ORIGINAL RESEARCH

Semen microbiota: cluster analysis of real-time PCR data

About authors

1 Ural State Medical University, Yekaterinburg, Russia

2 Medical Center “Garmonia”, Yekaterinburg, Russia

3 Yeltsin Ural Federal University, Yekaterinburg, Russia

4 Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

5 Ivanovo State Medical Academy, Ivanovo, Russia

Correspondence should be addressed: Еkaterina S. Voroshilina
Furmanova, 30, Yekaterinburg, 620142; moc.liamg@anilihsorov

About paper

Compliance with ethical standards: the study was approved by the Ethics Committee of Ural State Medical University, Federal State Budget Educational Institution of Higher Education under the Ministry of Health of the Russian Federation (Protocol № 7 of September 20, 2019). All patients signed the informed written consent to participation in the study.

Acknowledgments: the authors would like to thank VN Khayutin, director of "Garmonia" Medical Center, for allowing them to conduct the study in the clinic's laboratory department.

Author contribution: Voroshilina ES — organization of the study, data analysis, article authoring; Zornikov DL — data analysis, article authoring; Ivanov AV — statistical processing, data analysis, article authoring; Pochernikov DG — patients’ clinical profile, clinical data collection, data analysis, article authoring; Panacheva EA — literature review, data analysis, conducting PCR tests, article authoring.

Received: 2020-09-21 Accepted: 2020-10-09 Published online: 2020-10-28
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To this day semen microbiota is still poorly understood, and clinical significance of detecting specific microorganism groups has not been clearly determined. The aim of this work was to conduct cluster analysis of semen microbiota detected using real-time PCR. 634 semen samples of reproductive age men were analyzed. Microbial DNA in the quantity of no less than 103 GE/ml was detected in 460 samples (72.5%). From 1 to 14 microorganism groups were detected in 350 samples (55.2%) in the quantities that exceeded the threshold values (the detection rate of specific groups: 3.3–21.0%). In these 350 samples 4 stable microbiota clusters were determined. Each of the clusters was characterized by the prevalence of a specific microorganism group: obligate anaerobes (cluster 1; n = 172; detection rate — 49.1%), Lactobacillus spp. (cluster 2; n = 78; detection rate — 22.3%), gram-positive facultative anaerobes (cluster 3; n = 62; detection rate — 17.7%), Enterobacteriaceae / Enterococcoccus (cluster 4; n = 62; detection rate — 10.9%). Cluster 1 was less stable and was characterized by the larger species diversity compared to other clusters.

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