Greater Discriminatory Power of tuf Gene Sequence Analysis ...€¦ · 12/10/2011 · USA)....

1

Greater Discriminatory Power of tuf Gene Sequence Analysis in 1

Identification of Clinical Isolates of Coagulase-negative Staphylococci 2

Compared with 16S rRNA Sequence Analysis 3

4

Sang Mee Hwang1, Myoung Shin Kim2, Kyoung Un Park1,2*, Junghan Song1,2, Eui-5

Chong Kim1 6

1Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, 7

Korea 8

2Department of Laboratory Medicine, Seoul National University Bundang Hospital, 9

Seongnam, Korea 10

11

Running title: tuf sequencing for coagulase-negative staphylococci 12

13

*Corresponding author: Kyoung Un Park M.D., Department of Laboratory Medicine, Seoul 14

National University Bundang Hospital, 166 Gumiro, Bundanggu, Seongnamsi, Gyeonggido, 15

463-707, Korea 16

Fax: 82-31-787-7692 17

Phone: 82-31-787-4015 18

E-mail: [email protected] 19

Copyright © 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.J. Clin. Microbiol. doi:10.1128/JCM.05213-11 JCM Accepts, published online ahead of print on 12 October 2011

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ABSTRACT 20

21

22

We compared and analyzed 16S rRNA and tuf gene sequences for 97 coagulase-negative 23

staphylococci (CNS) clinical isolates using the GenBank, MicroSeq, EzTaxon and BIBI 24

databases. Discordant results for definitive identification were observed and varied according 25

to the different databases and target genes. Although higher percent sequence identities were 26

obtained with GenBank and MicroSeq for 16S rRNA analysis, the BIBI and EzTaxon 27

databases produced less ambiguous results. Greater discriminatory power and fewer multiple 28

probable identifications were observed with tuf gene analysis compared to 16S rRNA analysis. 29

The most pertinent results for tuf gene analysis were obtained with the GenBank database 30

when the cutoff values for percent identity were adjusted to be greater than or equal to 98.0% 31

with greater than 0.8% separation between species. Analysis of the tuf gene proved to be 32

more discriminative for certain CNS species; further, this method exhibited better distinction 33

in the identification of CNS clinical isolates. 34

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INTRODUCTION 35

36

37

Coagulase-negative staphylococci (CNS) are normal inhabitants of human skin and 38

mucous membranes and have been previously regarded as culture contaminants (31). 39

However, CNS have emerged as significant pathogens (26), especially in 40

immunocompromised patients (3), in premature neonates in intensive care (8, 16) and in 41

patients who have undergone complex medical procedures involving the implantation of 42

prosthetic or cardiac devices or indwelling catheters (25, 26). One of the frequently isolated 43

CNS species, Staphylococcus epidermidis, is the most commonly isolated etiological agent of 44

nosocomial infections (21). 45

Because of the increase in the clinical significance of CNS, there is a need for a more 46

accurate and sensitive method to identify CNS species in clinical samples. Many automated 47

identification systems are commercially available, such as Vitek 2 (bioMerieux, Marcy 48

l’Etoile, France), the BD Phoenix system (Becton Dickinson Diagnostic Systems, Sparks, 49

MD, USA) and MicroScan (Dade Behring, West Sacramento, CA, USA). These systems 50

allow for rapid and accurate identification compared to manual morphologic identification or 51

to biochemical tests. However, the accuracy of these tests can be compromised because of the 52

variable expression of phenotypic characteristics and the limited nature of the databases; 53

these limitations can result in ambiguous findings and the inability to identify uncommon 54

isolates (4, 15, 17). In addition, identification of CNS to the species level may also change the 55

diagnosis and therapeutic plans since uncommon isolates are being considered as causative 56

agents and unusual antibacterial resistance patterns appear (1, 13). Therefore, genotypic 57

methods of identifying CNS species are emerging as diagnostic tools for CNS infections. 58

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Sequence analysis of the 16S rRNA, a highly conserved region present in all bacteria, has 59

been implemented in clinical laboratories to identify CNS species (10, 22). Although widely 60

used and accurate, the high degree of similarity between closely related species limits its use 61

to identify several CNS species (27). Alternative target genes such as rpoB, tuf, dnaJ and 62

sodA are being assessed for their ability to discriminate between highly similar species (12, 63

14, 15, 24, 27, 28). The tuf gene, which encodes the elongation factor Tu, is an essential 64

constituent of the bacterial genome and is involved in peptide chain formation. Thus, due to 65

its essential nature, it is preferred for diagnostic purposes (19). Tuf gene analysis has been 66

shown to be a reliable and reproducible method to identify CNS; further, it has exhibited 67

better resolution for discriminating between certain CNS species than 16S rRNA analysis (15, 68

22). 69

In addition to the selection of a target gene for bacterial species identification, the 70

interpretation of the sequence analysis results utilizing different databases is also important. 71

However, the post-sequencing process of interpreting genotypic results has not been 72

emphasized in many studies (4, 22). Bioinformatic tools for bacterial identification are 73

continuously being developed and renovated to meet the needs for processing ever-increasing 74

amounts of data. Currently, multiple databases or tools exist for bacterial identification. The 75

most commonly used open database worldwide is GenBank (5), which incorporates DNA 76

sequences from all available public sources, making it comprehensive, easily accessible and 77

is considered as the primary data for other databases. Other databases incorporate other 78

information along with GenBank and can be regarded as secondary databases. One of the 79

secondary databases, BIBI combines the two well-known tools of BLAST (30) and 80

CLUSTALW (18) and utilizes phylogenetic data that is important for bacterial identification 81

(11). In addition, EzTaxon is another web-based tool that contains 16S rRNA sequences for 82

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prokaryotic type strains; it is constructed to enable the identification of isolates based on 83

pairwise nucleotide similarity values and phylogenetic inference methods (9). Additionally, 84

MicroSeq 500 (MicroSeq, Applied Biosystems Inc., Foster City, CA, USA) is a commercially 85

available software program for 16S rRNA sequence analysis (32). 86

We compared the genotypic results from 16S rRNA sequencing analyzed with GenBank, 87

MicroSeq, EzTaxon and BIBI for clinical isolates identified as CNS by phenotypic systems 88

(Vitek 2, MicroScan); further, the genotypic results from tuf gene analysis using GenBank 89

and BIBI were also compared. Few articles exist regarding guidelines for the interpretation of 90

DNA target sequences for bacterial identification. This is problematic as the results given by 91

the database are often inconclusive. The finding of multiple probable results or a rare species 92

may not have been reviewed by others, because many databases are open to the public and 93

are not validated thoroughly. Clinical and Laboratory Standards Institute (CLSI) Molecular 94

Method (MM) 18-A is so far the most commonly referenced material for bacterial DNA 95

identification (22). CLSI MM18-A focuses on the interpretation of bacterial 16S rRNA 96

sequence data including data for staphylococci, related gram-positive cocci, and fungi. 97

However, few guidelines exist for other DNA targets, such as the tuf gene, for bacterial 98

identification. Therefore, we evaluated the appropriateness of the CLSI guidelines for tuf 99

gene analysis and aimed to determine the optimal criteria for CNS species identification by 100

tuf gene analysis. Moreover, we compared the results of 16S rRNA and tuf gene analysis 101

using different databases and assessed whether tuf gene analysis can be used reliably in 102

clinical laboratories to identify CNS species. 103

104

MATERIALS AND METHODS 105

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Bacterial isolates 107

A total of 97 clinical isolates that were identified as CNS by either one of the two 108

phenotypic systems were included in this study. The phenotypic systems used were the 109

MicroScan Pos Combo Panel Type 1A (Dade Behring, West Sacramento, CA, USA) and 110

Vitek 2 GP-ID Cards (bioMerieux, Marcy l’Etoile, France). The identification procedures 111

were performed according to the manufacturers’ recommendations. The identification results 112

with less than 90% identity score by phenotypic systems were considered insufficient for a 113

result, but those isolates were included for the analysis. To exclude blood culture 114

contaminants, only blood culture isolates that grew in at least two of three blood cultures 115

were included. For additional or repeated tests, the isolates were suspended in skim milk and 116

stored at –70°C. 117

118

Extraction of genomic DNA 119

After overnight growth on blood agar plates at 37°C, genomic DNA of pure cultures was 120

extracted by Chelex matrix (Bio-Rad Laboratories, Hercules, CA., USA) according to the 121

manufacturer’s instructions. 122

123

16S rRNA and tuf gene sequencing 124

Polymerase chain reaction (PCR) amplifications were conducted in a total volume of 25 125

µL containing 2.5 mM dNTP, 10 pmol of each PCR primer, 0.6 units Taq polymerase, 2.5 µL 126

of 10X PCR buffer with 15 mM MgCl2 (Takara Bio, Inc., Shiga, Japan), and 2.5 µL of 127

template. In-house primers were designed using the Light Cycler Probe Design software 128

(version 2.0) (Roche, Penzberg, Germany); published studies were used as a reference (6, 9, 129

13). Amplification of 16S rRNA and tuf gene was achieved with the following primer pairs: 130

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16S rRNA MSQ-F (5'-TGAAGAGTTTGATCATGGCTCAG-3') and MSQ-R (5'-131

ACCGCGGCTGCTGGCAC-3'), tuf TUF-F (5'-GCCAGTTGAGGACGTATTCT-3') and 132

TUF-R (5'-CCATTTCAGTACCTTCTGGTAA-3'). The PCR conditions for 16S rRNA were 133

as follows: an initial denaturation period of 10 minutes at 95°C was followed by 35 cycles of 134

30 seconds of annealing at 60°C and 45 seconds of elongation at 72°C, with a final 10 minute 135

extension at 72°C. The PCR conditions for tuf were as follows: 15 minutes of initial 136

denaturation at 95°C, followed by 35 cycles of 30 seconds at 95°C, 30 seconds at 56°C, and 137

45 seconds at 72°C, with a final 10 minute extension at 72°C. Gel electrophoresis was used to 138

detect positive PCR signals and to confirm amplicon length of 527-bp for 16S rRNA and 139

412-bp for tuf gene. Prior to sequencing, the PCR products were purified using the ExoSAP-140

IT reagent (USB Corporation, Cleveland, Ohio, USA) according to the manufacturer’s 141

instructions. Forward and reverse sequencing reactions were conducted for each of the 142

amplified products. Sequencing reactions of 16S rRNA consisted of 10 µL of MicroSeq 500 143

sequencing mix (containing 1.6 pmol of MSQ-F and MSQ-R) primers, 2.9 µL of molecular-144

grade water, and 1 µL of purified PCR product. For tuf gene, sequencing reactions were 145

performed using BigDye Terminator v3.1 reagents (Applied Biosystems Inc., Foster City, CA, 146

USA). Briefly, sequencing reactions consisted of 1 µL of BigDye Ready Reaction mix, 3.5 147

µL of BigDye sequencing buffer (5X) (Applied Biosystems Inc.), 1.6 µL of 1 pmol primer, 148

2.9 µL of molecular-grade water, and 1 µL of purified PCR product; the final reaction volume 149

was 10 µL. The thermal cycling conditions were 25 cycles of 10 seconds at 96°C, 5 seconds 150

at 50°C, and 4 minutes at 60°C. The sequence products were purified using ethanol/sodium 151

acetate. Sequencing reactions were performed on an ABI Prism 3130xl Genetic Analyzer 152

(Applied Biosystems Inc.) according to the standard automated sequencer protocols. 153

154

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Sequence analysis 155

GenBank, MicroSeq 500 version 2.0, EzTaxon (http://www.eztaxon.org, 9), and BIBI 156

(http://umr5558-sud-str1.univ-lyon1.fr/lebibi/lebibiexecX.cgi, 11) were accessed most 157

recently on January 2011 for analysis. Complying with the CLSI guidelines for the 158

interpretation of 16S rRNA sequence analysis with GenBank and MicroSeq, a query 159

sequence with ≥ 99.0% identity for species identification and greater than 0.8% separation 160

between different species was considered acceptable. In cases where multiple species with 161

less than 0.8% separation with ≥ 99.0% identity were identified, all of the identifications (ID) 162

were considered as probable IDs. Because BIBI does not report results with identity scores, 163

the ID with the greatest number of sequences with “ID in cluster” to the query sequence was 164

considered the most probable ID; next, the ID with the greatest number of “type strain in 165

clusters” and then the ID with the greatest number of “type strain outside clusters” were 166

sequentially considered probable IDs. 167

For tuf gene analysis, GenBank and BIBI were used. Because no interpretative criteria 168

exist for species identification using tuf gene analysis with GenBank, we evaluated the results 169

with the current CLSI guidelines for 16S rRNA interpretation and arbitrarily set ≥ 98.0% 170

identity with greater than 0.8% separation as the rule for acceptable ID results for reporting at 171

the species level. The same rule applied for 16S rRNA analysis with BIBI was used for tuf 172

gene analysis with BIBI. 173

The “definitive ID” was defined as the ID with the most frequent result from the eight 174

different methods (two phenotypic results and six genotypic results); a minimum of five 175

results in concordance was required for definitive ID. If no results showed concordance in 176

five or more methods, the most frequent result was considered the definitive ID. 177

178

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Statistical analysis 179

The one-way ANOVA was used for comparisons of percent identity difference for 180

different methods. Kappa correlation statistics for the concordance of the results from 181

different methods were analyzed. IBM SPSS Statistics 19 (SPSS Inc., Chicago, Illinois, USA) 182

was used for statistical evaluation. All P-values are two sided, and P >0.05 was considered 183

statistically significant. 184

185

186

RESULTS 187

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Identification of coagulase-negative staphylococci 190

The identification results for the 97 clinical isolates obtained using four databases for 16S 191

rRNA sequencing and two databases for tuf gene sequencing and two automated phenotypic 192

identification systems are shown in Table 1-4. In general, at least five different methods were 193

in agreement for species identification. In the sole exception, only four of the identification 194

results were in concordance; this case was verified as Staphylococcus pettenkoferi (26), 195

which was regarded as the definitive ID. In summary, S. epidermidis (n = 37), 196

Staphylococcus hominis (n = 22), Staphylococcus capitis (n = 10), Staphylococcus 197

haemolyticus (n = 7), Staphylococcus lugdunensis (n = 6), Staphylococcus caprae (n = 5), 198

Staphylococcus cohnii (n = 3), Staphylococcus warneri (n = 3), Staphylococcus simulans (n = 199

2), Staphylococcus saprophyticus (n = 1), and S. pettenkoferi (n = 1) were identified from the 200

clinical specimens. For 16s rRNA analysis, identifications that were discordant with the 201

definitive ID occurred at a frequency of 2.1% for GenBank, 1.0% for MicroSeq, 10.3% for 202

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EzTaxon and 15.5% for BIBI. On the other hand, tuf gene analysis with GenBank showed 203

2.1% discrepancy; further, tuf gene analysis with BIBI did not result in any discrepant results. 204

The phenotypic ID systems Vitek 2 and MicroScan showed 8.8% and 19.5% discrepancy 205

with the definitive ID, respectively. The discordant results by different methods are listed in 206

Table 5. 207

Briefly, by 16S rRNA analysis with GenBank, two S. caprae isolates were misidentified; 208

with MicroSeq, one S. pettenkoferi isolate was misidentified. Using EzTaxon for 16S rRNA 209

analysis, ten S. capitis isolates were identified as S. caprae; using BIBI, one S. epidermidis, 210

one S. haemolyticus, two S. capitis and 11 S. hominis isolates were misidentified. By tuf gene 211

analysis with GenBank, one S. epidermidis and one S. pettenkoferi showed mismatch with the 212

definitive ID; with BIBI, no discrepant results were observed. 213

Because multiple species were considered as probable IDs when there was < 0.8% identity 214

difference between the IDs, we evaluated the species that were presented with probable IDs 215

(Table 5). 16S rRNA analysis with GenBank showed multiple answers for 45.4% of the 216

specimens, showing low discriminatory power between S. capitis, S. caprae and S. 217

epidermidis; between S. hominis, S. warneri, S. hyicus and S. haemolyticus; and between S. 218

warneri and S. hyicus. 16S rRNA analysis with MicroSeq provided multiple IDs for 15.5% of 219

the specimens and was unable to differentiate between S. capitis, S. caprae and S. 220

epidermidis and between S. saprophyticus and S. xylosus. 16S rRNA analysis with EzTaxon 221

showed ambiguous results for 7.2% of the specimens; further, all five S. caprae isolates were 222

given multiple IDs, including S. caprae, S. capitis, S. saccharolyticus and S. epidermidis. S. 223

hominis was identified as either S. hominis or S. haemolyticus, and S. saprophyticus was 224

identified as either S. saprophyticus or S. xylosus by 16S rRNA analysis with EzTaxon. 16S 225

rRNA analysis with BIBI revealed multiple probable IDS for 15.5% of the clinical isolates. 226

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The results were inconclusive, with S. capitis identified as S. capitis or S. caprae and S. 227

pettenkoferi as S. pettenkoferi or Staphylococcus pseudolugdunensis. The number of type 228

strain within clusters and type strain outside of clusters was same for the two species by BIBI, 229

respectively. In contrast, tuf gene analysis with GenBank was not able to differentiate 230

between S. warneri and S. pasteuri. Ambiguous results were not observed using tuf gene 231

analysis with BIBI. 232

The correlation of each method to the definitive ID was evaluated by multi-rater Kappa 233

statistics and the Kappa coefficient was 0.9735 for 16S GenBank, 0.9868 for 16S MicroSeq, 234

0.8684 for 16S EzTaxon, 0.8081 for 16S BIBI, 0.9736 for tuf GenBank, 1.00 for tuf BIBI, 235

0.8560 for Vitek2 and 0.7613 for MicroScan. Kappa coefficient with tuf analysis was higher 236

than the 16S rRNA analysis and automated identification systems. 237

238

239

Discriminatory power 240

The genotypic results generated from 16S rRNA analysis with MicroSeq, EzTaxon and tuf 241

gene analysis with GenBank provided identification scores as percent sequence identity to the 242

type culture collection or verified strains. Therefore, the results from these three methods 243

were compared for their discriminatory power and to determine the optimal sequence identity 244

cutoff value for the identification of CNS species by tuf gene analysis. Excluding the results 245

with 100% identity, MicroSeq and EzTaxon showed an average of 99.85% and 99.80% 246

sequence identity with the reference sequence, respectively; for tuf gene analysis with 247

GenBank, 99.29% sequence identity with the reference sequence was observed. The percent 248

sequence identity was significantly higher with 16S rRNA analysis with MicroSeq or 249

EzTaxon analysis compared to tuf gene analysis with GenBank (P < 0.001). When identity 250

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differences were compared between the first and the second most probable ID results, 251

MicroSeq had a 1.57% difference, EzTaxon, a 1.31% difference and a 2.82% difference by 252

tuf gene analysis. The average percentage identity difference between the first and the second 253

most probable ID was significantly higher in tuf gene analysis with GenBank than 16S rRNA 254

analysis with MicroSeq or EzTaxon (P < 0.001). 255

256

Interpretative criteria for tuf gene analysis of coagulase-negative staphylococci 257

Currently, there are no guidelines for the interpretation of data generated by tuf gene 258

analysis for bacterial identification. Therefore, we applied the CLSI MM18-A guidelines to 259

the results for tuf gene analysis. Overall, 88.7% of the specimens could be identified with ≥ 260

99.0% species identity with greater than 0.8% separation between species. Additionally, 9.3% 261

of the specimens were identified with ≥ 97.0% for genus identification. Results that could 262

only be reported up to the genus level according to the CLSI guidelines (≥ 97.0% identity) 263

were identified correctly to the definitive ID, once we adjusted the criteria for species 264

identification to ≥ 98.0% identity with > 0.8% separation between species., 98.0% of the 265

specimens could be identified correctly to the species level. 266

267

268

DISCUSSION 269

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271

Many laboratories use the 16S rRNA as a target for identification because it is widely used 272

and is supported with a large amount of data within public databases; further, guidelines exist 273

for data interpretation. However, because 16S rRNA analysis has low discriminatory power 274

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for the identification of certain CNS species (4, 12, 20), other target genes, such as the tuf 275

gene, have been studied for CNS identification and exhibit good resolution (2, 6, 14). In the 276

genomic era, post-sequencing analyses have become an important part of molecular 277

diagnostics. However, information regarding the interpretation of genotypic results with 278

different databases is limited. Here, for the first time, we compared 16S rRNA sequencing 279

results using four different databases, tuf gene analysis using two different databases and two 280

phenotypic identification systems. 281

The results of our study showed that tuf gene analysis generally exhibited better 282

discriminatory power for CNS species identification compared to 16S rRNA analysis. Further, 283

the use of multiple databases is important because the results vary depending on the database 284

used. Although disparity existed for the various databases used, there were less discordant 285

results with tuf gene analysis (1.0%) compared with 16S rRNA analysis (7.5%) and the 286

Kappa coefficients were higher for the tuf gene analysis than 16S rRNA analysis; these 287

results support the appropriateness of tuf gene analysis as another method for species 288

identification (2, 6). In addition, there were markedly fewer cases with multiple probable IDs 289

with tuf gene analysis; greater differences of percent sequence identity between the first and 290

the second probable IDs were present. This finding was also present in other studies and was 291

suggested through phylogenetic studies and assessment with different databases that tuf gene 292

had more intraspecies variability than 16S rRNA (15, 23). Therefore, further confirmation 293

with other methods or targets would be required less often with tuf gene analysis compared to 294

16S rRNA gene analysis. As it is particularly important to provide an accurate and clearly 295

defined result for a requested test in clinical laboratories, the discriminatory power of 296

identification tests is very important. 297

16S rRNA sequence analysis with all four different databases showed low discriminatory 298

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power for S. capitis and S. caprae, suggesting the use of an alternative target as 299

recommended in CLSI MM18-A. Tuf gene analysis was capable of discriminating between 300

these two species. S. warneri is reported to share approximately 98.7% sequence identity with 301

S. pasteuri (22), and tuf and rpoB gene analyses are generally known to provide better 302

resolution between these two species (6). However, sequence analysis with GenBank reported 303

both S. pasteuri and S. warneri as probable IDs regardless of the target (16S rRNA or tuf 304

gene). Using 16S rRNA analysis, EzTaxon reported S. warneri correctly, which could be due 305

to the fact that the database is based on different algorithms and data sources, curated from 306

the primary database GenBank. Although S. saprophyticus and S. xylosus are known to 307

exhibit approximately 100% sequence identity for their 16S rRNA sequence (22), the 308

sequence analysis results varied depending upon the database; the secondary databases, 309

MicroSeq and EzTaxon could not discriminate between these species, but GenBank did. 310

These results emphasize the need to utilize multiple databases, both primary and secondary 311

databases for the interpretation of sequencing analysis data for any DNA target. 312

Although our study included many CNS species with 97 clinical specimens, other species 313

that may be encountered in clinical laboratories such as Staphylococcus intermedius, 314

Staphylococcus schleiferi and Staphylococcus pseudointermedius and these species were not 315

included and should be examined in a future study. In addition, we chose the definitive ID as 316

the ID that appeared most frequently; however, this may not be the correct species ID. 317

Further analysis with type strains is required to support and confirm the definitive IDs of this 318

study. This study used partial sequencing for both 16S rRNA and tuf gene analysis and 319

provided reliable results, excluding one case (S. pettenkoferi), suggesting that partial 320

sequencing of the two genes is sufficient for clinical use. In addition, a minimal modification 321

of the CLSI MM18-A guidelines for species identification criteria of ≥ 98.0% identity with > 322

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0.8% separation between species yielded reliable results for tuf gene analysis. 323

From this study, it appears that tuf gene is an alternative useful target for CNS species 324

identification that exhibits increased discriminatory power compared to 16S rRNA. Although 325

no guidelines exist for tuf gene analysis, minimally modified CLSI MM18-A criteria can be 326

used to obtain reliable results with low ambiguity and high sensitivity. Integrating results 327

from different databases for post-sequencing analysis is important for accurate diagnosis. 328

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ambiguous biochemical profiles. J. Clin. Microbiol. 41:1996-2001. 440

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TABLE 1. Identification of Staphylococcus epidermidis (n = 37) by genotypica and phenotypic methods 441 442 16S GenBank Identity %, (No.)

16S MicroSeq Identity %, (No.)

16S EzTaxon Identity %, (No.)

16S BIBI

tuf GenBank Identity %, (No.)

tuf BIBI Vitek2 MicroScan Definite IDb

Isolates (n= 37)

S. epi.c S. epi. S. epi. S. epi. S. epi. 100.0 (12), 99.7 (8)

99.4 (6), 99.1(3) 98.8 (1)

S. epi. S. epi. S. epi. S. epi. 30

S. epi. S. epi. S. epi. S. epi. S. epi. 100.0 (1), 99.7 (1)

99.4 (1)

S. epi. S. hom.d S. epi. S. epi. 3

S. epi. S. epi S. epi. S. epi. S. epi. 99.1

S. epi. S. epi. S. au.e S. epi. 1

S. epi. S. epi S. epi. S. epi. S. hom. S. epi. S. epi. S. epi. S. epi. 1

S. epi./ S. cap.f/ S. capr.g

99.9

S. epi.

S. epi. S. epi. S. epi. S. epi. S. epi. S. epi. S. epi. 1

S. epi. S. epi.

S. epi. S. capr./ S. cap.

S. epi. S. epi. S. epi. S. epi. S. epi. 1

aPercent identity 100 for 16S GenBank, 16S MicroSeq, 16S EzTaxon and tuf GenBank, otherwise mentioned. 443 bID, identification. 444 cS. epi., S. epidermidis. 445 dS. hom., S. hominis. 446 eS. au., S. aureus. 447 fS. cap., S. capitis. 448 gS. capr., S.caprae. 449

450

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TABLE 2. Identification of Staphylococcus capitis (n = 10) and Staphylococcus caprae (n = 5) by genotypica and 451 phenotypic methods 452

16S GenBank Identity%, (No.)

16S MicroSeq Identity%, (No.)

16S EzTaxon Identity%, (No.) 16S BIBI tuf GenBank

Identity%, (No.) tuf BIBI Vitek 2 MicroScan Definitive IDb

Isolates (n = 15)

S. cap.c/ S. capr.c/ S. epi.c 99.8 (3),99.6 (1)

S. cap./ S. capr./ S. epi. 99.9 (4)

S. capr. S. cap./ S. capr.

S. cap. 100.0 (3), 99.4 (1)

S. cap. S. cap. S. cap. S. cap. 4

S. cap. S. cap./ S. capr./ S. epi. 99.9 (2)


S. cap. 99.1 (2)

S. cap. S. cap. S. cap. S. cap. 2

S. capr./ S. epi./ S. cap.

S. cap. S. capr. S. cap./ S. capr.

S. cap. S. cap. S. cap. S. cap. S. cap. 1

S. cap./ S. capr./ S. epi. 99.8


S. capr. S. epi. S. cap. 99.1

S. cap.

S. cap.

S. cap.

S. cap.

1




S. cap. 99.7

S. cap.

S. cap.

S. epi. S. cap.

1

S. capr./ S. epi./ S. cap. 99.8


S. capr.

S. haemo.d S. cap. 99.4

S. cap.

S. cap.

S. cap.

S. cap.

1

S. arlet.e/ S. cap./ S. capr. 99.8


S. capr./ S. sacch.f/ S. epi. 99.8

S. cap./ S. capr.

S. capr. 99.3

S. capr. S. capr. S. cap.

S. capr. 1

S. arlet./ S. cap./ S. epi. 99.6


S. capr./ S. cap./ S. sacch./ S. epi. 99.8

S. cap./ S. capr.

S. capr. 99.4

S. capr. S. sim.g S. haemo. S. capr. 1

S. arlet./ S. cap. S. capr. 99.8

S. cap. S. capr./ S. epi. 99.9

S. capr./ S. cap. S. sacch./ S. epi. 99.8

S. cap./ S. capr.

S. capr. 99.6

S. capr. S. capr. S. war.h S. capr. 1

S. arlet./ S. cap./ S. epi. 99.5

S. cap/ S. capr./ S. epi. 99.9

S. capr./ S. cap./ S. sacch./ S. epi. 99.8

S. capitis/ S. capr.

S. capr. S. capr. S. capr. S. epi. S. capr. 1

S. arlet/ S. cap./ S. capr. 99.6



S. cap./ S. capr.

S. capr. S. capr. S. capr. S. au.c S. capr. 1

aPercent identity 100 for 16S GenBank, 16S MicroSeq, 16S EzTaxon and tuf GenBank; otherwise mentioned. 453 bID, identification. 454 cSee Table 1 for abbreviations. 455 dS. haemo., S. haemolyticus. 456 eS. arlet., S. arlette. 457 fS. sacch., S. saccharolyticus. 458 gS. sim., S. simulans. 459 hS. war., S. warneri. 460

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TABLE 3. Identification of Staphylococcus haemolyticus (n = 7) and Staphylococcus hominis (n = 22) by genotypica 461 and phenotypic methods 462



16S EzTaxon Identity%, (No.)

16S BIBI tuf GenBank Identity%, (No.)

tuf BIBI Vitek 2 MicroScan Definitive IDb

Isolate (n = 29)

S. auri.c/ S. haemo.c/ S. war.c

S. haemo.

S. haemo.

S. haemo.

S. haemo. 99.7 (1), 99.6 (1) 99.1 (1)

S. haemo.

S. haemo.

S. haemo.

S. haemo.

4

S. auri.d S. haemo./ S. war.

S. haemo.

S. haemo. 99.8

S. cap.c/ S. capr.c

S. haemo.

S. haemo.

S. haemo.

S. haemo. S. haemo.

1

S. auri./ S. haemo./ S. war.

S. haemo.

S. haemo. 99.7

S. haemo.

S. haemo.

S. haemo.

S. epi.c S. epi. S. haemo.

1

S. auri./ S. haemo./ S. war.

S. haemo.

S. haemo. 99.7

S. haemo.

S. haemo.

S. haemo.

S. haemo.

S. sim.c S. haemo.

1

S. hom./ S. war./ S. hyicus 100.0 (7),

99.8 (2)

S. hom. 100.0 (3), 99.9 (1), 99.8 (5),

S. hom. 100.0 (3), 99.8(6)

S. hom. S.hom.100.0 (2), 99.7 (2), 99.6 (1), 99.4 (1), 99.3 (1), 98.6 (1), 98.3 (1)

S. hom. S. hom. S. hom. S. hom. 9

S. hom.

S .hom. 99.9 (1), 99.8 (1), 99.7 (4)

S. hom. 99.8 (6)

S. xylo.e S. hom. 99.4 (1), 99.1 (2), 98.9 (1), 98.8 (1), 98.0 (1)

S. hom. S. hom. S. au.c S. hom. 6

S. hom./ S. war./ S. hyicus 100.0 (2),

99.8 (1)

S. hom.

99.8 (3)

S. hom. 100.0 (1), 99.8 (2)

S. xylo. S. hom. 100.0(2), 99.1(1)

S. hom. S. hom. S. hom. S. hom. 3

S. hom./ S. war./ S. hyicus 99.8

S. hom. S. hom. S. hom. S. hom. S. hom. NAf S. hom. S. hom. 1

S. hom./ S. war./ S. hyicus

S. hom. 99.8

S. hom. 99.8

S. xylo. S. hom. 99.1

S. hom. Kocuria variant

S. hom. S. hom. 1


S. hom. 99.7

S. hom./ S. haemo. 99.8

S. xylo. S. hom. S. hom. S. hom. S. hom. S. hom. 1


S. hom. 99.7

S. hom. 99.8

S. hom. S. hom. 99.1

S. hom. S. hom. S. epi. S. hom. 1

aPercent identity 100 for 16S GenBank, 16S MicroSeq, 16S EzTaxon and tuf GenBank, otherwise mentioned. 463 bID, identification. 464 cSee Table 1 for abbreviations. 465 dS. auri., S. auricularis. 466 eS. xylo., S.xylosus. 467 fNA, not available. 468

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TABLE 4. Identification of other coagulase-negative staphylococci (n= 16) by genotypica and phenotypic methods 469



16S EzTaxon Identity%, (No.)

16S BIBI tuf GenBank Identity%, (No.)

tuf BIBI Vitek2 MicroScan Definitive IDb

Isolate (n= 16)

S. coh.c S. coh. S. coh. S. coh. S. coh. 98.3 (1), 97.3 (1) 97.1 (1)

S. coh. S. coh. S. coh. S. coh. 3

S. lug.d

100.0 (3), 99.8 (1), 99.3 (1)

S. lug. 99.9 (2),99.8 (3)

S. lug. S. lug. S. lug. 100 (1), 99.7 (3) 99.6 (1)

S. lug. S. lug. S. haemo.e S. lug. 5

S. lug. S. lug. 99.8

S. lug. S. lug. S. lug. 99.7

S. lug. NAe S. lug. S. lug. 1

S. petten.f/ S. pseudo.g

99.6

S. hyicus/ S. coh./S. capr.e/S. cap.e 96.6

S. petten.

S. pseudo./ S. petten.

S. pseudo.

S. petten. NA S. cap. S. petten. 1

S. sapro.h

S. sapro. /S. xylo.e 99.9

S. sapro. /S. xylo. 99.6

S. sapro. S. sapro. 98.9

S. sapro. S. sapro. S. sapro. S. sapro. 1

S. sim.e 99.6

S. sim. 99.9

S. sim..

S. sim. S. sim. 99.1

S. sim. S. sim. S. sim. S. sim. 1

S. sim. 99.6

S. sim. 99.9

S. sim. S. sim. S. sim. 99.1

S. sim. S. sim. S. coh. S. sim. 1

S. war.e/ S. past.i

S. war.

S. war.

S. war. S. war./ S. past.

S. war. S. sapro. S. war. S. war. 1

S. war./ S. past. 99.8

S. war./ S. past. 99.8

S. war.

S. war. S. war./ S. past. 99.6

S. war. S. war. S. auri.e S. war. 1

S. war./ S. past.

S. war./ S. past.

S. war.

S. war. S. war./ S. past.

S. war. S. war. S. war. S. war. 1

aPercent identity 100 for 16S GenBank, 16S MicroSeq, 16S EzTaxon and tuf GenBank, otherwise mentioned. 470 bID, identification. 471 cS. coh., S. cohnii. 472 dS. lug., S. lugdunensis 473 eSee Table 1 and 2 for abbreviations. 474 fS. pett., S. pettenkoferi. 475 gS. pseudo,. S. pseudolugdunensis. 476 hS. sapro., S. saprophyticus. 477 iS. past., S. pasteuri. 478 on N

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TABLE 5. Discordant results by different methods 479

Methods Single ID in concordance to definitive ID (%)

Multiple IDs, one of which is in agreement with the definite ID (%)

Discordant ID (%)

Total

16S GenBank 56 (57.7) 39 (40.2) 2 (2.1) 97 16S MicroSeq 81 (83.5) 15 (15.5) 1 (1.0) 97 16S EzTaxon 80 (82.5) 7 (7.2) 10 (10.3) 97 16S BIBI 67 (69.1) 15 (15.5) 15 (15.5) 97 tuf GenBank 92 (94.8) 3 (3.1) 2 (2.1) 97 tuf BIBI 97 (100.0) 0 (0.0) 0 (0.0) 97 Vitek 2 83 (91.2) 0 (0.0) 8 (8.8) 91 MicroScan 70 (80.5) 0 (0.0) 17 (19.5) 87

480

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