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.