CoChat
Sign Up
Cited by 48Reproductive tract infections researchGut microbiota and healthOral microbiology and periodontitis research

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Vaginal Dysbiotic Microbiome in Women With No Symptoms of Genital Infections

Open access
Pramanick, Rinku, Nathani, Neelam, Warke, Himangi, Mayadeo, Niranjan, Aranha, Clara·2022·Frontiers in Cellular and Infection Microbiology
DOI 10.3389/fcimb.2021.760459PMC PMC8790106
Open this paper in CoChat — free account

Abstract

The vaginal microbiome plays a critical role in determining the progression of female genital tract infections; however, little is known about the vaginal microbiota of Indian women. We aimed to investigate the vaginal microbial architecture of women with asymptomatic bacterial vaginosis (BV) (n=20) and normal microbiota (n=19). Microbial diversity was analyzed in vaginal swabs from regularly menstruating women (18-45yrs) by 16S rRNA V3-V4 amplicon (MiSeq Illumina) sequencing. Rarefaction analysis showed a higher number of species in normal flora compared to BV. Alpha diversity as measured by Pielou’s evenness revealed microbial diversity was significantly greater in BV samples than normal microbiota (p= 0.0165). Beta diversity comparison using UniFrac metrics indicated distinct microbial communities clustering between normal and BV flora. Firmicutes were the major phyla observed in vaginal specimens of normal microbiota whereas Actinobacteria, Fusobacteria, Bacteroidetes were significantly abundant in BV samples. Notably, the relative abundance of Lactobacillus was significantly high in normal microbiota. Conversely Gardnerella , Sneathia , Prevotella , Atopobium , Ureaplasma , Dialister significantly dominated dysbiotic microbiota. Relative frequency of Lactobacillus decreased significantly in BV (6%) as compared to normal microbiota (35.2%). L. fermentum, L. gasseri, L. iners, L. jensenii, L. mucosae, L. ruminis, L. salivarius , L. coleohominis was more exclusively present in normal microbiota. L. iners was detected from both the groups with a relative frequency of 50.4% and 17.2% in normal and BV microbiota respectively. Lefse analysis indicated Atopobium vaginae, Sneathia amnii, Mycoplasma hominis Prevotella disiens in the vaginal microbiota as a biomarker for dysbiosis and L. jensenii as a biomarker of a healthy microbiota . Firmicutes were negatively correlated to Tenericutes, Actinobacteria, Bacteroidetes , and Fusobacteria . Proteobacteria positively correlated to Tenericute s, and Bacteroidetes were shown to be positively correlated to Fusobacteria. Predicted functional analysis indicated differences in the functional profiles between BV and normal microbiota. Normal microbiota utilized pathways essential for phosphatidylglycerol biosynthesis I & II, peptidoglycan biosynthesis, geranylgeranyl diphosphate biosynthesis I, mevalonate pathway, CoA biosynthesis pathway I and pyrimidine nucleotide salvage; whereas BV bacteria had characteristic aromatic amino acid biosynthesis, pentose phosphate pathway, carbohydrate degradation. In conclusion, women with asymptomatic BV have vaginal microbiota significantly different than women with normal microbiota. Furthermore, the study provides insights into the vaginal microbial structure of Indian women that will enable us to explore the prospective candidates for restoring the vaginal microbiota.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fcimb.2021.760459

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 (A) Alpha-diversity represented by the Pielou evenness index in the BV and normal samples indicating significant difference (p-value = 0.0165) between the groups based on the Kruskal-Wallis test. The boxes represent the distributions of the alpha diversity index and show the median for each condition. Whiskers extend to the furthest data point. (B) PCoA generated using weighted Unifrac distance indicates distinct clustering of samples from each group. Each point corresponds to an individual sample. For each experimental group, an ellipse around the centroid is depicted. The first two components of the variance are represented by Plotting BV(blue) vs Normal (Red) samples with significant separation between the two groups. The first two components represent 78.32 percent of variance, individually depicted in parentheses next to Axis1 and Axis2.
Figure 1 (A) Alpha-diversity represented by the Pielou evenness index in the BV and normal samples indicating significant difference (p-value = 0.0165) between the groups based on the Kruskal-Wallis test. The boxes represent the distributions of the alpha diversity index and show the median for each condition. Whiskers extend to the furthest data point. (B) PCoA generated using weighted Unifrac distance indicates distinct clustering of samples from each group. Each point corresponds to an individual sample. For each experimental group, an ellipse around the centroid is depicted. The first two components of the variance are represented by Plotting BV(blue) vs Normal (Red) samples with significant separation between the two groups. The first two components represent 78.32 percent of variance, individually depicted in parentheses next to Axis1 and Axis2.CC BY · PubMed Central
Figure 2 Relative abundance of bacteria at the (A) phylum and (B) genus level in the normal and BV groups as computed by Welch’s t-test using STAMP software. The middle shows the difference between proportions of abundance in the 95% confidence interval, and the value at the right is the P-value. P < 0.05 represents the significant difference between the two groups.
Figure 2 Relative abundance of bacteria at the (A) phylum and (B) genus level in the normal and BV groups as computed by Welch’s t-test using STAMP software. The middle shows the difference between proportions of abundance in the 95% confidence interval, and the value at the right is the P-value. P < 0.05 represents the significant difference between the two groups.CC BY · PubMed Central
Figure 3 Genus level composition of the vaginal microbiota in normal and BV samples. The heatmap depicts the relative abundances of the most abundant genera. Each column represents one sample. Orange are samples with normal microbiota and Blue are BV samples.
Figure 3 Genus level composition of the vaginal microbiota in normal and BV samples. The heatmap depicts the relative abundances of the most abundant genera. Each column represents one sample. Orange are samples with normal microbiota and Blue are BV samples.CC BY · PubMed Central
Figure 4 Cladogram representation of differentially abundant bacterial families detected using LEfSe. The cladogram diagram shows the microbial species with significant differences in the two groups. Red and green, indicate BV and normal microbiota groups respectively, with the species classification at the level of phylum, class, order, family, and genus shown from the outside to the inside. The red and green nodes in the phylogenetic tree represent microbial species that play an important role in the BV and normal microbiota groups, respectively. Yellow nodes represent species with no significant difference. Significantly abundant bacterial groups observed in the study are shown in the list on the right hand side.
Figure 4 Cladogram representation of differentially abundant bacterial families detected using LEfSe. The cladogram diagram shows the microbial species with significant differences in the two groups. Red and green, indicate BV and normal microbiota groups respectively, with the species classification at the level of phylum, class, order, family, and genus shown from the outside to the inside. The red and green nodes in the phylogenetic tree represent microbial species that play an important role in the BV and normal microbiota groups, respectively. Yellow nodes represent species with no significant difference. Significantly abundant bacterial groups observed in the study are shown in the list on the right hand side.CC BY · PubMed Central
Figure 5 Bacterial correlation (A) at the phylum level. Positive correlations are represented as red and negative correlations as blue (B) Network of associations between the bacteria at the genus level. Red are samples with normal microbiota and green are BV samples. The nodes represent genera of bacteria; the edges represent the correlation coefficients between genera. Node size indicates the number of subjects in which the genus was seen.
Figure 5 Bacterial correlation (A) at the phylum level. Positive correlations are represented as red and negative correlations as blue (B) Network of associations between the bacteria at the genus level. Red are samples with normal microbiota and green are BV samples. The nodes represent genera of bacteria; the edges represent the correlation coefficients between genera. Node size indicates the number of subjects in which the genus was seen.CC BY · PubMed Central
Figure 6 Predicted functional profiling: Differential pathway abundance predicted by PICRUSt between normal and BV group. Pathways (BioCyc IDs) with an LDA score of 4 and above in normal (Green) and BV (Red) samples are represented.
Figure 6 Predicted functional profiling: Differential pathway abundance predicted by PICRUSt between normal and BV group. Pathways (BioCyc IDs) with an LDA score of 4 and above in normal (Green) and BV (Red) samples are represented.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 52 references

Cited by

Sorted by citations

See all 48

Related

Metadata

Authors
Pramanick, R., Nathani, N., Warke, H., Mayadeo, N., & Aranha, C.
Affiliations
  • ICMR National Institute for Research on Women's Health (IN)
  • Gujarat Technological University (IN)
  • King Edward Memorial Hospital and Seth G.S. Medical College (IN)
Corresponding author
Aranha, Clara
ORCID
Venue
Frontiers in Cellular and Infection Microbiology
Publisher
Frontiers Media SA
Published
2022-01-12
Volume
11
Pages
760459-760459
ISSN
2235-2988 (ISSN-L)
DOI
10.3389/fcimb.2021.760459
PMC
PMC8790106
OpenAlex
W4210716192
Cited by
47
References
52
Citations by year
2026: 6 · 2025: 13 · 2024: 14 · 2023: 5 · 2022: 9
FWCI
6.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Cellular and Infection Microbiology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Indian Council of Medical Research
Topics
  • Life Sciences › Immunology and Microbiology › Microbiology › Reproductive tract infections research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
Keywords
vaginal microbiota, bacterial vaginosis, dysbiosis, Lactobacillus, Lactobacillus jensenii, Lactobacillus iners, Atopobium vaginae, Sneathia amnii, Prevotella disiens, Firmicutes, microbial diversity, biomarkers, Indian women
MeSH terms
Female, Humans, Prospective Studies, RNA, Ribosomal, 16S, Vagina, Microbiota, Dysbiosis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review