Introduction

In China, Mongolian horses are a prized native breed of horses. Strong muscular endurance, low susceptibility to disease, and little preference for certain forage species1. Currently, study on Mongolian horses is concentrated on their genetic evolution2, athletic ability3, gut microbe4, and male reproductive5. However, mare reproduction has not yet been documented.

The follicle is located in the ovary cortex, which is also a unique structure encasing the oocyte. After estrus, the primordial follicles begin to mature and ovulate under regulation of reproductive hormones. At the same time, the follicle diameter increases, in which the follicle fluid accumulates. Follicular fluid (FF) is composed mainly of proteins, hormones, enzymes, polysaccharides, growth factors, cytokines, chemokines, metabolites, anticoagulants, electrolytes, reactive oxygen species, and antioxidants6. It is secreted partly by the follicles (granulosa cells, Theca of follicle cells, and oocytes) and the blood7,8,9. Follicular fluid provides a unique microenvironment for oocyte maturation. In addition, many studies have shown that follicular fluid plays a crucial role in determining the oocytes quality and influencing mammalian fertility and embryonic development10,11,12,13.

Many components in the follicular fluid change dynamically with the growth and development of the oocyte and play a role in the oocyte maturation14. Therefore, studying the composition of follicular fluid is beneficial for developing oocyte in vitro maturation research15. The key to in vitro maturation is to mimic oocyte in vivo development environment, so the culture environment is essential. Various media have been applied to the oocytes culture, including tissue culture medium 199 (TCM 199), equine maturation medium (EMMI), Dulbecco-modified Eagle medium/F12, and base media combinations16. On this basis, pyruvate, antibiotics, serum, albumin, or various hormones are usually added16,17. However, despite various media supplements, horse oocytes in vitro maturation rate is only 50% ~ 80%18, which is lower than other mammals, including pigs (70% ~ 85%)19and cattle (80% ~ 90%)20.

The FF composition varies throughout follicular development, which can be utilized as a marker to assess oocyte quality21. As fundamental elements of biological phenotypes, metabolites can contribute to a more direct and effective understanding of biological processes and their mechanisms. Metabolomics studies small-molecule end products of cellular processes and analyze a wide range of metabolites produced by various follicular cell types to characterize the microenvironment within which oocytes develop in the follicle22,23. The follicular fluid metabolomics analysis has been applied to humans and many animals and screened out the marker metabolites of reproductive diseases, such as polycystic ovary syndrome, which provides a broader idea for the female reproduction research24,25. However, no study has analysed changes in metabolites in equine follicular fluid during follicular development. Proteomics analysis of body fluids is a powerful approach to understand complex biological processes26. Previous studies have been reported on proteomics of horse follicular fluid, including effect of seasonal changes, late follicular development and protein group changes of horse follicular fluid during follicular development27,28,29. However, the joint analysis of horse follicular fluid proteomics and metabolomics has not been reported so far.

In order to understand the follicular development and oocyte maturation environment of Mongolian horses, improve the understanding of reproductive traits of Mongolian horses and the application of horse assisted reproductive technology, this study used metabonomic and proteomic methods to identify the metabolites and protein dynamic profiles in follicular fluid of Mongolian horses, hoping to provide reference for the study of reproductive performance of other breeds of mares.

Results

Protein identification, quantification, and statistical analysis.

The comparative analysis of follicular fluid with different follicular diameters was carried out by Tandem mass tag (TMT) liquid chromatography tandem mass spectrometry (LC–MS/MS). 539 proteins were quantified from the follicular fluid at different stages of development (Table S1). The most abundant proteins include albumin, serum transferrin and apolipoprotein. To understand differences in protein abundance, statistical analysis was performed by ANOVA with a fold change(FC) > 1.5 or fold change(FC) < 0.6667 and p-value < 0.05. The comparison between SFF and LFF had the highest number of differential proteins, including 9 over abundant and 15 less abundant in LFF compared to SFF. There are 20 differential proteins between LFF and MFF. The number of differential proteins between SFF and MFF was the least, with only three differential proteins (Fig. 1A, Table S2-S4). In order to analyse the different trends of DAPs , a clustering analysis by K means was carried out (Supplementary Figure S1). A total of 33 accession were detected, 25 differential proteins were annotated, 14 of which showed a decreasing trend with the follicular development, and the other 11 showed an increasing trend. The function of differential protein obtained by K- means clustering analysis is mainly immune and inflammatory response, which is consistent with previous studies.

Figure 1
figure 1

Statistical and functional notes of differential abundant proteins (DAPs). (A) statistical results of differential protein; (B) Go classification statistics of differentially expressed proteins in SFF vs LFF group; (C) Go classification statistics of differentially expressed proteins in LFF vs MFF; (D) Go classification statistics of differentially expressed proteins in SFF vs MFF.

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Functional annotation of differentially abundant proteins (DAPs).

To evaluate the functions of differentially abundant proteins, Gene Ontology (GO) annotation and analysis were performed. DAPs of each group were classified into cellular component (CC), molecular function (MF), and biological process (BP) categories. With 37 entries, DAPs of SFF vs LFF has the most GO term annotations. In LFF vs MFF, DAPs are annotated to 34 GO terms. Just 12 catalogues in SFF vs MFF were annotated for the DAPs.

Regardless of the stages of follicle development, DAPs are annotated to 38 GO terms. The top five annotations of each group were similar, with the highest proportions of DAPs associated with protein-containing complex, extracellular region, cell and cell part GO names of CC; binding GO names of MF; cellular process GO names of BP (Fig. 1B–D).

Functional enrichment and cluster analysis of DAPs

To further investigate the pivotal function of the DAPs, functional enrichment and cluster analyses of the DAPs were conducted. According to the ranking of P-value in the results of enrichment analysis, the top 50 Go-terms were selected to draw the column diagram of enrichment items. In LFF vs MFF, “Protein-containing complexes” was the most abundant term. (Fig. 2A). In SFF vs LFF (Fig. 2B) and SFF vs MFF(Fig. 2C), the “binding” enrichment was the highest in the “molecular function” category. This indicates that protein binding in follicular fluid plays an indispensable role in follicular development. In addition, each of the three comparison groups has distinct terms enrichment. With 149 unique terms, the SFF vs LFF group has the most, after of the MFF vs LFF group with 110 unique terms, and the SFF vs MFF group with just one unique term.

Figure 2
figure 2

Functional enrichment and cluster analysis of DAPs. (A), (B), (C). Go enrichment of differential proteins in comparison groups; (D), (E), (F). KEGG enrichment of different proteins in comparison groups.

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In addition, KEGG enrichment analysis was performed to identify potential regulatory pathways during follicular development. In LFF vs MFF group, the differential proteins were enriched to 36 KEGG pathway terms. The pathways with higher protein enrichment include “Neutrophil extracellular trap formation”, “Systemic lupus erythematosus”, “African trypanosomiasis” and “Transcriptional misregulation in cancer” (Fig. 2D). In SFF vs LFF group, the differential proteins were enriched to 110 KEGG pathway terms. “Pathogenic Escherichia coli infection”, “Epstein-Barr virus infection”, “Neutrophil extracellular trap formation” were the KEGG pathway with the highest concentration (Fig. 2E). In SFF vs MFF group, there are only four KEGG pathways, and have the same number of differential proteins (Fig. 2F).

Metabolite identification and quantification

In order to study the changes of physiological activity and metabolic process in follicular fluid during follicular development, the metabolites in follicular fluid were analyzed and compared quantitatively by LC–MS. A total of 3639 metabolites were detected in 3 groups of follicular fluid (Table S5-S6). 1,2-Dilinoleoyl-SN-glycero-3-phosphocholine, L-Saccharopine, Sulcotrione and 4-Hydroxyacetophenone were the highest in all detected metabolites.

To understand differences in metabolite abundance, statistical analysis was performed by ANOVA with a fold change(FC) > 1.5 or fold change(FC) < 0.6667 and p-value < 0.05. There were 88 different metabolites in LFF vs MFF group, more than the other two groups. The difference between LFF and SFF is 65 distinct metabolites. Between MFF and SFF, there exist 35 distinct metabolites (Table S6-S12). It is worth noting that there are seven identical differential metabolites in the three comparison groups (Supplementary Figure S2). In addition, the amount of differential metabolites detected in the positive mode of electrospray ionization (ESI +) was significantly higher than that in the negative mode of electrospray ionization (ESI-) (Table 1). The DAMs in the ESI- are separated into 8 sub classes, according to the DAM's K-means analysis, while those in the ESI + are separated into 7 sub classes(Supplementary Figure S3, S4).

Table 1 Number of differential metabolites in follicular fluid.
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Classification of differentially abundant metabolites (DAMs)

The classification with the most DAMs was “glycerophospholipid (GP)”, with 6 less abundant metabolites and 23 over abundant metabolites in MFF compared to LFF. All metabolites showed an overall upward trend, including: “Benzene and substituted derivatives” (9over abundant, 4 less abundant), “Amino acid and Its metabolites” (7over abundant, 2 less abundant) and so on. Among the different metabolites in SFF compared to LFF , “GP” (23over abundant, 3 less abundant) were the most, followed by “Amino acid and Its metabolites” (4over abundant, 3less abundant) and “Heterocyclic compounds” (5over abundant, 2less abundant). There are 35 DAMs in MFF vs SFF, mainly including “GP” and “Amino acid and Its metabolites”. These results suggest that Glycerophospholipid play a crucial role in follicular development.

The KEGG pathway of differential metabolite enrichment during follicular development mainly includes “Metabolic pathways”, “Linoleic acid metabolism”, “Glycerophospholipid metabolism” and “Arachidonic acid metabolism” (Fig. 3). Among them, “Metabolic pathways” and “Linoleic acid metabolism” are enriched in the comparison of all groups. In addition, LFF vs MFF group had four unique KEGG pathways, MFF vs SFF group had a unique KEGG pathway, and LFF vs SFF group had the largest number of differential protein enrichment pathways, with 33 unique pathways.

Figure 3
figure 3

KEGG enrichment map of differential metabolite. (A) Kegg enrichment of differential metabolite in LFF vs MFF under ESI-: (B) Kegg enrichment of differential metabolite in LFF vs MFF under ESI + ; (C) Kegg enrichment of differential metabolite in LFF vs SFF under ESI-; (D) Kegg enrichment of differential metabolite in LFF vs SFF under ESI + ; (E) Kegg enrichment of differential metabolite in MFF vs SFF under ESI + .

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Meta-proteomic integration analysis

A total of 7 KEGG pathways were enriched in proteomic and metabolomic combined analysis, of which the highest number of differential metabolites and differential protein enrichment were metabolic pathways(Fig. 4A, B). In addition, “Linoleic acid metabolism” and “alpha-Linolenic acid metabolism” showed significant differences in proteomics and metabolomics, suggesting that they play an important role in follicular development.

Figure 4
figure 4

KEGG enrichment analysis bubble diagram. (A) Proteomics and the ESI- of metabolomics-enriched KEGG pathways; (B) Proteomics and the ESI + of metabolomics-enriched KEGG pathways.

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Quantitative analysis of metabolites and proteins

The results of enzyme-linked immunosorbent assay showed that Serum amyloid A protein(SAA1)and Inhibin beta A chain(INH βA)protein levels were increasing with follicular development(Fig. 5A, C),while Versican (VCAN)and Olfactomedin like 2B (OLFML2B)showed the opposite expression trend(Fig. 5D, E); moreover, the four proteins' expression levels showed significant differences between large and small follicle groups. Inhibin alpha chain (INH A)protein levels did not change significantly during follicular development (Fig. 5B). This is consistent with the results observed in the proteome. Phosphatidylcholine levels increased during the development of small follicles to intermediate follicles but decreased significantly by the large follicle stage (Fig. 5F).

Figure 5
figure 5

Quantitative analysis of proteins and metabolites. (A) Level of SAA1 protein in follicular fluid; (B) Level of INH A protein in follicular fluid; (C) Level of INH βA protein in follicular fluid; (D) Level of VCAN protein in follicular fluid; (E) Level of OMFML2B protein in follicular fluid; (F) The level of phosphatidylcholine in follicular fluid.

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Discussion

In the present study, the molecular alterations of follicular fluid during follicular development were studied by non-targeted metabolomics and proteomics methods, and 3639 metabolites, 539 proteins were identified successfully.

The changing trend of protein expression level can directly show the relationship between protein and follicular development. The expression amounts of all differential proteins filtered according to screening criteria in all comparison groups were normalized and centrally processed, followed by K-means clustering analysis support. For instance, Vimentin (VIM) is a 57 kDa protein, which is expressed in many organs and normal cells. However, Vimentin expression increased during carcinogenesis. The main function of VIM is to maintain cell morphology, stabilize cytoskeleton, participate in mitosis and cell decomposition, promote cell adhesion and migration, etc. VIM has been identified as a marker of immature blood vessels. With the development of follicles, the expression of vim decreases, indicating that blood vessels gradually mature30,31. Ovulation is considered to be an inflammatory reaction involving blood vessels. We speculate that VIM may affect ovulation through angiogenesis. OLFML2B is an extracellular matrix protein, which is involved in early eye development and function. At present, the research on OLFML2B focuses on the expression of cancer prognosis characteristic genes32. Histone H3(H3-3B) is a replacement histone that can be synthesized instead of the main histone isomorphism without DNA replication33. The reason for the existence of alternative histones in follicular fluid is unclear, and it is speculated that this may be related to extracellular exosomes. The above three differential proteins showed a standard linear relationship in K-means analysis, but whether they had a direct regulatory relationship with follicular development needs further study. VCAN is a multifunctional proteoglycan, which is the extracellular matrix main component34. Versican also affects inflammation by interacting with various growth factors and cytokines that regulate inflammation. Follicle development and ovulation are also accompanied by inflammatory reaction, so there are changes in time and space for VCAN to play its role35. Thrombospondin 4 (THBS4) is a member of the platelet-reactive protein family. The expression of THBS4 is usually very low in adult tissues, but it is reported that it increases greatly after tissue injury and subsequent tissue repair and regeneration. In addition, the expression of THbs4 was also found in serum, which proved that it could promote the formation of cardiovascular system36. Bone morphogenetic proteins (BMPs) are antagonized by the action of many extracellular protein antagonists, including members from the gene aberration family selected by differential screening in neuroblastoma. BMPs signaling affects the oocytes maturation, and studies show that Neuroblastoma Suppressor of Tumorigenicity 1(NBL1) can effectively inhibit the function of BMPs37. Cysteine protease inhibitor C (CST3) is a low molecular weight, alkaline non-saccharified protein composed of 120 amino acids with a molecular weight of 13 kDa. It exists in almost all tissues and body fluids, and is an enzyme inhibitor of lysosomal protease, which can prevent extracellular protein decomposition. Therefore, we speculate that CST3 in follicular fluid is the key factor to prevent the hydrolysis of specific protein and maintain the follicular fluid stability microenvironment.

With the follicles development, the expression of 11 protein showed an upward trend. For example, immunoglobulin gamma 1 heavy chain constant region (IGHC1) is an immunoglobulin, which can promote inflammatory response. Alpha-2 Heremans Schmid Glycoprotein (AHSG) is a multifunctional protein, which can inhibit the precipitation of basic calcium phosphate and maintain the calcium content in body fluids. This protein can participate in the inactivation of macrophages and regulate the degree of inflammation; It can inhibit the insulin receptor phosphorylation and the tyrosine kinase activity, leading to insulin resistance and type II diabetes. It can bind with transforming growth factor-β (TGF-β), thus inhibiting the growth of intestinal tumors38. Previous studies have shown that TGF-β superfamily members can promote the proliferation of granulosa cells, thus blocking the channel connection between granulosa cells and oocytes, and reducing the content of cyclic adenosine monophosphate (cAMP) in oocytes, thus promoting oocytes to maturation. Moreover, the environment around the oocyte directly or indirectly regulates the expression of related genes through TGF-β pathway, thus regulating the oocytes development39. Globin A1(GLNA1) is a kind of respiratory protein that can reversibly bind oxygen through iron porphyrin ring. It is closely related to anemia, and its role in follicular fluid is unclear. SAA1 is serum amyloid, it can play a role in the immune system, and the level of SAA1 protein in blood and other tissues increases under inflammatory conditions. Inhibin A in follicular fluid is mainly secreted by granulosa cells of dominant follicles, and the insufficient secretion of inhibin is related to follicular stagnation observed in polycystic ovary syndrome. In addition, INH βA can activate TGF-β signaling pathway to play a greater role40,41. In human studies, INH βA affects the development of oocytes by forming activin A through disulfide bonds42. Phospholipase A2 (PLA2G1B) is a key factor in linoleic acid metabolism, and lecithin on cell membrane is converted into linoleic acid by A2 phospholipase. Linoleic acid can reduce blood cholesterol level and affect insulin and glucose metabolism43,44. IkappaB kinase is an immunomodulatory protein, which plays a role in many biological processes. Fibulin 5 (FBLN5) is a glycoprotein composed of 448 amino acids, which plays an important role in regulating the balance of extracellular environment, maintaining the stability of ECM structure, and regulating cell proliferation and tumorigenesis. In addition, Fibulin-5 reduces the expression of inflammatory factors in chondrocytes, thus reducing inflammatory reaction45,46,47. BBS family proteins are the key components in the construction of protocilia. Protocilia are antennae and antennas of cells, which are used to collect peripheral signals and environmental changes. It is rich in many development-related signal transduction receptors, which may play a vital role in the development of various cells in follicular fluid48. The expression levels of several protein changed during follicular development, indicating that there is a complex network in follicular fluid for follicular development and oocyte growth.

A total of 139 differential metabolites were detected in this study, and the differential metabolites detected in ESI + were far more than those in ESI-. In this study, the types of these differential metabolites mainly include Glycerol phospholipids (GP), Amino acid and Its metabolites, Benzene and substituted derivatives and so on. Phosphatidylcholine (PC) is the category with the largest number of differential metabolites in GP. PC accumulates in the process of follicular growth, and its availability significantly affects oocyte maturation, follicular development and follicular cavity formation49. Amino acid and Its metabolites are the second most numerous differential metabolites class in follicular fluid, mostly in the form of small peptides. The majority of the peptides present a net positive charge at physiological pH, so only detected in ESI + . Small peptides can not only supplement the source of amino acids, but also exert their unique physiological activities. The diversity of small peptides makes them have different functions, such as anti-inflammation, immunity, regulating enzyme activity and scavenging free radicals50,51. The effect of small peptides in follicular fluid on follicular development needs further study. Fatty acyl (FA) is the main form of fatty acids in organisms. They can transfer and store energy in cells and between cells, and also participate in many biological processes, such as signal transduction, hormone synthesis, cell membrane construction and immune system regulation. In addition, studies have shown that the decline of oocyte development ability caused by obesity is related to fatty acid metabolism in follicular fluid52. The different metabolites in each comparison group include 1-(6-((3-Methoxyestra-1,3,5(10)-trien-17-yl)amino)hexyl)-1H-pyrrole-2,5-dione, PE-NMe2(18:1(9Z)/20:3(5Z,8Z,11Z)), 1-tetradecanoyl-2-(13Z,16Z-docosadienoyl)-glycero-3-phosphocholine, PE-NMe2(16:0/16:0), 1-Oleoyl-2-palmitoyl-sn-glycero-3-phosphocholine, 1-Stearoyl-2-linoleoyl-sn-glycero-3-phosphoethanolamine and 1,2-di-[(11Z)-octadecenoyl]-sn-glycero-3-phosphocholine. Except that 1-(6-((3-Methoxyestra-1,3,5(10)-trien-17-yl)amino)hexyl)-1H-pyrrole-2,5-dione belongs to ketones, all other metabolites are GP. In this study, K-means analysis was also carried out on the differential metabolites in follicular fluid. In ESI-, With the development of follicles, the content of 1-Hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-3-phosphoserine increased continuously, while PC (18:2(9Z,12Z)/18:1(11Z)) and Mitomycin showed the opposite trend. In ESI + , according to the expression trend of differential metabolites, it is found that most of the differential metabolites with linear relationship with follicular development belong to GP, which further indicates that GP plays a vital role in follicular development.

In this study, the joint analysis found that the linoleic acid metabolic pathway during follicular development deserved special attention. In the linoleic acid metabolic pathway, PLA2G1B functions after lecithin to create linoleic acid and influences the fatty acid metabolism of arachidonic acid44. Linoleic acid is an essential fatty acid in animal nutrition, which can reduce blood fat and cholesterol (53). Conjugated linoleic acid is the general name of linoleic acid isomers. Cis-9, trans-11 CLA (c9t11 CLA) and trans-10, cis-12 CLA (t10c12 CLA) are two widely studied isomers of CLA, which have similar physiological functions to linoleic acid53. Jia et al.54 found that adding the optimal concentration of t10c12 CLA to the in vitro maturation medium is beneficial to the cytoplasmic porcine oocytes maturation, improving the intracellular MAPK activity, regulating the redox state of cells and improving the in vitro development ability. Whether linoleic acid and its isomers have a positive effect on equine oocytes in vitro maturation needs further study. This study enables us to understand the dynamic changes of follicular fluid composition during follicular development of Mongolian horses, and screen out some proteins and metabolites that may affect the growth and development of oocytes, so as to establish the basis for improving the reproductive performance of local stallions.

Materials and methods

Animals and ethics statement

21 Mongolian horses ( 2 ~ 8 years) were slaughtered in a commercial slaughterhouse. All horses have no health problems .All animal procedures were completed per the approval of the Inner Mongolia University ethics committee.

Follicular fluid collection

The ovaries were rinsed three times with saline to prepare for the collection of follicular fluid. FF samples were collected by a puncture at three developmental stages according to follicular diameter. FF samples collected from follicles with a diameter of 10 mm to 20 mm are defined as a small follicular fluid (SFF) group. Those collected from follicles with more than 20 mm diameter and less than 30 mm are defined as a medium follicular fluid (MFF) group. Those collected from more than 30 mm diameter follicles are defined as a large follicular fluid (LFF) group. After centrifuging the FF samples for ten minutes at 3000 g, the supernatant was collected and kept in liquid nitrogen. It was placed in − 80 till the following stage of detection after 12 h. A total of 15 FF samples were analyzed. One FF represents 1–3 mares, and each developmental stage has 5 biological replicates.

Metabolomics Analysis

The sample stored at − 80 °C refrigerator was thawed on ice and vortexed for 10 s. A 150 μl extract solution (ACN: Methanol = 1:4, V/V) containing internal standard was added to a 50 μl sample and vortexed for 3 min. Then the sample was centrifuged at 12,000 rpm for 10 min (4 °C), and the supernatant (150 μl) was collected to a centrifuge tube. Incubate at − 20 ℃ for 30 min and then centrifuge at 12,000 rpm for 3 min (4℃). 120 μl aliquots of supernatant were transferred for LC–MS analysis.

The LC–MS system acquired all samples following machine orders. The analytical conditions were as follows, UPLC: column, Waters ACQUITY UPLC HSS T3 C18 (1.8 µm, 2.1 mm*100 mm); column temperature, 40 ℃; flow rate, 0.4 ml/min; injection volume, 2 μl; solvent system, water (0.1% formic acid): acetonitrile (0.1% formic acid); The column was eluted with 5% mobile phase B (0.1% formic acid in acetonitrile) at 0 min followed by a linear gradient to 90% mobile phase B (0.1% formic acid in acetonitrile) over 11 min, held for 1 min, and then come back to 5% mobile phase B within 6 s, held for 114 s. The data quality control was perfomed by QC plots and PCA (Supplementary Figure S5).

Proteomic Analysis

SDT (4% (w/V) SDS, 100mmtris/HCl, pH 7.6, 0.1mdtt) was used to extract protein from the sample, and then BCA protein analysis kit (bio rad, USA) was used to quantify the protein Protein digestion by trypsin was performed according to filter-aided sample preparation (FASP) procedure described by Wiśniewski55. The digest peptides of each sample were desalted, concentrated by vacuum centrifugation and reconstituted in 40 µl of 0.1% (v/v) formic acid.

200 μg of protein of each sample was added to 30 μ l of SDT buffer (4% SDS, 100 mM DTT, 150 mM Tris–HCl pH 8.0). After repeated ultrafiltration with UA buffer (8 M urea, 150 mM Tris–HCl pH 8.0), 100 μl iodoacetamide was added, and the sample was incubated in the dark for 30 min. Promega suspension was digested with trypsin at 37℃ overnight, and the obtained peptide was collected as filtrate. The peptides of each sample were desalted, concentrated by vacuum centrifugation, and reconstructed in 40 µL of 0.1% (v/v) formic acid.

The LC–MS/MS analysis was performed on a Q Exactive mass spectrometer (Thermo Scientific) coupled with Easy nLC (Thermo Fisher Scientific). The peptides were loaded onto a reverse phase trap column (Thermo Scientific Acclaim PepMap100, 100 μm*2 cm, nanoViper C18) connected to the C18-reversed phase analytical column (Thermo Scientific Easy Column, 10 cm long, 75 μm inner diameter, 3 μm resin) in buffer A (0.1% Formic acid), and separated with a linear gradient of buffer B (84% acetonitrile and 0.1% formic acid). MS data was acquired using a data-dependent top10 method dynamically choosing the most abundant precursor ions from the survey scan (300–1800 m/z) for HCD fragmentation, and dynamic exclusion is allowed. The proteome data were normalized.

Data processing, Bioinformatics, and analysis

The original data file acquisition by LC–MS was converted into mzML format by ProteoWizard software and corrected. After that, metabolic identification information was obtained by searching the laboratory’s self-built database, integrated public database, AI database, and metDNA.

Unsupervised PCA (principal component analysis) was performed by statistics function prcomp within R (www.r-project.org). The data was unit variance scaled before unsupervised PCA. For two-group analysis, differential metabolites were determined by P-value (P-value < 0.05, Student’s t test) and absolute Log2FC (|Log2FC|≥ 1.0).

MS raw data were analyzed using the MASCOT engine (Matrix Science, London, UK; version 2.2) embedded into Proteome Discoverer v1.4. Spectra files were searched against the homo sapiens swissprot database (20,425 entries) concatenated with decoy database using the following parameters: Quantification Mathord was seted up in relation to n-plex of TMT for quantification; Carbamidomethyl on Cys and TMT6/10/16 plex on Lys and peptide N-terminal were specified as stabi modification, and oxidation on Met and TMT6/10/16 plex on Tyr were specified as dynamic modifications with a maximum of 5 variable modifications; Trypsin was specified as digestive enzyme allowing up to 2 missing cleavages; The peptide mass tolerance was set as 20 ppm and the fragment mass tolerance was set as 0.1 Da; Search results were filtered with 1% FDR at peptide levels. The proteins which contained at least one unique peptide were used for further identification and quantification analysis. When the difference is divided into two groups of samples, fold change (FC) > 1.5 or fold change(FC) < 0.6667, and P-value < 0.05 is defined as the protein with significant difference.

Identified metabolites and protein were annotated using KEGG Compound database (http://www.kegg.jp/kegg/compound/), annotated metabolites and protein were then mapped to KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html)56,57,58. Significantly enriched pathways are identified with a hypergeometric test’s P-value for a given list of metabolites and protein.

Verification of proteins and metabolite levels by enzyme-linked immunosorbent assay

Detection of protein and metabolite levels in follicular fluid using enzyme-linked immunosorbent assay kits (shanghai hengyuan biologic technology co., LTD,China). We detected the level of OLFML2B in follicular fluid using horse Olfactomedin like 2B immunoassay kit. We used the same method to detect the level of SAA1,INH βA,VCAN,INH A and Phosphatidylcholine in follicular fluid. Briefly, the standards and samples were added to the enzyme plate and washed at 37℃ for 30 min. 90ul of follicular fluid was used for each protein level test, which was the same as the histological test. Then the enzyme reagent was added and washed at the same time. Finally, the reaction was terminated by adding the colour developer to avoid the light and then adding the termination solution. The absorbance values were measured within 15 min. Protein and metabolite levels were calculated from the standard curve.

Ethical statement

The manuscript introduces the research on animals, which has been approved by the ethics committee of Inner Mongolia Agricultural University.

Conclusions

This study investigated the follicular fluid proteomic and metabolomic signature of the follicular development using a meta-proteomic approach. A large number of differentially expressed proteins and metabolites were identified, indicating that the proteins and metabolites in follicular fluid were constantly changing during follicular development. In addition, linoleic acid metabolic pathway changed most significantly during follicular development.

Data availability

Data is provided within the manuscript or supplementary information files.