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325（ 1 ）Optimization of cluster analysis based on drug
resistance profiles of MRSA isolates
HIROYA TANI1, 2, TAKAHIKO KISHI2, MINEHIRO GOTOH2,
YUKA YAMAGISHI3 and HIROSHIGE MIKAMO1, 3
1

Department of Clinical Infectious Diseases, Aichi Medical University
Graduate School of Medicine
2
Department of Clinical Laboratory, Aichi Medical University Hospital3
Department of Clinical Infectious Diseases, Aichi Medical University Hospital(Received for publication August 25, 2015)

We examined 402 methicillin-resistant Staphylococcus aureus (MRSA) strains
isolated from clinical specimens in our hospital between November 19, 2010 and
December 27, 2011 to evaluate the similarity between cluster analysis of drug
susceptibility tests and pulsed-ﬁeld gel electrophoresis (PFGE). The results showedthat the 402 strains tested were classiﬁed into 27 PFGE patterns (151 subtypes ofpatterns). Cluster analyses of drug susceptibility tests with the cut-off distanceyielding a similar classiﬁcation capability showed favorable results—when the MIC
method was used, and minimum inhibitory concentration (MIC) values were useddirectly in the method, the level of agreement with PFGE was 74.2% when 15 drugs
were tested. The Unweighted Pair Group Method with Arithmetic mean (UPGMA)
method was effective when the cut-off distance was 16. Using the SIR method inwhich susceptible (S), intermediate (I), and resistant (R) were coded as 0, 2, and 3,
respectively, according to the Clinical and Laboratory Standards Institute (CLSI)
criteria, the level of agreement with PFGE was 75.9% when the number of drugs
tested was 17, the method used for clustering was the UPGMA, and the cut-off
distance was 3.6. In addition, to assess the reproducibility of the results, 10 strains
were randomly sampled from the overall test and subjected to cluster analysis. Thiswas repeated 100 times under the same conditions. The results indicated good
reproducibility of the results, with the level of agreement with PFGE showing amean of 82.0%, standard deviation of 12.1%, and mode of 90.0% for the MIC
method and a mean of 80.0%, standard deviation of 13.4%, and mode of 90.0% for
the SIR method. In summary, cluster analysis for drug susceptibility tests is usefulfor the epidemiological analysis of MRSA.

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Introduction
Methicillin-resistant Staphylococcus aureus (MRSA), ﬁrst reported in the United Kingdom
in 19611), includes approximately 50% of the nosocomial isolates of S. aureus2) and remains themost important microorganism in healthcare-associated infections. Epidemiological analyses of
MRSA have become increasingly important because MRSA presents problems such as infections
or outbreaks in long-term hospitalized patients, dialysis patients, patients with indwelling medical
devices, or compromised hosts in hospital settings3〜5). Additionally, community-acquired MRSA
has also attracted attention as the causative agent of community-acquired infections in recent
years6, 7).
Pulsed-ﬁeld gel electrophoresis (PFGE) is the gold standard used for the epidemiologicalanalyses of MRSA8, 9), but this method has many disadvantages including procedural complexity,
the long period of 5 days or more required to obtain results, and the need for special equipment10〜12). Thus, new techniques such as multi-locus sequence typing13), phage open reading
frame typing14), and repetitive sequence-based PCR15) have been developed16〜20). However, although these methods are more rapid and convenient than PFGE, they are difﬁcult to perform inroutine laboratories because of the need to familiarize the operating personnel with the dedicatedequipment or procedures. In contrast, although drug susceptibility testing is routinely performedin hospital laboratories and produces a wide range of results, there are limited reports of the useof drug susceptibility testing for epidemiological analysis.
In this study, we investigated the similarity between PFGE and cluster analysis of drug susceptibility tests (“drug cluster analysis”) to evaluate the usefulness of drug cluster analysis in epidemiological analyses of MRSA.

Materials and Methods
1. Bacterial strains used in this study
A total of 402 MRSA was strains isolated from clinical specimens in our hospital between
November 19, 2010 and December 27, 2011. When two or more strains were detected in a single
patient, only the ﬁrst detected strain was included if they were the same strains. All detectedstrains were included if they were different.
2. PFGEHeart infusion broth (Nippon Becton Dickinson Company, Ltd., Tokyo, Japan) was inoculated and incubated with test strains at 37°C overnight and the obtained bacterial suspension wasused. A DNA plug was prepared using the CHEF Bacterial Genomic DNA Plug Kit (Bio-Rad,
Hercules, CA, USA) and the restriction enzyme SmaI (Takara Bio, Shiga, Japan). Electrophoresis

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327（ 3 ）was performed in a CHEF-DR III system (Bio-Rad) according to the program 5 for the CHEFMapper: voltage of 6.0 V/cm, angle of 120°, total run time of 20.0 h, initial switch time of 5.3 s,
and ﬁnal switch time of 34.9 s. Lambda Ladder (Bio-Rad) was used as a control.
To analyze the PFGE patterns, a dendrogram was generated using FP Quest Plus software(Bio-Rad) and patterns with a similarity of 80% or higher were considered to represent the samestrain21〜23). Similarity among strains was determined by the Dice method and clustering was performed using the unweighted pair group method with arithmetic mean (UPGMA) method.
3. Drug susceptibility testing
Drug susceptibility testing was performed using RAISUS (Nissui Pharmaceutical, Tokyo,
Japan), a routinely used and fully automated system for identiﬁcation and susceptibility testing,using the broth microdilution method. Isolates were tested against a total of 17 drugs, including
oxacillin (MPIPC), ampicillin (ABPC), ABPC/sulbactam (SBT), cefazolin (CEZ), cefoxitin(CFX), imipenem (IPM), gentamicin (GM), arbekacin (ABK), clarithromycin (CAM), clindamycin (CLDM), levoﬂoxacin (LVFX), minocycline (MINO), linezolid (LZD), vancomycin (VCM),
teicoplanin (TEIC), fosfomycin (FOM), and sulfamethoxazole/trimethoprim (ST), and interpreted
according to the Clinical and Laboratory Standards Institute (CLSI) criteria24, 25). Staphylococcus
aureus isolates with a minimum inhibitory concentration (MIC) of 4 μg/mL or more for MPIPC
were deﬁned as MRSA.
4. Cluster analysis
Drug cluster analysis was performed using the MIC method, in which MIC values were directly used as data, and the SIR method, in which based on the method of SATO, et al.26), isolates
that were interpreted as susceptible (S), intermediate (I), and resistant (R) according to CLSI criteria24, 25) were coded as 0, 2, and 3, respectively. A dendrogram was generated by selecting
squared Euclidean distances to determine similarity, and the UPGMA method27, 28), which is commonly used in PFGE, and Wardʼs method29), which typically yields clear clusters for clustering.
Dendrograms were generated using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA). In the
MIC method, the results for ABPC/SBT and ST were excluded from the data because they were
difﬁcult to directly quantify, and results for the other drugs with an inequality sign but no equals
sign (＞ or ＜) were used; a value higher or lower by one dilution than the ﬁgure and results withan inequality sign with an equal sign (≧ or ≦) were directly used as the ﬁgure. In the SIR
method, the MIC of VCM was converted to 1 if the value was not more than 1 μg/mL and to 2 if
the value was 2 μg/mL based on VCM MIC creeping30〜33). Using these methods, cluster analyses
were performed to investigate cut-off distances that yielded the number of clusters similar to
those observed using PFGE, the number of clusters, and the level of agreement with PFGE in thefollowing cases: i) when all drugs (15 drugs for the MIC method and 17 drugs for the SIR

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method) were included, ii) drugs that were highly correlated to other antimicrobials (a Pearson
correlation coefﬁcient of ≧0.7) were excluded (14 drugs, excluding IPM, for the MIC method
and 15 drugs, excluding ABPC/SBT and CLDM, for the SIR method), iii) 5 representative drugs
from antimicrobial classes (penicillin: ABPC, cephem: CEZ, carbapenem: IPM, aminoglycoside:
GM, and quinolone: LVFX) were selected, and iv) 9 drugs, consisting the 5 representative drugs
plus 4 anti-MRSA drugs (ABK, LZD, VCM, and TEIC), were selected.
The level of agreement was calculated based on the assumption that 2 clusters form a pair ifthey contained a large number of shared strains. The number of strains that were shared withpairs of drug clusters based on PFGE clusters was counted.
5. Reproducibility of cluster analyses results
Ten strains were randomly sampled from the overall population of 402 test strains using theSAS9.3 and drug cluster analysis was performed under the conditions with the highest level ofagreement with PFGE method using the MIC and SIR methods. This was repeated 100 times toassess reproducibility by calculating the mean, standard deviation, and mode of the agreementlevel with PFGE.

Results
1. PFGE method
According to PFGE results, the isolates tested were classiﬁed into the following 27 patterns(151 subtypes) from type A to type AA. In addition, 309 strains belonged to type C, accounting
for 76.9% of the total strains (Figure 1, Table 1).
2. Drug susceptibility testing
As shown in Table 2, drug susceptibility testing revealed that the isolates generally showed
resistance to many antimicrobials. Among anti-MRSA drugs, 5 strains were considered as I only
for ABK, but the susceptibility to VCM, TEIC, or LZD was S in all strains.
3. Cluster analysis
The cut-off distance yielding a number of clusters close to that obtained using PFGE, the number of clusters, and the level of agreement with PFGE were shown in Table 3. The results revealed ahigher level of agreement with PFGE using the UPGMA method compared to using the Wardʼs
method. In addition, the level of agreement with PFGE was generally higher for the UPGMA
method when increasing number of drugs were tested. Moreover, the level of agreement between C
type of PFGE method and a cluster analysis under the analysis conditions for the highest level ofagreement with the PFGE method (Table 4) was 86.7% in MIC method and 88.7% in SIR method.

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Fig. 1. Dendrogram of PFGE method

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Table 1. Result of PFGE method

Table 2. Result of drug susceptibility testing

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331（ 7 ）Table 3. Result of cluster analysis

Table 4. The analysis conditions for the highest level of agreement with the PFGE method

4. Reproducibility of results of cluster analyses
The reproducibility of the analysis conditions for the highest level of agreement with the
PFGE method (Table 4) was favorable using both the MIC method, with a mean of 82.0%, standard deviation of 12.1%, and mode of 90.0%, and the SIR method, with a mean of 80.0%, standard deviation of 13.4%, and mode of 90.0% (Table 5). There was no signiﬁcant difference inreproducibility between these two methods (P＝0.3920: Wilcoxon rank sum test).

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Table 5. The reproducibility of cluster analysis for the highest level of agreement with
the PFGE method

Discussion
The PFGE results were classiﬁed into 27 types, ranging from type A to type AA, and eachtype was further divided into 1〜79 subtypes (151 subtypes in total). Individual types ranged fromthose comprising a single strain to those comprising numerous strains, such as type C (which included 309 strains, accounting for 76.9% of the total) and were divided into major types and minor
types showing diversity. Our results were consistent with those of previous studies34, 35), and the
predominant type C strains were detected nearly continuously during the study period covered by
the study, suggesting that these strains may cause healthcare-related infections in our hospital.
Although drug susceptibility testing of the strains showed a trend towards resistance to multiple drugs, except anti-MRSA drugs (ABK, LZD, VCM, and TEIC) and ST, the susceptibility toantimicrobials varied among drugs.
Based on these results, drug cluster analyses were conducted using the UPGMA and Wardʼs
methods and by changing the types of drugs tested. The results revealed a higher level of agreement with PFGE using the UPGMA method compared to using the Wardʼs method. In addition,
the level of agreement with PFGE was generally higher for the UPGMA method when increasing
number of drugs were tested, with the highest level of agreement of 75.9% observed using the
SIR method when all drugs were included. This was likely due to the following reasons: for theformer result of the higher level of agreement with PFGE in the UPGMA method than the Wardʼs
method, the UPGMA method was considered appropriate for drug cluster analyses since cluster
analysis is a multivariate analysis that does not include clear classiﬁcation criteria36) and can be
performed using the method most convenient for the analyst29); and for the latter result of the tendency for a higher level of agreement with PFGE with increasing numbers of the drugs tested, thevariability in the results of the drug susceptibility tests was increased with increasing numbers of
drugs. In addition, this trend was observed using both the MIC method and the SIR method, suggesting that application of the MIC method without requiring data conversion in facilities using acommonly employed drug susceptibility panel or application of the SIR method in facilities using
a breakpoint panel only for qualitative interpretation may enable facilities to perform drug susceptibility tests for strain typing. Moreover, the reproducibility of conditions showing the bestDec. 2015

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333（ 9 ）agreement with PFGE was highly favorable for both the MIC method and the SIR method, indicating that the results were not speciﬁc to the population studied. However, because the cut-off
distance associated with clustering varied according the type of drugs tested, a facility performing
drug cluster analyses should determine an appropriate cut-off distance based on the drugs tested
in that facility. Although the present study showed that if a standard drug susceptibility panel isused, the same cut-off distance can be used by another facility that uses the same panel, the procedure for determining cut-off distances cannot be omitted in facilities using an ordered panel;
therefore, a simple method for determining cut-off distances remains to be identiﬁed.
Although few studies have examined these issues, there have been a variety of reports regarding the relationship between drug cluster analysis and PFGE, including reports showing that
drug cluster analysis is useful for strain typing26, 37) and that this analysis has a limited relationship with PFGE and a low capability of typing strains9, 38), indicating that the results are inconclusive. This may be because most reports to date evaluated the usefulness of drug cluster analysis
based only on the results of the studies and did not verify whether the analysis parameters included were suitable for other situations. The present study demonstrated that application of the
UPGMA method for clustering with increasing numbers of drugs tested yielded a high level ofsimilarity to the results of PFGE, although drug cluster analysis requires determination of an appropriate cut-off distance based on the drugs used by facilities.
Drug susceptibility tests are essential for determining the type and dose of antimicrobials
that should be used to treat infections. In recent years, drug susceptibility testing has also been
applied as antibiograms in empirical therapy39, 40) and has become increasingly important. Our
results suggest that drug susceptibility testing is useful for epidemiological analyses and that theresults indicate the importance of further drug susceptibility tests.
Disclosure: The authors have declared no conﬂicts of interest.

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