Molecular and antimicrobial susceptibility profiling of ...

Infection, Genetics and Evolution 44 (2016) 376–381

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Research paper

Molecular and antimicrobial susceptibility profiling of atypical Streptococcus species from porcine clinical specimens Luisa Z. Moreno a, Carlos E.C. Matajira a, Vasco T.M. Gomes a, Ana Paula S. Silva a, Renan E. Mesquita a, Ana Paula G. Christ b, Maria Inês Z. Sato b, Andrea M. Moreno a,⁎ a Departamento de Medicina Veterinária Preventiva e Saúde Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Av Prof Dr. Orlando Marques de Paiva, 87, Cidade Universitária, CEP 05508 270, São Paulo, Brasil b Companhia Ambiental do Estado de São Paulo (CETESB). Av Prof Frederico Hermann Jr, 345, Pinheiros, CEP 05489 900, São Paulo, Brasil

a r t i c l e

i n f o

Article history: Received 7 June 2016 Received in revised form 28 July 2016 Accepted 31 July 2016 Available online xxxx Keywords: Streptococcus Swine MALDI-TOF Sequencing Susceptibility Profiling

a b s t r a c t The Streptococcus species present broad phenotypic variation, making identification difficult using only traditional microbiological methods. Even though Streptococcus suis is the most important species for the worldwide swine industry, other Streptococcus species appear to be able to cause disease in swine and could represent a higher underestimated risk for porcine health. The aim of this study was to identify Streptococcus-like isolates by MALDI-TOF MS and 16S rRNA sequencing and further molecular and antibiotic susceptibility characterization of the atypical Streptococcus species capable of causing disease in swine. Fifty presumptive Streptococcus isolates from diseased pigs isolated from different Brazilian States between 2002 and 2014 were evaluated. Among the studied isolates, 26% were identified as Streptococcus hyovaginalis, 24% as Streptococcus plurianimalium, 12% as Streptococcus alactolyticus, 10% as Streptococcus hyointestinalis, and the remaining isolates belonged to Streptococcus henryi (6%), Streptococcus thoraltensis (6%), Streptococcus gallolyticus (6%), Streptococcus gallinaceus (4%), Streptococcus sanguinis (4%), and Streptococcus mitis (2%). The Streptococcus isolates were successfully identified by spectral cluster analysis and 16S rRNA sequencing with 96% of concordance between the techniques. The SE-AFLP analysis also supported Streptococcus species distinction and enabled further observation of higher genetic heterogeneity intra-species. The identified Streptococcus species presented variable MIC values to β-lactams, enrofloxacin and florfenicol, and high resistance rates to tetracyclines and macrolides, which appear to be directly related to the industry's antimicrobial usage and resistance selection. © 2016 Elsevier B.V. All rights reserved.

1. Introduction The Streptococcus genus is composed of Gram-positive cocci that are characterized as non-motile, catalase negative and facultative anaerobes. Several pathogenic species comprise the genus and may cause serious impact on both human and animal health (Köhler, 2007). For the swine industry, however, the Streptococcus suis is the most important species worldwide (Goyette-Desjardins et al., 2014). Most veterinary diagnostic laboratories are limited to S. suis identification by species-specific PCR, while the non S. suis isolates are just classified as Streptococcus-like or Streptococcus sp. The Streptococcus species present broad phenotypic variation, making identification difficult using only traditional microbiological methods. The application of molecular techniques, especially 16S rRNA sequencing, revolutionized the Streptococcus genus taxonomy with over 50 species and six species groups (Gao et al., 2014; Richards

et al., 2014). However, the introduction of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), as a rapid and accurate tool for microbiological diagnosis, appears to enable proper Streptococcus differentiation (Arinto-Garcia et al., 2015; Doern and Burnham, 2010; Wang et al., 2012) facilitating the identification of underestimated Streptococcus species that may represent a risk for animal health. The aim of this study was to identify Streptococcus-like isolates, previously characterized as non S. suis, by MALDI-TOF MS and 16S rRNA sequencing and further molecular and antibiotic susceptibility characterization of the atypical Streptococcus species capable of causing disease in swine.

2. Material and methods 2.1. Samples and bacterial isolation

⁎ Corresponding author at: FMVZ/USP, Av. Prof. Dr. Orlando Marques de Paiva, 87, Cidade Universitária, CEP 05508 270, São Paulo, Brazil. E-mail address: [email protected] (A.M. Moreno).

http://dx.doi.org/10.1016/j.meegid.2016.07.045 1567-1348/© 2016 Elsevier B.V. All rights reserved.

Fifty presumptive Streptococcus isolates were studied. The Streptococcus-like colonies were isolated from lung, heart, central nervous

L.Z. Moreno et al. / Infection, Genetics and Evolution 44 (2016) 376–381

system (CNS), joint, urine, vaginal discharge, and skim of diseased pigs, from different Brazilian States between 2002 and 2014, presenting encephalitis, arthritis, pneumonia, metritis, urinary tract infection and septicemia. Porcine samples were plated on Columbia Blood Agar base (Oxoid Limited, Basingstone, Hants, England), containing 5% bovine sterile blood and the SR0126 supplement (Oxoid Limited, Basingstone, Hants, England), for Streptococcus selective isolation and incubated for 24 h at 37 °C. At first the isolates were characterized as Streptococcuslike by colony morphology and were screened for S. suis by PCR (Okwumabua et al., 2003).

377

2.4. SE-AFLP genotyping Single enzyme amplified fragments length polymorphism (SE-AFLP) was applied for Streptococcus genotyping according to McLauchlin et al. (2000) protocol. DNA fragments were detected through electrophoresis at 24 V for 26 h in 2% agarose gel stained with BlueGreen® (LGC Biotecnologia, São Paulo, Brazil). SE-AFLP fingerprints were analyzed using the Dice coefficient by means of Bionumerics 7.5 software (Applied Maths NV, Saint-Martens-Latem, Belgium) to generate the dendrogram. A cut-off value of 90% of genetic similarity was applied for clusters analysis (van Belkum et al., 2007).

2.2. MALDI-TOF MS bacterial identification

2.5. Antimicrobial susceptibility profiling

For MALDI-TOF MS sample preparation, ethanol/formic acid protocol (Kuhnert et al., 2012) was applied for bacterial protein extraction. The protein suspension (1 μL) was transferred to a polished steel MALDI target plate (Bruker Daltonik) and overlaid with 1 μL of matrix (10 mg α-cyano-4-hydroxy-cinnamic acid ml − 1 in 50% acetonitrile/ 2.5% trifluoroacetic acid). Bacterial mass spectra (2–20 kDa range) were acquired using Microflex™ mass spectrometer (Bruker Daltonik). Each sample was distributed over three spots and measured twice. Primarily, for MALDI-TOF MS identification, the captured spectra were loaded into MALDI BioTyper™ 3.0 and compared with the manufacturer's library. Standard Bruker interpretative criteria were applied; scores ≥2.0 were accepted for species assignment and scores ≥1.7 but ≤2.0 for genus identification. For further analysis, the spectral replicates were used to generate a main spectrum for each isolate in BioNumerics 7.5 (Applied Maths). The cluster analysis was performed using the number of different peaks detected and UPGMA method to generate a dendrogram.

The minimal inhibitory concentration (MIC) was determined by broth microdilution technique as recommended by the Clinical and Laboratory Standards Institute for fastidious organisms (CLSI, 2013) using Sensititre® Standard Susceptibility MIC Plates BOPO6F (TREK Diagnostic Systems/Thermo Fisher Scientific, Waltham, MA, USA). S. pneumoniae ATCC 49619 and S. aureus ATCC 29213 were used as internal quality control. The MIC50 and MIC90 values for the respective antimicrobials were determined according to Schwarz et al. (2010) for the Streptococcus species with more than six isolates.

2.3. Species confirmation by 16S rRNA sequencing For the species confirmation, 16S rRNA sequencing was performed on all studied isolates. DNA extraction was performed according to Boom et al. (1990) protocol with previous enzymatic treatment with lysozyme (100 mg) and proteinase K (20 mg) (US Biological) at 37 °C for 60 min. Twomey et al. (2012) primers were applied for partial gene amplification and sequencing was performed by the Human Genome Research Center (University of São Paulo, Brazil). A phylogenetic tree was constructed using the maximum-likelihood method by Mega 5.10 (Tamura et al., 2011). The DNA sequences from this study were deposited in GenBank under accession numbers KR819485, KR819488, KR819489, KR819493 - KR819502, KX485314 - KX485315, KX500122 - KX500156.

3. Results A total of 50 presumptive Streptococcus isolates were identified as non S. suis and were selected for further phenotypic and molecular analysis. Initially, the isolates were grouped according to their isolation sites (Table 1). MALDI-TOF MS successfully identified all isolates with log (score) values N 2.0. Among the fifty studied isolates, 26% were identified as Streptococcus hyovaginalis, 24% as Streptococcus plurianimalium, 12% as Streptococcus alactolyticus, 10% as Streptococcus hyointestinalis, and the remaining isolates belonged to Streptococcus henryi (6%), Streptococcus thoraltensis (6%), Streptococcus gallolyticus (6%), Streptococcus gallinaceus (4%), Streptococcus sanguinis (4%), and Streptococcus mitis (2%) (Table 1). The MALDI-TOF MS spectral cluster analysis enabled the distinction of the Streptococcus isolates according to the identified species (Fig. 1). The 16S rRNA sequencing presented 96% agreement with MALDI-TOF MS for species identification; only two S. plurianimalium isolates were misidentified as S. hyovaginalis by MALDI-TOF MS (SS224 and SS296). The topology of both spectral dendrogram and phylogeny tree was also maintained (Fig. S1), demonstrating the closest relationship between the clusters comprised by: S. hyovaginalis, S. plurianimalium and S. thoraltensis; S. gallolyticus and S. alactolyticus; S. mitis and S. sanguinis.

Table 1 Distribution of Streptococcus species according to isolation site and identified species. Species Identification

S. hyovaginalis S. plurianimalium S. alactolyticus S. hyointestinalis S. henryi S. thoraltensis S. gallolyticus S. sanguinis S. gallinaceus S. mitis Total a b

Isolation site CNSa N (%)

Respiratory N (%)

Genitourinary N (%)

Joints N (%)

Othersb N (%)

3 (37.5) 3 (37.5) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 2 (25.0) 0 (0.0) 0 (0.0) 0 (0.0) 8 (100)

0 (0.0) 0 (0.0) 4 (28.6) 5 (35.7) 1 (7.1) 0 (0.0) 0 (0.0) 2 (14.3) 1 (7.1) 1 (7.1) 14 (100)

10 (45.5) 5 (22.7) 2 (9.1) 0 (0.0) 0 (0.0) 3 (13.6) 1 (4.5) 0 (0.0) 1 (4.5) 0 (0.0) 22 (100)

0 (0.0) 1 (100.0) 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 (100)

0 (0.0) 3 (60.0) 0 (0.0) 0 (0.0) 2 (40.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 5 (100)

CNS - central nervous system. Others – peritoneum, heart or skin lesion.

Total N (%)

13 (26.0) 12 (24.0) 6 (12.0) 5 (10.0) 3 (6.0) 3 (6.0) 3 (6.0) 2 (4.0) 2 (4.0) 1 (2.0) 50 (100)

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Fig. 1. Dendrogram showing the relationship among Streptococcus spp. isolate protein spectral profiles (*S. plurianimalium identification confirmed by 16S rRNA sequencing).

The SE-AFLP enabled the genotyping of all studied isolates into 26 band patterns (P1 to P26) and presented higher genetic heterogeneity for some Streptococcus species (Fig. 2). The fifty isolates were grouped into three main clusters with over 60% of genetic similarity. This composition maintained the genus taxonomic relationship as previously observed for the spectral cluster analysis and 16S rRNA phylogeny. The S. henryi, S. gallolyticus, S. gallinaceus, and S. sanguinis presented the highest genetic heterogeneity. The S. plurianimalium and S. hyovaginalis were typed into six and four AFLP profiles (P13–P18 and P22–P25, respectively) with over 70% of genetic similarity, while for S. alactolyticus and S. hyointestinalis three profiles were detected for each (P19–P21 and P10–P12, respectively) with over 80% of genetic similarity.

The MIC values are presented in Tables 2 and 3. The S. plurianimalium isolates presented high MICs to tetracyclines, fluoroquinolones, macrolides, sulfonamides, clindamycin and tiamulin, while they were all susceptible to the tested β-lactams (Table 2). Even though most isolates presented low MIC to gentamicin and neomycin, they were all resistant to spectinomycin. Similarly, the S. hyovaginalis isolates also presented high MIC values to tetracyclines, macrolides and clindamycin, and the lowest MICs to gentamicin and neomycin (Table 2). A group of eight (urine) isolates presented distinct susceptibility profile with low MIC values to the β-lactams and florfenicol. Interestingly, these isolates comprise the SE-AFLP profile P25 that demonstrate the isolates clonality and its segregation from the remaining S. hyovaginalis studied. Nevertheless, S. hyovaginalis presented greater MIC variation to β-lactams, enrofloxacin and florfenicol than S. plurianimalium isolates.

L.Z. Moreno et al. / Infection, Genetics and Evolution 44 (2016) 376–381

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Fig. 2. Dendrogram showing the relationship among the SE-AFLP patterns from porcine Streptococcus spp. isolates.

In contrast, the studied S. alactolyticus and S. hyointestinalis isolates presented the highest MIC values to most of the tested antimicrobials (Table 3), including the β-lactams. For the remaining studied species, greater MIC values variation was observed (Table 3), and S. sanguinis and S. mitis were considered the most susceptible species for the tested antimicrobials. 4. Discussion Different Streptococcus species appear to be able to cause disease in swine and could represent a higher underestimated risk for porcine health. To date, S. hyovaginalis, S. alactolyticus, S. thoraltensis, S. hyointestinalis, S. plurianimalium, S. gallolyticus and S. sanguinis had already been reported as porcine isolates (Baele et al., 2001; Devriese et al., 1997; Facklam, 2002; Janda, 2014). Here we also present S. henryi, S. gallinaceus and S. mitis as potential swine pathogens. Most S. hyovaginalis and S. thoraltensis were isolated from the genitourinary system as previously described (Devriese et al., 1997). The remaining identified species were originated from distinct isolation sites suggesting their ability to colonize different tissues and possibly a higher potential for dissemination. It is also noteworthy that herds H1, H2 and H4 presented more than one Streptococcus species isolated from diseased animals. This supports the hypothesis that some unusual Streptococcus species are indeed an underrated risk for swine health and go unnoticed in most production systems. The Streptococcus isolates were successfully identified by spectral cluster analysis and 16S rRNA sequencing with high concordance between the techniques. The only two misidentified isolates (SS224

and SS296) by MALDI-TOF MS correspond to the closely related species S. plurianimalium and S. hyovaginalis. Even though the phylogenetic proximity between the species (Facklam, 2002) could justify the misidentification, the spectral cluster analysis already demonstrated that these isolates clustered among S. plurianimalium (Fig. 1). Therefore, the protein spectral cluster analysis appears to present higher discriminatory power than the direct identification by BioTyper™. MALDI-TOF MS typing has been applied to few Streptococcus species specific studies (Arinto-Garcia et al., 2015; Doern and Burnham, 2010; Wang et al., 2012) and proved to be a valuable tool for genus and species identification. The technique presents high reproducibility, as observed for the technical replicates, and also presents high specificity; the sensibility appears to be more affected by closely related species and even subspecies typing (Arinto-Garcia et al., 2015). Our results corroborate the significance of protein spectral analysis for Streptococcus diagnosis; furthermore, the topology of the spectral dendrogram appears to sustain the species phylogenetic similarities which support MALDI-TOF MS typing as a rapid and accurate alternative for 16S rRNA sequencing. The SE-AFLP analysis also enabled the distinction of the Streptococcus isolates into three main clusters. This composition sustained the genus taxonomic relationship as previously observed for the spectral cluster analysis. Even though SE-AFLP presented almost complete species differentiation, higher genetic heterogeneity was observed intra-species. With the exception of S. mitis that represented only one isolate, all the remaining identified Streptococcus species presented more than one SE-AFLP profiles which appear to be closely related to the isolates origin, especially herd.

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Table 2 MIC values distribution (mg/L), MIC50 and MIC90 of S. hyovaginalis and S. plurianimalium strains against tested antibiotics. Strain

Species

PEN

AMP

TIO

CTET

OXY

DANO

ENRO

SDM

SXT

FFN

GEN

NEO

TIA

U3 U9 U10 U19 U23 U24 U36 U38 U39 U52 SS73 Ss311 Ss312 MIC50 MIC90 U25 U26 U34 U37 U41 Ss222 Ss223 Ss224 Ss243 Ss244 Ss295 Ss296 MIC50 MIC90

S. S. S. S. S. S. S. S. S. S. S. S. S.

hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis hyovaginalis

S. S. S. S. S. S. S. S. S. S. S. S.

plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium plurianimalium

≤0.12 ≤0.12 ≤0.12 ≤0.12 ≤0.12 ≤0.12 ≤0.12 ≤0.12 4.0 0.25 2.0 4.0 8.0 ≤ 0.12 4.0 0.5 1.0 1.0 0.25 0.25 0.25 0.25 0.25 0.25 0.5 0.5 0.5 0.25 1.0

≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 4.0 ≤0.25 1.0 4.0 8.0 ≤0.25 4.0 ≤0.25 0.5 0.5 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 0.5 ≤0.25 0.5

≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 1.0 ≤0.25 ≤0.25 1.0 2.0 ≤0.25 1.0 ≤0.25 ≤0.25 0.5 ≤0.25 ≤0.25 0.5 0.5 ≤0.25 ≤0.25 ≤0.25 0.5 0.5 ≤0.25 0.5

N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 1.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0

N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 1.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0

N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 1.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0

1.0 2.0 2.0 2.0 1.0 2.0 2.0 1.0 N4.0 2.0 1.0 0.5 N4.0 2.0 N4.0 2.0 2.0 N4.0 2.0 2.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0

N512.0 N512.0 N512.0 N512.0 N512.0 ≤256.0 N512.0 N512.0 N512.0 N512.0 N512.0 ≤256.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0

≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 N4/72 N4/72 ≤2/38 ≤2/38 N4/72 ≤2/38 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72 N4/72

1.0 1.0 1.0 1.0 1.0 1.0 1.0 4.0 N16.0 N16.0 2.0 N16.0 N16.0 1.0 N16.0 N16.0 N16.0 2.0 N16.0 N16.0 1.0 2.0 2.0 2.0 2.0 N16.0 N16.0 2.0 N16.0

≤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 ≤1.0 ≤1.0 2.0 ≤1.0 ≤1.0 ≤1.0 ≤1.0 ≤1.0 ≤1.0 2.0 N32.0 N32.0 ≤1.0 2.0

≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 N64.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 8.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0

≤0.5 N128.0 ≤0.5 N128.0 1.0 N128.0 2.0 N128.0 4.0 N128.0 ≤0.5 N128.0 1.0 N128.0 1.0 N128.0 N64.0 N128.0 N64.0 N128.0 1.0 N128.0 2.0 16.0 ≤0.5 N128.0 1.0 N128.0 N64 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0 N64.0 N128.0

SPE

TYLT

TIL

TUL

CLI

N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 1.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 2.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0

N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 16.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 8.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0

N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 ≤1.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 ≤1.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0

N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 ≤ 0.25 N 32.0 N 32.0 N32.0 N32.0 N 32.0 N 32.0 8.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N 32.0 N32.0 N32.0

PEN – penicilin, AMP – ampicillin, TIO – ceftiofur, CTET – chlortetracyclin, OXY – oxytetraciclin, DANO – danofloxacin, ENRO – enrofloxacin, SDM – sulphadimethoxine, SXT - trimethoprim/ sulfamethoxazole, FFN – florfenicol, GEN – gentamicin, NEO – neomycin, TIA – tiamulin, SPE – spectomycin, TYLT – tylosin tartrate, TIL – tilmicosin, TUL – tulathromycin, CLI – clindamycin.

Pulsed-field gel electrophoresis (PFGE) has also been applied to few pathogenic Streptococcus species genotyping, especially S. pneumoniae, S. agalactiae and S. suis, and presented relative high genetic variability with few correlations with strain origin and serotypes (Pillai et al., 2009; Richter et al., 2002; Vela et al., 2003). In this study, SE-AFLP is proposed not only for Streptococcus intra-species analysis as a faster,

more economically viable and less troublesome genotyping technique, but also for primary Streptococcus species differentiation. Interestingly, we also observed the existence of co-infection of different Streptococcus species in one animal; strains U34 (S. plurianimalium) and U35 (S. alactolyticus) were isolated from the urine of the same animal of herd H1. It is also noteworthy that in H1 it was observed the

Table 3 MIC values distribution (mg/L) of S. alactolyticus, S. hyointestinalis, S. gallolyticus, S. henryi, S. thoraltensis, S. gallinaceus, S. sanguinis and S. mitis against tested antibiotics. Strain

Species

PEN

AMP

TIO

CTET

OXY

DANO

ENRO

SDM

SXT

FFN

GEN

NEO

TIA

SPE

TYLT

TIL

TUL

CLI

U35 Ss253 Ss229 Ss306 U27 Ss221 Ss192 Ss240 Ss275 Ss307 Ss148 Ss298 SS319 U118 Ss185 Ss284 Ss285 U49 U53 U54 Ss255 Ss286 Ss162 Ss264 Ss233

S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S. S.

N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 ≤0.12 ≤0.12 4.0 0.25 4.0 0.25 0.5 ≤0.12 N16.0 ≤0.12 ≤0.12 ≤0.12 ≤0.12 ≤0.12 ≤0.12

N32.0 N32.0 N32.0 N32.0 N32.0 16.0 8.0 16.0 16.0 N32.0 ≤0.25 ≤0.25 8.0 ≤0.25 ≤0.25 0.5 0.5 ≤0.25 N32.0 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25

N16.0 N16.0 1.0 ≤0.25 1.0 2.0 N16.0 N16.0 N16.0 N16.0 ≤0.25 ≤0.25 ≤0.25 ≤0.25 0.5 ≤0.25 ≤0.25 0.5 2.0 0.5 ≤0.25 ≤0.25 ≤0.25 ≤0.25 ≤0.25

N16.0 N16.0 N16.0 N16.0 4.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 2.0 ≤0.5 ≤0.5 ≤0.5

N16.0 N16.0 N16.0 N16.0 2.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 2.0 ≤0.5 ≤0.5 ≤0.5

N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 N2.0 0.25 0.5 N2.0 1.0 1.0 1.0 1.0 N2.0

N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 N4.0 1.0 2.0 2.0 2.0 N4.0 N4.0 N4.0 0.25 0.5 N4.0 0.5 0.5 1.0 0.25 2.0

N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 ≤256.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 N512.0 ≤256.0 ≤256.0 ≤256.0 N512.0

N4/72 N4/72 ≤2/38 N4/72 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 N4/72 ≤2/38 ≤2/38 N4/72 N4/72 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38 ≤2/38

2.0 1.0 N16.0 8.0 N16.0 1.0 1.0 N16.0 1.0 N16.0 1.0 1.0 N16.0 1.0 N16.0 N16.0 N16.0 N16.0 N16.0 N16.0 2.0 0.5 ≤0.25 ≤0.25 0.5

2.0 ≤1.0 ≤1.0 ≤1.0 ≤1.0 ≤1.0 ≤1.0 N32.0 4.0 2.0 ≤1.0 4.0 2.0 ≤1.0 ≤1.0 2.0 2.0 ≤1.0 ≤1.0 2.0 2.0 ≤1.0 ≤1.0 ≤1.0 2.0

N64.0 ≤4.0 N64.0 ≤4.0 ≤4.0 N64.0 N64.0 N64.0 32.0 8.0 ≤4.0 N64.0 ≤4.0 ≤4.0 8.0 8.0 8.0 ≤4.0 ≤4.0 8.0 16.0 ≤4.0 ≤4.0 ≤4.0 8.0

N64 N64 N64 N64 N64 N64 N64 N64 N64 N64 16.0 16.0 N64 32.0 1.0 N64 N64 N64 N64 N64 N64 1.0 ≤0.5 ≤0.5 2.0

N128.0 N128.0 32.0 N128.0 16.0 32.0 32.0 64.0 N128.0 64.0 N128.0 N128.0 16.0 N128.0 64.0 64.0 64.0 N128.0 N128.0 N128.0 ≤8.0 16.0 ≤8.0 ≤8.0 32.0

N64.0 N64.0 N64.0 N64.0 ≤5.0 1.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 N64.0 ≤0.5 ≤0.5 ≤0.5 ≤5.0

N128.0 N128.0 N128.0 N128.0 16.0 ≤4.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 ≤4.0 ≤4.0 ≤4.0 ≤4.0

N128.0 N128.0 N128.0 N128.0 ≤1.0 ≤1.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 N128.0 ≤1.0 ≤1.0 ≤1.0 2.0

N32.0 N32.0 N32.0 N32.0 1.0 4.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 N32.0 ≤0.25 ≤0.25 ≤0.25 ≤0.25

alactolyticus alactolyticus alactolyticus alactolyticus alactolyticus alactolyticus hyointestinalis hyointestinalis hyointestinalis hyointestinalis hyointestinalis gallolyticus gallolyticus gallolyticus henryi henryi henryi thoraltensis thoraltensis thoraltensis gallinaceus gallinaceus sanguinis sanguinis mitis

PEN – Penicilin, AMP – Ampicillin, TIO – Ceftiofur, CTET – Chlortetracyclin, OXY – Oxytetraciclin, DANO – Danofloxacin, ENRO – Enrofloxacin, SDM – Sulphadimethoxine, SXT - Trimethoprim/sulfamethoxazole, FFN – Florfenicol, GEN – Gentamicin, NEO – Neomycin, TIA - Tiamulin, SPE - Spectomycin, TYLT – Tylosin tartrate, TIL - Tilmicosin, TUL – Tulathromycin, CLI – Clindamycin.

L.Z. Moreno et al. / Infection, Genetics and Evolution 44 (2016) 376–381

isolation of S. plurianimalium, S. alactolyticus and S. hyovaginalis from animal's urine with high genetic heterogeneity. Further studies are necessary to fully understand if the higher variability of Streptococcus species is characteristic for urine samples and even the impact of Streptococcus co-infection for the infection severity and prognosis. The studies of Streptococcus antimicrobial resistance profiling mainly focuses on the species that are pathogenic to human, S. pyogenes and S. pneumoniae, and few livestock pathogens such as S. agalactiae and S. suis (Albrich et al., 2004; Palmieri et al., 2011; Passali et al., 2007; Piccinelli et al., 2015). Interestingly, penicillin-nonsusceptible and macrolide-resistant S. pneumoniae and S. pyogenes have been used as markers for antibiotic usage evaluation, and the streptococcal resistance appears to be directly associated with antibiotic selection pressure (Albrich et al., 2004). The veterinary streptococci described with high tetracyclines and macrolides resistance rates also appear to be related with antibiotic usage by the livestock industry (Palmieri et al., 2011; Piccinelli et al., 2015). The antimicrobial susceptibility profiles observed in the present study corroborate the above findings; the variable MIC values to βlactams, enrofloxacin and florfenicol, and the high resistance rates to tetracyclines and macrolides suggest antibiotic selection pressure due to the continue usage by the Brazilian swine industry. Even the S. sanguinis and S. mitis that were considered the most susceptible species identified in this study have been described to most likely develop resistance to macrolides and β-lactams, respectively (Doern and Burnham, 2010). Therefore, these atypical Streptococcus species may represent a higher underestimated risk for porcine health and should be properly identified by veterinary diagnostic laboratories. Furthermore, they present high resistance rates that appear to be directly related to the industry's antimicrobial usage and could be further applied as biological markers for resistance monitoring. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.meegid.2016.07.045. Conflict of interest The authors declare that they have no conflict of interest. Acknowledgments CAPES and CNPq research grants are gratefully acknowledged. V.T.M.G., L.Z.M. and C.E.C.M. are recipients of PhD fellowships from FAPESP (2013/16946-0, 2013/17136-2 and 2015/26159-1). A.M.M. is a CNPq fellow. References Albrich, W.C., Monnet, D.L., Harbarth, S., 2004. Antibiotic selection pressure and resistance in Streptococcus pneumoniae and Streptococcus pyogenes. Emerg. Infect. Dis. 10, 514–517. Arinto-Garcia, R., Pinho, M.D., Carriço, J.A., Melo-Cristino, J., Ramirez, M., 2015. Comparing matrix-assisted laser desorption ionization-time of flight mass spectrometry and phenotypic and molecular methods for identification of species within the Streptococcus anginosus group. J. Clin. Microbiol. 53, 3580–3588.

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