Conclusion:
This research was based on a cross-sectional study with a large sample
size to analyze the structure of VMB in PCOS. The VMB of PCOS patients
showed a reduction in the abundance of Lactobacillus, but
increases in the abundance of potential pathogenic bacteria, such asU. parvum , A.baumannii , Prevotellaspp ., and G. vaginalis. There were interactions
between serum levels of testosterone, AMH, LH, and changes in the VMB.G. vaginalis , U. parvum , P. L.crispus , P. timonensis, and P. buccalis were
identified as key bacteria that drive changes in the vaginal microbial
interaction network of PCOS patients. Due to the complex etiology of
PCOS, the way forward for VMB research may not be as straightforward as
it seems. However, the results of this study not only enhance our
understanding of the PCOS vaginal microbiome but also provide a basis
for future research on the potential mechanism by which pathogenic
bacteria are involved in PCOS vaginal microbial imbalance and to develop
relevant methods of treatment.
Acknowledgements
The authors would like to thank the laboratory staff for the support in
maintaining and analyzing the samples and they are also grateful to all
women who participated in this study.
Authors’ contributions
C. J., L. Q. and H. Z. contributed to design of the study and drafted
the manuscript; C. J., L. Q., T. S. , Q. S. , Z.L. and Y.C. did the
experiment and were involved in the analysis of data. C. J., L. Q. X.
L., G. X., J. W., T.H., L.Y., J.S., F.Z., F.L., Y.Z., Y.H., Y.P., Y.L.,
Z.Y., H.C., Z.Z., S.Z., Y.F., Y.Z., and Q.Y. collected the samples . C.
J., L. Q. and H. Z. revised the article. The author(s) read and approved
the final manuscript.
Funding
This study was supported by the National Key Research and Development
Program of China (2021YFC2700400, 2021YFC2700701); the National Natural
Science Foundation of China (82192874, 31871509, 82071606); the Taishan
Scholars Program of Shandong Province (ts20190988) ; and Wenzhou
Municipal Science and Technology Bureau(Y20190261).
Availability of data and materials
The datasets used or analyzed during the current study are available
from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate.
This study was approved by the Institutional Review Board of Center for
Reproductive Medicine of Shandong University(2019LSZ14). All the
enrolled subjects gave a written consent.
Consent for publication
Written informed consent for publication was obtained from all
participants.
Competing interests
The authors declare that they have no competing interests.
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Figure legend
Fig 1. Composition of the vaginal microbiota in two groups. The mean
percentage of relative abundances at the phylum (a) , genus (b) in two
groups.
Fig 2. By shannoon index (a)and simposon index (b),box plots present the
vaginal microbiome diversity between PCOS and control group; comparation
among the control gorup,PA group, PD group by shannoon index(c) and Chao
1 index(d).PA:OD+HA+PCOM;PD:OD+PCOM.OD: ovulatory dysfunction ;HA:
hyperandrogenism ;PCOM: polycystic ovarian morphology.* p<0.05
Fig 3. β-diversity of microbiome was performed using PCoA analysis:
(a) comparaion between pcos and control (b).comparation among three
groups ; (c) box plot present within−group median BC distance.***
P<0.001
Fig.4 Feature selection analysis (Boruta algorithm) to identify the taxa
important to the classification of PCOS(a). ROC curve based on the taxa
that were selected as confirmed species by Boruta algorithmb(b).
Fig 5. Comparation in control group and PCOS group , heat map of
Spearman’s correlation analysis between the intersected different
vaginal microbiome and the clinical indices.( *p
<0.05,**P<0.01,***P<0.001)
Fig 6. Microbiota community structure was evaluated by networks of ASVs
using (SparCC) . Each node represents an ASV, and the identified ASV is
annotated with its species, lines show the connection among species. The
top five modules in the network in terms of module size were colored.
Numbers of different colored nodes represent the relative content of the
microbiota composition.( ASV noted in Suppl table ). Scatter diagrams
show the connectivity among and within modules in PCOS(c)and in
Ccontrol(d), nodes represent each specie.
Fig7. (a) The plot shows the most common sub-network between the control
and PCOS network. All nodes belonging to a same community are randomly
assigned a similar color. Nodes which are big and red are particularly
important ‘drivers’ . Red edges are present only in PCOS, Green edge are
present only in control and Blue are present in both.(b) The Community
shuffle plot enables one to understand the extent to which the
identified communities in the control and case networks are similar
using a heatmap. Similar (or conserved) communities are shown starting
with a blue (deep blue indicated most similar community pairs) gradient
in the heatmap while dissimilar ones are shown as green gradient (deep
green indicates most dissimilar pairs). The values in the cells are
calculated as the intersecting set count between the sets of node
contents of the two compared communities (vertical axis as ’control’ and
horizontal axis as ’case’). More community splits (from ‘Control’ to
‘Case’) represents increased community shuffling. Hence, plots having
less shuffling will show less horizontal splits (in the blocks of the
matrix) and individual blocks will have a higher cell values.
Fig 8. Functions influenced by PCOS (n = cases, controls). (a)KEGG
gene enriched in the samples with and without PCOS. The pathways were
detect by Wilcoxon test.(* P<0.05)。(b)The pathways were arranged by
unsupervised hierarchical clustering.