Bioinformatics analysis suggests base modifications of tRNAs and miRNAs in Arabidopsis thaliana
© Iida et al; licensee BioMed Central Ltd. 2009
Received: 27 October 2008
Accepted: 09 April 2009
Published: 09 April 2009
Modifications of RNA bases have been found in some mRNAs and non-coding RNAs including rRNAs, tRNAs, and snRNAs, where modified bases are important for RNA function. Little is known about RNA base modifications in Arabidopsis thaliana.
In the current work, we carried out a bioinformatics analysis of RNA base modifications in tRNAs and miRNAs using large numbers of cDNA sequences of small RNAs (sRNAs) generated with the 454 technology and the massively parallel signature sequencing (MPSS) method. We looked for sRNAs that map to the genome sequence with one-base mismatch (OMM), which indicate candidate modified nucleotides. We obtained 1,187 sites with possible RNA base modifications supported by both 454 and MPSS sequences. Seven hundred and three of these sites were within tRNA loci. Nucleotide substitutions were frequently located in the T arm (substitutions from A to U or G), upstream of the D arm (from G to C, U, or A), and downstream of the D arm (from G to U). The positions of major substitution sites corresponded with the following known RNA base modifications in tRNAs: N1-methyladenosine (m1A), N2-methylguanosine (m2G), and N2-N2-methylguanosine (m22G).
These results indicate that our bioinformatics method successfully detected modified nucleotides in tRNAs. Using this method, we also found 147 substitution sites in miRNA loci. As with tRNAs, substitutions from A to U or G and from G to C, U, or A were common, suggesting that base modifications might be similar in tRNAs and miRNAs. We suggest that miRNAs contain modified bases and such modifications might be important for miRNA maturation and/or function.
In rRNAs, tRNAs, snRNAs, and some mRNAs, the bases of nucleotides are often modified [1–5]. Especially in tRNAs, many types of base modifications have been characterized [1, 2]. These modifications often involve methylation or acetylation, and may contribute to the stability of tRNA molecules when they form tertiary structures [4, 6]. Another well-characterized tRNA modification is RNA editing from adenine (A) to inosine (I) [7, 8]. This A to I editing is explained by deamination of A by adenosine deaminase . In the yeast alanine tRNA, adenosine bases on the anticodon are deaminated to I. The edited inosine can form base pairs with uridine (U), cytosine (C), or adenosine in codons of mRNAs, and thereby expands the decoding capacities . Although RNA modification in tRNAs, especially mitochondrial tRNAs, has been studied in Arabidopsis , the understanding of RNA modification in Arabidopsis tRNAs is still limited.
MicroRNAs (miRNAs) are a recently described class of functional RNAs and were initially found in C. elegans . Several editing events in miRNAs have been reported. One example is A to I editing in primary miRNA precursor transcripts (pri-miRNAs). A to I editing has been documented for human pri-miR-22 and pri-miR-142 [11–13]. In the case of human pri-miR-142, A to I editing represses the maturation process carried out by the cleaving enzyme, Drosha [12, 13]. Another example of RNA editing in miRNAs is found in primary transcripts of the human miR-376 cluster . In this case, the edited pri-miRNAs are processed into mature miRNAs, and the mature miRNA targets different transcripts than the non-edited miRNA. These examples demonstrated the importance of RNA editing on miRNAs. Like humans, Arabidopsis thaliana also has hundreds of miRNA genes . In Arabidopsis, the methyltransferase enzyme HEN1 introduces 2'-O methyl groups to the ribose of 3' terminal nucleotide of miRNAs [16, 17]. This modification is important for the stability and accumulation of miRNAs. However, there has been no report of RNA modification in the bases of miRNAs in Arabidopsis or other plants.
The current study is focused on RNA base modifications in Arabidopsis. Because some modified bases are read differently from unmodified bases by reverse transcriptases during cDNA synthesis , we expect that the modified nucleotides will be read as different nucleotides from genomic ones if the bases of RNAs are modified. Our analysis utilized several sets of high throughput cDNA sequences of small RNAs (sRNAs). We used our own sequences generated by the "454" technology [19, 20]. During the analysis of our sequences, we observed that many of the sequences could not be mapped to either the nuclear or organellar genomes . We suspected that such non-mapped sequences may contain information of RNA modifications. We found that the sRNAs that were not mapped to the genome, mapped perfectly with the genome sequences except for one base mismatches (OMM). In the current work, we also used public sequences obtained with the 454 technology and the "massively parallel signature sequencing" (MPSS) method [21, 22]. The MPSS dataset is especially important here since the sequences were generated by a completely different technology from the 454. We listed substituted sites only if the sites were supported by sequences from both 454 technology and MPSS. This strategy allowed us to avoid detecting simple sequencing errors resulting from one or the other of the sequencing technologies. In this study, we first analyzed RNA base modifications in tRNA molecules. Next, we investigated potential RNA base modifications in miRNAs. Ours is the first report of large scale analysis of RNA base modifications of tRNAs in Arabidopsis. More importantly, our bioinformatics analysis suggests similar base modifications (e.g. methylation and acetylation) in miRNAs.
Results and discussion
Mapping sRNAs to the genome
Data set and mapping results
OMM excluding terminal miss match
OMM excluding terminal miss match
454 data set 1a
454 data set 2b
MPSS data setc
Finding candidate sRNAs with base modifications
We also considered possibilities of single nucleotide polymorphisms (SNPs) contributing to the OMMs. In the case of SNPs, sequences from the same genetic background (such as Col-0) must have the same substitutions and should not have perfect matches. In our analysis, 1,166 sites out of all 1,187 substitution sites had mixtures of substituted and non-substituted nucleotides from plants of a single genetic background. Besides, in the remaining 21 sites, perfectly modified sites, such as A to I editing sites were included. These results suggest that OMMs did not come from SNPs.
We examined the genomic loci and classified the substitution sites based on the corresponding genome annotation. Out of 1,187 substitution sites, 703 sites corresponded with tRNAs (59.2%), 147 with miRNAs (12.4%), and 88 with ribosomal RNAs (7.4%).
Base modifications in tRNAs
Modified nucleotides found in tRNA
TPQ for non-modified bases
# of tRNA
Uniq # of sRNA
Total TPQ of sRNA
TPQ of edited nucleotides
Type(s) of tRNA
The second most abundant type of substitutions were in the 3' regions of D loop (Table 2, Fig. 2). In these substitutions, genome encoded guanine (G) was read mainly as U. This position is known as the position of the modified nucleotide, N2-dimethylguanosine (also called N2-N2-methylguanosine or m22G) [30–32]. Interestingly, this modification was found only in tRNAs for asparagine, methionine, and tyrosine, which did not have the m1A modification in the T loop. In the tertiary structure of tRNAs, m1A does not contact m22G . The relationship of these two types of modifications is unclear.
The third most abundant type of substitutions occurred between the D loop and acceptor stem, at the position of N2-methylguanosine (m2G) [5, 6]. These substitutions may also involve modifications of guanine, as in m22G, but the substitution pattern observed in cDNAs was different. We observed C, U, and A with similar abundance. We suggest that this difference in substitution patterns reflects the different effects of m2G and m22G on reverse transcription, and may be used to predict m2G and m22G modifications.
The fourth most abundant type of substitutions was found in the 5' part of the D stem, where A substituted for C (Table 2). This site corresponds to the known modification site of N4-acetylcytidine (ac4C) .
The fifth most abundant type of substitutions involved the well-known A to I editing in the anticodon loop (Table 1, ref. ). In these cases, adenine was read as G but not as U or C. This type of RNA editing was found in only six cases of threonine tRNA (Table 2). We noted that the editing rate in this case was 100% (Table 2). In the current analysis, we used sRNA sequences that included fragments of tRNAs. These tRNA fragments rarely contain the anticodon regions. Out of 5,657 OMM sRNAs supporting substitutions in tRNAs, only 274 (4.8%) contain anticodon nucleotides. These results indicate that most tRNA fragments were products of cleavage around anticodon regions, as is true for tRNA fragments found in humans or Tetrahymena thermophila [25, 26]. This is probably one reason why few cases of A to I editing were observed in this study. Our elimination of OMM sRNAs with substitutions in the initial or terminal three bases might also have reduced the chance to observe modified bases on aniticodon loops. It is known that the anticodon loop has several modified nucleotides besides A to I editing [reviewed in ref. ]. Despite the small number of tRNA fragments containing the anticodon loop, we found some substitutions which correspond to the well-known modified base, 1-methylguanosine (m1G) at position 37 of the anticodon loop (Table 2, substitution of G to U/C).
The locations of predicted RNA modifications based on OMMs corresponded well with locations of known modifications reported in the literature. Out of 703 substitution sites found in tRNAs, 604 sites (86%) can be accounted for by the 6 known base modifications discussed above. Clearly these bias were not caused by chance, which was supported by binomial test with p-value less than 1*10-15. Substitution patterns were also consistent with the known chemistry of reverse transcription in the literature. Research on reverse transcription of tRNAs showed that m1A nucleotides formed base pairs with A, U, G, or C . In agreement with Steinberg and Cedergren , we found that the substituted nucleotides at known m22G sites were mainly U and A (Table 2). Therefore, our predictions of base modifications in tRNAs are consistent with previous studies. We suggest that RNA modification types can be classified based on substitution patterns found in OMM sRNAs.
Base modifications in miRNAs
Substitutions found in miRNAs
Name of microRNA
Summary of substitutionsa
5:G(27900.95)>CA(21), 12:G(28142.15)>CA(45.4), 13:G(35092.7)>UA(144.86)
6:G(20643.65)>CA(50.2), 9:A(20057.15)>GCU(109.2), 13:C(18208.1)>GA(8.3)
MIR165a, MIR166a, MIR166b, MIR166c, MIR166d, MIR166e, MIR166f, MIR166g
4:G(54554.7)>CUA(134.7), 6:C(54554.7)>UA(209.21), 7:C(44547.6)>U(149.93), 8:A(35038.15)>GU(83.7), 9:G(47675.1)>CUA(328.62), 10:G(54539.9)>A(106.29), 11:C(54539.9)>GUA(278.15), 12:U(54539.9)>GCA(379.38), 13:U(54539.9)>CA(256.11)
MIR167a, MIR167b, MIR167d
4:A(219813.04)>GCU(76.99), 5:G(219813.04)>CUA(325.81), 7:U(158835.06)>GCA(466.4), 8:G(158835.06)>CUA(326.2), 9:C(158835.06)>GUA(602.6), 10:C(158835.06)>GU(431.4), 11:A(219826.94)>GCU(3686.02), 12:G(219826.94)>CU(1018.4), 13:C(158835.06)>GU(336.74)
9:U(15948.36)>CA(36.6), 10:G(15933.26)>A(24.3), 12:A(15933.26)>GC(22.2), 13:G(15933.26)>UA(43.1), 14:G(15933.26)>UA(41.3)
MIR169a, MIR169b, MIR169c, MIR169d, MIR169e, MIR169h, MIR169i, MIR169j, MIR169k, MIR169l, MIR169m, MIR169n
6:C(24472.01)>GU(40.32), 8:A(87930.11)>GCU(77.31), 9:G(58812.37)>CUA(501.61), 10:G(37156.57)>CUA(161.5), 11:A(10397.81)>GC(26.88), 13:G(87930.11)>CUA(1043.6), 14:A(34341.26)>G(44.23), 15:C(38019.92)>GUA(109.1)
MIR172a, MIR172b, MIR172c
PRE74:A(6807.15)>G(21.5), PRE76:C(6807.15)>U(78.6), 6:C(41119.9)>UA(27.6), 7:A(20563.45)>G(48.5)
A to I editing is found in human miRNAs . Based on the results with tRNAs, A to I editing was expected to be observed as substitution to G (Table 2). Such substitutions were found in miR169 (site 14) and miR319 (site 7) (Table 3). However, A to G substitutions did not represent the major substitution pattern (Fig. 4). Therefore, although A to I editing seems to occur in Arabidopsis miRNAs, such modifications are evidently uncommon.
Potential biological impacts of base modifications in miRNAs
Our bioinformatics analysis strongly suggested RNA base modifications in miRNAs. What would be the biological effects of base modifications in miRNAs? We considered two possibilities. The first one is that the modification may enhance or prohibit the recognition of mRNA targets or cause recognition of novel targets, as is the case for RNA editing in human miR-376 . The other possibility is that RNA base modifications may affect the maturation of miRNA, as with RNA editing in human pri-miR-142 [12, 13].
Our data on tRNAs clearly show that substitutions in cDNAs can indicate RNA base modifications. This research thus found a new analytical strategy for predicting RNA base modifications and provided the first detailed bioinformatics analysis on RNA modifications in Arabidopsis tRNAs.
More importantly, this is the first report of the possibility of RNA base modifications in miRNAs with methyl or acetyl groups. Our results, which are based on carefully chosen sRNA sequences, strongly suggest that some RNA bases might be modified in miRNAs. We showed that one-base mismatches in sRNAs were not random and much more abundant than expected, and that many substitution sites were supported by both MPSS and 454 technologies. It follows that these substitutions cannot be explained by sequencing errors. More importantly, substitution patterns in adenine and guanine of miRNAs were similar to those of tRNAs. This similarity suggests common base modifications in miRNAs and tRNAs, although the nature of base modifications in miRNAs can only be determined experimentally in the future. Base modifications likely provide another regulatory mechanism for the biogenesis and/or functioning of miRNAs.
We used 238,538 sRNA sequences (of which 98,035 were unique) sequenced by the 454 technology (Table 1) [19, 20]. The small RNA libraries were constructed from plants treated withabiotic stresses (cold, drought, salt, copper, UV and ABA, respectively) or infected with bacterial or fungal pathogens . We also used public sRNA sequences obtained by Lu et al. [21, 22] with 454 and MPSS technologies, which have 58,178 reads and 3,126,002 reads respectively. The read numbers are used with normalization in TPQ (Transcripts Per Quarter million), according to ref. . When a single type of sRNA had multiple loci on the genome, the TPQ value was divided by the number of the loci.
Mapping sRNAs to the genome
We mapped sRNA sequences to the genome sequences with the alignment program, SOAP . We used the program with the "-v 1" option, enabling the program to find one mismatch when the sRNA is mapped to the genome. We added a further criterion for the sRNAs that mapped to the genome with one mismatch. If a single sRNA had more than two OMM loci, with substitutions in different positions or with different nucleotides, we discarded it. After the analysis of the substitution sites (Table 1, Fig 1), we selected OMM sRNAs only if the substitution sites were not in the initial or terminal three bases. Then, we compared the results of sRNAs from 454 and MPSS technologies. If a substitution of a genomic locus was supported with sRNAs from both 454 and MPSS technologies, we selected it for further analysis. These criteria decreased technology-dependent errors.
We used probabilities of OMM sRNAs caused by sequencing errors based on reported sequencing error rates [24, 21]: 0.25% for MPSS sequences and 0.004% for 454 sequences. We calculated the expected numbers of OMM sRNAs caused by sequencing errors for each substitution site. Then we performed chi-square tests to assess whether the read numbers of OMM sRNAs were significantly larger than expected from sequencing errors.
Analysis of tRNAs and miRNAs
In the analysis of RNA modification in tRNAs and miRNAs, we had to determine the clover leaf structure of tRNAs and fold-back structures of miRNAs. For this purpose, we used tRNAscan-SE  and information about miRNAs published at miRBase . Perl scripts were used to summarize the results.
This work was supported by National Institutes of Health grants R01GM070795 and R01GM059138 (J.-K. Zhu) and a long-time fellowship from TOYOBO bio-technology foundation (K. Iida). We greatly appreciate Mr. Satoshi Takahasi for his initial efforts on analysis of sRNA data.
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