Based Upon Repeat Pattern (BURP): an algorithm to characterize the long-term evolution of Staphylococcus aureus populations based on spa polymorphisms.

ral
ssBioMed CentBMC Microbiology
Open AcceResearch article
Based Upon Repeat Pattern (BURP): an algorithm to characterize
the long-term evolution of Staphylococcus aureus populations based
on spa polymorphisms
Alexander Mellmann†1, Thomas Weniger†2, Christoph Berssenbrügge1,
Jörg Rothgänger3, Michael Sammeth4, Jens Stoye5 and Dag Harmsen*2
Address: 1Institute for Hygiene, University Hospital Münster, Münster, Germany, 2Department of Periodontology, University Hospital Münster,
Münster, Germany, 3Ridom GmbH, Würzburg, Germany, 4Center for Genomic Regulation, Barcelona, Spain and 5Faculty of Technology,
University Bielefeld, Bielefeld, Germany
Email: Alexander Mellmann - mellmann@uni-muenster.de; Thomas Weniger - tweniger@gmx.de;
Christoph Berssenbrügge - ch.berssenbruegge@web.de; Jörg Rothgänger - jrothgaenger@ridom.de; Michael Sammeth - micha@sammeth.net;
Jens Stoye - stoye@techfak.uni-bielefeld.de; Dag Harmsen* - dharmsen@gmx.net
* Corresponding author †Equal contributors
Abstract
Background: For typing of Staphylococcus aureus, DNA sequencing of the repeat region of the protein A (spa)
gene is a well established discriminatory method for outbreak investigations. Recently, it was hypothesized that
this region also reflects long-term epidemiology. However, no automated and objective algorithm existed to
cluster different repeat regions. In this study, the Based Upon Repeat Pattern (BURP) implementation that is a
heuristic variant of the newly described EDSI algorithm was investigated to infer the clonal relatedness of different
spa types.
For calibration of BURP parameters, 400 representative S. aureus strains with different spa types were
characterized by MLST and clustered using eBURST as "gold standard" for their phylogeny. Typing concordance
analysis between eBURST and BURP clustering (spa-CC) were performed using all possible BURP parameters to
determine their optimal combination. BURP was subsequently evaluated with a strain collection reflecting the
breadth of diversity of S. aureus (JCM 2002; 40:4544).
Results: In total, the 400 strains exhibited 122 different MLST types. eBURST grouped them into 23 clonal
complexes (CC; 354 isolates) and 33 singletons (46 isolates). BURP clustering of spa types using all possible
parameter combinations and subsequent comparison with eBURST CCs resulted in concordances ranging from
8.2 to 96.2%. However, 96.2% concordance was reached only if spa types shorter than 8 repeats were excluded,
which resulted in 37% excluded spa types. Therefore, the optimal combination of the BURP parameters was
"exclude spa types shorter than 5 repeats" and "cluster spa types into spa-CC if cost distances are less than 4"
exhibiting 95.3% concordance to eBURST. This algorithm identified 24 spa-CCs, 40 singletons, and excluded only
7.8% spa types. Analyzing the natural population with these parameters, the comparison of whole-genome micro-
array groupings (at the level of 0.31 Pearson correlation index) and spa-CCs gave a concordance of 87.1%; BURP
spa-CCs vs. manually grouped spa types resulted in 95.7% concordance.
Conclusion: BURP is the first automated and objective tool to infer clonal relatedness from spa repeat regions.
Published: 29 October 2007
BMC Microbiology 2007, 7:98 doi:10.1186/1471-2180-7-98
Received: 7 June 2007
Accepted: 29 October 2007
This article is available from: http://www.biomedcentral.com/1471-2180/7/98
© 2007 Mellmann et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 5
(page number not for citation purposes)
It is able to extract an evolutionary signal rather congruent to MLST and micro-array data.

BMC Microbiology 2007, 7:98 http://www.biomedcentral.com/1471-2180/7/98
Background
Staphylococcus aureus, a human commensal living on the
skin and mucosa, can cause a broad range of infections
including endocarditis, septicemia, skin infections, soft
tissue infections, and osteomyelitis. Moreover, S. aureus is
the leading cause of nosocomial infections [1]. The appli-
cation of several new genotypic typing methods gave
many new insights into the epidemiology and population
structure of S. aureus [2]. Recently, Koreen et al. investi-
gated a collection of 36 S. aureus isolates (methicillin
resistant and methicillin sensible S. aureus, MRSA and
MSSA, respectively), which was recovered from 10 coun-
tries on four continents over a period of four decades as a
representative of the breadth of diversity within S. aureus
[3]. They used whole-genome micro-array analysis (com-
prising approximately 2,800 open reading frames) as typ-
ing reference to evaluate the capability of several typing
techniques, among them partial S. aureus protein A (spa)
gene sequencing. The spa repeat region consists of a varia-
ble number of 21–27 bp long repeats (VNTRs) varying in
composition that result in different spa types.
Previously it was shown that spa typing is fast, discrimina-
tory, and very reproducible [4,5]. It was hypothesized by
Koreen and colleagues that by manual grouping of similar
spa types this region contains evolutionary signals nearly
comparable to whole-genome micro-array data [3]. Until
recently, however, no automated and objective algorithm
existed to cluster different repeat regions. The Based Upon
Repeat Pattern (BURP) implementation that is a heuristic
variant of the newly described EDSI algorithm [6], was
investigated in this study to infer the clonal relatedness of
different spa types. We first calibrated the BURP parame-
ters using multilocus sequence typing (MLST) data from a
representative strain collection as "gold standard" and
then evaluated BURP using the Koreen et al. dataset.
Methods
S. aureus strains (MRSA and MSSA) were used from our
strain collection comprising 400 of the initial and most
frequently to the SpaServer reported spa types [7]. From
these strains, MLST sequence types (ST) were determined
as previously [8]. STs that showed at least six of seven
identical alleles were grouped into clonal complexes (CC)
using eBURST [9]. BURP – as implemented in the
StaphType software v. 1.5 (Ridom GmbH, Würzburg, Ger-
many) – was used to cluster (spa-CC) spa types [10].
Repeat-duplication and -excision in addition to substitu-
tion and base-insertion and -deletion events were taken
into account when the relatedness of different spa types
was calculated. BURP offers two user-defined parameters
that influence clustering: exclusion of spa types that are
shorter than "x" repeats and the maximum number of
because their information content is limited and no relia-
ble evolutionary history can be inferred. The costs account
for the "steps" of evolution between two different spa
types, whereas the algorithm tries to minimize these steps
("parsimony assumption"). To find out the optimal com-
bination of these two parameters, clustering of all possible
combinations of both parameters (values: 1 to 10) was
performed. A prerequisite was that the number of
excluded spa types should be as low as possible and not
exceed 10% of all investigated spa types. Subsequently, the
typing concordance [11] between BURP and eBURST
groupings were determined to elucidate the best parame-
ter combination with the highest concordance on the one
side and the lowest number of excluded spa types on the
other. BURP calibrated in this manner was finally used to
cluster the strains from the study of Koreen et al. [3].
Results and discussion
In total, the 400 investigated strains exhibited 122 differ-
ent STs. The eBURST algorithm clustered the STs into 23
CCs (354 isolates) and 33 singletons (46 isolates). BURP
clustering of spa types using all possible parameter combi-
nations and subsequent comparison with eBURST CCs
resulted in concordances ranging from 8.2 to 96.2%.
These concordances are illustrated in Figure 1 using the
Visual-XSel 9.0 software (CRGraph, München, Germany).
To determine the optimal combination between the
BURP parameters, a graph showing the dependence of the
concordance from the minimal repeat length of included
spa types (vice versa the percentage of excluded spa types)
for the different costs were drawn by MS Excel XP. The
Concordance analysis of eBURST and BURP clustering in dependence of all po sibl BURP parametersFigure 1
Concordance analysis of eBURST and BURP cluster-
ing in dependence of all possible BURP parameters. A
surface curve displaying the dependence of concordance (in
%) between eBURST MLST CCs and BURP spa-CCs applying
all possible combinations of the BURP parameters "exclude
spa types that are shorter than x repeats" and "spa types are
Length of excluded spa types (no. of repeats)
1
2
3
4
5
6
7
8
9
10
C
o
n
c
o
rd
a
n
c
e
i
n
%
10
20
30
40
50
60
70
80
90
100
Costs
12345678910
C
o
n
c
o
rd
a
n
c
e
i
n
%
0
10
20
30
40
50
60
70
80
90
100Page 2 of 5
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costs "y" for clustering spa types into the same group.
Short spa types can be excluded from further analysis
clustered if costs are less or equal than y".

BMC Microbiology 2007, 7:98 http://www.biomedcentral.com/1471-2180/7/98
overall highest concordance (96.2%) lay on the cost value
4 curve (Figure 2). Analyzing this curve, the closest integer
to the first inflection point – representing the first local
maximum – was chosen as the optimal combination of
the BURP parameters with "exclude spa types that are
shorter than 5 repeats" and "spa types are clustered if costs
are less or equal than 4". In this way a concordance of
95.3% could be achieved (Figure 2). Using these parame-
ters, BURP clustered the 400 spa types into 24 spa-CCs and
40 singletons. Only 31 (7.8%) spa types were excluded by
using these parameters. In contrast, analysis of ungrouped
spa types vs. eBURST CCs resulted in 92.8% concordance,
only. A population snapshot of the 369 included strains
after BURP grouping is displayed in Figure 3. It shows
clusters of linked spa types in spa-CCs, linked doublets,
and individual unlinked spa types. In Table 1, exemplarily
the spa-CC004, its spa types, corresponding STs, and CCs
is shown. In general, a high concordance between BURP
and eBURST clustering can be observed. Of the 50 spa
types that were clustered in spa-CC004, only three spa
types were grouped into another CC and another three
were judged as singletons by MLST.
Comparing whole-genome micro-array groupings (at the
level of 0.31 Pearson correlation index) and spa-CCs of
the 36 strains from the study of Koreen et al. using the cal-
ibrated BURP gave a concordance of 87.1% – that is in the
same range as reported. BURP spa-CCs vs. manually
grouped spa types resulted in 95.7% concordance.
The underlying alignment model of BURP takes repeat-
duplication and -excision into account [6] – in contrast to
widely-used multiple alignment strategies like ClustalW
[12]. The proposed molecular mechanism of the evolu-
tion of such repeat regions is slipped-strand mispairing
(SSM) during DNA duplication [13]. The presence of
those evolutionary events within the spa repeat region was
already detected in vivo, when sequential S. aureus isolates
from long-term pulmonary S. aureus colonization/infec-
tion of cystic fibrosis patients were spa typed [14].
The high concordance of BURP spa-CCs in comparison to
eBURST CCs using a diverse strain collection demon-
strated that spa indeed contains long-term evolutionary
signals. Recent comparisons between spa-CCs and PFGE
clustering corroborated these findings [15,16]. In future,
the integration of BURP into the already established early-
warning system for MRSA-outbreaks based on spa typing
Population snapshot of the 400 S. aureus strains after BURP groupingFig re 3
Population snapshot of the 400 S. aureus strains after
BURP grouping. Population snapshot of the 400 S. aureus
strains after grouping with the calibrated BURP ("exclude spa
types that are shorter than 5 repeats" and "spa types are
clustered if costs are less or equal than 4", 31 spa types were
excluded). Clusters of linked isolates correspond to spa-
CCs. Whereas eBURST uses the number of relatives (single
locus variants, SLVs) to define founders and subfounders of
groups, BURP sums up costs to define a founder-score for
each spa type in a cluster. The spa type with the highest
founder-score is defined founder of the cluster (blue color).
Subfounders are the spa types with the second highest
founder-score and are labeled in yellow. If two or more spa
types exhibit the same highest founder-score, they are all
colored in blue. For clarity, only the spa-CCs are labeled.
Note that the spacing between linked spa types and between
unlinked spa types and spa-CCs provides no information
concerning the genetic distance between them.
spa-CC002
spa-CC008
spa-CC324
spa-CC012
spa-CC100
spa-CC044
spa-CC166/240
spa-CC164
spa-CC084
spa-CC127
spa-CC186
spa-CC004
spa-CC160
spa-CC099
spa-CC005spa-CC159
spa-CC316
spa-CC078
High range of concordance between eBURST and BURP for optimal BURP calibratioFi ure 2
High range of concordance between eBURST and
BURP for optimal BURP calibration. Graph showing
curves for cost integers in the high concordance range.
Curves labeled "Costs: 1 to 10" represent different cost val-
ues. For the curve with the overall highest concordance
(Costs: 4) the first inflection point is marked (arrow) and
corresponds to the first local optimum giving a good balance
92.8
93.3
93.8
94.3
94.8
95.3
95.8
96.3
2 (0) 3 (1.8) 4 (4.3) 5 (7.8) 6 (14.5) 7 (24.5) 8 (37.0) 9 (50.0) 10 (63.3)
Repeat length of excluded spa types (% of excluded spa types)
%
C
o
n
c
o
rd
a
n
c
e
Costs: 1
Costs: 2
Costs: 3
Costs: 4
Costs: 5
Costs: 6
Costs: 7
Costs: 9Page 3 of 5
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will help to detect clonal diversification during extended
outbreaks [17].
between concordance and percentage of excluded spa types.

BMC Microbiology 2007, 7:98 http://www.biomedcentral.com/1471-2180/7/98
There are some limitations using spa-CCs for long-term
analysis. First, the strains must be spa-typeable. Having
typed more than 8,000 isolates, however, very few isolates
(approximately 0.1%) were not typeable – probably due
to mutations within the primer binding regions. Second,
BURP analyses are limited to spa types that pass the
parameter of a certain number of repeats. However, when
analyzing the SpaServer content (accessed at 12th Septem-
ber 2007) comprising 38,978 isolates with 2964 different
spa types, only 204 (6.88%) of all spa types and 881
(2,26%) of all submitted isolates are effected, respectively.
Finally, in very few instances, discrepancies can occur
between spa and other typing methods as observed in this
study and in two recent other publications [15,16]. These
discrepancies are most probably due to recombinational
events. Large chromosomal replacements that give rise to
such typing incongruences have been experimentally doc-
umented for two STs previously [18].
Conclusion
In summary, BURP is the first automated and objective
tool to infer clonal relatedness from spa repeat regions. It
is able to extract an evolutionary signal rather congruent
to MLST and micro-array data.
Abbreviations
BURP – Based Upon Repeat Pattern; CC – clonal complex;
eBURST – electronic Based Upon Related Sequence Types;
EDSI – excision and duplication of repeats, and substitu-
tion and indels of bases; MLST – multilocus sequence typ-
ing; MRSA – methicillin resistant S. aureus; MSSA –
methicillin sensible S. aureus; PFGE – pulsed-field gel elec-
trophoresis; spa – S. aureus protein A encoding gene; ST –
sequence type; VNTR – variable number tandem repeats
Competing interests
developers of the Ridom StaphType software mentioned
in the manuscript. The software is distributed and sold by
the company Ridom GmbH that is partially owned by
them. All other authors have declared that no competing
interests exist.
Authors' contributions
The project was coordinated by DH. AM and CB per-
formed the laboratory work and data analysis. MS and JS
developed the EDSI algorithm. TW and JR implemented
BURP. AM, TW, and DH wrote the main part of the paper.
All other authors gave useful comment on the analysis of
data and text of the manuscript. All authors have read and
approved the final version of the manuscript.
Acknowledgements
A. Mellmann was funded by a grant from the Deutsche Forschungsgemein-
schaft (ME 3205/1-1). This work was supported by a post-doctoral fellow-
ship of the German Academic Exchange Service to M. Sammeth.
The authors thank K. Becker, B.C. Kahl, B. Sinha (Münster, Germany), M.C.
Enright (London, UK), H. Grundmann (Bilthoven, The Netherlands), A.M.
Kearns (London, UK), A.C. Peterson (Lund, Sweden), A. Sabat (Warsaw,
Poland), U. Vogel (Würzburg, Germany), K. Boye, H. Westh (Kopenhagen,
Denmark), B. Strommenger, and W. Witte (Wernigerode, Germany) for
supplying strains. Furthermore, the skillful technical assistance of I. Ram-
minger and U. Keckevoet is gratefully acknowledged.
This study was presented in part at the 106th General Meeting of the Amer-
ican Society for Microbiology, Orlando, FL, May 22–25, 2006.
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