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Am J Perinatol. Author manuscript; available in PMC 2016 December 19. Published in final edited form as: Am J Perinatol. 2016 March ; 33(4): 401–408. doi:10.1055/s-0035-1565919.
Vaginal Microbiota in Pregnancy: Evaluation Based on Vaginal Flora, Birth Outcome, and Race Akila Subramaniam1, Ranjit Kumar2, Suzanne P. Cliver1, Degui Zhi3, Jeff M. Szychowski1,3, Adi Abramovici1, Joseph R. Biggio1, Elliot J. Lefkowitz2,4, Casey Morrow5, and Rodney K. Edwards1 1Division
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of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Center for Women's Reproductive Health, Birmingham, Alabama
2Biomedical
Informatics, Center for Clinical and Translational Sciences, Birmingham, Alabama
3Department
of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama
4Department
of Microbiology, University of Alabama at Birmingham, Birmingham, Alabama
5Department
of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
Abstract
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Objective—This study aims to evaluate vaginal microbiota differences by bacterial vaginosis (BV), birth timing, and race, and to estimate parameters to power future vaginal microbiome studies. Methods—Previously, vaginal swabs were collected at 21 to 25 weeks (stored at −80°C), and vaginal smears evaluated for BV (Nugent criteria). In a blinded fashion, 40 samples were selected, creating 8 equal-sized groups stratified by race (black/white), BV (present/absent), and birth timing (preterm/term). Samples were thawed, DNA extracted, and prepared. Polymerase chain reaction (PCR) with primers targeting the 16S rDNA V4 region was used to prepare an amplicon library. PCR products were sequenced and analyzed using quantitative insight into microbial ecology; taxonomy was assigned using ribosomal database program classifier (threshold 0.8) against the modified Greengenes database.
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Results—After quality control, 97,720 sequences (mean) per sample, single-end 250 base-reads, were analyzed. BV samples had greater microbiota diversity (p < 0.05)—with BVAB1, Prevotella, and unclassified genus, Bifidobacteriaceae family (all p < 0.001) more abundant; there was minimal content of Gardnerella or Mobiluncus. Microbiota did not differ by race or birth timing, but there was an association between certain microbial clusters and preterm birth (p = 0.07). To evaluate this difference, 159 patients per group are needed.
Note The authors report no conflicts of interest. This study was presented in part at the 34rd Annual Meeting of the Society for MaternalFetal Medicine; February 6–10, 2014; New Orleans, LA and the Annual Meeting of the Infectious Diseases Society for Obstetrics and Gynecology; August 7–9, 2014; Stowe, VT.
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Conclusions—There are differences in the vaginal microbiota between patients with and without BV. Larger studies should assess the relationship between microbiota composition and preterm birth. Keywords bacterial vaginosis; birth timing; pregnancy; race; vaginal microbiome
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Different surfaces of the human body harbor a mixture of microorganisms in a symbiotic relationship that vary not only over time, but also in response to external stimuli such as illness and antibiotics.[1] The lower genital tract similarly hosts mutualistic bacterial communities. In the vagina of healthy women, the predominating Lactobacillus species produce lactic acid, lowering the vaginal pH, and thereby creating a chemical barrier to urogenital infections.[2] [3] [4] [5] However, colonization of the vaginal cavity extends beyond Lactobacilli, and other bacteria may be identified with culture.[3] [6] Recently, culture-independent molecular techniques using next-generation sequencing of the bacterial 16S rDNA genes have emerged to further qualitatively describe the bacteria present on and in human body surfaces and cavities (microbiota).[6] [7] [8] Differences in the vaginal microbiota between ethnicities have been documented in asymptomatic, nonpregnant reproductive-age women.[8] Similarly, differences in the vaginal microbes between nonpregnant women with and without bacterial vaginosis (BV) have also been described.[6] [7] [8] [9]
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Descriptions of the vaginal microbiota during pregnancy are less well-characterized. Moreover, how changes in the vaginal microbiota of pregnant women affect pregnancy outcomes historically associated with bacterial colonization, such as spontaneous preterm birth (PTB), is unknown. BV is an established risk factor for spontaneous PTB. The syndrome of BV is characterized by colonization of the vagina with a heterogeneous mix of anaerobic and gram-negative aerobic bacteria. However, current methods to diagnose BV in pregnancy (e.g., Nugent score on Gram stain) do not provide a comprehensive spectrum of the microbial complexity required to discriminate interpatient differences in the bacteria present in the vagina. As such, our objective of this preliminary study was to test the differences in the vaginal microbiome of pregnant women stratified by the presence or absence of BV, birth timing (preterm or term), and race. By assessing these relationships, in this pilot study, we sought to estimate parameters for power calculations for future vaginal microbiome–based studies.
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Materials and Methods Sample Collection In this institutional review board-approved study (X120125008), we utilized stored cervicovaginal samples collected at our institution during a population-based study of risk factors for PTB. Details of this parent study have previously been published.[10] Briefly, during a vaginal speculum examination, Dacron swab samples of the mucus on the ectocervix and in the posterior vaginal fornix were collected from women at a routine
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prenatal visit between 21 and 25 6/7 weeks gestation. Each swab was left in place for 10 seconds, withdrawn, and placed in 750 μL of fetal fibronectin buffer. These samples were then stored at −80°C. Of note, these samples had not been subject to previous thawing. Concurrently, vaginal smears were also obtained and evaluated for the presence of BV. Gram stains of these vaginal smears were evaluated in a single experienced center by the Nugent criteria: decreased Lactobacillus morphotypes (score 0–4), increased Gardnerella morphotypes (score 0–4), and increased Mobiluncus morphotypes (score 0–2) (scores of 0–4 indicative of normal flora, 5–6 consistent with “intermediate flora,” and 7–10 diagnostic of BV).[9] Isolation of Microbial DNA and Creation of 16S V4 Amplicon Library
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In our current study, microbial genomic DNA was isolated using the fecal DNA isolation kit from Zymo Research (Irvine, CA) (catalog no. D6010) following the manufacturer's instructions (there are no kits specific for vaginal DNA isolation). Once the sample DNA was prepared, polymerase chain reaction (PCR) was used with unique bar coded primers to amplify the V4 region of the 16S rDNA gene to create an “amplicon library” from individual samples.[11] [12] While previous studies, including those from the Human Microbiome Project have used V1–V3 or V3–V5 primers, we opted to use V4 primers. Specific to the vaginal microbiome, the V1–V3 region is not recommended for the identification of Gardnerella. In addition, recent literature has shown that for both short and longer read sequences, the V4 region is an appropriate region for capturing microbial diversity.[11] [13] [14] [15] [16] [17] [18] [19] As such, we used the following V4 primers: 5′ Primer:
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5′AATGATACGGCGACCACCGAGATCTACACTATGGTAATTGTGTGCCAGCMGCCG CGGTAA 3′ 3′ Primer: 5′CAAGAGAAGACGGCATACGAGATNNNNNNAGTCAGTCAGCCGGACTACHVGGG TWTCTAAT3′ The primers were ordered at 50 nmol scale with desalting purification from Eurofins (Huntsville, AL) MWG Operon. The primers were diluted with 10 mM Tris, pH 8.0 to 100 μM, then diluted 10× in water to 10 μM for use in PCR reactions.
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The individual PCR reactions were set up using the following: 10 μL of 5× reaction buffer, 1.5 μL of dNTPs, 2 μL of the 5′ primer (diluted as described above), 2 μL of the 3′ primer (diluted as described above), 1.5 μL of the “LongAmp” enzyme kit (New England Biolabs [Ipswich, MA] LongAmp Taq PCR kit; catalog no. E5200S), 30 μL of the template DNA (prepared using the fecal DNA isolation kit with the concentration of DNA at 2–5 ng/μL), and 3 μL of H2O.[11] The total reaction volume was 50 μL. Cycling conditions for the PCR were an initial denature at 94°C for 1 minute, followed by 32 cycles of 94°C for 30 seconds, 50°C for 1 minute, and 65°C for 1 minute. The final extension period was 65°C for 3 minutes and then a final hold at 4°C.
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Following PCR, the entire PCR reaction was electrophoresed on a 1.0% agarose/tris-borateEDTA gel. The PCR product (~380 base pair predicted product size) was visualized by ultraviolet illumination. The band was excised and purified from the agarose using Qiagen (Venlo, the Netherlands) QIAquick gel extraction kit (catalog no. 28704) according to the manufacturer's instructions. DNA Sequencing and Bioinformatics
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The samples were first quantitated using PicoGreen (Molecular Probes, Inc., Eugene, OR), adjusted to a concentration of 4 nM, and then used for sequencing.[12] The PCR amplicon covered around 255 bases of the V4 region of the 16S rDNA. All the samples were multiplexed using bar codes, and 251 base paired end reads were sequenced using the Illumina MiSeq platform (San Diego, CA).[11] [12] FASTQ conversion of the raw data files was performed following demultiplexing. Quality assessment of the FASTQ files was performed using FASTQC, and then quality filtering was done using the FASTX toolkit.[20] [21] Since the overlap between fragments was approximately 245 bases, both paired reads were merged to generate a single high-quality read using the module “fastq_mergepairs” of USEARCH (Drive 5). Read pairs with an overlap of less than 200 bases or with too many mismatches (> 25) in the overlapping region were discarded. Chimeric sequences were also filtered using the “identify_chimeric_seqs.py” module of USEARCH.[22] The resulting reads with an average base quality Q score of < 20 were discarded. The remainder of the steps (explained below) was performed with the quantitative insight into microbial ecology (QIIME) suite, version 1.8 and in-house developed Perl scripts.[20] [23] [24]
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Sequences were grouped into operational taxonomic units (OTUs) using the clustering program UCLUST at a similarity threshold of 97%.[22] The ribosomal database program (RDP) classifier was used to make taxonomic assignments (to the genus level) for all OTUs at a confidence threshold of 80% (0.8). The RDP classifier was trained using the modified version (explained below) of Greengenes (v13_8) 16S rRNA database.[25] The resulting OTU table included all OTUs, their taxonomic identification, and abundance information. To account for differences in read depth across different sample, the OTUs are rarified at a common sequence depth. OTUs whose average abundance was less than 0.0005% were filtered out. OTUs were then grouped together to summarize taxon abundance at different hierarchical levels of classification (e.g., phylum, class, order, family, genus). These taxonomy tables were also used to generate bar charts of taxon abundance. Multiple sequence alignment of OTUs was performed with PyNAST.[21] Alpha diversity (within sample diversity) was calculated using a variety of diversity metrics, including Shannon, Chao1, and Simpson, as implemented in QIIME.[26] Beta diversity (between sample diversity) among different samples was measured using UniFrac analysis.[23] Principal coordinates analysis was performed by QIIME to visualize the dissimilarity matrix (βdiversity) between all the samples, such that samples that are more similar are closer in space than samples that are more divergent.[27] A heatmap with the top 15 most highly abundant taxa across all samples was generated using the “heatmap.2” function in R package (available at: http://CRAN.R-project.org/package=gplots).
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Bacterial Vaginosis-Associated Bacteria Prediction
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Since the 16S Greengenes databases do not have annotated sequences for any of the socalled BV-associated bacteria (BVAB), the BVAB sequences were downloaded and included in the Greengenes 16S database. The following sequences BVAB1 (AY959097.1), BVAB2 (AY958888.1), BVAB3 (AY995273.1) were downloaded from the National Center for Biotechnology Information.[7] [28] Since the exact genus names of these sequences are not known, the term Bvab1, Bvab2, Bvab3 are assigned as a genus identifier to fulfill the formatting criteria for including them in the database and making them available for genus level identification. The following taxonomy was assigned for the sequences.
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•
BVAB1: k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Bvab1; s__
•
BAVB2: k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Bvab2; s__
•
BVAB3: k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Bvab3; s__
Subject Characteristics and Statistical Analysis
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Out of a total of 4,057 samples from the parent study, 40 samples were randomly selected for analysis to create 8 equal-sized groups in this pilot study. These groups were stratified by three design variables: race, BV, and birth timing. Samples were included where race was black or white, patients had BV, or normal flora defined by Nugent score, and the pregnancy during which sample collection occurred, resulted in a spontaneous preterm birth (delivery < 35 weeks) or a term birth ([Fig. 1]). No indicated preterm deliveries were included. Patients included in the current analysis tended to have Nugent scores at either extreme (normal flora median 0, range 0–2; BV median 10, range 7–10), and no patients with “intermediate flora” were included.
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The overall difference in microbiome composition between groups defined by each of the design variables was determined by the analysis of variance using distance matrices (Adonis) test with 9,999 permutations as implemented in the R package vegan version 2.0– 10 using the unweighted UniFrac beta-diversity as the distance measure. To identify the genera that were responsible for the overall difference in microbiome composition, the Wilcoxon rank sum test was used to compare the mean percent content of each of the top 10 genera with highest abundance (percent content) reported from QIIME across each of the design variables. p Values of 0.05 or lower were deemed statistically significant. Bonferroni correction was applied to adjust for multiple testing. For each of the design variables, posthoc power, and sample size was estimated under a t-test. All statistical analyses except PERMANOVA were done in JMP Pro 10.0.2 (SAS Institute, Inc., Cary, NC).
Results Of the 40 samples submitted for analysis, 39 were successfully analyzed—1 sample was omitted from sequencing secondary to a duplicated index primer. The 251 base paired end sequencing resulted in a mean of 137,176 sequences (range, 66,567–205,488) per sample.
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After quality control steps, a mean of 97,720 sequences (range, 50,727–145,290) of single end read length of 250 bases were used for analysis. All the samples were normalized at a sequencing depth of 50,670. A total of 243 unique OTUs were identified. Demographics of patients included for analysis are presented in [Table 1]. Regarding birth timing, the mean gestational age in the preterm group was 30.1 weeks (standard deviation, 3.6; range, 21–35 weeks) compared with the term group with a mean gestational age of 40.3 weeks (standard deviation, 0.8; range, 39–41 weeks).
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Comparing the vaginal microbiota between pregnant women with BV and those with normal flora revealed significant differences (β-diversity) in overall microbiota composition (permutation-based Adonis over all three β-diversity measures: Bray–Curtis, p < 0.001; weighted UniFrac, p < 0.001; and unweighted UniFrac, p = 0.013) ([Fig. 2]). Moreover, there was a greater microbial diversity within the BV samples (α-diversity measures significantly different in both Shannon [p = 0.0043] and Simpson [p < 0.001] between BV and normal samples). A closer examination of the top 10 genera revealed that BVAB1 (p < 0.001), Prevotella (p < 0.001), Megasphaera (p < 0.001), and an unclassified genus in the Bifidobacteriaceae family (p < 0.001) were significantly more abundant in women with BV compared with those with normal flora ([Table 2]). At the genus level, Lactobacillus was the dominant genus in patients with normal flora (average relative abundance 88.5% compared with 12.7% in BV samples; p < 0.001). In fact, Lactobacillus had > 60% abundance in 19 samples with normal flora, and > 80% in 17 out of 20 patients with normal flora. Of note, there was one BV sample outlier that had 98.4% Lactobacillus (second bar in [Fig. 2]).
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In an unsupervised clustering analysis of the data, there were three identified clusters purely based on the β-diversity (Bray–Curtis) of microbiome composition ([Fig. 3]—heat map). The first cluster contained 16 BV samples; the second cluster contained 3 BV samples and 9 normal samples, while the third cluster contained 11 normal samples. Although the latter two clusters were mostly normal flora, they had distinct microbiome compositions—cluster 2 was predominantly Lactobacillus (96%), while cluster 3 was more diverse (74% Lactobacillus). Although not statistically significant, cluster 2 was associated with a nominally higher rate of preterm birth (9 out of 12) than cluster 1 (7 out of 16) and cluster 3 (4 out of 11) (p = 0.07, Fisher exact test).
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However, when comparing vaginal microbial diversity (β-diversity) in samples by race or birth timing (independent of vaginal flora), there were no statistically significant differences (p = 0.89 for race and 0.25 for birth timing by Bray–Curtis; p = 0.90 for race and 0.73 for birth timing by weighted UniFrac; and p = 0.59 for race and 0.58 for birth timing by unweighted UniFrac). No individual taxa level analysis for preterm birth or race was performed given that these associations in our small cohort were not statistically significant. Posthoc power analyses indicated that we were underpowered for the comparisons related to birth timing and race—power between 0.05 and 0.40. The minimal sample size to achieve sufficient power (> 0.8) to detect significant differences for at least one OTU would be n = 159 for birth timing, and n = 52 for the race. In contrast, the maximal power for detecting OTUs different between normal flora versus BV was 1.0 and the minimal required sample size was n = 6.
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Comment We demonstrated marked diversity in microbial composition between pregnant women with BV and those with normal flora. In addition, while there were no statistically significant differences when comparing microbiome diversity by race or birth timing, there was a nonsignificant association between certain microbial clusters and preterm birth (p = 0.07). As such, we have now determined the sample sizes needed to further evaluate the relationships between race and preterm birth (independent of BV) in our future larger studies.
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Prior microbiome studies have shown significant differences according to race and BV status in the microbial composition in the vagina among nonpregnant asymptomatic patients.[8] Ravel et al, described inherent vaginal microbial differences between asymptomatic reproductive-age women of four races (white, black, Hispanic, and Asian). They also described distinct microbial patterns associated with high- or low Nugent scores.[8] It has been noted that the vaginal microbiome is different in both composition and stability between pregnant and nonpregnant individuals.[29] [30] However, Hyman et al, in a prospective study of 88 participants, noted that diversity in flora by race persisted in pregnancy.[31] We did not observe distinct differences in microbiota by race. However, posthoc analysis showed that we were underpowered for this comparison. The fact that our results differ from what has been previously reported may be due to our small sample size, techniques used to sequence, and classify DNA (for example, choice of primers), or it may be true that pregnancy results in amelioration of racial differences in the vaginal microbiota.
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Moreover, given the established link between BV and preterm birth, we were surprised to note no statistically significant differences in this study in the β-diversity of the microbiota of women who did and did not deliver preterm—both independently and considering BV colonization. We did observe some clustering associated with preterm birth, yet this was not statistically significant. However, we were underpowered for that comparison as well, and we are currently conducting a prospective study of vaginal microbiota and preterm birth to further evaluate this association. The above-mentioned study by Hyman et al, measured the microbial diversity in women with either a low- or high risk of preterm birth. While there were no clear groups of microbes implicated with preterm birth, it was noted that there were differences in the overall diversity of microbes between women with a preterm birth and those with a term birth.[31] Given the fact that there was no single organism or group of organisms identified in that study or in our study, perhaps it is dynamic, changes in vaginal microbial communities that are linked to spontaneous preterm birth rather than a specific organism or group of organisms. This possibility should be addressed in future studies evaluating the vaginal microbiome at different time points during pregnancy. Furthermore, differences between the organisms in our clustered analysis should be investigated in a larger sample to elucidate if there is a relationship between those microbial compositions and preterm birth. As are others, our group currently is conducting such a study. The strengths of our current study include the use of a newer microbial analysis technique to characterize samples from a well-described cohort of patients. Demographic characteristics, Nugent score, and birth outcomes of these patients were prospectively collected at the time
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of the parent study. However, given that this is a retrospective analysis of previously collected samples, one limitation is the possibility of contamination affecting DNA analysis. Moreover, the relatively small sample size resulted in a small number of patients in each subgroup, limiting our ability to make statistical comparisons. Our posthoc power calculations have enabled us to determine the number of samples required in future studies. In addition, the samples evaluated in our current study reflect one point in time; it may be that changes in the vaginal microbiome over time are at least as important as the prevalence of various organisms at a single point in time.
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Vaginal microbiome analysis to study pregnancy-related complications is a relatively new tool in obstetrics. Given our preliminary data, we think that additional studies evaluating the vaginal microbiome in relation to pregnancy outcomes, particularly preterm birth, are warranted and hold promise to improve our understanding of infection-related preterm birth. Potentially, such studies may identify a target organism or group of organisms associated with adverse outcome, thus yielding a way to identify a group at high risk of PTB who could be targeted for current or putative therapeutic interventions.
Acknowledgments The following are acknowledged for their support of the Microbiome Resource at the University of Alabama at Birmingham: School of Medicine, Comprehensive Cancer Center (P30AR050948), Center for AIDS Research (5P30AI027767), and Center for Clinical and Translational Science (UL1TR000165) and Heflin Center.
References
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Fig. 1. Flow diagram describing sample selection and stratification
This flow diagram describes sample selection and stratification *BV = bacterial vaginosis by Nugent score
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Beta-diversity in vaginal microbial composition between pregnant women with BV and those with normal flora. This figure describes and defines the diversity of different genera (operational taxonomic units) between pregnant women with BV and normal flora. BV, bacterial vaginosis.
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Unsupervised clustered analysis of beta-diversity in microbiome composition. This figure portrays clusters of microbiome composition based on beta-diversity. The figure shows three distinct clusters.
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Table 1
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Patient characteristics N = 40
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Race White Black
20 (50%) 20 (50%)
Marital status Single Married
32 (80%) 8 (20%)
Less than 12 year education
24 (60%)
Nulliparity
22 (55%)
Smoking
21 (53%)
Maternal age at delivery (weeks)
21.4 ± 4.3
Body mass index (kg/m2)
23.9 ± 5.1
Pre-pregnancy weight (kg)
67.9 ± 18.0
Data are presented as n (%) or mean ± standard deviation.
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Table 2
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The ten most commonly identified genera in the women with BV Mean % genus content Identified genus
BV (n = 19)
Normal flora (n=20)
p-value
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1
BVAB1
39.5
0.4
< 0.001
2
Prevotella
15.4
0.4
< 0.001
3
Lactobacillus
12.7
88.5
< 0.001
4
Megasphaera
11.6
0.4
< 0.001
5
Bifidobacteriaceae family; unspecified genus
6.3
0.4
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Vaginal Microbiota in Pregnancy: Evaluation Based on Vaginal Flora, Birth Outcome, and Race Akila Subramaniam1, Ranjit Kumar2, Suzanne P. Cliver1, Degui Zhi3, Jeff M. Szychowski1,3, Adi Abramovici1, Joseph R. Biggio1, Elliot J. Lefkowitz2,4, Casey Morrow5, and Rodney K. Edwards1 1Division
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of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Center for Women's Reproductive Health, Birmingham, Alabama
2Biomedical
Informatics, Center for Clinical and Translational Sciences, Birmingham, Alabama
3Department
of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama
4Department
of Microbiology, University of Alabama at Birmingham, Birmingham, Alabama
5Department
of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
Abstract
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Objective—This study aims to evaluate vaginal microbiota differences by bacterial vaginosis (BV), birth timing, and race, and to estimate parameters to power future vaginal microbiome studies. Methods—Previously, vaginal swabs were collected at 21 to 25 weeks (stored at −80°C), and vaginal smears evaluated for BV (Nugent criteria). In a blinded fashion, 40 samples were selected, creating 8 equal-sized groups stratified by race (black/white), BV (present/absent), and birth timing (preterm/term). Samples were thawed, DNA extracted, and prepared. Polymerase chain reaction (PCR) with primers targeting the 16S rDNA V4 region was used to prepare an amplicon library. PCR products were sequenced and analyzed using quantitative insight into microbial ecology; taxonomy was assigned using ribosomal database program classifier (threshold 0.8) against the modified Greengenes database.
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Results—After quality control, 97,720 sequences (mean) per sample, single-end 250 base-reads, were analyzed. BV samples had greater microbiota diversity (p < 0.05)—with BVAB1, Prevotella, and unclassified genus, Bifidobacteriaceae family (all p < 0.001) more abundant; there was minimal content of Gardnerella or Mobiluncus. Microbiota did not differ by race or birth timing, but there was an association between certain microbial clusters and preterm birth (p = 0.07). To evaluate this difference, 159 patients per group are needed.
Note The authors report no conflicts of interest. This study was presented in part at the 34rd Annual Meeting of the Society for MaternalFetal Medicine; February 6–10, 2014; New Orleans, LA and the Annual Meeting of the Infectious Diseases Society for Obstetrics and Gynecology; August 7–9, 2014; Stowe, VT.
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Conclusions—There are differences in the vaginal microbiota between patients with and without BV. Larger studies should assess the relationship between microbiota composition and preterm birth. Keywords bacterial vaginosis; birth timing; pregnancy; race; vaginal microbiome
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Different surfaces of the human body harbor a mixture of microorganisms in a symbiotic relationship that vary not only over time, but also in response to external stimuli such as illness and antibiotics.[1] The lower genital tract similarly hosts mutualistic bacterial communities. In the vagina of healthy women, the predominating Lactobacillus species produce lactic acid, lowering the vaginal pH, and thereby creating a chemical barrier to urogenital infections.[2] [3] [4] [5] However, colonization of the vaginal cavity extends beyond Lactobacilli, and other bacteria may be identified with culture.[3] [6] Recently, culture-independent molecular techniques using next-generation sequencing of the bacterial 16S rDNA genes have emerged to further qualitatively describe the bacteria present on and in human body surfaces and cavities (microbiota).[6] [7] [8] Differences in the vaginal microbiota between ethnicities have been documented in asymptomatic, nonpregnant reproductive-age women.[8] Similarly, differences in the vaginal microbes between nonpregnant women with and without bacterial vaginosis (BV) have also been described.[6] [7] [8] [9]
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Descriptions of the vaginal microbiota during pregnancy are less well-characterized. Moreover, how changes in the vaginal microbiota of pregnant women affect pregnancy outcomes historically associated with bacterial colonization, such as spontaneous preterm birth (PTB), is unknown. BV is an established risk factor for spontaneous PTB. The syndrome of BV is characterized by colonization of the vagina with a heterogeneous mix of anaerobic and gram-negative aerobic bacteria. However, current methods to diagnose BV in pregnancy (e.g., Nugent score on Gram stain) do not provide a comprehensive spectrum of the microbial complexity required to discriminate interpatient differences in the bacteria present in the vagina. As such, our objective of this preliminary study was to test the differences in the vaginal microbiome of pregnant women stratified by the presence or absence of BV, birth timing (preterm or term), and race. By assessing these relationships, in this pilot study, we sought to estimate parameters for power calculations for future vaginal microbiome–based studies.
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Materials and Methods Sample Collection In this institutional review board-approved study (X120125008), we utilized stored cervicovaginal samples collected at our institution during a population-based study of risk factors for PTB. Details of this parent study have previously been published.[10] Briefly, during a vaginal speculum examination, Dacron swab samples of the mucus on the ectocervix and in the posterior vaginal fornix were collected from women at a routine
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prenatal visit between 21 and 25 6/7 weeks gestation. Each swab was left in place for 10 seconds, withdrawn, and placed in 750 μL of fetal fibronectin buffer. These samples were then stored at −80°C. Of note, these samples had not been subject to previous thawing. Concurrently, vaginal smears were also obtained and evaluated for the presence of BV. Gram stains of these vaginal smears were evaluated in a single experienced center by the Nugent criteria: decreased Lactobacillus morphotypes (score 0–4), increased Gardnerella morphotypes (score 0–4), and increased Mobiluncus morphotypes (score 0–2) (scores of 0–4 indicative of normal flora, 5–6 consistent with “intermediate flora,” and 7–10 diagnostic of BV).[9] Isolation of Microbial DNA and Creation of 16S V4 Amplicon Library
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In our current study, microbial genomic DNA was isolated using the fecal DNA isolation kit from Zymo Research (Irvine, CA) (catalog no. D6010) following the manufacturer's instructions (there are no kits specific for vaginal DNA isolation). Once the sample DNA was prepared, polymerase chain reaction (PCR) was used with unique bar coded primers to amplify the V4 region of the 16S rDNA gene to create an “amplicon library” from individual samples.[11] [12] While previous studies, including those from the Human Microbiome Project have used V1–V3 or V3–V5 primers, we opted to use V4 primers. Specific to the vaginal microbiome, the V1–V3 region is not recommended for the identification of Gardnerella. In addition, recent literature has shown that for both short and longer read sequences, the V4 region is an appropriate region for capturing microbial diversity.[11] [13] [14] [15] [16] [17] [18] [19] As such, we used the following V4 primers: 5′ Primer:
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5′AATGATACGGCGACCACCGAGATCTACACTATGGTAATTGTGTGCCAGCMGCCG CGGTAA 3′ 3′ Primer: 5′CAAGAGAAGACGGCATACGAGATNNNNNNAGTCAGTCAGCCGGACTACHVGGG TWTCTAAT3′ The primers were ordered at 50 nmol scale with desalting purification from Eurofins (Huntsville, AL) MWG Operon. The primers were diluted with 10 mM Tris, pH 8.0 to 100 μM, then diluted 10× in water to 10 μM for use in PCR reactions.
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The individual PCR reactions were set up using the following: 10 μL of 5× reaction buffer, 1.5 μL of dNTPs, 2 μL of the 5′ primer (diluted as described above), 2 μL of the 3′ primer (diluted as described above), 1.5 μL of the “LongAmp” enzyme kit (New England Biolabs [Ipswich, MA] LongAmp Taq PCR kit; catalog no. E5200S), 30 μL of the template DNA (prepared using the fecal DNA isolation kit with the concentration of DNA at 2–5 ng/μL), and 3 μL of H2O.[11] The total reaction volume was 50 μL. Cycling conditions for the PCR were an initial denature at 94°C for 1 minute, followed by 32 cycles of 94°C for 30 seconds, 50°C for 1 minute, and 65°C for 1 minute. The final extension period was 65°C for 3 minutes and then a final hold at 4°C.
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Following PCR, the entire PCR reaction was electrophoresed on a 1.0% agarose/tris-borateEDTA gel. The PCR product (~380 base pair predicted product size) was visualized by ultraviolet illumination. The band was excised and purified from the agarose using Qiagen (Venlo, the Netherlands) QIAquick gel extraction kit (catalog no. 28704) according to the manufacturer's instructions. DNA Sequencing and Bioinformatics
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The samples were first quantitated using PicoGreen (Molecular Probes, Inc., Eugene, OR), adjusted to a concentration of 4 nM, and then used for sequencing.[12] The PCR amplicon covered around 255 bases of the V4 region of the 16S rDNA. All the samples were multiplexed using bar codes, and 251 base paired end reads were sequenced using the Illumina MiSeq platform (San Diego, CA).[11] [12] FASTQ conversion of the raw data files was performed following demultiplexing. Quality assessment of the FASTQ files was performed using FASTQC, and then quality filtering was done using the FASTX toolkit.[20] [21] Since the overlap between fragments was approximately 245 bases, both paired reads were merged to generate a single high-quality read using the module “fastq_mergepairs” of USEARCH (Drive 5). Read pairs with an overlap of less than 200 bases or with too many mismatches (> 25) in the overlapping region were discarded. Chimeric sequences were also filtered using the “identify_chimeric_seqs.py” module of USEARCH.[22] The resulting reads with an average base quality Q score of < 20 were discarded. The remainder of the steps (explained below) was performed with the quantitative insight into microbial ecology (QIIME) suite, version 1.8 and in-house developed Perl scripts.[20] [23] [24]
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Sequences were grouped into operational taxonomic units (OTUs) using the clustering program UCLUST at a similarity threshold of 97%.[22] The ribosomal database program (RDP) classifier was used to make taxonomic assignments (to the genus level) for all OTUs at a confidence threshold of 80% (0.8). The RDP classifier was trained using the modified version (explained below) of Greengenes (v13_8) 16S rRNA database.[25] The resulting OTU table included all OTUs, their taxonomic identification, and abundance information. To account for differences in read depth across different sample, the OTUs are rarified at a common sequence depth. OTUs whose average abundance was less than 0.0005% were filtered out. OTUs were then grouped together to summarize taxon abundance at different hierarchical levels of classification (e.g., phylum, class, order, family, genus). These taxonomy tables were also used to generate bar charts of taxon abundance. Multiple sequence alignment of OTUs was performed with PyNAST.[21] Alpha diversity (within sample diversity) was calculated using a variety of diversity metrics, including Shannon, Chao1, and Simpson, as implemented in QIIME.[26] Beta diversity (between sample diversity) among different samples was measured using UniFrac analysis.[23] Principal coordinates analysis was performed by QIIME to visualize the dissimilarity matrix (βdiversity) between all the samples, such that samples that are more similar are closer in space than samples that are more divergent.[27] A heatmap with the top 15 most highly abundant taxa across all samples was generated using the “heatmap.2” function in R package (available at: http://CRAN.R-project.org/package=gplots).
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Bacterial Vaginosis-Associated Bacteria Prediction
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Since the 16S Greengenes databases do not have annotated sequences for any of the socalled BV-associated bacteria (BVAB), the BVAB sequences were downloaded and included in the Greengenes 16S database. The following sequences BVAB1 (AY959097.1), BVAB2 (AY958888.1), BVAB3 (AY995273.1) were downloaded from the National Center for Biotechnology Information.[7] [28] Since the exact genus names of these sequences are not known, the term Bvab1, Bvab2, Bvab3 are assigned as a genus identifier to fulfill the formatting criteria for including them in the database and making them available for genus level identification. The following taxonomy was assigned for the sequences.
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•
BVAB1: k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Bvab1; s__
•
BAVB2: k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Bvab2; s__
•
BVAB3: k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Bvab3; s__
Subject Characteristics and Statistical Analysis
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Out of a total of 4,057 samples from the parent study, 40 samples were randomly selected for analysis to create 8 equal-sized groups in this pilot study. These groups were stratified by three design variables: race, BV, and birth timing. Samples were included where race was black or white, patients had BV, or normal flora defined by Nugent score, and the pregnancy during which sample collection occurred, resulted in a spontaneous preterm birth (delivery < 35 weeks) or a term birth ([Fig. 1]). No indicated preterm deliveries were included. Patients included in the current analysis tended to have Nugent scores at either extreme (normal flora median 0, range 0–2; BV median 10, range 7–10), and no patients with “intermediate flora” were included.
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The overall difference in microbiome composition between groups defined by each of the design variables was determined by the analysis of variance using distance matrices (Adonis) test with 9,999 permutations as implemented in the R package vegan version 2.0– 10 using the unweighted UniFrac beta-diversity as the distance measure. To identify the genera that were responsible for the overall difference in microbiome composition, the Wilcoxon rank sum test was used to compare the mean percent content of each of the top 10 genera with highest abundance (percent content) reported from QIIME across each of the design variables. p Values of 0.05 or lower were deemed statistically significant. Bonferroni correction was applied to adjust for multiple testing. For each of the design variables, posthoc power, and sample size was estimated under a t-test. All statistical analyses except PERMANOVA were done in JMP Pro 10.0.2 (SAS Institute, Inc., Cary, NC).
Results Of the 40 samples submitted for analysis, 39 were successfully analyzed—1 sample was omitted from sequencing secondary to a duplicated index primer. The 251 base paired end sequencing resulted in a mean of 137,176 sequences (range, 66,567–205,488) per sample.
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After quality control steps, a mean of 97,720 sequences (range, 50,727–145,290) of single end read length of 250 bases were used for analysis. All the samples were normalized at a sequencing depth of 50,670. A total of 243 unique OTUs were identified. Demographics of patients included for analysis are presented in [Table 1]. Regarding birth timing, the mean gestational age in the preterm group was 30.1 weeks (standard deviation, 3.6; range, 21–35 weeks) compared with the term group with a mean gestational age of 40.3 weeks (standard deviation, 0.8; range, 39–41 weeks).
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Comparing the vaginal microbiota between pregnant women with BV and those with normal flora revealed significant differences (β-diversity) in overall microbiota composition (permutation-based Adonis over all three β-diversity measures: Bray–Curtis, p < 0.001; weighted UniFrac, p < 0.001; and unweighted UniFrac, p = 0.013) ([Fig. 2]). Moreover, there was a greater microbial diversity within the BV samples (α-diversity measures significantly different in both Shannon [p = 0.0043] and Simpson [p < 0.001] between BV and normal samples). A closer examination of the top 10 genera revealed that BVAB1 (p < 0.001), Prevotella (p < 0.001), Megasphaera (p < 0.001), and an unclassified genus in the Bifidobacteriaceae family (p < 0.001) were significantly more abundant in women with BV compared with those with normal flora ([Table 2]). At the genus level, Lactobacillus was the dominant genus in patients with normal flora (average relative abundance 88.5% compared with 12.7% in BV samples; p < 0.001). In fact, Lactobacillus had > 60% abundance in 19 samples with normal flora, and > 80% in 17 out of 20 patients with normal flora. Of note, there was one BV sample outlier that had 98.4% Lactobacillus (second bar in [Fig. 2]).
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In an unsupervised clustering analysis of the data, there were three identified clusters purely based on the β-diversity (Bray–Curtis) of microbiome composition ([Fig. 3]—heat map). The first cluster contained 16 BV samples; the second cluster contained 3 BV samples and 9 normal samples, while the third cluster contained 11 normal samples. Although the latter two clusters were mostly normal flora, they had distinct microbiome compositions—cluster 2 was predominantly Lactobacillus (96%), while cluster 3 was more diverse (74% Lactobacillus). Although not statistically significant, cluster 2 was associated with a nominally higher rate of preterm birth (9 out of 12) than cluster 1 (7 out of 16) and cluster 3 (4 out of 11) (p = 0.07, Fisher exact test).
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However, when comparing vaginal microbial diversity (β-diversity) in samples by race or birth timing (independent of vaginal flora), there were no statistically significant differences (p = 0.89 for race and 0.25 for birth timing by Bray–Curtis; p = 0.90 for race and 0.73 for birth timing by weighted UniFrac; and p = 0.59 for race and 0.58 for birth timing by unweighted UniFrac). No individual taxa level analysis for preterm birth or race was performed given that these associations in our small cohort were not statistically significant. Posthoc power analyses indicated that we were underpowered for the comparisons related to birth timing and race—power between 0.05 and 0.40. The minimal sample size to achieve sufficient power (> 0.8) to detect significant differences for at least one OTU would be n = 159 for birth timing, and n = 52 for the race. In contrast, the maximal power for detecting OTUs different between normal flora versus BV was 1.0 and the minimal required sample size was n = 6.
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Comment We demonstrated marked diversity in microbial composition between pregnant women with BV and those with normal flora. In addition, while there were no statistically significant differences when comparing microbiome diversity by race or birth timing, there was a nonsignificant association between certain microbial clusters and preterm birth (p = 0.07). As such, we have now determined the sample sizes needed to further evaluate the relationships between race and preterm birth (independent of BV) in our future larger studies.
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Prior microbiome studies have shown significant differences according to race and BV status in the microbial composition in the vagina among nonpregnant asymptomatic patients.[8] Ravel et al, described inherent vaginal microbial differences between asymptomatic reproductive-age women of four races (white, black, Hispanic, and Asian). They also described distinct microbial patterns associated with high- or low Nugent scores.[8] It has been noted that the vaginal microbiome is different in both composition and stability between pregnant and nonpregnant individuals.[29] [30] However, Hyman et al, in a prospective study of 88 participants, noted that diversity in flora by race persisted in pregnancy.[31] We did not observe distinct differences in microbiota by race. However, posthoc analysis showed that we were underpowered for this comparison. The fact that our results differ from what has been previously reported may be due to our small sample size, techniques used to sequence, and classify DNA (for example, choice of primers), or it may be true that pregnancy results in amelioration of racial differences in the vaginal microbiota.
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Moreover, given the established link between BV and preterm birth, we were surprised to note no statistically significant differences in this study in the β-diversity of the microbiota of women who did and did not deliver preterm—both independently and considering BV colonization. We did observe some clustering associated with preterm birth, yet this was not statistically significant. However, we were underpowered for that comparison as well, and we are currently conducting a prospective study of vaginal microbiota and preterm birth to further evaluate this association. The above-mentioned study by Hyman et al, measured the microbial diversity in women with either a low- or high risk of preterm birth. While there were no clear groups of microbes implicated with preterm birth, it was noted that there were differences in the overall diversity of microbes between women with a preterm birth and those with a term birth.[31] Given the fact that there was no single organism or group of organisms identified in that study or in our study, perhaps it is dynamic, changes in vaginal microbial communities that are linked to spontaneous preterm birth rather than a specific organism or group of organisms. This possibility should be addressed in future studies evaluating the vaginal microbiome at different time points during pregnancy. Furthermore, differences between the organisms in our clustered analysis should be investigated in a larger sample to elucidate if there is a relationship between those microbial compositions and preterm birth. As are others, our group currently is conducting such a study. The strengths of our current study include the use of a newer microbial analysis technique to characterize samples from a well-described cohort of patients. Demographic characteristics, Nugent score, and birth outcomes of these patients were prospectively collected at the time
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of the parent study. However, given that this is a retrospective analysis of previously collected samples, one limitation is the possibility of contamination affecting DNA analysis. Moreover, the relatively small sample size resulted in a small number of patients in each subgroup, limiting our ability to make statistical comparisons. Our posthoc power calculations have enabled us to determine the number of samples required in future studies. In addition, the samples evaluated in our current study reflect one point in time; it may be that changes in the vaginal microbiome over time are at least as important as the prevalence of various organisms at a single point in time.
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Vaginal microbiome analysis to study pregnancy-related complications is a relatively new tool in obstetrics. Given our preliminary data, we think that additional studies evaluating the vaginal microbiome in relation to pregnancy outcomes, particularly preterm birth, are warranted and hold promise to improve our understanding of infection-related preterm birth. Potentially, such studies may identify a target organism or group of organisms associated with adverse outcome, thus yielding a way to identify a group at high risk of PTB who could be targeted for current or putative therapeutic interventions.
Acknowledgments The following are acknowledged for their support of the Microbiome Resource at the University of Alabama at Birmingham: School of Medicine, Comprehensive Cancer Center (P30AR050948), Center for AIDS Research (5P30AI027767), and Center for Clinical and Translational Science (UL1TR000165) and Heflin Center.
References
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Fig. 1. Flow diagram describing sample selection and stratification
This flow diagram describes sample selection and stratification *BV = bacterial vaginosis by Nugent score
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Beta-diversity in vaginal microbial composition between pregnant women with BV and those with normal flora. This figure describes and defines the diversity of different genera (operational taxonomic units) between pregnant women with BV and normal flora. BV, bacterial vaginosis.
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Unsupervised clustered analysis of beta-diversity in microbiome composition. This figure portrays clusters of microbiome composition based on beta-diversity. The figure shows three distinct clusters.
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Table 1
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Patient characteristics N = 40
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Race White Black
20 (50%) 20 (50%)
Marital status Single Married
32 (80%) 8 (20%)
Less than 12 year education
24 (60%)
Nulliparity
22 (55%)
Smoking
21 (53%)
Maternal age at delivery (weeks)
21.4 ± 4.3
Body mass index (kg/m2)
23.9 ± 5.1
Pre-pregnancy weight (kg)
67.9 ± 18.0
Data are presented as n (%) or mean ± standard deviation.
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Table 2
Author Manuscript
The ten most commonly identified genera in the women with BV Mean % genus content Identified genus
BV (n = 19)
Normal flora (n=20)
p-value
Author Manuscript
1
BVAB1
39.5
0.4
< 0.001
2
Prevotella
15.4
0.4
< 0.001
3
Lactobacillus
12.7
88.5
< 0.001
4
Megasphaera
11.6
0.4
< 0.001
5
Bifidobacteriaceae family; unspecified genus
6.3
0.4
