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Journal of Microbiological Methods 6

Parity among interpretation methods of MLEE patterns and

disparity among clustering methods in epidemiological

typing of Candida albicans

Marcelo Fabiano Gomes Boriollo a,*, Edvaldo Antonio Ribeiro Rosa b,

Reginaldo Bruno Goncalves a, Jose Francisco Hofling a

aMicrobiology and Immunology Laboratory, Dental School of Piracicaba, State University of Campinas, Piracicaba, BrazilbStomatology Laboratory, Center of Biological and Health Sciences, Pontifical Catholic University of Parana, Curitiba, Brazil

Received 25 May 2004; received in revised form 17 May 2005; accepted 24 May 2005

Available online 11 July 2005

Abstract

The typing of C. albicans by MLEE (multilocus enzyme electrophoresis) is dependent on the interpretation of enzyme

electrophoretic patterns, and the study of the epidemiological relationships of these yeasts can be conducted by cluster analysis.

Therefore, the aims of the present study were to first determine the discriminatory power of genetic interpretation (deduction of

the allelic composition of diploid organisms) and numerical interpretation (mere determination of the presence and absence of

bands) of MLEE patterns, and then to determine the concordance (Pearson product-moment correlation coefficient) and

similarity (Jaccard similarity coefficient) of the groups of strains generated by three cluster analysis models, and the

discriminatory power of such models as well [model A: genetic interpretation, genetic distance matrix of Nei (dij) and

UPGMA dendrogram; model B: genetic interpretation, Dice similarity matrix (SD1) and UPGMA dendrogram; model C:

numerical interpretation, Dice similarity matrix (SD2) and UPGMA dendrogram]. MLEE was found to be a powerful and

reliable tool for the typing of C. albicans due to its high discriminatory power (N0.9). Discriminatory power indicated that

numerical interpretation is a method capable of discriminating a greater number of strains (47 versus 43 subtypes), but also

pointed to model B as a method capable of providing a greater number of groups, suggesting its use for the typing of C.

albicans by MLEE and cluster analysis. Very good agreement was only observed between the elements of the matrices SD1 and

SD2, but a large majority of the groups generated in the three UPGMA dendrograms showed similarity SJ between 4.8% and

75%, suggesting disparities in the conclusions obtained by the cluster assays.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Candida albicans; MLEE patterns; Interpretation methods; Cluster analysis

0167-7012/$ - s

doi:10.1016/j.mi

* Correspondin

University of C

E-mail addre

4 (2006) 346–365

ee front matter D 2005 Elsevier B.V. All rights reserved.

met.2005.05.012

g author. Laboratatory of Microbiology and Immunology, Department of Oral Diagnostic, Dental School of Piracicaba, State

ampinas, Av. Limeira 901, CEP13414-903 CP052, Piracicaba, SP, Brazil. Tel.: +55 19 3412 5321; fax: +55 19 3412 5218.

ss: marcelofgb@yahoo.com.br (M.F.G. Boriollo).

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 347

1. Introduction

Multilocus enzyme electrophoresis (MLEE or

MEE), also known as isoenzymatic typing, has been

employed for a number of decades as a standard

method for the genetic analysis of populations in

eukaryotes (Ayala, 1976; Nevo et al., 1980; Selander

and Whittam, 1983). Pioneer work conducted in the

1980s employing MLEE in the genetic analysis of

Escherichia coli and Shigella, stirred enormous inter-

est among medical microbiologists (Selander and

Levin, 1980; Selander et al., 1986). Since then, nu-

merous studies were performed with innumerable

results contributing to the understanding of the natural

history of infectious diseases. MLEE has been con-

sidered the gold standard in the study of the popula-

tion genetics of microorganisms (Boerlin, 1997). In

the field of medical mycology, isoenzymatic typing

has been shown to be of great potential in taxonomy,

systematics, genetics, evolution and epidemiology,

especially in the characterization of C. albicans

(Arnavielhe et al., 1997; Barchiesi et al., 1998; Boer-

lin et al., 1996; Boriollo et al., 2005; Caugant and

Sandven, 1993; Lehmann et al., 1989; Mata et al.,

2000; Pujol et al., 1993a,b, 1997; Rosa et al., 1999,

2000a,b, 2001, 2003).

Enzyme reactions can be carried out in gels (for

example, amide gels) and electrophoretic bands visu-

alized according to enzyme activity, indicating the

existence of isoenzymes or isozymes (Markert and

Moller, 1959). Isoenzymes constitute multiple molec-

ular forms of the same enzyme with individual affinity

for the same substrate, catalyzing the same reaction in

the cell (Dixon and Webb, 1979). Their expression is

controlled genetically by one or more alleles or genes,

situated at one or various loci (Harris, 1975; Markert,

1975; Scandalios, 1969). Isoenzymes controlled by

alleles of a single locus are called alloenzymes or

allozymes (Conkle et al., 1982; Prakash et al.,

1969). Because the net electrostatic charge and,

hence, the rate of migration of a protein during elec-

trophoresis are determined by its amino acid sequence,

mobility variants (electromorphs or allozymes) of an

enzyme can be directly equated with alleles at the

corresponding structural gene locus (Selander et al.,

1986).

The capacity of isoenzymatic analysis in the dis-

tinction of fungal species depends on intrapopula-

tional genetic variability. Organisms with high

genetic variability can express highly variable pheno-

types and therefore conceal inter-or intraspecific

differences. This is evident mainly with various

non-metabolic enzymes which exhibit high structural

variation as a result of intense environmental selective

pressure (Brown and Langley, 1979; Huettermann et

al., 1979; Johnson, 1974; Newman, 1985; Racine and

Langley, 1980). On the contrary, metabolic enzymes

show low vulnerability to environmental selection,

whereby they are usually employed as isoenzyme

markers (Whittam et al., 1983). The patterns of iso-

enzyme electrophoretic bands are frequently predict-

able, since they depend on the genetic and nuclear

conditions of each organism. However, various

mycologists limit the interpretations of electrophoretic

results to mere counting of bands (Rosa et al., 1999,

2000a,b, 2001, 2003; Shannon et al., 1973; Shecter,

1973). Genetic interpretation, when possible, fur-

nishes a large amount of additional information

about the nuclear, genetic and taxonomic conditions

of a group of organisms (Harris and Hopkinson, 1976;

Micales et al., 1998; Siciliano and Shaw, 1976).

Therefore, different criteria for interpretation have

been employed for haploid and diploid organisms

(Harris and Hopkinson, 1976; Murphy et al., 1990;

Pasteur et al., 1987; Selander et al., 1986). Based on

these criteria, allelic composition has been determined

from a group of 10 to 30 metabolic enzymes consid-

ered representative of the total genome (Boerlin,

1997; Soll, 2000).

Since each form of data (genetic and numerical

interpretation) has its own peculiarities that must be

taken into consideration for computer-assisted analy-

sis and storage, another final interpretation must

involve a measurement of similarity or distance

among the data collected for every possible pair of

isolates analyzed. These measurements are then used

to generate similarity matrices or distance matrices

and dendrograms (phylogenetic trees) for cluster

analysis (Soll, 2000). Such dendrograms can then

be generated by the unweighted pair-group method

using arithmetic averages (UPGMA), first used by

Rohlf (1963) and discussed in detail by Sneath and

Sokal (1973), or a comparable method. UPGMA,

which has been used frequently to generate dendro-

grams based on matrices of a variety of distance or

similarity coefficients, is relatively straightforward

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365348

and has been widely used in fingerprinting infectious

fungi (Soll, 2000).

The typing of C. albicans by MLEE is initially

dependent on the interpretation of enzyme electro-

phoretic patterns, and the study of the epidemiolog-

ical relationship of these yeasts can be determined by

cluster analysis. Therefore, the aim of the present

study was (i) to determine the discriminatory power

of genetic interpretation (deduction of the allelic

composition of diploid organisms) and the numerical

interpretation (mere counting of bands present and

absent) of MLEE patterns, and then (ii) to determine

the parity or disparity among groups of identical and

highly related isolates obtained by three cluster

assays commonly used for the typing of fungal

infections.

2. Materials and methods

2.1. Yeast isolates

The study involved 75 specimens of C. albicans

isolated from the oral cavity of 75 clinically healthy

children, characterized previously by the group of

investigators at the Laboratory of Microbiology and

Immunology of the Department of Oral Diagnostics,

Dental School of Piracicaba, State University of Cam-

pinas (Moreira et al., 2001; Boriollo et al., 2005).

2.2. Enzyme extraction

Yeast cultures were grown in flasks containing 50

mL of YEPD medium [1% (wt/vol) yeast extract, 2%

(wt/vol) peptone and 2% (wt/vol) D-glucose] at 37 8Cfor 18h, under constant shaking at 150 rpm (Shaker

Incubator mod. NT 712, Nova Tecnica Instrumentos e

Equipamentos de Laboratorio Ltda.). After growth,

cells were centrifuged at 3000 �g for 5 min and

washed twice in 0.9% (wt/vol) NaCl, submitting

each wash to the same centrifugal force. Pellets

(~500 AL) were transferred to 2-mL microtubes (Bios-

pec Products, Inc.) containing cold distilled water

(approximately 8 8C) and glass beads (1 :1 :1).

These mixtures were kept on ice (4 8C) for 5 min

and afterwards agitated 4 times in a BeadBeater ma-

chine (Biospec Products, Inc.) at 4200 rpm for 30 s, at

one-minute intervals. Cell fragments were centrifuged

at 5000 �g at 4 8C for 5 min. The resulting upper

aqueous phase was applied to Whatman n3 filter

papers (wicks), 12�5mm in size, and maintained at

�70 8C until time of use (Rosa et al., 2003; Boriollo

et al., 2005).

2.3. Electrophoresis and specific enzyme staining

Enzymes were separated in starch gels (Penetrose

30R- Refinacoes de Milho Brasil Ltda) at 13% (wt/

vol), with dimensions of 200�120�10mm. Wicks

were then immediately soaked in 5 AL (0.02% wt/

vol) of bromophenol-blue solution, and afterwards

applied perpendicularly onto a gel cut longitudinally

(20 mm). Electrophoresis was performed in a hori-

zontal and continuous system, at 130 V at 4 8Covernight (bromophenol-blue migration equivalent

to 80 mm). To assure reproducible results, the C.

albicans CBS-562 enzymes (Centralbureau voor

Schimmelcultures, Delft, The Netherlands) were in-

cluded in each gel. After the electrophoretic run, the

gel was put on an acrylic base and sliced into 1.5 mm

sections with the aid of rulers and n15 nylon thread.

The sections were carefully placed inside white por-

celain containers and submitted to a staining process

by methods previously described for 11 enzymes (15

enzyme loci) (Alfenas, 1998; Boriollo et al., 2005;

Pujol et al., 1997; Selander et al., 1986). The enzy-

matic activities determined were: alcohol dehydroge-

nase, sorbitol dehydrogenase, manitol-1-phosphate

dehydrogenase, malate dehydrogenase, isocitrate de-

hydrogenase, glucose dehydrogenase, glucose-6-phos-

phate dehydrogenase, aspartate dehydrogenase,

catalase, peroxidase, and leucine aminopeptidase

(Table 1). Enzymatic expressions of malate dehydro-

genase, isocitrate dehydrogenase, and sorbitol dehy-

drogenase showed two and three genetically

interpretative loci (Mdh-1, Mdh-2, and Mdh-3; Idh-1

and Idh-2; Sdh-1 and Sdh-2).

2.4. Genetic interpretation of MLEE patterns

Pattern interpretation was performed following the

general rules commonly accepted in the deduction of

the allelic composition and of the genotype of dip-

loid organisms. The bands on the gels were num-

bered in order of decreasing mobility, and the

corresponding alleles were numbered by using the

Table 1

Systems and solutions utilized for MLEE analysis from metabolic enzymes of C. albicans

Enzyme Compound for staining

EC number Name Symbol Substrate Buffer Salt Coenzyme Dye catalyser

1.1.1.1. Alcohol dehydrogenase ADH Ethanol (3 mL)

isopropanol (2 mL)

200mM Tris–HCl

pH 8.0 (50 mL)aNAD 1%

(2 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.1.1.14. Sorbitol dehydrogenase SDH Sorbitol (250 mg) Tris–HCl 50 mM

pH 8.0 (50 mL)bNAD 1%

(2 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.1.1.17. Mannitol-1-phosphate

dehydrogenase

M1P Mannitol-1-phosphate

(5 mg)

Tris–HCl 100 mM

pH 8.5 (50 mL)cNAD 1%

(2 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.1.1.37. Malate dehydrogenase MDH 2M Malic acid

(6 mL)dTris–HCl 200 mM

pH 8.0 (40 mL)aNAD 1%

(2 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.1.1.42. Isocitrate dehydrogenase IDH 1M Isocitric acid

(2 mL)eTris–HCl 200 mM

pH 8.0 (40 mL)a100 mM

MgCl2(1 mL)f

NADP 1%

(1 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.1.1.47. Glucose dehydrogenase GDH D-glucose (500 mg) Tris–HCl 200 mM

pH 8.0 (50 mL)aNAD 1%

(2 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.1.1.49. Glucose-6-phosphate

dehydrogenase

G6PDH Glicose-6-phosphate

disodium salt (100 mg)

Tris–HCl 200 mM

pH 8.0 (50 mL)a100 mM

MgCl2(1 mL) f

NADP 1%

(1 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.4.3.x. Aspartate dehydrogenase ASD Aspartic acid (50 mg) Sodium phosphate

pH 7.0 (50 mL)gNAD 1%

(2 mL)

PMS 1% (500 AL)MTT 1.25% (1 mL)

1.11.1.6. Catalaseh CAT

1.11.1.7. Peroxidase PO H2O2 3% (1 mL) 100 mM Sodium

acetate pH 4.5 (50 mL)io-dianisidine 2HCl

(16 mg)

3.4.11.1. Leucine aminopeptidase LAP L-leucine

b-naphthylamide HCl

(30 mg)

100 mM Potassium

phosphate pH 5.5

(50 mL)j

100 mM

MgCl2(1 mL)f

Black K (30 mg)

Electrode buffer: Tris–citrate pH 8.0 [83.2 g of C4H11NO3 (Tris), 33.09 g of C6H8O7 . H2O (Citric acid), 1 L of H2O]; Gel buffer: Electrode buffer diluted 1 :29.a 24.2 g of C4H11NO3 (Tris), 1 L of H2O (pH adjusted with HCl);b 6.05 g of C4H11NO3 (Tris), 1 L of H2O (pH adjusted with HCl);c 12.1 g of C4H11NO3 (Tris), 1 L of H2O (pH adjusted with HCl);d 26.8 g of C4H6O5 (DL-malic acid) and 16 g of NaOH in 0.1 L of H2O (caution: potentially explosive reaction);e 29.41 g of C6H5O7Na3. 2H2O (DL-isocitric acid) in 0.1 L of H2O;f 2.03 g of MgCl2. 6HCl (Magnesium chloride) in 0.1 L of H2O;g Mix equal parts of 27.6 g of NaH2PO4. H2O (Sodium phosphate monobasic monohydrate) in 1 L of H2O and 53.6 g of Na2HPO4. 7H2O (Sodium phosphate dibasic heptahydrate)

in 1 L of H2O, then dilute the mixture 1 :25 with H2O;h Incubate gel slice for 30 min at 0 8C in 50 mL of 0.1 M sodium phosphate pH 7.0 buffer, then pour off solution, and immerse it in 50 mL of 1.5% potassium iodide solution (KI)

for 2 min. Therefore, rinse gel slice with water, and immerse it in 50 mL of 0.03% hydrogen peroxide (H2O2) solution. Mix gently and remove stain solution when white zones appear

on dark-blue background;i 13.61g of C2H3O2Na. 3H2O (Sodium acetate), 1 L of H2O;j 13.61 g of KH2PO4 (Potassium phosphate), 1 L of H2O.

M.F.G.Borio

lloet

al./JournalofMicro

biologica

lMeth

ods64(2006)346–365

349

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365350

same nomenclature. Lack of demonstrable activity

for an enzyme was scored as two null alleles at

the corresponding gene locus. Each unique combi-

nation of alleles over the 15 enzyme loci examined

results in an electrophoretic type (ET)–subtype or

strain (Alfenas, 1998; Harris and Hopkinson, 1976;

Pasteur et al., 1987; Selander et al., 1986; Soll,

2000).

2.5. Numerical interpretation of MLEE patterns

Pattern interpretation was performed employing

values of relative mobility (Rf), given by: Rf ¼ dD�

100, where d corresponds to the distance traveled by

the isoenzyme (mm) and D corresponds to the dis-

tance traveled by the bromophenol-blue front (mm).

Each unique combination of bands for the 11 en-

zymes examined results in an electrophoretic type

(ET)–subtype or strain (Alfenas, 1998; Boriollo et

al., 2000; Rosa et al., 1999, 2000a,b, 2001, 2003;

Sneath and Sokal, 1973).

2.6. Evaluation of interpretation methods by the

numerical index of discriminatory power

The discriminatory power of the MLEE method

based on both genetic and numerical interpretation of

the electrophoretic patterns was established by the

numerical index of discrimination (D), according to

the probability that two unrelated strains sampled

from the test population will be placed into different

typing groups. This probability can be calculated by

Simpson’s index of diversity, which was developed

for the description of species diversity within an

ecological habitat (Simpson, 1949). This index can

be derived from elementary probability theory (Armi-

tage and Berry, 1987) and is given by the following

equation: D ¼ 1� 1N N�1ð Þ

PSj¼1 nj nj � 1

� �, where N

is the total number of strains in the sample population,

s is the total number of types described, and nj is the

number of strains belonging to the jth type. This

equation is derived as follows. The probability that

two strains sampled consecutively will belong to that

group isnjðnj�1ÞNðN�1Þ. These probabilities can be summed

for all the described types to give the probability that

any two consecutively sampled strains will be the

same type. This summation can be subtracted from

1 to give the equation above. This equation can be

applied both to a direct comparison of the discrim-

inating power of typing methods and to analysis of

the discriminating power of combined typing

schemes. An index of greater than 0.90 would be

desirable if the typing results are to be interpreted

with confidence (Hunter and Fraser, 1989; Hunter

and Gaston, 1988).

2.7. Cluster analysis

The set of data furnished by genetic interpretation

of MLEE patterns was submitted to cluster analysis

(final interpretation of data) by two different models:

1) Model A: the statistic dij of Nei (1972) was used

to estimate the genetic distance among all the

isolates of C. albicans: dij¼� Inh P

k xkixkj�� ��� �

=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPk x

2kix

2kj

q� i, a measure of genetic distance

(range from 0 to infinity) based on the identity

of genes (frequency of alleles for all loci, includ-

ing monomorphic loci) among populations. This

genetic distance measures the accumulated allele

differences per locus, and it can also be estimated

from amino acid sequences of proteins even for a

distantly related species. Thus, if enough data are

available, genetic distance between any pair of

organisms can be measured in terms of dij. In

addition, this measure is applicable to any kind

of organism regardless of ploidy or mating

scheme. Its interpretation in terms of enzyme

loci infers that, on average, 0 to an infinite

number of allelic substitutions are detected (by

electrophoresis) in every 100 loci, from a com-

mon ancestral strain (Nei, 1972; Selander et al.,

1986; Alfenas, 1998).

2) Model B: The similarity coefficient Dice (1945)

was used to estimate the genetic similarity among

all of the isolates (Pujol et al., 1997; Sneath and

Sokal, 1973; Soll, 2000): SD ¼ 2a2aþbþc

, a pair-

function that measures the agreement (range

from 0 to 1) between pairs of OTUs (operational

taxonomic units) over an array of two-state char-

acters, which are for convenience coded 0 or 1

(Sneath and Sokal, 1973). In this case, the code 0

or 1 represents respectively the absence of pres-

ence of a given isoenzyme genotype. This coeffi-

cient omits consideration of negative matches (d)

and gives more weight to the positive matches (a)

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 351

than to mismatches (b and c). This SD1 measures

the proportion of identical genotypes of two iso-

lates, where a is the number of genotypes shared

by strains A and B, b is the number of genotypes

unique to strain A, and c is the number of geno-

types unique to strain B. For the present model,

an SD1 of 1.00 represents identically matched

genotypes (i.e., all genotypes of isolates A and

B match), an SD1 of 0.0 represents no matches,

and SD1 ranging from 0.01 to 0.99 represent

increasing proportions of matched genotypes.

The combined data furnished by the numerical

interpretation of MLEE patterns was submitted to

cluster analysis (final interpretation of data) by a

single model:

1) Model C: the similarity coefficient Dice (1945)

was also used to determine the similarity among all

the isolates (Pujol et al., 1997; Sneath and Sokal,

1973; Soll, 2000). The code 0 or 1 was used to

represent respectively the absence or presence of a

given isoenzyme activity band, (Sneath and Sokal,

1973). This SD2 measures the proportion of bands

with the same relative mobility (Rf) in the patterns

of two isolates, where a is the number of bands shared

by strains A and B, b is the number of bands unique to

strain A, and c is the number of bands unique to strain

B. For the present model, an SD2 of 1.00 represents

identically matched bands (i.e., all bands in the pat-

terns of isolates A and B match), an SD2 of 0.0

represents no matches, and SD2 ranging from 0.01

to 0.99 represent increasing proportions of matched

bands.

Trees with two-dimensional classifications, called

dendrograms, based on matrix dij (model A), matrix

SD1 (model B), and matrix SD2 (model C) were

generated by the SAHN method (sequential, agglom-

erative, hierarchic, nonoverlapping clustering meth-

ods) UPGMA algorithm (unweighted pair-group

method using an arithmetic average) (Sneath and

Sokal, 1973). Since MLEE provide all levels of relat-

edness that must be resolved by DNA fingerprinting

methods (i.e., identify the same strain in independent

isolates, identify microevolutionary changes in a

strain, identify cluster of moderately related isolates,

and identify completely unrelated isolates), thresholds

(average values: dijP

; SD1P

and SD2P

) were established

in dendrograms to identify clusters of identical iso-

lates and highly related isolates (dijP

Ndijz0; SD1P

bSD1V1; and SD2

PbSD2V1) (Pujol et al., 1997; Soll, 2000).

All of these analyses were obtained employing the

program NTSYS-pc 1.70 (Rohlf, 1988).

2.8. Evaluation of the cluster models

The Pearson product-moment correlation coeffi-

cient was used as a measure of the agreement

(range from �1 to +1) between elements of two

matrices (dij�SD1, dij�SD2, and SD1�SD2)

obtained by different techniques or based on different

characters. This coefficient, computed between OTUs

j and k, is

rjk ¼

Xni¼1

Xij � XP

j

� �Xik � X

Pk

� �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXni¼1

Xij � XP

j

� �2 Xni¼1

Xik � XP

k

� �2s

where Xij stands for the character state value of

character i in OTU j, XP

j is the mean of all state

values for OTU j, and n is the number of characters

sampled (Sneath and Sokal, 1973). Such concordance

was interpreted in the following manner: 0.9V r—very

good concordance; 0.8V rb0.9—good concordance;

0.7V r b0.8—weak concordance; r b0.7—very weak

concordance (Rohlf, 1988).

The similarity among the clusters containing

identical isolates and highly related isolates (dijPNdijz

0versusSD1P

bSD1V1versusSD2P

bSD2V1 ) was estab-

lished by the Jaccard coefficient: SJ ¼ aaþbþc

, a pair-

function that also measures the agreement (range from

0 to 1) between pairs of OTUs over an array of two-

state characters, which are for convenience coded 0 or

1 (Sneath and Sokal, 1973). In this case, the code 0 or

1 represents respectively the absence or presence of a

given strain of C. albicans. This coefficient omits also

consideration of negative matches (d) and gives the

same weight to positive matches (a) and mismatches

(b and c). SJ measures the proportion of identical

strains of two clusters, where a is the number of

strains shared by clusters A and B, b is the number

of strains unique to cluster A, and c is the number of

strains unique to cluster B. For the present model, an

SJ of 1.00 represents identically matched strains (i.e.,

all strains of clusters A and B match), an SJ of 0.0

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365352

represents no matches, and SJ ranging from 0.01 to

0.99 represent increasing proportions of matched

strains.

The discriminatory power of the three models of

cluster analysis was calculated by Simpson’s index of

diversity, based on the probability that two unrelated

strains sampled from the test population will be placed

into different typing clusters (dijP

Ndijz0; SD1P

bSD1V1orSD2

PbSD2V1) (Hunter and Fraser, 1989; Hunter

and Gaston, 1988).

3. Results

3.1. Genetic and numerical interpretations of MLEE

patterns

The genetic interpretation of MLEE patterns of

C. albicans isolates allowed the identification of 14

(93.3%) polymorphic loci for one, two or three

alleles [1 alelle: Idh-2 (frequency of allele b or c

b1%); 2 alleles: Cat, Gdh (frequency of allele c or

db1%), Mdh-2, Mdh-3 (frequency of allele a

b1%), Po and Sdh-2 (frequency of allele c or d

b1%); 3 alleles: Adh, Asd, G6pdh, Idh-1, Lap,

M1p, Mdh-1]. Only 1 (6.7%) locus was monomor-

phic for one allele (Sdh-1). The combinations of the

alleles existing in all the loci showed 43 ETs in the

population of isolates (Fig. 1). These results indi-

cate that 31 healthy children harbored in the oral

cavity distinct ETs of the yeast C. albicans. Still,

12 ETs shared among 44 children (Table 2). The

discriminatory power of the MLEE method based

on the genetic interpretation of electrophoretic pat-

terns was 0.966, that is, 96.6% probability of two

C. albicans isolates sampled from the test popula-

tion belonging to different typing groups (i.e., elec-

trophoretic type- ET).

The numerical interpretation of the MLEE pat-

terns of the population of isolates of C. albicans

permitted the identification of polymorphism in all

the enzymes studied (i.e., for each enzyme the fre-

quency of the most common band was b99%) total-

ing 43 distinct types of electrophoretic bands (Fig.

1). The combination of bands for the 11 enzymes

examined showed 47 ETs in the population of iso-

lates. These results indicate that 36 healthy children

harbor orally distinct ETs of the yeast C. albicans.

Moreover, 11 ETs were shared among 39 children

(Table 3). The discriminatory power of the MLEE

method based on the numerical interpretation of the

electrophoretic patterns was 0.970, that is, 97% prob-

ability of two C. albicans isolates sampled from the

test population belonging to different typing groups

(i.e., electrophoretic type- ET).

3.2. Evaluation of the cluster assays

Very good agreement was observed among the

elements of the matrices SD1 and SD2 (r =0.909),

while negative agreement (correlation) was observed

among elements of the matrices dij and SD1

(r =�0.548) or dij and SD2 (r=�0.487). In addition,

when the elements dij of the genetic distance matrix

were converted into similarity elements (Sdij =1�dij)

a very poor agreement was observed among the ele-

ments of the matrices dij and SD1 (r =0.505) or dij and

SD2 (r =0.566).

The three cluster assays showed the following

results (Fig. 2):

a) Model A: 63 isolates distributed among 10 groups

(mean of 6.3 isolates/cluster; standard deviation of

F6.53 isolates; discriminatory power equal to

0.810) and 13 moderately related isolates and/or

unrelated isolates;

b) Model B: 60 isolates distributed among 13 groups

(mean of 4.61 isolates/cluster; standard deviation

of F4.64 isolates; discriminatory power equal to

0.874) and 16 moderately related isolates and/or

unrelated isolates;

c) Model C: 63 isolates distributed among 12 groups

(mean of 5.25 isolates/cluster; standard deviation

of F5.61 isolates; discriminatory power equal to

0.847) and 13 moderately related isolates and/or

unrelated isolates.

Only 3 pairs of groups showed 100% similarity SJ[group 7 of model A (A7) and group 12 of model B

(B12); group 10 of model B (B10) and group 11 of

model C (C11); group 13 of model B (B13) and group

12 of model C (C12)], all with a very small number of

isolates (only 2 isolates/group or 2.6% of the sample

population). One pair showed 95% similarity SJ[group 3 of model B (B3–19 isolates or 25% of the

sample population) and group 2 of model C (C2–20

Fig. 1. Zymogram of MLEE patterns of oral isolates of C. albicans and resultant genetic (above) and numerical (below) interpretations. The migration of enzymes occurred from the

negative pole (cathode) to the positive pole (anode). The isolates are labeled from left to right 1 to 76 (52=CBS 562).

M.F.G.Borio

lloet

al./JournalofMicro

biologica

lMeth

ods64(2006)346–365

353

Fig.1(continued).

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365354

Fig.1(continued).

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 355

Fig.1(continued).

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365356

Table 2

Allelic profiles of 43 ETs of C. albicans isolated from oral cavity of 75 clinically healthy children

ET No. of

isolates

Alleles of 15 enzyme locia

Adh Asd Cat G6pdh Gdh Idh-1 Idh-2 Lap M1p Mdh-1 Mdh-2 Mdh-3 Po Sdh-1 Sdh-2

CBS 562 bb bb aa cc bb aa aa aa bb ab ab cc ab aa bb

1 2 ab ab aa ab bb aa aa aa bb aa ab cc ab aa bb

2 1 ab ab aa bb ab ac aa aa bb aa ab cc ab aa bb

3 1 ab ab aa cc ab ac aa aa aa aa ab cc aa aa aa

4 2 ab ab aa cc ab ac aa aa ab aa ab cc aa aa ab

5 1 ab ab aa cc bb aa aa aa bb aa ab cc aa aa ab

6 1 ab ab aa cc bb aa aa aa bb aa ab cc aa aa bb

7 1 ab bb aa bb bb aa aa aa bb aa ab cc ab aa bb

8 1 ab bb aa cc bb aa aa aa bb aa ab cc aa aa ab

9 2 ab bb aa cc bb aa aa aa bb aa ab cc aa aa bb

10 4 ab bb aa cc bb aa aa aa bb aa ab cc ab aa bb

11 1 ab bb aa cc bb aa aa aa bb aa ab – aa aa bb

12 1 ab bb aa cc bb aa aa aa bb aa ab – ab aa ab

13 1 ab bb aa cc bb aa aa aa bb aa ab – ab aa bb

14 1 ab bb aa cc bb aa aa ab bb ab ab – aa aa bb

15 1 ab bb aa cc bb aa – aa bb ab bb – ab aa bb

16 1 bb ab aa aa bb ac aa aa bb aa ab cc aa aa bb

17 1 bb ab aa aa bb – aa aa bb aa ab cc ab aa bb

18 1 bb ab aa bb bb aa aa aa bb aa ab – ab aa bb

19 1 bb ab aa bb bb ac aa aa bb aa ab cc ab aa bb

20 1 bb ab aa cc ab ac aa aa ab aa ab cc aa aa bb

21 1 bb ab aa cc ab ac aa aa ab aa ab cc ab aa bb

22 1 bb ab aa cc ab ac aa ab bb aa ab cc ab aa bb

23 6 bb ab aa cc bb aa aa aa bb aa ab cc aa aa bb

24 6 bb ab aa cc bb aa aa aa bb aa ab cc ab aa bb

25 1 bb ab aa cc bb aa aa aa bb aa ab – ab aa bb

26 1 bb ab aa cc bb aa aa aa bc aa ab bb aa aa bb

27 1 bb ab aa cc bb ac aa aa bb aa ab cc aa aa bb

28 3 bb ab aa cc bb ac aa aa bb aa ab cc ab aa bb

29 1 bb ab aa cc cd bb bc aa cc cc – ab ab aa bb

30 1 bb bb aa bb bb aa aa aa bc aa ab bb ab aa bb

31 2 bb bb aa cc bb aa aa aa bb aa ab cc aa aa bb

32 10 bb bb aa cc bb aa aa aa bb aa ab cc ab aa bb

33 3 bb bb aa cc bb aa aa aa bb aa ab – aa aa bb

34 2 bb bb aa cc bb aa aa aa bb aa ab – ab aa bb

35 1 bb bb aa cc bb aa – aa bb aa ab cc aa aa bb

36 1 bb bb aa cc bb aa – aa bb aa ab cc ab aa bb

37 2 bb bb aa cc bb aa – aa bb aa ab – aa aa bb

38 1 bb bb aa cc bb aa – aa bb aa ab – ab aa aa

39 1 bb bb aa cc bb aa – aa bb aa ab – ab aa bb

40 1 bb bb aa cc bb aa – aa bb aa bb – ab aa bb

41 1 bb cc bb bb ab ac aa aa ab aa ab cc ab – cd

42 1 bc bb aa cc bb aa – aa bb aa ab cc aa aa bb

43 1 cc bb aa cc bb aa aa cc bb aa ab cc bb aa bb

Genetic interpretation.a Heterozygotes are present as ab, ac, bc, and cd. [–] null allele.

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 357

isolates or 26.3% of the sample population)]. Forty-

two pairs of groups showed 4.8–42.9% similarity SJand sixteen pairs of groups showed 53.8–75% simi-

larity SJ (Table 4).

4. Discussion

Specific and reliable laboratory procedures are

needed to determine the genetic relationship of micro-

Table 3

Electromorphic profiles of 47 ETs of C. albicans isolated from oral cavity of 75 clinically healthy children

Eletromorphs of 11 enzymes*ET No. of isolates

Adh Asd Cat G6pdh Gdh Idh Lap M1p Mdh Po Sdh

CBS 562 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 1 0 0 1 1 1 1 0 1 0 02 1 1 0 1 1 0 1 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 1 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 1 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 1 1 0 01 1 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 0 01 1 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 1 0 0 1 1 0 1 1 0 0 01 1 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 1 1 0 01 1 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 01 1 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0 1 0 1 0 01 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 1 1 1 1 0 1 0 01 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 0 01 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 1 0 03 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 02 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 01 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 01 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 01 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 1 1 1 0 1 0 01 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 01 0 1 0 1 1 0 1 0 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 01 0 1 0 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 0 1 0 02 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 1 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 1 1 1 1 0 1 0 05 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 06 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 01 0 1 0 1 1 0 1 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1 0 01 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0

10 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 02 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 02 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 03 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 01 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 0 0 01 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 01 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 01 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 02 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 01 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 0 01 0 1 0 0 0 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 1

123456789

1011121314151617181920212223242526272829303132333435363738394041424344454647 1 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0

Numerical interpretation.

* 1 or 0 correspond to presence or absence of bands (electromorphs), respectively.

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365358

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 359

organisms of medical interest. Such procedures have

been essential to understand the dynamics of infectious

organisms in human populations, decipher the com-

plex relationship between infection and commensal-

ism, to identify the origin of an infection or to monitor

the emergence of drug-resistant strains (Soll, 2000).

Along this line, various methods have been employed

to determine the genetic relationship of C. albicans,

which include electrophoretic karyotyping (Barchiesi

et al., 1995; Doi et al., 1994; Vazquez et al., 1994; Voss

et al., 1995), RFLP analysis (Bart-Delabesse et al.,

1993; Magee et al., 1992; Vazquez et al., 1991; Whe-

lan et al., 1990), RAPD analysis (Bostock et al., 1993;

Holmberg and Feroze, 1996; Lehmann et al., 1992;

Robert et al., 1995), Southern blot hybridization with a

variety of moderately repetitive DNA probes (Lasker

et al., 1992; Lockhart et al., 1995; Mahrous et al.,

1990; Scherer and Stevens, 1988) and MLEE (Boerlin

et al., 1995, 1996; Boriollo et al., 2005; Caugant and

Sandven, 1993; Le Guennec et al., 1995; Lehmann et

al., 1989; Pujol et al., 1993a, 1993b; Reynes et al.,

1996). In the majority of these investigations, the

patterns generated by the fingerprinting methods

were not characterized with regard to discriminatory

power (Pujol et al., 1997).

Typabililty, reproducibility and discriminatory

power have been developed and recommended to

determine the efficiency of various methods. Typabil-

ity and reproducibility represent quantitative systems

and are frequently expressed as percentages. In this

case, the typability of a method corresponds to the

percentage of distinct strains obtained and the repro-

ducibility corresponds to the percentage of strains that

show the same results in repeated assays (Hunter and

Gaston, 1988). In the present study, the high repro-

ducibility of the results obtained by MLEE was guar-

anteed by the inclusion of the enzymes of C. albicans

CBS-562 in each gel and by obtaining identical ETs

for the same isolate with the three electrophoretic

assays. In turn, the discriminatory power of a method

corresponds to its ability to differentiate between un-

related strains (Hunter and Gaston, 1988).

The use of Simpson’s diversity index has been

suggested for comparing the discriminatory power

among phenotypic or genotypic typing methods of

C. albicans (Boerlin et al., 1996; Clemons et al.,

1997; Hunter and Fraser, 1989; Hunter and Gaston,

1988). The discriminatory power of MLEE based on

both genetic and numerical electrophoretic interpreta-

tions was shown to be higher than 0.9, therefore

proving to be a powerful and reliable tool for typing

C. albicans in epidemiological studies (Hunter and

Fraser, 1989; Hunter and Gaston, 1988). Such result

corroborates those obtained by Boerlin et al. (1996)

and Pujol et al. (1997), and is even shown to be

superior to that described previously for MLEE

(Hunter, 1991). We should mention that the different

discriminatory values for the MLEE method observed

in various studies is due to the different enzymes

analyzed, size of the microbial populations and origin

of the pathogen (immunocompetent or immunocom-

promised patients). Moreover, numerical interpreta-

tion of the MLEE patterns discriminated a greater

number of oral strains of C. albicans in a population

of clinically healthy children (47 ETs identified by

numerical interpretation versus 43 ETs identified by

genetic interpretation).

The present study also applied Simpson’s diversity

index to compare the discriminatory power of three

cluster analysis models (based on the probability that

two unrelated strains sampled from the test population

will be placed into different typing clusters). The three

models for cluster analysis that were evaluated

showed a discriminatory higher than 0.8 and lower

than 0.9. However, further inferences on the interpre-

tation of these values were not made, except for the

fact that a greater discriminatory power was observed

in model B [discriminatory power equal to 0.874; 60

isolates distributed among 13 groups (genetic inter-

pretation; matrix SD1; UPGMA dendrogram); mean of

4.61 isolates/cluster; standard deviation of F4.64 iso-

lates; 16 moderately related isolates and/or unrelated

isolates], followed by model C [discriminatory power

equal to 0.847; 63 isolates distributed among 12 groups

(numerical interpretation; matrix SD2; UPGMA den-

drogram); mean of 5.25 isolates/cluster; standard de-

viation of F5.61 isolates; 13 moderately related

isolates and/or unrelated isolates] and model A [dis-

criminatory power equal to 0.810; 63 isolates distrib-

uted among 10 groups (genetic interpretation; matrix

dij; UPGMA dendrogram); mean of 6.3 isolates/clus-

ter; standard deviation of F6.53 isolates; 13 moder-

ately related isolates and/or unrelated isolates].

The determination of discriminatory power point-

ed to numerical interpretation of MLEE patterns as a

method capable of discriminating a greater number of

1 8 58 72 69 70 63 5 74 9 29 21 37 3 4 67 76 68 62 25 27 28 57 35 41 42 47 48 50 52 24 56 60 20 32 34 31 33 38 7 26 44 23 45 30 51 39 40 46 11 61 64 71 43 54 55 6 17 18 36 66 75 16 19 12 13 22 49 2 10 59 73 65 53 15 14

18 58726970635 743 4 62766768252728573541425047482456527 26449 296 6020313234383621375433552345404643303951532 1011161961647166171875121322495973651415

16164713 4 627667688 58726970635 13669 29252728573541425047485220313234213730513954743675122 10115973655316171819156 7 26442456602233385523454046434914

0.0 0.3 0.5 0.8 1.0 0.0 0.3 0.5 0.8 1.0 1.0 0.8 0.5 0.3 0.0

0.0 0.3 0.5 0.8 1.0 0.0 0.3 0.5 0.8 1.0 1.0 0.8 0.5 0.3 0.0

945.02 =DS

1

2°

3

4

5

6

7

8

9

10

11

12

1

2

3°

4

5

6

7

8

9

10

11

12

13

1

2

3

4

5

6

7

8

9

10

903.01 =DS 012.0=ijd

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365360

Table 4

Similarity (SJ) among groups of C. albicans obtained from three cluster analysis models

Similaridade (SJ)

1–10% 11–20% 21–30% 31–40% 41–50% 51–60% 61–70% 71–80% 81–90% 91–100%

A1�B2 A1�B3 A9�C3 A1�B1 A2�B3 B1�C1 A3�B9 A10�B8 – A7�B12

A2�B4 A1�B11 A10�C4 A1�C1 B4�C4 – A3�C5 B6�C3 – B3�C2

A2�B7 A1�C2 B9�C6 A2�C2 – – A5�B10 B11�C7 – B10�C11

A4�C1 A1�C7 – A4�B2 – – A5�C11 – – B13�C12

A8�B3 A2�B6 – A4�B7 – – A6�B13 – – –

B5�C2 A2�C1 – A4�C8 – – A6�C12 – – –

– A2�C3 – A8�B4 – – A7�C9 – – –

– A3�C6 – A8�C4 – – B12�C9 – – –

– A8�B5 – A10�C6 – – – – – –

– A8�C2 – B8�C4 – – – – – –

– A9�B6 – B9�C5 – – – – – –

– A9�B7 – – – – – – – –

– B2�C1 – – – – – – – –

– B4�C1 – – – – – – – –

– B6�C9 – – – – – – – –

– B7�C1 – – – – – – – –

– B7�C3 – – – – – – – –

– B7�C8 – – – – – – – –

– B8�C6 – – – – – – – –

– B11�C10 – – – – – – – –

R =6 R =20 R =3 R =11 R =2 R =1 R =8 R =3 R =0 R =4

The letters A, B and C correspond to the models A (dij), B (SD1) and C (SD2) of the cluster assays, respectively.

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 361

strains of C. albicans, but then also indicated model

B (based on genetic interpretation of MLEE patterns;

matrix SD1; UPGMA dendrogram) as a method ca-

pable of furnishing a greater number of groups of

highly related strains of C. albicans. Therefore, we

recommend the use of model B, and thus the initial

genetic interpretation of MLEE patterns for the typing

of C. albicans by MLEE and cluster analysis. This

could be very useful in the identification of different

groups of highly related or unrelated C. albicans in

one or more types of infections in a variety of im-

munocompromised or immunocompetent patients.

Moreover, possible correlations among groups and

host parameters (age, sex, weight, medical character-

istics, predisposing conditions, prosthetic devices,

geographic location, socioeconomic factors, associa-

tion with other individuals, etc.) and/or pathogen

characteristics (patterns of sugar assimilation, antige-

nicity, secretion of proteinases, drug sensitivity pro-

file, hyphae formation, phenotypic switching, etc)

Fig. 2. Genetic diversity of oral isolates of C. albicans derived from clinic

cluster analysis models. The symbols5,E andn indicate groups that are 1

could be determined (Soll, 2000). In addition, genetic

interpretation, which not always it is possible to use

for certain molecular markers such as RAPD, pro-

vides additional information about the nuclear, genet-

ic and taxonomic conditions of a group of organisms

(Harris and Hopkinson, 1976; Micales et al., 1998;

Siciliano and Shaw, 1976). In this manner, such

results also suggest that the number of strains of C.

albicans is overestimated by the numerical interpre-

tation of MLEE patterns and the number of groups of

strains is reduced by cluster analysis (model C: nu-

merical interpretation of MLEE patterns, matrix SD2;

UPGMA dendrogram).

The agreement between elements of two matrices

(dij�SD1, dij�SD2, and SD1�SD2), obtained from

the use of different coefficients on the combined

findings from the interpretations of MLEE patterns,

was established by the Pearson product-moment cor-

relation coefficient. Very good agreement (r =0.909)

was only observed between the elements of the ma-

ally healthy children. UPGMA dendrograms obtained from the three

00% similar (SJ) and the symbol B indicates groups 95% similar (SJ).

M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365362

trices SD1 (model B) and SD2 (model C). The appli-

cation of this correlation coefficient as a measure of

concordance between (i) similarity values derived

from dendrograms and those of the original similarity

matrices, (ii) matrices of cophenetic values represent-

ing two dendrograms or ordinations, or (iii) two sim-

ilarity matrices determined by different techniques or

based on different characters was widely discussed by

Sneath and Sokal (1973). However, the great majority

of groups generated in the three UPGMA dendro-

grams (models A, B, and C) showed similarity SJbetween 4.8% and 75%. Together, these findings

suggest disparities in the conclusions obtained by

the three cluster analysis models, such as distinct

origins of infection, transmission route, correlations

among groups and host parameters or pathogen char-

acteristics, and relationships among highly related

and/or moderately related groups of C. albicans.

From a practical point of view, we can conclude in

part the following: clinically healthy children A, B

and C shared highly related strains [such strains could

have occurred from a common ancestral strain as a

consequence of the loss of an allele by mitotic recom-

bination or chromosomal rearrangement (Reynes et

al., 1996; Scherer and Magee, 1990); children A, B,

C and D shared highly related strains; and children A,

B, C, E,. . ., R and S shared such strains (Fig. 2: group

7 in SD1, group 11 in SD2 and group 1 in dij). It should

be noted that this fact could have great epidemiolog-

ical relevance especially in the identification of nos-

ocomial outbreaks of C. albicans.

A reason for employing only the UPGMA algo-

rithm for cluster assays in the present study is that it

has been used frequently to generate dendrograms

from matrices of a variety of distance/similarity coef-

ficients; and furthermore, it is relatively straightfor-

ward and has been used widely in fingerprinting

infectious fungi (Soll, 2000). In addition, when com-

pared with other algorithms, such as WPGMA

(weighted pair-group method using arithmetic

averages), UPGMC (unweighted pair-group centroid

method), complete linkage and single linkage, the

UPGMA algorithm was able to generate dendrograms

based on similarity/distance matrices that gave the

best representation when measured by cophenetic cor-

relation coefficients (Sneath and Sokal, 1973).

The lack of consensus among investigators with

regard to the choice of similarity, dissimilarity or

distance coefficient, because the interpretation data

of the fingerprinting patterns allow the use of one or

another, has been observed among typing studies of

C. albicans by MLEE and cluster analysis. However,

various investigators have employed the genetic dis-

tance coefficient of Nei (1972) and the similarity

coefficient Dice (1945), for the elaboration of matrices

or distance and similarity, respectively, in studies on

molecular epidemiology of bacterial and fungal infec-

tions, especially for the diploid yeast C. albicans and

its typing by MLEE (Alfenas, 1998; Boerlin et al.,

1995, 1996; Le Guennec et al., 1995; Nebavi et al.,

1998; Nei, 1972; Selander et al., 1986; Pujol et al.,

1997; Soll, 2000). However, such fact could represent

a limited way to evaluate all the possible relationships

among strains (or clusters of strains) once innumerous

coefficients of similarity, dissimilarity or genetic dis-

tance were written in the literature (Alfenas, 1998;

Sneath and Sokal, 1973; Legendre and Legendre,

1983). Therefore, studies aimed at identifying a coef-

ficient or a group of coefficients (including coeffi-

cients of similarity, dissimilarity or genetic distance)

capable of providing highly discriminatory, concor-

dant and similar results for cluster analysis and mo-

lecular typing of C. albicans should be conducted

separately. Such a study could be easily conducted

by different commercially available computer pro-

grams, and could also help investigators in choosing

a standard model for cluster analysis capable of being

stored and compared directly with other data from

identical models in the same line of research.

Acknowledgements

The authors acknowledge the financial support of

FAPESP – Fundacao de Amparo a Pesquisa do Estado

de Sao Paulo (Proc. 00/03045-5). Dr. A. Leyva pro-

vided English language editing.

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