Phosphate is an important nutrient for all living organisms. This chemical group forms part of the backbone of DNA molecules, and has a crucial role in many chemical reactions that occur inside cells. Plants in particular need a source of phosphate to grow. This is why agricultural fertilizers are rich in phosphate, but unfortunately, the use of fertilizers is not sustainable. Many researchers are now looking for new ways to maintain high crop yields without chemical fertilizers, and understanding how crops are affected in times of shortage will be pivotal to achieving this goal.

DNA contains coded information in the form of genes, which can either be switched on or off. Chemical marks added to the DNA can earmark genes for activation or inactivation, a bit like handwritten annotations in an instruction manual. One example is the addition of a chemical tag called a methyl group to one of the letters of the DNA code—so-called ‘cytosine methylation’. However, little is known about how the pattern of these chemical marks on DNA changes in response to changes in the environment.

Secco et al. investigated changes in cytosine methylation in both rice and Arabidopsis plants that had been deprived of phosphate. Arabidopsis, or thale cress, is a model plant that is often studied by plant biologists because it is small and grows quickly. The experiments showed that when rice plants were not given enough phosphate, the pattern of DNA methylation changed. This was particularly true around certain genes that help the plants survive in these difficult conditions. Notably, in the absence of phosphate, methylation also occurs more often in DNA sequences called transposable elements that sit close to these useful genes, and less often around other genes.

Transposable elements, also known as ‘jumping genes’ can move within the genome and thus potentially have damaging effects through altering the DNA sequence. However, DNA methylation normally prevents this from happening. Therefore, the extra methylation observed by Secco et al. may be a cautionary measure to inactivate these transposable elements and limit their potential deleterious effects. Further experiments went on to show that these useful genes seem to be switched on before the DNA of these transposable elements is methylated, implying that the extra methylation observed in these transposable elements is a consequence of the activation of these nearby useful genes.

By contrast, similar experiments performed in Arabidopsis reveal a very limited change in DNA methylation when the plants are grown under stressful conditions. This might be because Arabidopsis has considerably fewer transposable elements than rice. The next challenge will be to explore how significant the environmentally induced silencing of transposable elements is to the stress responses and genome integrity of crop plants.

Phosphorus (P) is one of the most important macronutrients for all living organisms, being a key component of nucleic acids and membrane phospholipids, as well as being an essential element for energy-mediated metabolic processes. Plants preferentially absorb this nutrient as inorganic phosphate (Pi), a form of P with low availability and mobility in the soil (Poirier and Bucher, 2002). As a consequence, Pi is one of the most limiting nutrients for plant growth and development in most agricultural soils. To overcome these issues, application of large quantities of Pi fertilizers has been the primary strategy to maintain crop yields. Yet, this approach is increasingly economically and environmentally unsustainable, with the reserves of Pi rocks greatly diminishing. It is therefore critical to better understand the molecular mechanisms involved in Pi homeostasis in order to generate plants with increased P acquisition and use efficiency, associated with sustained yields that will contribute to improve global food security. Plants have developed a wide set of sophisticated responses aimed at acquiring and utilizing Pi efficiently in order to maintain cellular Pi homeostasis even under Pi limiting conditions (−Pi) (Rouached et al., 2010; Chiou and Lin, 2011; Peret et al., 2011). In −Pi, the expression level of genes encoding high affinity Pi transporters (PTs) in the roots increases in order to increase Pi uptake, as well as inducing and secreting acid phosphatases and ribonucleases to mobilize organically bound P. In contrast, the levels of P-containing intermediates such as nucleotides, RNA and phospholipids are dramatically reduced under −Pi, with phospholipids being replaced with sulpho- and galactolipids. As a consequence, genes involved in sulpho- and galactolipids synthesis, such as sulpholipid synthases (SQDs) and monogalactosyl diacylglycerol synthases (MGDs) are highly up-regulated in −Pi conditions (Misson et al., 2005; Secco et al., 2013a). Members of the SPX-domain containing protein family (e.g., SPX, PHO1 and NLA) have also been shown to be key regulators of Pi homeostasis, being involved in Pi transport and signaling (Wang et al., 2009; Rouached et al., 2010; Kant et al., 2011; Secco et al., 2012; Secco et al., 2013a; Puga et al., 2014; Wang et al., 2014).

In addition to the complex regulation observed at the transcriptome level, studies have shown that Pi homeostasis is also regulated by several post-transcriptional mechanisms involving non-coding RNAs, such as miR827, miR399 and IPS1 (Franco-Zorrilla et al., 2007; Chiou and Lin, 2011) as well as post-translational changes (Bayle et al., 2011; Lin et al., 2013; Park et al., 2014). However, only a limited number of studies have assessed the potential involvement of altered DNA or histone modifications in response to Pi starvation, and stresses in general (Sahu et al., 2013). Smith and colleagues previously reported that in Arabidopsis (Arabidopsis thaliana) the histone variant H2A.Z was deposited at a number of Pi starvation-induced (PSI) genes and that a loss H2A.Z resulted with their de-repression (Smith et al., 2010). In addition, using low resolution, non-quantitative and locus-specific methods, several studies have shown the potential involvement of altered DNA methylation in response to stresses (Labra et al., 2002; Chinnusamy and Zhu, 2009; Wang et al., 2011a, 2011b; Karan et al., 2012; Chen and Zhou, 2013; Sahu et al., 2013). Deep sequencing technologies now enable whole genome single base resolution analysis of DNA methylation (Cokus et al., 2008; Lister et al., 2008), thus enabling global assessment of changes in DNA methylation in response to environmental and developmental cues. Indeed, Dowen et al. (2012) previously reported that biotic stress could induce dynamic changes in DNA methylation of repetitive sequences or transposons, often coupled to transcriptional changes of neighbouring genes (Dowen et al., 2012). In addition, Zhong et al. (2013) recently reported that changes in DNA methylation patterns play a role in the process of tomato fruit ripening (Zhong et al., 2013).

Cytosine DNA methylation (mC) is involved in a range of important biological processes, including silencing of repetitive sequences and transposable elements (TEs), genomic imprinting, and stable gene silencing. In plants, DNA methylation exists in all sequence contexts (CG, CHG, CHH, where H = A, C or T) through the activity of multiple genetically distinct pathways (Law and Jacobsen, 2010; Matzke and Mosher, 2014; Mirouze and Vitte, 2014). De novo DNA methylation is mediated by the RNA-directed DNA methylation (RdDM) pathways. In the canonical RdDM pathway, transcripts produced from the RNA polymerase IV (Pol IV) are then copied into dsRNAs by the RNA-dependent RNA polymerase (RDR2) before being processed into 24-nucleotides (nt) small interfering RNAs (siRNAs) by DICER-LIKE 3 (DCL3). These newly generated siRNAs are then loaded onto ARGONAUTE 4 (AGO4) before being guided towards the nascent scaffold of RNAs transcribed by the RNA polymerase V (Pol V) through sequence complementarity. Finally, this complex recruits the DNA methyltransferase DOMAINS REARRANGED METHYLTRANSFERASE 2 (DRM2) to perform de novo methylation in all sequence contexts (Matzke and Mosher, 2014). Recently, another RdDM pathway, which is independent of Pol IV and DCL3, has been identified and is referred to as RDR6-RdDM (Nuthikattu et al., 2013; Panda and Slotkin, 2013; Creasey et al., 2014; Matzke and Mosher, 2014; Bond and Baulcombe, 2015). In this pathway, Pol II derived transcripts are copied by RDR6 into dsRNAs before being processed into 21-22-nt siRNAs by DCL2 and DCL4. These siRNAs can then either induce post-transcriptional gene silencing (PTGS) when loaded onto AGO1 or initiate de novo DNA methylation when associated with AGO2, thus ultimately triggering the canonical RdDM pathway. Maintenance of DNA methylation through replication is mediated by METHYLTRANSFERASE 1 (MET1) and CHROMOMETHYLASE 3 (CMT3) methyltransferases in the CG and CHG contexts, respectively, and are thus referred to as symmetrical methylation, while methylation in the non-symmetrical CHH context has to be established de novo after DNA replication and involves the activities of the DOMAINS REARRANGED METHYLTRANSFERASE 1 and 2 (DRM1/DRM2) and CMT2 methyltransferases (Finnegan et al., 1996; Du et al., 2012; Zemach et al., 2013). Within the plant species studied to date, the general methylation state of particular genomic features are relatively conserved, with TEs often highly methylated in all contexts, and CG methylation commonly located in gene bodies (Feng et al., 2010; Zemach et al., 2010; Mirouze and Vitte, 2014). However, large differences in global DNA methylation levels can be observed amongst plant species potentially associated with different TE content in the various plant genomes. Indeed, the TE-rich genome (∼40%) of rice has a much higher aggregate level of DNA methylation than the TE-poor (∼15%) Arabidopsis genome (Li et al., 2012; Ragupathy et al., 2013; Mirouze and Vitte, 2014).

Given the paucity of past studies assessing the impact of abiotic stresses upon the plant DNA methylome and the temporal relationship between DNA methylation and transcriptional changes, we performed a comprehensive spatio-temporal assessment of the impact of limiting a central plant macronutrient, Pi, upon DNA methylation patterns and transcription, in rice (Oryza sativa) and Arabidopsis. Using whole genome bisulfite sequencing, we identified species-specific, widespread and mitotically heritable changes in DNA methylation in response to Pi starvation that are particularly enriched at stress responsive genes. These changes in DNA methylation occur after changes in nearby gene expression, and are thus likely a consequence of induced transcription of nearby Pi responsive genes, as well as being largely independent of DCL3a. Altogether, we demonstrate a species-specific process in which Pi starvation causes highly localized changes in the genomic DNA methylation patterns in rice, which may act to repress the potentially deleterious activity of TEs located near genes that are highly induced upon stress.

Plants have developed a wide array of mechanisms to tolerate changing environmental conditions. However, despite our understanding of Pi homeostasis greatly increasing during the past decade, very little is known about the potential role of changes in the epigenome in response to Pi starvation, and to stresses in general. Comprehensive methylome profiling coupled with an extensive time course experiment enabled investigation of the presence and potential role of dynamic epigenomic changes upon stress, revealing the temporal relationship between changes in DNA methylation and transcription, and the stability of altered DNA methylation states through mitosis and meiosis. Indeed, this study provides insights into the dynamic changes in DNA methylation in response to Pi starvation, with the identification of widespread changes in DNA methylation near PSI genes, in a species-dependent manner, likely correlated with the TE content of the genome. It is well known that plant genomes exhibit a very large diversity of size and repeat content as well as methylation levels, which were recently shown to be positively correlated, thus highlighting the central role of DNA methylation in repressing the potentially deleterious activity of TEs through hypermethylation (Mirouze and Vitte, 2014). However, the majority of DNA methylation analyses performed in plants to date have focused on Arabidopsis, despite being relatively depleted of TEs (15–20% of the genome) and being poorly methylated compared to other plant genomes (Mirouze and Vitte, 2014). Potentially, the differences in DNA methylation changes observed in rice and Arabidopsis in response to Pi starvation could primarily be due to the differential frequency of TEs in the genome of each species.

To date, only a limited number of studies have comprehensively investigated the involvement of DNA methylation in response to adverse environmental conditions (Dowen et al., 2012, Liang et al., 2014). Several studies have reported that changes in the environment can affect the methylation status of some regions of the genome, using low resolution and non-quantitative techniques such as methylation-sensitive amplification polymorphism (Choi and Sano, 2007; Hauser et al., 2011; Wang et al., 2011a, 2011b, Karan et al., 2012; Shaik and Ramakrishna, 2012; Sahu et al., 2013; Yu et al., 2013). However, these studies have lacked the resolution to provide the specific context and genomic location of the changes in DNA methylation, thus offering limited insights into the potential role of stress-induced changes in DNA methylation. While this study primarily focused in the main organ involved in nutrient sensing and uptake, namely the root, both roots and shoots appeared to exhibit similar changes in DNA methylation in response to Pi starvation. In addition, all but one of these DMRs overlapped with TEs. While, several studies have suggested that there is a negative relationship between methylation of TEs and nearby gene transcription (Hollister and Gaut, 2009; Ahmed et al., 2011; Eichten et al., 2012), we observed the opposite trend, with hypermethylated DMRs being located in close proximity to PSI genes. Consequently, we sought to shed light upon the temporal hierarchy, and thus potential causal relationships, between the observed environmentally-induced differential DNA methylation and the changes in gene transcript abundance. Using our time-course experiment, we demonstrated that upon Pi stress, changes in transcription occurred prior to changes in DNA methylation levels, thus suggesting that induction of gene transcription may be causal for the differential DNA methylation. In addition, since almost all the regions gaining DNA methylation in response to Pi deprivation overlap with TEs, such a process could potentially constitute a mechanism to repress the activity of specific TEs that are near highly induced PSI genes when plants are confronted with −Pi conditions. Indeed, vonHoldt and colleagues recently showed that the methylation pattern of TEs is a complex function of TE size, age and distance to a gene, with young TEs being preferentially poorly methylated (vonHoldt et al., 2012). During Pi stress, PSI genes are highly expressed, presumably requiring increased chromatin accessibility to facilitate access of Pol II and other transcription machinery, which may facilitate the transcription of poorly methylated nearby TEs, with potentially deleterious effects upon the genome such as insertional inactivation of genes and ectopic recombination. This hypothesis is strengthened by the fact that the majority of the hypermethylated PSI DMRs are independent of DCL3a, a key component of the canonical RdDM pathway. Thus, we hypothesize that the RDR6-RdDM pathway, another entry point into RdDM that recognizes Pol II-derived TE transcripts, may act to methylate these TEs. Overall, the observed increase in DNA methylation in specific TEs located close to PSI genes provides insights into a cellular activity that may act to maintain localized suppression of transposable elements in genomic regions that must be transcriptionally activated to respond to environmental perturbation (Figure 7).

Figure 7

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In contrast, hypomethylated rice root PSI DMRs overlapped less frequently with TEs, and were often affected by knockdown of DCL3a, suggesting a different function of this subset of PSI DMRs, and likely involving the canonical RdDM pathway. Among the 11 DCL3a-dependent PSI DMRs, those associated with SPX1 and SPX2 were the most affected by reduced transcription of DCL3a. It has recently been shown that both SPX1 and SPX2 can inhibit phosphate starvation responses through Pi-dependant direct interaction with PHR2, a key transcription factor controlling the majority of Pi-responsive genes. Under Pi sufficient conditions, SPX1 and SXP2 are tightly bound to PHR2, preventing PHR2 from interacting with its cognate binding sites, and suppressing induction of Pi responsive genes (Puga et al., 2014, Wang et al., 2014). Surprisingly, SPX2 transcript abundance was >65 fold lower in the DCL3a RNAi line compared to WT, potentially enabling PHR2 to interact with its target binding sites and induce transcription of PSI genes. Such a mechanism would thus explain the high number of differentially expressed genes observed between WT and the DCL3a RNAi line under +Pi conditions (3531), compared to −Pi (1791), where SPX2 is expressed at similar levels between WT and the DCL3a RNAi line. However, it remains unclear how the knockdown of DCL3a can repress SPX2 to such an extent in +Pi conditions. It is unlikely that the repression of SPX2 is a direct consequence of DCL3a-dependent changes in DNA methylation at this locus, since similar DNA methylation patterns are observed in DCL3a RNAi plants under +Pi and −Pi, while SPX2 is highly induced in −Pi and not expressed in +Pi (Figure 5F). In addition, several studies have shown that changes in DNA methylation within or near DNA binding elements could interfere with the binding of cognate transcription factors (Deng et al., 2001; Bird, 2002; Zhong et al., 2013). Zhong and colleagues (2013) recently showed in tomato that binding sites for RIN (Ripening Inhibitor), one the main transcription factors involved in fruit ripening, were frequently demethylated during ripening, thus enabling the induction of ripening genes (Zhong et al., 2013). Such a mechanism could potentially occur in rice, fine-tuning the expression of specific genes involved in Pi homeostasis by regulating the binding capacity of specific transcription factors to PSI genes. However, the involvement of such a mechanism in the Pi starvation response remains to be determined.

The Pi resupply experiment allowed us to determine whether PSI DMRs could be maintained despite an extensive period of Pi recovery and tissue growth. While the majority of PSI DMRs returned towards +Pi DNA methylation levels, 14 root PSI DMRs showed significant sustained changes in DNA methylation despite 31 days of Pi resupply, suggesting that these epialleles are likely to be transmitted to newly generated cells, and can thus be mitotically inherited. In addition, unlike animals where DNA methylation is reset in primordial germ cells and during embryogenesis, DNA methylation states in plants can be stably transmitted from parents to offspring (Kinoshita et al., 2007; Becker et al., 2011; Schmitz et al., 2011; Weigel and Colot, 2012), and thus could potentially establish a transgenerational ‘memory’ of the stress. However, to date few reports have studied the potential heritability of stress-induced DNA methylation changes, and no evidence of transgenerational transmission of stress-induced differential DNA methylation exists (Hauser et al., 2011, Pecinka and Scheid, 2012; Sahu et al., 2013). Boyko and colleagues reported that the progeny of stress-treated plants had increased homologous recombination frequency and global DNA methylation levels, as well as higher tolerance to stress (Boyko et al., 2010). However, no evidence of transgenerational transmission of differences in DNA methylation was observed, and the increase in global genome methylation levels did not persist in successive generations during the absence of stress (Boyko et al., 2010). Additionally, several recent studies suggest that specific mechanisms exist to prevent transgenerational inheritance of stress-induced epigenetic states (Baubec et al., 2014; Crevillen et al., 2014; Iwasaki and Paszkowski, 2014). Furthermore, Hagmann and colleagues, using a near-clonal North American A. thaliana population that has diverged under natural conditions for at least a century, recently showed that environment-induced changes are only minor contributors to durable genome-wide heritable epigenetic variation, with more than 97% of the total methylated genome space not being altered by the environment across dozens of generations (Hagmann et al., 2015). In this study, we found no evidence of transgenerational inheritance of PSI DMRs, suggesting that PSI DMRs, and potentially most stress-induced differential methylation in general, are not transmitted to their progeny and are thus not likely to contribute to transgenerational stress memory.

Altogether, this study reveals a species-specific process in which an abiotic stress induces dynamic and widespread changes in DNA methylation in rice. In addition, we establish the temporal relationship between differential transcription and DNA methylation, the limited stability of such induced DNA methylation events through mitosis, and the absence of their transmission through meiosis. These findings have important implications for interpreting the capacity of environmentally induced epialleles to influence genic transcription, and their stability through plant growth and reproduction.

1. 1. Secco D
   2. Wang C
   3. Shou H
   4. Schultz M
   5. Chiarenza S
   6. Nussaume L
   7. Ecker JR
   8. Whelan J
   9. Lister R

   (2015) Stranded RNA-seq for rice after 3, 24 and 52 days of Pi stress

   Publicly available at the NCBI Short Read Archive (Accession no: SRP061678).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRP061678
2. 1. Secco D
   2. Wang C
   3. Shou H
   4. Schultz M
   5. Chiarenza S
   6. Nussaume L
   7. Ecker JR
   8. Whelan J
   9. Lister R

   (2015) Rice methylomes under Pi stress

   Publicly available at the NCBI Short Read Archive (Accession no: SRP032765).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRP032765
3. 1. Secco D
   2. Wang C
   3. Shou H
   4. Schultz M
   5. Chiarenza S
   6. Nussaume L
   7. Ecker JR
   8. Whelan J
   9. Lister R

   (2015) Methylomes and stranded RNA-seq for Arabidopsis under Pi stress

   Publicly available at the NCBI Short Read Archive (Accession no: SRP040029).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRP040029
4. 1. Secco D
   2. Wang C
   3. Shou H
   4. Schultz M
   5. Chiarenza S
   6. Nussaume L
   7. Ecker JR
   8. Whelan J
   9. Lister R

   (2015) Stranded RNA-seq and methylomes for the rice DCL3a RNAi experiment

   Publicly available at the NCBI Short Read Archive (Accession no: SRP061677).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRP061677
5. 1. Secco D
   2. Wang C
   3. Shou H
   4. Schultz M
   5. Chiarenza S
   6. Nussaume L
   7. Ecker JR
   8. Whelan J
   9. Lister R

   (2015) Data from: Phosphate starvation induces widespread modulation of DNA methylation at stress responsive genes

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication. Figure 1—source data 1: FPKM and fold change of all genes in rice roots and shoots upon Pi treatments. Figure 2—source data 1: List of 175 PSI DMRs identified in the root, with DNA methylation levels and transcription levels of DMR-associated genes. Figure 2—source data 2: Transcription levels of TEs overlapping PSI DMRS after 7 days and 21 days of Pi deprivation. Figure 2—source data 3: List of 341 PSI DMRs identified in the shoots, with DNA methylation levels and transcript abundance levels of DMR-associated genes. Figure 3—source data 1: Levels of DNA methylation and FDR values for the 100 PSI DMRs associated with changes in nearby gene transcript abundance. Figure 4—source data 1: List of 14 root persisting PSI DMRs, despite 31 days of Pi resupply. Figure 4—source data 2: List of 36 panicle PSI DMRs. Figure 4—source data 3: Average methylation levels observed in the roots of the progeny of stressed- and non-stressed plants in all contexts and methylation levels for each replicate in the CNN context in the 175 root PSI DMRs. Figure 5—source data 1: FPKM and fold change of all genes between WT +Pi and DCL3a RNAi line +Pi as well as WT −Pi and DCL3a RNAi line −Pi (FDR < 0.05). Figure 5—source data 2: 11 DCL3a-dependant PSI DMRs, with methylation levels and associated gene transcript abundance. Figure 5—source data 3: 409 PSI DMRs in the DCL3a RNAi line, and 88 PSI DMRs in the DCL3a RNAi line, associated with significant changes in nearby gene expression. Figure 6—source data 1: FPKM and fold change of all genes in Arabidopsis roots upon Pi starvation.

   http://datadryad.org/review?doi=doi:10.5061/dryad.40gd6

1. 1. Secco D
   2. Jabnoune M
   3. Walker H
   4. Shou H
   5. Wu P
   6. Poirier Y
   7. Whelan J

   (2013) RNA-seq data for rice after 7, 21, 22 days of Pi stress

   Publicly available at the NCBI Short Read Archive (Accession no: SRA097415).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA097415

1. 1. Ahmed I
   2. Sarazin A
   3. Bowler C
   4. Colot V
   5. Quesneville H

   (2011) Genome-wide evidence for local DNA methylation spreading from small RNA-targeted sequences in Arabidopsis

   Nucleic Acids Research 39:6919–6931.

   https://doi.org/10.1093/nar/gkr324

   • Google Scholar
2. 1. Ames B

   (1966)

   Assay of inorganic phosphate, total phosphate and phosphatases

   Methods in Enzymology pp. 115–118.

   • Google Scholar
3. 1. Bancroft T
   2. Du CL
   3. Nettleton D

   (2013) Estimation of false discovery rate using sequential permutation p-values

   Biometrics 69:1–7.

   https://doi.org/10.1111/j.1541-0420.2012.01825.x

   • Google Scholar
4. 1. Baubec T
   2. Finke A
   3. Mittelsten Scheid O
   4. Pecinka A

   (2014) Meristem-specific expression of epigenetic regulators safeguards transposon silencing in Arabidopsis

   EMBO Reports 15:446–452.

   https://doi.org/10.1002/embr.201337915

   • Google Scholar
5. 1. Bayle V
   2. Arrighi JF
   3. Creff A
   4. Nespoulous C
   5. Vialaret J
   6. Rossignol M
   7. Gonzalez E
   8. Paz-Ares J
   9. Nussaume L

   (2011) Arabidopsis thaliana high-affinity phosphate transporters exhibit multiple levels of posttranslational regulation

   The Plant Cell 23:1523–1535.

   https://doi.org/10.1105/tpc.110.081067

   • Google Scholar
6. 1. Becker C
   2. Hagmann J
   3. Muller J
   4. Koenig D
   5. Stegle O
   6. Borgwardt K
   7. Weigel D

   (2011) Spontaneous epigenetic variation in the Arabidopsis thaliana methylome

   Nature 480:245–249.

   https://doi.org/10.1038/nature10555

   • Google Scholar
7. 1. Bird A

   (2002) DNA methylation patterns and epigenetic memory

   Gene Development 16:6–21.

   https://doi.org/10.1101/gad.947102

   • Google Scholar
8. 1. Bond DM
   2. Baulcombe DC

   (2015) Epigenetic transitions leading to heritable, RNA-mediated de novo silencing in Arabidopsis thaliana

   Proceedings of the National Academy of Sciences of USA 112:917–922.

   https://doi.org/10.1073/pnas.1413053112

   • Google Scholar
9. 1. Boyko A
   2. Blevins T
   3. Yao Y
   4. Golubov A
   5. Bilichak A
   6. Ilnytskyy Y
   7. Hollunder J
   8. Meins F Jr
   9. Kovalchuk I

   (2010) Transgenerational adaptation of Arabidopsis to stress requires DNA methylation and the function of Dicer-like proteins

   PLOS ONE 5:e9514.

   https://doi.org/10.1371/journal.pone.0009514

   • Google Scholar
10. 1. Chen XS
    2. Zhou DX

    (2013) Rice epigenomics and epigenetics: challenges and opportunities

    Current Opinion in Plant Biology 16:164–169.

    https://doi.org/10.1016/j.pbi.2013.03.004

    • Google Scholar
11. 1. Chinnusamy V
    2. Zhu JK

    (2009) Epigenetic regulation of stress responses in plants

    Current Opinion in Plant Biology 12:133–139.

    https://doi.org/10.1016/j.pbi.2008.12.006

    • Google Scholar
12. 1. Chiou T-J
    2. Lin S-I

    (2011) Signaling network in sensing phosphate availability in plants

    Annual Review of Plant Biology 62:185–206.

    https://doi.org/10.1146/annurev-arplant-042110-103849

    • Google Scholar
13. 1. Choi CS
    2. Sano H

    (2007) Abiotic-stress induces demethylation and transcriptional activation of a gene encoding a glycerophosphodiesterase-like protein in tobacco plants

    Molecular Genetics and Genomics 277:589–600.

    https://doi.org/10.1007/s00438-007-0209-1

    • Google Scholar
14. 1. Cokus SJ
    2. Feng S
    3. Zhang X
    4. Chen Z
    5. Merriman B
    6. Haudenschild CD
    7. Pradhan S
    8. Nelson SF
    9. Pellegrini M
    10. Jacobsen SE

    (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning

    Nature 452:215–219.

    https://doi.org/10.1038/nature06745

    • Google Scholar
15. 1. Creasey KM
    2. Zhai JX
    3. Borges F
    4. Van Ex F
    5. Regulski M
    6. Meyers BC
    7. Martienssen RA

    (2014) miRNAs trigger widespread epigenetically activated siRNAs from transposons in Arabidopsis

    Nature 508:411.

    https://doi.org/10.1038/nature13069

    • Google Scholar
16. 1. Crevillen P
    2. Yang H
    3. Cui X
    4. Greeff C
    5. Trick M
    6. Qiu Q
    7. Cao X
    8. Dean C

    (2014) Epigenetic reprogramming that prevents transgenerational inheritance of the vernalized state

    Nature 515:587–590.

    https://doi.org/10.1038/nature13722

    • Google Scholar
17. 1. Deng G
    2. Chen A
    3. Pong E
    4. Kim YS

    (2001) Methylation in hMLH1 promoter interferes with its binding to transcription factor CBF and inhibits gene expression

    Oncogene 20:7120–7127.

    https://doi.org/10.1038/sj.onc.1204891

    • Google Scholar
18. 1. Dowen RH
    2. Pelizzola M
    3. Schmitz RJ
    4. Lister R
    5. Dowen JM
    6. Nery JR
    7. Dixon JE
    8. Ecker JR

    (2012) Widespread dynamic DNA methylation in response to biotic stress

    Proceedings of the National Academy of Sciences of USA 109:E2183–E2191.

    https://doi.org/10.1073/pnas.1209329109

    • Google Scholar
19. 1. Du JM
    2. Zhong XH
    3. Bernatavichute YV
    4. Stroud H
    5. Feng SH
    6. Caro E
    7. Vashisht AA
    8. Terragni J
    9. Chin HG
    10. Tu A
    11. Hetzel J
    12. Wohlschlegel JA
    13. Pradhan S
    14. Patel DJ
    15. Jacobsen SE

    (2012) Dual binding of chromomethylase domains to H3K9me2-containing nucleosomes directs DNA methylation in plants

    Cell 151:167–180.

    https://doi.org/10.1016/j.cell.2012.07.034

    • Google Scholar
20. 1. Duan K
    2. Ding XZ
    3. Zhang Q
    4. Zhu H
    5. Pan AH
    6. Huang JH

    (2008) AtCopeg1, the unique gene originated from AtCopia95 retrotransposon family, is sensitive to external hormones and abiotic stresses

    Plant Cell Reports 27:1065–1073.

    https://doi.org/10.1007/s00299-008-0520-2

    • Google Scholar
21. 1. Eichten SR
    2. Ellis NA
    3. Makarevitch I
    4. Yeh CT
    5. Gent JI
    6. Guo L
    7. McGinnis KM
    8. Zhang XY
    9. Schnable PS
    10. Vaughn MW
    11. Dawe RK
    12. Springer NM

    (2012) Spreading of Heterochromatin is limited to specific families of Maize retrotransposons

    PLOS Genetics 8:.

    https://doi.org/10.1371/journal.pgen.1003127

    • Google Scholar
22. 1. Fei QL
    2. Xia R
    3. Meyers BC

    (2013) Phased, secondary, small interfering RNAs in posttranscriptional regulatory networks

    The Plant Cell 25:2400–2415.

    https://doi.org/10.1105/tpc.113.114652

    • Google Scholar
23. 1. Feng SH
    2. Cokus SJ
    3. Zhang XY
    4. Chen PY
    5. Bostick M
    6. Goll MG
    7. Hetzel J
    8. Jain J
    9. Strauss SH
    10. Halpern ME
    11. Ukomadu C
    12. Sadler KC
    13. Pradhan S
    14. Pellegrini M
    15. Jacobsen SE

    (2010) Conservation and divergence of methylation patterning in plants and animals

    Proceedings of the National Academy of Sciences of USA 107:8689–8694.

    https://doi.org/10.1073/pnas.1002720107

    • Google Scholar
24. 1. Finnegan EJ
    2. Peacock WJ
    3. Dennis ES

    (1996) Reduced DNA methylation in Arabidopsis thaliana results in abnormal plant development

    Proceedings of the National Academy of Sciences of USA 93:8449–8454.

    https://doi.org/10.1073/pnas.93.16.8449

    • Google Scholar
25. 1. Franco-Zorrilla JM
    2. Valli A
    3. Todesco M
    4. Mateos I
    5. Puga MI
    6. Rubio-Somoza I
    7. Leyva A
    8. Weigel D
    9. Garcia JA
    10. Paz-Ares J

    (2007) Target mimicry provides a new mechanism for regulation of microRNA activity

    Nature Genetics 39:1033–1037.

    https://doi.org/10.1038/ng2079

    • Google Scholar
26. 1. Hagmann J
    2. Becker C
    3. Muller J
    4. Stegle O
    5. Meyer RC
    6. Wang G
    7. Schneeberger K
    8. Fitz J
    9. Altmann T
    10. Bergelson J
    11. Borgwardt K
    12. Weigel D

    (2015) Century-scale methylome stability in a recently diverged Arabidopsis thaliana lineage

    PLOS Genetics 11:.

    https://doi.org/10.1371/journal.pgen.1004920

    • Google Scholar
27. 1. Hauser MT
    2. Aufsatz W
    3. Jonak C
    4. Luschnig C

    (2011) Transgenerational epigenetic inheritance in plants

    Biochimica et Biophysica Acta 1809:459–468.

    https://doi.org/10.1016/j.bbagrm.2011.03.007

    • Google Scholar
28. 1. Hollister JD
    2. Gaut BS

    (2009) Epigenetic silencing of transposable elements: a trade-off between reduced transposition and deleterious effects on neighboring gene expression

    Genome Research 19:1419–1428.

    https://doi.org/10.1101/gr.091678.109

    • Google Scholar
29. 1. Iwasaki M
    2. Paszkowski J

    (2014) Identification of genes preventing transgenerational transmission of stress-induced epigenetic states

    Proceedings of the National Academy of Sciences of USA 111:8547–8552.

    https://doi.org/10.1073/pnas.1402275111

    • Google Scholar
30. 1. Jiang CF
    2. Gao XH
    3. Liao L
    4. Harberd NP
    5. Fu XD

    (2007) Phosphate starvation root architecture and anthocyanin accumulation responses are modulated by the gibberellin-DELLA signaling pathway in Arabidopsis(1[OA])

    Plant Physiology 145:1460–1470.

    https://doi.org/10.1104/pp.107.103788

    • Google Scholar
31. 1. Kant S
    2. Peng M
    3. Rothstein SJ

    (2011) Genetic regulation by NLA and MicroRNA827 for maintaining Nitrate-dependent phosphate homeostasis in Arabidopsis

    PLOS Genetics 7:e1002021.

    https://doi.org/10.1371/journal.pgen.1002021

    • Google Scholar
32. 1. Karan R
    2. DeLeon T
    3. Biradar H
    4. Subudhi PK

    (2012) Salt stress induced variation in DNA methylation pattern and its influence on gene expression in contrasting Rice genotypes

    PLOS ONE 7:.

    https://doi.org/10.1371/journal.pone.0040203

    • Google Scholar
33. 1. Kinoshita Y
    2. Saze H
    3. Kinoshita T
    4. Miura A
    5. Soppe WJJ
    6. Koornneef M
    7. Kakutani T

    (2007) Control of FWA gene silencing in Arabidopsis thaliana by SINE-related direct repeats

    Plant Journal 49:38–45.

    https://doi.org/10.1111/j.1365-313X.2006.02936.x

    • Google Scholar
34. 1. Kobayashi K
    2. Awai K
    3. Nakamura M
    4. Nagatani A
    5. Masuda T
    6. Ohta H

    (2009) Type-B monogalactosyldiacylglycerol synthases are involved in phosphate starvation-induced lipid remodeling, and are crucial for low-phosphate adaptation

    Plant Journal 57:322–331.

    https://doi.org/10.1111/j.1365-313X.2008.03692.x

    • Google Scholar
35. 1. Labra M
    2. Ghiani A
    3. Citterio S
    4. Sgorbati S
    5. Sala F
    6. Vannini C
    7. Ruffini-Castiglione M
    8. Bracale M

    (2002) Analysis of cytosine methylation pattern in response to water deficit in pea root tips

    Plant Biology 4:694–699.

    https://doi.org/10.1055/s-2002-37398

    • Google Scholar
36. 1. Law JA
    2. Jacobsen SE

    (2010) Establishing, maintaining and modifying DNA methylation patterns in plants and animals

    Nature Reviews. Genetics 11:204–220.

    https://doi.org/10.1038/nrg2719

    • Google Scholar
37. 1. Li X
    2. Zhu JD
    3. Hu FY
    4. Ge S
    5. Ye MZ
    6. Xiang H
    7. Zhang GJ
    8. Zheng XM
    9. Zhang HY
    10. Zhang SL
    11. Li Q
    12. Luo RB
    13. Yu C
    14. Yu J
    15. Sun JF
    16. Zou XY
    17. Cao XF
    18. Xie XF
    19. Wang J
    20. Wang W

    (2012) Single-base resolution maps of cultivated and wild rice methylomes and regulatory roles of DNA methylation in plant gene expression

    BMC Genomics 13:.

    https://doi.org/10.1186/1471-2164-13-300

    • Google Scholar
38. 1. Liang D
    2. Zhang Z
    3. Wu H
    4. Huang C
    5. Shuai P
    6. Ye CY
    7. Tang S
    8. Wang Y
    9. Yang L
    10. Wang J
    11. Yin W
    12. Xia X

    (2014) Single-base-resolution methylomes of populus trichocarpa reveal the association between DNA methylation and drought stress

    BMC Genetics 15:S9.

    https://doi.org/10.1186/1471-2156-15-S1-S9

    • Google Scholar
39. 1. Lin WY
    2. Huang TK
    3. Chiou TJ

    (2013) Nitrogen limitation adaptation, a target of microRNA827, mediates degradation of plasma membrane-localized phosphate transporters to maintain phosphate homeostasis in Arabidopsis

    The Plant Cell 25:4061–4074.

    https://doi.org/10.1105/tpc.113.116012

    • Google Scholar
40. 1. Lister R
    2. Mukamel EA
    3. Nery JR
    4. Urich M
    5. Puddifoot CA
    6. Johnson ND
    7. Lucero J
    8. Huang Y
    9. Dwork AJ
    10. Schultz MD
    11. Yu M
    12. Tonti-Filippini J
    13. Heyn H
    14. Hu SJ
    15. Wu JC
    16. Rao A
    17. Esteller M
    18. He C
    19. Haghighi FG
    20. Sejnowski TJ
    21. Behrens MM
    22. Ecker JR

    (2013)

    Global epigenomic reconfiguration during mammalian brain development

    Science 341:629.

    • Google Scholar
41. 1. Lister R
    2. O'Malley RC
    3. Tonti-Filippini J
    4. Gregory BD
    5. Berry CC
    6. Millar AH
    7. Ecker JR

    (2008) Highly integrated single-base resolution maps of the epigenome in Arabidopsis

    Cell 133:523–536.

    https://doi.org/10.1016/j.cell.2008.03.029

    • Google Scholar
42. 1. Matzke MA
    2. Mosher RA

    (2014) RNA-directed DNA methylation: an epigenetic pathway of increasing complexity

    Nature reviews. Genetics 15:394–408.

    https://doi.org/10.1038/nrg3683

    • Google Scholar
43. 1. Mirouze M
    2. Vitte C

    (2014) Transposable elements, a treasure trove to decipher epigenetic variation: insights from Arabidopsis and crop epigenomes

    Journal of Experimental Botany 65:2801–2812.

    https://doi.org/10.1093/jxb/eru120

    • Google Scholar
44. 1. Misson J
    2. Raghothama KG
    3. Jain A
    4. Jouhet J
    5. Block MA
    6. Bligny R
    7. Ortet P
    8. Creff A
    9. Somerville S
    10. Rolland N
    11. Doumas P
    12. Nacry P
    13. Herrerra-Estrella L
    14. Nussaume L
    15. Thibaud MC

    (2005) A genome-wide transcriptional analysis using Arabidopsis thaliana Affymetrix gene chips determined plant responses to phosphate deprivation

    Proceedings of the National Academy of Sciences of USA 102:11934–11939.

    https://doi.org/10.1073/pnas.0505266102

    • Google Scholar
45. 1. Misson J
    2. Thibaud MC
    3. Bechtold N
    4. Raghothama K
    5. Nussaume L

    (2004) Transcriptional regulation and functional properties of Arabidopsis Pht1;4, a high affinity transporter contributing greatly to phosphate uptake in phosphate deprived plants

    Plant Molecular Biology 55:727–741.

    https://doi.org/10.1007/s11103-004-1965-5

    • Google Scholar
46. 1. Nuthikattu S
    2. McCue AD
    3. Panda K
    4. Fultz D
    5. DeFraia C
    6. Thomas EN
    7. Slotkin RK

    (2013) The initiation of epigenetic silencing of active transposable elements is Triggered by RDR6 and 21-22 nucleotide small interfering RNAs

    Plant Physiology 162:116–131.

    https://doi.org/10.1104/pp.113.216481

    • Google Scholar
47. 1. Panda K
    2. Slotkin RK

    (2013) Proposed mechanism for the initiation of transposable element silencing by the RDR6-directed DNA methylation pathway

    Plant Signaling & Behavior 8:e25206.

    https://doi.org/10.4161/psb.25206

    • Google Scholar
48. 1. Park BS
    2. Seo JS
    3. Chua NH

    (2014) NITROGEN LIMITATION ADAPTATION recruits PHOSPHATE2 to target the phosphate transporter PT2 for degradation during the regulation of Arabidopsis phosphate homeostasis

    The Plant Cell 26:454–464.

    https://doi.org/10.1105/tpc.113.120311

    • Google Scholar
49. 1. Pecinka A
    2. Scheid OM

    (2012) Stress-induced chromatin changes: a critical view on their heritability

    Plant & Cell Physiology 53:801–808.

    https://doi.org/10.1093/pcp/pcs044

    • Google Scholar
50. 1. Peret B
    2. Clement M
    3. Nussaume L
    4. Desnos T

    (2011) Root developmental adaptation to phosphate starvation: better safe than sorry

    Trends in Plant Science 16:442–450.

    https://doi.org/10.1016/j.tplants

    • Google Scholar
51. 1. Perkins W
    2. Tygert M
    3. Ward R

    (2011) Computing the confidence levels for a root-mean-square test of goodness-of-fit

    Applied Mathematics and Computation 217:9072–9084.

    https://doi.org/10.1016/j.amc.2011.03.124

    • Google Scholar
52. 1. Poirier Y
    2. Bucher M

    (2002)

    The arabidopsis book

    The arabidopsis book, Rockville, MD, American Society of Plant Biologists.

    • Google Scholar
53. 1. Puga MI
    2. Mateos I
    3. Charukesi R
    4. Wang Z
    5. Franco-Zorrilla JM
    6. de Lorenzo L
    7. Irigoyen ML
    8. Masiero S
    9. Bustos R
    10. Rodriguez J
    11. Leyva A
    12. Rubio V
    13. Sommer H
    14. Paz-Ares J

    (2014) SPX1 is a phosphate-dependent inhibitor of Phosphate Starvation Response 1 in Arabidopsis

    Proceedings of the National Academy of Sciences of USA 111:14947–14952.

    https://doi.org/10.1073/pnas.1404654111

    • Google Scholar
54. 1. Ragupathy R
    2. You FM
    3. Cloutier S

    (2013) Arguments for standardizing transposable element annotation in plant genomes

    Trends in Plant Science 18:367–376.

    https://doi.org/10.1016/j.tplants.2013.03.005

    • Google Scholar
55. 1. Reymond M
    2. Svistoonoff S
    3. Loudet O
    4. Nussaume L
    5. Desnos T

    (2006) Identification of QTL controlling root growth response to phosphate starvation in Arabidopsis thaliana

    Plant, Cell & Environment 29:115–125.

    https://doi.org/10.1111/j.1365-3040.2005.01405.x

    • Google Scholar
56. 1. Rouached H
    2. Arpat AB
    3. Poirier Y

    (2010) Regulation of phosphate starvation responses in plants: signaling players and cross-talks

    Molecular Plant 3:288–299.

    https://doi.org/10.1093/mp/ssp120

    • Google Scholar
57. 1. Sahu PP
    2. Pandey G
    3. Sharma N
    4. Puranik S
    5. Muthamilarasan M
    6. Prasad M

    (2013) Epigenetic mechanisms of plant stress responses and adaptation

    Plant Cell Reports 32:1151–1159.

    https://doi.org/10.1007/s00299-013-1462-x

    • Google Scholar
58. 1. Sato Y
    2. Antonio B
    3. Namiki N
    4. Motoyama R
    5. Sugimoto K
    6. Takehisa H
    7. Minami H
    8. Kamatsuki K
    9. Kusaba M
    10. Hirochika H
    11. Nagamura Y

    (2011) Field transcriptome revealed critical developmental and physiological transitions involved in the expression of growth potential in japonica rice

    BMC Plant Biology 11:10.

    https://doi.org/10.1186/1471-2229-11-10

    • Google Scholar
59. 1. Schmitz RJ
    2. Schultz MD
    3. Lewsey MG
    4. O'Malley RC
    5. Urich MA
    6. Libiger O
    7. Schork NJ
    8. Ecker JR

    (2011) Transgenerational epigenetic instability is a source of novel methylation variants

    Science 334:369–373.

    https://doi.org/10.1126/science.1212959

    • Google Scholar
60. 1. Secco D
    2. Wang C
    3. Arpat BA
    4. Wang Z
    5. Poirier Y
    6. Tyerman SD
    7. Wu P
    8. Shou H
    9. Whelan J

    (2012) The emerging importance of the SPX domain-containing proteins in phosphate homeostasis

    The New Phytologist 193:842–851.

    https://doi.org/10.1111/j.1469-8137.2011.04002.x

    • Google Scholar
61. 1. Secco D
    2. Jabnoune M
    3. Walker H
    4. Shou HX
    5. Wu P
    6. Poirier Y
    7. Whelan J

    (2013a) Spatio-temporal transcript profiling of Rice roots and shoots in response to phosphate starvation and recovery

    The Plant Cell 25:4285–4304.

    https://doi.org/10.1105/tpc.113.117325

    • Google Scholar
62. 1. Secco D
    2. Jabnoune M
    3. Walker H
    4. Shou H
    5. Wu P
    6. Poirier Y
    7. Whelan J

    (2013) NCBI Short Read Archive

    NCBI Short Read Archive, SRA097415. http://www.ncbi.nlm.nih.gov/sra/?term=SRA097415.

    http://www.ncbi.nlm.nih.gov/sra/?term=SRA097415

    • Google Scholar
63. Data

    1. Secco D
    2. Wang C
    3. Shou H
    4. Schultz M
    5. Chiarenza S
    6. Nussaume L
    7. Ecker JR
    8. Whelan J
    9. Lister R

    (authors) (2015) Dryad Digital Repository

    Dryad Digital Repository.

    https://doi.org/10.5061/dryad.40gd6
64. 1. Shaik R
    2. Ramakrishna W

    (2012) Bioinformatic analysis of epigenetic and microRNA mediated regulation of drought responsive genes in rice

    PLOS ONE 7:e49331.

    https://doi.org/10.1371/journal.pone.0049331

    • Google Scholar
65. 1. Smith AP
    2. Jain A
    3. Deal RB
    4. Nagarajan VK
    5. Poling MD
    6. Raghothama KG
    7. Meagher RB

    (2010) Histone H2A.Z regulates the expression of several classes of phosphate starvation response genes but not as a transcriptional activator

    Plant Physiology 152:217–225.

    https://doi.org/10.1104/pp.109.145532

    • Google Scholar
66. 1. Song XW
    2. Li PC
    3. Zhai JX
    4. Zhou M
    5. Ma LJ
    6. Liu B
    7. Jeong DH
    8. Nakano M
    9. Cao SY
    10. Liu CY
    11. Chu CC
    12. Wang XJ
    13. Green PJ
    14. Meyers BC
    15. Cao XF

    (2012) Roles of DCL4 and DCL3b in rice phased small RNA biogenesis

    Plant Journal 69:462–474.

    https://doi.org/10.1111/j.1365-313X.2011.04805.x

    • Google Scholar
67. 1. Trapnell C
    2. Hendrickson DG
    3. Sauvageau M
    4. Goff L
    5. Rinn JL
    6. Pachter L

    (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq

    Nature Biotechnology 31:46.

    https://doi.org/10.1038/nbt.2450

    • Google Scholar
68. 1. Trapnell C
    2. Roberts A
    3. Goff L
    4. Pertea G
    5. Kim D
    6. Kelley DR
    7. Pimentel H
    8. Salzberg SL
    9. Rinn JL
    10. Pachter L

    (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

    Nature Protocols 7:562–578.

    https://doi.org/10.1038/nprot.2012.016

    • Google Scholar
69. 1. vonHoldt BM
    2. Takuno S
    3. Gaut BS

    (2012) Recent Retrotransposon Insertions Are Methylated and Phylogenetically Clustered in Japonica Rice (Oryza sativa spp. japonica)

    Molecular Biology and Evolution 29:3193–3203.

    https://doi.org/10.1093/molbev/mss129

    • Google Scholar
70. 1. Wang C
    2. Ying S
    3. Huang H
    4. Li K
    5. Wu P
    6. Shou H

    (2009) Involvement of OsSPX1 in phosphate homeostasis in rice

    The Plant Journal 57:895–904.

    https://doi.org/10.1111/j.1365-313X.2008.03734.x

    • Google Scholar
71. 1. Wang WS
    2. Pan YJ
    3. Zhao XQ
    4. Dwivedi D
    5. Zhu LH
    6. Ali J
    7. Fu BY
    8. Li ZK

    (2011a) Drought-induced site-specific DNA methylation and its association with drought tolerance in rice (Oryza sativa L.)

    Journal of Experimental Botany 62:1951–1960.

    https://doi.org/10.1093/jxb/erq391

    • Google Scholar
72. 1. Wang WS
    2. Zhao XQ
    3. Pan YJ
    4. Zhu LH
    5. Fu BY
    6. Li ZK

    (2011b) DNA methylation changes detected by methylation-sensitive amplified polymorphism in two contrasting rice genotypes under salt stress

    Journal of Genetics and Genomics 38:419–424.

    https://doi.org/10.1016/j.jgg.2011.07.006

    • Google Scholar
73. 1. Wang Z
    2. Ruan W
    3. Shi J
    4. Zhang L
    5. Xiang D
    6. Yang C
    7. Li C
    8. Wu Z
    9. Liu Y
    10. Yu Y
    11. Shou H
    12. Mo X
    13. Mao C
    14. Wu P

    (2014) Rice SPX1 and SPX2 inhibit phosphate starvation responses through interacting with PHR2 in a phosphate-dependent manner

    Proceedings of the National Academy of Sciences of USA 111:14953–14958.

    https://doi.org/10.1073/pnas.1404680111

    • Google Scholar
74. 1. Wei LY
    2. Gu LF
    3. Song XW
    4. Cui XK
    5. Lu ZK
    6. Zhou M
    7. Wang LL
    8. Hu FY
    9. Zhai JX
    10. Meyers BC
    11. Cao XF

    (2014) Dicer-like 3 produces transposable element-associated 24-nt siRNAs that control agricultural traits in rice

    Proceedings of the National Academy of Sciences of USA 111:3877–3882.

    https://doi.org/10.1073/pnas.1318131111

    • Google Scholar
75. 1. Weigel D
    2. Colot V

    (2012) Epialleles in plant evolution

    Genome Biology 13:.

    https://doi.org/10.1186/gb-2012-13-10-249

    • Google Scholar
76. 1. Wu L
    2. Zhou H
    3. Zhang Q
    4. Zhang J
    5. Ni F
    6. Liu C
    7. Qi Y

    (2010) DNA methylation mediated by a microRNA pathway

    Molecular Cell 38:465–475.

    https://doi.org/10.1016/j.molcel.2010.03.008

    • Google Scholar
77. 1. Yan Y
    2. Zhang Y
    3. Yang K
    4. Sun Z
    5. Fu Y
    6. Chen X
    7. Fang R

    (2011) Small RNAs from MITE-derived stem-loop precursors regulate abscisic acid signaling and abiotic stress responses in rice

    The Plant Journal 65:820–828.

    https://doi.org/10.1111/j.1365-313X.2010.04467.x

    • Google Scholar
78. 1. Yu Y
    2. Yang X
    3. Wang H
    4. Shi F
    5. Liu Y
    6. Liu J
    7. Li L
    8. Wang D
    9. Liu B

    (2013) Cytosine methylation alteration in natural populations of Leymus chinensis induced by multiple abiotic stresses

    PLOS ONE 8:e55772.

    https://doi.org/10.1371/journal.pone.0055772

    • Google Scholar
79. 1. Zemach A
    2. Kim MY
    3. Hsieh PH
    4. Coleman-Derr D
    5. Eshed-Williams L
    6. Thao K
    7. Harmer SL
    8. Zilberman D

    (2013) The Arabidopsis Nucleosome Remodeler DDM1 allows DNA methyltransferases to access H1-containing heterochromatin

    Cell 153:193–205.

    https://doi.org/10.1016/j.cell.2013.02.033

    • Google Scholar
80. 1. Zemach A
    2. McDaniel IE
    3. Silva P
    4. Zilberman D

    (2010) Genome-wide Evolutionary analysis of eukaryotic DNA methylation

    Science 328:916–919.

    https://doi.org/10.1126/science.1186366

    • Google Scholar
81. 1. Zhong S
    2. Fei Z
    3. Chen YR
    4. Zheng Y
    5. Huang M
    6. Vrebalov J
    7. McQuinn R
    8. Gapper N
    9. Liu B
    10. Xiang J
    11. Shao Y
    12. Giovannoni JJ

    (2013) Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening

    Nature Biotechnology 31:154–159.

    https://doi.org/10.1038/nbt.2462

    • Google Scholar

Author details

1. David Secco

   ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia

   Contribution

   DS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   david.secco@uwa.edu.au

   Competing interests

   The authors declare that no competing interests exist.

2. Chuang Wang

   1. State Key laboratory of Plant Physiology and Biochemistry, College of Life Science, Zhejiang University, Hangzhou, China
   2. Joint Research Laboratory in Genomics and Nutriomics, Zhejiang University, Hangzhou, China

   Contribution

   CW, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

3. Huixia Shou

   1. State Key laboratory of Plant Physiology and Biochemistry, College of Life Science, Zhejiang University, Hangzhou, China
   2. Joint Research Laboratory in Genomics and Nutriomics, Zhejiang University, Hangzhou, China

   Contribution

   HS, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Matthew D Schultz

   Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   MDS, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

5. Serge Chiarenza

   UMR 6191 CEA, Centre National de la Recherche Scientifique, Laboratoire de Biologie du Développement des Plantes, Université d'Aix-Marseille, Saint-Paul-lez-Durance, France

   Contribution

   SC, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

6. Laurent Nussaume

   UMR 6191 CEA, Centre National de la Recherche Scientifique, Laboratoire de Biologie du Développement des Plantes, Université d'Aix-Marseille, Saint-Paul-lez-Durance, France

   Contribution

   LN, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

7. Joseph R Ecker

   Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   JRE, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

8. James Whelan

   1. Joint Research Laboratory in Genomics and Nutriomics, Zhejiang University, Hangzhou, China
   2. Department of Animal, Plant and Soil Science, School of Life Science, ARC Centre of Excellence in Plant Energy Biology, La Trobe University, Bundoora, Australia

   Contribution

   JW, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   J.Whelan@latrobe.edu.au

   Competing interests

   The authors declare that no competing interests exist.

9. Ryan Lister

   ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia

   Contribution

   RL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   ryan.lister@uwa.edu.au

   Competing interests

   The authors declare that no competing interests exist.

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