Limits...
Analysis of the Vaginal Microbiome by Next-Generation Sequencing and Evaluation of its Performance as a Clinical Diagnostic Tool in Vaginitis

View Article: PubMed Central - PubMed

ABSTRACT

Background: Next-generation sequencing (NGS) can detect many more microorganisms of a microbiome than traditional methods. This study aimed to analyze the vaginal microbiomes of Korean women by using NGS that included bacteria and other microorganisms. The NGS results were compared with the results of other assays, and NGS was evaluated for its feasibility for predicting vaginitis.

Methods: In total, 89 vaginal swab specimens were collected. Microscopic examinations of Gram staining and microbiological cultures were conducted on 67 specimens. NGS was performed with GS junior system on all of the vaginal specimens for the 16S rRNA, internal transcribed spacer (ITS), and Tvk genes to detect bacteria, fungi, and Trichomonas vaginalis. In addition, DNA probe assays of the Candida spp., Gardnerella vaginalis, and Trichomonas vaginalis were performed. Various predictors of diversity that were obtained from the NGS data were analyzed to predict vaginitis.

Results: ITS sequences were obtained in most of the specimens (56.2%). The compositions of the intermediate and vaginitis Nugent score groups were similar to each other but differed from the composition of the normal score group. The fraction of the Lactobacillus spp. showed the highest area under the curve value (0.8559) in ROC curve analysis. The NGS and DNA probe assay results showed good agreement (range, 86.2-89.7%).

Conclusions: Fungi as well as bacteria should be considered for the investigation of vaginal microbiome. The intermediate and vaginitis Nugent score groups were indistinguishable in NGS. NGS is a promising diagnostic tool of the vaginal microbiome and vaginitis, although some problems need to be resolved.

No MeSH data available.


Related in: MedlinePlus

ROC curves of the 11 predictors of diversity and three vaginitis criteria. The ROC curves of the 11 predictors are shown according to (A) vaginitis criterion 1, (B) vaginitis criterion 2, and (C) vaginitis criterion 3.*P<0.05.(1) Vaginitis criteriaVaginitis criterion 1: vaginitis when the Nugent score≥4.Vaginitis criterion 2: vaginitis when the Nugent score≥7.Vaginitis criterion 3: vaginitis otherwise the culture results are either normal flora or Lactobacillus spp.(2) PredictorsThe specimen indicated vaginitis when the fraction of that taxon in total reads, including both the 16S rRNA and ITS genes, was more than zero: (a), 0.1% (b), 1% (c), and 5% (d).The specimen indicated vaginitis when the fraction of that taxon in total reads, including only the 16S rRNA gene, was more than zero: (e), 0.1% (f), 1% (g), and 5% (h).(i): The fraction of Lactobacillus spp. in the specimen.(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).Abbreviations: AUC, area under the curve; ITS, internal transcribed spacer; rRNA, ribosomal RNA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4940487&req=5

Figure 3: ROC curves of the 11 predictors of diversity and three vaginitis criteria. The ROC curves of the 11 predictors are shown according to (A) vaginitis criterion 1, (B) vaginitis criterion 2, and (C) vaginitis criterion 3.*P<0.05.(1) Vaginitis criteriaVaginitis criterion 1: vaginitis when the Nugent score≥4.Vaginitis criterion 2: vaginitis when the Nugent score≥7.Vaginitis criterion 3: vaginitis otherwise the culture results are either normal flora or Lactobacillus spp.(2) PredictorsThe specimen indicated vaginitis when the fraction of that taxon in total reads, including both the 16S rRNA and ITS genes, was more than zero: (a), 0.1% (b), 1% (c), and 5% (d).The specimen indicated vaginitis when the fraction of that taxon in total reads, including only the 16S rRNA gene, was more than zero: (e), 0.1% (f), 1% (g), and 5% (h).(i): The fraction of Lactobacillus spp. in the specimen.(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).Abbreviations: AUC, area under the curve; ITS, internal transcribed spacer; rRNA, ribosomal RNA.

Mentions: The highest area under the curve (AUC), which was 0.8559, was obtained on the basis of the fraction of lactobacilli and the first criterion for vaginitis (Fig. 3A). When this parameter and criterion combination was applied with a 12.45% lactobacilli fraction cutoff, the sensitivity was 83.78% (95% CI: 68.0-93.8%) and the specificity was 80.00% (95% CI: 61.4-92.3%). All of the predictors showed significant AUC values with the first vaginitis criterion (Nugent score: ≥4).


Analysis of the Vaginal Microbiome by Next-Generation Sequencing and Evaluation of its Performance as a Clinical Diagnostic Tool in Vaginitis
ROC curves of the 11 predictors of diversity and three vaginitis criteria. The ROC curves of the 11 predictors are shown according to (A) vaginitis criterion 1, (B) vaginitis criterion 2, and (C) vaginitis criterion 3.*P<0.05.(1) Vaginitis criteriaVaginitis criterion 1: vaginitis when the Nugent score≥4.Vaginitis criterion 2: vaginitis when the Nugent score≥7.Vaginitis criterion 3: vaginitis otherwise the culture results are either normal flora or Lactobacillus spp.(2) PredictorsThe specimen indicated vaginitis when the fraction of that taxon in total reads, including both the 16S rRNA and ITS genes, was more than zero: (a), 0.1% (b), 1% (c), and 5% (d).The specimen indicated vaginitis when the fraction of that taxon in total reads, including only the 16S rRNA gene, was more than zero: (e), 0.1% (f), 1% (g), and 5% (h).(i): The fraction of Lactobacillus spp. in the specimen.(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).Abbreviations: AUC, area under the curve; ITS, internal transcribed spacer; rRNA, ribosomal RNA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940487&req=5

Figure 3: ROC curves of the 11 predictors of diversity and three vaginitis criteria. The ROC curves of the 11 predictors are shown according to (A) vaginitis criterion 1, (B) vaginitis criterion 2, and (C) vaginitis criterion 3.*P<0.05.(1) Vaginitis criteriaVaginitis criterion 1: vaginitis when the Nugent score≥4.Vaginitis criterion 2: vaginitis when the Nugent score≥7.Vaginitis criterion 3: vaginitis otherwise the culture results are either normal flora or Lactobacillus spp.(2) PredictorsThe specimen indicated vaginitis when the fraction of that taxon in total reads, including both the 16S rRNA and ITS genes, was more than zero: (a), 0.1% (b), 1% (c), and 5% (d).The specimen indicated vaginitis when the fraction of that taxon in total reads, including only the 16S rRNA gene, was more than zero: (e), 0.1% (f), 1% (g), and 5% (h).(i): The fraction of Lactobacillus spp. in the specimen.(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).Abbreviations: AUC, area under the curve; ITS, internal transcribed spacer; rRNA, ribosomal RNA.
Mentions: The highest area under the curve (AUC), which was 0.8559, was obtained on the basis of the fraction of lactobacilli and the first criterion for vaginitis (Fig. 3A). When this parameter and criterion combination was applied with a 12.45% lactobacilli fraction cutoff, the sensitivity was 83.78% (95% CI: 68.0-93.8%) and the specificity was 80.00% (95% CI: 61.4-92.3%). All of the predictors showed significant AUC values with the first vaginitis criterion (Nugent score: ≥4).

View Article: PubMed Central - PubMed

ABSTRACT

Background: Next-generation sequencing (NGS) can detect many more microorganisms of a microbiome than traditional methods. This study aimed to analyze the vaginal microbiomes of Korean women by using NGS that included bacteria and other microorganisms. The NGS results were compared with the results of other assays, and NGS was evaluated for its feasibility for predicting vaginitis.

Methods: In total, 89 vaginal swab specimens were collected. Microscopic examinations of Gram staining and microbiological cultures were conducted on 67 specimens. NGS was performed with GS junior system on all of the vaginal specimens for the 16S rRNA, internal transcribed spacer (ITS), and Tvk genes to detect bacteria, fungi, and Trichomonas vaginalis. In addition, DNA probe assays of the Candida spp., Gardnerella vaginalis, and Trichomonas vaginalis were performed. Various predictors of diversity that were obtained from the NGS data were analyzed to predict vaginitis.

Results: ITS sequences were obtained in most of the specimens (56.2%). The compositions of the intermediate and vaginitis Nugent score groups were similar to each other but differed from the composition of the normal score group. The fraction of the Lactobacillus spp. showed the highest area under the curve value (0.8559) in ROC curve analysis. The NGS and DNA probe assay results showed good agreement (range, 86.2-89.7%).

Conclusions: Fungi as well as bacteria should be considered for the investigation of vaginal microbiome. The intermediate and vaginitis Nugent score groups were indistinguishable in NGS. NGS is a promising diagnostic tool of the vaginal microbiome and vaginitis, although some problems need to be resolved.

No MeSH data available.


Related in: MedlinePlus