Mass Identification of Chloroplast Proteins of Endosymbiont Origin by Phylogenetic Profiling Based on Organism-Optimized Homologous Protein Groups

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1 56 Genome Informatics 16(2): (2005) Mass Identification of Chloroplast Proteins of Endosymbiont Origin by Phylogenetic Profiling Based on Organism-Optimized Homologous Protein Groups Naoki Sato 1 Masayuki Ishikawa 1 naokisat@bio.c.u-tokyo.ac.jp Ishimasa@bio.c.u-tokyo.ac.jp Makoto Fujiwara 1 Kintake Sonoike 2 mtf1@bio.c.u-tokyo.ac.jp sonoike@k.u-tokyo.ac.jp 1 Department of Life Sciences, Graduate School of Arts and Sciences, University of Tokyo, Komaba, Meguro-ku, Tokyo , Japan 2 Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, , Japan Abstract Chloroplasts originate from ancient cyanobacteria-like endosymbiont. Several tens of chloroplast proteins are encoded by the chloroplast genome, while more than hundreds are encoded by the nuclear genome in plants and algae, but the exact number and identity of nuclear-encoded chloroplast proteins are still unknown. We describe here attempts to identify a large number of unidentified chloroplast proteins of endosymbiont origin (CPRENDOs). Our strategy consists of whole genome protein clustering by the homolog group method, which is optimized for organism number, and phylogenetic profiling that extract groups conserved in cyanobacteria and photosynthetic eukaryotes. An initial minimal set of CPRENDOs was predicted without targeting prediction and experimentally validated. Keywords: genomic clustering, homolog group, CPRENDO, Gclust, endosymbiosis 1 Introduction Chloroplast is a photosynthetic organelle within plant and algal cells. It is also present as chromoplast, amyloplast, elaioplast, and leucoplast, depending on types of cells in flowering plants. A general term for all these organelles related to chloroplast is plastid. Plastid is also involved in various metabolism such as biosynthesis of fatty acids, isoprenoids, tetrapyrrols, amino acids, and some plant hormones. It is also the sole site of assimilation of nitrogen and sulfur in plant cells. Plants (and algae) acquired chloroplasts by endosymbiosis, which occurred 1.6 Ga (billion years ago) [22]. The endosymbiont was closely related to present-day cyanobacteria [4], but it is still not clear which cyanobacterium was the most related to the chloroplast ancestor. Such endosymbiosis theory is supported by the fact that the genes encoded in the chloroplast genomes are phylogenetically most related to the orthologs in cyanobacteria. Indeed, the endosymbiosis was a big event of massive transfer of genes from cyanobacteria to photosynthetic eukaryotes, and is a good target of comparative genomic studies. In algae and plants, many chloroplast proteins are encoded by the nuclear genome, and many of them are supposed to be transferred from the ancient endosymbiont. Chloroplasts also use proteins of eukaryotic origin. Therefore, chloroplast proteome is a chimera of proteins originated from both endosymbiont and eukaryotic host [1, 13, 17]. However, photosynthesis-related proteins and the enzymes involved in chloroplast biogenesis (transcription and translation) are mostly of endosymbiont origin. Based on this consideration, we tried to estimate the list of chloroplast proteins that were acquired by the

2 Mass Identification of Chloroplast Proteins of Endosymbiont Origin 57 endosymbiotic event. This is a good (maybe the best of all similar examples) challenge of comparative genomics [3, 5]. We present here a generally applicable method of phylogenetic profiling, which focuses on unidentified proteins that are conserved in a certain group of organisms that share a common physiological property or pathway. After the initial presentation in GIW three year ago [18], we made efforts in both computational and experimental works [19, 20]. In the computational efforts, the Gclust software has been revised as described above and implemented the clique mode. In addition, use of an intermediate file facilitated rapid analysis with different parameters. In the experimental efforts, a minimal set of CPRENDOs as estimated using the old version of Gclust was analyzed. In the present communication, we present results of revised prediction of CPRENDOs based on current version of Gclust as well as results of experimental verification of the minimal set CPRENDOs, and discuss on the effectiveness of phylogenetic profiling in comparative genomics. 2 Method 2.1 Major Features of the Methodology The following points are emphasized in the present study: (1) Use of homolog groups but NOT ortholog groups (based on bidirectional best hit) A usually used method for phylogenetic clustering relies on ortholog groups. Two genes (or proteins) are defined as orthologs if they originate from an identical ancestral gene. However, in computational biology, orthologs are operationally defined by bi-directional best-hit relationship inferred by BLAST or SSEARCH analysis. In practice, several paralogs or highly related genes are present in every genome such as those encoding protein families, and it is not always easy and practical to identify the correct orthologs (as originally defined) without phylogenetic analysis. We have been using homolog group method [7], in which all homologous proteins are included in each cluster. Such method allows detailed phylogenetic analysis of the homolog group to identify true orthologs. (2) Use of both E-value and homologous regions of BLASTP output for clustering Many clustering practices use BLASTP or SSEARCH data for hierarchical clustering using a single criterion such as E-value. Use of such simple criterion in BLASTP-based homolog group method produces large aggregates of various proteins bridged by multidomain proteins [18, 19]. To avoid this, we use homologous region information to infer both overlap score and domain structure. Overlap score for two proteins is defined as sum of total overlap region in both proteins devided by total length of both proteins, namely, (a1 + a2 + b1 + b1 + b2)/(length1 + length2) in the example shown in Figure 1. Constraint for E-value, overlap score, and domain structure are used for clustering to infer really homologous proteins by excluding functionally different proteins sharing one or several domains. Figure 1: An example showing calculation of overlap score homologous regions are indicated.

3 58 Sato et al. (3) Organism-optimized clustering If homolog group is constructed solely based on sequence similarity, clusters are not always suitable for phylogenetic profiling. A cluster may contain many proteins of the same family, or a single protein family is split into several different clusters according to phylogenetic positions. This problem is partially solved during the initial cluster formation using the 2D table (see below) and at the last stage of clustering. (4) Experimental validation of computational estimation We believe that any bioinformatics inference should be experimentally validated. In many informatics studies, logical consistency is the sole criterion of evaluation of computational estimation. But biologically meaningful results are most important in bioinformatics. Inference of chloroplast proteins of endosymbiont origin (CPRENDOs) may be one of the best applications of phylogenetic profiling that can be experimentally verified. 2.2 Preparation for Clustering All proteins in selected genomes were clustered by homolog group method. To this end, one of the authors (NS) developed a software called Gclust, which reads all-against-all BLASTP results and outputs a list of homologous protein groups (homolog groups). The software was written in C, and runs on any common UNIX machines if enough memory is available. The overall flow of data processing is shown in Figure 2. A typical source of genomic protein data is a GenBank flat file. The gbk file was processed to produce a FASTA file and a file of annotation. Such data of various genomes were assembled to get a single FASTA file and an annotation table. For eukaryotic organisms, nuclear as well as organellar (mitochondrial and chlorplast, if present) genomes were used. The two files were processed to give another FASTA file (**.gfa) and an annotation table (**.g.table). In the **.gfa file, all protein names were converted to numbers to save disk space during the BLASTP search. The numbers can be converted back to the original protein names by referencing the **.g.table. Next, all-against-all BLASTP search (versions ) [2] was done using the FASTA file (**.gfa) as an input. The output was directly pipelined into bl2ls3.pl to produce a list of homology regions and E-values, using a threshold for E-value at 1e-3. The resultant file was then used for input into Gclust software. The BLASTP step is the most time-consuming step, and is done as multiple jobs with split files on several different servers. All sequence file manipulation such as format conversion and file splitting was done with the SISEQ software (version 1.30) [16]. 2.3 Organism-Optimized Clustering with Gclust Software Gclust software [18] version [23] was run in the clique mode. The BLASTP results were processed in the following two steps (Figure 2): first, the data were read and partially transformed into intermediate data format and saved in a large file data.out for further analysis with various different settings of parameters. Low homology data were removed with keeping data with E-values for short sequences (from 1e-6 for >100 aa to 1e-3 for <40 aa). All single-path relations were picked up from the homology data. Domain composition of each protein was also estimated using the homology regions with different subject proteins. At this stage, multi-domain proteins as well as very large proteins (>2,000 aa, for example) were marked with a flag. In the second step, Gclust reads the data.out file, and performs clustering using the -clique option, which produces a good clustering result in a relatively short time (within one day for a dataset containing 141 organisms). In the clique mode, the homology data were converted to a structure called match, which held data of binary (i.e., protein-to-protein) similarity, namely, E-value, overlap score, and domain composition estimated as above. Normally, clique mode uses a list of organisms provided by the org list file. For each protein, all match data were tabulated in 2D, using E-value and overlap score (Figure 4A). The 2D table lists distribution of match array data using a pre-defined

4 Mass Identification of Chloroplast Proteins of Endosymbiont Origin 59 category scheme, which can be customized using a configuration file called var list. Match data were selected one by one starting from the initially selected best local maximum. The search scaned in a circular or diamond manner around the initial starting point. The scanning to lower overlap score and higher E-value stopped, if the number of members re-increased. This is a sign of another group of homologs with a lower similarity. This operation was done on a shadow table (Figure 4B), in which non-negative values indicate selected area, and the increasing number indicates path of search. By applying such criteria among others, a clearly defined cluster of match data with respect to E-value and overlap score was selected (boxed area). In addition, match data were selected to include as many organisms as possible but without picking up very low similarity data (the output below Figure 4A and B). After such purification of match data, a list of homologs was made for each protein. The threshold E-value and overlap score were also stored. Then, homolog clusters were formed by merging individual lists. At this stage, clusters with very diffent threshold E-values were not merged. After a repeat of merging and removing, orphan entries generated by removal step were again incorporated into the most adequate cluster. Clusters were again optimized for number of organisms. Homolog groups were sorted according to the number of entries. Finally, homolog groups were printed out to a large file as a catenated similarity matrix (Figure 5A). The matrix may be expressed in 1/0 (similar/dissimilar), E-value, and/or overlap score, depending on output options, 1, r, and/or s, respectively. 3 Results 3.1 Prediction of CPRENDOs by Phylogenetic Profiling Using a perl script homologtableg3b.pl, the homology matrix was transformed into a table showing members of each homolog group (Figure 3 and 5B). This table was used to extract homolog groups that are shared by various combinations of organisms (phylogenetic profiling). Note that proteins encoded by both organellar and nuclear genomes were included in the data set of eukaryotic organisms. Therefore, we selected organisms rather than genomes in the phylogenetic profiling. For the prediction of CPRENDOs, a data set CZ16Y containing all predicted proteins in nine species of cyanobacteria, Arabidopsis thaliana (plant) [21], Cyanidioscyzon merolae (red alga) [14], three species of photosynthetic bacteria, two species of bacteria, and two eukaryotes was used. All data were taken from the GenBank data repository, except for those of Cyanidioschyzon, which were obtained from the Cyanidioschyzon Genome Project [24]. Cyanidioschyzon is a representative of the red lineage of photosynthetic eukaryotes, and we expected that the use of a plant (green lineage) and a red alga increases accuracy of phylogenetic profiling. The homolog groups that are shared by cyanobacteria, Arabidopsis and Cyanidioscyzon were selected (Table 1). At this step, various constraints were tested in the selection. Conservation in cyanobacteria was one constraint, and allowance for presence in other organisms was another constraint. The first constraint could be complete conservation in all cyanobacteria (nine species), but many homologs of chloroplast proteins are not completely conserved in all cyanobacteria. A phylogenetic analysis suggests that plastids are sister to Anabaena-Synechocystis clade (Sato, unpublished results). Therefore, Anabaena [11] and Synechocystis [12] could be used as representatives of cyanobacteria. But we fould that all chloroplast proteins are not conserved in both cyanobacteria. We finally adopted a strategy in which any proteins conserved in a certain number of cyanobacteria were selected, irrespective of combination of cyanobacteria. The number of cyanobacteria was also a variable, but five species (out of nine) gave satisfactory results (Figure 6 and Table 1). Allowance for presence in other organisms was also tested. Table 1 compares effects of allowance in photosynthetic bacteria and non-photosynthetic organisms. Photosynthetic bacteria perform photosynthesis without oxygen evolution, with a single photosystem using machineries that are distantly related to those of cyanobacteria and plants. Therefore, the inclusion of photosynthetic bacteria could affect phylogenetic profiling of CPRENDOs. In addition, paralogs of some photosynthesisrelated proteins (ATP synthase, ribosomal proteins, and even a RuBisCO subunit) are present in

5 60 Sato et al. Figure 3: Flow chart of data processing for further analysis towards phylogenetic analysis. Figure 2: Flow chart of data processing until formation of homology matrix. Figure 4: Selection of match data in the clique mode. A. 2D table (rows, overlap score; lines, E-value) showing distribution of match data. A best local maximum is selected first (circle). Other local maxima with lower similarity are indicated by dotted circles. B. A shadow table for working. Zero is the start of search. Non-negative values show selected groups.

6 Mass Identification of Chloroplast Proteins of Endosymbiont Origin 61 Figure 5: Example output of Gclust (A) and tabular summary of homologs as generated by further processing (B). In (A), protein name (combination of genome name and gene identifier), number of amino acid residues, similarity matrix, and annotation in the original database are listed from left to right. The similarity matrix is a square matrix, having identical set of proteins in both vertical and horizontal directions. Each protein belongs to a single group. The similarity detected by BLASTP but not incorporated into the clustering is listed below the main matrix as Related groups. Each line in related groups consists of group number (number of members in parenthesis) and protein name. In (B), all homolog groups are listed with number of members in each genome. The annotation is taken from the first member.

7 62 Sato et al. Figure 6: A Venn diagram showing homolog groups shared by the three organism categories, Arabidopsis thaliana (green plant), Cyanidioschyzon merolae (red alga) and 5 cyanobacteria. Here, 5 Cyanos indicates >=5 of 9 cyanobacteria analyzed. This result was obtained with the selection method G shown in Table 1. In this diagram, each area is drawn proportional to the number of groups using a tcl/tk software called TriGraph (Sato, unpublished). Table 1: Number of homolog groups selected with different criteria. Number of homolog groups that are conserved in at least 5 among 9 cyanobacteria, Arabidopsis (Ath), and Cyanidioschyzon (Cme) are listed with varying additional conservation in photosynthetic bacteria (PhotoBact) and other organisms (Others). Others include C. elegans, S. cerevisiae, E. coli and B. subtilis. Number of homolog groups consisting of known chloroplast proteins or unknown proteins is listed. Each number in parenthesis indicates proportion of groups. Finally, number of members in Ath and Cme belonging to selected groups is listed. Selection PhotoBact Others of Groups Known cp proteins Unknowns of Ath proteins of Cme proteins A (0.44) 44 (0.52) D (0.46) 55 (0.49) E (0.44) 72 (0.48) F (0.48) 97 (0.31) G (0.49) 127 (0.29)

8 Mass Identification of Chloroplast Proteins of Endosymbiont Origin 63 non-photosynthetic organisms. These facts may make the profiling complicated. However, the results in Table 1 show that the allowance for other organisms has little effect on the proportion of clusters containing known chloroplast proteins, which was in different selections. In contrast, the proportion of clusters containing unknown proteins decrease with increasing allowance for other organisms. These results suggest that allowance for other organisms may be as wide as possible. Conservation in cyanobacteria, Arabidopsis and Cyanidioschyzon may be, therefore, a reliable criterion for selecting CPRENDOs. A Venn diagram (Figure 6) shows that there are smaller numbers of homolog groups that are shared by cyanobacteria and Arabidopsis, or cyanobacteria and Cyanidioschyzon. These groups could include proteins conserved in only green or red lineages, and may be studied as CPRENDO-like proteins. The groups shared by Arabidopsis and Cyanidioschyzon represent eukaryotic proteins, and are not candidate for CPRENDOs. 3.2 Minimal Set of CPRENDOs To verify the predicted CPRENDOs experimentally, we planned to analyze plastid localization of the predicted CPRENDOs (see 3.3.1). The data that we used for the experimental study as described below was predicted two years ago [18] using an older version of Gclust (verion 2.1.2) and an older database (dataset CZ16). At that time, homolog groups were constructed simply using various different E-values, and the homolog groups that were conserved in eight cyanobacteria, a red alga and a green plant but not in other non-photosynthetic organisms were selected at each cutoff E-value. The selected groups were combined, and used as a minimal set of CPRENDOs (Table 3). In total, 51 homolog groups were selected. Among them, 19 were clusters of known chloroplast proteins, such as Psa and Psb proteins. The remaining 32 groups were selected as targets of initial experimental study. These homolog groups were generally included in the selection A (Table 1) of the current CZ16Y dataset, with minor inconsistency. 3.3 Experimental Verification of the Minimal Set of CPRENDOs We performed experimental verification of the minimal set of CPRENDOs to test our idea that phylogenetic profiling is useful in predicting CPRENDOs, since we believe that all informatic prediction should be experimentally verified. The experimental verification consists of the following four analyses: localization of proteins, light-regulated expression, phenotype of cyanobacterial disruptants and plant tag-lines Localization of Predicted CPRENDOs in A. thaliana Localization of the predicted CPRENDOs was analyzed by using Green Fluorescent Protein (GFP)- fusion constructs. Each construct was prepared by successive PCR and either linear DNA or plasmids were transiently transformed into onion epidermis by particle bombardment. The localization of GFPfusion protein was analyzed by fluorescence microscopy on the next day. The results (Table 2) showed that 49 out of 52 proteins were targeted to plastids. Interestingly, five proteins were also targeted to mitochondria. Such dual targeting is common in plant organellar proteins [10]. It should be noted that the localization as predicted by TargetP [6] (not shown) was generally in good agreement, six proteins were not correctly predicted to be targeted to chloroplasts Light-Dependent Expression of the Genes for CPRENDOs in A. thaliana Expression of the predicted CPRENDOs was analyzed by RNA-blot analysis using 7-day-old seedlings, and the results are also shown in Table 2. As many as 36 genes encoding predicted CPRENDOs showed light-dependent expression, which is also expected for proteins involved in photosynthesis or chloroplast biogenesis. Nine genes were constitutively expressed, while expression of seven genes was

9 64 Sato et al. below the detection limit of the method employed. Cross-examination of localization and expression indicates abundance (31 proteins) of light-regulated chloroplast proteins Analysis of Synechocystis Disruptans The genes for the cyanobacterial homologs of predicted CPRENDOs were disrupted in Synechocystis sp. PCC For this purpose, a rapid method of preparation of disruption construct was developed using repeated PCR. Among the 41 genes, 33 were disrupted completely, while five were not completely segregated, and might represent essential genes. Three constructs were not successfully made, due to technical problems in PCR ( PCR problem in Table 3). Table 2: Summary of localization and expression of predicted minimal set of CPRENDOs. Cp, chloroplasts (plastids); Mt, mitochondria; Cyto, cytoplasm; nuc, nucleus. L > D, expression in the light was higher than that in the dark; L = D, expression was comparable in the light and the dark; No exp, no expression was detected by RNA-blot analysis. Expression Localization L > D L = D No Exp. Cp Cp & Mt Mt Cyto & nuc Total Fluorescence induction kinetics was measured as an indicator of photosynthetic performance (Table 3). Growth defect was also noted for some disruptants. In 22 disruptants, defects in growth or fluorescence kinetics was noted. These results suggest that the selected genes are important for the normal growth in cyanobacteria Analysis of A. thaliana Mutant Lines Mutants of the predicted CPRENDOs were analyzed using the SALK T-DNA tag-lines [25]. The analysis is still in progress, but we obtained homozygous lines for 25 CPRENDOs. During our experiments in the past two years, reports were published on four of the CPRENDOs, namely, Tab2 (in Chlamydomonas), Psb29/Thf1, APE1 and HY2. These are not components of photosynthetic machinery except Psb29, but are involved in its biogenesis. This demonstrates the correctness of our strategy, and many of the remaining CPRENDOs are also likely to be important in the biogenesis of photosynthetic machinery. However, only two of the Arabidopsis mutant lines showed visible phenotypes, such as variegation. The CPRENDO gene in one of them has been already annotated as ycf65, a hypothetical chloroplast reading frame, because it is encoded in the chloroplast genome in some algae such as Cyanidioschyzon. A mutant of ycf65 in Synechocystis also showed growth defect. Ycf65 protein is likely to be important in both chloroplasts and cyanobacteria. 4 Discussion 4.1 Evaluation of the Prediction Strategy of CPRENDOs The present study shows that phylogenetic profiling is useful in predicting CPRENDOs. Essential methodology for predicting CPRENDOs consists of (1) constructing homolog groups from total predicted proteins of both photosynthetic and non-photosynthetic organisms, and (2) selecting groups that are conserved in photosynthetic organisms under appropriate constraints. A probable estimate of

10 Mass Identification of Chloroplast Proteins of Endosymbiont Origin 65 Table 3: Summary of results of functional analysis. Phenotypes of Synechocystis disruptants, localization and light regulation of Arabidopsis genes, and the number of homozygous tag-lines are listed. For Synechocystis mutants, mutant ID is indicated with phenotypes (segregation state, growth properties, and fluorescence properties, in this order), if present. Localization is shown in abbreviated words (see Table 2). Blue underline indicates light-regulated expression. Confirmed CPRENDOs are marked by bold characters. For homozygous tag-lines, visible phenotype is marked by bold red characters. In annotation, Ycf stands for hypothetical chloroplast ORF. ID Function Synechocystis mutants Arabidopsis reported during the work Annotation Mutant ID: Phenotype (segregation, growth, fluorescence) # of localization (UL=L>D, Bold=CPRENDO) Ath, # of homozygous 5 Hypothetical 10; 11 2 cp, 1 mt 3 6 Hypothetical 12; (13: PCR problem) 1 cp 7 Ycf52 6: -, slow, - 2 cp 1 8 Hypothetical 14 2 cp 3 9 Yes Tab2 (Chlamydomonas) 10 Hypothetical tag-lines 15; (16: PCR problem) 1 cp Not available 17: incomplete, light sensitive, low peak 12 Hypothetical (18: PCR problem) 13 Membrane protease 14 Hypothetical 19: -, light sensitive, very low peak 20: -, slow, high second peak; 21: -, -, Low peak 1 cp 1 cp, 1 cp, 1 (cp), 1 nuc 1 cp, 1 cp, 1 (cp, mt) 15 Hypothetical 22: -, slow, low peak 1 cp 16 Probable ferredoxin (2Fe-2S) 23: -, -, low second peak 1 (cp) 1 cp 1 18 Ycf cp 2 32 Ycf19-like 4: incomplete, -, - 1 cp Not available 19 Hypothetical 24 1 (cp) 21 Ycf60 25: -, pale green and light sensitive, - 1 cp, 1 cp 2 22 Hypothetical 26: -, -, low peak 1 (cp, mt) 1 23 Ycf65 27: -, slow, - 2 cp 1, 1 27 Hypothetical 28 1 (cp) 33 Yes Psb29/Thf1/APG5 29 (sll1414): no phenotype 2g20890 (cp) (Not tried) 34 Rubredoxin 30: -, -, low peak 1 cp Not available 35 Hypothetical 31: incomplete, slow, high peak 1 cp 1 39 Yes APE1 32(slr0575): -, -, low peak 5g38660 (cp) (Not tried) 40 Hypothetical 8, -, slow, - 1 cp 1 41 Hypothetical 33: -, -, very low peak 1 cp, 1 (cp) Not available 43 Hypothetical 34 1 cp Not available 44 Hypothetical 35: -, -, no decrease after peak 1 cp Not available 3

11 66 Sato et al. ID Table 4: Continuation of Table 3. Function Synechocystis mutants Arabidopsis reported during the work 46 Yes Annotation HY2 (phycobilin synthesis) Mutant ID: Phenotype (segregation, growth, fluorescence) 36(slr0116): incomplete, -, high peak # of localization (UL=L>D, Bold=CPRENDO) 3g09150 (1 cp) Ath, # of homozygous tag-lines (Not tried) 47 Ycf20 37: -, slow, - 3 (cp, mt) 2 49 Hypothetical 38: -, slow, - 1 cp 51 Hypothetical 38 3 cp 3 54 Hypothetical 40 1 cp 55 Hypothetical 59 ATP-dependent proteinase 62 Hypothetical 41: -, slow, no decrease after peak 1 (cp) 45 2 cp 47: incomplete, light sensitive and slow, - 1 (cp) the number of CPRENDOs is 1192 in Arabidopsis and 676 in Cyanidioscyzon. A previous study [13] estimated the upper limit of chloroplast proteins of endosymbiont origin as about 4,500 in Arabidopsis, and another study [1] suggested about plant proteins originated from cyanobacterial endosymbiont. A more recent estimate was about 880 [15]. These estimates were done by calculation, but not by complete enumeration. These studies also showed that a significant proportion of proteins of cyanobacterial origin might be located in non-chloroplast compartment, which is not the case in our result. This could be partly due to the limitation of targeting prediction [15], but also to the inaccuracy in the prediction. In contrast, the results of present study on the minimal set of predicted CPRENDOs clearly indicate that almost all of them are chloroplast proteins, although no targeting prediction was used in the prediction process. A reasonable explanation of the discrepancy may be that we used conservation in 5 cyanobacteria, Arabidopsis, and Cyanidioschyzon as a criterion, while previous studies used conservation in only Arabidopsis and Synechocystis, or a similar simple criterion, which overestimates number of proteins conserved in plants and cyanobacteria. In addition, these previous studies used simple one plant vs one cyanobacterium relationship using a single cutoff E-value for all proteins. Our approach using phylogenetic profiling based on homolog groups gives robust clusters, which could yield a more solid prediction. 4.2 General Usefulness of Phylogenetic Profiling General success of our approach of comparative genomics prompted us to extend phylogenetic profiling to prediction of various other proteins that are conserved in a certain group of organisms. Prediction of pathogenicity-related proteins was done in various bacterial groups including strains with or without pathogenicity [8, 9]. Such analysis might not need sophisticated strategy of genomic comparison. But identification of proteins, which are conserved in a wide range of organisms that are not closely related phylogenetically, requires a solid clustering and phylogenetic profiling. The phylogenetic profiling with Gclust database will be a powerful tool for identifying plant-specific proteins and proteins specific to flowering plants, if more plant genomic sequences are available.

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