A Novel BLAST-Based Relative Distance (BBRD) Method Can Effectively Group Members of
Protein Arginine Methyltransferases and Suggest their Evolutionary Relationship
Yi-Chun Wang1, 2, †, *, Jing-Doo Wang3, 4, †, Chin-Han Chen1, Yi-Wen Chen5, Chuan Li1, 2, *
1
Department of Biomedical Sciences, Chung Shan Medical University. No.110, Sec.1, Jianguo N.
Rd., Taichung 40201, Taiwan
2
Department of Medical Research, Chung Shan Medical University Hospital. No.110, Sec.1,
Jianguo N. Rd., Taichung 40201, Taiwan.
3
Department of Computer Science and Information Engineering, Asia University, No. 500, Lioufeng
Rd. Wufeng District, Taichung 41354, Taiwan.
4
Department of Medical Research, China Medical University Hospital, China Medical University,
Taichung, Taiwan.
5
Department of Life Science, Tunghai University. No.1727, Sec.4, Taiwan Boulevard, Xitun District,
Taichung 40704, Taiwan R.O.C.
†
Co-first author
*
Co-corresponding author: Yi-Chun Wang: ycw@csmu.edu.tw; Chuan Li: cli@csmu.edu.tw
Abstract
We developed a novel BLAST-Based Relative Distance (BBRD) method by Pearson's correlation
coefficient to avoid the problems of tedious multiple sequence alignment and complicated outgroup
selection. We showed its application on reconstructing reliable phylogeny for nucleotide and protein
sequences as exemplified by the fmr-1 gene and dihydrolipoamide dehydrogenase, respectively. We
then used BBRD to resolve 124 protein arginine methyltransferases (PRMTs) that are homologues of
nine mammalian PRMTs. The tree placed the uncharacterized PRMT9 with PRMT7 in the same
clade, outside of all the Type I PRMTs including PRMT1 and its vertebrate paralogue PRMT8,
PRMT3, PRMT6, PRMT2 and PRMT4. The PRMT7/9 branch then connects with the type II
PRMT5. Some non-vertebrates contain different PRMTs without high sequence homology with the
mammalian PRMTs. For example, in the case of Drosophila arginine methyltransferase (DART) and
Trypanosoma brucei methyltransferases (TbPRMTs) in the analyses, the BBRD program grouped
them with specific clades and thus suggested their evolutionary relationships. The BBRD method
thus provided a great tool to construct a reliable tree for members of protein families through
evolution.
Keywords: BLAST; Pearson's correlation coefficient; evolutionary relationship
1. Introduction
1.1. Motivation of developing the BBRD method
With the vast number of sequences from widely diverse species in the databases, it is feasible for
scientists to trace the molecular evolution of their interested targets. Numerous phylogenetic methods,
mostly character-based or distance-based, have been developed to reconstruct the phylogeny and
greatly enhanced our analyzing power of the molecular data. However, there are still some
limitations or drawbacks that may hinder their application.
The character-based method such as maximum likelihood and maximum parsimony, use standard
statistical techniques for inferring probability distributions to assign probabilities to particular
possible phylogenetic trees. Identification of the potential phylogenetic tree requires the smallest
total number of evolutionary events to explain the sequence data (Felsenstein, 1973). Other
character-based Bayesian methods implement Markov chain Monte Carlo algorithms to recover a
tree that represents the most likely clades (Yang and Rannala, 1997; Mau et al., 1999). However,
these analyses all involve many computational steps and thus require strong computing power if a
large number of sequences are to be analyzed.
The distance-based methods are generally straightforward and can be easily conducted in molecular
biology laboratories. Most of the methods require a multiple sequence alignment (MSA) in their
initial step (Blair et al., 2010) and a guide tree must be constructed based on the distance scores
derived from those sequences. However, manual adjustment of the multiple sequence alignment can
be time-consuming as identification of the common conserved region for all of sequences in
comparison might need exhaustive browsing and inspection of the MSA results (Goertzen et al.,
2003). It is even difficult to conduct the analyses with sequences that are very diverse or contain
more than one common conserved region. Furthermore, the order of sequence inclusion may affect
the result significantly (Thompson et al., 1994). To avoid the complexity of multiple sequence
alignment, many distance-based phylogenetic methods use pairwise alignment methods such as the
basic local alignment search tool (BLAST) (Altschul et al., 1990) and its derivatives gapped BLAST
and PSI BLAST (Altschul et al., 1997). Other methods evaluate the similarity between sequences,
such as Scoredist based on the logarithmic correction of observed divergence among others
(Sonnhammer and Hollich, 2005), and the ones based on movements of segments (Varr et al., 1999)
using Lempel-Ziv complexity for the distance of sequences (Otu and Sayood, 2003). Kelil et al.
(2007) developed a similarity measure called substitution matching similarity (SMS) and derived an
alignment-free method CLUSS for protein clustering. However, those alignment-dependent distance
measurement methods are either inefficient to reveal the subtle differences among similar proteins or
dependent on the quality of the alignments (Kelil et al., 2007). Additionally, almost all
distance-based and most character-based molecular phylogenetic analysis programs require an
outgroup (or outgroups) for reference. Appropriate selection of the outgroup requires a sequence that
is moderately, but not too closely or distantly related to the sequences of interest (Mount, 2004).
Different outgroups may affect the phylogenetic tree markedly (de la Torre-Bárcena et al., 2009,
Kirchberger et al., 2014).
In this study we showed our development of a BLAST-Based Relative Distance (BBRD) method by
Pearson's correlation coefficient to avoid the problems of MSA adjustment, input order, and
outgroup selection. The method simply combines the basic local alignment search tool (BLAST)
(Altschul et al., 1997) for sequence comparison, calculates Pearson's correlation coefficient for
relative distance, and performed hierarchical clustering for tree construction. In this method we
evaluated the distance between two sequences not only based on the direct (absolute) distance
between them, but also considered the distances of the two sequences to other input (reference)
sequences. The proposed method can eliminate the requirement of an outgroup sequence and the
phylogenetic tree constructed is unaffected by the input sequence order. After validating the
feasibility and reliability of the method for constructing phylogenetic trees for both nucleotide and
protein sequences, we used it to analyze our interested targets, protein arginine methyltransferases
(PRMTs, Table 1).
1.2. Protein arginine methyltransferases
PRMTs catalyze the transfer of methyl groups from the methyl group donor S-adenosylmethionine
(AdoMet) to specific guanidino nitrogen atoms of arginine and can be divided by the ways the
methyl groups are attached. Type I, type II and type III PRMT catalyzes the formation of asymmetric
-NG, NG dimethylarginine (aDMA), symmetric -NG, NG’ dimethylarginine (sDMA), and- NG
monomethylarginine (MMA) respectively. The type IV activity that catalyzes the formation of - NG
methylarginine has only been reported for yeast RMT2 (Bedford and Clarke, 2009; Wang and Li,
2012). After the identification of PRMT1 (Lin et al., 1996), the PRMT genes were designated by
sequence homology and numbered according to the identification order. PRMT1, 2, 3, 4, 6 and 8
belong to type I and PRMT5 belongs to type II PRMT (Wang and Li, 2012). Recently PRMT7 has
been shown to be a type III enzyme (Zurita-Lopez et al., 2012; Feng et al., 2013).
We have analyzed homologous genes for PRMT1–8 identified in humans and fish (puffer fish, Fugue
rubripes, and zebrafish, Danio rerio) and have shown their conserved amino acid sequences and
gene structures (Hung and Li, 2004). We reviewed the studies of PRMTs in non-mammalian systems
and also used nine PRMTs identified in mammals (human) as the templates to survey homologous
PRMTs in ten non-mammalian animal species including vertebrates, invertebrate chordates,
echinoderms, arthropods, nematodes and cnidarians that have a completed genomic sequence (Wang
and Li 2012). As more species have been sequenced and more sequence data are available, we would
like to construct a more detailed and thorough phylogenetic tree of PRMTs. In this study we
analyzed 124 PRMT sequence entries collected from 22 different species by the newly developed
BLAST-based relative distance (BBRD) method to construct the tree.
2. Materials and methods
2.1. Methods used in validation
The methods, DNA_ML, DNA_Pars, Pro_ML, and Prodist_Fitch, used to verify the feasibility and
reliability of our method are programs from the PHYLIP program package (Felsenstein, 1989).
2.2. Procedures of the BBRD method
The BLAST used in BBRD is NCBI BLAST-2.2.28+. The latest BLAST program can be
downloaded at ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/.
The relationship of sequences was then constructed by 5 steps:
1. A self pairwise alignment of sequence dataset was performed with BLAST.
2. The BLAST total score matrix (BTSM) was constructed from the BLAST results. BTSM(Pi, Pj)
denotes the value of the entry in the ith row and jth column of the BTSM. It refers to the
summation of the scores of all high-scoring segment pairs (HSPs) of sequence Pi to another
sequence Pj.
3. The BLAST total score matrix was converted to a sequences relative distance matrix (SRDM).
The relative distance of Pi and Pj was determined by the Pearson's correlation coefficient (Bulmer,
1979) between the series of total scores of Pi related to the other sequences and that of Pj related
to the others. Let Series(Pi) denote a series of values in the ith row of the BTSM matrix.
Intuitively, the values within the Series(Pi) represent the similarities of Pi in relation to other
sequences, respectively. The series of Series(Pi) is similar to that of Series(Pj) if Pi is similar to
the Pj. The similarities between the two series of Series(Pi) and Series(Pj) were evaluated by
defining and computing the relative distance of two sequences. Basically, sequence relative
distance matrix (SRDM) denotes the matrix containing the relative distances of any two
sequences. The value of the SRDM(Pi; Pj) was defined as
SRDM(Pi; Pj) = (1−PCCM(Series(Pi); Series(Pj)).
The PCCM(Series(Pi); Series(Pj)) is the matrix of Pearson's Correlation Coefficient of the two
series, Series(Pi) and Series(Pj). Notably, the value of SRDM(Pi; Pj) was between 0 and 2.
4. Sequences relationships were constructed via SRDM. The relationship diagram of selected
sequences was constructed based on the single-linkage algorithm (Han et al., 2006). It is one of
the hierarchical clustering methods that repeatedly merges two groups with the smallest relative
distance until only one group is left. The elements in matrix SRDM were sorted based on their
values in an increasing order list. One SRDM(Pi; Pj) (1 ≤ i; j ≤ n) with the smallest values was
then extracted from the sorted list, as mentioned above. Next, whether two groups, containing Pi
and Pj respectively, were the same or not was verified. The two groups were merged if Pi and Pj
were not in the same group, followed by recording the value of SRDM(Pi; Pj) as the relative
distance of those two groups. The steps were repeated until all sequence groups were merged into
one or the distance between two nearest remaining groups was larger than a given threshold.
5. Sequences relationship diagrams were constructed. Dendroscope version 3.2.10 built 19 (Huson
and Scornavacca, 2012) was used to present and adjust the sequence relationship.
2.3. Domain Structure comparison
Human PRMT5, PRMT7, and PRMT9 domain structures were depicted from GenPept records and
aligned together according to the AdoMet_MTase region.
3. Results
3.1. Developing of the BBRD method
At the initial stage of developing the BBRD method, we selected the highest high-scoring segment
pair (HSP) of each sequence pair to calculate the sequence relative distance matrix (SRDM). Yet, the
tree constructed with the matrix was not satisfactory. We then took all HSPs of a given sequence pair
into account to build the BLAST total score matrix (BTSM) and transformed BTSM to SRDM.
Utilizing the BLAST total score overcame the limitations of low sensitivity for high similarity
sequences, especially those with tandem domains, inverted repeats (e.g., regulatory elements in
DNA), domain swaps or scattered conservative short motifs. The influence (false positive hits) of
short tandem repeats or low complexity sequences was eliminated by employing the "low complexity
filter" or "repeat mask" in BLAST parameter settings. BLAST total score calculation increased the
scoring range of compared sequences, thus raising the resolution power for evolutionarily closely
related (high similarity) sequences. Consequently, modification of the scoring scheme greatly
improved the output tree.
The BBRD tools can be downloaded at http://tm.asia.edu.tw/tools/. The hardware requirement for
installing the program is low (memory more than 4 GB is recommended). BBRD can be run in Linux,
Mac OS or Windows system.
3.2. Validation of the BBRD method using cDNA Sequences
To evaluate whether the BBRD method could efficiently construct a reasonable phylogenetic tree, we
first conducted a tree construction with nucleotide sequences. The selected nucleotide sequence was
the cDNA sequence of fragile X mental retardation 1 (fmr-1) gene that widely exists in vertebrates.
Its protein product fragile X mental retardation protein (FMRP) is responsible for nucleocytoplasmic
shuttling of mRNA, dendritic mRNA localization, synaptic protein synthesis (Antar et al., 2005), and
synaptic plasticity (Huber et al., 2002). We used human FMR-1 as the template to identify its
homologues in eighteen vertebrate species that have diverged approximately 340 million years ago
(Supplementary Table 1).
Even though the evolutionary tree of a specific gene at the DNA or protein level may not be
consistent with that of the species due to horizontal gene transfer, genetic recombination or even
convergent evolution, a taxonomy common tree of the species would still be a good indication for
the evolutionary relationships of the sequences. We thus used the NCBI taxonomy common tree
(species tree) as the reference for both DNA and protein data validation. It is apparent that the taxa
grouping and the tree topology of BBRD tree for fmr-1 correlate well with that of the NCBI
taxonomy common tree (Fig. 1a, b).
In addition, we performed the analyses with two commonly used methods, maximum likelihood
method (DNA_ML) and maximum parsimony method (DNA_Pars). We used two character-based
methods for comparison because the frequently used distance-based method DNAdist_NJ of the
PHYLIP package was not able to complete the analyses with the default settings due to the large
sequence differences. Topologies of the DNA_ML tree (Fig. 1c) and DNA_Pars tree (Fig. 1d) closely
resembled each other, yet differed from the taxonomy common tree and BBRD tree markedly.
3.3. Validation of the BBRD method using Protein Sequences
We then assessed whether the BBRD method could also construct reliable trees from protein
sequences. Dihydrolipoamide dehydrogenases, a common component of dehydrogenase complexes
highly conserved from prokaryotes to eukaryotes, were selected to evaluate the feasibility of our
method. We used the human sequence as the query to search against the protein sequence database.
Twenty five sequences belonging to different eukaryotic species that have diverged approximately
760–1060 million years ago (Lucking R et al., 2009) (Supplementary Table 2) were subjected to
BBRD and two commonly used methods, Pro_ML (character-based) and Prodist_Fitch
(distance-based). The taxa clustering and topology of the BBRD tree are very similar to those of the
NCBI common tree (Fig. 2a, b), whereas those of the Pro_ML and Prodist_Fitch trees were
apparently different (Fig. 2c, d).
3.4. Phylogenetic tree of PRMTs
We used nine human PRMTs as the templates to search for homologues in different species
belonging to different phyla/classes. One hundred and twenty four PRMT proteins from twenty two
representative species were included. The entries used in this study are summarized in Table 1. We
then constructed the overall PRMT tree. Our method successfully separated one PRMT from other
PRMTs with no exception (Supplementary Fig. 1; Supplementary file Fig. S1.nexml). To illustrate
the results concisely, we showed an outline tree in Fig. 3a. PRMT1 and its vertebrate paralogue
PRMT8 grouped together clearly. This clade then connects with PRMT3 (Fig. 3b). The method then
put other type I PRMT 2 and 6, then 4 into one subdivision (Fig. 3c). The other subdivision contains
PRMT7 and 9, and then 5 (Fig. 3d). The tree is very similar to the tree we constructed previously
(Hung and Li, 2004), with the addition of PRMT9 (PRMT9 4q31, previously designated as
PRMT10). Even though PRMT9 has been identified and suggested as a PRMT for a few years, no
further analyses have been conducted on this gene and no catalytic activity of this PRMT has been
suggested. As shown in Fig. 3, PRMT9 is subgrouped with PRMT7, outside all of the type I PRMTs.
Even though type II activity was suggested for PRMT7 in early studies, recent investigations provide
strong evidence for the type III activity of PRMT7 (Zurita-Lopez et al., 2012; Feng et al., 2013).
PRMT5 is present in a different branch with all other PRMTs, indicating its specific type II activity.
In addition to our method, two other methods were also performed to analyze the PRMT families.
The popular traditional methods DNA(Pro)_ML, DNA_Pars, and Prodist_Fitch used in the previous
BBRD method validation stage did not generate a tree with satisfactory evolutionary relatedness. We
thus chose two other methods that were developed more recently. One of these methods is a
distance-based CLUSS program (web service:
http://prospectus.usherbrooke.ca/CLUSS/Server/Index.html). The Supplementary Fig. 2 shows that
the CLUSS tree has a "binary clustering" property. The clustering of families is similar to our tree
but with some taxa placed in different PRMT groups. The clearest example is that PRMT6 of
Strongylocentrotus purpuratus is grouped with PRMT2, and PRMT3 of Drosophila malanogaster is
outside the PRMT1/8-3-6-2 branch. Another method performed is a character-based Mr. Bayes
program (download site: http://mrbayes.sourceforge.net/download.php), a very popular Bayesian
method. The major differences between Bayes tree and our tree are the family relationship and
multi-furcating root in the Bayes tree (Supplementary Fig. 3). A minor difference is that the Bayes
tree placed a PRMT9 from Ciona into the PRMT7 family.
Interestingly, PRMT9 of C. elegans and A. suum tend to be placed outside the PRMT9 group by
these two methods but not BBRD. The major difference of the trees is their placement of PRMT5, 7,
and 9. CLUSS places PRMT7 with other type I PRMTs, then PRMT9, then PRMT5. Mr. Bayes
places PRMT5 with other type I PRMTs first and then the branch of PRMT7 with PRMT9.
3.5. Determination of the evolutionary relationship of PRMTs outside of the vertebrate systems
It is known that some eukaryotes that are evolutionarily distant from mammals contain different
PRMTs that do not show high sequence homology with the mammalian PRMTs. The protein
arginine methyltransferases in Drosophila melanogaster (designated as DARTs) have been described
by Boulanger et al., 2004. There are nine DARTs and four of them, DART1, 4, 5 and 7 share high
sequence homologies with the mammalian PRMTs with the same numeral. Other DARTs were
reported to share highest similarity with PRMT1. In this analysis we re-examined the phylogenetic
relationships of these DARTs. DART3 appeared to be closely related with other PRMT3s in Fig. 1.
We then included the Drosophila DART2, 6, 8, and 9 for the phylogenetic analyses (Supplementary
Table 3). DART 2, 6, 9 are restricted in Drosophila species while DART 8 appears to be present
broadly in insects. To avoid the bias of a single sequence presentation, we included DART 2, 6, 8, 9
from five different Drosophila species, and DART8 from six more insect species.
As outlined in Fig. 4a and detailed in Supplementary Fig. 4 (Supplementary file Fig. S4.nexml), we
found that DART2s are most closely related to the PRMT1/3 cluster and are located between all
other PRMT3s and the PRMT3 of S. pombe. DART6s are located between the PRMT1/3 cluster and
PRMT6. DART8s are widely distributed in insects and are between PRMT6 and PRMT2. DART9 is
outside of other type I PRMTs and is between the putative type I/non-type I (PRMT7, 9 and 5)
branches.
PRMTs and protein arginine methylation have been intensely investigated as an early branching
eukaryote Trypanosoma brucei, the etiological parasitic protozoan that causes African sleeping
sickness (Pasternack et al., 2007; Fisk et al., 2009; Fisk et al., 2010; Fisk and Read, 2011; Lott et al.,
2013; Lott et al., 2014). Five PRMT sequences of Trypanosoma brucei gambiense DAL 972
(TbPRMT1, 3, 5, 6, and 7; Supplementary Table 4) were obtained by BLAST search from a protein
database with Trypanosoma brucei brucei TREU927 queries. As the outlined tree shows in Fig. 4b
and the detailed tree shows in Supplementary Fig. 5 (Supplementary file Fig. 5S.nexml), TbPRMT1,
TbPRMT5, and TbPRMT7 cluster with the PRMT clades with the same numeral as the most outside
one of each clade. TbPRMT3 and TbPRMT6 are at the outmost of the PRMT1/8-3-6-2 branch and
are placed between that branch and the PRMT4 clade.
4. Discussion
In this study we introduced the novel BBRD method to reconstruct reliable phylogenies. We used the
NCBI taxonomy common tree (species tree) as the reference for both DNA and protein data
validation. The DNA/protein tree of specific genes determined by BBRD may not be completely
consistent with the species tree, but the variation is limited and small and might be explained by
varying evolutionary rates of the genes among the species. However, the tree determined by some
popular traditional methods showed some significant deviation from the species tree. We thus are
confident that BBRD can analyze both nucleotide and protein sequences with superior tree
construction.
The two critical steps of the BBRD method are calculation of the total score of a given sequence pair
using BLAST, and then determination of the relative distance of two sequences by Pearson's
correlation coefficient. The advantage of employing Pearson's correlation coefficient to construct the
relative distance is that the correlation coefficient of a given sequence set is determined by the
sequences within the set, that is, the outgroup is not needed. If two sequences have no homology or
the homology is low enough, the BLAST total score will be zero, and then the relative distance of
these two sequences will not be evaluated by each other. In this way we can reduce the potential
problem of Pearson's correlation coefficient that might distort the results due to inappropriate data
(outliers) in the dataset. Theoretically, more homologous entries in the dataset will have more
accurately clustering output. However, from the preliminary analysis of PRMT sequences we found
that partial sequences affect the clustering result. Therefore, we carefully examined the PRMT
sequences and all partial PRMT sequences were excluded from this study.
When inspecting each PRMT family grouping by BBRD and Mr. Bayes trees (Supplementary Fig. 1
and Supplementary Fig. 3), both trees have their specific strong points in taxa clustering. For
example, Mr. Bayes can well separate the vertebrate PRMT1 with PRMT8 while BBRD separated all
other PRMTs without confusion. However, compared to the Mr. Bayes method that requires a
two-week calculation period using personal computer analyses, BBRD only used less than 5 minutes
to complete the tree construction. Therefore, the BBRD method we developed is an efficient method
in both time consumption and tree accuracy.
In addition, the phylogenetic analyses of the PRMT family members revealed the strength of BBRD
in resolving related protein sequences. As indicated previously, homologues of the nine mammalian
PRMT genes were distributed differently in eukaryotes and probably are evolved from different
times with different rates (Wang and Li, 2012). With the broad selection of PRMTs from
representative species, in this study we showed that for the type I enzymes, PRMT1associates with
its vertebrate paralogue PRMT8, and then groups with PRMT3. They then connect with PRMT2,
PRMT6 and finally PRMT4 (CARM1). The order is the same as determined by CLUSS, but deviates
from that by Mr. Bayes, which place PRMT6 as the most outside group of the type I PRMTs.
Over all the major difference of the three methods is their placement of PRMT5, 7, and 9. BBRD
puts PRMT9 with PRMT7 in one division, outside all of the type I PRMTs, and then connects with
the type II PRMT5. In this way PRMT5 is present in a different branch with all other PRMTs,
indicating a specific type II activity. Mr. Bayes also placed PRMT7 with PRMT9 in one cluster as
BBRD. However, it puts PRMT5 with all type I PRMTs and then the whole branch connects with the
PRMT7/9 cluster. CLUSS groups PRMT7 with other type I PRMTs, then PRMT9, then the type II
PRMT5.
In principle, the BLAST total score is positively correlated with the sequence length for homologous
sequences. It thus raised the possibility that the clustering of PRMT9 and PRMT7 are both much
longer (about 700~850 amino acid residues) than other PRMTs (about 300~500 amino acid residues).
However, the length would not affect our clustering result because the relative distance of a given
sequence pair calculated by Pearson's correlation coefficient is determined by the rest of the
sequences in the dataset (see 2.2. Procedures of BBRD method, step 3). Thus, PRMT9 clustered with
PRMT7 is not due to the long protein sequence.
The activity of PRMT9 has not been biochemically characterized. Recent investigations showed that
PRMT7 is most likely to be a type III enzyme that catalyzes the formation of monomethylarginines
but not dimethylarginines (Zurita-Lopez et al., 2012; Feng et al., 2013). Structural analyses showed
that the tandem PRMT core domain in PRMT7 can form a homodimer-like structure (Hasegawa et
al., 2014), which is similar to the dimers of other PRMTs that are required for their methyltransferase
activities. However, only the N-terminal but not the degenerate C-terminal core of PRMT7 can bind
to AdoMet and catalyze methylarginine formation (Hasegawa et al., 2014). Recent annotation of
human PRTM9 (gi: 74758248) shows that it also contains a PRMT domain with an AdoMet binding
motif in the N-terminal half and another PRMT domain without the binding motif in the C-terminal
half. We carefully examined the sequences and the alignment of the designated domain structures of
PRMT5, PRMT7 together with PRMT9. The results, illustrated in Fig.5,show that PRMT9 and
PRMT7 both contain a C-terminal degenerate PRMT domain with a degenerate AdoMet binding
motif. We thus consider the phylogenetic relationship with PRMT9 to becloser to PRMT7 than to
PRMT5. As BBRD groups PRMT9 with PRMT7 in one cluster and separates the cluster with both
type I and type II PRMTs, we suspect that PRMT9 might also be a type III enzyme. However, the
classification requires further experimental validation.
We have also used BBRD to analyze the PRMT genes that are not orthologues of the mammalian
PRMTs, such as the Drosophila-specific PRMTs DART 2, 6, 8 and 9. We showed that BBRD can
quickly demonstrate the phylogenetic relationships of these PRMTs. Similarly, we revealed that two
Trypanosome type I PRMTs TbPRMT3 and TbPRMT6 are placed between the PRMT2 and PRMT4
clades but not clustered with the PRMT3 or PRMT6 clades. The method thus should be a powerful
tool to trace the PRMT modification system in different species.
Posttranslational modification (PTM) can exert its function through the network of the writes
(enzymes that add the modification), erasers (enzymes that remove the modification) and readers
(factors that can bind to the modification). Compared with other posttranslational modification
systems, the writers (PRMTs) of protein arginine methylation are best studied. Putative demethylase
erasers such as peptidylarginine deiminases (PADIs) that may convert MMA residues into citrullines
(Cuthbert et al., 2004; Wang et al., 2004) or JMJD6 that is likely to remove one methyl group from
DMAs (Chang et al., 2007) have been reported. However, there were few follow-up studies and the
reaction catalyzed by PADI4, the only PADI that can convert MMA, is deimination but not
demethylation. Moreover, besides demethylation, JMJD6 appears to catalyze lysyl-hydroxylation
reactions (Webby et al., 2009). Unlike many reader domains identified for methyllysines, the only
well studied methylarginine reader is the Tudor domain (Gayatri et al., 2014; Chen et al., 2011; Côté
et al., 2005). However, only a subset of Tudor domains with narrower aromatic cages binds to
methylarginine rather than methyllysines.
5. Conclusions
In this study we traced the evolutionary relationship of PRMTs by the BBRD method that we
developed; similar studies can be conducted for the putative protein arginine methylation erasers and
readers to have a better view on the modification network. We expect that the BBRD method will
contribute to this kind of study.
Acknowledgements
This work was partially supported by the grants NSC 101-2320-B-040-004 from National Science
Council, Taiwan and CSMU-INT-102-10 from Chung Shan Medical University to Chuan Li and
102-asia-38 of Asia University, Taiwan to Jing-Doo Wang.
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Figure legends
Fig. 1. Trees of fmr-1 cDNAs. (a) BBRD tree. (b) NCBI taxonomy common tree. (c) DNA_ML tree.
(d) DNA_Pars tree. The NCBI taxonomy common tree is for reference. The Western
clawed frog is most distant in evolution from other species in this analysis, and thus was
dedicated as the outgroup for DNA_ML and DNA_Pars.
Fig. 2. Trees of dihydrolipoamide dehydrogenases. (a) BBRD tree. (b) NCBI taxonomy common tree.
(c) Pro_ML tree. (d) Prodist_Fitch tree. The NCBI taxonomy common tree is for reference.
The sequence of the most distant Schizosaccharomyces pombe was dedicated as the
outgroup.
Fig. 3. Phylogenetic tree of PRMTs. (a) The summary of the relationships of 9 different PRMTs. (b)
Sub-tree of PRMT1, PRMT8, and PRMT3. (c) Sub-tree of PRMT6, PRMT2, and PRMT4.
(d). Sub-tree of PRMT7, PRMT9, and PRMT5.
Fig. 4. Phylogenetic tree of DARTs and TbPRMTs. (a) Different PRMTs are outlined with DARTs
shown in detail. (b) Different PRMTs are outlined with TbPRMTs shown in detail.
Fig. 5. Alignment of domain structure of human PRMT5, PRMT7, and PRMT9. We obtained
GenPept records for PRMT5 (gi: 32171585), PRMT7 (gi: 20137529) and PRMT9 (gi: 74758248)
and aligned sequences. The "PRMT domains" in purple are the regions that can align with human
PRMT1 and are consistent with the regions designated as the "SAM-dependent MTase PRMT
domain" (ECO:0000255|PROSITE-ProRule:PRU01015) in the annotation. The "degenerate PRMT
domains" in the C-termini of PRMT7 and 9 only partially align with PRMT1 but can fully align with
their N-terminal PRMT domains. The "AdoMet binding motifs" shown in green are according to the
alignment with that in PRMT1 and are consistent with the "S-adenosylmethionine binding site" and
"SAM-dependent MTase" features in the annotation. The "degenerate AdoMet binding motif"
indicates the region that can be aligned with low similarity but are not functional in AdoMet binding.
The "double E loop" and the "THW loop" that are critical for PRMT activity have also been
illustrated. The amino acid sequences corresponding to the typical "THW" residues in the PRMTs are
shown. TRP motifs (tetratricopeptide repeat domain) are only present in the N-terminus of PRMT9.
Supplementary Fig. 1. Complete BBRD tree of PRMT1 to PRMT9.
Supplementary Fig. 2. CLUSS tree of PRMT1 to PRMT9.
Supplementary Fig. 3. Mr. Bayes tree of PRMT1 to PRMT9.
Supplementary Fig. 4. Complete BBRD tree of PRMT1 to PRMT9 with DART2, 6, 8, and DART9.
Supplementary Fig. 5. Complete BBRD tree of PRMT1 to PRMT9 with five TbPRMTs.
Fig. 1
Fig. 2
Fig. 3a
Fig. 3b
Fig. 3c
Fig. 3d
Fig. 4a
Fig. 4b
Fig. 5
Table 1 PRMTs collected by using blastp with human PRMT queries.
Taxonomy
Species
PRMT1
PRMT2
PRMT3
PRMT4
PRMT5
PRMT6
PRMT7
PRMT8
PRMT9
Trichoplax adhaerens
195996377
N. D.
N. D.
196008967†
196012142
196013265
196004252
N. D.
196005341
Nematostella vectensis
156402592
156378558†
156408760
N. D.
156385530
N. D.
N. D.
N. D.
156377274
Hydra vulgaris
221110103
N. D.
221116391
449665705
N. D.
N. D.
221111548
N. D.
N. D.
Schistosoma mansoni
256076138
N. D.
N. D.
256093042
256087695
N. D.
256075371
N. D.
N. D.
Chelicerata
Ixodes scapularis
241814081
N. D.
N. D.
N. D.
241678429
N. D.
N. D.
N. D.
N. D.
Hexapoda
Drosophila melanogaster
116812047
N. D.
21357573
24645553
17137240
N. D.
221330572
N. D.
N. D.
Crustacea
Daphnia pulex
321473398
N. D.
N. D.
321474633
321460483
N. D.
321471031
N. D.
N. D.
Caenorhabditis elegans
17566490
N. D.
N. D.
N. D.
17552468
N. D.
75028868
N. D.
115532940
Parazoa
Placozoa
Eumetazoa
Cnidaria
Platyhelminthes
Arthropoda
Nematoda
313004784
Ascaris suum
324507728
N. D.
N. D.
324500392
324506253
N. D.
324507004
N. D.
324501492
Echinodermata
Strongylocentrotus purpuratus
390355639
390364501
115963954
115974295†
390355786
115920250
72006035
N. D.
N. D.
Hemichordata
Saccoglossus kowalevskii
296434234
N. D.
291220836
N. D.
N. D.
291236949
291230242
N. D.
291238931
Urochordata
Ciona intestinalis
198425450
N. D.
198428082
198429701
198432044
198420727
198422468
N. D.
198432519
Cephalochordata
Branchiostoma floridae
260826854
260807553
260825710
260823914
260834801
260814339†
260832940
N. D.
260833330
Danio rerio
347800730
122114561
169159053
54261803
118722330
255652853
182507159
326675987
195546796
Chordata
Vertebrata
Actinopterygii
Amphibia
Lissamphibia
Xenopus (Silurana) tropicalis
123892812
322518398
45360587
301627458
171847328
187607708
62859951
301618678
213982995
Reptilia
Anolis carolinensis
327281163
N. D.
327259799
327264171
327289321
N. D.
327276497
327273289
327274011
Neognathae
Gallus gallus
N. D.
N. D.
313747440
N. D.
53132882
N. D.
57524811
363728290
50746307
Mammalia
Homo sapiens
154759421
46255047
44771198
40288288
19684070
49522881
12652789
74099699
40538772
Mus musculus
9790109
117938280
29789323
50511310
188528624
126432554
21703808
357527369
124486973
Bos taurus
528992278
528938233
156120799
528907143†
296483630
62460618
296477955
332308961
149642603
Schizosaccharomyces pombe
295443078
N. D.
74626597
N. D.
1706961
N. D.
N. D.
N. D.
N. D.
6319508
N. D.
N. D.
N. D.
323349738
N. D.
N. D.
N. D.
N. D.
Fungi
Ascomycota
Saccharomyces cerevisiae
N.D.: not detected
†
partial sequence. It was excluded from the phylogenetic analysis.
Supplementary Fig. 1.
Supplementary Fig. 2.
Supplementary Fig. 3.
Supplementary Fig. 4.
Supplementary Fig. 5.
Supplementary Table 1. fmr-1 cDNA sequences used in this study.
gi number
296587
Species
Common name
Homo sapiens
human
197102197
Pongo abelii
Sumatran orangutan
297304932
Macaca mulatta
rhesus monkey
301766716
Ailuropoda melanoleuca
giant panda
291408561
Oryctolagus cuniculus
rabbit
30794227
Rattus norvegicus
Norway rat
354485792
Cricetulus griseus
Chinese hamster
363732912
Gallus gallus
chicken
326924657
Meleagris gallopavo
turkey
410057072
Pan troglodytes
chimpanzee
402911662
Papio anubis
olive baboon
300798635
Bos taurus
cattle
395859574
Otolemur garnettii
small-eared galago
Mus musculus
house mouse
395545958
Sarcophilus harrisii
Tasmanian devil
345306646
Ornithorhynchus anatinus
platypus
209915622
Taeniopygia guttata
zebra finch
53749721
Xenopus tropicalis
western clawed frog
51036612
Supplementary Table 2. Dihydrolipoamide dehydrogenase (E3) sequences used in this study.
gi Number
Species
Common name
1339989
Homo sapiens
Human
326911238
Meleagris gallopavo
Turkey
27882508
Danio rerio
Zebra fish
229291927
Branchiostoma floridae
Florida lancelet
291240754
Saccoglossus kowalevskii
Acorn worm
24817518
Caenorhabditis elegans
Nematode
321469305
Daphnia pulex
Water flea
307209020
Harpegnathos saltator
Jerdon's jumping ant
399108168
Tribolium castaneum
Red flour beetle
71897021
Gallus gallus
Chicken
148236207
Xenopus laevis
African clawed frog
47209763
Tetraodon nigroviridis
Puffer fish
198423786
Ciona intestinalis
Sea squirt
115752588
Strongylocentrotus purpuratus
Purple sea urchin
4836454
Ascaris suum
Large roundworm of pigs
322790475
Solenopsis invicta
Red imported fire ant
66526646
Apis mellifera
Western honey bee
167867657
Culex quinquefasciatus
Southern house mosquito
157114623
Aedes aegypti
Yellow fever mosquito
7293932
Drosophila melanogaster
Fruit fly
353231245
Schistosoma mansoni
Trematodes
221116615
Hydra magnipapillata
Hydra
156228862
Nematostella vectensis
Starlet sea anemone
171390
Saccharomyces cerevisiae
Budding yeast
1911177
Schizosaccharomyces pombe
Fission yeast
Supplementary Table 3. DARTs collected by using blastp with Drosophila melanogaster DART
queries.
Classification
Species
DART2
DART6
DART8
DART9
Drosophila melanogaster
24581602
7299824
24583607
7299823
Drosophila sechellia
195342423
195329128
195340029
195329132
Drosophila simulans
195576487
195571037
195578413
195571039
Drosophila erecta
194855986
194901322
194861897
194901324
Drosophila yakuba
195471228
195500943
195472058
195500941
Solenopsis invicta
N.D.
N.D.
322795848
N.D.
Harpegnathos saltator
N.D.
N.D.
307195304
N.D.
Tribolium castaneum
N.D.
N.D.
91085911
N.D.
Culex quinquefasciatu
N.D.
N.D.
170049565
N.D.
Aedes aegypti
N.D.
N.D.
157136843
N.D.
Acyrthosiphon pisum
N.D.
N.D.
193713732
N.D.
Hexapoda
Insecta
N.D.: not detected
Supplementary Table 4. Five PRMTs identified in Trypanosoma brucei gambiense DAL972.
Classification
Species
gi number
Protein name
261326330
PRMT1
261332363
PRMT3
261331991
PRMT5
261328430
PRMT6
261329762
PRMT7
Euglenozoa
Kinetoplastea
Trypanosoma brucei gambiense DAL972