Mutations and Epimutations
A story of two cultivars
and their children.
Matteo Pellegrini
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)
Why Study Crosses?
• Crosses of Indica and
Japonica are often
sterile
• Show hybrid vigor in
agronomic traits
Overview
• 2 rice ecotypes: Nipponbare and 93-11
• Generated BS-seq data for NPB, 93-11, and 2 reciprocal crosses
NPB
9311
• Identify SNPs between ecotypes.
– SNP generation
P
• Identify epiMutations between
ecotypes.
– Identify methyl-inheritance
F1
• Identify allele-specific expression
• Identify RNA editing
Detecting Cytosine Methylation
A, 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
Mapping Approach: BS Seeker
BS reads are C/T converted, so normal aligners are not applicable
Three letter alignment:
Convert C to T
BS read:
AATCGTA
Bowtie mapping
AATTGTA
AATTGTA
TTAATTGTAGG
Ref.
CTAATCGCAG
genome:
G
Restore to 4 letters
Compare alignments
m u
AATCGTA
CTAATCGCAG
G
TTAATTGTAGG
Chen et al (2010) BMC Bioinformatics
Methylation levels at single-base resolution
tagtgcgtggtg
cattttagtgcgtgg
ttttagcgcgtggtg
Ref.
5’--attgagacatcctagcgcgtggtgacaataata—-3’
genome:
1/(1+2)=33.3%
3/(3+0)=100%
 Calculate methylation level at each covered cytosine
 Methylation level= #C/(#C+#T)
7
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.
Detecting Alelle-Specific methylation
parent1/parent2
SNP
BS-seq
parent1
parent2
Methylation level
at CG sites
Methylation level
at CG sites
Library statistics
Methyl-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%
-NPB
-93-11
RNA-Seq
NPB
42M
17M
42%
93-11
42M
13M
31%
NPB x 93-11
48M
12M
26%
43M
11M
25%
-NPB
-93-11
93-11 x NPB
-NPB
-93-11
25.77
7.45
6.59
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)
SNPs - NPB vs 93-11
A
C
G
T
216,135
42,513
A
86,677,300 42,553
C
43,336
65,771,387 34,146
G
226,045
34,146
65,771,387 43,336
T
42,513
216,135
42,553
226,045
86,677,300
• 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
SNPs - NPB vs F1 (9N-NPB)
A
C
G
T
A
3,188,414
-
3
-
C
-
2,695,005
-
3
G
2
-
2,548,205
-
T
-
4
-
3,253,196
• 12 mutations
• Are these real or
false?
• Similar numbers
amongst all F1
comparisons
Identifying epimutations
Min/max
• 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
Identifying epimutations (cont)
• Called different if:
– mean(sample1) <
min(sample2) &
mean(sample2) >
max(sample1)
Epimutation rate
1 in 300 CG sites spontaneously mutate across one generation
Epimutation clusters
9311 cross
9311 cross
9311 parent
NPB cross
NPB cross
NPB parent
Epimutation clusters II
9311 cross
9311 cross
9311 parent
NPB cross
NPB cross
NPB parent
Epimutations
are enriched
in regions
where parents
differ
Half of the
epimutations between
parents and crosses
occur at sites where
parents differ
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).
Expression
• Many genes (~7800/25640) are differentially
expressed between ecotypes.
• GO term: choroplast related terms, response
to cadmiumion.
Expression cont.
• Across generations, only 78 genes
differentially expressed
• Of these only 2 were differentially expressed
in the parents
Allele Specific Expression
• 681 examples
of allele
specific
expression
• Partially
explain hybrid
vigor?
NPB cross
NPB parent
9311 parent
9311 cross
NPB cross
9311 cross
Allele-Specific Genes Accumulate
Mutations
SNP Density
All genes
Allele-specific genes
And are also enriched for differentially methylated sites
Allele-specific
Expression
cont.
And are also
enriched for
differentially
methylated sites
RNA Editing
• Cytidine
deamination : C
to U
• Adenosine
deaminase: A to
I (G)
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.
RNA Editing in Rice
NPB - RNA
A
NPB - DNA
C
G
T
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….
On Closer Inspection…
Alignments are often off by
one or more bases at splice
sites
But a Few Real Ones Remain?
But more Filtering Should be done…
Position of edit site along read
Current Numbers
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…
Acknowledgements
–Krishna Chodavarapu (Pellegrini Lab)
–Suhua Feng (Steve Jacobsen Lab)
–Blake Myers, Guo-liang Wang, Yulin Jia