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Rep-PCR Genomic Fingerprinting of Plant-Associated Bacteria and Computer-Assisted Phylogenetic Analyses

F.J. de Bruijn1,2,3*, J. Rademaker2, M. Schneider1,2

U. Rossbach1 and F.J. Louws1,2,4

1MSU-DOE Plant Research Laboratory, 2NSF Center for Microbial Ecology, 3Dept. of Microbiology, Michigan State University, E. Lansing, MI 48864, USA; 4Dept. of Plant Pathology, N. Carolina State University, Raleigh, NC 27695
Introduction
Interspersed repetitive DNA elements and rep-PCR fingerprinting.
Rep-PCR genomic fingerprinting of plant-associated bacteria using different templates
Computer assisted phylogenetic pattern analysis programs based on rep-PCR generated genomic fingerprints.
Conclusions
Acknowledgements
Literature Cited.

The utility of a recently developed method to classify bacteria on the basis of their genomic fingerprint patterns was investigated, using collections of both symbiotic and pathogenic plant-associated bacteria. The genomic fingerprinting method employed is based on the use of DNA primers corresponding to naturally occurring sed repetitive elements in bacteria, such as the REP, ERIC and BOX elements, and the PCR reaction (rep-PCR). We have been able to show that rep-PCR fingerprinting is a highly reproducible and simple method to distinguish closely related strains, to deduce phylogenetic relationships between strains and to study their diversity in a variety of ecosystems. We have also used computer assisted pattern analysis programs (e.g. GelCompar) for microbial identification and phylogenetic analysis of complex data sets, and describe the application of these methods for the creation of databases for bacterial diagnosis.

Introduction

The identification and classification of symbiotic and pathogenic plant-associated bacteria are important both in terms of their agricultural applications, as well as for basic studies on plant-microbe interactions. A variety of phenotypic methods has been traditionally used to type these bacteria, including serotyping, phage typing, microscopic identification, substrate utilization screening (e.g. BIOLOG), multilocus enzyme electrophoresis (MLEE), fatty acid methyl ester analysis (FAME), 2-D PAGE of total proteins, and intrinsic antibiotic resistance profiling.

In addition, in the case of symbiotic bacteria, such as rhizobia, plant nodulation tests are performed, and in the case of pathogenic bacteria, pathogenicity tests on specific hosts are carried out. The majority of these techniques require purification and cultivation of the bacteria and/or can be quite laborious and time consuming.

More recently, DNA-based (genotypic) approaches have increasingly been applied to microbial identification and classification. In fact, these "molecular" approaches have resulted in the birth of a new ecology subspecialty, Molecular Microbial Ecology (see Akkermans et al. 1995). Generally, these methods tend to be less dependent on bacterial growth variables, more 'stable', less time-consuming and are very useful for determining phylogenetic relationships between microbial isolates and for assigning strains into specific groups. Such methods include DNA-DNA hybridization studies (which constitute the basis for bacterial genus/species designations), characterization of rRNA sequences, and other methodologies, including digestions of total genomic DNA with infrequently (rare) or frequently cutting restriction enzymes, followed by pulse field gel electrophoresis (PFGE) or hybridization with specific probes (RFLP), respectively, and plasmid profiling (see Louws et al. 1996 for a discussion of the relative utilities of these techniques and references).

Clearly, the utility of DNA-based approaches has been enhanced tremendously by the application of PCR. A most useful, application of PCR to bacterial identification and classification has been in the area of genomic fingerprinting using, for example, primers corresponding to endogenous interspersed repetitive sequences (Lupski and Weinstock, 1992; de Bruijn, 1992; Versalovic et al. 1991). The latter approach has been named mic fingerprinting and will be discussed here.

Interspersed repetitive DNA elements and rep-PCR fingerprinting.

As mentioned above, naturally occurring interspersed repetitive DNA elements, found in many (if not all) bacteria, can serve as primer sites for genomic DNA amplification (Versalovic et al. 1991;1994; de Bruijn 1992). Several families of repetitive sequences are interspersed throughout the genome of diverse bacterial species (see Lupski and Weinstock 1992). Three families of repetitive sequences have been studied in most detail, including the 35-40 bp repetitive extragenic palindromic (REP) sequence, the 124-127 bp enterobacterial repetitive intergenic consensus (ERIC) sequence, and the 154 bp BOX element (see Versalovic et al. 1994). These sequences appear to be located in distinct, intergenic positions all around the chromosome. The repetitive elements may be present in both orientations on the chromosome, and PCR primers have been designed to "read outward" from the inverted repeats in REP and ERIC, and from the boxA subunit of BOX (Versalovic et al. 1994). The use of the above primer(s) and PCR leads to the selective amplification of distinct genomic regions located between REP, ERIC or BOX sequences. The corresponding protocols are referred to as REP-PCR, ERIC-PCR and BOX-PCR, respectively, and rep-PCR collectively (Versalovic et al. 1991;1994). Amplified bands are size fractionated through a gel matrix to yield fingerprint patterns resembling "bar codes" (Lupski, 1993), analogous to UPC codes used in grocery stores, and function as a signature for specific bacterial strains (see Figure 1).



Figure 1. General rep-PCR genomic fingerprinting protocol using different templates.

Rep-PCR genomic fingerprinting of plant-associated bacteria using different templates

For the identification and classification of rhizobial strains from soils or from nodules induced on plants, or pathogenic bacteria from lesions on plant leaves and fruits, it is particularly useful to have a rapid, simple and highly reproducible method that is not absolutely dependent on purified DNA. We, and others, have been able to show that the rep-PCR fingerprinting patterns of purified rhizobial DNA, single colonies from a plate, liquid cultures or direct extracts of nodule tissues are identical in most cases (de Bruijn et al. 1992; Nick and lindstrom 1994; Schneider and de Bruijn 1996). The same applies to purified DNA versus whole cells and extracts from plant lesions caused, for example, by xanthomonads (Louws et al. 1994; 1995; 1996). Thus, rep-PCR can be carried out with different templates (see Figure 1).

Computer assisted phylogenetic pattern analysis programs based on rep-PCR generated genomic fingerprints.

The banding patterns generated by rep-PCR genomic fingerprinting analysis of large collections of strains are too complex to analyse by eye. Therefore, we have tested the utility of several computer assisted banding pattern determination and phylogenetic analysis programs.




We previously reported the use of the AMBIS System (Scanalytics, Waltham, Mass. USA) for phylogenetic analysis of rep-PCR generated genomic fingerprints of different rhizobia (Rossbach et al. 1995; Schneider and de Bruijn 1996).

More recently, we have been using the GelCompar system (Applied Math, Kortrijk, Belgium) for our analyses, since it appears to be more user friendly, more versatile and allows the construction/screening of rep-PCR generated "bar codes" for diagnostic purposes (see below), as well as for phylogenetic analyses.

We have applied these analyses to the identification and classification of plant pathogenic xanthomonads (e.g. Louws et al. 1994;1995). In order to test our hypothesis that rep-PCR generated fingerprints directly reflect genomic structure, we carried out a cluster analysis of 19 Xanthomonas strains belonging to 6 homology groups (Vauterin et al. 1995). Using the GelCompar program, we analysed the combined REP, ERIC and BOX fingerprints of the 19 strains (kindly provided by Drs. Swings and Vauterin, University of Ghent, Belgium) in duplicate. The results of this analysis is shown in Figure 2 and reveals that: 1. Virtually all duplicates ended up alligned next to eachother; 2. The rep-PCR generated groupings correspond directly to the DNA homology groups (numbered on top); 3. Even very closely related strains can be distinguished by this method.

We are also interested in being able to use the rep-PCR fingerprinting method as a tool for strain diagnosis. For this purpose, we are constructing a database of rep-PCR patterns of a large collection (600-800) of Xanthomonas isolates using the GelCompar (library search) program, in collaboration with the laboratory of Prof. Swings (Ghent, Belgium). A limited database has been constructed and pilot searches with unknown input strains conducted. The fingerprint pattern of an "unknown" (X. cassavae) isolate was run through the library of fingerprints, and the four most closely related patterns were found to be indeed from X. cassavae strains present in the database.

Conclusions

In conclusion, we believe that rep-PCR genomic fingerprinting coupled to computer assisted phylogenetic analysis and library search programs will constitute a useful method for the identification or diagnosis of plant pathogenic, as well as symbiotic bacteria (see also Versalovic et al. 1994; Schneider and de Bruijn 1996; Louws et al. 1996 and references contained therein). This technique has already been successfully used to study the

population structure of plant pathogens, to follow green house infections, to examine nodule occupancy, to identify (brady)-rhizobial strains that could not be distinguished by any other method, and to carry out phylogenetic analyses of world-wide collections of rhizobia and plant pathogens (for references see Versalovic et al., 1994; Louws et al. 1996). It therefore is another useful molecular approach in molecular microbial ecology.

Figure 2. Cluster analysis of 19 Xanthomonas strains belonging to 6 DNA homology groups using combined REP, BOX and ERIC fingerprints (Pearson correlation; UPMGA).

Acknowledgements

This work has been supported by the DOE (DE FG 0290ER20021, The NSF Center for Microbial Ecology (DIR 8809640) and the Consortium for Plant Biotechology Research.




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