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Molecular Plant Pathology Laboratory, USDA–Agricultural Research Service, Beltsville, MD 20705, USA
Correspondence
Yan Zhao
zhaoy{at}ba.ars.usda.gov
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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), are small, cell wall-less prokaryotes that descended from an ancestral low G+C Gram-positive bacterium, possibly a Clostridium-like member of the Lactobacillus lineage (Woese, 1987; Weisburg et al., 1989). Along with mycoplasmas, spiroplasmas, acholeplasmas and other cell wall-less bacteria, phytoplasmas are classified in the class Mollicutes. Phytoplasmas are obligate intracellular parasites that reside in the sieve cells of plant phloem tissue and cause diseases in hundreds of plant species worldwide (McCoy et al., 1989; Lee et al., 2000). In nature, phytoplasmas are transmitted from diseased to healthy plants by phloem-feeding insect vectors, mainly leafhoppers and psyllids (Tsai, 1979). To date, no phytoplasma culture has been established in a cell-free medium; thus, differentiation and classification of phytoplasmas by means of the biophysical- and biochemical-based phenotypic criteria that are routinely used for culturable micro-organisms has been impossible. When the aetiological agents of phytoplasmal diseases (yellows diseases) were mistakenly believed to be viruses, differentiation of these presumed ‘viruses’ was based on the symptoms exhibited by diseased plants and on the identity of specific plant hosts and insect vectors (Chiykowski, 1962; Freitag, 1964; Granados & Chapman, 1968; Chiykowski & Sinha, 1989; McCoy et al., 1989). Given that the same phytoplasma strain may induce different symptoms in different hosts and different phytoplasma strains may share a common vector(s) or cause diseases characterized by similar symptoms, this ‘guilty by affiliation’ approach could not provide an accurate means for phytoplasma classification.
In the 1980s and early 1990s, the employment of serological (Lin & Chen, 1985; Lee et al., 1993a) and nucleic acid-based (Lee & Davis, 1988; Lee et al., 1992a, b; Griffiths et al., 1994) assay techniques revealed new insights into the diversity and genetic interrelationships of phytoplasmas. In particular, based on restriction fragment length polymorphism (RFLP) analysis of polymerase chain reaction (PCR)-amplified 16S rRNA, Lee and colleagues constructed the first comprehensive phytoplasma classification scheme (Lee et al., 1993b, 1998, 2000), providing a reliable means for the differentiation of a broad array of phytoplasmas. To date, this system has classified phytoplasmas in 18 groups and more than 40 subgroups and has become the most comprehensive and widely accepted phytoplasma classification system (Lee et al., 1998, 2004a, b; Arocha et al., 2005; Lee et al., 2006). Over the last few years, numerous and diverse phytoplasmas have been discovered at an increasingly rapid pace in emerging diseases worldwide. These developments have raised expectations that the number of 16S rRNA RFLP groups (16Sr groups) and subgroups could rise considerably, warranting expansion of the existing phytoplasma classification scheme. However, attempts to update the classification scheme using conventional RFLP analysis have been hindered by the lack of a complete, or near-complete, collection of phytoplasma strains as sources of DNA, emphasizing the need for a method to circumvent this obstacle.
Recent technological advancements now make possible an alternative approach for updating the phytoplasma classification scheme: the cost of DNA sequencing has dramatically reduced while the accuracy of the sequencing data has significantly improved and novel bioinformatic approaches for handling nucleotide sequence data have emerged. At the time of writing, more than 800 phytoplasma 16S rRNA gene sequences have been deposited into the National Center for Biotechnology Information's (NCBI) nucleotide sequence database. The availability of high-quality sequence data makes it possible to simulate restriction digestions in silico and to generate virtual RFLP patterns for high throughput identification and classification of diverse phytoplasmas. Here, we report the exploitation of a computer-simulated RFLP analysis method for classification of phytoplasma strains that resulted in the identification of new phytoplasma groups, significantly expanding the 16S RNA gene sequence-based phytoplasma classification scheme and unveiling putative novel phytoplasma species.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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). The retrieved sequences were kept in a Microsoft Excel-based mini-database, in which sequence accessions were organized into groups according to the existing 16S rRNA RFLP-based phytoplasma classification scheme as outlined in the ‘Candidatus Phytoplasma’ Taxonomy Browser (http://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Tree&id=33926). For the purpose of cladistic analysis, 16S rRNA gene sequences from 90 non-phytoplasma bacterial taxa and two cyanobacterial taxa were also retrieved from the nucleotide sequence database at the NCBI. Of the 90 non-phytoplasma eubacterial taxa, 64 were cell wall-less bacteria from eight genera in the class Mollicutes (Acholeplasma, Anaeroplasma, Asteroleplasma, Entomoplasma, Mesoplasma, Mycoplasma, Spiroplasma and Ureaplasma), 18 were Gram-positive low G+C walled bacteria from three representative groups (orders Bacillales, ‘Clostridia’ and ‘Lactobacillales’) and eight were Gram-positive high G+C bacteria in the class Actinobacteria. The two cyanobacterial taxa were Synechocystis sp. and Nostoc sp.
Alignment of 16S rRNA gene sequences and cladistic analysis.
For multiple sequence alignment, nucleotide sequences were compiled in FASTA format. Compiled sequences were aligned using CLUSTAL_X (version 1.83) by selecting the ‘do complete alignment’ option with default parameters (gap opening penalty 15.00, gap extension penalty 6.66, delay divergent sequences 30 %, DNA transition weight 0.5) (Thompson et al., 1997; Jeanmougin et al., 1998). Each aligned sequence was trimmed to an approximately 1.25 kb fragment (termed the F2nR2 region hereafter) that was bounded by the two conserved nucleotide blocks corresponding to the annealing sites for the phytoplasma-universal 16S rRNA primer pair R16F2n/R16R2 (Gundersen & Lee, 1996). Accessions not encompassing the full F2nR2 region and accessions containing two or more consecutive undetermined nucleotides were considered inadmissible and were excluded from further analyses. The trimmed sequences were realigned and the final alignment was converted to MEGA format for cladistic analyses.
Maximum-parsimony cladistic analysis was conducted with MEGA3 software (Kumar et al., 2004) using the close neighbour interchange (CNI) algorithm. The initial tree for the CNI search was created by random addition for 10 replications. The reliability of the analysis was subjected to a bootstrap test with 100 replicates. The choice of these settings was a compromise because the present study was comparing up to 616 sequences which made an exhaustive search by heuristic algorithms prohibitive. In phylogenetic tree reconstruction, the two cyanobacterial taxa served as an out-group.
In silico restriction enzyme digestions and virtual gel plotting.
The aligned and trimmed sequences were exported to the in silico restriction analysis and virtual gel plotting program pDRAW32, developed by AcaClone Software (http://www.acaclone.com). Each aligned DNA fragment was digested in silico with 17 distinct restriction enzymes that have been routinely used for phytoplasma 16S rRNA gene RFLP analysis (Lee et al., 1998). These enzymes were AluI, BamHI, BfaI, BstUI (ThaI), DraI, EcoRI, HaeIII, HhaI, HinfI, HpaI, HpaII, KpnI, Sau3AI (MboI), MseI, RsaI, SspI and TaqI. After in silico restriction digestion, a virtual 3.0 % agarose gel electrophoresis image was plotted automatically to the computer screen. The virtual gel image was then captured as a device-independent file in PDF format for subsequent RFLP pattern comparisons.
Comparison of virtual RFLP patterns and calculation of similarity coefficients.
Virtual RFLP patterns, i.e. the sum result from in silico digestions with 17 enzymes, were compared using the multiple layer function of the Photoshop graphics editing software (Adobe Systems). A similarity coefficient (F) was calculated for each pair of phytoplasma strains according to the formula described previously (Nei & Li, 1979; Lee et al., 1998), F=2Nxy /(Nx+Ny), in which x and y are two given strains under investigation, Nx and Ny are the total number of DNA fragments (bands) resulting from digestions by 17 enzymes for strains x and y, respectively, and Nxy is the number of fragments shared by the two strains.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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Sequence data validation and cladistic analysis
As of the end of August 2006, a total of 829 phytoplasma 16S rRNA gene sequence accessions had been deposited in the nucleotide databases of the DNA DataBank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL) and GenBank at the NCBI. The lengths of these registered sequences ranged from a few hundred bases to full-length rRNA operons. To remain consistent with the well-established, actual gel-based (conventional) phytoplasma 16S rRNA gene RFLP classification scheme (Lee et al., 1998), an admissible sequence accession for the present study had to encompass the complete F2nR2 region. This sequence admissibility test validated a total of 524 accessions. For an overwhelming majority of the 524 admissible accessions, the F2nR2 region varied from 1235 to 1254 bp. Nine accessions had an exceptionally long F2nR2 region (1371–1377 bp), whereas seven accessions had an exceptionally short F2nR2 region (1142–1225 bp).
The F2nR2 regions of the 524 phytoplasma 16S rRNA gene sequence accessions were used to reconstruct a maximum-parsimony phylogenetic tree (Fig. 1a). As indicated by the topology of the parsimony tree, the 524 phytoplasma accessions under investigation constituted a monophyletic clade (with a bootstrap value of 100 %) that subsumed 16 accessions that had either an exceptionally long or an exceptionally short F2nR2 region. The phytoplasma clade was paraphyletic to the clade formed by acholeplasmas, the closest known relatives of phytoplasmas.
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). Previously classified phytoplasma strains, each having been assigned to one of the 18 (16SrI to 16SrXVIII) already delineated 16Sr groups (Lee et al., 1998, 2004a, b, 2006; Arocha et al., 2005), fell into nine of the 14 subclades seen in the phytoplasma tree (Fig. 1b). The results of this cladistic analysis strongly suggested that, among the 524 phytoplasma accessions studied in the present work, there were previously unrecognized phytoplasma groups occupying at least five subclades.
Virtual RFLP analysis and expansion of the phytoplasma classification scheme
The F2nR2 regions from 524 phytoplasma 16S rRNA gene sequence accessions were each digested in silico with 17 restriction enzymes. Virtual RFLP analyses of the resulting DNA fragments generated 250 distinct pattern types that were sorted into 28 groups and around 100 subgroups. Delineation of groups was based on the previously established convention in which coefficients of 16S rRNA gene RFLP pattern similarity between two distinct groups were equal to or less than 90 % (Lee et al., 1998). The criteria used for the delineation of the rapidly growing number of subgroups will be addressed in a separate communication.
The virtual RFLP patterns of 53 16S rRNA gene sequence accessions from 51 phytoplasma strains representing 28 groups are shown in Fig. 2. Of the 51 phytoplasma strains (Table 1), 41 had previously been classified by means of the conventional phytoplasma 16S rRNA gene RFLP analysis (Lee et al., 1998, 2006; Arocha et al., 2005). The virtual 16S rRNA gene RFLP patterns of previously classified strains matched the RFLP patterns on real gels perfectly (data not shown). These results indicated that virtual RFLP analysis could serve as a convenient and reliable alternative to conventional RFLP analysis.
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, 2006; Arocha et al., 2005). The results from the present study revealed that at least 10 previously unrecognized phytoplasma 16Sr groups can also be delineated. The expansion of the existing phytoplasma classification scheme to include these 10 new groups (16SrXIX to 16SrXXVIII) is justified by their distinct 16S rRNA gene RFLP patterns (Fig. 2) and by their lower-than-threshold coefficients of similarity with other groups. From the data presented in Table 2, it can be seen that, for each strain of a new group, the similarity coefficient is less than 0.85 with all strains in previously delineated groups. Recognition of the new groups is strengthened by their distinct cladistic positions in the maximum-parsimony phylogenetic tree [with one exception, where 16SrXXVIII (GenBank accession number AY744945) clustered together with 16SrI] (Fig. 1b).
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). The new groups established in the present work, 16SrXIX, 16SrXX, and 16SrXXI, contain three recently named ‘Candidatus Phytoplasma’, ‘Candidatus Phytoplasma castaneae’ (Jung et al., 2002), ‘Candidatus Phytoplasma rhamni’ (Marcone et al., 2004a) and ‘Candidatus Phytoplasma pini’ (Schneider et al., 2005), respectively. Phytoplasma strains representing the other seven new 16Sr groups are Nigerian coconut lethal decline phytoplasma strain LDN (GenBank accession number Y14175, 16SrXXII) (Tymon et al., 1998), Buckland valley grapevine yellows phytoplasma (AY083605, 16SrXXIII) (Constable et al., 2002), sorghum bunchy shoot phytoplasma (AF509322, 16SrXXIV) (Blanche et al., 2003), weeping tea witches’-broom phytoplasma (AF521672, 16SrXXV), Mauritius sugar cane yellows phytoplasma strain D3T1 (diversity group 3, type 1) (AJ539179, 16SrXXVI), Mauritius sugar cane yellows phytoplasma strain D3T2 (diversity group 3, type 2) (AJ539180, 16SrXXVII) and Havana derbid phytoplasma (AY744945, 16SrXXVIII). All strains share less than 97.5 % 16S rRNA gene sequence similarity with each other and with any previously described ‘Candidatus Phytoplasma’ taxa; therefore, each may be recognized as a novel ‘Candidatus Phytoplasma’ taxon, in accordance with the concept put forward by Murray & Schleifer (1994) and the recommendation made by the Phytoplasma/Spiroplasma Working Team – Phytoplasma Taxonomy Group of the International Research Program on Comparative Mycoplasmology (IRPCM, 2004).
Thus far, 26 ‘Candidatus Phytoplasma’ taxa have been described (Lee et al., 2000, 2006; IRPCM, 2004; Arocha et al., 2005; Firrao et al., 2005; Valiunas et al., 2006). The results from the present study, which point to an additional seven ‘Candidatus’ taxa yet to be described, underscore the diversity within the phytoplasma clade. Conceivably, as more phytoplasma strains are discovered and become quickly characterized by virtual RFLP analysis, the total number of phytoplasma 16S rRNA gene RFLP pattern types will rise rapidly. The present work's accurate classification of 18 previously identified groups and delineation of 10 new groups has demonstrated that any phytoplasma strain can be readily classified based on RFLP patterns produced by in silico digestion. In fact, group level classification can be achieved by comparison of virtual RFLP patterns generated by digestion using three key restriction enzymes, namely, MseI, RsaI and HinfI. As shown in Fig. 3, 19 of the 28 groups could be sufficiently differentiated by MseI digestion alone and the remaining nine groups could be separated by comparison of MseI and RsaI digestion profiles or MseI and HinfI digestion profiles.
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Issues of rrn interoperon sequence heterogeneity
Many phytoplasma strains have two rRNA operons, rrnA and rrnB (Schneider & Seemüller, 1994; Firrao et al., 1996b; Lauer & Seemüller, 2000; Padovan et al., 2000a; Marcone & Seemüller, 2001). While rrnA and rrnB may be identical or nearly identical in some phytoplasma strains, apparent rrn interoperon sequence heterogeneity exists in other strains (Lee et al., 1993b; Firrao et al., 1996a; Liefting et al., 1996; Davis & Sinclair, 1998; Jomantiene et al., 2002). If sequence variations between two heterogeneous 16S rRNA genes affect the restriction sites in the F2nR2 region, in an actual gel an RFLP pattern may be a composite of the patterns from two sequence-heterogeneous rRNA operons. A composite pattern is suspected when the sum of the sizes of DNA fragments is greater than the expected size of the F2nR2 region (1.25 kb). Furthermore, RFLP analysis of a phytoplasma's sequence-heterogeneous rRNA operons in mutual isolation could result in erroneous assignment of the same phytoplasma to two different 16S rRNA subgroups, or putative taxa, in classification schemes that are based on RFLP patterns (Davis et al., 2003). Although such a composite banding pattern may not be encountered in virtual RFLP analysis, a ‘chimaeric’ banding pattern could arise due to nucleotide sequencing of an uncloned PCR product if the analysed sequence is a consensus that contains bases from two sequence-heterogeneous operons. Thus, for accurate classification of a phytoplasma strain, it is preferable to sequence 16S rRNA genes after separation of rrn operons by cloning.
We examined 16S rRNA gene sequences from both rrnA and rrnB operons of 17 phytoplasma strains. Of the 17 strains, four yielded identical virtual 16S rRNA gene RFLP patterns for the rrnA and rrnB operons (AYWB, OY-M, Carludovica palmata leaf yellowing phytoplasma and loofah witches'-broom phytoplasma strain LfWB). The remaining 13 strains yielded discrete 16S rRNA gene banding patterns for the rrnA and rrnB operons. However, the differences in the patterns were minor and did not affect the group classification of those phytoplasma strains.
Conclusion
The availability of a comprehensive set of phytoplasma 16S rRNA gene RFLP pattern types (Lee et al., 1993b, 1998, 2000) has made possible the accurate and reliable identification, differentiation and classification of a broad array of phytoplasmas and has greatly stimulated and expanded phytoplasma research over the past decade. Typically, RFLP analysis of DNA segments has been done in the absence of prior nucleotide sequence information. Nowadays, as sequence information has become readily available, either by database retrieval or by de novo determination, one may ask whether RFLP analysis still remains a useful tool for phytoplasma identification, differentiation, and classification. We suggest that it does. First, the already established phytoplasma 16S rRNA gene RFLP patterns have become authoritative expositions for scientists in the phytoplasma research community and have served as standard keys for phytoplasma strain identification and classification. Second, although sequence information-based analyses such as pairwise sequence comparisons and phylogenetic analyses can be used to assess the relationships among phytoplasma strains, neither percentage sequence similarities from pairwise comparisons nor tree topologies from phylogenetic analyses directly reveal informative sites along the sequences or ‘visible’ genetic footprints provided by RFLP analysis. While RFLP analysis remains a valuable tool for studying microbial diversity and classification, the method by which RFLP analysis is carried out has evolved (Moyer et al., 1996; Edwards & Turco, 2005; Ricke et al., 2005; Abdo et al., 2006). The virtual RFLP analysis method implemented in the present study simulates laboratory restriction enzyme digestions and subsequent gel electrophoresis, quickly generating reproducible RFLP patterns. These computer-generated patterns not only faithfully replicate the classical, authoritative pattern types that have been established by conventional RFLP analysis but also reveal new pattern types that have not been recognized previously, providing additional standard keys for future identification and classification of the rapidly growing numbers of phytoplasmas by either computer-simulated or conventional RFLP analyses. The value of virtual RFLP analysis was evident in the delineation of 10 new phytoplasma groups, in the elucidation of candidates for novel species descriptions and in the recognition of about 50 new, potentially significant subgroup lineages. The virtual 16S rRNA gene RFLP pattern types generated from 51 representative phytoplasma strains will be accessible online at http://www.ba.ars.usda.gov/data/mppl/virtualgel.html as reference patterns. A web interface will soon be developed for users to enter sequences, create and compare RFLP patterns and update the classification scheme as new pattern types are identified.
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X174DNA-HaeIII digestion.