Vaginal Microbiota Profiles of Native Korean Women and Associations with High-Risk Pregnancy
1Metabolic Regulation Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea, 2Department of Biological Science and Biotechnology, Hannam University, Daejeon 34054, Republic of Korea, 3Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea, 4Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea, 5Department of Obstetrics and Gynecology, College of Medicine, Myunggok Medical Research Center, Konyang University, 6Department of Bioprocess Engineering, KRIBB School of Biotechnology, Korea University of Science and Technology (UST)
J. Microbiol. Biotechnol. 2020; 30(2): 248-258
Published February 28, 2020
Copyright © The Korean Society for Microbiology and Biotechnology.
Since the development of pyrosequencing platform technologies, the human microbiota has received increasing attention because of its associations with aspects of human health and disease, including immune programming, protection from pathogens, and nutrient acquisition [1, 2]. In addition, the beneficial roles of the human microbiota have been investigated for application to many health issues, including obesity [3, 4], poor immune response , and inflammation . In particular, the vaginal microbial flora reportedly plays a significant role in pregnancy, protecting the health of the mother and fetus [5, 6]. Several studies have examined the vaginal microbiota during pregnancy using culture-independent molecular techniques [7-9]. These studies have consistently shown that the vaginal microbial communities of pregnant women are dominated by
Most research published to date on the human vaginal microbial ecosystem has focused on the microbiota of healthy asymptomatic women of reproductive age. The vaginal microbiota undergoes major compositional changes throughout a woman’s lifespan, from birth to puberty to menopause [10-12]. Vaginal microbial abnormality increases the risks for various obstetric and gynecological diseases and conditions, such as sexually transmitted infections , preterm birth (PTB) [14, 15], early and late miscarriage , recurrent abortion , histological chorioamnionitis  and postpartum endometritis . Recently, a
The vaginal microbial composition may provide useful information for accurate and rapid diagnosis or prediction of pregnancy conditions. For example, Haque
In the present study, the vaginal microbiota profiles of 137 Korean women were examined using a 454 GS Junior pyrosequencing system (Roche). The microbial community structure and representative microbial groups in normal and high-risk pregnancy groups were identified. We then analyzed correlations between community structure and delivery outcomes, such as PTB and miscarriage, to identify specific microbial taxonomic targets for prediction. This formulation could aid the prediction of possible pregnancy outcomes and prevent reproductive health complications in Korean women.
Study Population and Sampling
This study received ethical approval from Konyang University Hospital Institutional Review Board (IRB) (Approval Number 2014-06-009). All participants provided written informed consent and all methods were performed in accordance with the relevant guidelines and regulations. Women attending antenatal clinics of Department of Obstetrics and Gynecology, College of Medicine, Myunggok Medical Research Center, Konyang University (Korea) between September 2014 and August 2018 were invited to be part of a clinical trial to determine the vaginal microbiome structures of Korean women. This study was conducted as a prospective observational study. For non-pregnant women, samples were obtained as being non-menstrual. The vaginal swabs were not collected at any specific non-menstrual cycle time as previous report has demonstrated there is little variation in microbiota structures through the cycle . Vaginal swabs collected from pregnant women at 16-20 weeks of gestational age were used for bacterial community analysis. Vaginal swabs were collected under direct visualization using a speculum by either a physician or a nurse and placed in dry tubes prior to being placed in −80°C. A total of 137 women were enrolled in the vaginal microbiome study, including 67 pregnant women. After pyrosequencing, 11 of the 137 metagenome samples were found to have an average low read quality (Phred quality score <20), short average read length (< 250 bp) or low sequencing output (the number of reads per sample < 500). Therefore, the 11 metagenome data (7 pregnant & 4 non-pregnant) were excluded from further analysis. For the PTB group (n = 8), eligible participants for this study were women who had undergone preterm deliveries at greater than 16 weeks but less than 37 weeks, where onset of labor occurred spontaneously or in association with cervical incompetence or preterm premature rupture of membranes (PPROM). The microbial profiles of pregnant women with term-deliveries (n = 48) were compared to profiles generated from PTB (n = 8), miscarriage (n = 4) and non-pregnant Korean women (n = 66).
PCR Amplification of 16S rRNA Genes and Pyrosequencing
Frozen vaginal swabs were sent to Chunlab, Inc. (Korea) for pyrosequencing analysis. Total nucleic acid was extracted from swabs using Mobio Soil kit (Qiagen, USA) according to the manufacturer’s instruction. PCR amplification was performed using primers targeting from V1 to V3 regions of the 16S rRNA gene with extracted DNA. For bacterial amplification, barcoded primers of 9F 5’-CCTATCCCCTGTGTGCCTTGGCAGTC-TCAG-AC-
Pyrosequencing Data Analysis
The basic analysis was conducted according to the previous descriptions in other studies [25-27]. Obtained reads from the different samples were sorted by unique barcodes of each PCR product. The sequences of the barcode, linker, and primers were removed from the original sequencing reads. Any reads containing two or more ambiguous nucleotides were discarded. Potential chimera sequences were detected by the bellerophone method, which is comparing the BLASTN search results between forward half and reverse half sequences . After removing chimera sequences, the taxonomic classification of each read was assigned against the EzBioClud Database (https://www.ezbiocloud.net/ ) , which contains 16S rRNA gene sequence of type strains that have valid published names and representative species level phylotypes of either cultured or uncultured entries in the GenBank database with complete hierarchical taxonomic classification from the phylum to the species. The richness and diversity of samples were determined by Chao1 estimation and Shannon diversity index at the 3% distance. Random subsampling was conducted to equalize read size (n = 1,108) of samples for comparing different read sizes among samples. The overall phylogenetic distance between communities was estimated using the Fast UniFrac  and visualized using principal coordinate analysis (PCoA). Using CLcommunity program (Chunlab Inc.,), all the rarefaction curves were obtained (Fig. S1). To compare OTUs between samples, shared OTUs were obtained with the XOR analysis of CLcommunity program.
Heatmap and Principal Coordinates Analysis (PCoA)
QIIME v1.9.1-dev software suite  was used to analyze the generated 454 pyrosequencing reads. Briefly, all reads were truncated to an even length (515 nt) using the QIIME script truncate_fasta_qual_files.py. After removal of low quality reads, operational taxonomic units (OTUs) were clustered using the QIIME script pick_open_reference_otus.py at 97% identity. An additional filtering process was conducted by first aligning all OTU sequences to Greengenes 13_8 Database using PYNAST version 1.2.2 . OTU taxonomy was determined using Ribosomal Database Project classifier. Principle Coordinate Analysis (PCoA) was performed by calculating weighted and unweighted UniFrac distance between each pair of samples (QIIME script function beta_diversity_through_plots.py) on a normalized OTU table.
Raw sequence data files for the 126 samples described in this study are available in the European Nucleotide Archive under study accession PRJEB33541. Due to ethical and legal restrictions related to protecting participant privacy imposed by Konyang Medical School IRB, all other relevant data are available upon request pending ethical approval.
Sample Collection and Pregnancy Outcomes
In the present study, we characterized the vaginal microbiota profiles of pregnant and non-pregnant native Korean women. We collected vaginal swabs at 16–20 weeks of gestation. The possibility of PTB or miscarriage was usually assessed during the first or second trimester, and therapeutic interventions at this gestational stage have been considered to be efficacious . In addition, pregnant women in Korea first visit the hospital at this gestational age, at which time vaginal swabs are taken to screen for vaginal infection.
From September 2014 to August 2018, we collected more than 500 vaginal swabs from native Korean women (430 pregnant and 70 non-pregnant). A portion of the collected vaginal swabs (137 samples collected from September 2014 to October 2016) was sent to Chunlab, Inc. for next-generation sequencing (NGS) analysis. Initially, Chunlab, Inc. used a GS Junior sequencing system (Roche) for the metagenome analysis of 16S rRNA gene amplicons; due to the unavailability of the Roche 454 platform service, the Illumina MiSeq sequencing system (Illumina, USA) service has been used since January 2017. Therefore, our reporting on the vaginal microbiota profiles of pregnant and non-pregnant Korean women and their possible associations with undesirable delivery outcomes is based on the 137 initially collected vaginal swab samples [from 67 pregnant women (with 55 term deliveries, 8 preterm deliveries, and 4 miscarriages) and 70 non-pregnant women] analyzed using the Roche 454 NGS platform. The collection of vaginal samples is ongoing, and when the additional 30+ samples from women who experienced PTB are analyzed using the MiSeq platform, the updated results will be reported in combination with the present work. To date, 20 additional swabs from women who experienced PTB and 6 additional swabs from women who miscarried have been collected and are being analyzed using the Illumina MiSeq pyrosequencer. Currently, the total numbers of samples from women with PTB and miscarriage outcomes are 28 and 10, respectively.
Among the 137 samples submitted for NGS using the Roche 454 platform, 11 metagenome sequences (from 7 pregnant and 4 non-pregnant women) were of insufficient quality for further analyses, such as heatmap analysis and principal coordinates analysis (PCoA). Therefore, NGS data from only 126 samples [from 60 pregnant women (with 48 term births, 8 PTBs, and 4 miscarriages) and 66 non-pregnant women] were used; corresponding personal information is summarized in Table 1. The sociodemographic characteristics of 48 women who gave birth at term, 28 women who had preterm deliveries (8 samples analyzed with the Roche 454 and 20 analyzed with the Illumina MiSeq platform), and 10 women who miscarried (4 samples analyzed with the Roche 454 and 6 analyzed with the Illumina MiSeq platform) are summarized in Table 2 and in Supplemental Table 1, respectively.
Operational Taxonomic Unit Analysis and Microbiota Profiles
The Roche 454 NGS raw sequence data files for the 126 samples described in this study are available in the European Nucleotide Archive under study accession number PRJEB33541. In total, 1,068,077 16S rRNA gene reads were generated. The median and average read counts per sample were 4,021 and 6,698 (range, 398–42,166), respectively. The average read length was 431 bp. In total, 352 families, 235 genera, and 64 bacterial species were identified. Most (73.9%) reads were identified as
Fig. 1. Proportion of vaginal microbes in non-pregnant and pregnant women in term, preterm and miscarriage groups based on Roche/454 pyrosequencing analysis.
The proportion of reads from
Community State Type Analysis
Ravel and Gajer  previously clustered vaginal microbial communities into five groups: four were dominated by
Fig. 2. Heatmap of bacterial relative abundance by individual vaginal samples. Each column represents the relative bacterial abundance of an individual vaginal sample with the 50 most abundant species showed with their taxonomies. The dendrogram was drawn based on the hierarchical clustering solution (Ward’s method) of the 126 vaginal microbiome samples. Shannon diversity indices calculated from each vaginal samples.
The CSTs of pregnant women in the term delivery group were compared with those in the non-pregnant group. In the latter group, 26/66 (39.4%) samples were assigned to CST IV, with the remainder assigned to the
Fig. 3. Principal Coordinates Analysis (PCoA) of weighted UniFrac distances of microbial profiles from all participants. Samples are colored by clade types (
A), pregnancy status ( B), and miscarriage/preterm delivery cases ( C).
Alpha Diversity Analysis
The assessment of alpha diversity revealed that the microbiomes of pregnant women with term deliveries (
Prevalence and Abundance of
The NGS data for the vaginal microbiota profiles of 126 Korean women are summarized in Fig. 1. The microbial profiles from pregnant women clustered into three CSTs (I, III, and IV, originally defined by Ravel and Gajer ); CST II (
Overall, the microbiota profiles of pregnant women could not be distinguished from those of non-pregnant women. However, several differences were observed between the microbiota profiles of pregnant women with term deliveries and those of non-pregnant women. Pregnant women with term deliveries exhibited much greater relative abundances of
In the present study,
Supplementary data for this paper are available on-line only at http://jmb.or.kr.
This work was supported by the Korea Health Technology R&D Project (HI14C0368 & HI17C1238), Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2017R1A6A1A03015713) and partially supported by the Korea Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Program. All authors of the present paper declare that no-financial interest exists in relation to the work described.
Conflict of Interest
The authors have no financial conflicts of interest to declare.
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