Chapter 4
Seminal plasma metabolites and aging: Impact on male
91
Abstract
Aging impact on fertility is now considered an important problem in public health since delayed marriage and parenthood are hallmarks of modern societies. Delayed parenthood has renewed the interest in age related decline of male fertility. Ageing is associated with structural and functional changes in the organism that result in the declining of its functioning. Though the relation between women fertility and age is relatively known, in men it remains a matter of debate. The aim of this study was to investigate the comprehensive metabolism of human seminal fluid and to verify the potential of metabolites as biomarkers of male reproductive potential during aging using an NMR-based metabolomics approach. The study comprized three subgroups of male patients: (1) normozoospermic (NZ, n=35) who had a normal semen profile (≥ 15 x106 spermatozoa/mL, ≥ 32% motility, and ≥ 4% normal morphology); (2) oligozoospermic (OZ, n=11), who had a sperm concentration < 15x106/mL; and (3) asthenozoospermic (AZ, n=8), who had a sperm motility < 32% motility. Seminal plasma metabolic profiles of all samples, from males of infertile couples (from 26 to 50 years-of-age), were analysed. The partial least squares-discriminant analysis (PLS-DA) was performed on GC–MS dataset. A significant (p<0.05) age-associated increase in the amino acid glutamate (Glu) and a significant decrease in the amino acid creatine was observed in OZ group. In NZ samples a significant increase with age was observed in threonine. In AZ samples a significant increase with age was observed in leucine and proline, while malate values decreased significantly.
There was also, an age-associated increase in most amino acid metabolites, namely in branched-chain (BCAA). Seminal content of phospholipid precursors like choline, increased with age. Our results provide evidence for an influence of aging on global seminal metabolome, with a profound alteration of several key metabolic pathways associated with the male reproductive potential.
Keywords: aging; male fertility; Seminal fluid metabolic profile; metabolomics; nuclear magnetic resonance.
Introduction
Population aging is a phenomenon that represents a major challenge for health systems [1].
As population ages and delayed parenthood is a hallmark for modern societies, aging- associated infertility becomes a major problem in public health. Statistics show that in recent years nearly two-thirds of babies have been conceived by fathers aged 30 or over [2].
Currently, male infertility factor affects approximately up to 50% of infertile couples, with 60 to 75% of those cases being idiopathic (of unknown origin) [3]. The occurrence of male infertility
92 is usually accompanied by qualitative (asthenospermia, teratozoospermia, and necrospermia) and quantitative (azoospermia, cryptozoospermia, and oligoasthenozoospermia) abnormalities in semen characteristics [4].
Unlike the abrupt decline in reproductive capacity observed in women, men maintain their reproductive function during all lifetime, although it deteriorates gradually over time. Several age-associated changes have been reported in testicular physiology and hormonal profile.
Indeed, advanced paternal age is associated with changes in the production of reproductive hormones [5, 6], sexual dysfunction [7] and testicular morphology [8]. However, andrologists continue to debate whether sperm quality declines with age or not, as most of the studies on this subject lack a mechanistic approach and are mainly focused on the evaluation of sperm parameters and DNA integrity [5, 9-11]. Hence, the molecular mechanisms associated to sperm quality declines with age remain to be clarified.
Sperm ejaculate consists of a small volume of sperm cells (5%) and the seminal fluid where the sperm is bathed, with secretions being produced from various male reproductive organs, including the testes and epididymis (5-10%), seminal vesicles (65%), prostate gland (25%), and bulbourethral glands (less than 1%) [12, 13]. The seminal plasma has a complex molecular composition, comprising various soluble ions and molecules, including sugars, proteins, polyamines and other constituents [14, 15]. In addition, it is influenced by the physiological and metabolic status of the male at that particular moment [15]. These secretions provide not only a safe environment for sperm during ejaculation, with all the requirements that promote the survival of spermatozoa, but also a medium through which they can move or "swim" [16, 17].
Seminal plasma is composed by a set of metabolites that play several roles related to sperm function, such as energy production, motility, protection, pH control and regulation of metabolic activity [13]. Thus, its metabolic composition plays a crucial role for a successful fertilization [18].
Metabolomics has been gaining momentum as a means of studying and understanding biological processes related to reproduction [19], since it allows the identification and quantification of small molecules, such as amino acids, peptides, fatty acids and carbohydrates in fluids, cells, tissues and organs [20, 21]. This allows the disclosure of metabolic reactions and identifying biomarkers for phenotypes of interest [20-22]. So far metabolic studies related to aging were mostly focused on the analysis of tissues like muscle, liver, brain, blood or in whole organisms [23]. Herein, we propose to investigate the metabolic phenotype of human seminal fluid from asthenozoospermic, oligozoospermic and normozoospermic patients, with diferent ages, by using an NMR-based metabolomics approach. We aim to unveil age-related
93 changes in the total metabolome of fluid samples of asthenozoospermic, oligozoospermic and normozoospermic patients.
Methods Chemicals
All chemicals were all purchased from Sigma–Aldrich (St. Louis, MO, USA), unless specifically stated.
Study population
The study was conducted in full compliance with government policies and the Helsinki Declaration. The procedures were approved by institutional ethics committee of Maternity Dr.
Alfredo da Costa - Hospital Center of Central Lisbon.
The donors were men seeking for fertility treatment and recruited at the Maternity Dr. Alfredo da Costa - Fertility Center. Every participant was fully informed about the purpose of this research and written consent was obtained. A questionnaire was used to collect anonymous information including personal background, lifestyle factors, occupational and environmental exposures, genetic risk factors, sexual and reproductive status, medical history, and physical activity. Smokers or men who underwent oncological treatments were excluded from the study.
Men included in this study had normal physical, as well as metabolic and endocrine status.
A total of 65 sperm samples, from 25-50 years of age men were used to evaluate sperm quality (motility, morphology and concentration). The study comprized three subgroups of patients:
(1) normozoospermic (NZ, n=35) who had a normal semen profile (≥ 15 x106 spermatozoa/mL,
≥ 32% motility, and ≥ 4% normal morphology); (2) oligozoospermic (OZ, n=11), who had a sperm concentration < 15x106/mL; and (3) asthenozoospermic (AZ, n=8), who had a sperm motility < 32% [24]. The average age of the men was 35.8 years in the OZ group (range 30 – 46 years), 35.4 years old in the AZ group (range 30 – 46 years) and 34 years old in the NZ group (range 29 – 50 years). Some of the samples with asthenozoospermia were also oligozoospermic.
For the metabolomics analysis, a total of 65 seminal fluid samples, from 25-50 years of age men, were used. Samples were further divided in different age subgroups (G1: ages 30 to 35 years; G2: ages 35 to 40 years; G3: ages above 40 years) and according to their classification as normozoospermic (NZ), oligozoospermic (OZ) or asthenozoospermic (AZ).
94
Semen collection, analysis and storage
Semen samples were obtained in private by masturbation into a sterile wide-mouth and metal free glass container after an at least 3-day abstinence. As the number of days of abstinence may have influence on semen parameters, patients included in this study were
previously taught to observe 3 up to a maximum of 5 days of abstinence from intercourse before their planned analysis. Routine semen analyses were performed using a phase contrast microscope, volumetric dilution and hemocytometry, while referring to guidelines in the fifth edition World Health Organization (WHO) Laboratory Manual for the Examination of Human Semen [25]. After collection, the semen was liquefied at 37ºC for 30 min and then analyzed in accordance with WHO guidelines. The variables examined included semen volume, pH, concentration, number of spermatozoa per ejaculum, motility, vitality, progression, and motion parameters. Strict quality control measures were enforced throughout the entire study. Semen samples were assessed twice, and the counting procedure was automatic and performed by two embryologists according to WHO recommendations. After analysis, each semen sample was centrifuged at 500.g for 10 minutes for separating spermatozoa and seminal plasma in the laboratory. The seminal fluid was collected and stored in a new tube. Then all samples were stored at -80ºC prior to 1H-NMR analysis.
1H-NMR spectroscopy analysis
Seminal plasma samples were thawed at room temperature and homogenised using a vortex mixer. Then to 180 µl of each seminal plasma sample, was added, 45 μl of fumarate in D2O (2mM, PH=7). After centrifugation at 12,000 rpm for 5min at 4 °C, aliquots of 200 μl of the resulting supernatants were transferred into 3-mm NMR tubes.
1H-NMR spectra of seminal fluid samples were obtained using a Varian Inova 600 MHz (14.1 T) spectrometer equipped with a 3 mm QXI probe with a z-gradient. 1H 1D noesy experiments with water presaturation were acquired at 298 K, using a 7.2 kHz spectral width, 0.1 s mixing time, 4 dummy scans, 4 s relaxation delay with 3 s of water pre-saturation (total relaxation time 7 s), 90o pulse angle, 3 s acquisition time and 128 scans. Pulse durations and water saturation frequencies were optimized for each sample. Spectra were processed by multiplying FIDs with exponential window function (line broadening of 0.3 Hz) and were zero filled to 64k points prior to Fourier transformation using TopSpin (Bruker Biospin, Karlsruhe, Germany). Spectra were manually phased and line corrected using a forth degree polynomial function, with chemical shifts internally referenced to the alanine signal at 1.48 ppm. To help spectral assignment two-
95 dimensional spectra (TOCSY) were also acquired. TOCSY was acquired using sweep width of 5.4 kHz in both dimensions, 48 transients and 400 and 1024 points in t1 and t2 dimensions, respectively. It was processed by applying qsine window function and zero filled to 2048 point in both dimensions. Peaks were assigned by comparing recorded 1D and 2D spectra with reference spectra and public databases such as HMDB [26]. Metabolites were identified according to Metabolomics standards initiative (MSI) guidelines for metabolite identification [27].
Multivariate analysis of NMR data
After processing, 1H noesy spectra were bucketed using one-point bucket (all intensity values) in the region 0.6-9.0 ppm, with signal-free, water and fumarate regions excluded from data matrix used for multivariate analysis. Data matrix was built in Amix Viewer (version 3.9.15, BrukerBiospin, Rheinstetten). Bucketed spectra were aligned using icoshift algorithm [28] and probabilistic quotient normalization (PQN) [29] was applied. Multivariate statistical analysis was applied (SIMCA-P14.1, Umetrics, Sweden) on the aligned and normalized NMR matrix scaled to unit variance [30]. Principal Component Analysis (PCA) was used to provide qualitative information on the observed data set [31]. Partial Least Square-Discriminant Analysis (PLS-DA) was used to assess class separation and to identify the main metabolites that contribute to the class discrimination. PLS-DA models were validated by A 7-fold internal cross-validation and permutation test (n=100) [32]. The corresponding PLS-DA loadings were obtained by multiplying the loading weight factors (w) by the standard deviation of the respective variable, and were colour-coded according to variable importance to the projection (VIP). Qualitative information extracted from the PLS-DA loadings was complemented by the quantitative assessment of metabolite variations between groups through spectral integration.
Spectral peaks contributing to class separation were integrated in Amix-viewer, and normalized by the quotient computed from PQN. Statistical difference between normalized multiplet areas was assessed by Student’s t-test and all p-values were corrected for multiple comparisons using Benjamini-Hochberg False Discovery Rate (significance cut-off value of q=0.01 was used). The results are presented as bar plots as mean ± SEM. Additionally, for each metabolite, the magnitude of variation in each age group was assessed by calculating biologically relevant effect size (ES), adjusted for small sample numbers [33]. Identities of all differentially expressed metabolites were used to identify the most relevant metabolic pathways effected by aging using MetabAnalyst [34]. Pathway enrichment and topology analysis were used for metabolic pathway identification.
96
Statistical analysis
The statistical significance between the experimental groups was assessed by one-way ANOVA, followed by a Fisher’s LSD test. The experimental results are presented as mean ± SEM. Statistical analysis was performed using GraphPad Prism 6 (GraphPad software, San Diego, CA, USA). Possible outliers were removed using Grubbs’ method, α=0.2. p < 0.05 was considered as significant.
Results
Semen parameters show differential response to aging
The various semen parameters were examined and compared in relation to patient age. The sperm parameters (semen volume - mL; sperm concentration – millions/mL) were analyzed according to the ages individuals from which we obtained the different samples (Fig. 1A and Fig. 2A), according to different age groups (G1: ages 30 to 35 years; G2: ages 35 to 40 years;
G3: ages above 40 years) (Fig. 1B and Fig. 2B) and according to their classification as normozoospermic (NZ), oligozoospermic (OZ) or asthenozoospermic (AZ) (Fig. 1C and Fig.
2C).
Figure 1. Total semen volume of semen samples according to the age of the individual. 1A - Total semen volume (ejaculate volume in mL) of samples from males of all ages (25-50 years; N=65) according to the age of the individual. 1B - Ejaculate volume (mL) of the samples from males of different ages groups (G1 -group 1: 30 to 35 years, N=25; G2 - group 2: 35 to 40 years, N=24; G3 - group 3: above 40 years, N=13) according to the age of the individual. 1C - Ejaculate volume (mL) of the samples from males of the normozoospermic (N=35), oligozoospermic
97 (11) and asthenozoospermic (8) groups according to the age of the individual. The association between total semen volume and age was evaluated by computing Pearson correlation coefficients (r) assuming Gaussian distribution and a confidence interval of 95%. All P values < 0.05 were considered statistically significant.
We observed that the semen volume was not significantly affected by the individual’s age, when analysing all the samples together. Also, when we analyzed the different age groups individually (G1, G2 and G3), a gradual but not significant decrease in the samples from the individuals from the age group G3 (ages: above 40 years; 18 samples) was observed (Fig.
1B). We also observed that in while total sperm count per ejaculate suffered a decrease with age, inversely, sperm concentration per mililiter (ml) increased with age (Fig. 2A) however this value is not statistically significant. Moreover, when analyzing sperm concentration according to the age groups (Fig.2B) or according to the classification in NZ, OZ and AZ (Fig. 2C), significant differences were observed. G1 group exhibit a significant increase (p=0.039;r=0.416) with age.
Figure 2. Sperm counts of semen samples according to the age of the individual. 2A – Sperm count (millions/mL) of samples from males of all ages (25-50 years; N=65) according to the age of the individual. 2B - Sperm count (millions/mL) of the samples from males of different ages groups (G1 -group 1: 30 to 35 years, N=25;
G2 - group 2: 35 to 40 years, N=24; G3 - group 3: above 40 years, N=13) according to the age of the individual. 2C - Sperm count (millions/mL) of the samples from males of the normozoospermic (N=35), oligozoospermic (N=11) and asthenozoospermic (N=8) groups according to the age of the individual. The association between total sperm count and age was evaluated by computing Pearson correlation coefficients (r) assuming Gaussian distribution and a confidence interval of 95%. All P values < 0.05 were considered statistically significant.
98 NMR-based metabolomics analysis of fluid seminal samples
To evaluate the molecular effects of aging on samples of seminal fluid we use an NMR-based metabolomic approach. Figure 3 shows the typical 1H NMR spectra of a complete metabolic profile and chemical shift assignments of different resonances in seminal plasma samples used.
Figure 3. Representative 1H NMR spectrum of seminal plasma. Some major metabolites are indicated according to Table 1. Legend: Ala-alanine, BCAA-branched chain amino acids, Cho-choline, Cit-citrate, Gly glycine, His- histidine, Lac- lactate, Lys-lysine, Phe-phenylalanine, Tyr-tyrosine. Source: Ivana Jarak
As an initial approach, and in order to better analyze metabolome changes in patients according to age, we applied a non-directed multivariate analysis. The samples were grouped according to the age of the donors into three distinct groups (Class 1: ages 30 to 35 years;
Class 2: ages 35 to 40 years; Class 3: above 40 years) and results obtained were subjected to a multivariate analysis. Both the unsupervised exploratory method PCA and PLS-DA scatter scoring plots of all age groups analyzed at the same time reveal a significant influence of aging on seminal fluid sample metabolome composition, which can be observed in clustering trends of individual age groups. A clear separation of the 3 classes of samples of different ages can be observed in the PLS-DA scores plots (Fig. 4).
99 Figure 4. Scores scatter plots obtained by PLS-DA of 1H NMR spectra of seminal samples of individual different group ages (Class 1 - G1 group 1: 30 to 35 years; Class 2 - G2 group 2: 35 to 40 years; Class 3 - G3 group 3: above 40 years) as indicated in the legends above the plots. Source: Ivana Jarak.
By detailed characterization of the NMR spectra we were able to identify 37 different small metabolites in the seminal fluid, which can be grouped into several classes, such as amino acids and their derivatives (glycine, alanine, valine, isoleucine, leucine, aspartate, glutamate, aspargine, glutamine, lysine, proline, serine, threonine, phenilalanine, tyrosine, histidine), organic acids (citrate, malate), compounds related to the metabolism of lipids (choline, glycerophosphocholine (GPC)), compounds related to the energy metabolism (acetate, lactate, fructose, glucose, creatine and n-acetylglucosamine) and nucleotids (Uridine). The referred metabolites were unequivocally and ubiquitously present in all seminal plasma samples. We also found 1 metabolite we could not identify (U1). U1 is also a sugar, however it is in such a small concentration that it was impossible to identify with certainty. The chemical shift, resonance assignment, and multiplicity pattern of these metabolites are presented in Table 1. Statistical analyses of the metabolites present on the samples of all individuals did not exhibit any correlation with age (Supplementary Table S1). However, when we analysed the quantified metabolites in the seminal samples of the individuals from the subgroups NZ, OZ and AZ, we found significant correlations with age for some of them (Supplementary Table S2). The seminal content on threonine (Fig. 5A) in NZ patients were significantly increased (p=0.041; r=0.357) with age. Likewise, concentration of Glutamate and U1 were also significantly increased (p=0.047; r=0.609 and p=0.026; r=0.665) while on the contrary creatine significantly decreases (p=0.006; r=-0.764) in OZ patients (Fig. 5B). In AZ patients concentration of leucine and proline were also significantly increased (p=0.033; r=0.746 and p=0.007; r=0.855) while on the contrary malate significantly decreases (p=0.011; r=-0.829) (Fig. 5C).
100 Figure 5. Relative content (Integral Area – AU) of NMR-derived metabolites in seminal plasma of the samples from males of the normozoospermic, oligozoospermic and asthenozoospermic groups according to the age of the individual. Relative quantification (Integral Area – AU) of NMR-derived metabolites in seminal plasma of the samples from males of the normozoospermic (N=35), oligozoospermic (N=11) and asthenozoospermic (N=8) groups. Legend: U1- metabolite we could not identify. 5A- Relative quantification (AU) of threonine (Thr) of the normozoospermic group. 5B- Relative quantification (AU) of glutamate, creatine and U1 of the oligozoospermic group. 5C- Relative quantification (AU) of leucine, proline and malate of the asthenozoospermic group. The relative quantification of NMR-derived metabolites was evaluated by computing Pearson correlation coefficients (r) assuming Gaussian distribution and a confidence interval of 95%. All P values < 0.05 were considered statistically significant.
When we analysed association of the quantified metabolites with age, in the samples of the individuals from the different age groups (G1 -group 1: 30 to 35 years; G2 - group 2: 35 to 40 years; G3 - group 3: above 40 years), we observed a significant correlation for some metabolites in samples from the G1 and G2 groups (Fig. 6 and Supplementary Table S3). In samples from the G1 group, not only glutamine (p=0.0270; r=-0.4607) but also tyrosine (p=0.0035; r=-0.5831) content showed a significant decrease with age. In samples from the G2 group, a significant increase with age was observed on the content of glutamate (p=0.0102;
r= 0.4854), while creatine (p=0.0059; r=-0.5445), uridine (p=0.0173; r= -0.4812) and U1 (p=0.0214; r=-0.4670) showed a significant decrease with age.
The seminal content of metabolites related to lipid metabolism (choline and glycerophosphocholine - GPC), although showing a tendency to increase with age, did not