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Mutations and Epimutations
A story of two cultivars and their children.Matteo Pellegrini
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Nipponbare and 93-11
• Nipponbare:– Oryza sativa japonica
• Primarily Japan, China, Indonesia
• Agronomic differences:• Days to heading
• 93-11– Oryza sativa indica
• India, Bangladesh, Nepal, China
• Submerged growth
• Agronomic differences:• Seed fertility• Long grain• Taller (83 cm)
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Why Study Crosses?
• Crosses of Indica and Japonica are often sterile
• Show hybrid vigor in agronomic traits
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Overview
• Identify SNPs between ecotypes.– SNP generation
• Identify epiMutations between ecotypes.– Identify methyl-inheritance
• Identify allele-specific expression• Identify RNA editing
P
F1
NPB 9311
• 2 rice ecotypes: Nipponbare and 93-11• Generated BS-seq data for NPB, 93-11, and 2 reciprocal crosses
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Detecting Cytosine MethylationA, Cunmethylated, Cmethylated, G, T ?
… m mm …… ACCCGTACCCGATTAG …
… ATCTGTATCCGATTAG …
Apply sodium bisulfite and amplify: Unmethylated C → T, methylated C (and A/G/T) unchanged Try to align new sequence to known reference; compare
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Mapping Approach: BS Seeker
Chen et al (2010) BMC Bioinformatics
BS reads are C/T converted, so normal aligners are not applicable
Three letter alignment:
AATCGTA
CTAATCGCAGG
BS read:
Ref. genome:
TTAATTGTAGG
AATTGTA
Convert C to T
AATTGTATTAATTGTAGG
Bowtie mapping
CTAATCGCAGGAATCGTA
Restore to 4 letters
m u
Compare alignments
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7
Methylation levels at single-base resolution
Calculate methylation level at each covered cytosine Methylation level= #C/(#C+#T)
5’--attgagacatcctagcgcgtggtgacaataata—-3’ttttagcgcgtggtg
cattttagtgcgtgg
tagtgcgtggtg
3/(3+0)=100%
1/(1+2)=33.3%
Ref. genome:
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Workflow
• Alignments– BS-Seeker mapping of NPB and 9311 samples to NPB reference
genome.– Maps 9311 genome to NPB coordinates
• Parent genomes– Each read generates a small implied sequence fragment.– Use this to generate a parent genome.
• F1 read matching• Map reads to NPB reference genome to get location.• Compare each read to NPB and 9311 parent genomes and
determine better match.
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SNP
parent1
parent2
Methylation level at CG sites
Methylation level at CG sites
BS-seq
parent1/parent2
Detecting Alelle-Specific methylation
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Library statisticsMethyl-Seq Reads Mapped % Mapped Coverage
NPB 298M 134M 45% 17.58
93-11 157M 74M 47% 10.14
NPB x 93-11 594M 279M 47% 20.04
-NPB 6.51
-93-11 6.08
93-11 x NPB 543M 236M 43% 25.77 -NPB 7.45
-93-11 6.59
RNA-Seq
NPB 42M 17M 42%
93-11 42M 13M 31%
NPB x 93-11 48M 12M 26%
-NPB
-93-11
93-11 x NPB 43M 11M 25%
-NPB
-93-11
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Identifying SNPs
• If sites: – > 3 reads/strand– > 90% agreement within ecotype– Strands agree with each other (compensate for Cs).– (obviously) disagree with each other.
• Will miss indels, dups, inversions, other chr rearrangements.
• Will miss long runs of SNPs ( > 3 within ~55 bp) (BS-seeker limit)
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SNPs - NPB vs 93-11• 1,209,456 mutations /
306,106,830 sites with mutual base calls
• ~ 1/253 bases
• Mostly (73%) C->T (or G->A if C->T on opposite strand) or T->C & A->G if in other 93-11
A C G T
A 86,677,300
42,553
216,135
42,513
C 43,336
65,771,387
34,146
226,045
G 226,045
34,146
65,771,387
43,336
T 42,513
216,135
42,553
86,677,300
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SNPs - NPB vs F1 (9N-NPB)• 12 mutations
• Are these real or false?
• Similar numbers amongst all F1 comparisons
A C G T
A 3,188,414
-
3
-
C -
2,695,005
-
3
G 2
-
2,548,205
-
T -
4
-
3,253,196
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Identifying epimutations
• Use the binomial dist. to build min, max, and mean pct methylation at each C.
• Confidence intervals at 5% are min, max
As # of reads ^, interval size v
Reads
Min/m
ax
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Identifying epimutations (cont)
• Called different if:– mean(sample1) <
min(sample2) & mean(sample2) > max(sample1)
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1 in 300 CG sites spontaneously mutate across one generation
Epimutation rate
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Epimutation clusters
9311 parent
NPB parent
NPB cross
NPB cross
9311 cross
9311 cross
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Epimutation clusters II
9311 parent
NPB parent
NPB cross
NPB cross
9311 cross
9311 cross
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Epimutations are enriched in regions where parents differ
Half of the epimutations between parents and crosses occur at sites where parents differ
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Epimutations (continued)
• Epimutations within genes– 498 genes were significantly enriched for
epimutations– GO Term x-ecotypes indicates: ATP synthesizing
related activity (ATP synthesis coupled proton transport, hydrogen transport, ion transmembrane transport, etc).
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Expression
• Many genes (~7800/25640) are differentially expressed between ecotypes.
• GO term: choroplast related terms, response to cadmiumion.
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Expression cont.
• Across generations, only 78 genes differentially expressed
• Of these only 2 were differentially expressed in the parents
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Allele Specific Expression
• 681 examples of allele specific expression
• Partially explain hybrid vigor?
NPB parent
NPB cross
9311 parent
9311 cross
NPB cross
9311 cross
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Allele-Specific Genes Accumulate Mutations
SNP Density
All genes Allele-specific genes
And are also enriched for differentially methylated sites
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Allele-specific Expression
cont.
And are also enriched for differentially methylated sites
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RNA Editing
• Cytidine deamination : C to U
• Adenosine deaminase: A to I (G)
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How Widespread• Recent studies indicate that
RNA editing may be more widespread than originally thought
• Others have disputed this claim (Schrider et al, PlosOne)
• In plants RNA editing is thought to take place in the mitochondria and plastids
• Is there editing in nuclear genes?
Science. 2011 Jul 1;333(6038):53-8.
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RNA Editing in Rice
NPB - RNA
A C G T
NPB - DNA
A 5535334 6907 3063 2219
C 4758 4436282 4279 7054
G 3777 2437 4382636 4213
T 2210 3227 6949 5577323
Initially we found lots of examples….
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On Closer Inspection…
Alignments are often off by one or more bases at splice sites
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But a Few Real Ones Remain?
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But more Filtering Should be done…
Position of edit site along read
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Current Numbers
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Conclusions
• Epimutation rates are one in 300 cytosines across one generation– Clusters of epimutations are present– Are enriched in sites where parental epigenomes differ
• Allele-specific expression is widespread and associated with– Increased SNP densities– Higher differential methylation
• Find some evidence for RNA editing but…
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Acknowledgements
–Krishna Chodavarapu (Pellegrini Lab)–Suhua Feng (Steve Jacobsen Lab)–Blake Myers, Guo-liang Wang, Yulin Jia