Metabolomic profiling of goat seminal plasma: insights into sperm motility regulation

Abstract

Low sperm motility is a major limitation to the success of artificial insemination in goats, yet the metabolic basis underlying this trait remains poorly understood. Seminal plasma (SP) contains a diverse array of metabolites that support sperm function by providing energy substrates, antioxidants, and signaling molecules. This study investigated the metabolomic profile of goat seminal plasma associated with sperm motility, aiming to explore the metabolic mechanisms underlying variations in sperm motility and identify potential biomarkers associated with goat reproductive performance. Using the high-resolution liquid chromatography–mass spectrometry (LC–MS), a total of 7,374 metabolites were detected across all samples. All xenobiotic compounds detected in preliminary analyses were excluded following MS/MS confirmation. Multivariate and univariate analyses revealed several significantly different individual metabolites (false discovery rate < 0.05) between high-motility (≥ 75%) and low-motility (≤ 65%) groups. However, no metabolic pathways remained significant after false discovery rate (FDR) correction, indicating an exploratory level of evidence limited to single metabolite associations. Key discriminant metabolites included amino acids, carnitine derivatives, and antioxidants such as riboflavin and phosphocreatine, which were more abundant in the high-motility group. These findings suggest possible roles of energy metabolism, oxidative protection, and membrane stability in regulation of goat sperm motility.

This work presents the most comprehensive dataset to date for goat seminal plasma, generated under controlled conditions with rigorous quality assurance. The results provide preliminary insight into the metabolite–motility relationships and offer a foundation for future targeted validation using multiple reaction monitoring and functional fertility assays.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-025-05269-8.

Introduction

Sperm motility is a key determinant of male fertility and reproductive success in livestock. Reduced sperm motility is commonly associated with infertility, and in such cases, assisted reproductive technologies (ART) are often required to achieve fertilization [1, 2]. In goats, sperm motility is positively correlated with fertility rates, but the molecular and metabolic mechanisms underlying variations in motility remain largely unclear [3–5]. In goats (Capra hircus), variations in sperm motility among individuals or ejaculates represent an important limiting factor for artificial insemination and breeding programs. Understanding the molecular basis of these variations is therefore essential for improving reproductive efficiency [6]. In the context of semen evaluation, sperm motility remains one of the most reliable indicators of male fertility in goats and other livestock species. Previous studies have demonstrated that total motility above 70%–75% is typically associated with high fertilization potential, whereas values below 65% correspond to reduced conception rates in artificial insemination (AI) programs [2, 7]. These thresholds are therefore widely used as the reference criteria in ruminant reproductive studies, providing a biologically meaningful framework for classifying semen samples in the present investigation.

Seminal plasma (SP), the fluid portion of semen, plays a crucial role in maintaining sperm viability and function. It originates from the testes, epididymis, and accessory sex glands and contains a complex mixture of proteins, amino acids, lipids, ions, and small-molecule metabolites [8, 9]. These components support sperm motility, protect against oxidative stress, and aid in capacitation and membrane stabilization [10].The composition of seminal plasma is known to vary according to multiple factors including breed, age, nutrition, and season, as well as ejaculate-to-ejaculate variability within the same individual [11].

These factors can confound metabolomic analyses by altering the biochemical environment of semen independently of fertility status. To minimize such variability, our study focused on a uniform group of healthy adult bucks maintained under standardized management and nutritional conditions.

Metabolomic profiling has recently emerged as a powerful approach to explore small-molecule changes that reflect physiological and functional states of sperm and seminal plasma [12, 13]. Compared with genomic or proteomic markers, metabolites directly represent biochemical activities and can provide real-time insights into sperm quality and functionality. In ruminants, seminal plasma metabolomics has been applied in cattle, sheep, boars, and buffalo to identify metabolites associated with fertility status, antioxidant capacity, and sperm membrane integrity [14–18]. Furthermore, in humans, NMR and mass spectrometry-based analyses of SP have revealed significant differences in metabolites such as citrate and glycerylphosphorylcholine between fertile and infertile men [19, 20]. While such interspecies studies provide valuable comparative insight, it is important to note that seminal plasma composition and metabolic regulation differ between species; therefore, extrapolations from other ruminants or humans should be interpreted cautiously when applied to goats.

To address these knowledge gaps within a goat-specific context, we conducted a comprehensive metabolomic analysis of seminal plasma linked to sperm motility. Regarding goat SP, our prior work in goats identified 1,857 differentially expressed metabolites in seminal plasma (SP) associated with sperm motility, using a combined proteomic and metabolomic approach in a single ion mode [5]. However, that study was limited by lower analytical depth and did not capture the full spectrum of SP metabolites. In the present study, we aimed to significantly expand on those findings by applying high-resolution untargeted LC-MS/MS in both positive and negative ion modes, thereby improving metabolite coverage. We also refined sample classification using CASA-derived motility parameters, increasing the biological resolution of our comparisons. This updated strategy allows us to generate a more complete SP metabolome map, identify novel candidate biomarkers, and better understand the biochemical mechanisms regulating sperm motility in goats. As the final products of biochemical processes, metabolites provide a direct reflection of physiological status and serve as valuable indicators of sperm health and function [21, 22].

By combining untargeted and targeted LC–MS analyses, this work sought to provide an expanded dataset of seminal plasma metabolites, to describe the biochemical features associated with sperm motility, and to highlight promising molecules for subsequent targeted validation. The findings are expected to advance understanding of metabolic determinants of sperm function in goats and contribute to the development of fertility-improving strategies within the framework of sustainable animal reproduction.

Materials and methods

Chemical and reagents

Unless otherwise mentioned, all chemicals, reagents, and kits used were purchased from Sigma-Aldrich (St. Louis, MO, United States). The AndroMed extender was purchased from Minitüb GmbH (Hauptstrasse 41, 84184 Tiefenbach, Germany).

Animals, semen collection and evaluation

The healthy adult bucks (n = 20) of the same breed (Yunshang Black goat) used in this study were bought from Yixingheng Livestock Technology Co., Ltd., China. They are aged between 2 and 3 years maintaining under identical nutritional and housing conditions at the farm of Yixingheng Livestock Technology Co., Ltd. All experimental procedures were approved by the Ethics Committee for Animal Use at Yunnan Animal Science and Veterinary Institute (Kunming City, Yunnan Province, China; approval number: 201909006).

To ensure robustness, potential confounding factors, such as breed, age, nutritional status, collection season, and ejaculate variability, were carefully controlled or documented. All bucks were maintained under identical management conditions, and semen collection was standardized to minimize physiological and environmental variability. The feed was composed of corn (29.5%), soybean meal (23%), calcium monophosphate (1.5%), premix (1%), NaCL (0.5%), sodium bicarbonate (0.5%), broad bean bran (19%), alfalfa Grass (10%), and corn silage (15%).

Each buck provided three ejaculates collected at one-week intervals using an artificial vagina pre-warmed to 42 °C during their breeding season. Ejaculates were immediately transferred to the laboratory for analysis within 20 min of collection. Only ejaculates with minimum sperm concentration ≥ 2.5 × 10⁹/mL and morphology ≥ 85% were used in this study. To minimize variability, motility classification was based on averaged values of three ejaculates per buck.

Assessment of sperm motility

Sperm motility was evaluated using a computer-assisted sperm analysis (CASA) system (SCA) software, Version 5.1, from Microptic, Barcelona). All sperm samples were diluted using Andromed^® to a concentration of 20 × 10⁶ sperm/mL. 10 µl sperm suspension was placed on a slide and covered with a cover-slip (18 × 18 mm). A previously heated (37 °C) Makler chamber was placed on the phase-contrast microscope (Nikon, ECLIPSE E200, Japan) with magnification of 100X. For each sample, more than three fields per drop were analyzed and a minimum of 500 sperm were evaluated. The following parameters were recorded: total motility (%), progressive motility (%), curvilinear velocity (VCL, µm/s), straight-line velocity (VSL), average path velocity (VAP), and amplitude of lateral head displacement (ALH). After the motility assessment of these 20 samples, 10 bucks with sperm motility less than 65% were acted as the low motility group (LM), and 10 bucks with sperm motility more than 75% were acted as the high motility group (HM).

Preparation of seminal plasma

After initial semen evaluation, samples were centrifuged at 800 × g for 10 min to remove sperm cells, followed by a second centrifugation at 10,000 × g for 10 min at 4 °C to ensure complete removal of cellular debris. The resulting seminal plasma was filtered through a 0.22 μm (Millipore), aliquoted and stored at − 80 °C until metabolomic analysis.

Seminal plasma preparation and metabolite extraction

Seminal plasma was isolated via double centrifugation (10,000 × g, 10 min, 4 °C) and filtration through a 0.22 μm membrane (Millipore). Samples were stored at -80 °C until metabolomic analysis. For extraction, 100 µL of thawed seminal plasma was mixed with 400 µL of precooled 50% methanol (v/v) to precipitate proteins and extract polar metabolites. The mixture was vortexed for 30 s and incubated at -20 °C for 12 h to enhance extraction efficiency. After incubation, the samples were centrifuged at 15,000 × g for 20 min at 4 °C. The resulting supernatants were transferred to 96-well plates and stored at -80 °C until analysis. No chemical derivatization or drying steps were applied. Quality control (QC) samples were prepared by pooling equal aliquots from all samples and were injected periodically throughout the LC-MS run to assess instrument stability and data reproducibility.

LC–MS instrumentation

For each sample, 100 µL of seminal plasma was mixed with 400 µL of 50% methanol (v/v) prechilled at − 20 °C. The mixture was vortexed for 30 s and incubated at -20 °C for 12 h to enhance extraction followed by centrifugation at 14,000 × g for 20 min at 4 °C. The supernatant was transferred to a new tube and filtered through a 0.22 μm membrane prior to LC–MS injection.

We acknowledge that this single-phase methanolic extraction preferentially captures polar and semi-polar metabolites (amino acids, carnitines, nucleotides, organic acids), while nonpolar lipids such as triglycerides and phospholipids are underrepresented. Consequently, subsequent data interpretation was restricted to the polar metabolome, and lipid-related findings were discussed with appropriate caution.

This extraction protocol was adapted from our previous work [5] and follows standard metabolomic workflows used in seminal plasma analysis [15, 16].

Untargeted metabolomic profiling was performed on a TripleTOF 5600 + high-resolution mass spectrometer (AB Sciex, USA) equipped with an ExionLC UHPLC system. Analyses were conducted in both positive and negative ionization modes.

Targeted verification of selected metabolites was carried out using a QTRAP 6500 + mass spectrometer operating in multiple reaction monitoring (MRM) mode. Authentic standards were used to confirm the identities of key discriminant metabolites where available. Retention times and MS/MS spectra were compared with internal libraries and reference databases (HMDB, METLIN, MassBank).

ESI-QTRAP-MS/MS

To ensure analytical reproducibility, pooled quality control (QC) samples were prepared by mixing equal aliquots from all study samples. QC samples were injected every ten runs to monitor instrument stability and drift. Relative standard deviations (RSD) of QC replicates were maintained below 15% for major metabolites.

Blank samples were analyzed periodically to monitor carryover, and a LOESS-based signal correction was applied to account for intensity drift. Missing values were imputed using the k-nearest neighbor (k-NN) algorithm. Data normalization was conducted using total area normalization prior to statistical analysis.

Data analysis of metabolomics

Raw LC-MS data were converted to mzML format using ProteoWizard and processed with XCMS for peak detection, alignment, and normalization. Metabolite identification was performed using in-house and public databases (Metlin, metDNA). Statistical analysis included both univariate (t-test) and multivariate methods (PCA, PLS-DA, OPLS-DA) implemented in R, as described in the original study [5].

Metabolite features were annotated according to the Metabolomics Standards Initiative (MSI) framework:

Level 1: confirmed by MS/MS and authentic standard;

Level 2: MS/MS match with public database;

Level 3: putatively characterized compound class;

Level 4: unknown feature.

The identification level for each metabolite is indicated in Supplementary Tables S1–S4.

Statistical analysis

Data processing and feature extraction were performed using XCMS (v3.0) and MetaboAnalyst 5.0. Multivariate analysis included principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Model validity was evaluated through 200-permutation testing and cross-validation ANOVA (CV-ANOVA) (p < 0.05).

Univariate analysis was performed using Student’s t-test with FDR correction (Benjamini–Hochberg method). Features with adjusted p < 0.05 and variable importance in projection (VIP) > 1.0 were considered discriminant. Effect sizes (Cohen’s d) and 95% confidence intervals were calculated for key metabolites.

Results

Sperm motility parameters

20 goats were divided into two groups based on their sperm motility. 10 goats with higher motility and 10 goats with lower motility. Sperm motility in the HM group was significantly higher than in the LM group (80.43% ± 5.79% vs. 60.37% ± 3.56%, P < 0.01).

Basic data acquirement and assessment

Goat SP metabolomic data were obtained using a non-targeted metabolomic strategy in both positive and negative ion modes. In the present study, the overlapping display analysis of the total ion chromatogram (TIC) of different QC samples detected by mass spectrometry confirmed data reliability. As shown in Fig. 1., the repeatability of detection in each sample was good, confirming the reliability of the acquired data.

Fig. 1.

Total ion chromatogram (TIC) overlaps of mixed samples analyzed by LC–MS. The TIC represents the summed ion intensities detected at each retention time. The X-axis shows the retention time (RT) of metabolite detection, and the Y-axis indicates the ion current intensity (CPS, counts per second). Fig. 1A Negative ionization mode. Fig. 1B Positive ionization mode. A total of 20 samples were analyzed. The quality control (QC) sample was prepared by pooling 10 µL from each sample. Three injections were run in both the positive and negative ion modes

Metabolites scanning in positive and negative ion modes

A total of 7,374 metabolite features were detected across all seminal plasma samples in both positive and negative ionization modes (4,302 in ESI + and 3,072 in ESI−). Data quality was verified by the tight clustering of QC samples in PCA plots, confirming high analytical reproducibility (RSD < 15%). The detailed information associated with these identified metabolites, including index, mass, retention time (RT), compounds, mass, formula, etc., were presented in Supplementary Table S1 and S2.

The identified metabolite ions were analyzed using the Partial Least Squares Discriminant Analysis (OPLS-DA). As shown in Fig. 2A and B., the separation distance between sample groups represented their separation or clustering. Regardless of whether in negative ion mode (Fig. 2A) or positive ion mode (Fig. 2B), the R²X, R²Y, and Q² values (> 0.9) confirmed significant population stratification, and the samples were clearly separated. Based on P-value of less than 0.005, the results of OPLS-DA model validation further demonstrated the reliability of these two models (Fig. 2C and D).

Fig. 2.

Orthogonal partial least squares discriminant analysis (OPLS-DA) of goat seminal plasma samples. Fig.2A and Fig.2B OPLS-DA score plots in the negative and positive ion modes, respectively. The X-axis shows T score [1], and the Y-axis shows Orthogonal T score [1]. (Fig.2C and Fig. 2D) Corresponding permutation tests validating the OPLS-DA models for the negative and positive ion modes, respectively. Samples labeled “G” represent high-motility semen, and those labeled “B” represent low-motility semen (10 samples in total)

Determination of differential expressed metabolites

Based on Variable Importance in Projection (VIP) scores, fold change (FC), and P-values, the differential expressed metabolites between high and low motility groups are presented in.

Figure 3. After further identification and quantification, 774 differential expressed metabolites were detected between the high motility and low motility groups in the negative ion mode (Fig. 4). Among these metabolites, 416 metabolites were up-regulated and 358 metabolites were down regulated in the low motility group, respectively. Meanwhile, 1269 differential expressed metabolites were identified in these two groups in the positive ion mode. Among these metabolites, 532 metabolites were more abundant in the low motility group. On the contrary, 737 metabolites were enriched in the high motility group (Fig. 4). The detailed information related to these identified differential expressed metabolites was presented in the Supplementary Table S3 and S4. To better understand the biological roles of the differentially expressed metabolites, we manually mapped the top statistically significant candidates (FDR < 0.05, VIP > 1) to their associated metabolic pathways (Table 1). Compounds classified as xenobiotics (e.g., clopamide, halofenozide, and related drug metabolites) were excluded from the final dataset after MS/MS spectral comparison indicated low-confidence matches inconsistent with endogenous origin.

Fig. 3.

Volcano plots showing differentially abundant metabolites between low- and high-motility groups. F3.A Negative ion mode. Fig. 3B Positive ion mode. Red dots represent significantly up-regulated metabolites (fold change ≥ 2), and green dots represent down-regulated metabolites (fold change ≤ 0.5) in low-motility samples compared to high-motility ones. Grey dots indicate metabolites with no significant change

Fig. 4.

Distribution of metabolite classes showing significant differences between high- and low-motility groups. Numbers of metabolites belonging to each class (peptides, fatty acids, carbohydrates, amino acids, nucleosides, enzymes, and vitamins) are shown for both ionization modes

Table 1.

Mapping of key differential metabolites (FDR < 0.05) to metabolic pathways and biological functions related to sperm motility

Metabolite	Metabolic Pathway	Ion Mode	Biological Role in Sperm Function	Direction (HM vs. LM)
Proline	Amino acid metabolism	Negative	Osmoprotectant; membrane stabilizer; antioxidant precursor	↓ in LM
Arginine	Arginine and proline metabolism	Positive	Precursor for nitric oxide; supports capacitation and acrosome reaction	↓ in LM
Methionine	Cysteine and methionine metabolism	Positive	Antioxidant via glutathione synthesis	↓ in LM
Tyrosine	Phenylalanine, tyrosine, and tryptophan biosynthesis	Positive	Involved in sperm capacitation and signal transduction	↓ in LM
Carnitine	Fatty acid β-oxidation	Positive	Mitochondrial energy production; antioxidant	↑ in HM
Phosphocreatine	Creatine metabolism	Positive	Energy buffering via ATP regeneration	↑ in HM
Choline	Glycerophospholipid metabolism	Positive	Membrane integrity and fluidity	↑ in HM
Phosphoethanolamine	Glycerophospholipid metabolism	Positive	Membrane precursor; sperm capacitation	↑ in HM
Riboflavin	Riboflavin metabolism	Positive	Redox cycling; antioxidant support	↑ in LM
α-Tocopherol acetate	Vitamin E metabolism	Positive	Antioxidant; protects membrane lipids from oxidation	↑ in LM
Malic acid	TCA cycle	Negative	Supports mitochondrial respiration and energy generation	↓ in LM
Succinic acid	TCA cycle	Negative	Energy metabolism; modulates ROS	↓ in LM
Creatine	Creatine metabolism	Positive	ATP buffering system for motility	↑ in HM
Astaxanthin	Carotenoid metabolism	Positive	Strong antioxidant; improves membrane function	↑ in LM

Bioinformatics analysis of differential expressed metabolites

The most enriched differential expressed metabolites between the high and the low motility were shown in Fig. 5. In the negative ion mode (Fig. 5.A), 10 metabolites, such as L-4-Hydroxy-2-oxoglutarate (N2138), Clopamide (N2914), Halofenozide (N2697), etc., were most abundant in the high motility group. However, another 10 metabolites, such as 2-Phenylglycine (N2309), 2,3,6-Trihydroxypyridine (N2290), Pindone (N1406), etc., were mostly enriched in the low motility group. In the positive ion mode (Fig. 5B), 10 metabolites, including CL(i-14:0/i-16:0/i-12:0/a-21:0) (P3923), Fenothiocarb sulfoxide (P3365), TG (i-21:0/19:0/i-15:0) (P1430), etc., were most abundant in the high motility group. By contrast, another 10 metabolites, such as Probenazole (P2011), (10Z,13Z)-Nonadecadienoyl-CoA (P3051), Ser Arg His (P2361), etc., were enriched in the low motility group.

Fig. 5.

Top differentially expressed metabolites between high-motility (HM) and low-motility (LM) goat seminal plasma samples. A Negative ion mode. B Positive ion mode. Bar plots display the top 10 up-regulated (red) and down-regulated (green) metabolites ranked by log₂ fold change (log₂FC). The Y-axis lists metabolite identities, and the X-axis shows log₂FC values. Metabolites were selected using the criteria: VIP > 1, fold change ≥ 2 or ≤ 0.5, and FDR < 0.05

In addition, results of cluster analysis of different metabolites between these two groups were presented in (Fig. 6) The results demonstrated that the clustering pattern of differential metabolites in the high motility group was opposite to that in the low motility group in both negative and positive ion modes (Fig. 6A and B).

Fig. 6.

Hierarchical clustering heatmaps of significantly different metabolites. The X-axis shows the sample IDs, and the Y-axis represents differential metabolites. Color intensity reflects relative abundance, with red indicating higher and green indicating lower expression levels. Fig. 6A Negative ion mode. Fig. 6B Positive ion mode.Samples “B” correspond to low-motility semen, and “G” to high-motility semen

In this study, KEGG annotation was applied to explore potential pathways associated with differentially expressed metabolites. However, no pathways met statistical significance after multiple testing correction, and the majority of nominal p-values were above 0.05. A full list of identified pathways is provided in the Supplementary Materials (Supplementary Table S5 and S6). Accordingly, our interpretations focused on individual metabolites with significant differential abundance (FDR < 0.05), as detailed in Supplementary Tables S3 and S4.

Discussion

This study provides a comprehensive overview of the seminal plasma metabolome in goats, emphasizing metabolite differences associated with sperm motility. By integrating untargeted and targeted LC–MS approaches, we identified several candidate metabolites that may play key roles in sperm energy metabolism, antioxidant protection, and membrane stability. Although these results are exploratory, they establish an important reference for future studies on metabolic determinants of male fertility in small ruminants.

Semen quality is a fundamental determinant of male fertility, and sperm motility remains one of the most predictive parameters of fertilization success, particularly in goats where artificial insemination (AI) is widely used [1, 23]. Motility not only correlates with fertility but also influences the sperm’s ability to traverse the cervix and penetrate the oocyte, making it a central target for semen evaluation [7, 24]. Seminal plasma (SP), a complex fluid secreted by the testes and accessory sex glands, plays a critical role in modulating sperm physiology. It contains proteins, ions, amino acids, and metabolites that influence sperm motility, capacitation, and membrane stability [8, 25]. The composition of SP has been shown to affect sperm performance in pigs, bulls, and humans, but its role in goats remains less understood.

This study used untargeted LC-MS/MS to analyze the SP metabolome from goats with high (HM) and low motility (LM) sperm, in both positive and negative ion modes. A total of 7,374 metabolites were identified, with 2043 showing differential abundance. These figures surpass those reported in bull, goat, and boar studies, reflecting the high sensitivity and breadth of our method [14, 15, 26]. We found several amino acids—such as proline, lysine, methionine, and leucine—downregulated in the LM group, while D-phenylalanine, threonine, and aspartate were elevated. These shifts align with previous findings linking amino acid profiles to sperm motility and fertility potential [16, 20]. Amino acids not only serve as energy substrates but also act as antioxidants, osmoprotectants, and signaling molecules [27]. For example, proline and glycine, help preserve membrane integrity during cryopreservation, while methionine supports glutathione synthesis, mitigating oxidative stress [28, 29]. Among the most compelling candidates identified were tryptophan, arginine, and tyrosine—amino acids previously linked to sperm function in mammals and aquatic species. Supplementation with tryptophan or threonine has been shown to improve motility in extenders, while arginine plays a key role in capacitation and acrosome reaction [30–32].

Notably, we observed elevated levels of mannose, fructose, α-tocopherol acetate, and astaxanthin in LM samples. These compounds are known for their antioxidant and energy-related properties. Fructose and mannose serve as energy sources, while α-tocopherol acetate and astaxanthin offer potent protection against lipid peroxidation [33]. Astaxanthin, in particular, has a stronger antioxidant capacity than tocopherol and has been shown to improve sperm viability during cryopreservation [34, 35]. Riboflavin (Vitamin B2) was elevated in the LM group. Although essential for redox cycling and glutathione regeneration, excess riboflavin may paradoxically promote oxidative stress under certain conditions [36, 37]. Conversely, glycerol 3-phosphate, succinic acid, and malic acid—organic acids involved in energy metabolism—were decreased in LM samples, suggesting reduced mitochondrial activity or altered metabolic flux. Malic and succinic acids are known to modulate reactive oxygen species (ROS) and support energy generation, making them promising candidates for further functional validation [38, 39].

In the positive ion mode, choline, phosphocreatine, phosphoethanolamine, and carnitine were significantly more abundant in high-motility (HM) samples. Although the current extraction protocol primarily captures polar and semi-polar metabolites, these molecules are known intermediates in membrane turnover and energy metabolism. The elevated choline and phosphoethanolamine levels may reflect enhanced phospholipid remodeling or membrane stability rather than direct quantification of lipid species. Likewise, carnitine and phosphocreatine support mitochondrial ATP production and energy buffering during sperm motility, consistent with their established roles in sperm bioenergetics [40–42].

Carnitine acts as a mitochondrial cofactor and antioxidant, and elevated levels in SP have been linked to improved sperm quality across species [43]. Interestingly, we also found higher levels of creatine in the HM group. As an energy reservoir, creatine supports ATP synthesis and could enhance sperm performance [44]. While exploratory pathway analysis was conducted, no pathways reached statistical significance after correction for multiple testing, and nominal p-values were generally high. As such, we have not drawn conclusions from the pathway-level results.

Instead, we emphasize the roles of individual metabolites that showed strong statistical significance (FDR < 0.05), many of which are biologically linked to sperm physiology. For instance, several amino acids—including proline, arginine, lysine, methionine, and tyrosine—were significantly more abundant in the high motility group (HM). These molecules are known to serve as energy substrates, redox regulators, and precursors for polyamines, all of which are essential for sperm capacitation, motility, and membrane integrity [27].

In addition, elevated levels of carnitine, phosphocreatine, and choline in HM samples support enhanced mitochondrial β-oxidation and ATP buffering—mechanisms critical for sustained sperm motility [43, 44]. Phosphoethanolamine also contributes to membrane lipid remodeling, which is essential for sperm viability and acrosome integrity [41].

Among antioxidant molecules, α-tocopherol acetate (Vitamin E), riboflavin (Vitamin B2), and astaxanthin were significantly different between groups. These compounds play known roles in protecting sperm against reactive oxygen species (ROS)-induced damage. Astaxanthin, in particular, has shown superior efficacy in maintaining sperm membrane integrity during cryopreservation [34, 35].

Interestingly, metabolites such as succinic acid and malic acid—key intermediates in mitochondrial respiration—were decreased in low motility (LM) samples, suggesting potential impairment in mitochondrial function. Lower levels of these metabolites could translate to diminished energy output and reduced sperm kinetic performance [45]. Collectively, these findings reinforce the multifactorial biochemical regulation of sperm motility, with significant contributions from amino acid metabolism, antioxidant defense, membrane stabilization, and mitochondrial energy production.

These individual metabolites and their associated pathways are summarized in Table 1, which outlines their biological functions in the context of sperm physiology. This targeted mapping shows that the differentially abundant compounds play key roles in energy metabolism (e.g., carnitine, phosphocreatine, creatine, malic acid), antioxidant defense (e.g., riboflavin, α-tocopherol acetate, astaxanthin), and membrane stability (e.g., choline, phosphoethanolamine)—all of which are fundamental to maintaining sperm motility and fertilizing capacity. Notably, metabolites such as proline and arginine have previously been linked to sperm cryotolerance and motility in both ruminants and fish species, supporting their potential as conserved biomarkers across taxa [28, 30]. Meanwhile, energy-related metabolites like carnitine and phosphocreatine reflect enhanced mitochondrial efficiency, which is essential for sustained flagellar motion. These findings offer mechanistic insight into the biochemical underpinnings of sperm quality and suggest practical avenues for fertility screening in AI programs.

Together, these findings provide novel insights into the metabolomic landscape of goat seminal plasma and its relationship with sperm motility. The identification of distinct metabolite patterns—including key amino acids, antioxidants, energy-related compounds, and membrane stabilizers—highlights the multifaceted biochemical regulation of sperm function. Notably, the integration of both positive and negative ionization modes allowed for the most comprehensive metabolomic profiling of goat SP to date. This work advances our understanding of fertility biomarkers and lays a foundation for the development of metabolite-based strategies to improve semen evaluation and AI outcomes in goat reproduction.

This study has several limitations. First, the sample size (n = 20) is relatively small, which limits statistical power and generalizability. Second, many of the enriched pathways were supported by only a few metabolite hits, leading to high FDR values—a known issue in untargeted metabolomics. Third, although all animals were of similar age, breed, and nutritional status and were maintained under uniform management conditions, potential influences of seasonality, extender composition, and ejaculate-to-ejaculate variability cannot be entirely excluded. In addition, animals were drawn from a single goat population, so inter-breed and environmental variability were not assessed. The use of a single-phase methanolic extraction limited detection of highly nonpolar lipids such as triglycerides and phospholipids, and interpretations of lipid-related metabolites should therefore be considered tentative. Finally, although several potential biomarkers were identified, targeted validation using MRM/PRM-based assays and functional studies (e.g., artificial insemination trials) are needed to confirm their predictive value for fertility outcomes.

Conclusion

This study provides an expanded and high-coverage metabolomic dataset of goat seminal plasma, generated in both positive and negative ionization modes. Rather than relying on pathway enrichment, which yielded no statistically significant results after FDR correction, the analysis focused on individual metabolites that differed consistently between high- and low-motility groups. Compounds such as proline, arginine, methionine, carnitine, phosphocreatine, riboflavin, and α-tocopherol acetate were associated with sperm energy metabolism, antioxidant protection, and membrane stability. These findings highlight promising biochemical candidates for further targeted validation but should be interpreted as exploratory given the modest sample size and methodological scope. Overall, this work establishes a foundational metabolomic resource for goat seminal plasma and supports ongoing efforts to develop metabolite-based indicators of semen quality and fertility potential in small ruminants.

Authors’ contributions

B.J: Formal analysis, Writing-original draft. A.L: Formal analysis, Writing & editing. J.L and C.L: Semen analysis and seminal plasma extraction. C. Li: Methodology, semen analysis. B.B: Formal analysis, software, Writing-paper & editing. G.W: Conceptualization, Writing-paper & editing. G.Q: Resources, Investigation, Supervision, Methodology, Conceptualization, Writing-review & editing, Funding acquisition.

Funding

This study was funded by Yunnan Province Basic Research Project (Grant No. 202301AS070005) and Yunnan International Joint Laboratory of Conservation and Innovative Utilization of Sheep Germplasm Resources (Grant No. 202403AP140017).

Data availability

The processed data are already available in the supplementary materials.

Declarations

Ethics approval and consent to participate

The experimental protocol of this study was approved by Yunnan Animal Science and Veterinary Institute (Kunming city, Yunnan province, China) (approval number: 201909006). During the study, all authors strictly complied with Regulations on the Administration of Laboratory Animals (Order No.2 of the State Science and Technology Commission of the People’s Republic of China, 1988) and Regulations on the administration of experimental animals of Yunnan Province (the Standing Committee of Yunnan Provincial People’s Congress 2007.10). The manuscript was prepared according to the requirements of the journal.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.