Journal of Medical Microbiology (2004), 53, 35–45
DOI 10.1099/jmm.0.05365-0
Identification and interrogation of highly informative
single nucleotide polymorphism sets defined by
bacterial multilocus sequence typing databases
Gail A. Robertson,1 Venugopal Thiruvenkataswamy,1 Hayden Shilling,2
Erin P. Price,1 Flavia Huygens,1 Frans A. Henskens2 and Philip M. Giffard1
Correspondence
Philip M. Giffard
1
Cooperative Research Centre for Diagnostics, Queensland University of Technology (Gardens Point
Campus), GPO Box 2434 Brisbane, Queensland 4001, Australia
[email protected]
2
Discipline of Computer Science and Software Engineering, University of Newcastle, Newcastle, New
South Wales, Australia
Received 26 June 2003
Accepted 11 September 2003
A unified, bioinformatics-driven, single nucleotide polymorphism (SNP)-based approach to
microbial genotyping has been developed. Multilocus sequence typing (MLST) databases consist of
known variants of standardized housekeeping genes. Normally, seven fragments are defined; a
sequence type (ST) consists of the variants of these fragments that are found in a particular isolate. A
computer program that can identify highly informative sets of SNPs in entire MLST databases has
been constructed. The SNPs either define a particular user-specified ST or provide a high value for
Simpson’s index of diversity (D), and may thus be generally applicable to that species. SNP sets that
are diagnostic for Neisseria meningitidis ST-11 and ST-42, and high-D SNP sets for N. meningitidis
and Staphylococcus aureus, were identified and real-time PCR methods to interrogate these SNPs
were demonstrated. High-D SNP sets were also identified in other MLST databases. This widely
applicable approach allows rapid genetic fingerprinting of infectious agents.
INTRODUCTION
The last decade has seen a great increase in quantity of gene
sequence data that are contained in searchable and downloadable databases. These data are now acquiring a significant second dimension, in that there has been a recent rapid
accumulation of data concerning the intraspecific variation of genes. Important examples of this are the single
nucleotide polymorphism (SNP) databases for humans
(Sachidanandam et al., 2001) and the multilocus sequence
typing (MLST) databases for infectious micro-organisms
(Maiden et al., 1998). MLST databases consist of known
variants of standardized housekeeping genes. Normally,
seven fragments are defined; a sequence type (ST) consists
of the variants of these fragments that are found in a
particular isolate.
Known intraspecific variation can be interrogated to generate genetic fingerprints. For genetic fingerprinting procedures to be useful, their resolving power must be known. In
the case of human SNPs, low average linkage between SNPs
and complete allelic reassortment at each generation define
Abbreviations: CT , cycles to threshold; ˜CT , difference in cycles to
threshold; D, Simpson’s index of diversity; MLST, multilocus sequence
typing; MRSA, methicillin-resistant Staphylococcus aureus; SNP, single
nucleotide polymorphism; ST, sequence type.
the algorithms used to deduce the resolving power of SNP
combinations (Krawczak, 1999). Bacteria differ in that there
is usually a single haploid chromosome, recombination
occurs sporadically and at different rates in different species,
a given recombination event will probably not encompass the
entire genome, and mutation and recombination may occur
at comparable rates (Feil et al., 2001). As a consequence, in
some bacterial species, a degree of linkage may exist over an
appreciable fraction of the genome and observed de-linkage
may either be due to recombination or the appearance of new
alleles as a result of point mutation. This prevents estimation
of the resolving power of bacterial SNPs by using methods
that were developed for sexually reproducing diploid
organisms.
The aim of this study was to develop a straightforward
approach to SNP-based bacterial typing. We have made use
of computer software that can identify highly informative
sets of SNPs in databases of gene sequence variants and
applied this to MLST databases for a number of bacterial
pathogens. The algorithms used are free of assumptions
concerning modes of generation of diversity. We have also
developed kinetic PCR assays for interrogation of informative sets of SNPs that were identified in the Neisseria
meningitidis and Staphylococcus aureus MLST databases.
Kinetic PCR does not require any fluorescently labelled
probes and is potentially very cost-effective. It is similar in
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05365 & 2004 SGM Printed in Great Britain
35
G. A. Robertson and others
principle to the allele-specific PCR/amplification refractory
mutation system family of SNP interrogation methods,
which depend on reduced extension from 39 mismatched
PCR primers (Germer et al., 2000). In the non-time-resolved
format, these methods are difficult to optimize, as any
extension from a mismatched primer provides a template
for unimpeded amplification (Giffard et al., 2001). However,
in the real-time format, optimization is easier as SNP calling
can be done on the basis of a difference in the number of
cycles to threshold (˜CT ) between allele-specific reactions,
rather than a difference between the amounts of amplified
material at the reaction end points.
METHODS
Bacterial strains. Seven N. meningitidis isolates were used (Table 1);
these were obtained from Queensland Health Scientific Services. The
STs of these isolates were determined as part of this study. The 11 S.
aureus isolates that were used were all methicillin-resistant S. aureus
(MRSA) from Australian healthcare facilities (Table 1). Isolates 66460/
98 (ST-30), F829549 (ST-88), IP01M2046 (ST-88) and MC8011535
(ST-88) were from the Queensland Health Pathology Service collection
and their STs were determined as part of this study. The remaining
isolates (two with ST-30, three with ST-239 and two with ST-1) were
from the Australian Group on Antimicrobial Resistance collection. The
STs of these were determined as part of a study that is as yet unpublished.
Downloading of MLST databases. All MLST data were downloaded
from http://www.mlst.net in the form of alignments in FASTA format.
The first 2510 STs were used for all analyses of N. meningitidis STs unless
otherwise indicated, whereas for S. aureus, the following STs were
available for download and were used for all analyses unless otherwise
indicated: 1–103, 109, 120, 121, 123, 134, 145, 169, 178, 182, 188–190,
192–205, 207–215, 217, 220–222, 225, 228, 231, 235, 238–241, 243,
246–247 and 250.
Identification of informative SNP sets. SNP sets were identified by
using a computer program called ‘Minimum SNPs’. It is designed to
provide a suite of methods for extraction of informative SNP sets from
comparative sequence data. The functions of Minimum SNPs can be
summarized as follows:
(1) Minimum SNPs can either carry out SNP searches on alignments
derived from individual MLST loci or convert an MLST database into a
single concatenated alignment that can be searched in a single operation.
(2) Highly informative sets of SNPs are identified by an approximation
that we have termed the ‘anchored method’. This entails initial
identification of the most informative SNP, which is termed SNP1,
then identification of the next SNP (SNP2) that, in combination with
SNP1, forms the most informative pair. This process is iterated, creating
progressively larger combinations until a preset target, based on either
informative power or number of SNPs, is reached. If two or more sets of
SNPs exhibit equal informative power, then the program will provide all
these sets as output. Given that this may occur at more than one point in
the analysis, large numbers of different sets of SNPs may be produced.
Cumulative informative power for each SNP identified is included in the
output.
(3) The minimum number of loci required to define any given ST can be
calculated.
(4) The informative power of sets of SNPs can be calculated by two
different methods, depending on the user’s requirements. The ‘specified
variant’ algorithm identifies SNPs that are diagnostic for a userspecified variant, i.e. one sequence in the alignment. Such SNPs are
most useful for efficient determination of whether or not an unknown
isolate is a variant of particular interest. If this option is selected, the
program assesses informative power by calculating the proportion of
non-selected variants that have the same polymorphs as the selected
variant at the SNPs being assessed (the term ‘polymorph’ is used to
denote a particular state of an SNP; this is to avoid confusion with
the term ‘allele’, which can be used to denote variants of MLST loci). The
‘generalized’ algorithm identifies sets of SNPs that are suitable for the
efficient determination of whether two isolates are likely to be the same
or different. If this option is selected, the informative power of sets of
SNPs is assessed by calculating Simpson’s index of diversity (D)
(Simpson, 1949; Hunter & Gaston, 1988). In this context, this is the
probability that two sequences in the alignment, selected at random
without replacement, will type differently if interrogated at the SNPs in
question. This is calculated as follows:
Table 1. Bacterial isolates used in this study
Species/ST
Allelic profile
N. meningitidis:
ST-8
2, 3, 7, 2, 8, 5, 2
ST-11
2, 3, 4, 3, 8, 4, 6
ST-23
ST-32
ST-42
S. aureus:
ST-1
ST-30, -88
ST-239
ST-?
36
No.
isolates
1
3
10, 5, 18, 9, 11, 9, 17
4, 10, 5, 4, 6, 3, 8
10, 6, 9, 5, 9, 6, 9
1
1
1
1, 1 ,1, 1, 1, 1, 1
2, 2, 2, 2, 6, 3, 2
22, 1, 14, 23, 12, 4
2, 3, 1, 1, 4, 4, 3
2
3
3
3
Strain
98M3462
94M1541
02M5007, 97M2363
02M5044
99M2717
94M2114
–
–
–
–
Serological
phenotype
B : 4: P1.7, 4
C : 2a : P1.5, 2
C : 2a : P1.5
Y : NT: P1.5
B :1 5 : P1, 7
C : NT : P1, 2
–
–
–
–
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Journal of Medical Microbiology 53
Bacterial typing based on computer-identified SNP sets
D ¼ 1 1=[n(n 1)]
s
X
Integrity of kinetic PCRs was checked routinely by determining melt
curves of the products, to ensure that melting-points were consistent
with the size of the expected product and not with primer-dimers, and
also to ensure that a single peak was produced.
n j (n j 1)
J¼1
where n is the number of sequences in the alignment, s is the number of
classes defined by the SNPs under test and nj is the number of sequences
in the jth class.
(5) The program allows the performance of sets of SNPs to be explored
fully by incorporating a function that returns STs that conform to any
combination of SNPs and polymorphs defined by the user.
The program is menu-driven, with a comprehensive graphical user
interface. It was written by using the Java programming language
(http://java.sun.com). Its engineering is in accordance with the objectoriented methodology (Booch, 1993). This structure facilitates future
adaptation of the software to implement, for example, a web-based
interface that would allow clients to download a relatively small applet
that communicates with a remote-server-based compute and storage
engine.
The decision to use Java rather than, for instance, C++ as the
implementation language was predicated on a desire for maximum
portability, as the program files generated by the Java compiler on one
platform do not require recompilation to run on any alternative
platform for which there is an available Java Virtual Machine. Thus,
while the software is currently in use on Microsoft Windows-based PCs,
it will also run on systems such as Solaris, Linux or Macintosh OS-X. It is
recognized that programs written in Java execute more slowly than
those written in languages that compile to native code for a given
architecture (e.g. C++), but the benefits of portability were felt to far
outweigh the slight performance penalty that is imposed by execution in
a virtual environment. Moreover, the data structures used to store SNP
data and the algorithms used to manipulate those data were chosen
carefully, with maximum efficiency as a primary goal.
Interrogation of SNPs by kinetic PCR. All reactions were carried out
in an Applied Biosystems ABI 7000 Sequence Detection system by using
Applied Biosystems SYBR Green PCR MasterMix. Primer design was
carried out with the assistance of Primer Express 2.0 (Applied
Biosystems) on sequences that were aligned by using Clustal X 1.8
(Jeanmougin et al., 1998) [accessed through the Australian National
Genome Information Service (ANGIS), Sydney, Australia]. Primer
sequences are listed in Table 2. Primers were obtained from Proligo
and were unlabelled and purified by desalting only.
For N. meningitidis, cells were grown on chocolate agar that was made as
follows: 36 g GC agar base (Oxoid), 10 g haemoglobin powder (Oxoid),
0.4 of one vial of Vitox (Oxoid) and water to 1000 ml. To prepare
genomic DNA, individual colonies were suspended in 400 mM Tris/
EDTA buffer (10 mM Tris, 1 mM EDTA, pH 8.0) and boiled for 6 min,
then centrifuged at 13 400 g for 5 min to pellet debris. Aliquots of the
supernatant were transferred to fresh microfuge tubes and stored at
82 8C. Reactions contained 1 l extract, 2.5–5 pmol each primer and
13 SYBR Green PCR MasterMix in a final volume of 20 l. A two-step
temperature cycling protocol was used: 50 8C for 2 min and 95 8C for
10 min, followed by 40 cycles of 95 8C for 15 s and 59 8C for 30 s.
For S. aureus, genomic DNA was prepared from 1 ml aerated overnight
culture in brain heart infusion broth (Oxoid) by using a Qiagen DNA
Extraction kit according to the manufacturer’s instructions, with the
exception that to lyse the cells, cell pellets were incubated for 30 min at
37 8C in 180 l lysostaphin (200 g ml1 ). DNA was stored at 20 8C.
Reactions contained 2 l template DNA, a final concentration of 0.53
SYBR Green PCR MasterMix and 6.25 pmol each primer and were made
up to final volume of 25 l. A three-step temperature cycling procedure
was used: 50 8C for 2 min and 95 8C for 10 min, followed by 40 cycles of
95 8C for 15 s, 56 8C for 10 s and 72 8C for 33 s.
http://jmm.sgmjournals.org
˜CT values were calculated by subtracting the CT of the matched
primer/template pair from the CT of the mismatched primer template.
CT values were obtained by using the default threshold setting.
MLST determination. Genomic DNA was prepared as for kinetic PCR
reactions. Amplification and sequencing primers and amplification
conditions were as recommended at http://www.mlst.net/, with the
exception that, for the N. meningitidis fumC and pgm loci, the
recommended sequencing primers were used for amplification as, in
our hands, the recommended amplification primers were unreliable in
yielding product. Sequencing reactions were carried out by using ABI
PRISM BigDye Terminator mix (version 3.1) according to the manufacturer’s instructions. Sequencing products were electrophoresed at
the Australian Genome Research Facility (University of Queensland,
Brisbane, Australia).
RESULTS AND DISCUSSION
Experimental strategy
The strategy pursued involved initial studies that used noncomputer-aided sequence analysis, which confirmed that
informative SNP sets, composed of small numbers of SNPs,
are indeed defined by MLST databases (data not shown).
This was followed by software engineering, with incremental
improvements to the program over a period of time. The
software was used to test different data-mining strategies
and, finally, in order to demonstrate the potential of this
genotyping approach, we reduced to practice procedures for
interrogation of potentially useful examples of SNP sets.
Most of our focus was on N. meningitidis and S. aureus, as
both of these species are epidemiologically interesting, but
they differ greatly with respect to diversity in MLST loci.
Identification of informative SNPs in the
N. meningitidis MLST database
SNPs that were diagnostic for N. meningitidis ST-11 and
ST-42 were identified. ST-11 was chosen as this is the ST of
the electrophoretic type-15 variant that has been responsible
for outbreaks in the Czech Republic, Greece and Canada and
is also isolated frequently in Australia (Tribe et al., 2002). ST42 is a member of lineage 3, which is a genetically related
cluster of disease-causing strains (Caugant et al., 1990). Two
methods were used to identify SNP sets. Firstly, a semiempirical, two-step strategy was adopted, which used an
earlier version of Minimum SNPs that was unable to
accommodate concatenated databases. This entailed identification of subsets of loci that defined these STs with a high
degree of reliability, identification of SNPs that are diagnostic
for alleles at these loci and use of these SNPs as a pool for
empirical searches for highly discriminatory subsets. Discriminatory powers of these subsets were assessed by using
the Minimum SNPs facility that returns STs that conform to
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G. A. Robertson and others
Table 2. Kinetic PCR primers
Bases that provide allele specificity are underlined.
SNP
Primer name
N. meningitidis ST-specific SNPs:
ST-11-specific:
fumC435
fumC435-T*
fumC435-C
fumC435-Rev†
pdhC12
pdhC12-T
pdhC12-C
pdhC12-For
ST-42-specific:
abcZ411
abcZ411-T
abcZ411-C
abcZ411-For
aroE455
aroE455-A
aroE455-G
aroE455-For
fumC201
fumC201-A
fumC201-G
fumC201-Rev
pdhC274
pdhC274-C
pdhC274-T
pdhC274-For
N. meningitidis high-D SNPs:
pgm93
pgm93-A
pgm93-C
pgm93-G
pgm93-Rev
aroE283
aroE283-A
aroE283-C
aroE283-G
aroE283A-T{
aroE283G-T
aroE283-Rev
fumC114
fumC114-C
fumC114-T
fumC114-For
abcZ183
abcZ183-T
abcZ183-C
abcZ183-G
abcZ183-For
abcZ54
abcZ54-C
abcZ54-T
abcZ54-Rev
gdh60
gdh60-A
gdh60-G
gdh60-Rev
pdhC103
pdhC103-C
pdhC103-T
pdhC103-For
S. aureus high-D SNPs:
arcC210
arcC210-C
arcC210-T
arcC210-A
arcC210-For
38
Primer sequence (59!39)
ACCATTCCCTGATGCTGGTTACT
CCATTCCCTGATGCTGGTTACC
CAGCAAGCCCAACTCAACG
CCTTTCAAGATGTCTTGTTCCGCA
CTTTCAAGATGTCTTGTTCTGCG
CGTGTTCTACTACATCACCCTGATG
CAAGTTCGACAATCCGCGTA
CGAGTTCGACAATCCGCGTG
CTTGGTCGTCATTACCCACGA
TGTATTCGATAACAGGGCGGATATT
TGTATTCGATAACGGGGCGGATATC
TGGGTATGCTGGTCGGTCA
CGACCCAATGCGAAGCA
CGACCCAATGCGAAGCG
GTAACGTCGTTGCCGAACACT
GGACCGTCATGACCTTGCAG
GGACCGTCATGACCTTGCAA
GAACGCTTCAACCGCCTG
CCGCAATCCTAAAGCCAAAGTA
CCCGGCGCGAAAGTC
CCGCAATCCTAAAGCAAAAGTG
CCGCCGTGTTCTTTAATCCA
GGTCAGATTCCCGGTATTCCA
CAGCTTCCTGCCGTCAGC
GGTCAGATTCCCGATATTCCG
AGCTTCCGGCCGTCAAT
GTCAGCTTCCTGCCGTCAGT
CCGTACACCATATCGTAGGCAAG
TTCGCCCAAACCGCAG
TTCGCCCAAACCGCAA
AATCGCCAACGACATCCG
GTTTTCTGGCAAACCAAGTTCA
CCGGCAAACCGAGTTCG
CCGGCAAACCGAGTTCC
GAAGCGAAGGACGGCTGG
GCGATTTATTGCGCCGTTAC
GCGATTTATTGCGCCGTTAT
GCCGTCCTTCGCTTCGAT
CAGCTGACCATCGCCGAA
CAGCTGACCATCGCCGAG
TTTGCACCATATCGCGCA
CCGGCAATGACTTCTTGCAG
CCGGCAATCACTTCTTGCAA
AAAGGTATGTACCTGCTGAAAGCC
CGTATAAAAAGGACCAATTGGTTTG
CGTATAAAAAGGACCAATTGGTTTA
CGTATAAAAAGGACCAATTGGTTTT
TATGATAGGCTATTGGTTGGAAACTG
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Journal of Medical Microbiology 53
Bacterial typing based on computer-identified SNP sets
Table 2. cont.
SNP
tpi243
arcC162
tpi241
yqiL333
aroE132
gmk129
Primer name
tpi243-A
tpi243-G
tpi243-Rev
arcC162-T
arcC162-A
arcC162-Rev
tpi241-G
tpi241-A
tpi241-Rev
yqiL333-C
yqiL333-T
yqiL333-Rev
aroE132-A
aroE132-G
aroE132-Rev
gmk129-C
gmk129-T
gmk129-Rev
Primer sequence (59!39)
GTAAATCATCAACATCTGAAGATGCA
GTAAATCATCAACATCTGAAGATGCG
CTTCTTTGCTTGATAAGTCAGCAATAG
GTGATAGAACTGTAGGCACAATCGTT
GTGATAGAACTGTAGGCACAATCGTA
GGGTTATTGAATCGTGGATCATC
GGTAAATCATCAACATCTGAAGATG
GGTAAATCATCAACATCTGAAGATA
CTTCTTTGCTTGATAAGTCAGCAATAG
TGCTTGTCAACAACAGTCGCTTC
TGCTTGTCAACAACAGTCGCTTT
TCTGTTAAACCATCATATACCATGCTATC
GGCTTTAATATCACAATTCCTCATAAAGAA
GGCTTTAATATCACAATTCCTCATAAAGAG
CTTGTCATCTTTTATCAAAACAGTGTTAAC
GGATGCGTTTGAAGCTTTAATC
GGATGCGTTTGAAGCTTTAATT
TTGTATCTTTAACATATTGAACTGGTGTAC
*Allele-specific primers are named according to the position of the SNP and the base in the sense
strand at the SNP. The 39 end of these primers always coincides with the SNP.
†Common primers are named according to the position of the SNP and whether the primer is
oriented as for the antisense strand (-Rev) or the sense strand (-For).
{Primers aroE283A-T and aroE283G-T were used together in the T-specific reactions.
Degenerate positions in these primers are underlined.
any user-defined combination of SNPs and polymorphs.
Secondly, SNPs that were diagnostic for these STs were
identified in a single step by using the concatenated database.
The results of the semi-empirical method were as follows: for
ST-11, T at fumC435 and C at pdhC12 excluded 98.1 % of
known STs, whereas for ST-42, T at abcZ411, A at aroE455, A
at fumC201 and T at pdhC274 excluded 97.7 % of known STs.
It was concluded that significant resolution may be obtained
from a small number of SNPs.
The results of the direct approach that used the concatenated
database were broadly similar. With this method, it was
possible to identify sets of SNPs that would, within the
context of known STs, identify these STs unambiguously. For
ST-11, 26 SNPs were required. These are listed, with the
polymorph present in ST-11 and the cumulative percentage
of SNPs discriminated from ST-11, as follows: pgm124, A
(95.2 %); pdhC12, C (98.0 %); fumC435, T (98.4 %); gdh132,
T (98.7 %); abcZ27, T (98.8 %); adk108, C (99.0 %); aroE352,
A (99.2 %); gdh60, G (99.2 %); abcZ366, C (99.3 %);
abcZ375, G (99.3 %); adk29, G (99.4 %); adk135, A
(99.4 %); adk189, C (99.4 %); adk371, A (99.5 %); aroE43,
C (99.5 %); aroE126, C (99.6 %); aroE169, A (99.6 %);
aroE207, C (99.6 %); gdh290, G (99.7 %); gdh339, T
(99.7 %); pdhC201, C (99.8 %); pgm106, A (99.8 %);
pgm276, C (99.8 %); pgm373, G (99.9 %); pgm430, G
http://jmm.sgmjournals.org
(99.9 %); pgm433, G (100.0 %). For ST-42, only eight SNPs
were required for unambiguous identification: abcZ411, T
(88.4 %); gdh129, T (95.6 %); abcZ423, C (98.9 %); aroE82, T
(99.5 %); fumC9, G (99.8 %); pdhC129, A (99.9 %); adk21, T
(99.9 %); gdh492, C (100.0 %). This demonstrates that it is
technically feasible to identify STs unambiguously by using
SNPs, assuming that the unknown sample has a known ST.
However, it is striking that for ST-11, 20 SNPs are required to
move from 99 to 100 % discrimination, and that 95.2 %
discrimination is obtained with just one SNP. For many
applications, interrogation of a very small number of SNPs
may provide useful information.
SNPs that are discriminatory for specified STs are potentially
very useful, but only for specialized applications. In order to
define a set of SNPs that could be used to derive a
discriminatory fingerprint from any N. meningitidis isolate,
the Minimum SNPs program was used with the D option
selected as the criterion for assessment of the discriminatory
power of SNP sets. A set of seven SNPs was identified and
their cumulative D-values were: pgm93, 0.653; aroE283,
0.871; pdhC456, 0.933; fumC114, 0.965; abcZ54, 0.980;
abcZ183, 0.988; fumC330, 0.992. This demonstrates that
a small number of SNPs can provide potentially useful
resolution.
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G. A. Robertson and others
Identification of informative SNPs in the S. aureus
MLST database
The S. aureus MLST database is much smaller than the N.
meningitidis database, and the level of sequence diversity is
much less. Therefore, it was of interest to explore the
relationship between the number of SNPs identified and
resolving power for this species. Firstly, SNPs that were
diagnostic for a subset of the clonal complex progenitors and
one single-locus variant, as defined by Enright et al. (2002),
were identified. Clonal complex progenitors were tested
because we conjectured that these may be difficult to define
unambiguously, as they would need to be discriminated
individually from each descendant. The STs chosen were the
clonal complex progenitors ST-30, ST-22 and ST-45, and the
ST-30 single-locus variant ST-36. SNPs that identify these
STs unambiguously are as follows.
For ST-30, 14 SNPs were required: glpF66, T (83.7 %); tpi241,
G (88.3 %); arcC78, A (90.9 %); pta387, G (92.8 %); pta181,
G (94.1 %); arcC165, A (94.8 %); aroE121, C (95.4 %);
aroE310, G (96.1 %); aroE362, C (96.7 %); glpF124, G
(97.4 %); gmk331, C (98.0 %); gmk390, A (98.7 %); pta115,
A (99.3 %); tpi158, C (100.0 %).
For ST-22, seven SNPs were required: tpi13, A (96.1 %);
aroE184, G (96.7 %); glpF128, G (97.4 %); glpF213, C
(98.0 %); gmk303, A (98.7 %); gmk340, G (99.3 %);
gmk373, A (100 %).
For ST-45, six SNPs were required: glpF57, A (95.4 %);
aroE178, G (96.7 %); tpi60, T (98.0 %); aroE270, T (98.7 %);
gmk6, A (99.3 %); pta357, T (100 %).
For ST-36, two SNPs were required: pta181, A (99.3 %);
glpF178, G (100 %).
As expected, the single-locus variant required markedly
fewer SNPs for unambiguous identification than the clonal
complex progenitors. In addition, during these searches, the
program settings were adjusted so that up to 10 SNP sets
would be selected for each ST if they were comparable in
performance. For the clonal complex progenitor STs, 10
essentially equivalent pathways to 100 % were identified.
However, in the case of the single-locus variant ST-36, the set
of two SNPs was the only set identified. These results are
consistent with the notion that in the case of clonal complex
progenitors, each descendant must be discriminated separately, whereas single-locus variants need only be discriminated from the progenitor.
In order to obtain a set of SNPs with generalized ability to
discriminate S. aureus STs, a set of SNPs that provides a high
D-value was identified. These seven SNPs and their cumulative D-values are: arcC210, 0.51; tpi243, 0.75; arcC162, 0.84;
gmk129, 0.90; aroE132, 0.92; yqi333, 0.94; tpi241, 0.95.
Several alternatives to this set of SNPs with essentially
equivalent performance were identified. It is clear that,
although a potentially useful level of discrimination was
achieved, the D-values did not rise as swiftly as with the N.
meningitidis database.
40
Kinetic PCR interrogation of N. meningitidis SNPs
In order to test the practicality and robustness of kinetic PCR
interrogation of SNPs in N. meningitidis DNA, kinetic PCR
assays were developed for: (a) SNPs diagnostic for ST-11 and
identified by the semi-empirical two-step method (fumC435
and pdhC12); (b) SNPs diagnostic for ST-42 and identified
by the semi-empirical two-step method (abcZ411, aroE455,
fumC201 and pdhC274); (c) the high-D SNP set (pgm93,
0.653; aroE283, 0.871; fumC114, 0.933; abcZ183, 0.963;
abcZ54, 0.979; gdh60, 0.987; pdhC103, 0.992). This is slightly
different from the N. meningitidis high-D SNP set identified
above, as we were unable to reduce to practice a reliable
kinetic PCR assay for pdhC456. Therefore, this position was
deleted from the alignment and the Minimum SNPs analysis
was repeated, yielding the SNP set above.
After optimization, all assays were carried out at least twice
on each isolate. To assess the robustness of the assays, results
from each polymorph of each SNP were pooled and the mean
and SD of ˜CT values were calculated. The results are shown
in Tables 3–5. On all occasions, the assays clearly indicated
the correct polymorph. In the case of isolates 02M5007 (ST11) and 02M5044 (ST-23), MLST determination was not
carried out until the kinetic PCR assays had been completed,
so the SNP interrogation was carried out blind. The kinetic
PCR and MLST results were entirely consistent. As expected,
the high-D SNP set provided unique polymorph profiles for
each ST included in the study, despite there being no
reference to these STs in the selection process for these SNPs.
It was concluded that this method has potential as a rapid
means for obtaining an epidemiological type for N. meningitidis. An alternative strategy for reducing the time and cost
of MLST-based genotyping has been reported by Shlush et al.
(2002); this involved detection of mismatches between PCR
products from a standard isolate and a known isolate by
using denaturing HPLC. This approach is very effective for
determining whether or not an isolate has an ST of interest.
However, it requires post-PCR processing and, unlike our
high-D SNP sets, cannot yield a generally applicable genetic
fingerprint.
During the process of primer design and optimization, a
potentially useful observation was made. Scrutiny of the
results of kinetic PCR interrogation of fumC435 revealed that
in some fumC alleles, there was a subterminal mismatch 7 bp
from the 39 end of the primer. This mismatch is a consequence of diversity in the primer-binding site and is present
in ST-8 and ST-42, but not in ST-11 or ST-32. This mismatch
was shown to improve allele specificity by increasing CT for
reactions with mismatched primers, whilst having little or no
effect on CT values for matched primers (Fig. 1). The net
effect of this is to increase the ˜CT . The likely reason for this
effect is that the mismatch lowers the melting-point of the
target–primer duplex, thus reducing the probability that the
primer site will be occupied at any given time-point during
the annealing step.
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Journal of Medical Microbiology 53
Bacterial typing based on computer-identified SNP sets
Table 3. Kinetic PCR results on N. meningitidis DNA for ST-specific SNPs
All ˜CT values obtained were in the expected orientation. NA, Not applicable.
ST-11-specific SNP set
fumC435
ST-42-specific SNP set
pdhC12
abcZ411
ST SNP profiles:
ST-11
T
C
C
ST-8
C
T
C
ST-32
C
T
C
ST-42
C
T
T
ST-23
C
T
T
Mean SD CT values from pooled results for each polymorph:
G
NA
NA
NA
A
NA
NA
NA
T
7.86 1.06
10.87 0.45
7.52 2.28
C
7.17 3.18
15.74 3.7
13.87 0.79
aroE455
fumC201
pdhC274
G
G
G
A
G
G
G
A
A
A
T
T
C
T
T
7.34 2.97
4.13 0.79
16.74 1.97
9.91 1.26
NA
NA
NA
NA
NA
14.09 2.25
10.72 0.95
NA
Table 4. Kinetic PCR results on N. meningitidis DNA for high-D SNPs
All ˜CT values obtained were in the expected orientation. NA, Not applicable; NT, not tested.
pgm93
aroE283
fumC114
ST SNP profiles:
ST-11
G
G
C
ST-8
C
T
C
ST-32
G
G
T
ST-42
A
T
T
ST-23
G
C
T
Mean SD ˜CT values from pooled results for each polymorph:
G
14.05 2.32
10.15 1.64
NA
.
.
A
15 55 3 85
NT
NA
T
NA
12.14 3.83
9.18 3.27
C
18.29 4.38
4.24 1.07
18.06 1.96
abcZ183
abcZ54
gdh60
pdhC103
G
G
G
C
C
C
C
T
C
C
G
G
G
G
A
C
C
C
T
T
15.65 2.03
NA
NA
NA
NA
13.24 2.44
10.99
NT
7.62 0.01
6.11 1.03
NA
13.15 1.35
13.77 3.35
13.26 2.65
NA
NA
Table 5. ˜CT values from interrogation of high-D SNPs in ST-11 DNA
ST-11 ˜CT
Matched ( SD)
Mismatched ( SD)
NT,
pgm93
aroE283
fumC114
abcZ183
abcZ54
gdh60
pdhC103
17.45 1.76
35.66 3.59
18.15 3.19
35.14 5.88
20.82 1.38
39.79 0.54
17.05 0.69
35.34 3.75
17.08 1.18
24.97 4.31
19.85 1.06
34.15 4.47
15.93 0.74
30.38 5.41
Not tested.
Kinetic PCR interrogation of S. aureus SNPs
Kinetic PCR assays were developed for the S. aureus
high-D SNP set: arcC210, tpi243, arcC162, tpi241,
yqiL333, aroE132 and gmk129. The results are shown in
http://jmm.sgmjournals.org
Tables 6 and 7. As with the N. meningitidis SNPs, the
assay was very reliable. After optimization, ˜CT values
obtained were always in the expected orientation. This
method has potential as a means for rapid fingerprinting
of S. aureus.
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41
G. A. Robertson and others
Identification of high-D SNP sets in other
organisms and comparison of the relationship
between the number of SNPs and D-values
obtained
Fig. 1. Effect on allele discrimination for the N. meningitidis fumC435
SNP of a subterminal mismatch 7 bp from the 39 end of the allelespecific primers.
Our approach is applicable to all species for which there are
MLST databases. Therefore, MLST databases for a number of
different species were searched for high-D SNP sets. Species
subject to this analysis were Burkholderia pseudomallei/
Burkholderia mallei, Helicobacter pylori, Campylobacter jejuni, Streptococcus pneumoniae, Streptococcus pyogenes and
Enterococcus faecium (Feil et al., 2000; Enright et al., 2001;
Sa-Leao et al., 2001; Dingle et al., 2002; Homan et al., 2002;
Godoy et al., 2003; Meats et al., 2003). Ten different sets of
SNPs were identified for each species. For each species, the
relationship between D and the number of SNPs was
essentially identical for each SNP set, but there were large
differences between species (Fig. 2). Despite the differences, it
was possible to identify high-D SNP sets in all cases. It was
concluded that SNP-based typing could potentially be
applied to any bacterial species for which there is sufficient
comparative sequence data.
The basis for differences in the relationship between the
number of SNPs and D-values obtained is an intriguing
Table 6. Kinetic PCR on S. aureus genomic DNA for high-D SNPs
All ˜CT values obtained were in the expected orientation. NA, Not applicable; NT, not tested.
arcC210
tpi243
arcC162
ST SNP profiles:
ST-30
T
G
A
ST-88
T
A
T
ST-239
T
A
A
ST-1
C
A
T
Mean SD ˜CT values from pooled results for each polymorph:
G
NA
11.58 1.95
NA
A
NT
7.33 2.67
16.46 1.2
T
12.51 1.77
NA
15.46 0.85
C
11.54 1.34
NA
NA
tpi241
yqiL333
aroE132
gmk129
G
G
G
G
T
C
T
C
G
A
A
A
T
T
C
C
5.6 0.52
NA
NA
NT
NA
12.99 3.88
6.72 2.7
NA
6.63 1.35
9.57 2.66
NA
9.32 2.78
8.4 2.56
NA
NA
NA
Table 7. ˜CT values from interrogation of high-D SNPs in ST-30 DNA
ST-30 ˜CT
Matched ( SD)
Mismatched ( SD)
42
arcC210
tpi243
arcC162
tpi241
yqiL333
aroE132
gmk129
20.19 5.12
31.87 2.93
20.19 3.15
30.24 3.67
18.28 2.00
33.38 4.48
20.32 2.52
25.54 3.26
19.52 4.05
24.59 3.53
19.74 3.0
29.01 1.31
20.40 2.75
31.92 6.88
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Journal of Medical Microbiology 53
Bacterial typing based on computer-identified SNP sets
Therefore, this approach is suitable for bacterial pathogens,
as bacteria exhibit significant interspecies variation in their
diversity and clonality and, hence, also vary in the extent of
linkage disequilibrium between markers that are separated
by any given distance. The results presented here demonstrate that SNP combinations with quantitatively defined
and useful resolving powers can indeed be identified and can
also be interrogated by using efficient and convenient assays.
Fig. 2. Relationships between number of SNPs and D-values obtained for MLST databases for a number of different bacterial species.
STs analysed were: Helicobacter pylori, 1–370; Neisseria meningitidis, 1–2859; Streptococcus pneumoniae, 1–900; Campylobacter
jejuni, 1–812; Streptococcus pyogenes, 1–108; Haemophilus influenzae, 1–95; Burkholderia pseudomallei/Burkholderia mallei, 1–
101; Enterococcus faecium, 1–63; Staphylococcus aureus, the 202
STs available for download in September 2003. In the case of H. pylori,
YphC alleles 286, 288 and 310–315 contained 6 bp of missing
sequence; these were filled in manually by using the consensus
sequence for this region.
puzzle. It is tempting to look for a relationship between
clonality and D-values: a highly clonal organism would have
linkage disequilibrium that extended over the entire genome,
so different SNPs would be polymorphic only within certain
lineages, resulting in lower combinatorial power between
SNPs. The recent finding that S. aureus has a low recombination rate (Feil et al., 2003) may explain the low D-values that
were obtained with this species. However, there are other
factors. The extent of genetic diversity would be expected to
have an effect, simply because more genetic diversity means a
larger pool of SNPs and, all things being equal, a consequently higher probability of finding highly informative sets
of SNPs. Also, the presence of trimorphic or tetramorphic
SNPs potentially speeds up the accumulation of resolving
power as each SNP is added to the set; such SNPs are much
more common in more diverged species. Our current view is
that both the frequency of recombination and the number
and nature of SNPs available contribute to D-values that can
be obtained for a given number of SNPs.
Possible applications
The purpose of this study was to develop a systematic and
practical approach to making use of known genetic diversity
in micro-organisms, in order to assemble rapid and costeffective genetic fingerprinting methodologies. The algorithms used are entirely empirical and there are no assumptions made concerning population structure or diversity.
http://jmm.sgmjournals.org
Possible criticisms of our work are that SNP combinations
will almost never have the same resolution as the full ST, that
SNP combinations are ineffective at detecting new STs and
that sequencing technology is itself rapid and cost-effective.
The major counter to this is that MLST is itself somewhat
arbitrary in nature. There is no certainty that two isolates
with the same ST do not differ in regions that are subject to
high-frequency transposition or site-specific recombination
events, or even in housekeeping genes that are outside the
MLST-defined fragments and have undergone recombination or mutation. MLST appears to be an excellent method
for identifying clonal background and carrying out longrange studies of population structure and evolution. Higher
typing resolution, such as that required for reliable outbreak
detection, may require methods such as PFGE. This raises the
question as to the practical value of the increase in resolution
provided by MLST, as compared to our SNP-based approach. It is likely that highly effective typing methodologies
of the future will encompass interrogation of housekeeping
genes to determine clonal background, and interrogation of
hypervariable regions to increase resolution. A SNP-based
approach in combination with interrogation of hypervariable regions would be expected to have a very similar
performance to full ST determination in combination with
interrogation of hypervariable regions.
Although the ability of SNP-based typing to detect new STs is
limited, in the case of high-D SNP sets, it is not zero, as novel
SNP profiles can be detected. In the case of specified variant
SNPs, the concern is not relevant, as these SNPs are designed
only to allow efficient determination of whether or not an
isolate has an ST of interest. Also, SNP-based typing does not
rule out the use of MLST or other higher-resolution typing
methods. Specified variant SNPs for ST-x will never provide a
false-negative answer to the question ‘Does this isolate have
ST-x?’, whereas high-D SNPs will never provide a falsenegative answer to the question ‘Are these isolates identical?’.
When these questions are answered in the affirmative on the
basis of SNP profiles, it may be appropriate in some instances
to carry out full ST determination or other higher-resolution
typing procedures. We therefore see SNP-based typing to be
most suitable as a routinely applied, high-throughput screening tool that can flag potential outbreaks or the appearance of
variants of concern. Possible applications include infection
control in healthcare facilities, monitoring of food-borne
disease and biodefence.
It was in recognition of this that we tested the practicality of
kinetic PCR as an SNP-interrogation method. This approach
is single-step –there is no prior amplification required. It
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43
G. A. Robertson and others
uses unlabelled primers and no probes. There is a trend
toward lower reaction volumes and reduced reagent costs in
real-time PCR applications, and kinetic PCR has the potential to be extremely cost-effective. We envisage this method
being carried out on colonies on primary isolation plates or
on blood or enrichment cultures from normally sterile sites.
It could be combined easily with other gene-detection assays
for, for example, virulence factor-encoding genes or antibiotic-resistance determinants, to gain a complete picture of
a micro-organism in a very short time.
study was supported by the Australian Federal Government Cooperative Research Centres Programme and the Queensland State Government Department of Innovation and Information Economy.
In our hands, kinetic PCR is a tractable methodology. It is
well-known that different 39 mismatches are subject to
different mis-priming frequencies (Huang et al., 1992;
Ayyadevara et al., 2000), so the different mean ˜CT values
obtained at different SNPs were not unexpected. Remarkably, kinetic PCR reactions yielded approximately equal ˜CT
values (although always in the expected orientation) with
different polymorphs at any given SNP, indicating that the
allele-specific primers in any given primer pair (or triplet)
mis-primed at approximately equal frequencies. This was not
expected and quite fortuitous. Primer design for N. meningitidis reactions was more challenging than for S. aureus
reactions, as greater diversity in N. meningitidis means that
diversity within primer sites is a factor that must be
accommodated. However, we were only unsuccessful in
assembling a reliable kinetic PCR assay for one SNP
(pdhC456) and degenerate primers were only necessary for
one other SNP (aroE283). In the case of fumC435, primer site
diversity proved to be advantageous, as a mismatch 7 bp
from the 39 end increased the ˜CT . Deliberate inclusion of
mismatches is likely to prove a valuable tactic for maximizing
˜CT values, as has been found previously for conventional
allele-specific PCR (Ishikawa et al., 1995). It can be seen, in
Table 2, that the N. meningitidis allele-specific primers differ
markedly at locations other than the 39 end. This was
necessary to accommodate diversity in the primer sites and
appears to be a viable option, as examination of sequence
alignments indicated that the diversity was not distributed
evenly –particular bases at polymorphic sites were linked
strongly to certain sequences within the primer-binding sites.
This may be the effect of insertions, deletions or inversions in
the evolutionary past. With the much less diverse species
S. aureus, diversity within the primer-binding sites was not
an issue.
Caugant, D. A., Bol, P., Høiby, E. A., Zanen, H. C. & Frøholm, L. O.
(1990). Clones of serogroup B Neisseria meningitidis causing systemic
In conclusion, we have demonstrated a novel and widely
applicable approach to bacterial typing. It makes use of
known genetic diversity and allows the typing method to be
designed with reference to the resolving power required. It
would be worthwhile to test this approach against large
numbers of isolates.
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