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학 사 학 논

Analysis of the vaginal microbiome

by next-generation sequencing and

evaluation of its performance as a

clinical diagnostic tool in vaginitis

차 염 열분 이용한 질

미생 군집 분 질염에 있어

임상 검사법 가능 평가

2016 7 월

울 학 학원

학과 검사 학 공

A thesis of the Degree of Doctor of Philosophy








July 2016

The Department of Laboratory Medicine

Seoul National University

College of Medicine

Ki Ho Hong




지도

이 논 학 사 학 논 출함

2016 4 월

울 학 학원

학과 검사 학 공

학 사 학 논 인 함

2016 6 월

원 장 (인)

부 원장 (인)

원 (인)

원 (인)

원 (인)

i





by

Ki Ho Hong

A thesis submitted to the Department of Laboratory

Medicine in partial fulfillment of the requirements for the

Degree of Doctor of Philosophy in Medicine at Seoul

National University College of Medicine

June 2016

Approved by Thesis Committee:

Professor Chairman

Professor Vice chairman

Professor

Professor

Professor

ii

ABSTRACT

Introduction: Changes in the vaginal microbiome are associated with vaginal

symptoms and diseases. These changes are usually identified through

microscopic examination and microbiological culture. Next-generation

sequencing (NGS) can detect many more microorganisms in the vaginal

microbiome than these traditional methods. Several studies have analyzed

vaginal microbiomes with NGS; however, short read lengths and the

exclusion of microorganisms other than bacteria are common limitations of

these studies. The aim of this study was to analyze the vaginal microbiomes of

Korean women using NGS with long read lengths and the inclusion of

bacteria as well as other microorganisms. This study also compared NGS with

other assays and evaluated its feasibility for vaginitis prediction.

Methods: In total, 89 vaginal swab specimens were collected. Of these, 67

specimens were microscopically examined by Gram-staining and

microbiological culture. A GS Junior (454 Life Sciences, Branford, CT, USA)

system was used for NGS. The 16S rRNA, internal transcribed spacer (ITS),

and Tvk genes were used to detect bacteria, fungi, and Trichomonas vaginalis.

Data processing, operational taxonomic unit (OTU) table construction, and

chimeric sequence removal were performed with Usearch software.

Taxonomic assignment was performed using the Ribosomal Database Project

(RDP) website and Basic Local Alignment Search Tool (BLAST) database. A

DNA probe assay for Candida spp., Gardnerella vaginalis, and T. vaginalis

was performed.

iii

Results: In total, 202,958 reads of the 16S rRNA gene and 7,600 reads of the

internal transcribed spacer (ITS) gene were obtained from NGS of 89

specimens. ITS sequences were obtained in the majority of specimens (56.2%).

The 16S rRNA sequences and ITS sequences were clustered into 3,259 and

112 OTUs, respectively. The compositions of the intermediate Nugent score

group and vaginitis Nugent score group differed from those of the normal

score group; however, they were similar to each other. Shannon diversity

indices, the number of species, and the fraction of Lactobacillus spp. were

significantly different among the three groups. From the NGS data, various

predictors of diversity were analyzed to predict vaginitis, and a fraction of

Lactobacillus spp. was associated with the highest area under curve (AUC)

value (0.8559). NGS and DNA probe assay showed good agreement. NGS

and microbiological culture showed 73.1% agreement (range, 86.2–89.7%).

Conclusions: The intermediate Nugent score group and vaginitis group were

not significantly different in the microbiome analysis. ITS sequences were

common in normal specimens. NGS is a promising tool for examining vaginal

microbiomes and diagnosing vaginitis.

---------------------------------------------------------------------------------------------

Keywords: Vaginal microbiome, Next generation sequencing, Vaginitis

Student number: 2012-2172

iv

CONTENTS

Abstract ..................................................................................... i

Contents ................................................................................... iii

List of tables..............................................................................vi

List of figures ......................................................................... viii

List of abbreviations .................................................................ix

1. Introduction ........................................................................... 1

2. Materials and Methods ......................................................... 3

2.1. Specimens ......................................................................... 3

2.2. Next generation sequencing............................................. 4

2.3. Processing sequence data, building an operational

taxonomic unit (OTU) table and assigning

taxonomies ....................................................................... 5

2.4. DNA probe assay .............................................................. 8

2.5. Statistical analysis ............................................................ 9

3. Results .................................................................................. 10

v

3.1. Sequence read statistics. ................................................ 10

3.2. Operational taxonomic unit (OTU) statistics and

taxonomic allocation of 16S rRNA sequences. ............. 12


taxonomic allocation of internal transcribed spacer

(ITS) sequences.............................................................. 15

3.4. Diversity calculation ...................................................... 21

3.5. Cluster analysis of the microbiome compositions ........ 27

3.6. Association between diversity predictors and

Nugent scores. ............................................................... 32

3.7. The comparison of various predictors of diversity as

a diagnostic criterion of vaginitis. ................................ 35

3.8. Comparison of next generation sequencing (NGS),

DNA probe assay and microbiological culture in

the detection of Candida spp. and Gardnerella

vaginalis. ........................................................................ 39

3.9. Comparison of next generation sequencing (NGS)

vi

and microbiological culture .......................................... 41

4. Discussion............................................................................. 43

References ................................................................................ 48

Abstract in Korean .................................................................. 52

vii

LIST OF TABLES

Table 1. Results of clustering, taxonomic scores and read

lengths ......................................................................... 11

Table 2. BLAST results in operational taxonomic units (OTU)

with similarity scores of less than 0.5. ......................... 13

Table 3. Distribution of taxonomic rank after taxonomic

allocation... .................................................................. 14

Table 4. Taxonomic result of internal transcribed spacer (ITS)

gene sequencing... ........................................................ 16

Table 5. Total reads and positive specimen in the internal

transcribed spacer (ITS) gene taxonomy.. ..................... 20

Table 6. Results of clustering, the number of taxonomy and

Shannon diversity index............................................... 22

Table 7. The most abundant taxa in the three Nugent score

groups.. ........................................................................ 31

Table 8. Comparison of next generation sequencing (NGS),

viii

DNA probe assay and microbiological culture for

detection of Candida spp. and Gardnerella vaginalis .. 40

Table 9. Discordant results between next generation

sequencing (NGS) and microbiological culture. ........... 42

ix

LIST OF FIGURES

Figure 1. Summary of processing sequence data, building an

operational taxonomic unit (OTU) table and

assigning taxonomies. .................................................... 7

Figure 2. Heatmap of microbiome of 89 vaginal swab

specimens. ................................................................. 28

Figure 3. Association between diversity predictors and

Nugent score groups. ................................................. 33

Figure 4. Receiver-operating characteristic (ROC) curves of

eleven predictors of diversity and three vaginitis

criteria... .................................................................... 36

x

LIST OF ABBREVIATIONS

ANOVA analysis of variance

AUC area under curve

BLAST Basic Local Alignment Search Tool

bp base pair

CI confidence interval

κ Cohen’s kappa index

ITS internal transcribed spacer

NGS next generation sequencing

OTU operational taxonomic unit

RDP Ribosomal Database Project

ROC receiver operating characteristic

rRNA ribosomal RNA

SPA simple percent agreement

１

1. INTRODUCTION

The vaginal microbiome consists of the largest number of microorganisms of

all the microbiomes in the human female reproductive system. The vaginal

microbiome of healthy woman usually comprises 4–12 species, with Lactoba-

cillus being the most abundant genus. Under certain conditions, the composi-

tion of the vaginal microbiome can change. The total number of species may

increase, and microorganisms other than Lactobacillus spp., such as anaerobic

bacteria, fungus, and protozoa, overgrow. This change often accompanies

symptoms in the host female, such as pain, abnormal vaginal discharge, and

odor. These conditions are associated with infertility, preterm delivery, and

pelvic inflammatory diseases (1-3).

Currently, changes in the vaginal microbiome are usually identified by micro-

scopic examination and culture of vaginal swabs. Polymerase chain reaction

(PCR) and nucleic acid hybridization methods are also used. Recently, next-

generation sequencing (NGS) has been applied to the study of microbiomes.

NGS is massive, parallel sequencing that has enabled study of the human mi-

crobiome. It can identify many more microorganisms than microscopy or cul-

ture.

Several studies have analyzed the vaginal microbiome using NGS (4-9).

However, many of these studies have limitations, including short read lengths

and the exclusion of fungi. In this study, the vaginal microbiomes of Korean

women were analyzed by NGS with long read lengths and the detection of

２

both bacteria and fungi. The potential for using NGS data as a clinical diag-

nostic tool to predict vaginitis was estimated, and NGS data was compared

with that of other assays.

３

2. MATERIALS AND METHODS

2.1. Specimens

Sixty-nine vaginal swab specimens were collected from sixty-five patients

who visited the gynecological clinics of Seoul National University Hospital,

Seoul, Republic of Korea, for vaginal symptoms between December 2011 and

March 2012. Twenty additional vaginal swab specimens were collected from a

healthy control group. Among the 69 specimens from patients, sixty-seven

specimens had results from microscopic examination of Gram-stained smears

and microbiological cultures. Nugent scores of these 67 specimens were

calculated on the basis of Gram stain results (10). The specimens were

grouped into three categories according to Nugent scores: normal (scores 0–3),

intermediate (scores 4–6), and vaginitis (scores 7–10). The specimens were

cultured at 37°C and 5% CO2 for two days. The cultured organisms were

identified using VITEK II ID Cards (bioMérieux, Marcy-l'Étoile, France) and

MicroScan Pos ID Panels (Beckman Coulter, Brea, CA, USA). Lactobacillus

sp. was reported as normal vaginal flora or Lactobacillus spp.; species names

were not reported. This study was approved by the Institutional Review Board

of Seoul National University Hospital (IRB Number: H-1510-073-711).

４

2.2. Next generation sequencing

PrepMan Ultra Sample Preparation Reagent (Thermo Fisher Scientific,

Waltham, MA, USA) was used for nucleic acid extraction following the

manufacturer’s instructions. Sequencing of bacteria, fungi, and T. vaginalis

was performed on extracted nucleic acid. For bacteria, the V3–V5 region of

the 16S rRNA gene was the target. Primers 357F and 926R were used for

bacterial identification, with an expected amplicon size of 570 bp (12). For

fungi, the internal transcribed spacer (ITS) gene was the target. Primers ITS-5

and ITS-4 were used, yielding a 700-bp amplicon.(13). For T. vaginalis, the

Tvk gene was the target. Primers TVK3 and TVK7 were used, for an expected

263-bp amplicon (14). The GS Junior System (454 Life Sciences, Branford,

CT, USA) was used for NGS following the manufacturer’s instructions. The

minimum sequence length was 150 bp and the minimum exponential quality

score was 20. Only those sequences that fulfilled these two minimum criteria

were included in further analyses.

５

2.3. Processing sequence data, building an operational

taxonomic unit (OTU) table and assigning taxonomies.

A schematic diagram of data processing and further analyses is shown in

Figure 1. Usearch for Windows software (version 6.0.203) was used for

sequence processing, clustering, and removing chimeric sequences (15, 16).

The similarity threshold for clustering of two sequences into the same

operational taxonomic unit (OTU) was 0.97. For the removal of chimeric

sequences, both de novo and reference modes were used. The de novo mode

identifies possible chimeric sequences from the initial sequences and

compares them with the given sequences again. The reference mode finds the

chimeric sequences from a database of previously reported chimeric

sequences. When a sequence was identified as a chimeric sequence in both the

de novo and reference modes of Usearch, the sequence was regarded as a true

chimeric sequence and was removed from the OTU. Two open source

chimeric sequence databases for bacteria and fungi were used in the reference

mode (16, 17). However, there is no known chimeric sequence database for T.

vaginalis.

The online Ribosomal Database Project (RDP) database (version 10.3.2)

was used for matching and aligning the 16S rRNA sequences (18). Among the

RDP database sequences, near-full length (≥1,200 bp) sequences of good

quality were used for matching. After comparison with RDP sequences, each

OTU was matched with the single bacterial taxon with the highest similarity

score. If the similarity score was low (< 0.5), the sequence was analyzed again

６

using the nucleotide Basic Local Alignment Search Tool (BLAST) database

(19). The ITS and Tvk sequences were also analyzed by BLAST, since these

sequences are not included in the RDP database (20). PermutMatrix 1.9.3 was

used for drawing a heatmap to visualize the taxonomy (21). The Shannon

diversity index was calculated to estimate alpha diversity (22).

７

Figure 1. Summary of processing sequence data, building an

operational taxonomic unit (OTU) table and assigning

taxonomies.

Abbreviations: BLAST, Basic Local Alignment Search Tool; NGS, next

generation sequencing; OTU, operational taxonomic unit; and RDP,

Ribosomal Database Project.

Sequencing output from GS junior

(fasta format)

Clustering of sequences and producing OTU

(The similarity between two sequences > 97%)

Filtering of chimera sequences

(de novo mode, reference mode)

OTU table for bacteria, fungus, Trichomonas vaginalis

Assignment of taxonomy using database

(bacteria: RDP, fungus/Trichomonas: BLAST)

Heatmap generation, alpha diversity calculation

８

2.4. DNA probe assay

BD Affirm VPIII Microbial Identification Test (Becton Dickinson, NJ, USA)

is a direct specimen DNA probe-based diagnostic test for the detection of

Candida spp., Gardnerella vaginalis, and T. vaginalis. VPIII was performed

following the manufacturer’s instruction. In total, 87 specimens were tested

with this DNA probe assay. Two specimens could not be tested because of

small volumes.

９

2.5. Statistical analysis

Stata version 13.1 (StataCorp, TX, USA) was used for the statistical analysis.

A paired t-test and one-way analysis of variance (ANOVA) were performed to

compare the Shannon diversity indices among specimens. Pearson’s chi-

square and Cohen’s kappa index were calculated for a comparison of NGS,

DNA probe assay, and microbiological culture results. A receiver operating

characteristic (ROC) curve analysis was performed to evaluate the various

predictors of vaginitis. P values of less than 0.05 were considered statistically

significant.

１０

3. RESULTS

3.1. Sequence read statistics

After the removal of chimeric sequences, 202,958 reads of the 16S rRNA gene

and 7,600 reads of the ITS gene were obtained from 89 specimens (Table 1).

No Tvk gene read was obtained in the specimens. NGS of the Tvk gene was

performed on cultured T. vaginalis, and NGS correctly identified samples

with T. vaginalis. Therefore, T. vaginalis was presumed not to be present in

any of the clinical specimens. 16S rRNA reads were obtained from all 89

specimens, and the average number of reads per specimen was 2,280 (range,

13–6,825). ITS reads were obtained from 50 specimens, and the average

number of ITS reads per single specimen was 182 (range, 1–3,612). The

average size of reads was 364 bp (range, 151–580) for the 16S rRNA gene and

322 bp (range, 180–566) for the ITS gene.

１１

Table 1. Results of clustering, taxonomic scores and read

lengths.

Gene Taxonomic score OTUs Total reads Read length (bp)

Minimum Average Maximum

16S rRNA Similarity score

0.000-0.299 10 3,612 265 385 541

0.300-0.499 21 3,335 163 372 566

0.500-0.699 343 18,019 164 380 580

0.700-0.899 1,598 25,545 151 353 561

0.900-1.000 1,287 152,447 154 374 537

ITS Percent identity

<95.0% 11 928 190 350 566

95.0-96.9% 20 152 180 315 512

97.0-98.9% 43 6,422 184 321 509

99.0-100% 38 98 180 319 478

Sum 3,371 210,558 151 363 580

Abbreviations: bp, base pair; ITS, internal transcribed spacer; OTU,

operational taxonomic unit; and rRNA, ribosomal RNA.

１２


taxonomic allocation of 16s rRNA sequences

After clustering, 202,958 reads of 16S rRNA sequences were clustered into

3,259 OTUs (Table 1). After matching the 16S rRNA sequences with those in

the RDP database, each OTU was allocated to a single taxon with the highest

similarity. Fifty-one sequences with similarity scores of less than 0.5 were re-

matched using the nucleotide BLAST database. Twenty-one sequences did

not match any sequence with more than 97% percent identity. These sequenc-

es were excluded from further evaluation. BLAST results for the remaining 30

sequences are shown in Table 2. Of these, 20 sequences were identified as

human DNA by BLAST, and these sequences were excluded from further

evaluation. Ten sequences that were identified by BLAST were included in

later analyses. Finally, 645 OTUs were identified to the species level, and

2,451 OTUs were identified to the genus level (Table 3).

１３

Table 2. BLAST results in operational taxonomic units

(OTU) with similarity scores of less than 0.5.

RDP result Similarity score* BLAST result Percent identity† Read length (bp)

Bacteroides pyogenes 0.208 Lactobacillus sp. 97.51 401

Euryarchaeota 0.188 Lactobacillus sp. 98.40 437

Prevotellaceae 0.215 Lactobacillus sp. 99.13 345

Prevotellaceae 0.234 Lactobacillus sp. 99.30 287

Porphyromonadaceae 0.228 Streptococcus anginosus 98.59 213

Gammaproteobacteria 0.241 Streptococcus anginosus 100.00 219

Gammaproteobacteria 0.213 Candida albicans 98.86 416

Clostridiales 0.245 Candida albicans 98.65 269

Planomicrobium 0.257 Candida albicans 98.05 333

Prevotellaceae 0.286 Glomus mosseae 97.53 263

Bacteroidetes 0.234 Homo sapiens 98.05 256

Methanobrevibacter 0.201 Homo sapiens 98.96 385


Bacteroides pyogenes 0.205 Homo sapiens 98.68 379

Conexibacter 0.217 Homo sapiens 98.23 226


Thiomargarita namibiensis 0.186 Homo sapiens 99.74 379

Pelagibius 0.206 Homo sapiens 98.38 371


Porphyromonadaceae 0.236 Homo sapiens 98.66 224

Bacteroides coprocola 0.397 Homo sapiens 100.00 163





Archaeoglobus 0.249 Homo sapiens 97.16 176



Gammaproteobacteria 0.274 Homo sapiens 97.97 196


The BLAST results with high similarity were expressed in percent identities

score. *: RDP, †: BLAST.

Abbreviations: bp, base pair; BLAST, Basic Local Alignment Search Tool;

OTU, operational taxonomic unit; and RDP, Ribosomal Database Project.

１４

Table 3. Distribution of taxonomic rank after taxonomic allocation.

Gene Taxonomic rank Total reads OTUs Taxa Read length (bp) Average Maximum Minimum 16S rRNA Domain 5 4 1 399 416 374

Class 6 6 3 440 479 411

Phylum 14 5 3 403 485 337

Order 98 27 7 371 494 247

Family 5,451 121 18 376 525 160

Genus 106,782 2,451 118 364 580 151

Species 90,602 645 157 363 541 158

ITS Kingdom 2 1 1 217 217 217

Family 14 5 3 321 458 208

Genus 115 29 9 273 392 180

Species 7,469 77 18 342 566 180

Total 210,558 3,371 338 363 580 151

Abbreviations: bp, base pair; ITS, internal transcribed spacer; OTU, operational taxonomic unit; and rRNA, ribosomal RNA.

１５


taxonomic allocation of internal transcribed spacer (ITS)

sequences

After clustering, 7,600 reads of ITS sequences were clustered into 112 OTUs.

After taxonomic allocation, 77 OTUs were identified to the species level and

29 OTUs were identified to the genus level (Table 3). Candida spp. accounted

for the highest total number of reads, while Phialemonium curvatum was

detected in the highest number of specimens (Table 4, 5).

In both the 16S rRNA and ITS taxonomies, the average read lengths associated

with taxonomic groups with a low similarity score (16S rRNA) and percent

identity (ITS) were similar to those of taxonomic groups with a high similarity

score and percent identity; therefore, similarity/identity appears to be

independent of read length (Table 1). Similarly, the read lengths did not differ

by taxonomic level (Table 3).

１６

Table 4 Taxonomic result of internal transcribed spacer (ITS) gene sequencing.

Specimen Taxonomy Taxonomic rank Total reads Nugent score Fungi in culture Candida in DNA probe

101 Trichomeriaceae Family 7 1 Negative Negative

102 Cryptococcus mangaliensis Species 4 2 Negative Negative

Malassezia restricta Species 1

Nectriaceae Species 4

104 Malassezia Genus 2 0 Negative Negative


105 Lophiostoma Genus 3 6 Negative Negative

Phialemonium curvatum Species 2

106 Cladosporium silenes Species 3 5 Negative Positive

Malassezia Genus 8


Saccharomyces cerevisiae Species 13

Saccharomyces pastorianus Species 12

111 Epicoccum Genus 46 8 Negative Negative

Trichomeriaceae Family 4



117 Candida glabrata Species 4 0 Negative Positive

Cladosporium Genus 2

１７

Table 4. continued.


120 Cryptococcus Genus 4 8 Negative Negative


123 Candida albicans Species 7 0 Candida albicans Positive

Thanatephorus cucumeris Species 2


126 Malassezia restricta Species 1 5 Negative Negative

129 Phialemonium curvatum Species 5 4 Negative Negative


131 Candida albicans Species 13 0 Negative Negative


133 Candida Genus 1 0 Candida lusitaniae Positive

Candida albicans Species 3,611

134 Alternaria alternata Species 15 0 Negative Negative

Aspergillus ochraceus Species 3

Kazachstania telluris Species 2

Phoma Genus 33


Malassezia globosa Species 2




146 Rhodotorula Genus 1 0 Negative Negative

１８

Table 4. continued.






206 Ambrosia artemisiifolia Species 1 0 Negative Negative

Ascomycota Family 1


207 Dendronephthya Genus 1 3 Negative Negative

Erythrobasidiaceae Family 1


Malassezia Genus 5



211 Malassezia restricta Species 1 0 Negative Negative

213 Candida albicans Species 11 8 Candida albicans Positive



224 Stereum ostrea Species 3 4 Negative Negative

225 Candida Genus 1 4 Negative Positive

Candida albicans Species 3,523


Unclassified eukaryote Genus 1

１９

Table 4. continued.


226 Candida albicans Species 1 No data Negative

228 Phialemonium curvatum Species 3 No data Negative

229 Candida albicans Species 1

No data Negative

231 Fusarium Species 2

No data Negative


No data Negative

233 Phialemonium curvatum Species 9

No data Negative

Phyllodistomum folium Species 2

Unclassified fungi Kingdom 2

234 Candida glabrata Species 5

No data Positive


No data Negative


237 Phialemonium curvatum Species 1

No data Negative

239 Malassezia Genus 1

No data Negative


241 Malassezia Genus 2

No data Negative


No data Negative


No data Positive


246 Unclassified fungi Kingdom 1 No data Negative

２０

Table 5. Total reads and positive specimens in the internal

transcribed spacer (ITS) gene taxonomy.

*: genus or higher taxonomic rank.

Taxonomy* Total reads Number of positive specimens

Candida 7,214 13

Nakaseomyces 104 4

Phialemonium 76 21

Epicoccum 46 1

Malassezia 34 18

Phoma 33 1

Saccharomyces 25 2

Alternaria 15 1

Trichomeriaceae 11 2

Ascomycota 1 1

Cryptococcus 8 2

Cladosporium 5 2

Fusicolla 4 1

Aspergillus 3 1

Stereum 3 1

Lophiostoma 3 1

Fusarium 2 1

Kazachstania 2 1

Phyllodistomum 2 1

Thanatephorus 2 1

２１

3.4. Diversity calculation

Two Shannon diversity indices were calculated for each specimen. First, both

16S rRNA and ITS sequences were included in the calculations. Second, only

16S rRNA sequences were included. The mean Shannon diversity index of the

16S rRNA and ITS genes was 1.4137 (95% CI: 1.2414–1.5859) and that of 16s

rRNA alone was 1.3792 (95% CI: 1.2053–1.5530). The difference was

determined to be statistically significant by a paired t-test (p = 0.0005). Table

6 shows raw data for clustered OTUs, the numbers of taxa and Shannon

diversity indices.

２２

Table 6. Results of clustering, the number of taxonomy and Shannon diversity index.

Specimen Total reads OTU Fraction of reads

Number of taxa Shannon diversity index

16S rRNA + ITS 16S rRNA

16S rRNA ITS Lactobacillus Total† ≥0.1% ≥1% ≥5% Total* ≥0.1% ≥1% ≥5% Total* 16S rRNA

101 4,528 27 99.8% 0.2% 99.8% 9 8 3 1 8 7 3 1 0.8497 0.8395

102 2,856 64 99.7% 0.3% 77.0% 11 10 5 4 7 7 5 4 1.8654 1.8469

103 2,535 40 100.0% 0.0% 99.6% 11 3 2 1 11 3 2 1 1.1459 1.1459

104 3,154 19 99.9% 0.1% 99.8% 4 1 1 1 2 1 1 1 0.3832 0.3726

105 1,995 63 99.7% 0.3% 63.3% 26 22 4 2 24 20 4 2 2.0012 1.9870

106 2,354 29 98.2% 1.8% 0.0% 16 9 2 1 9 3 2 1 0.4112 0.2959

107 1,875 64 100.0% 0.0% 21.4% 21 16 6 5 21 16 6 5 2.0576 2.0576

108 3,860 26 100.0% 0.0% 99.8% 5 1 1 1 5 1 1 1 1.1134 1.1134

110 2,702 125 100.0% 0.0% 0.0% 24 15 8 2 24 15 8 2 2.7955 2.7955

111 767 42 93.5% 6.5% 0.0% 31 31 12 5 29 29 11 6 2.2287 2.0934

113 25 11 100.0% 0.0% 0.0% 11 11 11 5 11 11 11 5 1.9997 1.9997

114 255 28 96.9% 3.1% 1.2% 24 24 10 5 22 22 9 5 2.2934 2.2115

115 810 21 100.0% 0.0% 94.9% 18 18 4 2 18 18 4 2 1.1504 1.1504

116 2,717 23 100.0% 0.0% 100.0% 5 5 4 4 5 5 4 4 1.6336 1.6336

117 3,401 29 99.8% 0.2% 99.4% 13 3 1 1 11 2 1 1 0.4764 0.4631

118 3,243 33 100.0% 0.0% 99.6% 8 6 3 3 8 6 3 3 1.6656 1.6656

119 1,760 29 100.0% 0.0% 55.1% 14 8 5 2 14 8 5 2 1.6084 1.6084

120 2,069 23 99.7% 0.3% 96.9% 13 9 3 1 11 8 3 1 0.6622 0.6423

２３

Table 6. continued.




16S rRNA ITS Lactobacillus Total ≥0.1% ≥1% ≥5% Total ≥0.1% ≥1% ≥5% Total 16S rRNA

121 2,170 20 100.0% 0.0% 99.6% 11 5 3 1 11 5 3 1 0.4832 0.4832

122 19 1 100.0% 0.0% 100.0% 1 1 1 1 1 1 1 1 0.0000 0.0000

123 1,979 19 99.5% 0.5% 98.8% 8 6 2 2 6 4 2 2 1.0092 0.9822

124 1,711 77 99.9% 0.1% 0.0% 26 19 6 2 25 19 6 2 2.2817 2.2781

125 13 5 100.0% 0.0% 0.0% 5 5 5 5 5 5 5 5 1.0438 1.0438

126 889 28 99.9% 0.1% 0.4% 20 20 6 2 19 19 6 2 1.2896 1.2823

127 119 13 100.0% 0.0% 0.0% 12 12 9 3 12 12 9 3 1.6326 1.6326

128 3,308 39 100.0% 0.0% 0.0% 5 3 2 2 5 3 2 2 0.7471 0.7471

129 1,254 28 99.6% 0.4% 0.2% 19 11 4 1 18 10 4 1 0.7326 0.7094

130 1,936 17 99.8% 0.2% 98.7% 4 4 1 1 3 3 1 1 0.3720 0.3579

131 2,172 26 99.4% 0.6% 98.8% 9 8 2 1 8 7 2 1 0.8276 0.7957

132 2,197 15 99.5% 0.5% 99.3% 6 2 1 1 5 1 1 1 0.4549 0.4255

133 8,100 21 55.4% 44.6% 55.4% 5 3 2 2 3 2 1 1 1.0967 0.1069

133-1 3,612 10 100.0% 0.0% 0.0% 3 3 2 1 3 3 2 1 0.2516 0.2516

134 2,029 42 97.4% 2.6% 93.3% 22 13 4 1 18 12 3 1 1.0791 0.9563

135 43 8 100.0% 0.0% 9.3% 7 7 7 2 7 7 7 2 1.0921 1.0921

136 3,945 24 99.9% 0.1% 99.8% 7 1 1 1 5 1 1 1 1.2492 1.2435

137 2,978 84 100.0% 0.0% 0.5% 18 12 5 2 17 12 5 2 2.4477 2.4477

138 2,814 107 100.0% 0.0% 38.1% 17 14 7 4 17 14 7 4 2.5085 2.5085

２４

Table 6. continued.





138 2,814 107 100.0% 0.0% 38.1% 17 14 7 4 17 14 7 4 2.5085 2.5085

139 2,048 32 100.0% 0.0% 23.8% 14 8 5 2 13 8 5 2 1.2601 1.2601

140 472 35 99.8% 0.2% 49.2% 29 29 10 3 28 28 10 3 2.0700 2.0592

141 816 18 99.6% 0.4% 0.1% 14 14 4 3 13 13 4 3 1.0679 1.0451

142 1,596 27 99.9% 0.1% 95.6% 14 9 4 3 13 9 4 3 1.5414 1.5371

143 2,437 14 100.0% 0.0% 99.9% 6 2 2 1 6 2 2 1 0.3804 0.3804

144 1,696 54 100.0% 0.0% 0.5% 20 17 6 1 20 17 6 1 2.1255 2.1255

145 92 21 100.0% 0.0% 1.1% 18 18 18 3 18 18 18 3 2.0922 2.0922

146 1,291 24 99.9% 0.1% 95.3% 15 13 5 1 14 13 5 1 1.2216 1.2162

201 5,816 33 100.0% 0.0% 99.7% 7 2 1 1 5 2 1 1 0.8882 0.8852

202 356 40 99.4% 0.6% 2.8% 34 34 12 4 33 33 12 4 2.3937 2.3723

203 1,725 29 100.0% 0.0% 93.4% 18 10 3 1 18 10 3 1 1.1830 1.1830

204 596 32 100.0% 0.0% 0.3% 24 24 9 2 24 24 9 2 1.7143 1.7143

205 1,614 32 99.9% 0.1% 87.5% 27 17 4 1 26 17 4 1 1.0058 1.0012

206 4,664 22 99.9% 0.1% 99.9% 8 4 3 2 5 4 3 2 0.8972 0.8917

207 891 52 99.8% 0.2% 1.1% 43 43 16 4 41 41 16 4 2.6013 2.5896

208 6,871 45 99.3% 0.7% 99.3% 13 4 3 1 10 3 3 1 0.9400 0.9019

210 108 17 98.1% 1.9% 7.4% 16 16 10 5 15 15 9 5 2.1318 2.0781

211 5,702 22 100.0% 0.0% 99.9% 9 3 3 2 8 3 3 2 0.7068 0.7053

２５

Table 6. continued.





213 1,823 40 99.4% 0.6% 5.5% 24 17 5 3 23 16 5 3 1.8405 1.8146

214 4,057 51 100.0% 0.0% 0.0% 29 4 2 1 28 4 2 1 0.7829 0.7829

215 2,335 45 100.0% 0.0% 82.6% 31 15 8 2 30 15 8 2 1.2923 1.2923

216 4,414 145 100.0% 0.0% 0.4% 19 10 4 3 19 10 4 3 2.8650 2.8650

217 3,757 50 99.9% 0.1% 1.5% 25 10 3 1 23 9 3 1 0.4839 0.4778

218 3,059 45 100.0% 0.0% 93.9% 23 12 3 2 23 12 3 2 1.1614 1.1614

219 3,082 29 100.0% 0.0% 0.1% 14 4 3 1 13 3 2 1 0.8521 0.8521

220 3,638 23 99.9% 0.1% 99.2% 13 5 2 1 12 4 2 1 0.2112 0.2011

221 2,720 26 100.0% 0.0% 98.5% 14 6 3 3 14 6 3 3 1.0906 1.0906

222 27 3 100.0% 0.0% 96.3% 3 3 3 2 3 3 3 2 0.8259 0.8259

223 3,354 22 100.0% 0.0% 99.3% 15 6 3 2 15 6 3 2 0.5059 0.5059

224 205 15 98.5% 1.5% 1.0% 15 15 8 3 14 14 7 3 1.6712 1.6186

225 10,126 55 65.2% 34.8% 64.4% 11.5 5 3 2 9 5 2 1 1.1699 0.7245

226 3,317 30 100.0% 0.0% 98.2% 16 8 2 2 15 8 2 2 0.6378 0.6353

227 2,755 18 100.0% 0.0% 98.7% 12 4 1 1 12 4 1 1 0.1629 0.1629

228 145 23 97.9% 2.1% 4.1% 20 20 15 4 19 19 14 4 2.3201 2.2663

229 440 53 99.8% 0.2% 1.4% 38 38 10 3 37 37 10 3 2.5863 2.5761

230 3,468 20 100.0% 0.0% 97.9% 10 6 3 2 10 6 3 2 0.7210 0.7210

231 99 21 98.0% 2.0% 0.0% 20 20 20 5 19 19 19 5 2.4053 2.3540

２６

Table 6. continued.

Abbreviations: ITS, internal transcribed spacer; OTU, operational taxonomic unit; and rRNA, ribosomal RNA.

*: any fraction more than zero.


Number of Taxa Shannon diversity index



232 4,589 162 100.0% 0.0% 10.9% 24 13 5 4 23 13 5 4 2.9830 2.9816

233 197 30 93.4% 6.6% 0.5% 29 29 17 2 26 26 14 2 2.3682 2.2165

234 4,165 75 99.9% 0.1% 66.1% 26 12 6 3 24 11 6 3 2.0296 2.0228

235 3,253 122 100.0% 0.0% 10.6% 22 15 7 4 21 14 7 4 2.8136 2.8136

236 2,364 26 99.9% 0.1% 98.3% 20 5 2 1 18 5 2 1 0.2417 0.2314

237 752 32 99.9% 0.1% 1.3% 25 25 6 2 24 24 6 2 1.2990 1.2906

238 3,919 71 100.0% 0.0% 0.7% 17 9 3 2 17 9 3 2 1.7740 1.7740

239 3,557 21 99.8% 0.2% 98.7% 11 6 2 2 9 5 2 2 0.8520 0.8386

240 5,227 21 100.0% 0.0% 99.3% 9 4 2 1 9 4 2 1 0.2459 0.2459

241 68 18 97.1% 2.9% 7.4% 17 17 17 3 16 16 16 3 2.0842 2.0106

242 385 38 99.7% 0.3% 2.9% 33 33 14 5 32 32 14 5 2.6370 2.6258

243 4,350 20 97.8% 2.2% 97.7% 7 2 2 1 5 1 1 1 0.3351 0.2124

244 74 16 100.0% 0.0% 0.0% 16 16 16 5 16 16 16 5 2.1820 2.1820

245 125 22 100.0% 0.0% 0.0% 20 20 12 6 20 20 12 6 2.4690 2.4690

246 3727 154 100.0% 0.0% 0.0% 19 10 5 2 16 10 5 2 3.7206 3.7191

２７

3.5. Cluster analysis of the microbiome compositions

The bacterial compositions of 89 specimens are shown as a heatmap in Figure

2. The figure contains microorganisms that represent a fraction of sequences

that is greater than 0.1% of the total reads from a specimen. In Figure 2, each

row represents a taxon at order level and each column represents a specimen.

Sixty-seven specimens with Nugent scores were categorized into normal,

intermediate, and vaginitis groups. In Figure 2A, columns are clustered by

Nugent score categories. The pattern of the normal group was distinct from

the patterns of the intermediate and vaginitis groups. However, the patterns of

the intermediate group (yellow bar) and the vaginitis group (red bar) were

similar. In Figure 2B, columns were clustered by Euclidean distance

according to complete linkage rule and their similarities were visualized using

a neighbor-joining tree. Specimens could be grouped into four major clusters

(Group I-IV in Figure 2B). The four clusters consisted of heterogeneous

Nugent score groups.

The most abundant taxa of the three groups are shown in Table 7. The major

taxon of the normal group was Lactobacillus spp., and other taxa were

relatively rare. The genera that were more common in the intermediate and

vaginitis groups than in the normal group included Prevotella, Sneathia,

Aerococcus, Atopobium, Megasphaera, and Cupriavidus. The average

Lactobacillus fraction was higher in the vaginitis group than in the

intermediate group. (38.98% versus 25.19%, respectively).

２８

Figure 2. Heatmap of microbiome of 89 vaginal swab specimens (A) Sorting on the basis of Nugent score.

２９

Figure 2. Heatmap of microbiome of 89 vaginal swab specimens (B) Columns clustering.

３０

Figure 2. (continued)

Each row shows a taxon at the order level and each column shows a single

specimen. The bar above the first row shows the Nugent score of each

specimen. Green: normal group (Nugent score 0–3); Yellow: intermediate

group (Nugent score 4–6); Red: vaginitis group (Nugent score 7–10); Gray:

specimens without Nugent score data.

(a) Sorting on the basis of Nugent score

(b) Columns are clustered. The clustering rule was complete linkage.

Their similarity was visualized using a neighbor-joining tree. Four

groups according to clustering results are shown in the bottom panel.

３１

Table 7. The most abundant taxa in the three Nugent score

groups.

The value is the average fraction of corresponding taxonomy among Nugent

group. The lowest taxonomy is genus rank.

Normal group (n=30) Intermediate Group (n=25) Vaginitis group (n=12)

Lactobacillus 83.41% Lactobacillus 25.19% Lactobacillus 38.98%

Streptococcus 4.90% Cupriavidus 13.67% Prevotella 27.80%

Diaphorobacter 2.50% Sneathia 8.65% Sneathia 7.48%

Enterobacteriaciae 1.97% Streptococcus 8.27% Aerococcus 5.62%

Candida 1.54% Prevotella 6.04% Atopobium 4.46%

Cupriavidus 1.36% Atopobium 5.87% Megasphaera 1.72%

Prevotella 0.80% Megasphaera 5.28% Diaphorobacter 1.67%

Cloacibacterium 0.43% Enterobacteriaciae 4.37% Gardnerella 1.36%

Veillonella 0.34% Haemophilus 3.99% Porphyromonas 1.29%

Chlamydia 0.22% Diaphorobacter 2.90% Dialister 1.05%

Comamonas 0.20% Aerococcus 2.17% Cupriavidus 1.03%

Novosphingobium 0.18% Gp4 1.60% Saccharofermentans 0.94%

Staphylococcus 0.16% Sphingomonas 1.48% Peptoniphilus 0.69%

Haemophilus 0.14% Candida 1.47% Mobiluncus 0.69%

Gemella 0.13% Cloacibacterium 1.23% Anaerococcus 0.55%

Pseudomonas 0.11% Saccharofermentans 0.73% Epicoccum 0.50%

Acinetobacter 0.10% Corynebacterium 0.55% Coriobacteriaceae 0.38%

Alishewanella 0.09% Novosphingobium 0.55% Mycoplasma 0.35%

Sphingobium 0.08% Alishewanella 0.38% Moryella 0.35%

Dechloromonas 0.08% Propionibacterium 0.37% Fusobacterium 0.30%

３２

3.6. Association between diversity predictors and Nugent

scores.

Shannon diversity indices for the16S rRNA and ITS sequences showed a

significant association with Nugent score groups in a one-way ANOVA test

(Figure 3A, p = 0.0037). With Bonferroni correction, there was a significant

association between the normal Nugent score group and the vaginitis Nugent

score group (p = 0.033). The numbers of taxa representing more than 5% of

the reads differed significantly among the Nugent score groups (Figure 3B, p

= 0.0163). With Bonferroni correction, there was a significant association

between the normal Nugent score group and the vaginitis Nugent score group

(p = 0.004). The proportions of Lactobacillus spp. were significantly different

among the Nugent score groups (p < 0.0001) by a one-way ANOVA analysis,

but the proportions did not increase with the grade of vaginitis, represented by

the Nugent score group. The mean proportions of Lactobacillus spp. in the

normal, intermediate, and vaginitis groups were 83.4%, 25.2%, and 39.0%,

respectively.

３３

Figure 3. Association between diversity predictors and Nugent score groups

01

23

(n=30) (n=25) (n=12)

P = 0.033

0-3 4-6 ≥7

Shan

non d

ivers

ity

index

Nugent score

(A) Shannon index vs. Nugent score group

01

23

45

67

8

P = 0.004

0-3 4-6 ≥7(n=30) (n=25) (n=12)

Num

ber

of ta

xa(≥

5%

)

Nugent score

(B) Number of taxa vs. Nugent score group

３４

Figure 3. continued

A total of 67 specimens with Shannon diversity index were grouped into three

groups according to Nugent score. Normal group: score 0-3, intermediate

group: score 4-6, vaginitis group: score 7-10.

(a) Shannon diversity indices according to Nugent score group in the form of

Tukey’s box plot.

The Shannon diversity index including both bacteria and fungi was

calculated for each sample. In total, 67 Shannon diversity indices were

classified into three groups according to the Nugent score of the

specimen.

(b) Total number of taxa according to Nugent score group in the form of

Tukey’s box plot.

Only species more than 5% in total reads were included.

３５

3.7. The comparison of various predictors of diversity as

a diagnostic criterion of vaginitis.

An ROC curve analysis was performed for various predictors of diversity to

estimate their diagnostic value for vaginitis (Figure 4). Three criteria were

used to compare the diagnostic value of each method for vaginitis. First, a

Nugent score of ≥4 was considered vaginitis, including both the intermediate

and vaginitis groups in the original Nugent criteria (10). Second, a Nugent

score of ≥ 7 was considered vaginitis, similar to the original criterion (11).

Third, microbiological culture results other than those of normal vaginal flora

or Lactobacillus spp. were considered vaginitis. Since the Nugent criteria are

based on bacterial morphotypes and do not consider yeast morphotypes,

various other predictors were compared, including the Shannon diversity

index for 16S rRNA and the Shannon diversity indices for both 16S rRNA and

ITS sequences. Other parameters, such as the total number of taxa and the

fraction of sequences that were from Lactobacillus spp. were also compared.

All eleven predictors showed statistically significant associations with the first

criterion for vaginitis (Nugent score ≥ 4, Figure 4A). The highest AUC was

0.8559, which was obtained based on the fraction of sequences belonging to

lactobacilli and the first criterion for vaginitis. Applying this parameter and

criterion combination, with a 12.45% lactobacilli fraction as a cut-off, the

sensitivity of this algorithm was 83.78% (95% CI: 68.0–93.8%), and the

specificity was 80.00% (95% CI: 61.4–92.3%).

３６

Figure 4. Receiver-operating characteristic (ROC) curves of eleven predictors of diversity and three vaginitis

criteria.

0.0

00

.25

0.5

00

.75

1.0

0S

en

sitivi

ty

0.00 0.25 0.50 0.75 1.001-Specificity

a, AUC: 0.7158* b, AUC: 0.7536*

c, AUC: 0.7770* d, AUC: 0.7176*

e, AUC: 0.7252* f, AUC: 0.7568*

g, AUC: 0.7685* h, AUC: 0.7158*

i, AUC: 0.8559* j, AUC: 0.7234*

k, AUC: 0.7243* Reference line

(A) Vaginitis criterion 1

0.0

00

.25

0.5

00

.75

1.0

0S

en

sitivi

ty

0.00 0.25 0.50 0.75 1.001-Specificity

a, AUC: 0.7409* b, AUC: 0.7992*

c, AUC: 0.7500* d, AUC: 0.6545

e, AUC: 0.7515* f, AUC: 0.8053*

g, AUC: 0.7568* h, AUC: 0.6674

i, AUC: 0.6455 j, AUC: 0.7485*

k, AUC: 0.7606* Reference line

(B) Vaginitis criterion 2

0.0

00

.25

0.5

00

.75

1.0

0S

en

sitivi

ty

0.00 0.25 0.50 0.75 1.001-Specificity

a, AUC: 0.5910 b, AUC: 0.6290

c, AUC: 0.6248 d, AUC: 0.6018

e, AUC: 0.5788 f, AUC: 0.6220

g, AUC: 0.6140 h, AUC: 0.5835

i, AUC: 0.6932* j, AUC: 0.6144

k, AUC: 0.5882 Reference line

(C) Vaginitis criterion 3

３７

Figure 4. continued.

ROC curves of eleven predictors using (A) vaginitis criterion 1, (B) vaginitis

criterion 2 and (C) vaginitis criterion 3.

*: P < 0.05

Abbreviations: AUC, area under curve; CI, confidence interval; ITS, internal

transcribed spacer; and rRNA, ribosomal RNA.

(1) Vaginitis criteria

- Vaginitis criterion 1: When the Nugent score of a specimen was 4 or

more, the specimen was regarded as vaginitis.

- Vaginitis criterion 2: When the Nugent score of a specimen was 7 or

more, the specimen was regarded as vaginitis.

- Vaginitis criterion 3: When the microbiological culture of a

specimen showed results other than normal vaginal flora or

Lactobacillus spp., the specimen was regarded as vaginitis.

(2) Predictors.

a–d: The total number of taxa in the specimen, including both the 16S rRNA

gene and the ITS gene, when the fraction of that taxon is more than (a)

zero, (b) 0.1% , (c) 1% and (d) 5%.

e–h: The total number of taxa in the specimen, including only the 16S rRNA

gene, when the fraction of that taxon is more than (e) zero, (f) 0.1%, (g)

1% and (5) 5%.

i: The fraction of Lactobacillus spp. in the specimen.

３８

j–k: Shannon diversity index of the specimen, when the index was calculated

from both the 16S rRNA gene and the ITS gene (j) or was calculated from

the 16S rRNA gene only (k).

３９

3.8. Comparison of next generation sequencing (NGS),

DNA probe assay and microbiological culture in the

detection of Candida spp. and Gardnerella vaginalis.

The DNA probe assay and culture showed 95.5% and 76.1% agreement in the

detection of Candida spp. and G. vaginalis, respectively. NGS and culture

showed 88.1% and 86.6% agreement in the detection of Candida spp. and G.

vaginalis, respectively (Tables 8). NGS and the DNA probe assay showed

86.2% and 89.7% agreement in the detection of Candida spp. and G. vaginalis,

respectively. All three assays showed significant associations by Pearson’s

chi-square analysis. Cohen’s kappa index results showed fair to substantial

agreement among the three test methods (23). T. vaginalis was not found in

any specimen by any method.

４０

Table 8. Comparison of next generation sequencing (NGS),

DNA probe assay and microbiological culture for detection

of Candida spp. and Gardnerella vaginalis

Method1 Method2 N Organisms OPA P-value κ

NGS Culture 67 Candida spp. 88.1% 0.003 0.363

NGS Culture 67 Gardnerella vaginalis 86.6% 0.003 0.509

DNA probe Culture 67 Candida spp. 95.5% <0.001 0.776

DNA probe Culture 67 Gardnerella vaginalis 76.1% <0.001 0.335

NGS DNA probe 87 Candida spp. 86.2% <0.001 0.460

NGS DNA probe 87 Gardnerella vaginalis 89.7% <0.001 0.690

Abbreviations: κ, Cohen’s kappa index; SPA, simple percent agreement;.

P-values of Pearson’s chi-square analysis and Cohen’s kappa index were cal-

culated for each comparison.

４１

3.9. Comparison of next generation sequencing (NGS) and

microbiological culture.

The results of NGS and culture were difficult to compare directly, since NGS

detected many microorganisms and culture usually detected only a few. In our

study, NGS and culture were considered to be in complete agreement, if NGS

results included all of the cultured microorganisms. “Normal vaginal flora”

culture results included Lactobacillus spp. With this definition of complete

agreement, NGS and culture showed 73.1% (49/67) agreement. Two

specimens were considered in partial agreement. In one specimen showing

partial agreement, Enterococcus faecalis and Candida albicans grew, while

NGS detected only C. albicans. In the other specimen, Escherichia coli and G.

vaginalis grew in culture, while NGS detected only G. vaginalis. Table 9

shows the remaining 16 specimens with discordant results between these two

detection methods.

４２

Table 9. Discordant results between next generation

sequencing (NGS) and microbiological culture.

In these sixteen specimens, NGS could not find any microorganisms which

were grown in culture.

Specimen Culture result 16S rRNA sequence ITS sequence

103 Candida albicans 2,535 0

113 Streptococcus agalactiae 25 0

116 Yeast 2,717 0

122 Staphylococcus aureus 19 0

126 Streptococcus anginosus 888 1

128 Escherichia coli 3,308 0

142 Enterococcus faecalis 1,595 1

145 Escherichia coli 92 0


201 Enterococcus faecalis 5,814 1,495


206 Streptococcus agalactiae 4,661 3

210 Enterococcus faecalis 106 2

215 Staphylococcus epidermidis 2,335 0

221 Candida ciferrii 2,720 0

225 Escherichia coli 6,598 3,528

４３

4. DISCUSSION

Most read lengths in our study were long enough for sequences to be

identified at the species and genus levels, although they were shorter than we

expected. Several studies have analyzed vaginal microbiomes with NGS

technology (4-9). Hummelen et al. studied the vaginal microbiota of 132 HIV-

positive Tanzanian women using an Illumina system (5). Ravel et al. studied

the vaginal microbiota of 396 women comprising four ethnic groups (7). This

group used a 454 FLX system, and the average read length of 16S rRNA

sequences was 240 bp. Martin et al. studied vaginal swabs of 92 American

women (ethnicity unknown) with a 454 system, and the average read length

was 480 bp (24). This study showed the highest average read length of all of

these earlier studies. The studies described above analyzed only bacteria in

vaginal microbiomes and did not analyze fungi. As many bacterial vaginitis

cases are accompanied by vulvovaginal candidiasis, an analysis of fungi can

provide more information about the vaginal microbiome. In our study, the ITS

gene, indicating the presence of fungi, was found in 56.2% of specimens,

although many specimens included only a few reads of this gene. Shannon

diversity indices also showed significant differences with the inclusion of

fungi. Fungi should be considered in investigations of the vaginal microbiome.

The microbiota of the normal Nugent score group mainly comprised

Lactobacillus, as expected based on previous reports (Figure 2). The Shannon

diversity index, number of species, and proportion of sequences belonging to

Lactobacillus spp. showed significant differences among the Nugent score

４４

groups (Figures 3 and 4). However, the composition of the intermediate

Nugent score group and that of the vaginitis Nugent score group were similar.

One possibility is that there was bias by the examiner who assigned the

Nugent scores; another is that these results were skewed because of the

relatively small number of specimens in the vaginitis group. In our data, all 12

specimens comprising the vaginitis group (Nugent score ≥ 7) had a score of 8,

suggesting the possibility of bias when Nugent scores were assigned. As the

Nugent score is highly dependent on the examiner, bias is possible.

Yet another possibility is that the intermediate group and the vaginitis

group are on the same clinical spectrum; thus, they may share similar

microbiome patterns. In Figure 2, the Lactobacillus fraction showed good

correlation with the Nugent score when results from the normal group were

compared with those from the other two groups (intermediate and vaginitis

groups combined). Similarly, all 11 parameters of diversity were significantly

associated with evidence for vaginitis, including both the intermediate and

vaginitis group (a Nugent score of ≥ 4, Figure 4A). In short, we could not

distinguish between the intermediate group (Nugent score 4–6) and the

vaginitis group (Nugent score 7–10) with the microbiome analysis in this

study. Although clinical factors such as symptoms and treatment were not

considered in this study, the current cutoffs for Nugent scores might need to

be changed based on the results of this study.

In this study, NGS, DNA probe assay, and microbiological culture were

compared for their abilities to detect vaginal microorganisms. There are few

studies comparing NGS and culture in the investigation of vaginal

４５

microbiomes. Smidt et al. compared NGS, quantitative PCR, and culture-

based methods to identify Lactobacillus spp. (25). At present, this is the only

prior study that has compared culture and NGS using vaginal specimens.

These researchers compared agreement in the detection of species within the

Lactobacillus genus and reported general concordance among the three

methods in detecting L. crispatus, L. jensenii, and L. gasseri, but not L. iners.

The concordance between NGS and culture in the detection of Lactobacillus

spp. could not be determined, because Lactobacillus spp. in this study were

reported as normal vaginal flora, and species were not reported. In our study,

NGS and microbiological culture showed only 73.1% agreement, while NGS

and the DNA probe assay showed good correlation.

Salipante et al. reported a similar phenomenon, based on sputum

specimens from cystic fibrosis patients (26). In their study, 17.3% of

pathogens were identified only by culture, and the total agreement rate

between NGS and culture was 56.7%. Some of the reads were lost during the

de-noising steps of NGS, and this was a reason for some discordance. They

also suggested that discrepancies between the results by the two methods

reflect various factors, including inefficient DNA extraction from particular

organisms, primer bias, or properties of the specimens themselves, including

internal sample heterogeneity. Toma et al. also reported discrepancies between

culture and NGS in endotracheal aspirates (27). Interestingly, they used

multiple databases, and the discrepancy rates differed according to the

database used. NGS and culture results coincided in 85% of samples using

three databases. They suggested that short microbial reads and amplification

４６

bias caused by mismatches of universal primers in certain bacteria might have

caused such discrepancies. Although studies reporting discrepancies between

the results of NGS and culture methods in an investigation of the vaginal

microbiome were identified, the factors mentioned above could have had a

similar effect in our study.

The RDP is a very convenient pipeline for analyzing 16S rRNA sequences

from NGS data. It provides a taxonomic ranking of sequences in a form that

can easily be converted into data for use with various platforms. However, our

results showed that there might be significant mismatches based on low

similarity scores. Fettweis et al. noted similar mismatches (28). In our study,

several sequences showing RDP results with low similarity scores were

identified as human or Candida spp. DNA. Since vaginal swabs can include

nucleic acid from both humans and microorganisms other than bacteria,

sequences with poor similarity scores should be analyzed using other

databases.

This study had some limitations. First, we could not evaluate the various

analytical performance parameters, but we investigated the possibility of

using NGS as a clinical diagnostic tool. We evaluated the accuracy of NGS by

comparison with culture and DNA probe assay results. Second, NGS results

and microbiological culture results were indirectly compared.

Although there are some remaining problems to solve and further

optimization will be required before using NGS as an independent diagnostic

tool (26), NGS detected many more microorganisms than traditional detection

methods. NGS might become a useful and powerful method to investigate

４７

vaginal microbiomes that can provide information on clinical diseases

associated with specific vaginal microbiome profiles.

４８

REFERENCES

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５２

국

: 질 미생 군집 변 는 여러 증상 질 과 연 어

있 며, 통 는 미경 소견과 미생 양 그 변 를

인하 다. 차 염 열 분 법 (Next-generation sequencing;

NGS) 통 인 법에 해 훨씬 많 미생 진단할 있다.

과거 NGS 를 사용한 질 미생 연구들에는 산 이가

짧거나 균 외 미생 상 하지 않는 등 한계가

있었다. 이 연구는 한국 여 질 미생 군집 충분한 read

length 균 외 다른 미생 도 모 포함하여 분 하는 것

목 한다. 또한 NGS 를 다른 검사법과 하며, 질염

인자 가능 평가하 한다.

법: 89 개 질 도말 검체가 집 었 며, 그 67 개는 Gram

염색 도말 미경 소견과 미생 양 결과가 있었다. 균, 진균,

질편모충 분 해 16S rRNA, internal transcribed spacer (ITS), Tvk

자를 상 하 며, 454 사(Branford, CT, USA) GS junior

장 를 사용하여 NGS 를 시행하 다. 데이 분 , 조작상분 단

구 , 키 라 염 열 거에는 Usearch 소프트웨어를

사용하 며, Ribosomal database project (RDP), Basic Local Alignment

Search Tool (BLAST) 데이 베이스를 사용하여 미생 동 ,

５３

분 하 다. 잔여 도말 검체에 Beckton Dickinson (NJ, USA)사 BD

Affirm VPIII 검사법 사용하여 칸 다, 가드 라, 질편모충

검사를 시행하 다.

결과: 202,958 개 16S rRNA 염 열과 7,600 개 ITS

염 열이 인 었 며 Tvk 염 열 검출 지 않았다. ITS

염 열 체 검체 56.2% (50/89)에 검출 었다. 16S rRNA

ITS 염 열 각각 3,259 개 112 개 조작상분 단 다.

Nugent 가 상인 검체 미생 분포는 주

Lactobacilliales 목(目) 구 어 있었다. 간 또는 질염에

해당하는 검체는 Lactobacilliales 다른 다양한 미생

구 어 있었 나, 간군과 질염군 구 에 뚜 한 차이는

보이지 않았다. Shannon 다양 지 , 종(種) 자,

Lactobacillus 속(屬) 군에 뚜 한 차이를 보 다.

NGS 자료에 미생 군집 여러 가지 다양 지 를 얻어 질염

한 결과 Lactobacillus 속 이 가장 높 곡

아래 면 (area under curve) 값 보 다 (0.8559).

NGS 핵산 듬자법 가드 라 칸 다 검출에 있어 좋

일 도를 보 다 (범 86.2 – 89.7%).

결 : 간군과 질염군 미생 군집 뚜 이 구분 지 않았 며,

ITS 염 열 상 검체에 도 하게 검출 었다. NGS 는 질

５４

미생 군집 분 과 질염 진단에 있어 용한 도구가 것

보인다.

-------------------------------------

주요어 : 질 미생 군집, 차 염 열분 , 질염

학 번 : 2012-21726

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