 1074 CG hypomethylation DMRs identified in samples 13 to 17 (callus samples and wild-type plants regenerated from callus). (C) Heat map of CG methylation levels within 241 CG hypomethylation DMRs that were observed in all tested regenerated plants. (D) Boxplot representations of (C). Red lines, median; edges of boxes, 25th (bottom) and 75th (top) percentiles; error bars, minimum and maximum points within 1.5 × IQR (Interquartile range); red dots, outliers. DOI: http://dx.doi.org/10.7554/eLife.00354.016 Previous reports have indicated that certain genes are hypermethylated in Arabidopsis cell suspension culture and callus (Berdasco et al., 2008; Tanurdzic et al., 2008). Consistent with those data we found that rice callus showed hypermethylation throughout the genome (Figure 6A). Interestingly we found that the hypermethylation occurred specifically in CHH contexts (Figure 6A,B, Figure 6—figure supplement 1A), and showed high coincidence between the two callus samples (13 and 14) (Figure 6—figure supplement 1B). These CHH hypermethylated regions mostly corresponded to promoter regions (Figure 6C, Figure 6—figure supplement 1A). Hence in callus, certain promoters are CHH hypermethylated, while others are hypomethylated in all cytosine contexts. Interestingly, CHH hypermethylation observed in callus was completely lost in regenerated plants (Figure 6A,B, Figure 6—figure supplement 1A). This suggests that unlike tissue culture-induced DNA hypomethylation that is largely stable after regeneration, CHH hypermethylation is eliminated after regeneration. 10.7554/eLife.00354.017 Figure 6. Tissue culture-induced CHH hypermethylation is eliminated upon regeneration. (A) Genome browser views of DNA methylation. (B) Genome coverage of identified CHH hypermethylation and hypomethylation DMRs. Regenerated samples of the same line are grouped together in red boxes. (C) Overlap of callus CHH hypermethylation DMRs with indicated genomic elements. Observed overlap (dark bars) is compared to randomized regions of a similar number and size distribution as the DMRs (light bars). Error bars represent standard deviation. DOI: http://dx.doi.org/10.7554/eLife.00354.017 10.7554/eLife.00354.018 Figure 6—figure supplement 1. Callus induced CHH hypermethylation. (A) Average distribution of DNA methylation over defined CHH hypermethylated regions in callus, genes, and TE genes. Flanking regions are the same lengths as the middle region. (B) Overlap between defined CHH hypermethylation DMRs of the two callus samples in this study (13 and 14). DOI: http://dx.doi.org/10.7554/eLife.00354.018 Discussion In this report, we have investigated the effect that tissue culture processes have on the epigenome of regenerated plants by generating high-resolution maps of DNA methylation. Consistent with a previous study in Arabidopsis cell culture using microarray hybridization on chromosome 4 (Tanurdzic et al., 2008), we observed hypermethylation at certain genes in rice callus. We extend this observation by showing that hypermethylation predominantly occurs in CHH sequence contexts, most notably occurring at the promoters of genes. Interestingly, we found that this CHH hypermethylation was completely eliminated upon regeneration, suggesting that CHH hypermethylation may be linked specifically to the dedifferentiated state. In contrast to Arabidopsis cell culture, we did not observe global hypomethylation at TEs in rice callus. Instead, we found that DNA methylation was specifically lost at certain sites in the genome, appearing to affect individual plants somewhat differently despite coming from the same parent plant. We found that loss of DNA methylation was maintained upon plant regeneration, and was largely stable over subsequent generations. It is possible that some of the DMRs affected only one homologous chromosome and were segregating. However, because we required DMRs to have at least 70% reduction in DNA methylation compared to wild-type, the sites we analyzed in Figure 2 are likely homozygous for loss of DNA methylation, consistent with their stability across generations. Loss of DNA methylation occurred in all sequence contexts, and was associated with loss of 24-nt siRNAs. Notably, these sites were frequently associated with promoters of genes, and loss of DNA methylation was associated with misregulation of expression of proximal protein-coding genes, indicating a biological importance of this phenomenon. Interestingly, genes significantly up-regulated (fourfold upregulated compared to wild type, p<0.01) in each regenerated line were somewhat different (Figure 4—figure supplement 3). For this reason, it is difficult to assess the severity of impact of misregulated gene expression for any particular regenerated line, since some lines may have more biologically important genes affected than others. This would correlate with the observation that somoclonal variation affects only a proportion of plants that arise from regeneration experiments (Kaeppler and Phillips, 1993; Kaeppler et al., 2000; Thorpe, 2006; Rhee et al., 2010; Miguel and Marum, 2011; Neelakandan and Wang, 2012). Previous studies have shown that certain TEs such as Tos17 and mPing are reactivated in tissue culture, and are associated with changes in DNA methylation (Neelakandan and Wang, 2012). While our results suggest that most DNA hypomethylation occurs near genes and are relatively depleted at TE related sequences (Figure 4A), some of the hypomethylation did occur proximal to TE genes (average of 62.1 TE genes per line). The association of loss of methylation with TE gene reactivation was not clear (data not shown), however very subtle depletion of DNA methylation was observed over reactivated TE genes (Figure 4—figure supplement 4), suggesting that loss of methylation may in part be responsible for reactivation of TEs. Our results suggest that each regenerated plant has distinct DNA methylation profiles despite coming from the same parent (Figure 5A–D). It therefore appears that the tissue culture step induces DNA hypomethylation in a rather stochastic manner affecting individual plants differently. We further show that descendants of regenerated plants stably maintain most hypomethylation across plant generations (Figure 2). Indeed, lines derived from the same original regenerated plant show very similar methylation profiles (Figure 1E; samples 4–8 and 11–12). It has long been proposed that changes in the epigenome may be a source of somaclonal variation (Thorpe, 2006; Rhee et al., 2010; Miguel and Marum, 2011; Neelakandan and Wang, 2012). Our genome-wide data support this notion since we show that stochastic hypomethylation in individual regenerants is associated with misregulated expression of certain genes. These epigenetic changes likely explain a component of somaclonal variation that has been observed for decades in a number of plant species. In summary, our results suggest that use of tissue culture leaves behind an epigenetic footprint in regenerated plants that is stable over multiple generations and may partially explain somaclonal variation. Whereas the material used in this study were self-fertilized plants, a common practice in the development of agricultural biotechnology traits is to introgress new transgene loci into commercial genetic backgrounds, meaning that the plants used in agriculture are many generations removed from the initial regenerated plants (Bregitzer et al., 2008; Bennetzen and Hake, 2009; Johnson, 2009; Yang et al., 2012). The crosses utilized in these introgression schemes are likely to correct the vast majority of tissue culture-induced epigenetic changes. Material and methods Rice material Wild-type rice (Oryza sativa ssp japonica cv Nipponbare) and regenerated rice lines (in Nipponbare background) were used in this study (Zhou et al., 2006; Vega-Sanchez et al., 2008). Hygromycin was used as the selection marker in rice transformation. All the resistant plants were selfed for indicated generations (Table 1). Homozygosity was confirmed by PCR analysis of the transgene. Rice seeds were surface-sterilized and transferred to 1/2 MS medium. After germination, rice seedlings were transplanted into soil and kept in a growth chamber at 26/20°C under a 14-hr light/10-hr dark cycle. The rice plants regenerated from untransformed rice callus induced from Nipponbare seeds (WT2011) were prepared as previously described (Zhou et al., 2006; Vega-Sanchez et al., 2008). Rice leaf samples were collected at 3 weeks after transplanted into soil and the rice callus were harvested from the callus inducing media. Bisulfite sequencing (BS-seq) and analysis BS-seq libraries were generated as previously described using premethylated adapters (Feng et al., 2011) using 1 μg of genomic DNA isolated using DNeasy Plant Maxi Kit (Qiagen, Hilden, Germany). Libraries were single-end sequenced on a HiSeq 2000, and reads were base-called using the standard Illumina software. The read counts for these libraries are listed in Table 1. Reads (50 nt) were mapped to the MSU 6.1 version genome using BS-seeker (Chen et al., 2010) allowing up to two mismatches. Identical reads were collapsed into one read. Fractional methylation levels were calculated by #C/(#C+#T). DMRs for each sample were defined by comparing methylation levels to wild type in 100 bp bins across the genome. Fischer's exact test was used to identify bins that were significantly differentially methylated by comparing #C and #T (Benjamini-Hochberg corrected FDR < 0.01). In addition, we required an absolute methylation difference of 0.7, 0.5, 0.1, for CG, CHG, CHH methylation, respectively. Bins that were within 100 bp were merged. Finally, only bins that contained 10 informative cytosines (i.e., covered by ≥4 reads) in both the sample and wild type were considered as DMRs. Sample 1 was used for the wild type control for samples 2–12, whereas sample 18 was used for the wild type control for samples 13–17. This was because sample preparation (i.e., growth of plants and library constructions) were performed in two batches: 1∼12 and 13∼18. All heat maps in this study were generated by complete linkage and using Euclidean distance as a distance measure. Rows with missing values were omitted for presentation purposes but did not affect the conclusions in the paper. For determining overlap of DMRs with different genomic elements, we considered 1 bp overlap as an overlap. To assess significance, we generated 100 sets of ‘randomized DMRs' which mimicked both the number and size distributions as the observed DMRs, and examined their overlaps with the different genomic elements. mRNA/smRNA sequencing and analysis RNA-seq libraries were constructed from total RNA isolated using TRIzol reagent (Invitrogen, Life Technologies, Carlsbad, CA) from leaf tissues of samples 2∼10. Total RNA (10 μg) for each sample was used to purify poly-A mRNA; this mRNA was used for synthesis and amplification of cDNA. The RNA-seq libraries were prepared using the TruSeq RNA Sample Preparation Kit from Illumina (San Diego, CA). Libraries were sequenced on an Illumina HiSeq 2000. The read counts for these libraries are listed in Table 3. smRNA-seq libraries were constructed from total RNA isolated from the same tissues as described for the mRNA libraries, using the TruSeq Small RNA Sample Prep Kit from Illumina (San Diego, CA). The libraries were sequenced on the same Illumina HiSeq 2000 as the mRNA-seq libraries. The read counts for these libraries are listed in Table 2. Gene annotations (MSU 6.1) were obtained from the Rice Genome Annotation Project website (http://rice.plantbiology.msu.edu/). mRNA-seq reads were mapped and processed as previously described (Stroud et al., 2012). Briefly, reads were uniquely mapped to the genome using Bowtie (Langmead et al., 2009) allowing two mismatches, and differentially expressed genes were defined by applying fourfold and FDR < 0.01 cutoffs. smRNA-seq reads were uniquely mapped to the genome using Bowtie allowing no mismatches, and reads were categorized by their lengths for analyses. Reads per kilobase per million mapped reads (RPKM) was used to quantify RNA-seq datasets. Accession numbers All sequencing data have been deposited in the NCBI Gene Expression Omnibus with accession number GSE42410. Supplementary Material 10.7554/eLife.00354.005 Figure 1—source data 1. List of CG, CHG, CHH DMRs identified in this study. Defined hypomethylation DMRs for each sample are listed. DOI: http://dx.doi.org/10.7554/eLife.00354.005 10.7554/eLife.00354.010 Figure 4—source data 1. List of genes with CG hypomethylation DMRs at promoters and their expression levels. Genes with CG hypomethylation DMRs at the promoter regions (TSS minus 2 kb to TSS) in samples 4–10 along with their normalized expression levels are listed. Also indicated are whether they were significantly up- or down-regulated based on fourfold and FDR < 0.01 cutoffs. Descriptions of genes were directly taken from the rice genome annotation project website (http://rice.plantbiology.msu.edu/). DOI: http://dx.doi.org/10.7554/eLife.00354.010
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                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                27 October 2014
                : 9
                : 10
                : e108407
                Affiliations
                [1 ]Center for Plant Science Innovation, University of Nebraska Lincoln, Lincoln, Nebraska, United States of America
                [2 ]Department of Agronomy and Horticulture, University of Nebraska Lincoln, Lincoln, Nebraska, United States of America
                [3 ]Monsanto, Chesterfield Village Research Center, Chesterfield, Missouri, United States of America
                Agriculture and Agri-Food Canada, Canada
                Author notes

                Competing Interests: The manipulation of MSH1 for use in plant breeding has been submitted for U.S. patent (Mackenzie, S. and De La Rosa Santamaria, R., Plants with Useful Traits and Related Methods, Pending Appl. No. 61/481,519). Two authors in this study (GW and DON) were employed by the company Monsanto. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: RDLRS ID SAM. Performed the experiments: RDLRS MRS GW DONL HK YW. Analyzed the data: RDLRS MRS GW DONL HK YW. Wrote the paper: SAM.

                Article
                PONE-D-14-13390
                10.1371/journal.pone.0108407
                4209972
                25347794
                d3a688cc-062c-41aa-a31f-526082c6a9b5
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                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                : 25 March 2014
                : 26 August 2014
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                This work was supported by grants from the Bill & Melinda Gates Foundation (OPP1059119, http://www.gatesfoundation.org) and the National Science Foundation (IOS-1126935, http://www.nsf.gov). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Biology and Life Sciences
                Agriculture
                Agronomy
                Plant Breeding
                Genetics
                Heredity
                Quantitative Traits
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                The authors confirm that all data underlying the findings are fully available without restriction. The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the Supporting Information files.

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