Protein encoding genes in an ancient plant: analysis of codon usage, retained genes and splice sites in a moss, Physcomitrella patens
© Rensing et al; licensee BioMed Central Ltd. 2005
Received: 24 September 2004
Accepted: 22 March 2005
Published: 22 March 2005
The moss Physcomitrella patens is an emerging plant model system due to its high rate of homologous recombination, haploidy, simple body plan, physiological properties as well as phylogenetic position. Available EST data was clustered and assembled, and provided the basis for a genome-wide analysis of protein encoding genes.
We have clustered and assembled Physcomitrella patens EST and CDS data in order to represent the transcriptome of this non-seed plant. Clustering of the publicly available data and subsequent prediction resulted in a total of 19,081 non-redundant ORF. Of these putative transcripts, approximately 30% have a homolog in both rice and Arabidopsis transcriptome.
More than 130 transcripts are not present in seed plants but can be found in other kingdoms. These potential "retained genes" might have been lost during seed plant evolution. Functional annotation of these genes reveals unequal distribution among taxonomic groups and intriguing putative functions such as cytotoxicity and nucleic acid repair.
Whereas introns in the moss are larger on average than in the seed plant Arabidopsis thaliana, position and amount of introns are approximately the same. Contrary to Arabidopsis, where CDS contain on average 44% G/C, in Physcomitrella the average G/C content is 50%. Interestingly, moss orthologs of Arabidopsis genes show a significant drift of codon fraction usage, towards the seed plant. While averaged codon bias is the same in Physcomitrella and Arabidopsis, the distribution pattern is different, with 15% of moss genes being unbiased.
Species-specific, sensitive and selective splice site prediction for Physcomitrella has been developed using a dataset of 368 donor and acceptor sites, utilizing a support vector machine. The prediction accuracy is better than those achieved with tools trained on Arabidopsis data.
Analysis of the moss transcriptome displays differences in gene structure, codon and splice site usage in comparison with the seed plant Arabidopsis. Putative retained genes exhibit possible functions that might explain the peculiar physiological properties of mosses.
Both the transcriptome representation (including a BLAST and retrieval service) and splice site prediction have been made available on http://www.cosmoss.org, setting the basis for assembly and annotation of the Physcomitrella genome, of which draft shotgun sequences will become available in 2005.
Flowering plants have developed from a common ancestor with mosses, liverworts, ferns, and gymnosperms over the last 450 million years . Most recent angiosperms do not closely resemble their ancestors, as known from the fossil record. Quite a few gymnosperms (like Ginkgo or Cycas) still resemble the plants known from the fossil record, and this is even more true for "lower" land plants, namely mosses, liverworts and ferns [2, 3]. In addition, mosses seem to evolve with a slow molecular clock . So, if these plants appear to be more ancient than modern flowering plants as measured by morphological means and mutation rate, does this also hold true for how they employ their genetic system?
A lot of comparative studies on protein encoding genes have already been carried out within and between the two major groups of flowering plants, mono- and dicotyledons, with rice (Oryza sativa) and Arabidopsis thaliana as the most prominent examples. Currently more than two million Liliopsida (monocotyledons) EST are publicly available, the corresponding number for the Magnoliophyta (dicotyledons) even exceeds four million sequences. However, sequence information for other plant phyla is still scarce. There are only about 160,000 EST sequences available of both Coniferophyta (part of the gymnosperms) and Chlorophyta (green algae), 130,000 from Bryophyta (mosses) and 3,700 from Filicophyta (ferns) (all numbers from Genbank). For the moss Physcomitrella patens, more than 102,000 nucleic acid sequences (mainly EST) are publicly available to date. This "ancient" land plant therefore is an ideal candidate to unravel some details about how simple plants encode proteins and whether they do so in a different manner from "modern" plants, as represented by the monocotyledon rice and the dicotyledon Arabidopsis in this study.
Physcomitrella is increasingly being used as a model plant because of its unrivalled capability among plants to include ectopic DNA into its genome by means of homologous recombination (see e.g. [5–7]), thus enabling gene replacement in a straight forward manner. As in all mosses, the haploid gametophyte is the dominant generation in the heteromorphic life cycle. In this respect the moss is different from seed plants (gymnosperms and flowering plants), in which the polyploid sporophyte dominates the life cycle. It has been argued before [8, 9] that the set of genes of the respective dominant generation is equivalent, while a large proportion of moss transcripts cannot currently be assigned a putative function. These "orphan" genes might encode functions that are specific to mosses and are not present in other taxonomic groups. Besides species-specific orphan genes, mosses might also possess retained genes, that have been lost in seed plants during evolution. Both types of genes are candidates to encode functions that make mosses unique in terms of physiology and metabolism. For example, Physcomitrella exhibits increased tolerance towards abiotic stresses [10, 11], uses proteins derived from the same gene in different cellular compartments by dual targeting [12, 13] and displays secondary metabolite pathways not known in seed plants [14–16]. In this study, following up on the initial analyses by Nishiyama et al. , we aimed to increase our knowledge of the moss transcriptome.
Results and discussion
Comparative BLAST searches
Taxonomic constitution of the taxprot dataset
# of sequences1
Viridiplantae (plants and green algae)
Eubacteria (without Cyanobacteria)
The total number of clusters after EST clustering do not equal the number of protein encoding genes. This is mainly due to partial (as opposed to full-length) transcripts, i.e. a single gene is represented by more than one sequence because they do not overlap. How many of the clustered public (PPP) transcripts represent full-length coding sequences? Of those sequences that yield a filtered hit against plant mRNAs, 7.9% are putatively full-length. Of those, 53.9% start with Methionine, of the latter, 32.4% contain no X (X represents an indeterminable codon, which can be included by the ORF prediction).
Orthologs, paralogs and mapping
Taxonomic distribution and retained genes
The highest total number of non-redundant, filtered BLAST hits is (as expected) derived from the plant subset of the taxprot dataset (Table 1, Fig. 2a), followed by the animal and fungi subsets. When looking at the hits as percentage of the search space size (Fig. 2b), it becomes evident that quite a proportion of the sequence space of lower eukaryotes (including non-green algae) is covered. The comparatively high coverage of this "ancient" gene space suggests that mosses share many specialized genes with unicellular organisms.
Functional annotation of retained genes into broad functional categories, assembled transcripts can be retrieved via http://www.cosmoss.org.
Pp transcript (putative retained gene)
broad functional category (potential)
Membrane-bound lytic murein transglycosylase B
murein-degrading enzyme, may play a role in recycling of muropeptides during cell
involved in pore-formation
RTX toxins and related Ca2+-binding proteins
Enoyl-CoA hydratase/carnithine racemase
fatty acid metabolism
L-kynurenine 3-monooxygenase Fpk
amino acid metabolism
COMM (copper metabolism MURR1) domain containing 2
ribosomal protein in C. elegans dehydrogenases
related to aryl-alcohol dehydrogenases
homolog of eukaryotic DNA ligase III
nucleic acid binding / modification
nucleic acid binding / modification
Osa1 nuclear protein
nucleic acid binding / modification
nucleic acid binding / modification
nucleic acid modification
leucine zipper transcription factor, light- and starvation-induced response
serine/threonine protein kinase
serine/threonine protein kinase
calcium/calmodulin-dependent protein kinase II delta
tumor suppressor tout-velu
involved in diffusion of hedgehog
dual-specificity tyrosine phosphatase YVH1
Non-receptor class dual specificity subfamily
high-affinity iron permease
high affinity iron uptake
uric acid-xanthine permease
belongs to the Xanthine/Uracil oermeases family
inorganic phosphate transporter
probable inorganic phosphate transporter; yeast pho99 homologue
Gene structure and splice sites
The average rate of introns per gene (~5) is the same in Physcomitrella, Arabidopsis and human . The average Physcomitrella intron (252 bp) is longer than those of Arabidopsis (146 bp) and shorter than the typical human intron (740 bp). Furthermore, the Physcomitrella intron is longer than the exon, whereas in Arabidopsis it is the other way round. While the size distribution of Arabidopsis introns is centered around 70 bp, the longer moss introns are mainly clustered around 180 bp (data not shown). This fits the weak correlation of intron length and genome size generally found in eukaryotic organisms . Intron positions of close homologs between Physcomitrella and Arabidopsis are generally conserved (e.g., ).
The Physcomitrella G/C content of 40% in the intron and 50% in the exon differs significantly from that of Arabidopsis; 33% and 44%, respectively. Generally, Physcomitrella introns contain more thymine (T) than the exons. In terms of mononucleotide composition, T is overrepresented in the intron and C is underrepresented in the exon. In terms of dinucleotides, there is a significant overrepresentation of TT in the introns. Outstanding trinucleotide usage are the overrepresented TTT in the intron and the stop codon TGA in the exon, while the other two stop codons TAA and TAG are underrepresented in the moss.
Composition of coding sequences and codon usage
Codon usage of Physcomitrella retained genes, orthologs and paralogs
mean # of each triplet
# significant codon usage changes
codon usage towards At
codon usage away from At
codon over represented
codon under represented
significant changes per aa
Phe under represented
Pro under reprensented
In retained genes, Phenylalanine codons are underrepresented, in the orthologs this is the case for Proline codons. As can also be seen from the G/C content drift mentioned above, the majority of deviating codons in the orthologs changed in the direction of the Arabidopsis percentage usage. For retained genes, it is the direct opposite: the significantly deviating codons in these genes point away from the Arabidopsis codon fraction usage. Orthologs are thought to be functionally equivalent across taxonomic groups. The common ancestor of land plants might have had a G/C content similar to mosses, i.e. around 50%. In order to preserve efficient functioning of orthologs it might have been necessary to evolve a slightly different codon usage for these genes in mosses, as is e.g. the case in Arabidopsis. The retained genes, on the other hand, are not found in seed plants and do not reflect the codon usage found there.
The average number of synonymous codons that is used in Physcomitrella and Arabidopsis CDS is not significantly different (Fig. 6b, bar chart). However, the percentage distribution of synonymous codon usage, as measured by the effective number of codons (enc), is surprisingly dissimilar (Fig. 6b, scatter plot). Most Arabidopsis coding sequences use a lot of synonymous codons (enc 45–59), whereas Physcomitrella displays a linear percentage increase from low to high values. Interestingly, around 15% of the moss genes contain no codon bias at all (enc = 61).
The genome of the ancient land plant Physcomitrella patens, a moss, harbours genes of which at least 30% have a detectable homolog in seed plants. EST clustering yielded a database that covers a large proportion of the transcriptome, approximately 8% of the virtual transcripts contain full-length CDS.
Transcripts that are clear homologs of Arabidopsis genes were mapped against the Arabidopsis chromosomes, along with the set of paralogs and orthologs between the two organisms. All three sequence sets could be mapped evenly across the chromosomes, revealing neither hot nor cold spots (despite centromeric regions) nor differences in gene density.
While moss genes resemble those of Arabidopsis, there are significant differences. Introns are larger than those of the seed plant and are also longer than exons within moss, which is not the case in Arabidopsis. The G/C content of exons equals the A/T content. This might reflect a certain tenacity of the mosses' genetic system and its slow mutational rate. These might be necessary characteristics, as due to haploidy in the dominant gametophyte, the chance for the propagation of a disadvantageous change is higher than in a polyploid organism.
Whereas orthologs display a codon fraction usage drift towards Arabidopsis, the contrary is the case for retained genes. Thus, evolution of codon usage seems to be correlated with evolutionary history of protein genes. Mutational bias does not seem to play a role in the evolution of moss coding sequences. While the majority of Physcomitrella CDS displays codon bias, there is a significant fraction (~15%) of genes that is not biased at all, possibly representing a more ancient nucleotide composition than oberved in Arabidopsis. Splice sites in the moss resemble those in Arabidopsis, however, species-specific prediction models, like the one presented here, are necessary in order to avoid false positives. The same is true for the prediction of ORF based on EST data.
A high proportion of the sequence space of unicellular eukaryotes is covered by moss homologs, which apparently have not been lost since the days of the last common ancestor. The majority of moss genes find their best scoring homolog in plants. However, there are 134 putative retained genes that have their best BLAST hit among other taxonomic groups. Of those, 57 genes are specific to a single taxonomic group, putative functional annotation could be carried out for 25 of these proteins. The functional annotation revealed deviations in the taxonomic distributions: certain sets of genes seem to be shared with specific taxonomic groups, for example, transport proteins with fungi or signal transduction genes with bacteria and animals. Of special interest are genes that are possibly involved in cytotoxicity, metabolism and nucleic acid repair. These genes might be the reasons for some of the extraordinary capabilities of mosses, namely resistance against microbial pathogens, additional secondary pathways (as compared with seed plants) and a high rate of homologous recombination.
Clustering of EST data
All publicly available protein encoding DNA sequences of Physcomitrella were retrieved using Entrez  and divided into 399 "seeds" (full length mRNA sequences) as well as 102,535 EST and other sequences. This dataset is called the Physcomitrella patens public set, or PPP.
A set of 17 moss-specific repetitive elements, detected mainly in the untranslated regions of Physcomitrella genes  and used for filtering (see below) is available via cosmoss.org . Filtering, clustering and assembly of EST data were done using the Paracel transcript assembler, PTA . A species-specific parameter set has been developed and is available upon request.
For sequences where electropherograms were available, base-calling was carried out using phred . Base quality values of EST sequences without available sequencer raw data was set arbitrarily to a low value, 10%, and in the case of seed sequences to a higher confidence value of 50%. Filtering included steps for removal of synthetic (vector/linker) and low quality sequences as well as of contaminants (homologs of E. coli as well as Physcomitrella mitochondrial, rRNA and chloroplast genes). Low-complexity regions were annotated together with poly-A tails, untranslated regions (UTR, UTRdb see ) and repetitive elements (repeats, repbase see ), in order not to disturb clustering and assembly. In a final step, sequences containing less than 150 bases of sense characters were removed. For PPP, a total of 100,079 sequences went into the clustering.
Prior to clustering, homologs of the seed sequences were pulled out of the sequence pool and assembled independently. Where possible, sequences were placed into 5' and 3' partitions based on detected poly-A tails and inherent annotated information. Both during clustering and assembly, putative chimeras (cloning artefacts) were detected and tagged. During assembly, contigs were built within clusters and putative splice variants detected. After clustering and assembly, the PPP set contained a total of 26,131 sequences. By using only the longest sequence in each cluster, a non-redundant set of 22,218 sequences was produced. The PP dataset contained 63,685 sequences in the complete and 48,961 sequences in the non-redundant set.
Splice site prediction
For the splice site prediction, all publicly available pairs of genomic and cDNA/mRNA sequences were retrieved (40 genes). Together with 29 unpublished sequences, these sequences were aligned using MGAlign 1.3.6  in order to determine the splice sites. The procedure yielded a total of 438 exons and 368 introns. The complete dataset is available via cosmoss.org .
Suppor vector machine: The software used for training and classification was SVMlight , libsvm  and svmsplice . The complete set of splice sites was divided into training/testing sets of sizes 10–90%, for each set three samples were drawn. The set containing 90% of the sites for training proved to yield the best results. Optimization of parameters was done by 10-fold cross-validation, plotting precision vs. recall and chosing the best curve. The best performing model could be constructed using 50 nucleotides up- and downstream of the splice sites as context with the basepairing feature set of svmsplice and a polynomial kernel function of 4th order.
BLAST searches and filtering
BLAST searches were carried out using Paracel BLAST , a parallelized version of BLAST 2 , on amino acid level whenever applicable. In order to exclude random hits which are not based on true sequence homology, alignments had to contain at least 30% identical positions and a minimum length of 100 amino acid characters. This rigorous filtering excludes some true positive hits but removes almost all false positives . Putative full-length CDS had to pass the same filtering. In addition, in this case only those hits were counted that covered at least 90% of the subjects length. For the determination of identical sequences, BLASTN was performed and hits were filtered to be at least 95% identical and 300 nucleotides long. Non-redundant hits were counted by removing all subjects that were present more than once in the search result.
Additional sequence datasets
The predicted coding sequences of rice (56,056 sequences) and Arabidopsis (28,581 sequences) genes were taken from release 1.0 and 4.0 of the TIGR database , respectively. The taxprot dataset (3,346,100 sequences, see Table 1 for details) was created by downloading the respective sequences from Genbank  using appropriate Entrez queries. All three datasets consist of amino acid sequences. The Arabidopsis thaliana chromosome sequences were retrieved from Genbank .
ESTScan 2.0  was used to predict open reading frames. The species-specific model for Physcomitrella was built by using the 399 public full length seed sequences (complete mRNAs) mentioned above. ORF were predicted from the clustered EST data (non-redundant datasets). For the PPP set, 19,081 ORF were predicted; 34,981 for the PP set. Predictions were done using both the Arabidopsis and the Physcomitrella model for comparison. Manual inspection of several known CDS revealed that the Arabidopsis-based prediction contained false-positive stretches, which was not the case for the Physcomitrella-based prediction. Although the Physcomitrella model predicted a lower number of ORF, it was used in order to keep false-positives to a minimum.
Four different sets of coding sequences were used (see table 3). A set of 7,765 well annotated Arabidopsis mRNAs was retrieved using Entrez. The Physcomitrella datasets contained the predicted ORF for the complete PPP set (19,081 sequences), the Arabidopsis paralogs (1,659) and orthologs (1,476) described above as well as the putatively retained genes not found in higher plants (134). The smallest set contained 77,998 nucleotides and thus a theoretical average of 406 instances of each triplet, which allows significant analyses.
Nucleotide frequencies were calculated with the GCG 10.3  software composition. Codon usage fractions for individual datasets were calculated as percentage of the respective total amount of counted codons. Absolute deviations in comparison to the full Physcomitrella ORF set were calculated for the three subsets (retained genes, Arabidopsis orthologs and paralogs). The computed mean value over all sets (average absolute deviation) was 0.069. Codon fraction usage deviation was counted as significant only if it differed at least twice as much (+/- 0.138%) from the full set.
The effective number of codons (enc) was calculated using CodonW (J. Peden, ). The enc values range from 20 (maximum bias, i.e. only one synonymous codon is used per amino acid) to 61 (no bias, all synonymous codons are being used).
- CDS = coding sequence(s):
EST = expressed sequence tag(s), ORF = open reading frame(s)
We would like to thank Sven Degroeve (University of Gent, Belgium) for providing of and assistance with svmsplice as well as Hans Stenøien, Colette Matthewman and several anonymous reviewers for helpful comments on the manuscript.
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