Construction of the gyrB Database for the Identi cation
and Classi cation of Bacteria
Hiroaki Kasai 1
hkasai@kamaishi.mbio.co.jp
Amos Bairoch 2
bairoch@cmu.unige.ch

Kanako Watanabe 1
kanakow@kamaishi.mbio.co.jp
Katsumi Isono 3
isono@biol.kobe-u.ac.jp
Shigeaki Harayama 1

Elizabeth Gasteiger 2
Gasteiger@medecine.unige.ch
Satoshi Yamamoto 4
yamamotost@nichirei.co.jp

harayama@kamaishi.mbio.co.jp

1

Marine Biotechnology Institute, Kamaishi Laboratories
3-75-1, Heita, Kamaishi, Iwate 026-0001, Japan
2 Swiss Institute of Bioinformatics c/o Department of Medical Biochemistry1, rue Michel Servet CH - 1211, Geneva 4, Switzerland
3 Graduate School of Science and Technology, Kobe University
Rokkodai, Kobe 657-8501, Japan
4 Food Science Laboratory, Research and Development Center, Nichirei Corporation,
Shinminato 9, Chiba 261-8545 Japan

Abstract
Nucleotide sequences of small-subunit rRNA (16S rRNA) are most commonly used for the identication and characterization of bacteria and their complex communities. However, 16S rRNA evolves
slowly and is often not very convenient to resolve bacterial strains at the species level. We have therefore attempted to develop a rapid and more convenient system for bacterial identi cation using the
gyrB gene sequences. We chose the gyrB gene, because (i) it is rarely transmitted horizontally, (ii) its
molecular evolution rate is higher than that of 16S rRNA, and (iii) the gene is distributed ubiquitously
among bacterial species. We PCR-ampli ed the 1.2 kb-long gyrB segments from about 1,000 bacterial
species by using degenerate primers and determined their nucleotide sequences. The resultant data
have been assembled into the gyrB database accessible via WWW.

1 Introduction
For the identi cation and classi cation of new bacteria, microbiologists have been using various taxonomic
markers and performing comparative analysis of the data with those of type strains. Such studies are
generally very laborious, and yet the results obtained are not necessarily conclusive. During the past
decades, however, the method for taxonomy has evolved dramatically though the introduction of various
nucleotide sequencing techniques and resultant massive amounts of data. Especially, rRNA gene sequence
data have been proved to be useful in establishing the division of all living organisms into three domains,
namely, Archaea, Bacteria and Eucarya [18]. Needless to say that the development in the taxonomyof living organisms, microorganisms in particular, has mainly been achieved through the analysis ofrRNA sequence data. There are a variety of reasons why rRNA genes have been selected as standard
genes for molecular taxonomy. First, rRNA is an essential constituent in all living organisms. Second,the existence of many conserved regions in the rRNA genes allows the alignment of their sequences
derived from distantly related organisms, while their variable regions are useful for the distinction ofclosely related organisms. Furthermore, the horizontal transfer of rRNA genes is believed to be rare.
Currently, more than 20,000 rRNA gene sequences are available in the Ribosomal Database Project (RDP;
http://www.cme.msu.edu/RDP/) and GenBank at the National Center for Biotechnology Information(http://www.ncbi.nlm.nih.gov/). The RDP database is accessible via the internet and contains various
13analytical tools. However, in some cases, it seems that the resolution of rRNA-based analysis is too low
due to the small numbers of substitutions between compared rRNA sequences.
Therefore, we felt that a gene(s) which is not frequently transmitted horizontally, evolves at a higher
rate than the rRNA gene, and is found in most, if not all, bacterial species must be searched for andshould be used as an alternative taxonomic marker. We have surveyed the protein-coding genes which
satisfy these criteria, and found that several genes including gyrB appeared to be good candidates [4,19, 20, 21, 22]. Therefore, we determined the nucleotide sequences of the gyrB and other genes from awide variety of bacteria. We then construct the gyrB database for the identi cation and classi cation ofbacteria which can be accessible via the internet (http://www.mbio.co.jp; Fig. 3-A).

2 Molecular systematics based on gyrB
DNA topoisomerases are essential for DNA replication, transcription, recombination and repair andcontrol the level of supercoiling by cleaving and resealing the phosphodiester bond of DNA. They areclassi ed into type I (EC 5.99.1.2) and type II (EC 5.99.1.3) according to their enzymatic properties.
The bacterial DNA gyrase is a type II topoisomerase that is capable of introducing negative supercoilinginto a relaxed closed circular DNA molecule. This reaction is coupled with ATP hydrolysis. DNA gyrase
can also relax supercoiled DNA without ATP hydrolysis. DNA gyrase consists of two subunit proteinsin the quaternary structure of A2B2. The A protein (GyrA) has a molecular weight of approximately
100 kDa while the B protein (gyrB) has a molecular weight of either 90 kDa or 70 kDa. Comparisonof the primary structures of the 90 kDa class and of the 70 kDa class revealed that the 90 kDa typehas an insertion of about 170 amino acids at a site corresponding to residue 560 of the 70 kDa-typemolecule. The N-terminal portion of the B protein was thought to catalyze ATP-dependent supercoilingof DNA while the C-terminal portion supports complex formation with the A protein and is involved in
ATP-independent relaxation. The crystal structure of the N-terminal 43-kDa fragment of the B protein
has been established. The 43-kDa protein monomer comprises two domains with the ATP-binding site
being located at the center of the rst domain.
Topoisomerase IV is a bacterial enzyme that appears to be closely related to DNA gyrase. It was
identi ed through the analysis of mutations (parE) that let to a de ciency in the partition phase ofbacterial replication. The role of this enzyme may be to link the catenated daughter chromosomes prior
to partition into daughter cells. Sequence alignment shows that the parE gene product is very similar
to gyrB. Topoisomerase IV cannot catalyze supercoiling of DNA, however. It catalyzes DNA relaxation
by a mechanism that requires hydrolysis of ATP [11, 17]. Almost all bacteria are known to contain both
DNA gyrase and topoisomerase IV. However, the gene for topoisomerase IV was not found in the total
genome sequences of Helicobacter pylori [15]. Mycobacterium tuberculosis [2] and Synechocystis sp. PCC
6803 [5]. It is not known whether or not DNA gyrase in these bacteria acts also as topoisomerase IV.
We used the gyrB and its translated sequences for bacterial taxonomy. For this purpose, a set ofuniversal primers were designed from two conserved amino acid sequences in gyrBs from Escherichia
coli, Pseudomonas putida and Bacillus subtilis. The two conserved amino acid sequences were \reversetranslated", and a set of degenerate primers which contain 512 variations was designed. PCR ampli cation of gyrB was carried out by using the genomic DNA from bacteria of di erent taxonomic groups,
and PCR products with a size predicted from the known gyrB sequences (1.2 kb) were ampli ed fromvarious kinds of strain [19]. Thus, it was possible to amplify gyrBs from a broad range of bacteria: the, and subdivisions of Proteobacteria, the Cytophaga/Flavobacterium/Bacteroides complex, low andhigh G+C Gram-positive bacteria and some strains of Cyanobacteria. To avoid paralogous comparison,
we have developed the gyrB-speci c primers which do not amplify the genes for topoisomerase IV.
A validity of gyrB sequences as taxonomic markers was evaluated mainly from two points of view: therate of their base substitutions and the consistency between the results of gyrB-based analysis and thoseof the DNA-DNA hybridization analysis. Protein-coding genes are thought to evolve faster than rRNA
genes because synonymous substitutions mainly at the third positions of codons in the protein-coding
14

genes are permitted without causing any changes in the amino acid sequences of their gene products.
Actually, the average base-substitution rate of 16S rRNA genes was 1% per 50 million years, while thatof gyrB at the synonymous sites was estimated to be 0.7 - 0.8% per one million years [20]. Therefore,
some species with completely identical 16S rDNA sequences can be di erentiated by using their gyrB
gene sequences: four species belonging to Mycobacterium tuberculosis complex, Mycobacterium kansasii
and Mycobacterium gastri (Kasai et al. manuscripts in preparation) are such examples. Theoretically,
any protein-coding sequences can be used for phylogenetic analysis. However, many genes are known to
spread horizontally among di erent bacterial species. In the case of gyrB, there was a high correlation
between the phylogenetic distance based on the gyrB sequences and the total genome homology analyzed
by DNA-DNA hybridization [22]. This observation suggests that the horizontal transfer of gyrB is, ifanything, rare. Probably, the essential roles of the gyrB product in DNA replication and transcriptionfunctions against the introduction of foreign gyrB.
Two cases of the existence of multiple copies of gyrB have been reported in the genomes of the
Streptomyces family. In Streptomyces sphaeroides, two diverged gyrB genes were found, one encoding anovobiocin-sensitive and the other a novobiocin-resistant enzyme [13]. A similar case was found in the
genome project of Streptomyces coelicolor. Here, one of the gyrB genes was located just upstream of
gyrA in the oriC region as in the case of the gyrB genes found in almost all bacteria, while a second
gene was located about 1.85 Mbp far from oriC [9]. The phylogenetic analysis of the products of thesegenes indicated that one of them is related with other Streptomyces gyrBs while the other was outside
the Streptomyces cluster.

3 Comparison of the phylogenetic relationships deduced from gyrBwith those from 16S rRNA sequences
While 6205 data of 16S rRNA are available from the RDP [6], and a 16S rRNA-based phylogenetic
tree of bacteria has been constructed [7], the resolution of the 16S rRNA-based analysis is not so highas to distinguish very closely related bacteria. In general, it is said that organisms sharing more than
97% of identity in their 16S rRNA sequences might belong to one and the same species [16]. However,there are cases of bacteria exhibiting more than 99% identity in their 16S rRNA sequences, and yet
belonging to two distinct species as revealed from DNA hybridization analysis. Evidently, due to theslow speed of divergent evolution of the 16S rRNA gene, the resolution of 16S rRNA-based analysis
between closely related organisms is lower than that of DNA hybridization [12]. Furthermore, there arepolymorphisms found amongst di erent rRNA genes in some bacteria [10], and consequently alignmentof rRNA sequences requires some expertise. At the same time, it is often not very easy to obtain correct
sequence data of rRNAs and their genes largely due to their highly ordered structure.
Yamamoto and Harayama [21] compared the phylogenetic relationships of 20 Pseudomonas strains
deduced from the genes for 16S rRNA, RNA polymerase 70 factor (rpoD) and gyrB. They showed thatthe phylogenetic trees based on gyrB and rpoD are congruent, but the topology of the phylogenetic tree
based on the 16S rRNA sequences including their variable regions was not identical with those of thegyrB- and rpoD-based trees. When the variable regions were excluded from analysis, the topology ofthe phylogenetic tree based on the 16S rRNA sequences became less di erent from those of the gyrBand rpoD-based trees, although the resolution of the 16S rRNA-based tree was much lower than that of
the gyrB- and rpoD-based trees. They also showed that the genetic distances in the variable regions of
16S rRNA correlated poorly with the synonymous substitution distances in the gyrB and rpoD genes.
From these observations, they proposed that many base substitutions in the variable regions may not
be the results of successive point mutations, but may be caused by single-event mutations introducing
multiple substitutions. Hence, the variable regions should not be included in the calculation of the genetic
distance, and the base substitution rates outside such regions are too low to distinguish bacterial species.

15

Figure 1: Phylogenetic trees of 20 Pseudomonas strains based on the nucleotide sequences of gyrB (A),rpoD (B) and 16S rRNA genes (C, D). Two types of phylogenetic trees constructed from the 16S rRNA
sequences are presented: a tree based on the whole 16S rRNA sequences including the variable regions(C) and another based on the partial 16S rRNA sequences without the variable regions (D). Trees wereconstructed by using the UPGMA method [8]. Bars indicate the genetic distance of either 0.1 or 0.01.
Numbers at individual nodes indicate the percent bootstrap values of 1000 trials. The corresponding
Escherichia coli K-12 sequence was treated as the outgroup in each case.

16

Figure 2: Signature sequences found in the GyrB sequences of Mycobacteria and relatives. 349 GyrB
sequences of high G+C Gram-positive bacteria were aligned by running ClustalW 1.7. Consequently,
their GyrB sequences were found to contain a stretch of insertion sequence (shaded) as indicated.

4 Detection of signatures in multiply aligned GyrB sequences
We have been analyzing the gyrB of high G+C Gram-positive bacteria of so called actinomycetes. 349
GyrB sequences were aligned by using ClustalW 1.7. As the results, the GyrB sequences of this groupwere found to contain a stretch of insertion sequence which is not present in other taxonomic groups.
The site of insertion is located between the seventh and eighth beta sheet in the rst domain based on
the X-ray analysis of the Escherichia coli GyrB fragment [17]. The length of the inserted sequences
appeared to be variable: 3 to 4 amino acid residues in Micromonosporaceae, 10 to 15 in Streptomyces
and Pseudonocardiaceae except for a mycolic acid-containing group which possesses insertions of 25 to
55 amino acid residues (Kasai and Harayama, in preparation). The insertion sequence found in mycolic
acid-containing group, Mycobacterium tuberculosis for example, was predicted to form an alpha helix
through the analysis of a secondary structure estimation tool, PREDATOR [3]. Biological meanings ofthese insertions are unknown, although the nucleotide substitution rates and the codon usage patternin the insertion sequences are comparable with those of other regions. This observation suggests thatthese insertion sequences are not generated by the insertion of IS (insertion sequences) or transposon.
Consequently, they would serve as useful signature sequences for the identi cation of bacteria belonging
to high G+C Gram-positive bacteria.

175 The gyrB database for the identi cation and classi cation of bacteria
We have thus created a gyrB sequence database to facilitate easier Identi cation and Classi cation ofBacteria (for this reason, we have named the database the ICB database). The ICB database o ers usefuldata and information concerning bacterial phylogeny. It also provides tools for the identi cation andclassi cation of newly isolated bacteria by performing comparative analyses of the nucleotide sequences
and/or their derived amino acid sequences against the stored gyrB sequence database. The database
contains not only the nucleotide and amino acid sequence data of the gyrB gene and its protein products
from a large variety of microorganisms, but also other information such as useful hints for experimental
protocols and trouble shootings so as to reduce the time and e orts in the identi cation and classi cationof bacteria. The WWW pages and related cgi-scripts have been designed for these purposes as described
below.
In the data browsing pages, the gyrB sequence data are listed in the alphabetical order of genus
and species names. In the list, a check box is provided for each entry that can be used for retrieving
the corresponding sequence data in either Genbank or fasta format. Other information concerning eachspecies can also be accessed and retrieved by clicking species names. Furthermore, the database can besearched for any desired species and/or group of species by entering key words.
Currently, more than 1,000 gyrB sequences are stored in the database (some of them are open only
to authorized users). The data have been obtained from bacteria of various taxa. 501 gyrB sequences are
originated from Proteobacteria, 389 from Gram-positive bacteria, 64 from Cytophaga/Flavobacterium/
Bacteroides complex, and 52 from other taxonomic groups. The numbers of genera included are 61
Proteobacteria, 38 Gram-positive bacteria, 20 Cytophaga/Flavobacterium/Bacteroides complex, 14 othergroups and 36 unidenti ed species (the data were scored at the end of July 1998). We believe that there
is no other comparative database containing data of protein-coding genes from such a large number ofbacteria of taxonomical variety. In addition, the data have been found to contain almost no sequenceerrors, because it is quite easy to nd errors, if any, of a new sequence by comparing its amino acidtranslation against the database.
In the pages showing experimental procedures, how to obtain materials for gyrB sequence and how to
perform actual analysis are described in details. These include methods for bacterial DNA preparation,a list of universal PCR primers as well as genus- and/or group-speci c PCR primers, conditions for
successful PCR ampli cation for each bacteria, technical details of DNA sequencing, and so on. Trouble
shootings and know-how's for designing PCR primers are also described.
The ICB database also provides users with tools for the identi cation of their bacterial isolates byusing its gyrB gene sequence data. A typical analytical procedure will include the following steps: 1)
perform a similarity search of the obtained DNA/amino acid sequence data against the gyrB database;2) select several sequences showing higher similarity scores; 3) align the selected sequences; and, 4) nd
the most closely related species by analyzing the aligned sequence data. At step 1, we use the blast 2.0
program [1], in which both BLASTN and BLASTP are made available. At step 2, the results of blast
search will be listed together with a check box (Fig. 3-B). The checked data and query data will then be
analyzed by ClustalW ver. 1.7 [14] at step 3. Users can choose various options for ClustalW analysis at
this step. The results of multiple alignment of selected data along with a tree le will then be presented.
The aligned data can easily be subjected to construction of a phylogenetic tree by running a suitable
program.

6 Plans for future development of the ICB database
How to obtain a reasonable multiple alignment is one of the key questions for phylogenetic analysis basedon gene sequences. To solve various problems encountered in multiple alignment, we are currently seeking
the way to incorporate the secondary structure information based on the data obtained with E. coli gyrB
18

A.
B.
Figure 3: Presentation of data of the ICB database via internet. The rst page of the ICB databaseis shown in panel A. Blast search of query sequence against the whole data stored in ICB database isavailable. 20 resultant sequences will then be listed with check boxes for further analysis as shown inpanel B and the selected sequences will be subjected to ClustalW analysis with various selectable options.
protein to evaluate and modify the multiple alignment data produced by ClustalW. However, even if
the resultant multiple alignment appears reasonable, the taxonomical relationship based on it could be
biased, since only one protein-coding gene has been used for the phylogenetic analysis. Therefore, we arepreparing to incorporate the data for some other protein- coding genes into the database as well, so thatmultiple alignment of more than one protein-coding gene will become possible. Thus in the near future,
the data for rpoD as well as iC (the structural gene for agellin) will be incorporated into the ICB
database. In addition, eukaryotic type II topoisomerase genes from yeast strains will also be included forthe comparison of fungal microorganisms. The whole genomic nucleotide sequence data of as many as
fteen bacterial species have become available to date. However, the data are restricted to only modelorganisms or organisms of some pathogenic signi cance. In the natural environment, millions of bacterial
species are said to exist of which only less than 1% are currently known to be cultivated. The ICB
database, which stores the nucleotide sequence information for several protein-coding genes from a wide
variety of bacteria, is thus expected, at least more eciently, to cope with the analysis of the extremely
diverse bacterial world.

Acknowledgments
We are grateful to Tohru Hamada, Makoto Suzuki and Shoko Nakamura for their e orts to accumulate
gyrB data, and to Yuhka Takahashi, Yukiko Itazawa, Nakako Hayashi and Ikuko Hiramatsu for theirtechnical assistance. This work was supported by Industrial Science and Technology Frontier Programfrom New Energy and Industrial Technology Development Organization.

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