Abstract
Study question
What are the differences in the semen microbiota composition of patients with asthenozoospermia and normospermia according to cluster analysis of PCR data?
Summary answer
The detection rate of 4 stable semen microbiota clusters and the dominant bacteria groups varied in patients with asthenozoospermia and normospermia.
What is known already
Most of the research dedicated to analyzing normal and pathological semen microbiota is based on 16S rRNA gene specific Next generation sequencing (NGS). It has shown that microbiota is represented by polymicrobial communities (clusters) that consist of microorganisms from different genera and bacteria phyla. Despite it being highly informative, NGS has several weaknesses: complex sample preparation, difficult sample intake control, long analysis process, complicated results interpretation, high cost of equipment and reagents. These factors make it virtually impossible to use this approach in routine medical practice. Quantitative real-time PCR (RT-PCR) is far more suitable for this.
Study design, size, duration
Patients included in the study (n = 301) came to the “Garmonia” Medical Center (Yekaterinburg, Russia) either seeking preconception care or for infertility treatment. Depending on the spermiogram results, they were divided into two groups. Group 1 (n = 171) — asthenozoospermia, Group 2 (n = 130) — normospermia.
Participants/materials, setting, methods
Semen microbiota was analyzed using RT-PCR kit Androflor (DNA-Technology, Russia). Cluster analysis was performed for 201 samples with the total bacterial load (TBL) of at least 103 GE/ml (asthenozoospermia = 96, normospermia = 105). Cluster analysis was conducted using the k-means ++ algorithm, scikit-learn. The Silhouette index and the Davies–Bouldin index (DBI) were used to confirm the stability of clusters.
Main results and the role of chance
Both in the samples with normospermia and asthenozoospermia, four stable microbiota clusters were distinguished. Cluster I was characterized by the prevalence of obligate anaerobes, Lactobacillus spp. were prevalent in Cluster II, Gram-positive facultative anaerobes were prevalent in Cluster III, Enterobacteriaceae/Enterococcus spp. were prevalent in Cluster IV. Cluster I was detected the most often in both groups. However, in normospermia it was represented by various obligate anaerobes without pronounced quantitative predominance of any bacteria group. In samples with asthenozoospermia one of the bacteria groups were prevalent in Cluster I: Bacteroides spp./Porphyromonas spp./Prevotella spp., Peptostreptococcus spp./Parvimonas spp. or Eubacterium spp. In samples with asthenozoospermia Cluster II was characterized by the prevalence of Lactobacillus spp., while in samples with normospermia other bacteria groups were present along with lactobacilli, mainly obligate anaerobes. In samples with normospermia Corynebacterium spp. and Streptococcus spp., typical of normal microbiota of male UGT, were prevalent in Cluster III. In samples with asthenozoospermia Cluster III were characterized by the prevalence of Staphylococcus spp. In samples with asthenozoospermia Lactobacillus spp was present in Cluster IV along with Enterobacteriaceae/Enterococcus spp., which was not typical of the samples with normospermia.
Limitations, reasons for caution
Cluster analysis was not conducted for the samples with TBL lower than 103 GE/ml, since their results were incompatible with the data received for the negative control samples.
Wider implications of the findings
Further research could determine the detection rate of the described bacterial clusters in semen with other pathologies. Establishing the relationship between the characteristics of semen microbiota and infertility in men might allow the development of new algorithms for treating patients with reproductive disorders, depending on the composition of semen microbiota.
Trial registration number
not applicable
Semen microbiota: cluster analysis of real-time PCR data
