
==== Front
J Proteome Res
J Proteome Res
pr
jprobs
Journal of Proteome Research
1535-3893
1535-3907
American Chemical Society

39067049
10.1021/acs.jproteome.4c00060
Article
Proteomics and Metabolic Characteristics of Boar Seminal Plasma Extracellular Vesicles Reveal Biomarker Candidates Related to Sperm Motility
Zhang Yu
Ding Ning
Cao Jinkang
Zhang Jing
https://orcid.org/0000-0001-8247-1700
Liu Jianfeng
Zhang Chun
https://orcid.org/0000-0002-0648-2173
Jiang Li *
State Key Laboratory of Animal Biotech Breeding, College of Animal Science & Technology, China Agricultural University, Beijing 100193, P. R. China
* E-mail: lijiang@cau.edu.cn. Telephone/Fax: 8610-62732634.
27 07 2024
06 09 2024
23 9 37643779
04 02 2024
10 07 2024
27 06 2024
© 2024 The Authors. Published by American Chemical Society
2024
The Authors
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Although seminal plasma extracellular vesicles (SPEVs) play important roles in sperm function, little is known about their metabolite compositions and roles in sperm motility. Here, we performed metabolomics and proteomics analysis of boar SPEVs with high or low sperm motility to investigate specific biomarkers affecting sperm motility. In total, 140 proteins and 32 metabolites were obtained through differentially expressed analysis and weighted gene coexpression network analysis (WGCNA). Seven differentially expressed proteins (DEPs) (ADIRF, EPS8L1, PRCP, CD81, PTPRD, CSK, LOC100736569) and six differentially expressed metabolites (DEMs) (adenosine, beclomethasone, 1,2-benzenedicarboxylic acid, urea, 1-methyl-l-histidine, and palmitic acid) were also identified in WGCNA significant modules. Joint pathway analysis revealed that three DEPs (GART, ADCY7, and NTPCR) and two DEMs (urea and adenosine) were involved in purine metabolism. Our results suggested that there was significant correlation between proteins and metabolites, such as IL4I1 and urea (r = 0.86). Furthermore, we detected the expression level of GART, ADCY7, and CDC42 in sperm of two groups, which further verified the experimental results. This study revealed that several proteins and metabolites in SPEVs play important roles in sperm motility. Our results offered new insights into the complex mechanism of sperm motility and identified potential biomarkers for male reproductive diseases.

seminal plasma extracellular vesicles
proteomics
metabolomics
sperm motility
pyruvate
National Key Research and Development Program of China 10.13039/501100012166 2019YFE0106800 National Key Research and Development Program of China 10.13039/501100012166 2022YFD1302200 National Key Research and Development Program of China 10.13039/501100012166 2021YFD1200801 document-id-old-9pr4c00060
document-id-new-14pr4c00060
ccc-price
==== Body
pmcIntroduction

Many male reproductive disorders are associated with sperm quality, such as asthenozoospermia and oligozoospermia. It has been estimated that 4 million American men (aged 15–45) suffer from infertility.1 In addition, low sperm quality is associated with increased prostate cancer (PCa) risk.2 In the United States and Europe, PCa is the second most common cause of cancer-related mortality, and the incidence of PCa is on the rise worldwide.3 Sperm motility is one of the primary indicators of sperm quality. Pigs are an attractive biomedical model for studying many diseases in humans, because of their similar physiological metabolic features, anatomical structure, and disease pathogenesis.4 For example, pigs have been used as biological models to study diabetes, cardiovascular diseases, genetic diseases, tumors, etc. In addition, the pig model has the advantages of a uniform feeding mode and remitting confounding factors, such as smoking and drinking alcohol. In the modern breeding industry, a large number of pig semen quality phenotypes have been systematically recorded. Therefore, pigs may provide some important information as a good research model for studying male semen quality.

Extracellular vesicles (EVs) are small membranous vesicles that can be secreted by most cells.5,6 According to the latest instructions in the MISEV2018 guidelines, EVs can be classified as small EVs (sEVs, diameter <200 nm) and medium/large EVs (m/lEVs, diameter >200 nm).7 A recent study showed that there are fewer medium-sized EVs with a diameter of 200–800 nm than small EVs, and the number of large EVs with a diameter of ≥1 μm is the lowest.8 EVs participate in cellular communication between various types of cells by carrying nucleic acids, lipids, proteins, and metabolites.9 The function of EVs mainly depends on the source of cells and their contents.10,11 For example, cancer-derived EVs participate in intercellular communication through the transport of chemokines, small molecules, miRNAs, and growth factors.12 Seminal plasma contains a variety of EVs from different glands.13 These EVs come not only from the prostate but also from various tissues and organs, including the epididymis and other accessory gonads.14 It has been reported that seminal plasma extracellular vesicles (SPEVs) play an important role in sperm physiology and function, especially sperm motility.14−16 Accumulating evidence shows that it is particularly important for sperm to obtain the molecules and energy required for maturation from the interaction between sperm and SPEVs.17

In recent years, research on EV contents mainly focused on the transcriptome, especially microRNAs.18 EVs derived miRNAs have been proven to play important roles in many physiological processes. For example, miRNAs in EVs can promote angiogenesis19 and bone regeneration,20,21 inhibit cell apoptosis,22,23 and regulate neural development.24,25 Our previous study found that EV derived miR-222 can be transferred to sperm and regulate sperm survival and apoptosis by affecting mitochondrial function.26 In addition, we established a diagnostic model of PCa through some important EV derived miRNAs that are homologous to humans, such as hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-155-5p, and hsa-miR-378a-3p.27 These miRNAs exhibited good performance in discriminating patients with PCa from the controls. Moreover, we found that circ-CREBBP in SPEVs can regulate the expression of ssc-miR-143-3p and ssc-miR-10384 in sperm, as well as the expression of MCL1, CREB1, CREBBP, BAX, and CASP3, thereby affecting the apoptosis of sperm.28 Several studies have used proteomic techniques to detect proteins in SPEVs. Yang et al. identified 1,474 proteins in human seminal plasma exosomes, which were mainly involved in energy and metabolic pathways.29 In addition, Nixon et al. identified 1,640 proteins in mice epididymosomes, some of which can be delivered into sperm head.30 However, little research has been done on the metabolome of SPEVs, and its impact on sperm function is still unclear.

Metabolomics, as an emerging research field, has great potential in identifying specific biomarkers and understanding the mechanisms of pathological process. EVs are considered to be a new mode of intercellular communication in metabolic regulation. Previous studies showed that metabolites in extracellular vesicles can be used as disease markers. For example, a set of four biomarker candidates (phenylalanine, leucine, phosphatidylcholine, and sphingophospholipid) were identified for the diagnosis of tuberculosis and malignance through metabolomic and lipidomic studies of pleural effusion EVs.31 A recent study on urinary EVs of healthy people and PCa patients revealed that the levels of dehydroepiandrosterone sulfate in urinary EVs is a potential biomarker of prostate cancer.32

To date, few efforts have been made to explore coordinated disease characteristics in combination with SPEVs proteomic and metabolomic data. The purpose of this study was to identify key proteins, metabolites, and regulatory pathways that contribute to sperm motility and to understand the mechanisms that regulate sperm quality. Here, we compared the SPEVs obtained from boars with high and low sperm motility and conducted an integrated analysis of metabolomic and proteomic data. We identified a number of important proteins, metabolites, and pathways related to sperm motility. In addition, we explored the relationship between the key proteins and metabolites. Finally, we investigated the expression of important proteins in sperm and the interactions between these proteins. We hope that these findings can provide new insights into the molecular mechanisms underlying sperm motility and important information for the prediction and treatment of diseases related to male semen quality.

Materials and Methods

Animals and Semen Samples

The sperm motilities of 230 Yorkshire boars from a national boar station were measured by using a computer-assisted sperm analysis (CASA) system (IVOS II, France). The semen of these boars was collected one or two times a week, and the sperm motility from each boar was tested. Before collecting the experimental samples, the sperm motility phenotypic data of these boars were collected for over two months. At least eight consecutive semen parameter records were evaluated for each individual. The boars with consistently good or poor semen quality were selected as the experimental subjects. Finally, 12 Yorkshire boars with extremely high (H) or low (L) sperm motility were selected, with six individuals in each group. One ejaculate semen sample for each selected boar was collected by a gloved hand method. The total sperm motility and fast forward motility were measured using the CASA system (IVOS II, Hamilton Thorne, USA). All individuals were sexually mature, with ages between 14 and 36 months.

Analysis of Sperm Motility

One ejaculation from each boar was collected by using the gloved-hand method. Professionals took the sperm-rich fractions of ejaculates from each boar. We added 950 μL of preheated diluent to 50 μL of fresh semen and mixed gently. After incubation at 37 °C for 5 min, 7 μL of sperm suspension was placed on a prewarmed glass slide and covered with a glass coverslip. The glass slides were examined with a bright field under an optical microscope at a total magnification of 200×. The total sperm motility and fast forward motility were evaluated under five different microscopic fields for each sample using the CASA system.

SPEV Extraction and Particle Size Detection

SPEVs were isolated by ultracentrifugation as described in our previous studies.26 Briefly, 35 mL of semen plasma from each boar was put into a tube and centrifuged at 10,000g and 4 °C for 30 min to remove cellular debris (Centrifuge 5810 R, eppendorf, Germany). Then, the supernatant was centrifuged at 12,000g and 4 °C for 1 h (Centrifuge 5810 R, eppendorf, Germany). Next, the supernatant was transferred to an ultracentrifugation tube and centrifuged at 120,000g and 4 °C for 1.5 h (Optima XPN-100, Beckman, USA). The sediments were resuspended in DPBS (Gibco, USA) and again centrifuged at 120,000g and 4 °C for 1.5 h. Finally, the sediments were resuspended in 2 mL of DPBS and purified through 0.22-μm filters (Millipore, USA). The sizes of SPEVs were analyzed by a Zeta View PMX 110 analyzer (Particle Metrix, Meerbusch, Germany) which was equipped with a 405 nm laser. A 1 min video shot was used to analyze particle motion using Nanoparticle Tracking Analysis (NTA) software (ZetaView 8.02.28). Furthermore, microscopic images of SPEVs were obtained through transmission electron microscopy (HT770, Tokyo, Japan).

Protein Extraction, Trypsin Digestion, and LC-MS/MS Analysis

SPEVs were lysed with 8 M urea and 100 mM Tris-HCl (pH 8) buffer, followed by 1 min of sonication (3-s on and 3-s off, amplitude 25%). The protein concentration was determined using a Bradford protein assay Kit (Beyotime, Shanghai, China). The SPEV protein samples were reduced and alkylated with dithiothreitol (DTT) and iodoacetamide (IAA) and digested with trypsin using the Filter Aided Sample Preparation (FASP) method.33

LC-MS/MS analysis was performed on an Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific). Samples were separated using Nano high performance liquid chromatography (HPLC) Ultimate 3000. The mobile phases A and B were 0.1% formic acid in water and 80% acetonitrile with 0.1% formic acid in water, respectively. The column was equilibrated with 95% buffer A. The samples were loaded from an autosampler onto a C18 trap column (Bangfei Bioscience, 3 μm, 0.10 × 20 mm) and eluted onto an analytical C18 column (Bangfei Bioscience, 1.9 μm, 0.15 × 120 mm) at a flow rate of 600 nL/min. Proteins were identified by mass spectrometry analysis using a Q Exactive HF (Thermo Fisher Scientific, USA) coupled to a Nano HPLC. MS data was obtained using a data-dependent top 10 method dynamically choosing the most abundant precursor ions from the survey scan 300–1400 m/z for HCD fragmentation. MS1 scans were acquired at a resolution of 120,000 with an AGC target of 500,000 and a maxIT of 100 ms. MS2 scans were acquired at an isolation width of 1.6 m/z. The raw data of proteomics was uploaded to the ProteomeXchange Web site, with the identifier PXD050238.

Protein Identification and Quantitation

Proteome Discover 2.4.1.15 software (Thermo Fisher Scientific, Waltham, MA, USA) was employed for data analysis. Peptide identification was performed with the SEQUST search engine using pig proteome databases, which was downloaded from the Uniprot Web site (https://www.uniprot.org). The database search parameters are as follows: Trypsin was selected as the proteolytic enzyme, and two max missed cleavage sites were allowed. Carbamidomethyl (C) was set as a fixed modification, and oxidation (M) and N-terminal acetylation (Protein N-term) were set as the variable modifications. The peptide mass tolerance was set to 15 ppm, and 0.6 Da was used for fragment mass tolerance. The false discovery rates (FDRs) of the peptide-spectrum matches and proteins were set to less than 1%.

Metabolite Extraction and Untargeted Metabolomics Analysis

First, SPEVs were thawed slowly at 4 °C, and then, 200 μL samples were vortexed in 800 mL of a cold solution of methanol/acetonitrile/water (2:2:1, v/v). After sonication for 30 min on ice, the mixture was stored at −20 °C for 10 min to precipitate proteins. Next, the mixture was centrifuged for 20 min (14,000g, 4 °C), and the supernatant was dried through a vacuum drying system. Finally, the dried metabolites were dissolved and vortexed in 100 μL acetonitrile/water (1:1, v/v) and then centrifuged (14,000g, 4 °C) for 15 min. The supernatant was used for analysis.

Ultrahigh performance liquid chromatography (UHPLC) (Agilent, CA, USA) coupled with quadrupole time-of-flight mass spectrometry (Triple TOF 6600) (AB Sciex, Concord, Canada) was used for untargeted metabolomics analysis. In both ESI (Electron Spray Ionization) positive and negative modes, the mobile phase contained A, 25 mM ammonium acetate and 25 mM ammonium hydroxide in water, and B, acetonitrile. The gradient for A was 100% and for B was 95% (0 to 0.5 min), 95% to 65% (0.5 to 7 min), 65% to 40% (7 to 8 min), 40% (8 to 9 min), 40% to 95% (9 to 9.1 min), and 95% (9.1 to 12 min). After UHPLC analysis, the samples were analyzed under positive and negative ion modes, respectively. The conditions were as follows: ion source gas (GS1), 60 psi; Ion source gas 2 (GS2), 60 psi; curtain gas (CUR), 30 psi; source temperature, 600 °C; Ion Spray Voltage Floating (ISVF), ± 5500 V; TOF MS scan m/z range, 60 to 1000 Da; product ion scan m/z range, 25 to 1000 Da; collision energy, 35 ± 15 eV. The raw metabolomics data was uploaded to the Metabolights Web site with the identifier MTBLS9652.

Quantitative Analysis of Metabolites

The raw MS data were converted to MzXML files using ProteoWizard MSConvert before importing into XCMS software.34 For peak picking, the following parameters were used: centWave m/z = 25 ppm, peakwidth = c (10, 60), and prefilter = c (10, 100). For peak grouping, the parameters bw = 5, mzwid = 0.025, and minfrac = 0.5 were used. Collection of Algorithms of MEtabolite pRofile Annotation (CAMERA) was used for the annotation of isotopes and adducts. Among the extracted ion features, only variables with more than 50% of the nonzero measurement values, at least in one group, were kept. The compound identification of metabolites was carried out by comparing of the accuracy m/z value (<25 ppm) and MS/MS spectra with an in-house database established using available authentic standards.

Identification of Differentially Expressed Proteins and Metabolites

Proteomics data analysis was conducted using R software. The differentially expressed proteins (DEPs) included two parts: (i) When a protein was expressed in both groups and had quantitative information in more than half of the samples, it will be retained. The missing values were imputed using K-Nearest Neighbor (KNN) imputation function.35 A fold change (FC) > 1.5 or < 0.667 and P value < 0.05 were set as the thresholds of significantly differentially expressed proteins between the H and the L group; (ii) proteins identified in only one group were considered specifically expressed proteins. Both of them were deemed as DEPs. The metabolomics data were submitted to SIMCA software36 for orthogonal partial least-squares discriminant analysis (OPLS-DA). The OPLS-DA model was validated through 200 permutation tests to avoid overfitting. The differentially expressed metabolites were filtered according to the variable importance in the projection (VIP, VIP > 1) and the significance of the changes between the two groups (P value < 0.1).37

Weighted Coexpression Network Analysis

The coexpression network analysis was performed using weighted gene coexpression network analysis (WGCNA) implemented in the R software. For proteomic analysis, missing values were first filtered, and then, the original expression values of proteins were transformed by a log2 function as the input file of WGCNA. A threshold power of 14, R = 0.818, and a minimum module size of 5 were chosen for gene analysis.38 For metabolomics analysis, the original expression values of 181 metabolites annotated in the KEGG database were converted using the log2 function as the input file of WGCNA. A threshold power of 9, R = 0.839, and a minimum module size of 5 were set for metabolites analysis.38

Function Analysis of Proteome and Metabolome

Gene Ontology (GO) and pathway enrichment analysis were carried out through a clusterProfiler package in R software and KOBAS (http://bioinfo.org/kobas). PPI (protein–protein interaction network) analysis was performed using the STRING Web site (https://cn.string-db.org/) with default parameters. Metabolic pathway enrichment analysis was carried out through MetaboAnalyst (https://www.metaboanalyst.ca/).39 Furthermore, joint pathway analysis was performed using Metaboanalyst (https://www.metaboanalyst.ca/). The significance of metabolic pathways was determined by the hypergeometric test’s P-value (P < 0.05).39 Pearson correlation was calculated to analyze the relationships between key proteins and important metabolites.

Western Blot Analysis

SPEVs were lysed with RIPA buffer (Solarbio, Beijing, China) containing 1% protease inhibitor for 30 min. The protein samples (15 μg) were separated by SDS-PAGE and then transferred to PVDF membranes (Millipore, USA). Then, the membranes were blocked with 5% (w/v) skim milk for 2 h, washed 5 times with TBST, and incubated with TSG101, Alix, CD9 and Calnexin antibodies. The membranes were incubated with a secondary antibody and detected with an enhanced chemiluminescence (ECL) system (Beyotime, Shanghai, China).

Sperm was lysed in RIPA buffer A (Solarbio, Beijing, China) containing 1% protease inhibitor on ice for 30 min, and the proteins were quantified with a BCA assay kit (Beyotime, Shanghai, China). The protein samples were loaded into SDS-PAGE gels and then transferred onto 0.45 μm PVDF membranes (Millipore, USA). The membranes were blocked with 5% skim milk at room temperature for 2 h and incubated with primary antibodies at 4 °C overnight, followed by incubation with the HRP-conjugated secondary antibodies at room temperature for 2 h. Finally, the membranes were visualized using a Tanon 5200 (Tanon, Shanghai, China) with an Enhanced Chemiluminescence (ECL) Detection kit (Beyotime, Shanghai, China). The information on antibodies used in the experiments was provided in Table S1.

Co-IP Analysis

We conducted coimmunoprecipitation experiments using the Thermo Scientific Pierce Classic Magnetic IP/Co-IP Kit (Thermo Fisher Scientific, Waltham, MA, USA). Briefly, we added 2.5 mL of IP lysis buffer to approximately 1 mL of sperm sample, lysed it for 5 min, and then centrifuged it at 13,000g for 10 min at 4 °C to collect the supernatant. The protein concentration of the supernatant was quantified using a BCA assay kit (Beyotime, China). Subsequently, 5 μg of each antibody (IgG, CDC42 and GART) was added to supernatant samples with a total protein content of 800 μg; it was diluted to 500 μL with IP lysis buffer and incubated at room temperature for 90 min. The protein–antibody complexes were incubated with protein A and G magnetic beads for 1 h at room temperature. Then, the magnetic beads were collected using a magnetic rack and washed two times with 500 μL of IP lysis buffer. Next, 100 μL of elution buffer was added to the tube, and it was incubated for 10 min at room temperature. Then, 10 μL of neutralization buffer was added into the tube. Finally, 27.5 μL of 5× SDS-PAGE electrophoresis loading buffer was added to the tube, and it was mixed gently for subsequent Western blot detection. The information on antibodies used in the experiments was provided in Table S1.

Pyruvate Content Detection

First, we quantified the protein concentration of extracellular vesicles with a BCA assay kit (P0010, Beyotime, China). Then, the intracellular pyruvate (PA) content of samples was measured using a pyruvate assay kit (BC2205, Solarbio) according to the manufacturer’s protocol. We conducted four repeated measurements on each sample.

Results

Characterization of SPEVs

We performed proteomic and metabolic studies on SPEVs of adult boars with different sperm motility. Phenotypic analysis showed that there were significant differences in the total sperm motility and fast forward motility between the two groups (P value < 0.001) (Figure 1A). The average total sperm motility of the H group was 98.48%, while that of the L group was 57.52%, and the average fast forward motility of the H group was 75.97%, while that of the L group was 27.05% (Table S2). SPEVs were isolated from the two groups of boars via the ultracentrifugation method. The results of transmission electron microscopy (TEM) showed that most SPEVs had the bilayer structure and saucer shape (Figure 1B). In addition, the sizes of most SPEVs were around 100 nm in diameter. The particle size distribution peak of SPEVs in the H group was 107.1 nm, and the concentration was 5.5 × 1012 particles/mL. Similarly, the particle size distribution peak of SPEVs in the L group was 106 nm, and the concentration was 5.2 × 1012 particles/mL (Figure 1C). Western blotting analysis showed that EV markers (TSG101, Alix, and CD9) were detected in SPEVs. On the contrary, the negative marker of EVs (Calnexin) was not present in the SPEVs (Figure 1D and Figure S1A–D).

Figure 1 Characterization of SPEVs isolated from boars. (A) The difference of total sperm motility (%) and fast forward motility (%) between the high (H) and low (L) sperm motility group. The data are presented as the mean ± SD. ***: P < 0.001 (Student’s t-test), n = 6 for each group. (B) TEM image of SPEVs. Scale bars: 200 nm. (C) NTA results showed that EVs derived from seminal plasma were approximately 50–200 nm in diameter. (D) Western blotting results of the EV markers TSG101, Alix, and CD9 and the negative marker Calnexin.

Identification of Key Proteins

A total of 19,101 peptides were identified, and 2576 unique proteins were obtained. The results of protein identification and quantitative assessment were stable and reliable (Figure S2). Functional analysis results showed that most of the identified protein domain was the RAB family, followed by the RAS family (Figure 2A). Among these proteins, 2476 proteins were identified in both groups (Figure 2B).

Figure 2 Key proteins related to sperm motility in SPEVs. (A) Protein domain types of all identified proteins. (B) The number of identified proteins in the H and L groups. (C) Heatmap of differentially expressed proteins between the two groups. Red represents upregulated proteins and blue represents downregulated proteins. (D) Volcano plot of differentially expressed proteins. Red represents upregulated proteins in the L group, and blue represents upregulated proteins in the H group. (E) Weighted gene correlation network analysis (WGCNA) of SPEV proteins. The black modules exhibited the highest correlation with sperm motility. (F) Pie chart of key proteins identified in DEPs and WGCNA results.

In total, 51 DEPs were identified, of which 34 were upregulated and 17 were downregulated in the L group (Table S3). The protein abundance data of DEPs showed that the clustering of the two groups was obviously separated (Figure 2C). In addition, LMNA, TMEM52, IGSF8, HSPD1, and CD320 were the top five significantly downregulated proteins in the L group, while LOC100736569 (membrane cofactor protein-like), SHANK2, ARHGAP28, CD81, and BCHE were the top five significantly upregulated in the L group (Figure 2D and Table 1). Furthermore, 57 specifically expressed proteins were identified in the L group, and 9 specifically expressed proteins were identified in the H group, respectively (Table S3).

Table 1 Top 10 Differentially Expressed Proteins between the High (H) and Low (L) Sperm Motility Groups

Description	Gene Name	P Value	log2(L/H)	Regulate	
Prelamin-A/C	LMNA	0.05	–2.83	down	
Transmembrane protein 52	TMEM52	0.04	–2.22	down	
Immunoglobulin superfamily member 8	IGSF8	0.02	–2.03	down	
Uncharacterized protein	HSPD1	0.03	–1.73	down	
CD320 molecule	CD320	0.04	–1.59	down	
Carboxylic ester hydrolase (Fragment)	BCHE	0.04	2.31	up	
Tetraspanin	CD81	0.05	2.45	up	
Rho GTPase activating protein 28	ARHGAP28	0.02	2.89	up	
SH3 and multiple ankyrin repeat domains 2	SHANK2	0.03	2.93	up	
Uncharacterized protein	LOC100736569	0.03	3.70	up	

Moreover, we performed weighted gene coexpression network analysis (WGCNA) of proteomics data and identified 13 coexpressed modules (Figure 2E). Among them, three modules (including 30 proteins) were significantly associated with sperm motility (P value <0.05) (Table S4). Interestingly, the black module was the most significantly positive associated with low sperm motility (r = 0.7, P value = 0.01), which included 13 proteins (ADAM30, ADAM32, LOC110258345, ADAM29, FCN1, MYO5B, SPACA1, ADI1, ADIRF, EPS8L1, PRMT5, CDCP1, and PRCP). In addition, 7 proteins overlapped between DEPs and proteins in the significant modules, including ADIRF, EPS8L1, PRCP, CD81, PTPRD, CSK, and LOC100736569 (Figure 2F). Furthermore, CD81, and LOC100736569 were among the top five proteins with significant differential expression between the two groups.

Enrichment Analysis and PPI of Key Proteins

The DEPs and the proteins contained in three significant modules were considered to be key proteins. A total of 140 key proteins were identified. KEGG analysis results revealed that these genes enriched in the metabolic pathways were the most abundant, including NTPCR, ADI1, ADCY7, PLD1, PPT1, MAT1A, GRHPR, PLCD1, IL4I1, GART, etc. (Figure 3A and Table S5). The most significant pathway was the Rap1 signaling pathway, which contains some important proteins, such as CDC42, ADCY7, and MAPK14 (Figure 3A and Table S5). In addition, nine significant GO terms were enriched, involving 15 important proteins (Figure 3B and Table S5). Twelve out of them were upregulated in the L group, such as CDC42, CRKL, and TACSTD2. It is worth noting that MAPK14 is the only protein significantly downregulated in the L group. Interestingly, we found some significant GO terms related to immunity, such as “regulation of immune response”, “activation of immune response”, and “immune response-regulating signaling pathway”.

Figure 3 Functional enrichment analysis of key proteins in SPEVs. (A) A total of 27 KEGG pathways enriched with the key proteins (FDR < 0.05). (B) Nine significant GO terms were enriched for key proteins. Red represents upregulated proteins in the L group, and blue represents downregulated proteins in the L group. (C) PPI analysis of key proteins. CDC42 located at the center of the network can interact with several important metabolic pathway proteins. (D) Co-IP assays were used to detect the interaction between CDC42 and GART.

To explore the relationship between these key proteins, we conducted protein–protein interaction (PPI) analysis on 53 proteins that are included in significant GO terms and pathways. The results showed that CDC42 protein is located at the center of the PPI network (Figure 3C). We found that some important proteins directly or indirectly interact with CDC42 and participate in metabolic pathways. For example, GART can directly interact with CDC42. In addition, ADCY7 and NTPCR proteins are involved in Purine metabolism and can indirectly interact with CDC42. Besides, GRHPR takes part in pyruvate metabolism, which directly interacts with GART. Furthermore, IL4I1, ADI1, and MAT1A interact with each other and participate in cysteine and methionine metabolism (Figure 3C). Moreover, we performed coimmunoprecipitation (Co-IP) experiments to verify the interaction between CDC42 and GART. As shown in Figure 3D, GART was present in the CDC42 precipitates. Similarly, CDC42 was present in the GART precipitates. The results suggested that there is an interaction between these two proteins.

Metabolomic Profiling of SPEVs

We further examined the metabolic profiles of SPEVs from the two groups. A total of 364 metabolites (253 metabolites in ESI+ and 111 metabolites in ESI−) were identified by metabolomics analyses. The distribution of identified metabolites was consistent between the H and L groups. These metabolites mainly consist of lipids and lipid-like molecules, organic acids and derivatives, and benzenoids, accounting for 33.52%, 18.13%, and 12.63%, respectively (Figure 4A). Among them, 181 metabolites can be annotated in the KEGG database (Figure 4B). In the ESI+ mode, the expression of Glycerophosphocholine was the highest, while in the ESI– mode, cis,cis-muconic acid, was the highest (Table 2).

Figure 4 Overview of metabolites in SPEVs. (A) Proportion of total level metabolites in all the samples. (B) Pie chart of metabolite annotation in KEGG.

Table 2 Top 10 Most Abundant Metabolites in ESI+ and ESI– Modea

ID	Name	KEGG	Subclass	Mode	
M280T391	Glycerophosphocholine	C00670	Lipids and lipid-like molecules	ESI+	
M204T309	Acetylcarnitine	C02571	-	ESI+	
M118T277_2	Betaine	C00719	Organic acids and derivatives	ESI+	
M147T34	2-tert-Butyl-p-quinone	-	Organic oxygen compounds	ESI+	
M376T34	Tuberostemonine	-	Alkaloids and derivatives	ESI+	
M393T34	Chenodeoxycholate	C02528	Lipids and lipid-like molecules	ESI+	
M288T44_2	Heptadecasphinganine	-	Organic nitrogen compounds	ESI+	
M149T32_2	1,2-Benzenedicarboxylic acid	C01606	Benzenoids	ESI+	
M198T39_2	Dibenzylamine	-	Benzenoids	ESI+	
M515T31	Didodecyl 3,3′-thiodipropionate	-	Organic acids and derivatives	ESI+	
M141T334	cis,cis-Muconic acid	C02480	Lipids and lipid-like molecules	ESI–	
M265T29	Zinniol	C10840	Benzenoids	ESI–	
M255T52_2	Palmitic acid	C00249	Lipids and lipid-like molecules	ESI–	
M339T30	Gly-His-Lys	-	Organic acids and derivatives	ESI–	
M325T31_2	Hydroquinidine	C10696	-	ESI–	
M279T51	Linoleic acid	C01595	Lipids and lipid-like molecules	ESI–	
M374T40	N-Arachidonoyl-l-alanine	-	Organic acids and derivatives	ESI–	
M311T31	Thymol-beta-d-glucoside	-	Lipids and lipid-like molecules	ESI–	
M265T45	(e)-5-(4-Methoxy-5-methyl-6-oxopyran-2-yl)-3-methylhex-4-enoic acid	-	Lipids and lipid-like molecules	ESI–	
M293T28	Cinchonine	C06528	-	ESI–	
a Note: The ranking is in descending order of average expression.

Identification of Key Metabolites and Bioinformatics Analysis

To evaluate the differences in SPEVs metabolic profiles, we compared the metabolic characteristics of SPEVs between the H and L groups. The OPLS-DA score plot displays a clear demarcation between the H and L groups (Figure 5A). The permutation test results indicated that the establishment for the OPLS-DA model is reliable without overfitting (Figure 5B). A total of 18 differentially abundant metabolites were obtained, of which 13 metabolites were from the ESI+ mode and 5 metabolites were from the ESI– mode (Table S6). Among them, 12 metabolites can be annotated in the KEGG database (Figure 6A and Table S6). In addition, the contents of four metabolites (Beclomethasone, Carnitine, Adenosine, 3,4-dihydroxyhydrocinnamic acid) were upregulated in the H group, while eight metabolites (Creatinine, 1-methyl-l-histidine, 1,2-benzenedicarboxylic acid, ε-caprolactam, 2,2,6,6-tetramethyl-4 piperidone, Urea, Palmitic acid, Propiolic acid) were downregulated in the H group. Compared with the H group, adenosine was downregulated the most in the L group, and creatinine was upregulated the most in the L group (Table 3).

Figure 5 Orthogonal partial least-squares-discriminant analysis (OPLS-DA) for metabolites in SPEVs. (A) OPLS-DA for metabolites obtained after 7-fold cross-validation (positive mode on the left, negative mode on the right). Yellow spots denote the H group samples, and purple spots denote the L group samples. (B) Permutation test (200 times) showed good quality of OPLS-DA (positive mode on the left, negative mode on the right).

Table 3 Differentially Expressed Metabolites Annotated by KEGG

KEGG ID	Name	Subclass	VIP	P Value	log2(L/H)	Mode	
C00791	Creatinine	Organic acids and derivatives	5.51	0.05	1.82	ESI+	
C01152	1-Methyl-l-histidine	Organic acids and derivatives	3.48	0.06	0.43	ESI+	
C06842	Beclomethasone	Lipids and lipid-like molecules	3.12	0.00	–0.40	ESI+	
C01606	1,2-Benzenedicarboxylic acid	Benzenoids	2.78	0.04	0.12	ESI+	
C00318	Carnitine	Organic nitrogen compounds	2.61	0.09	–1.72	ESI+	
C00212	Adenosine	-	1.74	0.03	–2.31	ESI+	
C06593	ε-Caprolactam	Organoheterocyclic compounds	1.30	0.06	0.71	ESI+	
C11037	2,2,6,6-Tetramethyl-4 piperidone	Organoheterocyclic compounds	1.17	0.07	0.53	ESI+	
C00086	Urea	Organic acids and derivatives	1.04	0.09	0.47	ESI+	
C00249	Palmitic acid	Lipids and lipid-like molecules	8.11	0.02	0.88	ESI–	
C00804	Propiolic acid	Organic acids and derivatives	1.88	0.06	0.70	ESI–	
C10447	3,4-Dihydroxyhydrocinnamic acid	-	1.50	0.01	–0.33	ESI–	

Figure 6 Key metabolites related to sperm motility in SPEVs. (A) Scatter plot of differentially expressed metabolites. Red represents upregulated metabolites in the L group, and blue represents upregulated metabolites in the H group (the upper part represents positive mode, the lower part represents negative mode). (B) Weighted gene correlation network analysis (WGCNA) of SPEV metabolites. (C) Pie chart of key metabolites identified in DEM and WGCNA results. (D) Heatmap of key metabolites. Red represents upregulated metabolites and blue represents downregulated metabolites (* represents differentially expressed metabolites). (E) Metabolic pathway enrichment analysis of key metabolites.

In addition, a total of 181 KEGG annotated metabolites were used for WGCNA. The results showed that the blue and green modules were significantly correlated with sperm motility (P value < 0.05), including 26 metabolites (Figure 6B and Table S7). Furthermore, six (Adenosine, Beclomethasone, 1,2-benzenedicarboxylic acid, Urea, 1-methyl-l-histidine, and Palmitic acid) of them are differentially abundant metabolites (Figure 6C). We integrated differentially expressed metabolites (DEMs) and metabolites in the significant modules identified by WGCNA and identified 32 key metabolites (Figure 6D). Among them, 18 metabolites were upregulated in the L group. The pathway analysis revealed that seven significant metabolic pathways (P value <0.05), such as alanine, aspartate, and glutamate metabolism, tyrosine metabolism, arginine biosynthesis, citrate cycle, and pyruvate metabolism pathways, were enriched (Figure 6E and Table S8).

Joint Analysis of Proteomic and Metabolomic Data

Joint pathway analysis was conducted on 140 key proteins and 32 key metabolites. As a result, eight metabolic pathways were significantly enriched (P value < 0.05), involving 7 proteins and 7 metabolites (Figure 7A and Table 4). The most significant pathway is alanine, aspartate, and glutamate metabolism. In addition, we found that the purine metabolism pathway involves three DEPs (GART, ADCY7, and NTPCR) and two DEMs (Urea and Adenosine). Furthermore, fumarate, pyruvate, and IL4I1 were involved in many metabolic pathways, such as alanine, aspartate, and glutamate metabolism, pyruvate metabolism, and tyrosine metabolism, indicating their importance in sperm function.

Table 4 Joint Pathway Analysis of Key Proteins and Metabolites

Pathway	Matched_features	P Value	
Alanine, aspartate, and glutamate metabolism	GABA; Fumarate; Pyruvate; IL4I1	0.01	
Phenylalanine, tyrosine, and tryptophan biosynthesis	PhenylPyruvate; IL4I1	0.01	
Cysteine and methionine metabolism	Pyruvate; ADI1; MAT1A; IL4I1	0.01	
Pyruvate metabolism	Pyruvate; Fumarate; GRHPR	0.02	
Tyrosine metabolism	Tyramine; Fumarate; Pyruvate; IL4I1	0.02	
Phenylalanine metabolism	PhenylPyruvate; IL4I1	0.03	
Arginine biosynthesis	Urea; Fumarate	0.04	
Purine metabolism	Adenosine; Urea; GART; ADCY7; NTPCR	0.05	

Figure 7 Joint analysis of proteomic and metabolomic data. (A) Joint pathway analysis for key proteins and key metabolites. Eight metabolic pathways were significantly enriched (P value < 0.05). (B) Pearson correlation analysis of key proteins and key metabolites. (C) Key proteins and metabolites in metabolite pathways regulating sperm motility. Red represents upregulated expression in the L group, while green represents downregulated expression in the L group. * indicates significant differential expression between the two groups.

To explore the relationships between important metabolites and key proteins, the correlation coefficients between the contents of important metabolites and the expression levels of key proteins were calculated. As a result, we found significant positive correlations between some metabolites, such as fumarate and pyruvate (r = 0.8, P = 0.0017), urea and gamma-aminobutyric acid (GABA) (r = 0.85, P = 0.0005), and adenosine and tyramine (r = 0.72, P = 0.0084) (Figure 7B). In addition, a number of significant metabolite–protein pairs were identified. For example, there was a significant correlation between the expression levels of IL4I1 and the contents of GABA (r = 0.82, P value = 0.0011) and urea (r = 0.86, P value = 0.0004). Besides, obvious positive correlations exist between the expression levels of GART and the expression level of GRHPR (r = 0.8, P value = 0.0019). The metabolic pathways involved in these proteins and metabolites are shown in Figure 7C. As shown in the figure, upstream proteins IL4I1, ADI1, MAT1A, and GRHPR can affect the synthesis of pyruvate. On one hand, tyramine and pyruvate can be converted to fumarate and participate in the TCA cycle. In addition, gamma-aminobutyric acid (GABA) can be converted to succinate in the TCA cycle, which in turn affects the synthesis of fumarate. On the other hand, Glutamate from the TCA cycle participates in purine metabolism, affecting the synthesis of urea and adenosine. The above results demonstrated that these important pathways as well as the metabolites and proteins involved may play important roles in sperm motility.

To further verify these findings, we detected the protein expression levels of ADCY7 and GART in sperm from these two groups. The results showed that the expression level of ADCY7 was upregulated in sperm of the L group, while the expression level of GART was upregulated in sperm of the H group (Figure 8A,B and Figure S3A,D). Additionally, we detected the protein expression level of CDC42, which was the core of the PPI network and interacts with several key proteins. Our results showed that CDC42 was upregulated in sperm of the L group. The above results indicated that the expression trends of these proteins in sperm were consistent with those in SPEVs (Figure 8A,B and Figure S3C). Moreover, we measured the content of pyruvate in sperm samples of the two groups, which may affect many metabolic pathways. The results showed that the content of pyruvate was upregulated in sperm of the L group (Figure 8C), which was consistent with the expression trend of SPEVs samples.

Figure 8 Experimental verification of key proteins and metabolites in important metabolic pathways. (A) Western blot analysis of key proteins (ADCY7, CDC42, and GART) involved in metabolite pathways in sperm of two groups. (B) The relative quantified protein levels of the three proteins above. Data are presented as mean ± SD. *: P < 0.05 (Student’s t test), n = 3 for each group. (C) The content of pyruvate in sperm samples of the two groups.

Discussion

It is well-known that EVs play an important role in cell-to-cell communication. Due to the different origin cells of EVs, the contents of EVs are the most representative and can to some extent reflect the physiological state of the body, such as RNAs, proteins, metabolism, lipids, etc.9 Many studies have reported that EV derived molecules are related to sperm function, such as sperm maturation, sperm motility, capacitation, and acrosome reaction.40,41 However, there are few reports on the effects of EV derived proteins and metabolites on sperm function. In this study, we identified key metabolites and proteins related to sperm motility and their regulatory relationships through a comprehensive analysis of metabolomics and proteomics data.

In the present study, we found that PSP-I, PSP-II, and AWN were the most abundant proteins in SPEVs. Previous studies have reported that PSP-I, PSP-II, and AWN are the members of spermadhesins family, accounting for at least 45% of the total protein content in boar seminal plasma.42 In addition, PSP-I and AWN can inhibit premature sperm capacitation.43 These data indicated that proteins related to sperm physiological functions also have high expression levels in SPEVs, suggesting that SPEV proteins may play important roles in regulating sperm function through binding or delivery to sperms. It is worth noting that, although some proteins are highly expressed in both groups, there are still significant differences in their expression between the H and L groups, such as FN1. It has been reported that FN1 is a predominant protein in seminal plasma44 and is considered a marker of sperm freezability.45 In this study, FN1 was not only highly expressed in both the H and L groups but also differentially expressed between the two groups. Our results showed that the expression level of FN1 in the H group was significantly higher than that in the L group. These results indicated that FN1 plays an important role in sperm function and may contribute to high sperm motility.

A total of 140 key proteins were identified through differential expression analysis and WGCNA. Importantly, seven DEPs (ADIRF, CD81, CSK, EPS8L1, LOC100736569, PRCP, PTPRD) were also identified in WGCNA. KEGG enrichment analysis showed that the most significant pathway was the Rap1 signaling pathway, which includes some key proteins, such as CDC42, ADCY7, and MAPK14. CDC42 is located in the acrosome region of mammalian sperm. It not only participates in the acrosome reaction46 but also affects the sperm fertilizing potential.47 A previous study showed that CDC42 is necessary for male germline niche development.48 In this study, PPI analysis showed that CDC42 was the center of the protein–protein interaction network, suggesting that CDC42 plays an important role in sperm motility. Another important DEP was GART, which directly interacts with CDC42. GART is highly conserved in vertebrates and is required for de novo purine biosynthesis.49 It has been reported that GART encodes an enzyme involved in purine biosynthesis, and its folate-derived metabolites play a role in DNA methylation and mitochondrial redox processes.50 In the present study, GART was significantly overexpressed in the H group, suggesting that GART is important for maintaining sperm motility. Furthermore, PPI results showed that GART directly interacts with GRHPR. The expression level of GART was significantly positively correlated to the expression level of GRHPR. Therefore, we speculate that GART and GRHPR may play a synergistic role in the urea and pyruvate metabolic pathways, thereby affecting sperm motility. Notably, there is an indirect interaction between ADCY7 and CDC42. ADCY7 encodes a membrane-bound adenylate acylase that catalyzes ATP to form cAMP and plays an important role in cell signaling. Our results showed that ADCY7 is involved not only in the Rap1 signaling pathway but also in purine metabolism, indicating that ADCY7 may regulate sperm motility through multiple pathways.

Increasing evidence suggests that the balance between lipid metabolism is necessary to ensure normal physiological processes, such as sperm motility, capacitation, acrosome reaction or fusion.51 In this study, glycerophosphocholine was the most abundant metabolic product in SPEVs. Longobardi et al. found that glycerophosphocholine was overexpressed in fresh seminal plasma of high-fertility bulls.52 Our results showed that the content of metabolites such as palmitic acid, betaine, and acetylcarnitine was also high in SPEVs of boars. It has been found that palmitic acid contributes to the structure and function of the sperm membrane and participates in lipid metabolism to generate energy.53 Our results revealed that the level of palmitic acid in boar SPEVs of the L group was higher than those in the H group. Therefore, we speculate that the high palmitic acid concentration may produce excessive energy, thereby damaging the vitality of the sperm. Previous studies showed that betaine can improve the semen quality of different kinds of animals, such as ram,54,55 stallions,56,57 cattle,58 and boar.59 Besides, acetylcarnitine plays an important role in fatty acid oxidation and beta-oxidation in sperm.60,61

In this study, 12 DEMs were identified, which came from the subclasses of “Organic nitrogen compounds”, “Lipids and lipid-like molecules”, “Benzenoids”, “Organic acids and derivatives”, and “Organoheterocyclic compounds”. Especially, the number of metabolites from “organic acids and derivatives” was the most. It has been found that organic acids were the major metabolic class in pig seminal plasma62 and bull sperm.53 Interestingly, most DEMs were upregulated in SPEVs of the L group, with only adenosine, carnitine, beclomethasone, and 3,4-dihydroxyhydrocinnamic acid downregulated in SPEVs of the L group. In addition, adenosine, beclomethasone, 1,2-benzenedicarboxylic acid, 1-methyl-l-histidine, urea, and palmitic acid were also identified by WGCNA. Many studies demonstrated that these metabolites were related to sperm motility and function, such as adenosine,63 carnitine,64 urea,65 and palmitic acid.66 Urea is required for sperm capacitation and acrosome reaction and can produce ROS. Lavanya et al. found a significant negative correlation between the urea concentration in seminal plasma and sperm motility, membrane integrity, and mitochondrial membrane potential.67 Similarly, we found a significant increase in urea concentration in the L group, indicating that excessive urea can damage sperm motility through overproduction of ROS.65 In addition, many studies suggested that adenosine can increase sperm motility and dynein ATPase activity,68 regulate sperm capacitation, and affect sperm physiology and function.63 Adenosine inhibits spontaneous acrosome loss in frozen–thawed boar spermatozoa, and the presence of adenosine in the fertilization medium can improve monospermic penetration.69 Our results showed that the adenosine content was significantly upregulated in the H group, indicating that adenosine is beneficial to sperm motility. Interestingly, we found a negative correlation between ADCY7 and adenosine. The Western blot results confirmed that the expression of ADCY7 was indeed downregulated in the H group. Furthermore, we detected the expression of GART, which encodes an enzyme in purine metabolism. The results showed that the expression of GART is upregulated in the H group. Therefore, we speculated that GART can promote adenosine synthesis, which is beneficial for maintaining sperm motility.

Moreover, our results revealed seven important metabolic pathways related to sperm motility, including alanine, aspartate, and glutamate metabolism, tyrosine metabolism, arginine biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, and arginine and proline metabolism. Some of these pathways have been identified in previous studies. For example, Xu et al. found that “alanine, aspartate and glutamate metabolism” and “phenylalanine, tyrosine and tryptophan biosynthesis” played an important role in sperm freezability of goat.70 Li et al. revealed that phenylalanine, tyrosine, and tryptophan biosynthesis and arginine and proline metabolism pathways play an important role in the sperm motility of Holstein bulls.71

Joint analysis of proteins and metabolites was conducted to identify important metabolic pathways. The most significant pathway was alanine, aspartate, and glutamate metabolism, involving pyruvate, fumarate, GABA, and IL4I1 protein. Interestingly, pyruvate and fumarate and GABA and IL4I1 had a high correlation, and they were highly expressed in the L group. Therefore, we speculate that these metabolites and proteins may work together. Pyruvate can be carboxylated to oxaloacetate, which is a major way to replenish TCA cycle intermediates.72 Darr et al. showed that pyruvate is the most important energy source for stallion sperm.73 Our results indicated that pyruvate was involved in multiple metabolic pathways, such as alanine, aspartate, and glutamate metabolism pathways, cysteine and methionine metabolism, pyruvate metabolism, and tyrosine metabolism. It has been found that pyruvate metabolism was crucial for the oxidative phosphorylation process of spermatozoa.74 In this study, we detected the content of pyruvate in sperm and found a significant increase in pyruvate content in the L group, indicating that pyruvate may regulate sperm motility and function through multiple metabolic pathways. Previous studies suggested that GABA can affect the sperm acrosomal reaction.75−78 IL4I1 can produce reactive oxygen species (ROS) and play an important role in regulating sperm function.79 Our results showed that the protein expression level of IL4I1 in the L group was higher than that in the H group, indicating that it may cause damage to sperm by producing excessive ROS.80

In summary, this is the first study to identify important proteins and metabolites related to sperm motility through the comprehensive analysis of proteomics and metabolomics of boar SPEVs. A number of key proteins and metabolites that affect sperm motility were identified, which are involved in several important metabolic pathways, such as alanine, aspartate, and glutamate metabolism, pyruvate metabolism, and cysteine and methionine metabolism. In addition, we established a correlation between the expression of some proteins and metabolite content and found that these important proteins and metabolites may synergistically regulate sperm motility. These findings provide important information and a new perspective for understanding the mechanism of sperm motility.

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its Supporting Information.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00060.Figure S1, Western blotting results of SPEVs markers; Figure S2, Evaluation of proteomic data quality; Figure S3, Verification of key proteins in important metabolic pathways; Table S1, Information on antibodies used in the experiments; Table S2, Phenotype information on experimental samples; Table S3, Differentially expressed proteins identified between high (H) and low (L) sperm motility groups; Table S4, Important proteins identified by WGCNA; Table S5, Function enrichment analysis of key proteins; Table S6, Differentially expressed metabolites identified between the high (H) and low (L) sperm motility groups; Table S7, Important metabolites identified by WGCNA; Table S8, Metabolic pathway analysis of key metabolites (PDF)

Supplementary Material

pr4c00060_si_001.pdf

Author Contributions

L.J. conceived and designed the study. Y.Z., N.D., and J.Z. performed the experiments. Y.Z., J.C., and C.Z. analyzed the data. J.L. provided technical support. Y.Z. wrote the paper, and L.J. revised the manuscript. All authors read and approved the final manuscript.

The authors declare no competing financial interest.

Acknowledgments

This study was supported financially by the Scientific and Technological Innovation Major Project (2023ZD0404305) and National Key Research and Development Program of China (2022YFD1302200; 2021YFD1200801). We are grateful to the reviewers of this manuscript for their constructive suggestions. The authors are also indebted to the molecular quantitative genetics team at China Agricultural University for their expertise.
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