Introduction

Infertility has become one of the most serious public health problems affecting human reproduction and health, among which male infertility accounts for half1. The main cause of male infertility is decreased sperm quality, which manifests as azoospermia, oligospermia, asthenospermia, and teratospermia2. Although a great deal of research has been done on male infertility, routine semen analysis remains the main indicator for evaluating semen quality, including semen volume, pH, sperm count, motility, vitality, and morphology3,4,5. There are some researches using various diagnosis techniques (e.g., sperm DNA fragmentation index, genetic testing)6. However, there are few studies on oligozoospermia related to metabolomics.

Omics could simply fall into the following classes: genomics, transcriptomics, proteomics, and metabolomics, which may deepen our understanding of morphogenetic processes and the mechanisms of diseases. Metabolomics is the analysis of metabolites in biological fluids, cells, and tissues and is commonly used as a tool for biomarker discovery7. Studies have shown that metabolite analysis, such as blood, urine, saliva, and semen, can be used in the diagnosis and treatment of a variety of diseases8,9,10,11,12. Based on widely used detection equipment, including mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), metabolomics is characterized by high sensitivity, high accuracy, high resolution, and small sample volume13. A comparative analysis of the urinary metabolome of 158 fertile men and 135 infertile men with azoospermia elucidated a distinct metabolic profile associated with oligozoospermic infertility as discerned through urinary metabolomics. The result intimates that oligozoospermia may bear a proximate correlation to energetic expenditure and antioxidant defenses operative during spermatogenesis11. In a 2007 semen metabolomics study, the concentrations of oxidative stress biomarkers (-CH, -NH, -OH, and ROH) were found to be uniquely correlated in the semen of healthy men with idiopathic infertility, varicocele, and vasectomy reversal patients14. Moreover, recent studies have found that obesity can affect the composition of seminal plasma metabolites, and the abnormal energy metabolism of seminal plasma is closely related to asthenospermia caused by obesity, mainly including carbohydrate metabolism and amino acid metabolism15. The antecedent investigation has validated a correlation between alterations in serum metabolic profiles and oligozoospermic male infertility16. Nevertheless, the elucidation of seminal metabolic signatures in oligozoospermic males has remained inadequate hitherto. In our study, we characterized seminal metabolic signatures of oligozoospermic individuals and normal controls employing high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Extant research has intimated unique serum metabolic profiles in oligozoospermic patients, intimating potential metabolic perturbations operative in the pathogenesis of oligozoospermia. Further interrogation of the distinct metabolic state evident in oligozoospermic patients necessitates investigation with more substantial cohort sizes and stringent standards.

Methods

Ethical approval

This study was approved by the Ethics Committee of the The First Affiliated Hospital of Nanjing Medical University and it was performed per national and international guidelines. Each subject involved in the study gave their written informed consent.

Sample collection

The inclusion criteria for oligospermia were sperm concentration of 20 M/ml and total sperm count of 40 M/ml). Semen samples were procured via masturbation, the conventional methodology for semen acquisition. All donors remained abstinent for 3–7 days antecedent to the provision of specimens and washed hands with soap and water before self-collection into sterile wide-mouthed sample cups within a room adjoining the andrology laboratory. Donors were instructed to collect only the initial ejaculate whilst avoiding accrual of any spilled semen. Caps were promptly applied post-collection and specimens were immediately immersed in a 37 °C water bath until complete liquefaction transpired preceding analysis.

Sample processing

The semen samples obtained from volunteers were purified by centrifugation of semen(3000 rpm, 10 min, 4 °C), then the supernatant was collected in EP tube. The purity of all seminal plasma was examined by phase contrast microscope to ensure that there was no sperm cell in the collected seminal samples. All samples were placed in liquid nitrogen immediately after collection and then transferred to − 80 °C for further study.

Metabolite extraction

Firstly, 100µL of each sample was added to a new 1.5 ml Eppendorf (EP) tube, and 300µL of cold methanol was added to mix and shake. TissueLyser (frequency adjusted to 50 HZ, time 5 min) was used to grind and crush the sample, and it was placed at − 20 C for 2 h. Then centrifuge 15 min at 25,000*4 °C and take 350 µL of supernatant in a new EP tube and centrifuge at 25,000*4 °C for 15 min. Each sample was mixed with 50 ml supernatant quality control (QC), and the rest was transferred to a new 1.5 ml EP tube for computer detection.

LC–MS fingerprinting

The LC–MS fingerprinting process includes sample preparation, liquid chromatography, mass spectrometry and data analysis. The sample is prepared and often diluted to fit the analytical requirements. Then, the sample is injected into a liquid chromatography system where it is separated based on its chemical properties as it passes through a chromatographic column. The separated compounds are then ionized and analyzed by the mass spectrometer. The MS detects and measures the mass-to-charge ratio of the ions, providing information about the molecular weight and structure of the compounds. All samples were acquired by the LC–MS system followed machine orders. Firstly, all chromatographic separations were performed using an ultra performance liquid chromatography (UPLC) system (Waters, UK). The chromatographic separation was performed on an ACQUITY UPLC BEH C18 column (100 mm*2.1 mm, 1.7 μm, Waters, UK) at a column temperature of 50 °C and a flow rate of 0.4 ml/min. The mobile phase A consisted of water and 0.1% formic acid, while the mobile phase B consisted of methanol and 0.1% formic acid. The metabolites were eluted by the following gradients: 0-2min, 100% mobile phase A; 2-11min, 0–100% mobile phase B; 11-13min, 100% mobile phase B; 13-15min, 100% mobile phase A. The loading volume of each sample was 10μl. The small molecules eluted from the column were collected in positive and negative ion mode using a high resolution tandem mass spectrometer Xevo G2-XS QTOF (Waters, UK). In positive ion mode, the capillary voltage and cone voltage were 3.0 kV and 40.0 V, respectively. In negative ion mode, the capillary voltage and cone voltage were 2.0 kV and 40.0 V, respectively. The centroid data were collected in MSE mode with a first-order scanning range of 50–1200 Da and a scanning time of 0.2 s. All parent ions were fragmented at energy levels of 20 to 40 eV, and all fragment information was collected with a scanning time of 0.2 s. During the data collection, real-time quality correction was performed on the LE signal every 3 s. At the same time, a post-mixing quality control sample was collected every 10 samples to evaluate the stability of the instrument state during the sample collection.

Data analysis

Raw data were processed by Progenesis QI 2.0 data analysis software (Nonlinear Dynamics, Newcastle, UK) for peak picking, alignment, and normalization to produce peak intensities for retention time and m/z data pairs. Further statistical analysis was performed on the resulting normalized peak intensities using metaX software (http://www.bioconductor.org/packages/devel/bioc/html/metaX.html). In metaX processing, the metabolic features that were detected in < 50% of QC samples or < 80% of experimental samples were removed. In addition, all metabolic features with a coefficient of variation (CV), as calculated for the QC samples, of > 30% were also removed. Metabolite identification was conducted by comparing Progenesis Metascope from Progenesis QI 2.0 against the Human Metabolome Database with mass tolerance of 10 ppm for precursors. Pathway analysis was conducted using the MetaboAnalyst pathway tool (http://www.metaboanalyst.ca).

Results

Demographic and semen feature

A total of 20 oligozoospermia patients and 20 normal controls were enrolled in this study. There was no significant difference in age between the oligospermia group and the healthy control group. Demographic information and semen parameters are given in Table 1.

Table 1 characteristics of the sperm samples.
Full size table

Multivariate analysis

Figure 1 demonstrated typical peak chromatographic profiles showing the stability of the results in the positive and negative ion modes, and spectra were labeled with their metabolites. Multivariate statistical analysis was used to compare the metabolomes of oligozoospermia and control groups. Quality control samples in the principal component analysis (PCA) showed good aggregation (Figs. 2A, 3A). Similarly to typical peak chromatographic profiles overlap diagram, quality control samples QC can be relatively clustered together, the better the aggregation indicates the more stable the instrument and the better the quality of the collected data. A preliminary unsupervised PCA of the data was conducted in the positive and negative ion modes to examine clustering patterns between the different sample groups (Figs. 2B, 3B). As depicted, substantial overlap was observed between the oligozoospermia and control groups in the PCA analysis (Figs. 2C, 3C). Thus, partial least squares-discriminant analysis (PLS-DA) was subsequently performed on the entire dataset to discern differences. The model showed that the samples in the oligozoospermia group and control group are distributed in two separate areas, indicating a markedly different semen metabolome (Figs. 2C, 3C). To evaluate the PLS-DA, R2 and Q2 were used, and the positive ion mode parameters were R2 = 0.7088, Q2 = 0.043, which were very close to 1, the negative ion mode parameters were R2 = 0.7532, Q2 = -0.1204, which were very close to 1 thus indicating good ability of prediction and reliability of the model.

Fig. 1
figure 1

Typical peak chromatographic profiles of the semen samples from the healthy donors and oligospermia patients in (B) positive and (A) negative ion modes. Peaks are the parts of the chromatogram that rise above the baseline, representing the separated components of the sample. Peaks are the parts of the chromatogram that rise above the baseline, representing the separated components of the sample. Each peak corresponds to a specific compound or component. For unknown samples, compare the retention times with those of known standard compounds to help identify the unknown components. The resulting maps were continuously depicted with the time point as the abscissa and the sum of the intensities of all ions in the mass spectrogram at each time point as the ordinate. QC samples are the same sample, and the overlap map of their TIC (total ion chromatogram) can be used to preliminarily judge the instrument state. The higher the overlap degree, the more stable the instrument is.

Full size image
Fig. 2
figure 2

Multivariate analysis and identification of different expressed metabolites in oligozoospermia(positive ion modes). (A) Principal component analysis of QC samples. (B) A PCA score plot data from healthy controls versus oligospermia patients. (C) A PLS-DA scores plot data from healthy controls versus oligospermia patients. (D) Volcano plot of 72 significantly altered metabolites (p < 0.05). X axis: Fold change in log2 scale; Y axis: −log10 (p value). (E) The heatmap of hierarchical clustering analysis of healthy donors and oligospermia patients. (F) Correlation of differential metabolites in oligozoospermia patients.

Full size image
Fig. 3
figure 3

Multivariate analysis and identification of different expressed metabolites in oligozoospermia(negative ion modes). (A) Principal component analysis of QC samples. (B) A PCA score plot data from healthy controls versus oligospermia patients. (C) A PLS-DA scores plot data from healthy controls versus oligospermia patients. (D) Volcano plot of 89 significantly altered metabolites (p < 0.05). X axis: Fold change in log2 scale; Y axis: −log10 (p value). (E) The heatmap of hierarchical clustering analysis of healthy donors and oligospermia patients. (F) Correlation of differential metabolites in oligozoospermia patients.

Full size image

Identification of different expressed metabolites in oligozoospermia

A total of 72 and 89 differential ions were confirmed in the positive and negative ion modes respectively (the threshold: fold change < 0.667 or >  = 1.5 and p < 0.05) (Shown in volcano Figs. 2D, 3D; Supplementary Table 1,2). There were 31 differential metabolites up-regulated and 41 differential metabolites down-regulated in the positive ion mode. In addition, there were 33 differential metabolites up-regulated and 56 differential metabolites down-regulated in the negative ion mode. Most of the differences were between 1.5 and twofold changes. Among these differences, downregulated differential metabolites were more than upregulated differential metabolites. The heatmap of hierarchical clustering analysis (HCA) was used to analyze the possible differential metabolite profiles between oligozoospermia and healthy controls (Figs. 2E, 3E). To determine the relevance among the different expressed metabolites or the clusters, we investigated correlations in metabolites (Figs. 2F, 3F). The correlations between metabolites of the different classes were similar to the results of HCA. Furthermore, the pathway impact values of all differential metabolites were collected via a pathway topology analysis. The Propanoate metabolism pathway was ranked as the most important metabolic pathway. Glycine serine and threonine metabolism, Synthesis and degradation of ketone bodies, Valine, leucine, and isoleucine degradation were also enriched (Table 2).

Table 2 Metabolic pathway and linkages analysis.
Full size table

Discussion

In this research, we used high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) to identify potential metabolism between oligozoospermia and healthy humans. 72 and 89 different expressed metabolism was identified respectively in positive and negative ion modes by including 20 patients with oligospermia and 20 normal patients. By correlation analysis of these biomarkers, propanoate metabolism, glycine serine, and threonine metabolism, were suggested to be related to the initiation and maintenance of sperm motility4,17.

Metabolism is a key factor supporting sperm functions, such as capacitation, acrosome reaction, sperm–egg recognition, and fusion18. Fatty acid metabolism, a vital source of cellular energy metabolized, provides energy for cell metabolism and growth by degrading fatty acids19. At the same time, fatty acid metabolism is also related to reproduction20,21. In our research, the propanoate metabolism pathway was identified between the oligozoospermic patients and the normal men, which means fatty acid metabolism may play an important role in these patients. Propionate is a ubiquitous short-chain fatty acid (SCFA)22. Typically, SCFAs are generated by colonic bacteria and metabolized mostly by enterocytes and liver23. There have been studies showing that propionate was associated with cardiovascular diseases. It can lower blood pressure and improve I/R injury, reducing the risk of coronary artery disease (CAD) and atherosclerosis24. Moreover, the propionate may affect gut microbiota to influence the host health by regulating gut cells25. Our results show that the the propionate metabolism has a strong correlation with oligozoospermia, which may be explained by the combined impact of gut microbiota, and lifestyle.

In addition to being key nutrients for energy metabolism, amino acids and other metabolites are involved in physiological processes that regulate spermatogenesis26. Serine, glycine, and threonine metabolism is one of the most important metabolic pathways, participating in the structure and activity of life. It upregulated cancer and promoted tumor homeostasis, which is thought to play a key role in cancer development and progression27. Serine/threonine also takes part in sperm maturation and capacitation28. Recently, a newly founded serine/threonine kinase was reported to link doublet microtubules (DMTs) to outer dense fibers in mammalian sperm29. It can affect axonemal microtubules to regulate sperm motility, thus influencing human reproduction30. In our research, compared with normal humans, the oligozoospermic patients have fewer levels of serine, glycine, and threonine metabolism, which may imply low levels of serine, glycine, and threonine participate in the progression of oligozoospermia. The results indicated that glycine, and threonine supplementation can help to boost healthy sperm count and improve fertility. We will further explore the rescue effect of glycine and threonine supplementation on oligozoospermia in future studies. Moreover, the synthesis and degradation of ketone bodies, Valine, leucine, and isoleucine degradation were related to oligozoospermia. Moreover, our study found that the pathway of the synthesis and degradation of ketone bodies and the valine, leucine, and isoleucine degradation were downregulated in the oligozoospermic patient. The altered valine, leucine, and isoleucine metabolism was detected to be the etiology of teratozoospermia31.

Ketone bodies play essential roles in mammalian cell metabolism, homeostasis, and signaling in various physiological and pathological states. In addition to serving as an energy fuel for extra-hepatic tissues such as the brain, heart, or skeletal muscle, ketone bodies also play a key role as signaling mediators, drivers of protein post-translational modifications (PTM), and regulators of inflammation and oxidative stress32. Some studies indicated that ketone bodies are related to the progressive movement of human sperm33. Sperm maintenance in 4 mM β-hydroxy-butyrate (one of ketone bodies) after capacitation was associated with a significantly higher percentage of sperm cells with progressive motility compared to β-hydroxy-butyrate -lacking control. According to our results, the ketone bodies metabolism may play a pivotal role in the regulation of sperm function, which may trigger oligospermia.

Due to the limitations on research funds and time in the present study, this study still has a few inadequacies that need to be improved. First, the number of volunteers enrolled in this study is insufficient, more people should be enrolled to confirm the findings in the future. Moreover, metabolomics data are susceptible to multiple factors such as environment, lifestyle, and diet, and we do not control for interindividual confounders in study subjects, so more rigorous subject selection criteria and studies with controlled animal models are needed. Further experimental validation is needed to verify these differential metabolites, and we will use multi-omic analyses to explore the potential mechanisms.

Conclusion

In this study, we tested the metabolomic characteristics of oligozoospermia by using HPLC-MS/MS to assess their differences in metabolites and metabolic pathways. The metabolic patterns of semen samples from oligozoospermic and normal men were shown to be significantly different, reflecting a complex web of metabolic alterations in patients with oligozoospermia. Glycine serine and threonine metabolism valine, leucine, and isoleucine metabolism were enriched in the oligozoospermic patients and thus may influence spermatogenesis. Our finding also reveals that propanoate metabolism plays an important role in the development of oligozoospermia. These abnormal metabolic pathways may be related to the etiology of oligospermia. In conclusion, we portrayed the ground atlas of semen metabolism in oligozoospermia, which may provide a novel insight to identify the etiology of oligozoospermia. Further research needs to be conducted to unravel the potential underlying mechanisms.