Journal of Microbiological Methods 64 (2006) 346 – 365 www.elsevier.com/locate/jmicmeth 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 Gonçalves a, José Francisco Höfling a b a Microbiology and Immunology Laboratory, Dental School of Piracicaba, State University of Campinas, Piracicaba, Brazil Stomatology Laboratory, Center of Biological and Health Sciences, Pontifical Catholic University of Paraná, 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 (d ij ) and UPGMA dendrogram; model B: genetic interpretation, Dice similarity matrix (S D1 ) and UPGMA dendrogram; model C: numerical interpretation, Dice similarity matrix (S D2 ) 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 (N 0.9). 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 S D1 and S D2, but a large majority of the groups generated in the three UPGMA dendrograms showed similarity S J 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 * Corresponding author. Laboratatory of Microbiology and Immunology, Department of Oral Diagnostic, Dental School of Piracicaba, State University of Campinas, Av. Limeira 901, CEP13414-903 CP052, Piracicaba, SP, Brazil. Tel.: +55 19 3412 5321; fax: +55 19 3412 5218. E-mail address: marcelofgb@yahoo.com.br (M.F.G. Boriollo). 0167-7012/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.mimet.2005.05.012 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 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 interest among medical microbiologists (Selander and Levin, 1980; Selander et al., 1986). Since then, numerous studies were performed with innumerable results contributing to the understanding of the natural history of infectious diseases. MLEE has been considered the gold standard in the study of the population 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; Boerlin 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 visualized according to enzyme activity, indicating the existence of isoenzymes or isozymes (Markert and Moller, 1959). Isoenzymes constitute multiple molecular 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 electrophoresis 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 distinction of fungal species depends on intrapopula- 347 tional genetic variability. Organisms with high genetic variability can express highly variable phenotypes 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 isoenzyme electrophoretic bands are frequently predictable, 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, furnishes 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 considered 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 analysis 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 dendrograms based on matrices of a variety of distance or similarity coefficients, is relatively straightforward 348 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 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 electrophoretic patterns, and the study of the epidemiological 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 Campinas (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 8C for 18h, under constant shaking at 150 rpm (Shaker Incubator mod. NT 712, Nova Técnica Instrumentos e Equipamentos de Laboratório 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 (Biospec 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 machine (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- Refinações 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 horizontal and continuous system, at 130 V at 4 8C overnight (bromophenol-blue migration equivalent to 80 mm). To assure reproducible results, the C. albicans CBS-562 enzymes (Centralbureau voor Schimmelcultures, Delft, The Netherlands) were included 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 porcelain 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 enzymatic activities determined were: alcohol dehydrogenase, sorbitol dehydrogenase, manitol-1-phosphate dehydrogenase, malate dehydrogenase, isocitrate dehydrogenase, glucose dehydrogenase, glucose-6-phosphate dehydrogenase, aspartate dehydrogenase, catalase, peroxidase, and leucine aminopeptidase (Table 1). Enzymatic expressions of malate dehydrogenase, isocitrate dehydrogenase, and sorbitol dehydrogenase 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 diploid organisms. The bands on the gels were numbered 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 Name Symbol Substrate Buffer 1.1.1.1. Alcohol dehydrogenase ADH 1.1.1.14. Sorbitol dehydrogenase SDH Ethanol (3 mL) isopropanol (2 mL) Sorbitol (250 mg) 1.1.1.17. M1P 1.1.1.37. Mannitol-1-phosphate dehydrogenase Malate dehydrogenase MDH 1.1.1.42. Isocitrate dehydrogenase IDH 200mM Tris–HCl pH 8.0 (50 mL)a Tris–HCl 50 mM pH 8.0 (50 mL)b Tris–HCl 100 mM pH 8.5 (50 mL)c Tris–HCl 200 mM pH 8.0 (40 mL)a Tris–HCl 200 mM pH 8.0 (40 mL)a 1.1.1.47. Glucose dehydrogenase GDH D-glucose (500 mg) 1.1.1.49. Glucose-6-phosphate dehydrogenase G6PDH Glicose-6-phosphate disodium salt (100 mg) 1.4.3.x. Aspartate dehydrogenase ASD Aspartic acid (50 mg) Sodium phosphate pH 7.0 (50 mL)g 1.11.1.6. 1.11.1.7. Catalaseh Peroxidase CAT PO H2O2 3% (1 mL) 3.4.11.1. Leucine aminopeptidase LAP 100 mM Sodium acetate pH 4.5 (50 mL)i 100 mM Potassium phosphate pH 5.5 (50 mL)j Mannitol-1-phosphate (5 mg) 2M Malic acid (6 mL)d 1M Isocitric acid (2 mL)e L-leucine b-naphthylamide HCl (30 mg) Tris–HCl 200 mM pH 8.0 (50 mL)a Tris–HCl 200 mM pH 8.0 (50 mL)a Salt 100 mM MgCl2 (1 mL)f 100 mM MgCl2 (1 mL) f 100 mM MgCl2 (1 mL)f Coenzyme Dye catalyser NAD 1% (2 mL) NAD 1% (2 mL) NAD 1% (2 mL) NAD 1% (2 mL) NADP 1% (1 mL) PMS MTT PMS MTT PMS MTT PMS MTT PMS MTT 1% (500 AL) 1.25% (1 mL) 1% (500 AL) 1.25% (1 mL) 1% (500 AL) 1.25% (1 mL) 1% (500 AL) 1.25% (1 mL) 1% (500 AL) 1.25% (1 mL) NAD 1% (2 mL) NADP 1% (1 mL) PMS MTT PMS MTT 1% (500 AL) 1.25% (1 mL) 1% (500 AL) 1.25% (1 mL) NAD 1% (2 mL) PMS 1% (500 AL) MTT 1.25% (1 mL) o-dianisidine 2HCl (16 mg) Black K (30 mg) 349 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. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 EC number 350 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 same nomenclature. Lack of demonstrable activity for an enzyme was scored as two null alleles at the corresponding gene locus. Each unique combination 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). applied both to a direct comparison of the discriminating 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 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 distance traveled by the bromophenol-blue front (mm). Each unique combination of bands for the 11 enzymes 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 (Armitage and Berry, 1987) and P is given by the following equation: D ¼ 1  N ðN11Þ Sj¼1 nj nj  1 , where N is the total number of strains in the sample population, s is the total number of types described, and n j 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 n ðn 1Þ group is Nj ðNj 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 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 d ij of Nei (1972) was used to estimate the genetic distance hamong  P  all the    isolates of C. albicans: d ¼  In ij qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi k xki xkj = P 2 2ffi i , a measure of genetic distance k xki xkj (range from 0 to infinity) based on the identity of genes (frequency of alleles for all loci, including 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 d ij . 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 common 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 2a Sokal, 1973; Soll, 2000): SD ¼ 2aþbþc , a pairfunction that measures the agreement (range from 0 to 1) between pairs of OTUs (operational taxonomic units) 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 of presence of a given isoenzyme genotype. This coefficient 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 than to mismatches (b and c). This S D1 measures the proportion of identical genotypes of two isolates, 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 genotypes unique to strain B. For the present model, an S D1 of 1.00 represents identically matched genotypes (i.e., all genotypes of isolates A and B match), an S D1 of 0.0 represents no matches, and S D1 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 S D2 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 S D2 of 1.00 represents identically matched bands (i.e., all bands in the patterns of isolates A and B match), an S D2 of 0.0 represents no matches, and S D2 ranging from 0.01 to 0.99 represent increasing proportions of matched bands. Trees with two-dimensional classifications, called dendrograms, based on matrix d ij (model A), matrix S D1 (model B), and matrix S D2 (model C) were generated by the SAHN method (sequential, agglomerative, hierarchic, nonoverlapping clustering methods) UPGMA algorithm (unweighted pair-group method using an arithmetic average) (Sneath and Sokal, 1973). Since MLEE provide all levels of relatedness 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 P P P (average values: dij ; SD1 and SD2 ) were established in dendrograms to identify clusters of identical iso- 351 P P lates and highly related isolates (dij Ndij z 0; SD1 bSD1 P V 1; and SD2 bSD2 V1) (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 coefficient was used as a measure of the agreement (range from  1 to + 1) between elements of two matrices (d ij
S D1, d ij
S D2, and S D1
S D2) obtained by different techniques or based on different characters. This coefficient, computed between OTUs j and k, is n  X P P  Xij  X j Xik  X k i¼1 rjk ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n  X P 2 P 2 X  Xik  X k Xij  X j i¼1 i¼1 where X ij stands for the character state value of P character i in OTU j, X 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: 0.9 V r—very good concordance; 0.8 V r b 0.9—good concordance; 0.7 V r b 0.8—weak concordance; r b 0.7—very weak concordance (Rohlf, 1988). The similarity among the clusters containing P identical isolates and highly related isolates (dij Ndij z P P 0 versus SD1 b SD1 V1 versus SD2 b SD2 V1 ) was estaba lished by the Jaccard coefficient: SJ ¼ aþbþc , a pairfunction that also measures the agreement (range from 0 to 1) between pairs of OTUs over an array of twostate 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). S J 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 S J of 1.00 represents identically matched strains (i.e., all strains of clusters A and B match), an S J of 0.0 352 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 represents no matches, and S J 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 P P into different typing clusters (dij Ndij z 0; SD1 b SD1 V P 1 or SD2 bSD2 V 1) (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 d b 1%), 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 monomorphic 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 indicate 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 patterns was 0.966, that is, 96.6% probability of two C. albicans isolates sampled from the test population belonging to different typing groups (i.e., electrophoretic type- ET). The numerical interpretation of the MLEE patterns of the population of isolates of C. albicans permitted the identification of polymorphism in all the enzymes studied (i.e., for each enzyme the frequency of the most common band was b99%) totaling 43 distinct types of electrophoretic bands (Fig. 1). The combination of bands for the 11 enzymes examined showed 47 ETs in the population of isolates. 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% probability 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 S D1 and S D2 (r = 0.909), while negative agreement (correlation) was observed among elements of the matrices d ij and S D1 (r =  0.548) or d ij and S D2 (r =  0.487). In addition, when the elements d ij of the genetic distance matrix were converted into similarity elements (Sd ij = 1  d ij ) a very poor agreement was observed among the elements of the matrices d ij and S D1 (r = 0.505) or d ij and S D2 (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 S J [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 S J [group 3 of model B (B3–19 isolates or 25% of the sample population) and group 2 of model C (C2–20 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 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). 353 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 Fig. 1 (continued). 354 Fig. 1 (continued). M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 355 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 Fig. 1 (continued). 356 357 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 562 2 1 1 2 1 1 1 1 2 4 1 1 1 1 1 1 1 1 1 1 1 1 6 6 1 1 1 3 1 1 2 10 3 2 1 1 2 1 1 1 1 1 1 bb ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bc cc bb ab ab ab ab ab ab bb bb bb bb bb bb bb bb bb ab ab ab ab ab ab ab ab ab ab ab ab ab ab bb bb bb bb bb bb bb bb bb bb bb cc bb bb aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa bb aa aa cc ab bb cc cc cc cc bb cc cc cc cc cc cc cc cc aa aa bb bb cc cc cc cc cc cc cc cc cc cc bb cc cc cc cc cc cc cc cc cc cc bb cc cc bb bb ab ab ab bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb ab ab ab bb bb bb bb bb bb cd bb bb bb bb bb bb bb bb bb bb bb ab bb bb aa aa ac ac ac aa aa aa aa aa aa aa aa aa aa aa ac – aa ac ac ac ac aa aa aa aa ac ac bb aa aa aa aa aa aa aa aa aa aa aa ac aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa – aa aa aa aa aa aa aa aa aa aa aa aa aa bc aa aa aa aa aa – – – – – – aa – aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa ab aa aa aa aa aa aa aa ab aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa cc bb bb bb aa ab bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb ab ab bb bb bb bb bc bb bb cc bc bb bb bb bb bb bb bb bb bb bb ab bb bb ab aa aa aa aa aa aa aa aa aa aa aa aa aa ab ab aa aa aa aa aa aa aa aa aa aa aa aa aa cc aa aa aa aa aa aa aa aa aa aa aa aa aa aa ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab bb ab ab ab ab ab ab ab ab ab ab ab ab ab – ab ab ab ab ab ab ab ab ab ab bb ab ab ab cc cc cc cc cc cc cc cc cc cc cc – – – – – cc cc – cc cc cc cc cc cc – bb cc cc ab bb cc cc – – cc cc – – – – cc cc cc ab ab ab aa aa aa aa ab aa aa ab aa ab ab aa ab aa ab ab ab aa ab ab aa ab ab aa aa ab ab ab aa ab aa ab aa ab aa ab ab ab ab aa bb aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa aa – aa aa bb bb bb aa ab ab bb bb ab bb bb bb ab bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb bb aa bb bb cd bb bb Genetic interpretation. a Heterozygotes are present as ab, ac, bc, and cd. [–] null allele. isolates or 26.3% of the sample population)]. Fortytwo pairs of groups showed 4.8–42.9% similarity S J and sixteen pairs of groups showed 53.8–75% similarity S J (Table 4). 4. Discussion Specific and reliable laboratory procedures are needed to determine the genetic relationship of micro- 358 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 Table 3 Electromorphic profiles of 47 ETs of C. albicans isolated from oral cavity of 75 clinically healthy children ET No. of Eletromorphs of 11 enzymes* isolates Adh Asd Cat G6pdh Gdh CBS 562 1 2 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 10 1 11 1 12 1 13 3 14 2 15 1 1 16 1 17 1 18 19 1 1 20 21 1 1 22 1 23 1 24 25 1 2 26 1 27 28 1 29 1 1 30 31 5 6 32 33 1 1 34 1 35 10 36 2 37 2 38 3 39 40 1 41 1 42 1 43 1 44 2 45 1 46 1 47 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Idh 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Lap 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 M1p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Numerical interpretation. * 1 or 0 correspond to presence or absence of bands (electromorphs), respectively. 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 Mdh 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Po 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 Sdh 1 1 1 1 0 0 0 0 1 0 1 0 1 1 0 1 0 1 0 0 1 1 1 1 1 0 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 organisms of medical interest. Such procedures have been essential to understand the dynamics of infectious organisms in human populations, decipher the complex relationship between infection and commensalism, 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; Whelan 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. Typability 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 reproducibility corresponds to the percentage of strains that show the same results in repeated assays (Hunter and Gaston, 1988). In the present study, the high reproducibility of the results obtained by MLEE was guaranteed 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 unrelated 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 359 both genetic and numerical electrophoretic interpretations 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 immunocompromised patients). Moreover, numerical interpretation 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 interpretation 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 interpretation; matrix S D1; UPGMA dendrogram); mean of 4.61 isolates/cluster; standard deviation of F 4.64 isolates; 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 S D2; UPGMA dendrogram); mean of 5.25 isolates/cluster; standard deviation of F5.61 isolates; 13 moderately related isolates and/or unrelated isolates] and model A [discriminatory power equal to 0.810; 63 isolates distributed among 10 groups (genetic interpretation; matrix d ij ; UPGMA dendrogram); mean of 6.3 isolates/cluster; standard deviation of F6.53 isolates; 13 moderately related isolates and/or unrelated isolates]. The determination of discriminatory power pointed to numerical interpretation of MLEE patterns as a method capable of discriminating a greater number of 360 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 S D 2 = 0.945 0.0 0.0 0.3 0.3 0.5 0.5 0.8 0.8 1.0 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 0.0 1.0 0.0 S D1 0.3 0.5 0.8 1 2° 3 4 5 6 7 8 9 10 11 12 0.3 0.5 0.8 = 0.903 d ij 1.0 1 8 58 72 69 70 63 5 74 3 4 62 76 67 68 25 27 28 57 35 41 42 50 47 48 24 56 52 7 26 44 9 29 6 60 20 31 32 34 38 36 21 37 54 33 55 23 45 40 46 43 30 39 51 53 2 10 11 16 19 61 64 71 66 17 18 75 12 13 22 49 59 73 65 14 15 1.0 1.0 1.0 0.8 0.5 0.3 1 2 3° 4 5 6 7 8 9 10 11 12 13 0.8 0.5 0.3 = 0.012 0.0 1 61 64 71 3 4 62 76 67 68 8 58 72 69 70 63 5 13 66 9 29 25 27 28 57 35 41 42 50 47 48 52 20 31 32 34 21 37 30 51 39 54 74 36 75 12 2 10 11 59 73 65 53 16 17 18 19 15 6 7 26 44 24 56 60 22 33 38 55 23 45 40 46 43 49 14 0.0 1 2 3 4 5 6 7 8 9 10 361 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 Table 4 Similarity (S J ) among groups of C. albicans obtained from three cluster analysis models Similaridade (S J ) 1–10% 11–20% 21–30% 31–40% 41–50% 51–60% 61–70% 71–80% 81–90% 91–100% A1
B2 A2
B4 A2
B7 A4
C1 A8
B3 B5
C2 – – – – – – – – – – – – – – R =6 A1
B3 A1
B11 A1
C2 A1
C7 A2
B6 A2
C1 A2
C3 A3
C6 A8
B5 A8
C2 A9
B6 A9
B7 B2
C1 B4
C1 B6
C9 B7
C1 B7
C3 B7
C8 B8
C6 B11
C10 R = 20 A9
C3 A10
C4 B9
C6 – – – – – – – – – – – – – – – – – R =3 A1
B1 A1
C1 A2
C2 A4
B2 A4
B7 A4
C8 A8
B4 A8
C4 A10
C6 B8
C4 B9
C5 – – – – – – – – – R = 11 A2
B3 B4
C4 – – – – – – – – – – – – – – – – – – R =2 B1
C1 – – – – – – – – – – – – – – – – – – – R =1 A3
B9 A3
C5 A5
B10 A5
C11 A6
B13 A6
C12 A7
C9 B12
C9 – – – – – – – – – – – – R =8 A10
B8 B6
C3 B11
C7 – – – – – – – – – – – – – – – – – R =3 – – – – – – – – – – – – – – – – – – – – R =0 A7
B12 B3
C2 B10
C11 B13
C12 – – – – – – – – – – – – – – – – R =4 The letters A, B and C correspond to the models A (d ij ), B (S D1) and C (S D2) of the cluster assays, respectively. strains of C. albicans, but then also indicated model B (based on genetic interpretation of MLEE patterns; matrix S D1; UPGMA dendrogram) as a method capable 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 immunocompromised or immunocompetent patients. Moreover, possible correlations among groups and host parameters (age, sex, weight, medical characteristics, predisposing conditions, prosthetic devices, geographic location, socioeconomic factors, association with other individuals, etc.) and/or pathogen characteristics (patterns of sugar assimilation, antigenicity, secretion of proteinases, drug sensitivity profile, hyphae formation, phenotypic switching, etc) could be determined (Soll, 2000). In addition, genetic interpretation, which not always it is possible to use for certain molecular markers such as RAPD, provides 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). In this manner, such results also suggest that the number of strains of C. albicans is overestimated by the numerical interpretation of MLEE patterns and the number of groups of strains is reduced by cluster analysis (model C: numerical interpretation of MLEE patterns, matrix S D2; UPGMA dendrogram). The agreement between elements of two matrices (d ij
S D1, d ij
S D2, and S D1
S D2), obtained from the use of different coefficients on the combined findings from the interpretations of MLEE patterns, was established by the Pearson product-moment correlation coefficient. Very good agreement (r = 0.909) was only observed between the elements of the ma- Fig. 2. Genetic diversity of oral isolates of C. albicans derived from clinically healthy children. UPGMA dendrograms obtained from the three cluster analysis models. The symbols 5, E and n indicate groups that are 100% similar (S J ) and the symbol B indicates groups 95% similar (S J ). 362 M.F.G. Boriollo et al. / Journal of Microbiological Methods 64 (2006) 346–365 trices S D1 (model B) and S D2 (model C). The application 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 representing two dendrograms or ordinations, or (iii) two similarity 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 dendrograms (models A, B, and C) showed similarity S J between 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 characteristics, 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 recombination 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 S D1, group 11 in S D2 and group 1 in d ij ). It should be noted that this fact could have great epidemiological relevance especially in the identification of nosocomial outbreaks of C. albicans. A reason for employing only the UPGMA algorithm 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 coefficients; and furthermore, it is relatively straightforward and has been used widely in fingerprinting infectious fungi (Soll, 2000). In addition, when compared 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 correlation 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 distance 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 infections, especially for the diploid yeast C. albicans and its typing by MLEE (Alfenas, 1998; Boerlin et al., 1995, 1996; Le Guennec et al., 1995; Nébavi 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 distance were written in the literature (Alfenas, 1998; Sneath and Sokal, 1973; Legendre and Legendre, 1983). Therefore, studies aimed at identifying a coefficient or a group of coefficients (including coefficients of similarity, dissimilarity or genetic distance) capable of providing highly discriminatory, concordant and similar results for cluster analysis and molecular typing of C. albicans should be conducted separately. Such a study could be easily conducted by different commercially available computer programs, 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. 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