Patterns of tRNA Modificationsbenasque.org/2018rna/talks_contr/1710_Stadler-Benasque...Patterns of...
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Patterns of tRNA Modifications Peter F. Stadler Bioinformatics Group, Dept. of Computer Science & Interdisciplinary Center for Bioinformatics, University of Leipzig Max Planck Institute for Mathematics in the Sciences,Leipzig RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig Institute for Theoretical Chemistry, Univ. of Vienna (external faculty) The Santa Fe Institute (external faculty) Joint work with Anne Hoffmann, J¨ org Fallmann, Fabian Amman, and the M¨orl Lab Benasque, Jul 17 2018
Transcript of Patterns of tRNA Modificationsbenasque.org/2018rna/talks_contr/1710_Stadler-Benasque...Patterns of...
Patterns of tRNA Modifications
Peter F. Stadler
Bioinformatics Group, Dept. of Computer Science & Interdisciplinary Center forBioinformatics, University of Leipzig
Max Planck Institute for Mathematics in the Sciences,LeipzigRNomics Group, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig
Institute for Theoretical Chemistry, Univ. of Vienna (external faculty)The Santa Fe Institute (external faculty)
Joint work withAnne Hoffmann, Jorg Fallmann, Fabian Amman, and the Morl Lab
Benasque, Jul 17 2018
Conservation of Modification Patterns
How conserved are chemical modifications of RNAs
between different speciesbetween different tissues
Can this be studied with widely available data?tRNAs (or at least fragments) appear in high coverage inmost small-RNA-seq datasets.Can this information be mined?
The answer is a partial yes: Many but not all chemicalmodification leave detectable traces in normal RNA-seq data
Hallmarks of Modifications in RNA-seq data
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43 48 53 58 63 68 73
err
or
rate
tRNA nucleotide position
1-Methyladenosine
canonical 3’-end
0.25 0.50
A→A
A→C
A→G
A→T
Figure 1: Frequency of mismatches between RNAseq reads and ge-
nomic reference sequence for reads mapping close to the 3’-ends
of human (solid line; triangles) and macaque (dashed line; circles)
tRNAs. Note that the distribution of the error rates is highly cor-
related between the two species. The inset shows the frequency of
nucleotides observed at position 58. Apart from the being read “cor-
rectly” as , this post-transcriptional modification is typically seen as
an A-to-T transversion by the sequencer. Again, human (dark gray
bars) and macaque (light gray bars) have highly similar substitution
patterns.
for this modification seems to be largely invariant to the
library preparation.
This modification is the most prominent one that is
directly visible from the superposition of the error pro-
files of all tRNAs. Several other modifications are de-
tectable as conspicuous accumulations of mismatches
in individual tRNAs. Notably, most of the detectable
positional variations are located either towards the 5’-
or towards the 3’-end of the tRNA. This is caused
by the very uneven coverage of tRNAs with small se-
quencing reads, which is heavily biased towards the
ends and the fact that the error-prone 3’- and 5’-termini
of sequencing reads naturally coincide with the ends
of the tRNA (see Fig. S1). Besides the very strong
ect of 1-methyladenosine on the accuracy of the
cDNA, RNAseq data additionally exhibits moderately
increased error rates for N2,N2-dimethylguanosinesand
dihydrouridines. This is consistent with the find-
ings that the major substitution sites in plant tRNAs
correspond to known RNA base modifications: N1-
methyladenosine (m1A), N2-methylguanosine (m2G),
and N2-N2-methylguanosine (m22G) (Iida et al., 2009;
Ebhardt et al., 2009).
Fig. 2 summarizes the genomic distribution of mis-
matches that can be interpreted as sites of chemical
10 10 10 10 10 10
other
CDS
miRNA
tRNA
Number of potential RNA modifications (log)
Figure 2: Distribution of potential modifications among human (dark
gray) and macaque (light gray) RNA classes. Despite the fact that the
data set generated in the macaque RNAseq experiments is about 50-
times larger than the human one (Tab. 1) the total number of possible
modifications in tRNA and miRNA does not di er proportionally. In
the case of tRNAs, 862 modifications were found in macaque, com-
pared to 657 in human sets; for microRNAs, there are 1400 potential
modifications in macaque and about 500 in human.
modifications. As a consequence of the short RNA se-
quencing protocol, tRNAs and miRNAs, as measured
by the proportion of their genome sequence, accumu-
late most of the positional sequence variations seen in
the data sets used here. For tRNAs, the numbers are
comparable between human and macaque (657 vs. 862),
indicating that even the comparably small human li-
brary is nearly saturated, while for microRNAs there
are about 500 positional sequence variation in human
and 1400 in macaque, leading us to expect that the true
number of putative modifications in human microRNAs
will be larger than observed here. Such an saturation
ect cannot be expected for long transcripts, including
CDS, as a consequence of the small RNA sequencing
protocol. The much higher di erence of observable nu-
cleotide variations within these classes clearly supports
this assumption. Surprisingly, more than two third of
the detectable loci in human fall into coding sequences,
while only 8% are located in tRNAs. In macaque, the
ratio is even smaller, suggesting that chemical modifica-
tions indicated by the observed sequence variations are
a common phenomenon in general.
A detailed analysis of individual modification sites
with respect to their substitution patterns requires a suf-
ficiently large coverage. We have identified two fre-
quent potential modifications in human mir-124-3 and
mir-125b-2 transcript tags. In the case of the human
mir-124-3 (Fig. 3) 33 di erent tags cover nucleotide
77G but only 11 tags are in accordance with the refer-
ence sequence. The other nucleotides A, C, and T were
counted 6, 5, and 11 times, respectively. No genomic
11
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D-loop Ac-loop T-loopV-region
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mismatches RT stops
Mapping of tRNA reads
Many very similar tRNA paralog
tRNAs with introns
Specialized mapping pipeline
remove pre-tRNA reads that map across the boundaries ofmature tRNAs (and everyhing that maps well outside tRNAs)
mask out tRNA genes from genome
cluster very similar mature tRNAs and use a consensus
add these “tRNA prototypes” as artificial chromosomes
disregard positions with variations between paralogs in thesame cluster
Variations between tissues
More Results
Very similar results for rat tissues
Evidence for a few modifications in miRNAs
the method also picks up A-I editing in miRNAs (and othersmall RNAs)
modifications seem to be common also in snoRNAs
Many modifications require chemical treatment
Temperature dependence of Dihydrouridine
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Ala−GGC−1Ala−TGC−1Arg−ACG−1Arg−CCG−1Arg−TCT−1Asn−GTT−1Asp−GTC−1Cys−GCA−1Gln−TTG−1Glu−TTC−1Glu−TTC−2Gly−GCC−1Gly−TCC−1Gly−TCC−2His−GTG−1His−GTG−2
Ile−GAT−1Leu−CAA−1Leu−CAG−1Leu−GAG−1Leu−TAA−1Leu−TAG−1Lys−TTT−1Lys−TTT−2Met−CAT−1Met−CAT−2Met−CAT−3
Phe−GAA−1Pro−TGG−1Ser−CGA−1Ser−GCT−1Ser−GGA−1Ser−TGA−1Thr−GGT−1Thr−TGT−1Trp−CCA−1Tyr−GTA−1Val−GAC−1Val−TAC−1
1 2 3 4 5 6 7 8 10 11 12 13 16 17 17a20 20a
20b24 25 26 27 28 29 31 32 33 34 35 36 38 39 41 42 43 44 45 11e
12e13e
15e2e 3e 4e 5e 24e
22e21e
46 47 48 50 51 54 55 59 60 62 63 64 65 66
U tRNA position
tRN
A c
lust
er
10
20
30
ratio
20◦C
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Ala−GGC−1Ala−TGC−1Arg−ACG−1Arg−CCG−1Arg−TCT−1Asn−GTT−1Asp−GTC−1Cys−GCA−1Gln−TTG−1Glu−TTC−1Glu−TTC−2Gly−GCC−1Gly−TCC−1Gly−TCC−2His−GTG−1His−GTG−2
Ile−GAT−1Leu−CAA−1Leu−CAG−1Leu−GAG−1Leu−TAA−1Leu−TAG−1Lys−TTT−1Lys−TTT−2Met−CAT−1Met−CAT−2Met−CAT−3
Phe−GAA−1Pro−TGG−1Ser−CGA−1Ser−GCT−1Ser−GGA−1Ser−TGA−1Thr−GGT−1Thr−TGT−1Trp−CCA−1Tyr−GTA−1Val−GAC−1Val−TAC−1
1 2 3 4 5 6 7 8 10 11 12 13 16 17 17a20 20a
20b24 25 26 27 28 29 31 32 33 34 35 36 38 39 41 42 43 44 45 11e
12e13e
15e2e 3e 4e 5e 24e
22e21e
46 47 48 50 51 54 55 59 60 62 63 64 65 66
U tRNA position
tRN
A c
lust
er
20
40
60ratio
30◦C
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Ala−GGC−1Ala−TGC−1Arg−ACG−1Arg−CCG−1Arg−TCT−1Asn−GTT−1Asp−GTC−1Cys−GCA−1Gln−TTG−1Glu−TTC−1Glu−TTC−2Gly−GCC−1Gly−TCC−1Gly−TCC−2His−GTG−1His−GTG−2
Ile−GAT−1Leu−CAA−1Leu−CAG−1Leu−GAG−1Leu−TAA−1Leu−TAG−1Lys−TTT−1Lys−TTT−2Met−CAT−1Met−CAT−2Met−CAT−3
Phe−GAA−1Pro−TGG−1Ser−CGA−1Ser−GCT−1Ser−GGA−1Ser−TGA−1Thr−GGT−1Thr−TGT−1Trp−CCA−1Tyr−GTA−1Val−GAC−1Val−TAC−1
1 2 3 4 5 6 7 8 10 11 12 13 16 17 17a 20 20a 20b 24 25 26 27 28 29 31 32 33 34 35 36 38 39 41 42 43 44 45 11e 12e 13e 15e 2e 3e 4e 5e 24e 22e 21e 46 47 48 50 51 54 55 59 60 62 63 64 65 66
U tRNA position
tRN
A cl
uste
r
20
40
60ratio
Exiguobacterium sibir-icum
3 biological replicates
simple model to determine ap-value for theover-representation of RT-stopsat each position
Benjamini-Hochberg correctionwith 5% FDR
cutoff for minimum fold change
Temperature dependence of Pseudouridine
10◦C
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Ala−GGC−1Ala−TGC−1Arg−ACG−1Arg−CCG−1Arg−TCT−1Asn−GTT−1Asp−GTC−1Cys−GCA−1Gln−TTG−1Glu−TTC−1Glu−TTC−2Gly−GCC−1Gly−TCC−1Gly−TCC−2His−GTG−1His−GTG−2
Ile−GAT−1Leu−CAA−1Leu−CAG−1Leu−GAG−1Leu−TAA−1Leu−TAG−1Lys−TTT−1Lys−TTT−2Met−CAT−1Met−CAT−2Met−CAT−3
Phe−GAA−1Pro−TGG−1Ser−CGA−1Ser−GCT−1Ser−GGA−1Ser−TGA−1Thr−GGT−1Thr−TGT−1Trp−CCA−1Tyr−GTA−1Val−GAC−1Val−TAC−1
1 2 3 4 5 6 7 8 10 11 12 13 16 17 17a20 20a
20b24 25 26 27 28 29 31 32 33 34 35 36 38 39 41 42 43 44 45 11e
12e13e
15e2e 3e 4e 5e 24e
22e21e
46 47 48 50 51 54 55 59 60 62 63 64 65 66
U tRNA position
tRN
A c
lust
er
3
6
9
ratio
20◦C
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Ala−GGC−1Ala−TGC−1Arg−ACG−1Arg−CCG−1Arg−TCT−1Asn−GTT−1Asp−GTC−1Cys−GCA−1Gln−TTG−1Glu−TTC−1Glu−TTC−2Gly−GCC−1Gly−TCC−1Gly−TCC−2His−GTG−1His−GTG−2
Ile−GAT−1Leu−CAA−1Leu−CAG−1Leu−GAG−1Leu−TAA−1Leu−TAG−1Lys−TTT−1Lys−TTT−2Met−CAT−1Met−CAT−2Met−CAT−3
Phe−GAA−1Pro−TGG−1Ser−CGA−1Ser−GCT−1Ser−GGA−1Ser−TGA−1Thr−GGT−1Thr−TGT−1Trp−CCA−1Tyr−GTA−1Val−GAC−1Val−TAC−1
1 2 3 4 5 6 7 8 10 11 12 13 16 17 17a20 20a
20b24 25 26 27 28 29 31 32 33 34 35 36 38 39 41 42 43 44 45 11e
12e13e
15e2e 3e 4e 5e 24e
22e21e
46 47 48 50 51 54 55 59 60 62 63 64 65 66
U tRNA position
tRN
A c
lust
er
5
10
15
20
ratio
30◦C
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Ala−GGC−1Ala−TGC−1Arg−ACG−1Arg−CCG−1Arg−TCT−1Asn−GTT−1Asp−GTC−1Cys−GCA−1Gln−TTG−1Glu−TTC−1Glu−TTC−2Gly−GCC−1Gly−TCC−1Gly−TCC−2His−GTG−1His−GTG−2
Ile−GAT−1Leu−CAA−1Leu−CAG−1Leu−GAG−1Leu−TAA−1Leu−TAG−1Lys−TTT−1Lys−TTT−2Met−CAT−1Met−CAT−2Met−CAT−3
Phe−GAA−1Pro−TGG−1Ser−CGA−1Ser−GCT−1Ser−GGA−1Ser−TGA−1Thr−GGT−1Thr−TGT−1Trp−CCA−1Tyr−GTA−1Val−GAC−1Val−TAC−1
1 2 3 4 5 6 7 8 10 11 12 13 16 17 17a20 20a
20b24 25 26 27 28 29 31 32 33 34 35 36 38 39 41 42 43 44 45 11e
12e13e
15e2e 3e 4e 5e 24e
22e21e
46 47 48 50 51 54 55 59 60 62 63 64 65 66
U tRNA position
tRN
A c
lust
er
10
20
ratio
Exiguobacterium sibir-icum
![bAcids Nucleosides, Nucleotides and Nucleic - UMEXPERT · Role of Initiator tRNA i met in Fidelity of Initiation of Protein Synthesis 727 (aa-tRNA) ternary complex.[1] The tRNA binding](https://static.fdocuments.net/doc/165x107/5c25d16309d3f28d198c11f7/bacids-nucleosides-nucleotides-and-nucleic-umexpert-role-of-initiator-trna.jpg)
