Given the results presented in Chapter 4, using the epiRIL directly for epigenomic mapping as originally intended is more difficult than first anticipated. This raises the issue - how to analyze an epigenome? Despite our efforts to exclude genetic variation, the activity of transposable elements allows the possibility that genetic variation is present.
Secondly, the complex, locus-specific patterns of parental and non-parental methylation, with variable stability, is a concern. As discussed in Chapter 4, it may be possible to perform epigenomic mapping utilizing linkage disequilibrium to associate DNA
methylation polymorphisms with the trait of interest. Yet, additional options can also be considered.
One option may to focus on the epigenetic mechanisms of gene regulation in the Col-0 background. If an epiRIL is back-crossed to the WT parent (Col-0), and a trait of interest can be detected in the segregating progeny, transcriptome profiling of individual progeny may identify regulatory polymorphisms in association with phenotypic variance, as shown in “expression QTL” or “genetical genomics” experiments 13-16. This could include characterizing an epi-allelic series, where different epiRILs are grouped
according to variant methylation patterns at a locus and then the groups are examined for regulatory outcomes to identify which epigenetic modifications control gene expression.
This approach was demonstrated using genome-wide maps of nucleosome occupancy within promoters of yeast genes and indicated variable nucleosome occupancy could
indeed be associated with variable expression 17. Therefore, the epiRIL population could be used in a similar manner to study epigenome interactions between altered DNA methylation states and histone modifications, nucleosome occupancy or positioning, and/or histone variants that lead to variable phenotypic outcomes.
Alternatively, it may also be possible to use the epiRILs in reverse genetics approaches. Notably, the combination of met1-3 with other mutants with impaired epigenetic regulation has already proved successful, but has the disadvantage of
pleiotropic effects conferred by the met1-3 mutation. Therefore an epiRIL may be useful as an “epi-morph”, at least within chromosomal region(s) with altered epigenomes, analogous to the use a partial-loss of function mutant. This may allow the loss of mCG to be combined with another genetic mutation, but possibly with fewer pleiotropic effects relative to the use of met1-3 in creating a double mutant. Such experiments are ongoing, e.g. to explore mechanisms responsible for the met-like methylation patterns near MET1 on chromosome 5.
Last, genetic mapping of a selected epiRIL possessing a trait of interest remains a possible option, as illustrated in Chapter 5. With this approach, the mode of inheritance (dominant, recessive) and trait heritability can be confirmed within the newly-created genetic mapping population. Provided a map-based genetic interval, fine mapping and expression profiling could be useful for identifying the candidate gene(s) and to confirm that epigenetic regulation controls gene expression. Importantly, even if a large
insertion/deletion is absent at the candidate gene, DNA sequencing would be necessary to confirm a genetic lesion as small as a single nucleotide polymorphism is not present at
the locus. Assuming no evidence for a genetic mutation can be found, it would remain necessary to identify the epigenetic variation conferring the phenotypic variation.
In summary, the metastable nature of Arabidopsis DNA methylation patterns and their interactions with additional epigenetic modifications suggests a singular analysis method will most likely be insufficient. Thus, determining the architecture of complex traits affected by epigenetic regulation remains challenging not only because it remains difficult to identify only the subset of epigenetic modification(s) significantly affecting phenotypic variation, but also because numerous interactions between many loci may be involved. In this regard, systems biology attempts to accumulate detailed knowledge from each part of a biological system e.g., DNA, RNA, genes, proteins, cells, tissues, organs, organisms, and ecologies to clarify such complexities. Therefore incorporating epigenetic information into a “systems biology” approach, especially in respect to DNA sequence variation and transcriptional regulation, for the analysis of complex traits may allow a better understanding of the cumulative effect of epigenetic variation on complex genetic networks controlling trait(s) of interest. With the inclusion of epigenetic
information into a systems approach, it should improve the ability to identify epialleles at genes affecting complex traits and for determining the role of epigenetic modifications within a genetic network.
6.5 References
1. Zhang, X. et al. Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis. Cell 126, 1189-201 (2006).
2. Zilberman, D., Gehring, M., Tran, R.K., Ballinger, T. & Henikoff, S. Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an
interdependence between methylation and transcription. Nat Genet 39, 61-69 (2007).
3. Reinders, J. et al. Genome-wide, high-resolution DNA methylation profiling using bisulfite-mediated cytosine conversion. Genome Res. 18, 469 - 476 (2008).
4. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523-36 (2008).
5. Cokus, S.J. et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215-9 (2008).
6. Bernatavichute, Y.V., Zhang, X., Cokus, S., Pellegrini, M. & Jacobsen, S.E.
Genome-wide association of histone H3 lysine nine methylation with CHG DNA methylation in Arabidopsis thaliana. PLoS ONE 3, e3156 (2008).
7. Clarke, J. et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nature Nanotech. advanced online publication(2009).
8. Chan, S.W., Henderson, I.R. & Jacobsen, S.E. Gardening the genome: DNA methylation in Arabidopsis thaliana. Nat Rev Genet 6, 351-60 (2005).
9. Suzuki, M.M. & Bird, A. DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9, 465-76 (2008).
10. Mathieu, O., Reinders, J., Caikovski, M., Smathajitt, C. & Paszkowski, J.
Transgenerational Stability of the Arabidopsis Epigenome Is Coordinated by CG Methylation. Cell 130, 851-862 (2007).
11. Teixeira, F.K. et al. A Role for RNAi in the Selective Correction of DNA Methylation Defects. Science in press(2009).
12. Kato, M., Miura, A., Bender, J., Jacobsen, S.E. & Kakutani, T. Role of CG and non-CG methylation in immobilization of transposons in Arabidopsis. Curr Biol 13, 421-6 (2003).
13. Yvert, G. et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat Genet 35, 57-64 (2003).
14. Bystrykh, L. et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. Nat Genet 37, 225-232 (2005).
15. Chesler, E.J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37, 233-242 (2005).
16. Hubner, N. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37, 243-253 (2005).
17. Choi, J.K. & Kim, Y.-J. Intrinsic variability of gene expression encoded in nucleosome positioning sequences. Nat Genet advanced online
publication(2009).
6 4 2 1 0.5 0.2
Entry C m2 C C m2 C Bisulfite + + - + + -Time (hr) 1 1 1 2 2 2
kb
DNA C m2 m4 C
Bisulfite + + + - no-template control
A
B
600bp
Appendix A.1. Technical assays related to BiMP. DNA samples: “C” for Columbia, “m2” for met1-3 second generation, “m4” for met1-3fourth generation, template-free control is indicated by “no-template control”, with (+) or without (-) bisulfite-conversion (see also Methods). A. Relative amplification efficiency for the locus-specific AT3G08650 assay prior to random amplification. B.
Comparison of DNA fragmentation at two times (1 or 2 hours). The fragmentation reactions (see Methods) were incubated at 37°C for 1 hour and 1µl aliquots were removed and stored at -20°C. The reactions were allowed to proceed for a second hour. Fragmentation analysis was then performed using a Bioanalyzer 2100 (Agilent) for both fragmentation times. Following this experiment, all samples were incubated for 2 hours to obtain a more uniform distribution of probe sizes.
0.E+00 1.E+06 2.E+06 3.E+06
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Signal Intensity Bins
Feature Frequency
Col treatedCol BS+ met1-3 treatedmet1-3BS+ non treated gDNAnon-treated
Appendix A.2. Histogram of the global signal intensity distribution for the BiMP hybridization datasets. The number of features (y-axis), referred to as the “Feature Frequency”, observed with average signal intensities (x-axis), referred to as “Signal Intensity Bins” (log2 scale), with 1 and 20 representing the lowest and highest signal intensities, respectively, were calculated. For each entry (ColBS+, met1-3BS+and non-treated), the average number of features (+/- standard error) per intensity bin were calculated using the hybridization values observed from the three biological replicates. The histogram distributions were obtained using TAS (Affymetrix).
Chr.1 Chr.2
Chr.3 Chr.5
non-treated ColBS+
met1-3BS+
Appendix A.3. Bisulfite methylation profiling results visualized at the chromosomal level for
Arabidopsischromosomes 1, 2, 3 and 5. The graphs represent the average signal intensity per 100 kb and are color coded as in Figure 3.3A.
tandem repeats AT4G25530 (FWA)
AT3G23130 (SUP)
A
B
Appendix A.4. Publicly available mCIP analysis (http://epigenomics.mcdb.ucla.edu/DNAmeth/) for (A) AT4G25530 (FWA) and (B) AT3G23130 (SUP) loci. This approach determined “methylated”
probes as present if the a posteriorprobability was > 0.5 (scale = 0 to 1.0) and then derived
“methylated regions” by combining neighboring methylated probes, as previously described (Zhang et al. 2006). For (A) and (B) the red boxes represents the region encoding the methylated repeat sequences in the wild type (Soppe et al. 2000) and the region that was bisulfite sequenced (see Supp Fig. A5), respectively.
Bisulfite Sequencing at AT3G23130 (SUP)
0 5 10 15 20 25 30 35
CG (9/11) CHG (56/72) CHH (536/688)
%methylated
Col met1-3
Appendix A.5. Bisulfite sequencing at the AT3G23130 (SUP) locus. The graph is designed as described in Fig. 5C and D. The total number of cytosines analyzed per sequence motif is provided within the parentheses. The numbers (separated by “/”) within the parentheses indicate the number of cytosines analyzed for the Col and met1-3samples, respectively.
Appendix A.6. Comparison of the mCIP and BiMP results at previously reported methylated targets in the Col accession. Graphs were designed as in Fig. 3.3B. (A) AT1G02850 (MEA). The red box at the bottom represents the inverted repeat region previously reported to be methylated in Col (Henderson et al. 2006) (B) AT2G34710 (PHB). The red box at the bottom represents the exons previously reported to be methylated in Col (Bao et al. 2004) (C) Chromosome 1: 12673292-12673468. The red box at the bottom represents the region previously reported to be methylated in Col (Zhang et al. 2006). (D) Chromosome 3: 13024832-13025103. The red box at the bottom represents the region previously reported to be methylated in Col (Zhang et al. 2006).
Appendix A.7. Comparison of the mCIP and BiMP results at previously reported methylated and unmethylated regions in Col. Graphs were designed as in Fig. 3.3B. (A) AT2G32415. The red box at the bottom represents the methylated target (Zhang et al. 2006). (B) AT3G04810. The red box at the bottom represents the methylated target (Zhang et al. 2006) (C) AT1G65380. The red and blue boxes at the bottom represent methylated and unmethylated regions, respectively (Zhang et al. 2006). (D) AT5G07350 locus. The red and blue boxes at the bottom represent methylated and unmethylated regions, respectively (Zhang et al. 2006).
mCIP Col
Appendix A.8. Comparison of the mCIP and BiMP results at the BONSAI(BNS) locus. Graphs were designed as in Fig. 3B. The red box at the bottom indicates the region within the AT1G73177 (BNS) locus previously reported as hypermethylated (Saze and Kakutani 2007), and with hypermethylation confirmed in met1-3(H. Saze, pers. comm). The flanking LINE retroelement (AT1G73175),
observed here to be hypomethylated, was also confirmed in met1-3relative to Col (H. Saze, pers.
comm).
A
B
AT2G36490 (ROS1)
AT3G54340 (AP3)
Appendix A.9. Publicly available mCIP analysis (http://epigenomics.mcdb.ucla.edu/DNAmeth/) for AT2G36490 (ROS1) (A) and AT3G54340 (AP3) (B). The red boxes represent the regions assayed by bisulfite sequencing in Fig. 3.5C and 3.5D for the two loci, respectively.
Differentially Methylated Cytosines at AT2G36490 (ROS1)
0 10 20 30 40 50 60 70 80 90
5_CG 20_CHG 39_CHG 56_CHH 63_CHG 69_CHH 88_CHH 90_CHH 120_CHG 123_CG 136_CHG 140_CHG 143_CHG 149_CHH 161_CHH 167_CHH 183_CHG 186_CHH 197_CHG 202_CHH 218_CHH 226_CHH 236_CHH 239_CG
% methylated
Col met 1-3
* *
Appendix A.10. DNA methylation levels at the AT2G36490 (ROS1) locus for each cytosine in Col (white) and met1-3 (dark gray). The percent of DNA methylation (% methylated) was determined for the wild type and met1-3 methylation levels from five and six independent clones, respectively. The position of each residue is annotated according to the corresponding sequence motif. The asterisks indicate de novoCG methylation.
AT3G14800 AT3G42170
Log2 scale Log2 scale
8
Appendix A.11. Analysis of methylation patterns observed for mCIP and BiMP. All graphs were designed as in Fig. 3B for the following loci: AT3G14800 (A) AT3G42170 (B) AT2G18580 locus (C) and AT2G31290 (D). The red boxes at the bottom represent the regions that were bisulfite
Appendix A.12. Validation of BiMP using bisulfite sequencing. Bisulfite sequencing at the following loci: AT3G14800 (A) AT3G42170 (B) AT2G18580 (C) and AT2G31290 (D). (A) and (B) were used to analyze the differences observed between the mCIP and BiMP met1-3BS+profiles. (C) was used to analyze the BiMP result indicating similar levels between ColBS+and met1-3BS+. (D) was used to validate a novel BiMP DNA methylation polymorphism between ColBS+and met1-3BS+. The graphs were designed as described in Fig. 3.5C. The total number of cytosines analyzed per sequence motif is provided within the parentheses. The numbers (separated by “/”) within the parentheses indicate the number of cytosines analyzed for the Col and met1-3samples, respectively.
Bisulfite Sequencing at AT3G42170
CG (30/25) CHG (24/20) CHH (132/110)
% methylated
CG (66/55) CHG (48/40) CHH (96/80)
% methylated
Appendix B Supplementary Tables
B1. Correlation coefficients between the biological replicates corresponding to the BiMP analysis. The average correlation coefficient per entry is indicated below the replicate values.
non-treated
replicates Col met1-3 gDNA
1 & 2 0.951 0.900 0.842
1 & 3 0.945 0.895 0.916
2 & 3 0.974 0.954 0.968
Average 0.96 0.92 0.89
Bisulfite treated
B2. Correlation coefficients between the biological replicates corresponding to the mCIP analysis. The average correlation coefficient per entry is indicated below the replicate values.
replicates Col met1-3
1 & 2 0.910 0.890
1 & 3 0.933 0.895
2 & 3 0.968 0.969
Average 0.94 0.92
mCIP
B3. Bisulfite sequencing and PCR assay details. For each assay, the two primer sequences and annealing temperatures are provided. For the bisulfite assays, degenerate sites are indicated as Y or R (Y= C ,T; R = G, A).
Locus Assay Forward Reverse Tm
AT1G30490 dPHV_1 TGGGATTATAGTGATGTTATATTGTGTT TTTAATATCTAACATAACCAACCTTT 50 AT1G31290 d1G31290_1 TGATTTGTTAYTTYAYATTYAYAAGTTTTG ATTTACCCAACCACCATCCCTATCTTRAAC 52 AT2G18580 d2G18580_1 TTYAAYTTTYYAAYTYATGGAGGA TRCAACCARTTTCTTRCACAAA 50 AT2G36490 dROS1_1 TAGAGGAGGYGTTTTTTG CATTTTTAACCTAAAAACRAAAAAA 50 AT3G08650 d3G08650_1 TTATTAGYGTGTGGAGYG CCTTTCCTTCTCTCTAACCTCA 50 AT3G14800 d3G14800_1 AATGGTGTYAAGGAGYTTYTTG CARTCATRTRCACAGAACAAARCC 53.5 AT3G23130 dSUP_1 TYATTTTATTAAATYTAAGATGGGGATTTG CATRAAAACCCTARAARATAATCATRATCC 50 AT3G42170 d3G42170_1 YATYTGAAGGTGGGAAAGYAGA CTCAARAARCCATTCCCTTRC 53.5 AT3G54340 dAP3_1 GATATYTAGAAATGTAGTGTAGATTGAT TTTARCAACACCATRCCTTATRTTT 50
Repeat C AGCAGCACCTTGGTCTTTGT TTTGGATGACAGCAAGATGG 50
Repeat E CGAGTCCGAAGTCGTTTACC GAACCCGACATGCACAATTT 50
Repeat Z GGGCTTTTCTTTCCACTTCC TTTGACCTTGTTGTCGGGTCA 52
Locus Assay Forward Reverse Tm
AT1G30490 dPHV_2 GTTTGGGAATTGTYTGATTATTTTTTGG TATCATCAACAACTTTCCACACC 50 AT1G31290 d1G31290_2 TTGTTAYTTYAYATTYAYAAGTTTTGAAAG TACCCAACCACCATCCCTATCTTRAACAAA 52 AT2G18580 d2G18580_2 TGTAAGAAYTGGAAATTYAGYTTT TRCAACCARTTTCTTRCACAAA 50 AT2G36490 dROS1_2 GAGGYGTTTTTTGAGGAT CATTTTTAACCTAAAAACRAAAAAA 48 AT3G08650 d3G08650_2 TTAGYGTGTGGAGYGGTTGGG TTTCCTTCTCTCTAACCTCACT 53.5 AT3G14800 d3G14800_2 GGTGAAGATTYYTTYTYTGGAT CARTCATRTRCACAGAACAAARCC 53.5 AT3G23130 dSUP_2 TTAAATYTAAGATGGGGATTTGATAATGYG CATRAAAACCCTARAARATAATCATRATCC 50
Primary Reaction
Secondary Reaction
B4. EpiRIL Phenotype Analysis Summary Table. 150 mM NaCl Germination Rates (%)
biomass (mg / FW)
Abiotic Stress Developmental Phenotypes
0 mM NaCl Germination Rates (%)
flowering time (dps) Pst response
Table footnotes: days post sowing (dps); standard deviation (S.D.); Fisher´s least significant difference (LSD); (*) indicates Kruskal-Wallis rank sum test probability deemed significant at the 95% confidence level (α = 0.05) corresponding to seedling germination rates at 4 days post sowing (dps) under 150 mM NaCl treatment; Pseudomonas syringae pv tomato isolate DC3000 infection (Pst); CACTA activity represented by silent (“WT”) immobilization and active (“ac”) mobilization.
B5. Chapter Four assay information and primer sequences. c5-b 5 NA intergenic GCACTAGAAGTGCAGT
CTTCTTGA
DNA methylation validation assays
Assay chr Tile
c4-a 4 937394 protein coding gacccaactgaaagtgaggaag aggtttggcacaacactaaggt c4-b 4 2193275 transposable
element gene
atgcattctcccaccaaatatc gttttctccaatgaatgtgcaa c4-c 4 2425380 protein coding acagggaccagtttacatcacc ggttccaagtgcataaaagacc c4-d 4 3825777 transposable c4-f 4 6117473 protein coding tctttgtattcatcgcaaaacg taacttcatcaccacaggatgc c4-g 4 8559067 protein coding ttccaagcctcaggatacattt accacaatactggatgtgttcg c4-h 4 8706876 other rna ttccaaaactaagcgggagtaa cgacttttccttcaacccttta
c4-i 4 8989667 protein coding aatgagaagtgtgccctgagtt tacatgttcggattggagattg c4-j 4 9485957 transposable
element gene
aataagggtgacgaagagcgta ctcacatcccaaaattctctcc
c4-k 4 15648935 protein coding gacttctgctgatgaccctttt cctcggaggagtcagagtaaga c4-l 4 17170935 protein coding cttccagaatggctgtaatggt tccaatttgtggtttcaatgtc
RT_PCR assays
Assay ch r
Gene ID Gene model (TAIR 7) Forward Primer (5’-3’)
1 At1G32010 myosin heavy chain-related
cggagaaccggaacatgag tccactccttggcctcgt NRPD1 1 At1G63020 NUCLEAR RNA
POLYMERASE D1
tagctgatagtctctctgttacgg g
ggagaatgcgtttcaatgactgg CMT3 1 At1G69770 CHROMOMETHYLASE
3 acgaaattgtccccactgttgtc gcaagctcggaaggaagagttg At215800 2 At215800 unknown ccggaagtatgacctgaagaac gtagaagacaggcggcttagaa ROS1 2 AT2G3649
0
REPRESSOR OF SILENCING 1
agaagaaattcctaccatca accgttcttcgacgtaattc NRPE1 2 At2G40030 NUCLEAR RNA
POLYMERASE E1 atgatgacaagacgtttgtcctgg gcctgagcctgagatggagactga IBM1 3 AT3G0761
0 INCREASE IN BONSAI
METHYLATION 1 acaacaagtccaaaatgttg taaacactcgctgacattttcagg ACT2 3 At3G18780 ACTIN 2 ctaagctctcaagatcaaaggc aacattgcaaagagtttcaag NRPE2/D
0 3 At3G43310 hypothetical protein tgagagaggctcaactcagga tcacgggaacttctcaacct DRM2 5 At5G14620 DOMAINS
REARRANGED MET1 5 At5G49160 METHYLTRANSFERA
SE 1
c5-01b 5 19985175 intergenic agygtatttgattaaaayataaatgttttg cctrattattrattatattctracatta
B6. DNA methylation marker class summary and data table.
A.) DNA methylation marker class summary
epi-allele
n (%) n (%) n (%) n (%) origin
transgressive 1,580 6.4 1,100 4.5 1,144 4.7 767 5.2 non-parental
intermediate 7,136 28.8 6,942 28.5 7,350 30.1 4,297 29.1 non-parental
met-like 6,532 26.4 2,117 8.7 1,358 5.6 2,008 13.5 parental
WT-like 9,496 38.4 14,233 58.4 14,591 59.7 7,683 52.1 parental
subtotal 24,744 24,392 24,443 24,568
epi01 epi12 epi28 average
Marker Class
B.) DNA methylation marker analysis table
Parental met /Col epi/Col epi/met condition epi01 epi12 epi28 Marker Class
1 1,1,1 1 0 0 transgressive
0 1,1,0 112 23 35 met-like
-1 1,1,-1 2 0 0 intermediate
1 1,0,1 0 0 0
0 1,0,0 1,444 1,357 1,292 intermediate -1 1,0,-1 910 1,101 1,157 WT-like
1 1,-1,1 0 0 0
0 1,-1,0 0 0 0
-1 1,-1,-1 26 14 11 transgressive
1 0,1,1 761 298 502 transgressive
0 0,1,0 485 94 159 discard
-1 0,1,-1 0 0 0
1 0,0,1 4,431 6,397 8,091 discard
0 0,0,0 17,973 14,474 12,026 Non-significant (NS)
-1 0,0,-1 3,187 6,177 6,931 discard
1 0,-1,1 0 0 0
0 0,-1,0 981 267 151 discard
-1 0,-1,-1 626 737 584 transgressive
1 -1,-1,1 125 35 46 transgressive
0 -1,1,0 0 0 0
-1 -1,1,-1 0 0 0
1 -1,0,1 8,586 13,132 13,434 WT-like 0 -1,0,0 5,513 5,463 5,744 intermediate
-1 -1,0,-1 0 0 0
1 -1,-1,1 177 122 314 intermediate 0 -1,-1,0 6,420 2,094 1,323 met-like
-1 -1,-1,-1 41 16 1 transgressive
17,973 14,474 12,026 Non-significant (NS) 9,084 12,935 15,332 discards
16,028 16,350 15,949 Parental 8,716 8,042 8,494 Non-parental
33,828 37,327 39,775 Sub-total excluding NS
24,744 24,392 24,443 Sub-total excluding NS & discards TOTAL 51,801 51,801 51,801
B7. Chapter Five assay information
Appendix C. Supplementary Methods
C1. Phenotypic analysis
For flowering time, the epiRILs were analyzed in a randomized design with 16 plants per line. Plants were grown on soil under long day (LD) growth conditions (16 h light, 8 h dark at 22°C). The average time to flowering, scored as the first open flower in days post sowing (dps), was determined in order to compare each epiRIL to the wild type (WT) using a two-sample t-test (α = 0.05).
The aerial biomass, expressed as milligrams fresh weight per plant (mg FW / plant), was determined using three biological replicates, each consisting of three plants grown for 3 weeks in short-day (SD) conditions (8 h light at 23°C, 16 h darkness at 19°C). Analysis of variance (ANOVA) was performed and the results compared using Fisher´s least significant difference (LSD) at the 95% confidence interval to detect epiRILs significantly different from the WT (P< 0.05).
The abiotic response to 150 mM NaCl was performed in aseptic conditions. Seeds were surface sterilized by washing with a solution of 1 volume 2.4% sodium
hypochlorite, 5 volumes 99% ethanol for 10 min shaking at 1,100 rpm. Seeds were rinsed three times with 99% ethanol and dried under a laminar flow. Sterile seeds were sown on plates containing half-strength Murashige and Skoog (half MS) medium and 0.7% agar (Sigma) 1. For the salt-stress treatments, the half MS medium was amended with NaCl to a final concentration of 150 mM. Seeds were stratified at 4°C for 3 days and transferred to LD growth conditions. Three replicate plates, each seeded with 100 seeds, were examined for each entry. The germination rates (radicle emergence) were determined
across the three replicates. Because of the non-parametric germination rate distributions, a comparison of means was performed using a Kruskal-Wallis rank sum (α = 0.05) to estimate differences relative to the WT parent.
The biotic response of the epiRILs to Pseudomonas syringae pv tomato (Pst) isolate DC3000 infection was determined using 3-week-old plants grown under SD conditions. Five independent experiments with a minimum of two biological replicates per genotype were examined, with three individual plantlets per replicate. Plants were spray-inoculated with a bacterial solution (2.5•107 cfu) and 72 h later the log-scale colony forming units per milligram fresh weight (Log cfu / mg FW) were determined for each epiRIL per experiment. The following lines were included as controls: transgenic line over-expressing non expressor of pathogenesis-related genes1 (NPRH) 2, constitutive pathogenesis related 5 (cpr5) mutant 3, non expressor of pathogenesis-related genes1 (npr1) mutant 4, and phytoalexin deficient 4 (pad4) mutant 5. The results of different experiments were not significantly different at the 95% confidence level (F-ratio = 0.48, P= 0.7900). Hence, ANOVAs for the effect of genotype in response to the treatments were determined as described for the biomass experiment. Repeated analyses in four independent experiments with four biological replicates per genotype were made by infiltrating leaves of 5-week-old SD-grown plants with a bacterial solution (105 cfu).
Each replicated bacterial titer measurement (Log cfu / cm2) was obtained by sampling the combined leaf disks (n = 4 individuals/genotype) at 72 h post inoculation. ANOVAs for the effect of genotype were determined as described above.
C2. Bisulfite sequencing
For each entry, two replicate DNA samples extracted from 10 pooled individuals were independently bisulfite-modified and used for PCR-based cloning, as previously described (Mathieu et al. 2007). The primers are listed in Supplementary Table 2. After sequencing (8 clones per entry), the percentages of 5-methylcytosine at each cytosine position were calculated and represented according to the cytosine sequence motif (CG, CHG, and asymmetric CHH).
C3. RT-PCR analysis
Messenger RNA levels were examined for the following loci:
METHYLTRANSFERASE 1 (MET1) 6, DOMAINS REARRANGED METHYLASE 2 (DRM2) 7, CHROMOMETHYLASE 3 (CMT3) 8-11, DECREASED DNA METHYLATION 1 (DDM1) 12-14, SUVH4/KRYPTONITE (KYP) 15, NUCLEAR RNA POLYMERASE D1, E1, and D2/E2 (NRPD1, NRPE1, NRPD2/ NRPE2, respectively) 16, INCREASE IN BONSAI METHYLATION 1 (IBM1) 17, and REPRESSOR OF SILENCING 1 (ROS1) 18. See Supplementary Table 2 for primer details.
C4. Supplemental References
1. Weigel, D. & Glazebrook, J. Arabidopsis: A Laboratory Manual, 343 (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York USA, 2002).
2. Cao, H., Li, X. & Dong, X. Generation of broad-spectrum disease resistance by overexpression of an essential regulatory gene in systemic acquired resistance PNAS 95, 6531-6536 (1998).
3. Yoshida, S., Ito, M., Nishida, I. & Watanabe, A. Identification of a novel gene HYS1/CPR5 that has a repressive role in the induction of leaf senescence and pathogen-defence responses in Arabidopsis thaliana. Plant J 29, 427-37 (2002).
4. Cao, H., Glazebrook, J., Clarke, J.D., Volko, S. & Dong, X. The Arabidopsis NPR1 gene that controls systemic acquired resistance encodes a novel protein containing ankyrin repeats. Cell 88, 57-63 (1997).
5. Glazebrook, J., Rogers, E.E. & Ausubel, F.M. Isolation of Arabidopsis mutants with enhanced disease susceptibility by direct screening. Genetics 143, 973-82 (1996).
6. Saze, H., Mittelsten Scheid, O. & Paszkowski, J. Maintenance of CpG
methylation is essential for epigenetic inheritance during plant gametogenesis.
Nat Genet 34, 65-9 (2003).
7. Cao, X. & Jacobsen, S.E. Role of the Arabidopsis DRM Methyltransferases in De Novo DNA Methylation and Gene Silencing. Curr Biol 12, 1138-44 (2002).
8. Bartee, L., Malagnac, F. & Bender, J. Arabidopsis cmt3 chromomethylase
mutations block non-CG methylation and silencing of an endogenous gene. Genes Dev 15, 1753-8 (2001).
9. Lindroth, A.M. et al. Requirement of CHROMOMETHYLASE3 for maintenance of CpXpG methylation. Science 292, 2077-80 (2001).
9. Lindroth, A.M. et al. Requirement of CHROMOMETHYLASE3 for maintenance of CpXpG methylation. Science 292, 2077-80 (2001).