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Plant Resistance Inducers against Pathogens in Solanaceae Species—From Molecular Mechanisms to Field Application
review-article
Author(s):
Erik Alexandersson
1 ,
Tewodros Mulugeta
2 ,
Åsa Lankinen
1 ,
Erland Liljeroth
1 ,
Erik Andreasson
1
,
*
Publication date (Electronic):
02 October 2016
Publisher:
MDPI
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Abstract
This review provides a current summary of plant resistance inducers (PRIs) that have been successfully used in the Solanaceae plant family to protect against pathogens by activating the plant’s own defence. Solanaceous species include many important crops such as potato and tomato. We also present findings regarding the molecular processes after application of PRIs, even if the number of such studies still remains limited in this plant family. In general, there is a lack of patterns regarding the efficiency of induced resistance (IR) both between and within solanaceous species. In many cases, a hypersensitivity-like reaction needs to form in order for the PRI to be efficient. “-Omics” studies have already given insight in the complexity of responses, and can explain some of the differences seen in efficacy of PRIs between and within species as well as towards different pathogens. Finally, examples of field applications of PRIs for solanaceous crops are presented and discussed. We predict that PRIs will play a role in future plant protection strategies in Solanaceae crops if they are combined with other means of disease control in different spatial and temporal combinations.
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The tomato genome sequence provides insights into fleshy fruit evolution
Introductory Paragraph Tomato (Solanum lycopersicum) is a major crop plant and a model system for fruit development. Solanum is one of the largest angiosperm genera 1 and includes annual and perennial plants from diverse habitats. We present a high quality genome sequence of domesticated tomato, a draft sequence of its closest wild relative, S. pimpinellifolium 2 , and compare them to each other and to potato (S. tuberosum). The two tomato genomes show only 0.6% nucleotide divergence and signs of recent admixture, but show >8% divergence from potato, with nine large and several smaller inversions. In contrast to Arabidopsis, but similar to soybean, tomato and potato, small RNAs map predominantly to gene-rich chromosomal regions, including gene promoters. The Solanum lineage has experienced two consecutive genome triplications: one that is ancient and shared with rosids, and a more recent one. These triplications set the stage for the neofunctionalization of genes controlling fruit characteristics, such as colour and fleshiness. Main Text The genome of the inbred tomato cultivar ‘Heinz 1706’ was sequenced and assembled using a combination of Sanger and “next generation” technologies (Supplementary Section 1). The predicted genome size is ~900 Mb, consistent with prior estimates 3 , of which 760 Mb were assembled in 91 scaffolds aligned to the 12 tomato chromosomes, with most gaps restricted to pericentromeric regions (Fig. 1A; Supplementary Fig. 1). Base accuracy is approximately one substitution error per 29.4 kb and one indel error per 6.4 kb. The scaffolds were linked with two BAC-based physical maps and anchored/oriented using a high-density genetic map, introgression line mapping and BAC fluorescence in situ hybridisation (FISH). The genome of S. pimpinellifolium (accession LA1589) was sequenced and assembled de novo using Illumina short reads, yielding a 739 Mb draft genome (Supplementary Section 3). Estimated divergence between the wild and domesticated genomes is 0.6% (5.4M SNPs distributed along the chromosomes (Fig. 1A, Supplementary Fig. 1)). Tomato chromosomes consist of pericentric heterochromatin and distal euchromatin, with repeats concentrated within and around centromeres, in chromomeres and at telomeres (Fig. 1A, Supplementary Fig. 1). Substantially higher densities of recombination, genes and transcripts are observed in euchromatin, while chloroplast insertions (Supplementary Sections 1.22-1.23) and conserved miRNA genes (Supplementary Section 2.9) are more evenly distributed throughout the genome. The genome is highly syntenic with those of other economically important Solanaceae (Fig. 1B). Compared to the genomes of Arabidopsis 4 and sorghum 5 , tomato has fewer high-copy, full-length LTR retrotransposons with older average insertion ages (2.8 versus 0.8 mya) and fewer high-frequency k-mers (Supplementary Section 2.10). This supports previous findings that the tomato genome is unusual among angiosperms by being largely comprised of low-copy DNA 6,7 . The pipeline used to annotate the tomato and potato 8 genomes is described in Supplementary Section 2. It predicted 34,727 and 35,004 protein-coding genes, respectively. Of these, 30,855 and 32,988, respectively, are supported by RNA-Seq data, and 31,741 and 32,056, respectively, show high similarity to Arabidopsis genes (Supplementary section 2.1). Chromosomal organisation of genes, transcripts, repeats and sRNAs is very similar in the two species (Supplementary Figures 2-4). The protein coding genes of tomato, potato, Arabidopsis, rice and grape were clustered into 23,208 gene groups (≥2 members), of which 8,615 are common to all five genomes, 1,727 are confined to eudicots (tomato, potato, grape and Arabidopsis), and 727 are confined to plants with fleshy fruits (tomato, potato and grape) (Supplementary Section 5.1, Supplementary Fig. 5). Relative expression of all tomato genes was determined by replicated strand-specific Illumina RNA-Seq of root, leaf, flower (2 stages) and fruit (6 stages) in addition to leaf and fruit (3 stages) of S. pimpinellifolium (Supplementary Table 1). sRNA sequencing data supported the prediction of 96 conserved miRNA genes in tomato and 120 in potato, a number consistent with other plant species (Fig. 1A, Supplementary Figures 1 and 3, Supplementary Section 2.9). Among the 34 miRNA families identified, 10 are highly conserved in plants and similarly represented in the two species, whereas other, less conserved families are more abundant in potato. Several miRNAs, predicted to target TIR-NBS-LRR genes, appeared to be preferentially or exclusively expressed in potato (Supplementary Section 2.9). Supplementary section 4 deals with comparative genomic studies. Sequence alignment of 71 Mb of euchromatic tomato genomic DNA to their potato 8 counterparts revealed 8.7% nucleotide divergence (Supplementary Section 4.1). Intergenic and repeat-rich heterochromatic sequences showed more than 30% nucleotide divergence, consistent with the high sequence diversity in these regions among potato genotypes 8 . Alignment of tomato-potato orthologous regions confirmed 9 large inversions known from cytological or genetic studies and several smaller ones (Fig. 1C). The exact number of small inversions is difficult to determine due to the lack of orientation of most potato scaffolds. 18,320 clearly orthologous tomato-potato gene pairs were identified. Of these, 138 (0.75%) had significantly higher than average non-synonymous (Ka) versus synonymous (Ks) nucleotide substitution rate ratios (ω), suggesting diversifying selection, whereas 147 (0.80%) had significantly lower than average ω, suggesting purifying selection (Supplementary Table 2). The proportions of high and low ω between sorghum and maize (Zea mays) are 0.70% and 1.19%, respectively, after 11.9 Myr of divergence 9 , suggesting that diversifying selection may have been stronger in tomato-potato. The highest densities of low-ω genes are found in collinear blocks with average Ks >1.5, tracing to a genome triplication shared with grape (see below) (Fig. 1C, Supplementary Fig. 6, Supplementary Table 3). These genes, which have been preserved in paleo-duplicated locations for more than 100 Myr 10,11 are more constrained than ‘average’ genes and are enriched for transcription factors and genes otherwise related to gene regulation (Supplementary Tables 3-4). Sequence comparison of 32,955 annotated genes in tomato and S. pimpinellifolium revealed 6,659 identical genes and 3,730 with only synonymous changes. A total of 22,888 genes had non-synonymous changes, including gains and losses of stop codons with potential consequences for gene function (Supplementary Tables 5-7). Several pericentric regions, predicted to contain genes, are absent or polymorphic in the broader S. pimpinellifolium germplasm (Supplementary Table 8, Supplementary Fig. 7). Within cultivated germplasm, particularly among the small-fruited cherry tomatoes, several chromosomal segments are more closely related to S. pimpinellifolium than to ‘Heinz 1706’ (Supplementary Figures 8-9), supporting previous observations on recent admixture of these gene pools due to breeding 12 . ‘Heinz 1706’ itself has been reported to carry introgressions from S. pimpinellifolium 13 , traces of which are detectable on chromosomes 4, 9, 11 and 12 (Supplementary Table 9). Comparison of the tomato and grape genomes supports the hypothesis that a whole-genome triplication affecting the rosid lineage occurred in a common eudicot ancestor 11 (Fig. 2B). The distribution of Ks between corresponding gene pairs in duplicated blocks suggests that one polyploidisation in the solanaceous lineage preceded the rosid-asterid (tomato-grape) divergence (Supplementary Fig. 10). Comparison to the grape genome also reveals a more recent triplication in tomato and potato. While few individual tomato/potato genes remain triplicated (Supplementary Tables 10-11), 73% of tomato gene models are in blocks that are orthologous to one grape region, collectively covering 84% of the grape gene space. Among these grape genomic regions, 22.5% have one orthologous region in tomato, 39.9% have two, and 21.6% have three, indicating that a whole genome triplication occurred in the Solanum lineage, followed by widespread gene loss. This triplication, also evident in potato (Supplementary Fig. 11) is estimated at 71 (+/-19.4) mya based on Ks of paralogous genes (Supplementary Fig. 10), and therefore predates the ~7.3 mya tomato-potato divergence. Based on alignments to single grape genome segments, the tomato genome can be partitioned into three non-overlapping ‘subgenomes’ (Fig. 2A). The number of euasterid lineages that have experienced the recent triplication remains unclear and awaits complete euasterid I and II genome sequences. Ks distributions show that euasterids I and II, and indeed the rosid-asterid lineages, all diverged from common ancestry at or near the pan-eudicot triplication (Fig. 2B), suggesting that this event may have contributed to formation of major eudicot lineages in a short period of several million years 14 , partially explaining the explosive radiation of angiosperm plants on earth 15 . Supplementary section 5 reports on the analysis of specific gene families. Fleshy fruits (Supplementary Fig. 12) are an important means of attracting vertebrate frugivores for seed dispersal 16 . Combined orthology and synteny analyses suggest that both genome triplications added new gene family members that mediate important fruit-specific functions (Fig. 3). These include transcription factors and enzymes necessary for ethylene biosynthesis (RIN, CNR, ACS) and perception (LeETR3/NR, LeETR4) 17 , red light photoreceptors influencing fruit quality (PHYB1/PHYB2) and ethylene- and light-regulated genes mediating lycopene biosynthesis (PSY1/PSY2). Several cytochrome P450 subfamilies associated with toxic alkaloid biosynthesis show contraction or complete loss in tomato and the extant genes show negligible expression in ripe fruits (Supplementary Section 5.4). Fruit texture has profound agronomic and sensory importance and is controlled in part by cell wall structure and composition 18 . More than 50 genes showing differential expression during fruit development and ripening encode proteins involved in modification of wall architecture (Fig. 4A and Supplementary Section 5.7). For example, a family of xyloglucan endotransglucosylase-/hydrolases (XTHs) has expanded both in the recent whole genome triplication and through tandem duplication. One of the triplicated members, SlXTH10, shows differential loss between tomato and potato (Fig. 4A, Supplementary Table 12), suggesting genetically driven specialisation in the remodelling of fruit cell walls. Similar to soybean and potato and in contrast to Arabidopsis, tomato sRNAs map preferentially to euchromatin (Supplementary Fig. 2). sRNAs from tomato flowers and fruits 19 map to 8,416 gene promoters. Differential expression of sRNAs during fruit development is apparent for 2,687 promoters, including those of cell wall-related genes (Fig. 4B) and occurs preferentially at key developmental transitions (e.g. flower to fruit, fruit growth to fruit ripening, Supplementary Section 2.8). The genome sequences of tomato, S. pimpinellifolium and potato provide a starting point for comparing gene family evolution and sub-functionalization in the Solanaceae. A striking example is the SELF PRUNING (SP) gene family, which includes the homolog of Arabidopsis FT, encoding the mobile flowering hormone florigen 20 and its antagonist SP, encoding the ortholog of TFL1. Nearly a century ago, a spontaneous mutation in SP spawned the “determinate” varieties that now dominate the tomato mechanical harvesting industry 21 . The genome sequence has revealed that the SP family has expanded in the Solanum lineage compared to Arabidopsis, driven by the Solanum triplication and tandem duplication (Supplementary Fig. 13). In potato, SP3D and SP6A control flowering and tuberisation, respectively 22 , whereas SP3D in tomato, known as SINGLE FLOWER TRUSS, similarly controls flowering, but also drives heterosis for fruit yield in an epistatic relationship with SP 23,24,25 . Interestingly, SP6A in S. lycopersicum is inactivated by a premature stop codon, but remains functionally intact in S. pimpinellifolium. Thus, allelic variation in a subset of SP family genes has played a major role in the generation of both shared and species-specific variation in Solanaceous agricultural traits. The genome sequences of tomato and S. pimpinellifolium also provide a basis for understanding the bottlenecks that have narrowed tomato genetic diversity: the domestication of S. pimpinellifolium in the Americas, the export of a small number of accessions to Europe in the 16th Century, and the intensive breeding that followed. Charles Rick pioneered the use of trait introgression from wild tomato relatives to increase genetic diversity of cultivated tomatoes 26 . Introgression lines exist for seven wild tomato species, including S. pimpinellifolium, in the background of cultivated tomato. The genome sequences presented here and the availability of millions of SNPs will allow breeders to revisit this rich trait reservoir and identify domestication genes, providing biological knowledge and empowering biodiversity-based breeding. Methods Summary A total of 21 Gb of Roche/454 Titanium shotgun and matepair reads and 3.3 Gb of Sanger paired-end reads, including ~200,000 BAC and fosmid end sequence pairs, were generated from the ‘Heinz 1706’ inbred line (Supplementary Sections 1.1-1.7), assembled using both Newbler and CABOG and integrated into a single assembly (Supplementary Sections 1.17-1.18). The scaffolds were anchored using two BAC-based physical maps, one high density genetic map, overgo hybridization and genome-wide BAC FISH (Supplementary Sections 1.8-1.16 and 1.19). Over 99.9% of BAC/fosmid end pairs mapped consistently on the assembly and over 98% of EST sequences could be aligned to the assembly (Supplementary Section 1.20). Chloroplast genome insertions in the nuclear genome were validated using a matepair method and the flanking regions were identified (Supplementary Sections 1.22-1.24). Annotation was carried out using a pipeline based on EuGene that integrates de novo gene prediction, RNA-Seq alignment and rich function annotation (Supplementary Section 2). To facilitate interspecies comparison, the potato genome was re-annotated using the same pipeline. LTR retrotransposons were detected de novo with the LTR-STRUC program and dated by the sequence divergence between left and right solo LTR (Supplementary Section 2.10). The genome of S. pimpinellifolium was sequenced to 40x depth using Illumina paired end reads and assembled using ABySS (Supplementary Section 3). The tomato and potato genomes were aligned using LASTZ (Supplementary Section 4.1). Identification of triplicated regions was done using BLASTP, in-house generated scripts and three way comparisons between tomato, potato and S. pimpinellifolium using MCscan (Supplementary Sections 4.2-4.4). Specific gene families/groups (genes for ascorbate, carotenoid and jasmonate biosynthesis, cytochrome P450s, genes controlling cell wall architecture, hormonal and transcriptional regulators, resistance genes) were subjected to expert curation/analysis, (Supplementary Section 5). PHYML and MEGA were used to reconstruct phylogenetic trees and MCSCAN was used to infer gene collinearity (Supplementary Section 5.2). Supplementary Material 1 2 3 4
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Induced systemic resistance by beneficial microbes.
Beneficial microbes in the microbiome of plant roots improve plant health. Induced systemic resistance (ISR) emerged as an important mechanism by which selected plant growth-promoting bacteria and fungi in the rhizosphere prime the whole plant body for enhanced defense against a broad range of pathogens and insect herbivores. A wide variety of root-associated mutualists, including Pseudomonas, Bacillus, Trichoderma, and mycorrhiza species sensitize the plant immune system for enhanced defense without directly activating costly defenses. This review focuses on molecular processes at the interface between plant roots and ISR-eliciting mutualists, and on the progress in our understanding of ISR signaling and systemic defense priming. The central role of the root-specific transcription factor MYB72 in the onset of ISR and the role of phytohormones and defense regulatory proteins in the expression of ISR in aboveground plant parts are highlighted. Finally, the ecological function of ISR-inducing microbes in the root microbiome is discussed.
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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)
Author and article information
Contributors
Marcello Iriti: Role: Academic Editor
Journal
Journal ID (nlm-ta): Int J Mol Sci
Journal ID (iso-abbrev): Int J Mol Sci
Journal ID (publisher-id): ijms
Title:
International Journal of Molecular Sciences
Publisher:
MDPI
ISSN
(Electronic):
1422-0067
Publication date
(Electronic):
02
October
2016
Publication date Collection:
October
2016
Volume: 17
Issue: 10
Electronic Location Identifier: 1673
Affiliations
[1
]Department of Plant Protection Biology, Swedish University of Agricultural Sciences,
P.O. Box 102, 23053 Alnarp, Sweden;
erik.alexandersson@
123456slu.se
(E.A.);
asa.lankinen@
123456slu.se
(Å.L.);
erland.liljeroth@
123456slu.se
(E.L.)
[2
]Department of Zoological Science, Addis Ababa University, 1176 Addis Ababa, Ethiopia;
mulugetatewodros@
123456gmail.com
Author notes
Article
Publisher ID: ijms-17-01673DOI: 10.3390/ijms17101673
PMC ID: 5085706
PubMed ID: 27706100
SO-VID: e6b5a470-7ed2-4165-a1c5-286b1997c5f9
Copyright © © 2016 by the authors; licensee MDPI, Basel, Switzerland.
License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).
History
Date
received
: 04
July
2016
Date
accepted
: 21
September
2016
Categories
Subject:
Review
ScienceOpen disciplines: Molecular biology
Data availability:
ScienceOpen disciplines: Molecular biology