Comparison of Microsatellite Length Polymorphism and Multilocus ...

JOURNAL OF CLINICAL MICROBIOLOGY, Dec. 2007, p. 3958–3963 0095-1137/07/$08.00⫹0 doi:10.1128/JCM.01261-07 Copyright © 2007, American Society for Microbiology. All Rights Reserved.

Vol. 45, No. 12

Comparison of Microsatellite Length Polymorphism and Multilocus Sequence Typing for DNA-Based Typing of Candida albicans䌤 Dea Garcia-Hermoso,1 Odile Cabaret,2 Gael Lecellier,3 Marie Desnos-Ollivier,1 Damien Hoinard,1 Dorotheé Raoux,1 Jean-Marc Costa,2 Françoise Dromer,1* and Ste´phane Bretagne1,2 Centre National de Re´fe´rence de Mycologie et des Antifongiques, Unite´ de Mycologie Molećulaire, CNRS URA 3012, Institut Pasteur, Paris, France1; Laboratoire de Parasitologie-Mycologie, Ho ˆpital Henri Mondor-APHP and UMR BIPAR 956, Cre´teil, France2; and Laboratoire de Geńe´tique et Biologie Cellulaire, Equipe Complexes Proteíques Mitochondriaux, Universite´ de Versailles Saint Quentin en Yvelines, CNRS (UMR8159)/EPHE, Versailles, France3 Received 22 June 2007/Returned for modification 23 July 2007/Accepted 30 September 2007

For genotyping Candida albicans isolates, two PCR-based methods have recently emerged: multilocus sequence typing (MLST), based on the sequence of selected genes, and microsatellite length polymorphism (MLP), based on the length of PCR products containing variable numbers of short DNA repeats. To compare the two methods in their abilities to differentiate and group C. albicans isolates, we selected 50 independent isolates collected at the National Reference Center for Mycoses and Antifungals. MLST typing was performed using sequencing of seven loci as described at http://test1.mlst.net. The MLP method consisted of a single multiplex PCR testing three different loci. Dendrograms were constructed by the unweighted pair group cluster method with Euclidean metric for both methods. The correlation between the distance matrices was performed with a Mantel test tested with 1,000 random permutations. The sensitivity and specificity of the MLP typing system were determined after allocating MLST groups for the greater number of isolates of each distinct MLP group. The discriminatory power index was >0.99, and the distances between the isolates were highly correlated with both systems. The Mantel coefficient and the Pearson product-moment correlation coefficient were 35,699 and 0.32, respectively (P < 1.2 ⴛ 10ⴚ6). Using MLP, the average specificity and sensitivity of clustering compared to MLST were 83% and 73%, respectively, when the singletons were excluded. The two methods are similarly discriminatory and can be interchangeable depending on the objectives. MLP is less expensive and faster than MLST. However, MLST is currently more accurate and additional standardization is needed for MLP. quence typing (MLST) and the analysis of microsatellite length polymorphism (MLP). MLST relies on DNA sequence analysis of nucleotide polymorphisms within housekeeping genes. The consensus system is based on fragments of seven C. albicans genes: the AAT1a, ACC1, ADP1, MPIb, SYA1, VPS13, and ZWF1b genes (2, 19). MLST is able to differentiate heterozygous strains, in contrast to restriction fragment length polymorphism and RAPD, which is important for diploid microorganisms such as C. albicans. Each isolate can be assigned a diploid sequence type (DST), and the data can be compared to those available at http://test1.mlst.net. The analysis of MLP relies on the amplification of microsatellite sequences, defined as tandemly repetitive stretches of two to five nucleotides. Microsatellite alleles generally refer to DNA fragments of different sizes obtained after amplification with primers flanking the microsatellite region. As MLP tests the presence of different alleles at a given locus, distinguishing heterozygotes is possible. Several studies have already reported the use of this technique for C. albicans genotyping (1, 3, 4, 6, 7, 14). For epidemiological purposes, we have implemented at the National Reference Center for Mycoses and Antifungals (Pasteur Institute) an active surveillance program on yeasts isolated from blood cultures (YEASTS program). Therefore, we are interested in genotyping the large collection of C. albicans isolates available to compare with the demographic, medical,

Among the yeasts that have emerged as major fungal pathogens in recent years, the commensal Candida albicans is the most prevalent and acts as an opportunistic agent in immunocompromised patients. Most of these infections are nosocomial and raise the issue of their prevention. The process of subtyping C. albicans is epidemiologically important for recognizing outbreaks of infection, detecting cross-transmission, determining the source of the infection, recognizing particularly virulent strains if any, or detecting the emergence of drugresistant strains (5, 6, 17). Another wide field of investigation is the study of population genetics of C. albicans (9, 10, 15, 18, 20). Several typing methods have been developed to differentiate C. albicans strains and isolates. Strain typing techniques such as electrophoretic karyotyping, restriction length polymorphic DNA with hybridization with a C. albicans-specific probe, and random amplified polymorphic DNA (RAPD) have been reviewed elsewhere (16). Two other methods are PCR based and, as a consequence, amenable to high-throughput capability for investigating large collections of isolates: multilocus se-

* Corresponding author. Mailing address: Centre National de Re´férence de Mycologie et des Antifongiques, Unite´ de Mycologie Molećulaire, CNRS URA 3012, Institut Pasteur, Paris, France. Phone: 33 1 45 68 83 54. Fax: 33 1 45 68 84 20. E-mail: [email protected]. 䌤 Published ahead of print on 10 October 2007. 3958

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and biological data collected. The aim of the present study was thus to compare the performances of MLST and MLP in order to later choose the appropriate method for large-scale study of C. albicans isolates. MATERIALS AND METHODS Isolates of C. albicans. We analyzed 50 epidemiologically independent bloodstream isolates of C. albicans obtained from 16 different hospitals in the Paris area. The isolates were cultured on Sabouraud agar for 24 h at 30°C, and the identification was confirmed using the phenotypic method (ID32 C; BioMe´rieux, France). The genomic DNA was extracted from a few colonies using the High Pure PCR template preparation kit as described by the manufacturer (Roche Applied Biosciences, Germany). MLP analysis with the previously described microsatellite markers CDC3, EF3, and HIS3 (1). Multiplex PCR was performed in a 20-␮l reaction volume combining 1⫻ PCR buffer, 0.2 mM each of four deoxynucleoside triphosphates, 5 mM MgCl2, 5 pmol of the EF3 primers, 2 pmol of the CDC3 and HIS3 primers, and 1.25 U of Taq Gold polymerase (Applied Biosystems) as described elsewhere (1). The PCR program consisted of an initial denaturation step at 95°C for 10 min, followed by 30 cycles of 30 s at 95°C, 30 s at 55°C, and 1 min at 72°C, with a final extension step of 5 min at 72°C. Following PCR, 2 ␮l of the amplification product was added to 20 ␮l of formamide and to 0.5 ␮l of the 6-carboxytetramethylrhodamine Genescan 500 size standard (Applied Biosystems). The samples were denatured at 95°C for 2 min and then chilled in an ice bath. The denatured samples were then run on an ABI Prism 310 genetic analyzer, and the allele sizes were calculated with the Genescan software (version 2.1; Applied Biosystems). To check the reproducibility of the technique, we tested the B311 reference strain in 10 separate experiments. This strain is heterozygous for the three markers, and the length in base pairs of each allele is known (1, 6). The standard deviation was ⫾0.20 bp for CDC3, ⫾0.24 bp for EF3, and ⫾0.42 bp for HIS3. To assign a specific length to a PCR fragment, we systematically tested the B311 strain in all the PCR runs. All the PCR results were aligned with this reference strain. Therefore, each allele was named according to the length in bp of the amplified fragment after alignment with the reference strain. For each marker and for a given isolate, one or two peaks were observed. Since C. albicans is diploid and since each marker tested a single locus, each peak observed was assigned to an allele. When we observed electromorphs harboring one signal for a given locus, we considered the isolates to be homozygous for this locus (4). Each isolate was therefore characterized by a profile of six alleles. MLST analysis using the seven consensual loci (2, 19). PCRs were carried out in a 50-␮l reaction volume containing about 1 ␮l of extracted DNA, 5 ␮l of 10⫻ PCR buffer, a 2.5 mM concentration of MgCl2, a 0.200 mM concentration of each deoxynucleotide triphosphate, 10 pmol of each primer, and 1.25 U of FastStart Taq DNA polymerase (Roche Applied Science). The PCR conditions consisted of an initial denaturation step of 6 min at 95°C, followed by 35 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 1 min, with a final extension step of 5 min at 72°C. The amplified fragments were sequenced by using the same primers as those used in the initial amplification. Sequencing reactions were prepared using the ABI PRISM Big Dye Terminator v3.1 cycle sequencing kit (PE Applied Biosystems) and were analyzed, after a purification step by Sephadex columns, on an ABI PRISM 3130xl genetic analyzer (PE Applied Biosystems). For all strains all seven loci were sequenced on both strands. The data were stored and analyzed with SeqScape software (Applied Biosystems). For each gene, distinct alleles were identified and numbered using the C. albicans MLST website (http://test1.mlst.net). The DST, the result of combination of the alleles at the different loci, was determined by using the same database. Statistical analysis. The numerical index of discriminatory power, based on the probability that two unrelated isolates sampled from the test population will be placed into different typing groups, was calculated for each method from the formula (8).

DP ⫽ 1 ⫺

1 n共n ⫺ 1兲

冘 S

xj共xj ⫺ 1兲

j⫽1

where DP ⫽ discriminatory power, s ⫽ the number of profiles, xj ⫽ the number of the population falling into the jth type, and n ⫽ the size of the population (n ⫽ 50). For MLST, a matrix of distances between pairwise sequences was constructed using the following distances: 0 for identical homozygous or heterozygous sites, 0.5 for homozygous or heterozygous sites sharing one

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nucleotide, and 1 otherwise. Relationships among isolates were shown as a dendrogram constructed by the unweighted pair group cluster method with Euclidean metric from the matrix of pairwise sequences. For MLP, the matrix of distances between isolates was constructed with Euclidean metric of the length of the PCR fragment at each locus and shown as a dendrogram constructed by the unweighted pair group cluster method. For both MLST and MLP clusterings, the cophenetic correlation coefficient was calculated with NTSYS-PC software (Numerical Taxonomy System; Exeter Software, Setauket, NY) to verify the adjustment between distance matrices and respective dendrogram-derived matrices. The number of clusters was determined with a 60% similarity cutoff value. The correlation between the distance matrices was performed with a Mantel test. Its statistical significance was tested with 1,000 random permutations. To assess the performance of the MLP typing system, we allocated one MLST group for each distinct group of MLP, corresponding to the greater number of isolates in the MLP group belonging to a MLST group. We calculated for each MLP group the number of isolates belonging to the allocated MLST group (true positives) and the number of isolates belonging to another MLST group (false positives). The remaining isolates of the allocated MLST group, not included in the corresponding MLP group, determined the false-negative values. The performance was determined in terms of the sensitivity and specificity. The following formulas were used: sensitivity ⫽ true positives/(true positives ⫹ false negatives) and specificity ⫽ true positives/(true positives ⫹ false positives). The average performance is the mean of the performance values (sensitivity or specificity) according to the number of isolates in each group.

RESULTS For MLP typing, the 50 isolates studied yielded 38 different profiles, with a discriminatory power index of 0.998. Five alleles and eight different combinations were detected for the CDC3 gene, 13 alleles and 18 combinations for the EF3 gene, and 17 alleles and 23 combinations for the HIS3 gene. For MLST typing, the 50 isolates yielded 40 distinct DSTs, with a discriminatory power index of 0.997. Twenty-one of these DSTs have been identified previously and appeared in the MLST database (http://test1.mlst.net) on 3 September 2007. They correspond to strains collected worldwide (10). Among the seven fragments sequenced, VPS13 generated the highest number of alleles (n ⫽ 21), three of which are newly identified in this study, followed by ZWF1 (19 alleles, three of which are newly identified in this study), AAT1a (15 alleles, one newly identified), MPIb (14 alleles, one newly identified), ACC1 (14 alleles, one newly identified), ADP1 (12 alleles, two newly identified), and SYA1 (10 alleles). The interpretation of one heterozygous position with uneven height of the peaks on the chromatogram was problematic for a given isolate. MLP clearly showed that a third allele was present for that isolate. When clonal subcultures were tested, the MLP strongly suggested that the initial clone was a mixture of two different isolates. This isolate was removed from the analysis and replaced by the following unrelated isolate of the CNR collection to keep the sample size at 50 isolates. The linear correlation between the two distance matrices was determined by a Mantel test, evaluating the Mantel coefficient and the Pearson product-moment correlation coefficient at 35,698.88 and 0.32, respectively. One thousand random permutations were performed, and no higher correlation coefficient was obtained. The level of significance of these coefficients was estimated at a P value of 1.2 ⫻ 10⫺6. Therefore, the distances between the isolates were highly correlated with both typing systems. To visualize this correlation, unweighted pair group method with arithmetic mean (UPGMA) trees were constructed. The

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FIG. 1. Dendrogram showing the genetic relatedness among 50 independent C. albicans isolates using MLST typing. Groups M1 to M8 were delineated with a 40% dissimilarity cutoff value. The percent dissimilarity (or genetic distance) is indicated on the horizontal bar. The percent values above 50% for groups represent 1,000 bootstrap cycles.

clustering techniques revealed a good adjustment between distances and cophenetic matrices with cophenetic correlation coefficients of 0.89 and 0.94 with MLST and MLP, respectively. With MLST, eight groups, called M1 to M8, were distinguished (Fig. 1). This clustering was identical to that obtained by using

a Burst analysis (http://linux.mlst.net/burst.htm) based on the frequency of MLST alleles (not shown). With MLP, nine groups, called S1 to S9, were determined, of which two were subdivided into two subgroups, S1.1 and S1.2 and S3.1 and S3.2, to correspond with the MLST group names (Fig. 2).

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FIG. 2. Dendrogram showing the genetic relatedness among 50 independent C. albicans isolates using MLP typing. Groups S1 to S9 were delineated with a 40% dissimilarity cutoff value. The percent dissimilarity (or genetic distance) scale is indicated on the horizontal bar. The percentage values above 50% for groups represent 1,000 bootstrap cycles.

Using the MLP, S3.1 and S3.2 were distant by several nodes while S1.1 and S1.2 were separated by one node only. The number of singletons was higher with the MLP partitioning (n ⫽ 8) than with the MLST partitioning (n ⫽ 5). Nevertheless, the performance measures of the classification by MLP versus MLST partitioning showed high levels of specificity and sensitivity of the subgroups determined by MLP

clustering (Table 1). Three groups (S2, S5, and S6) and four subgroups (S1.1, S1.2, S3.1, and S3.2) had 100% specificity, matching the corresponding MLST groups: S1.1 and S1.2 with M1, S2 with M2, S3.1 and S3.2 with M3, S5 with M5, and S6 with M6. The specificity average was about 76% on the whole, when all the isolates were considered, and increased to 83% when the singletons were excluded. The

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TABLE 1. Performance measures of the classification by MLP compared with that by MLST MLP groupa

No. of isolates

Specificity (%)

Corresponding MLST group

No. of isolates in MLST group

Sensitivity (%)

Singletons S1.1 S1.2 S2 S3.1 S3.2 S4 S5 S6 S7 S8 S9

8 11 2 6 4 2 2 2 2 3 4 4

38 100 100 100 100 100 50 100 100 67 25 50

Singletons M1

5 17

80 76

M2 M3

9 8

67 75

M4 M5 M6 M7 M8

3 2 2 2 2

33 100 100 100 50

a Group names refer to the groups defined in Fig. 1 and 2. The performance was measured for each MLP group in terms of sensitivity and specificity. These are defined as follows: sensitivity ⫽ TP/(TP ⫹ FN) and specificity ⫽ TP/(TP ⫹ FP), where TP, FN, and FP denote the numbers of true positives, false positives, and false negatives, respectively.

sensitivity average was 74% on the whole and 73% when the singletons were excluded. DISCUSSION Both MLP and MLST fulfill several criteria for a broadly useful tool for genotyping C. albicans isolates. First, all the isolates were typeable by both methods. Second, the variability within the selected sequence was sufficient to differentiate 50 unrelated isolates and the discriminatory power was ⬎0.99 for both methods. Third, the resulting data can be stored in digital form for subsequent analysis with the aid of specialized software. A theoretical disadvantage of both methods is that only very small regions of the C. albicans genome are analyzed, in contrast to techniques such as RAPD, which potentially examines the entire genome. However, the microsatellite loci selected are on different chromosomes (1) as well as the housekeeping genes for the MLST (2). Another major concern is reproducibility. This point is crucial for the construction of reliable databases containing all known strains within a species to which unknown organisms can be compared for classification (11). The reproducibility of MLST has been reported elsewhere (18) and is exceptionally high (99.72%). This is mainly due to the reliable sequencing kits currently used which generate easy-to-read chromatograms. However, to obtain reliable data, especially at the DNA strand ends, both DNA strands must be sequenced and compared in order to minimize incorrect base identification. For some heterozygous positions, the height of the peaks for different nucleotides may be unequal. This finding raised the possibility of a mixture of different clones or the possibility to have microorganisms polyploid for this specific sequence. Then, MLP is more suitable to visualize the number of alleles and to assess a mixture of different clones as reported here for one of the originally selected isolates. Reproducibility of MLP is more problematic since the migration conditions can interfere with the allocation of a length to a specific PCR product. The fragment calculated by the

GenScan software can slightly vary depending on the capillary and the dyes used (21). This is of little importance when all the isolates are tested using the same equipment and under the same conditions as done in a given laboratory. In our hands, the C. albicans reference strain used gives consistent results (6). Nevertheless, it seems mandatory to use a reference strain with all the alleles known as internal control standards. Additional standardization is needed when comparisons between laboratories are considered, as recently underlined for Aspergillus fumigatus typing using MLP (12). MLST has already been compared with random amplified polymorphic DNA, multilocus enzyme electrophoresis, and Ca3 Southern hybridization probe techniques (13) but never with MLP. Here we show that the two methods generate similar UPGMA trees and similar groupings. Moreover, distances between isolates were highly correlated with both typing systems as assessed by two statistical analyses, even if singletons were more frequent with MLP than with MLST. For the present study, the MLST typing required seven PCRs per isolate, leading to 700 chromatogram analyses. In contrast, for each isolate, only one PCR multiplex reaction and one analysis were necessary for MLP typing. Even though the cost of MLST typing could be reduced if not all seven loci were tested, if new software were to improve chromatogram analysis, and if the number of markers were increased for MLP to reduce the number of singletons, the workload and the cost would remain higher for MLST than for MLP. In conclusion, both methods are discriminatory and can be used interchangeably depending on local facilities and on the purpose of the study. For us, when a rapid result is requested on a limited number of isolates, such as when investigating a C. albicans epidemic in a clinical ward or when a large number of isolates are genotyped to find association with phenotypic and epidemiological data, as in the YEASTS program, MLP is less expensive and faster than MLST. In contrast, to compare new isolates to a large reference library of genotyped C. albicans strains, MLST is currently more accurate since no public database is available for MLP and additional standardization is needed before this can be achieved. ACKNOWLEDGMENTS We thank the following principal investigators of the YEASTS Group who contributed to the current database: in alphabetical order by city (other than Paris), Claire Bouges-Michel (Ho ˆpital Avicenne, Bobigny), Isabelle Poilane (Ho ˆpital Jean Verdier, Bondy), Jean Dunand (Ho ˆpital Ambroise Pare´, Boulogne), Guy Galeazzi (Ho ˆpital Louis Mourier, Colombes), Nathalie Fauchet (Centre Hospitalier Intercommunal de Cre´teil, Cre´teil), Elisabeth Forget (Ho ˆpital Beaujon, Clichy), Françoise Botterel and Christine Bonnal (Ho ˆpital du KremlinBiceˆtre, Biceˆtre), Odile Eloy (Ho ˆpital Mignot, Le Chesnay), Christine Lawrence (Ho ˆpital Raymond Poincare´, Garches), Marie-Françoise David and Liliana Mihaila (Ho ˆpital Paul Brousse, Villejuif), Elisabeth Chachaty and Olivier Adam (Institut Gustave Roussy, Villejuif); in Paris, Christian Chochillon (Ho ˆpital Bichat); Andre´ Paugam and Marie-The´re`se Baixench (Ho ˆpital Cochin); Muriel Cornet (Ho ˆpital de l’Ho ˆtel Dieu); Marie-Christine Escande (Curie); Svetlana Challier and Marie-Elisabeth Bougnoux (Necker); Ve´ronique Lavarde and Eric Dannaoui (Ho ˆpital Europeén Georges Pompidou); Annick Datry, Houria Laklache, Bader Lminmouni, and Sophie Brun (Ho ˆpital de la Pitie´-Salpe´trie`re); Jean-Louis Poirot (Ho ˆpital Saint Antoine); Claire Lacroix (Ho ˆpital Saint Louis); Didier Moissenet (Ho ˆpital Trousseau); Michel Develoux (Ho ˆ pital Tenon); Ste´phane Bonacorsi (Ho ˆ pital Robert Debre´).


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