Potential Pathogenic Bacteria in Seminal Microbiota of Patients with Different Types of Dysspermatism

Huijun Yang, Jiaming Zhang, Zhiwei Xue, Changying Zhao, Lijun Lei, Yan Wen, Yunling Dong, Junjie Yang, Lei Zhang, Huijun Yang, Jiaming Zhang, Zhiwei Xue, Changying Zhao, Lijun Lei, Yan Wen, Yunling Dong, Junjie Yang, Lei Zhang

Abstract

Human microbiota play an important role in the health of their human hosts. Recent studies have demonstrated that microbiota exist in seminal plasma. The current study aims to elucidate whether seminal microbiota exist in patients with different types of dysspermatism and whether bacterial biomarkers can be identified for them. A total of 159 study participants were recruited, including 22 patients with oligoasthenospermia, 58 patients with asthenospermia, 8 patients with azoospermia, 13 patients with oligospermia, and 58 matched healthy controls. Seminal microbiota composition was analyzed using 16S rRNA gene-based sequencing. The results showed that the composition of seminal microbiota of patients with dysspermatism differed from those of healthy controls. Comparison of the microbiota composition in semen samples from patients with different types of dysspermatism showed that microbiota in patients with asthenospermia and oligoasthenospermia were distinct from healthy controls in beta diversity (P < 0.05). Characteristic biomarkers, including Ureaplasma, Bacteroides, Anaerococcus, Finegoldia, Lactobacillus and Acinetobacter lwoffii, were identified based on LEfSe analysis. Inferred functional analysis based on seminal microbiome data further indicated the presence of potential pathogenic biomarkers in patients with asthenospermia and oligoasthenospermia. These results provided profiles of seminal microbiota exhibited in different types of dysspermatism, thus providing new insights into their pathogenesis.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Relative abundance of taxa among five groups. Comparison of OTUs and relative taxa abundance among asthenospermia, oligospermia, oligoasthenospermia, azoospermia, and healthy controls. (A) At phylum level; (B) at genus level.
Figure 2
Figure 2
Comparison of alpha diversity and relative abundance at phylum level based on the OTUs profile. Box plots depict differences in microbiome diversity based on (A) Shannon index and (B) ACE index between patients with dysspermatism and healthy controls. (C,D) show Shannon index and ACE index among patients with asthenospermia, oligospermia, oligoasthenospermia, and azoospermia, and healthy controls. The p value was calculated using the Wilcoxon rank-sum test.
Figure 3
Figure 3
PCoA analysis of microbiota between patients with dysspermatism and healthy controls. (A) Unweighted unifrac PCoA; (B) Weighted unifrac PCoA. PCoA analysis of the microbiota among patients with asthenospermia, oligospermia, oligoasthenospermia, and azoospermia, and healthy controls; (C) Unweighted unifrac PCoA; (D) Weighted unifrac PCoA.
Figure 4
Figure 4
The most differentially abundant taxa between patients with dysspermatism and healthy controls (LDA score above 3) generated using LEfSe analysis.
Figure 5
Figure 5
LEfSe analysis between patients asthenospermia and healthy controls. (A,B) Comparison of the most differentially abundant taxa between patients with asthenospermia and healthy controls (LDA score above 3) generated using LEfSe analysis. We selected 4 biomarkers to predict the probability of patients with asthenospermia. (C–F) These biomarkers are Propionibacterium, Pelomonas, Lactobacillus, and Propionibacterium acnes. The ROC curves as well as the AUC (Area Under the Curve) values were calculated using SPSS.
Figure 6
Figure 6
LEfSe analysis between patients with oligoasthenospermia and healthy controls. (A,B) Comparison of the most differentially abundant taxa between patients with oligoasthenospermia and healthy controls (LDA score above 3), were generated using LEfSe analysis. (C–H) These biomarkers are Lactobacillus, Acinetobacter, Propionibacterium, Pelomonas, Prevotella copri, and Propionibacterium acnes. The ROC curves as well as the AUC (Area Under the Curve) value was calculated using SPSS.
Figure 7
Figure 7
Predicted metagenome function based on KEGG pathway analysis. Extended error bar plot showed significantly different KEGG pathways between (A) patients with asthenospermia and healthy controls; (B) between patients with oligoasthenospermia and healthy controls.

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