Reagentless identification of human bifidobacteria by intrinsic fluorescence

Journal of Microbiological Methods 69 (2007) 100 – 106 www.elsevier.com/locate/jmicmeth

Reagentless identification of human bifidobacteria by intrinsic fluorescence Mohammed Salim Ammor ⁎, Susana Delgado, Pablo Álvarez-Martín, Abelardo Margolles, Baltasar Mayo Instituto de Productos Lácteos de Asturias (CSIC), Carretera de Infiesto s/n, 33300 Villaviciosa, Asturias, Spain Received 6 October 2006; received in revised form 21 November 2006; accepted 11 December 2006 Available online 16 December 2006

Abstract A new identification method for bifidobacteria species from the human gastrointestinal tract was developed based on the measurement and statistical analysis of the intrinsic fluorescence of aromatic amino acids (AAA) and nucleic acids (NA), following their excitation at 250 nm. The model was constructed by recording the fluorescence spectra of 53 Bifidobacterium strains of 10 different species, including the corresponding type strains, and validated by analyzing the spectra data from nine further problem strains. Principal components analysis (PCA) and factorial discriminant analysis (FDA) of the results showed the technique to distinguish between the isolates at the species level; the Bifidobacterium pseudolongum subspecies (globosum and pseudolongum) could also be distinguished. The proposed method provides a powerful, inexpensive and convenient means of rapidly identifying intestinal bifidobacteria, which could be of help for large probiotic surveys. © 2006 Elsevier B.V. All rights reserved. Keywords: Bifidobacterium; Bifidobacteria; Identification method; Fluorescence spectroscopy; Chemiometry

1. Introduction Bifidobacteria are Gram positive, non-spore-forming, nonmotile, rod-shaped, saccharolytic anaerobes that produce acetic and lactic acids from carbohydrates without the generation of CO2 (Scardovi, 1986). They belong to the class Actinobacteria, and are characterized by a high G + C content (between 55 and 67%) (Stackebrandt et al., 1997). Bifidobacteria are normal inhabitants of the human and animal gastrointestinal ecosystems, where they are found in relatively high numbers (around 109–1010 cfu g− 1) shortly after birth (Biavati et al., 2000). To date, more than 30 species have been described (www.dsmz.de/ microorganisms/html/bacteria.genus/bifidobacterium.html), of which 12 have been associated with the human gastrointestinal tract (GIT) (Ventura et al., 2004); these are thought to be important in maintaining the microbial balance necessary for health through their metabolic, trophic and protective activities (Ouwehand et al., 2002; Guarner and Malagelada, 2003). Because of their health-promoting effects, bifidobacteria species are common components of probiotics (Heller, 2001; ⁎ Corresponding author. Tel.: +34 985 89 12 31; fax: +34 985 89 22 33. E-mail address: [email protected] (M.S. Ammor). 0167-7012/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.mimet.2006.12.005

Leahy et al., 2005) and are the target organisms of most prebiotics (Tuohy et al., 2005). Over the last two decades, the development of molecular methods has led to a renewed interest in the microbiology of the GIT (Tannock, 1999a; Vaughan et al., 2000; Zoetendal et al., 2004), and searches for new probiotic bacteria have been undertaken (Dunne et al., 1999). These have involved the identification of a large number of strains of human origin and the demonstration of their beneficial properties (Dunne et al., 2001; Ouwehand et al., 2002) and safety traits (Salminen et al., 1998). However, the identification of bifidobacteria by conventional phenotypic procedures based on morphology, carbohydrate fermentation and enzymatic activity is tedious, time consuming, involves a large number of reagents, and is not reliable (Tannock, 1999b). Molecular methods are much more accurate (Satokari et al., 2003; Ward and Roy, 2005), but they are expensive and need qualified personnel. Fourier-transform infrared (FT-IR) spectroscopic analysis has been evaluated as an alternative to these methods (Mayer et al., 2003), but, although reliable, it requires expensive, dedicated equipment and trained personnel. Live bacteria contain a variety of intracellular biomolecules, such as tryptophan and other amino acids, nucleic acids and coenzymes, etc., that emit photons following their excitation in

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Table 1 Inventory of the strains used in this study Species (abbreviation)

Strain/isolate

Bifidobacterium adolescentis (Bad) Bifidobacterium animalis (Ban) Bifidobacterium bifidum (Bbf) Bifidobacterium breve (Bbv) Bifidobacterium catenulatum (Bct) Bifidobacterium dentium (Bdt) Bifidobacterium longum (Blg) Bifidobacterium pseudocatenulatum (Bpc) Bifidobacterium pseudolongum (Bpl) Bifidobacterium thermophilum (Btp) Escherichia coli Lactobacillus johnsonii Lactobacillus rhamnosus

LMG10502T; D114B; D115B; D118B; D120B; D121B; D122B1; D123B LMG10518T; E43 D11; D14; D115; D123; L22; L24; L64; L66; L71; L72; L73; L74 LMG13208T; NCIMB8807T LMG11043T; L21; L48; L51 F101; G61 LMG13197T; B93; B94; C56; E44; E71; E111; H64; H66; H95; L26; L42; L45; L47; M21; M25 LMG10505T; C35; C52; C53; E72; E91; E113; E134; E137; E138; H85; M41; M63 LMG11569T, LMG1157T LMG21813T DL232 G41 E42

LMG collection of the Belgian Coordinated Collections of Microorganisms, BCCM™, Microbiology Laboratory, University of Ghent, Ghent, Belgium. NCIMB, National Collection of Industrial, Marine and Food Bacteria, Aberdeen, Scotland. 1 Underlined strains were used as test strains and were not included in model construction. 2 Well known laboratory strain.

the ultraviolet region (Cantor and Schimmel, 1980). The fluorescent characteristics of many such molecules have been studied and, since they all have specific excitation and emission wavelength spectra (Hairston et al., 1997), they could be of use in biological detection techniques. The high sensitivity of fluorescence-based methods, their short data collection times, the possibility of performing in-situ measurements requiring no human contact with samples, and the possibility of monitoring large sample volumes continuously, are attractive properties for the detection (Estes et al., 2003) and identification (Leblanc and Dufour, 2002; Mason et al., 2003) of microorganisms. This paper reports a reagentless method for the identification of bifidobacteria requiring statistical analyses of the differential emission of intrinsic fluorescence by the aromatic amino acids (AAA) and nucleic acids (NA) following excitation at 250 nm. The spectra of 62 Bifidobacterium strains of 10 different species (including those of the type strains) were recorded in order to construct a model for identification purposes, which was subsequently validated using a series of nine test strains. 2. Methods and materials

Bifidobacteria strains cryopreserved in glycerol were subcultured twice in MRS agar (Biokar Diagnostics, Beauvais, France) containing 0.25% cysteine (Sigma, St. Louis, Mo., USA) (MRS + C) prior to the assays. Incubation proceeded at 37 °C for 48 h in an anaerobic chamber (MAC500; Down Whitley Scientific, West Yorkshire, UK) containing an atmosphere of 85% N2, 10% H2 and 5% CO2. E. coli was subcultured twice in LB medium (Biokar Diagnostics) at 37 °C. 2.2. Sample preparation One colony of each strain was propagated in 10 ml of MRS + C (LB for E. coli) broth for 24–48 h under the incubation conditions mentioned above. Cells were pelleted by centrifugation at 3800 rpm for 10 min, washed twice with 2 ml of 50 mM HEPES buffer (pH 7) (Sigma), and suspended in 500 μl of the same. The optical density (OD) of this suspension was determined at 600 nm using a Kontron spectrophotometer (Tegimenta AG, Rotkreuz, Switzerland). The OD600 nm of the suspension was adjusted to 0.5 prior to the measurement of fluorescence. Three independent cultures were assayed for each strain and 2 spectra each were collected — except for the strains used for the validation of the model, for which only one spectrum per independent culture was collected.

2.1. Strains and growth conditions 2.3. Fluorescence spectroscopy The 10 type strains used in this work were obtained from either the LMG collection of the Belgian Coordinated Collections of Microorganisms (BCCM™, University of Ghent, Ghent, Belgium) or the National Collection of Industrial, Marine and Food Bacteria, (NCIMB, Aberdeen, Scotland) (Table 1). Fifty-two wild Bifidobacterium isolates (previously identified by phenotypic and genotypic methods Delgado et al., 2006) belonging to the dominant populations of the feces and colonic mucosa of healthy individuals were also used. Two Lactobacillus strains and an Escherichia coli strain were assayed as outsider controls (Table 1).

The fluorescence spectra of the bacterial samples were obtained using a Cary Eclipse fluorescence spectrophotometer (Varian Inc., Palo Alto, Ca., USA) equipped with a thermostatcontrolled, right-angled, single cuvette holder. Samples (2 ml) were placed in a quartz cuvette and the fluorescence emission spectra (λEmi = 280–480 nm; resolution: 1 nm; slits width: 5 nm) recorded at 37 °C at an excitation wavelength of λExc = 250 nm. Under these conditions, a large number of fluorophores are excited, though these settings mainly corresponded to the fluorescence properties of aromatic amino acids in proteins and nucleotides in nucleic acids (Cantor and Schimmel, 1980).

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Fig. 1. AAA and NA fluorescence spectra of three Bifidobacterium species following excitation at 250 nm.

2.4. Mathematical treatment of the data The spectral data were analyzed using XLStat pro 7.5 software (Addinsoft, Paris, France). Pearson principal component analysis (normed PCA) was performed to transform the large number of potentially correlated factors into a smaller number of uncorrelated factors (i.e., principal components), and thus reduce the size of the data set. This multivariate treatment allows score plots of the samples to be drawn that represent the spectral patterns (Jolliffe, 2002). Neighboring points on these score plots represent similar spectra. The linearly independent principal components resulting from PCA were subjected to factorial discriminant analysis (FDA) by examining the spectral fluorescence data. The aim of this technique is to predict the likelihood of an observation (spectrum data) belonging to a previously-defined qualitative group (Ammor et al., 2004). Since the raw spectral data could not be used because of the strong correlation between the variables (the wavelengths), the uncorrelated principal component resulting from PCA was employed. 3. Results and discussion 3.1. Intrinsic fluorescence spectra of bifidobacteria and other bacterial species The intrinsic fluorescence of some bacteria has already been shown to allow their discrimination and identification at the genus, species and subspecies levels (Leblanc and Dufour, 2002; Estes et al., 2003; Ammor et al., 2004; Bhatta et al., 2006). AAA (phenylalanine, tryptophan, and tyrosine), NA, NADH and FAD have all been used as intrinsic fluorophores with that aim. However, with bifidobacteria species better results were obtained when targeting aromatic amino acids (AAA) and nucleic acids (NA) than when exciting NADH or FAD (Ammor et al., 2004; Leblanc and Dufour, 2002). The latter are metabolic coenzymes (the reduced form of a pyridine nucleotide and the oxidized form of flavine adenine dinucleotide), the cellular quantities of which are related to the

metabolic state: hence the fluorescence intensity obtained is closely related to this metabolic state. While these fluorophores show dramatic variation in their fluorescence intensities according to the growth stage, those of AAA and NA are unaffected until cell death (Estes et al., 2003; Ammor et al., 2004). Since bifidobacteria are strict anaerobes, and therefore their metabolic state is affected by the presence of oxygen during the period of sample preparation and fluorescence measurement, AAA and NA were selected as the intrinsic fluorophores to be measured in the present study. The fluorescence intensities of these fluorophores were not influenced by the growth stage of the bacteria and remained unchanged through the cellular cycle (data not shown). AAA and NA emission spectra were collected between 280 and 480 nm (λExc: 250 nm) for all strains. Fig. 1 shows the spectra for three Bifidobacterium species. They are characterized by a maximum located between 336 and 340 nm, and a small shoulder at about 417 nm which is shifted slightly to lower or higher wavelengths depending on the bacterium in question. Similar results have been reported for AAA and NA emission maxima in other bacteria (Leblanc and Dufour, 2002; Ammor et al., 2004). Large variations in the shape of the spectra were observed for the different Bifidobacteria, suggesting that their fluorescence spectra afford a type of ‘fingerprint’. A large variation in the shape of the spectra was also observed between the Bifidobacterium species and other dominant intestinal species such as Lactobacillus johnsonii, Lactobacillus rhamnosus and E. coli (data not shown), suggesting that this method could be used to detect and identify bifidobacteria species from human intestinal samples. 3.2. Model building A model was constructed using the results for 53 Bifidobacterium strains, which was then validated with nine test strains. The 318 spectral data collected for the 53 strains were pooled into one matrix and the data examined by PCA. The linear-independent factors resulting from this were then used as new variables to perform FDA based on the defined groups

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Table 2 Confusion matrix (learning-sample) for the Bifidobacterium species after performing FDA on the AAA + NA spectral data

From Bad From Ban From Bbf From Bbv From Bct From Bdt From Blg From Bpc From Bpl From Btp Sum

To Bad a

To Ban

To Bbf

To Bbv

To Bct

To Bdt

To Blg

To Bpc

To Bpl

To Btp

Sum

42 13.2% – – – – – – – – – – – – – – – – – – 42 13.2%

– – 12 3.8% – – – – – – – – – – – – – – – – 12 3.8%

– – – – 60 18.9% – – – – – – – – – – – – – – 60 18.9%

– – – – – – 12 3.8% – – – – – – – – – – – – 12 3.8%

– – – – – – – – 24 7.5% – – – – – – – – – – 24 7.5%

– – – – – – – – – – 12 3.8% – – – – – – – – 12 3.8%

– – – – – – – – – – – – 78 24.5% – – – – – – 78 24.5%

– – – – – – – – – – – – – – 60 18.9% – – – – 60 18.9%

– – – – – – – – – – – – – – – – 12 3.8% – – 12 3.8%

– – – – – – – – – – – – – – – – – – 6 1.9% 6 1.9%

42 13.2% 12 3.8% 60 18.9% 12 3.8% 24 7.5% 12 3.8% 78 24.5% 60 18.8% 12 3.8% 6 1.9% 318 100%

Apparent error rate (resubstitution error rate calculated with the learning-sample): 0%. a See Table 1 for a summary of species abbreviations.

constituting the dependent variable. The latter was composed of 10 groups representing the total number of Bifidobacterium species examined (see Table 1). The confusion matrix resulting from the FDA provided 100% correct classification of the observations (strain spectra) to the corresponding taxonomic group (Table 2). The model was therefore considered well constructed and validation experiments were performed. Fig. 2 shows the factorial map defined by the first discriminant factors (DF), DF1 and 2. These factors explained 52.4% of the total variance (nine factors explained the total variance). The observations belonging to the same statistical

(taxonomic) group are close to each other on this map, confirming the accuracy of the method. Further, clear discrimination between the strains at the species level was observed. According to DF1, which explained 31.2% of the total variance, Bifidobacterium bifidum isolates are well discriminated from all other Bifidobacterium species; this agrees with the results of other authors who report its clear distinction from other species of the same genus at the genetic (Scardovi, 1986; Germond et al., 2002) and physiological (Germond et al., 2002; Ventura et al., 2004) levels. Moreover, DF2 explained 21.2% of the total variance, establishing two

Fig. 2. Discriminant analysis similarity map determined by discriminant factors 1 and 2, for the AAA and NA fluorescence spectral data of the different Bifidobacterium species used in this study.

104

Table 3 Classification, membership probability and squared distances to the Bifidobacterium species’ group centroids D2 [i, centroid (group)]

Probability Group

Bad

Ban

Bbf

Bbv

Bct

Bdt

Blg

Bpc

Bpl

Btp

Bad

Ban

Bbf

Bbv

Bct

Bdt

Blg

Bpc

Bpl

Btp

D122B-1-1 D122B-1-2 D122B-1-3 L71-1-1 L71-1-2 L71-1-3 D115-1-1 D115-1-2 D115-1-3 B93-1-1 B93-1-2 B93-1-3 L42-1-1 L42-1-2 L42-1-3 M21-1-1 M21-1-2 M21-1-3 C53-1-1 C53-1-2 C53-1-3 E138-1-1 E138-1-2 E138-1-3 M63-1-1 M63-1-2 M63-1-3

Bct Bad Bad Bbf Bbf Bbf Bbf Bbf Bbf Blg Blg Blg Blg Blg Blg Blg Blg Blg Bpc Bpc Bpc Bpc Bpc Bpc Bpc Bpc Bpc

– 1.0 1.0 – – – – – – – – – – – – – – – – – – – – – – – 0.1

– – – – – – – – – – – – – – – – – – – – – – – – – – –

– – – 1.0 1.0 0.9 1.0 1.0 1.0 – – – – – – – – – – – – – – – – – –

– – – – – – – – – – – – – – – – – – – – – – – – – – –

1.0 – – – – – – – – 0.1 – – – – – – – – – – – – – – – – –

– – – – – – – – – – – – – – – – – – – – – – – – – – –

– – – – – 0.1 – – – 0.9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 – – – – – – – – –

– – – – – – – – – – – – – – – – – – 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9

– – – – – – – – – – – – – – – – – – – – – – – – – – –

– – – – – – – – – – – – – – – – – – – – – – – – – – –

515.2 596.8 576.9 706.3 600.8 591.3 859.5 747.8 727.5 489.3 552.8 536.0 399.3 443.3 675.2 624.7 536.4 681.9 728.4 909.0 748.2 699.3 877.1 3041.9 813.7 1010.1 725.9

564.5 684.0 625.6 744.2 658.7 643.5 857.5 742.8 732.2 476.8 565.9 531.6 395.4 429.5 758.5 592.8 546.4 745.9 725.6 888.7 781.8 783.1 966.1 3123.4 851.4 1070.1 793.6

574.7 696.9 635.2 612.9 552.5 534.3 704.3 622.3 599.5 459.5 578.0 529.0 445.6 477.5 765.5 613.2 535.9 683.8 813.5 950.7 800.2 690.5 862.5 3027.2 836.8 1102.8 826.5

593.3 730.1 616.8 714.7 668.9 713.1 996.1 727.5 821.9 453.7 565.4 525.7 389.5 498.2 676.2 657.1 568.5 720.0 789.0 1027.5 868.2 850.0 897.8 2884.7 787.0 1009.4 878.5

494.0 617.6 608.6 677.8 642.1 596.4 966.2 726.4 729.3 423.6 554.5 506.1 369.1 488.3 728.7 596.9 505.8 631.2 740.7 914.1 700.0 703.1 801.4 3000.8 793.4 1027.8 842.5

574.5 662.4 686.9 681.3 687.9 614.0 915.0 749.5 707.7 558.3 646.8 586.7 412.3 561.5 759.5 656.1 605.3 789.4 814.6 920.8 802.7 733.7 931.0 3361.4 818.4 1138.9 790.8

513.4 638.3 636.9 686.7 581.5 539.2 840.8 710.6 685.5 421.3 539.6 485.4 353.9 424.5 663.9 534.1 492.8 575.1 700.9 904.4 739.0 733.0 877.8 3057.0 796.7 1028.8 767.8

511.2 604.8 638.3 733.5 639.6 630.2 906.6 774.9 745.3 469.7 625.0 554.7 418.6 436.7 679.6 607.5 566.2 703.4 666.7 856.6 663.2 636.0 796.8 2840.2 752.4 960.7 721.1

612.8 717.0 696.0 833.6 680.7 669.2 994.4 847.9 817.4 427.9 639.8 555.8 421.5 497.2 708.6 664.7 565.1 693.6 690.9 969.5 803.4 879.4 1018.7 2899.7 789.2 1020.0 835.0

620.0 735.0 679.2 819.7 608.1 677.1 851.8 828.2 780.3 540.3 653.9 606.3 455.6 478.0 695.6 671.4 601.5 779.6 798.6 949.1 804.0 781.8 997.0 3045.6 890.3 1091.7 798.8

See Table 1 for a summary of species abbreviations.

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Observation

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105

Table 4 Prior-classification, post-classification, membership probability, observation scores and squared distances to the B. pseudolongum subspecies’ group centroids Observation

LMG11569-1-1 LMG11569-1-2 LMG11569-2-1 LMG11569-2-2 LMG11569-3-1 LMG11569-3-2 LMG11571-1-1 LMG11571-1-2 LMG11571-2-1 LMG11571-2-2 LMG11571-3-1 LMG11571-3-2 a b

Prior

Bpl-g Bpl-g Bpl-g Bpl-g Bpl-g Bpl-g Bpl-p Bpl-p Bpl-p Bpl-p Bpl-p Bpl-p

Post

Bpl-g Bpl-g Bpl-g Bpl-g Bpl-g Bpl-g Bpl-p Bpl-p Bpl-p Bpl-p Bpl-p Bpl-p

Probability of belonging to group Bpl-g a

Bpl-l b

1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0

0 0 0 0 0 0 1.0 1.0 1.0 1.0 1.0 1.0

F1

27.2 27.2 29.3 26.0 27.8 25.9 − 26.8 − 28.0 − 27.2 − 27.5 − 27.9 − 26.2

Square distance (D2) from the centroid of group i, centroid (Bpl-g)

i, centroid (Bpl-l)

8.2 8.3 7.6 5.6 5.1 8.0 2926.0 3052.5 2969.5 3003.1 3039.9 2855.9

2973.9 2973.5 3197.0 2838.4 3035.1 2834.4 7.9 6.7 8.1 8.2 2.7 3.8

B. pseudolongum subsp. globosum. B. pseudolongum subsp. pseudolongum.

clusters, the first encompassing Bifidobacterium adolescentis, Bifidobacterium catenulatum and Bifidobacterium pseudocatenulatum, and the second including Bifidobacterium species. These results concur with published data on the high DNArelatedness and similarities in fermentation profiles between B. adolescentis, B. catenulatum and B. pseudocatenulatum (Scardovi, 1986), and with their inclusion in a distinct cluster at the genetic level (Germond et al., 2002). 3.3. Model validation To validate the model, nine strains not included in its construction were examined. The spectral kinetics data for these test strains were collected and pooled in a matrix. PCA was then used with the model and validating data matrices. The model matrix provided the principal observations, while the validating matrix provided the supplementary observations; thus, the supplementary observations did not contribute to the inertia of the model. The linear-independent factors (principal and supplementary data) were used as new variables (principal and supplementary data) in FDA analysis. Although, one of the 27 observations corresponding to these supplementary observations was misclassified (Table 3), it did not invalidate the overall results, and all nine test strains were correctly identified (see Table 1). The misclassified observation corresponded to one observation of a B. adolescentis strain, which was classified as B. catenulatum — a closely related species. This misclassification might be explained by the small number of B. adolescentis strains (6) used in the construction of the model. However, since new data can be added to the model, the analysis of new Bifidobacterium species/strains would increase its robustness. 3.4. Investigation of the ability of intrinsic fluorescence to discriminate bifidobacteria at the subspecies level The ability of intrinsic fluorescence to discriminate at the subspecies level was investigated using type strains of B.

pseudolongum subsp. globosum and B. pseudolongum subsp. pseudolongum. The AAA and NA spectra of both strains were pooled in one matrix and Pearson PCA performed. The linearly independent factors were then used as new variables to perform FDA. Before performing this analysis, two groups were created, one formed by the B. pseudolongum subsp. globosum (Bpl-g) observations, and the other by B. pseudolongum subsp. pseudolongum (Bpl-p) observations. Both groups constituted the dependent variable. Table 4 shows the prior-classification, post-classification, and membership probability analysis, of the 12 observations following FDA, after determining the FD1 score, and after calculating the squared distances to the B. pseudolongum subsp. globosum and B. pseudolongum subsp. pseudolongum groups’ centroids. The model unambiguously classified 100% of observations correctly. The Wilkis’ Lambda test showed that at the level of significance α = 0.050 the difference between the group centroids was significant. Thus, intrinsic fluorescence is able to discriminate bifidobacteria at the subspecies level. However, the analysis of more strains of different subspecies is needed for the results to be conclusive. 4. Conclusions These results show that fluorescence spectroscopy is a rapid and accurate method for identifying Bifidobacterium isolates at the species and subspecies levels. The model could easily be adapted for other bacterial species. The method is amenable to semi- or complete automation, which could be useful for large probiotic surveys, complementing or even replacing the more expensive and time-consuming phenotypic and genotypic assays currently in use. Acknowledgments M. S. Ammor was the recipient of a postdoctoral fellowship from the “Secretaría de Estado de Universidades e Investigación”, Spanish Ministry of Education and Science (Ref.

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