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Integrated lipid metabolomics and proteomics analysis reveal the pathogenesis of polycystic ovary syndrome



Polycystic ovary syndrome (PCOS) is an endocrinological and metabolic disorder that can lead to female infertility. Lipid metabolomics and proteomics are the new disciplines in systems biology aimed to discover metabolic pathway changes in diseases and diagnosis of biomarkers. This study aims to reveal the features of PCOS to explore its pathogenesis at the protein and metabolic level.


We collected follicular fluid samples and granulosa cells of women with PCOS and normal women who underwent in vitro fertilization(IVF) and embryo transfer were recruited. The samples were for the lipidomic study and the proteomic study based on the latest metabolomics and proteomics research platform.


Lipid metabolomic analysis revealed abnormal metabolism of glycerides, glycerophospholipids, and sphingomyelin in the FF of PCOS. Differential lipids were strongly linked with the rate of high-quality embryos. In total, 144 differentially expressed proteins were screened in ovarian granulosa cells in women with PCOS compared to controls. Go functional enrichment analysis showed that differential proteins were associated with blood coagulation and lead to follicular development disorders.


The results showed that the differential lipid metabolites and proteins in PCOS were closely related to follicle quality,which can be potential biomarkers for oocyte maturation and ART outcomes.


Polycystic ovary syndrome (PCOS) is the most prevalent endocrine and metabolic disorder in women, and it is also the primary cause of anovulatory infertility and hyperandrogenism [1,2,3]. The main clinical manifestations of the patient were oligomenorrhea, infertility, hyperandrogenism, obesity, hirsutism, acne, insulin resistance(IR) and polycystic ovarian changes under B-ultrasound. Severe metabolic disorders can cause long-term complications of PCOS,including diabetes, hyperlipidemia, cardiovascular disease and even endometrial cancer [4], which affect women's physical and psychological health. The oocytes collected from PCOS patients undergoing assisted-reproductive techniques (ART) always have poor quality, which causes a high cancelation rate and a low fertilization rate. With the pathogenesis of PCOS remaining unclear, further investigation into the etiology of PCOS and discovery of relevant biomarkers through appropriate screening, early accurate diagnosis, and effective intervention are needed to prevent long-term complications.

The most prevalent metabolic disorder in patients with PCOS is dyslipidemia. A variety of metabolic pathways involve lipids, such as steroid hormone biosynthesis, sphingolipid and fatty acid metabolism. Dyslipidemia and abnormalities in amino acid metabolism were discovered in PCOS serum and urine [5, 6]. The levels of TG, and Apo-B increased in correlation with BMI among Chinese PCOS patients [7]. Additionally, the ratios of AI, TG/HDL, and Apo-B/Apo-A were linked to specific PCOS features like insulin resistance and obesity. It has been confirmed that women with PCOS have reduced levels of sphingosine, LPE, and especially LPE (22:5) and LPC (18:2) [8]. When compared to controls, PCOS patients had significantly higher levels of lactose, stearic acid, palmitic acid, and lower levels of succinic acid in urine metabolites [9]. Sun et al. [10] found levels of bioactive lipids such as lysophosphatidylcholines (LysoPC) (16:0), phytosphingosine, LysoPC (14:0), and LysoPC (18:0) were dramatically lowered in women with PCOS, levels of free fatty acids, 3-hydroxynonanoylcarnitine carnitine, and eicosapentaenoic acid were significantly raised. The growing body of research indicates that dyslipidemia may contribute to the adverse outcome of pregnancy by eliciting an oxidative stress response.In this study,samples of follicular fluid were collected from women with PCOS and normal women who underwent IVF and embryo transfer for lipidomic analysis and granulosa cells for proteomic analysis.

Follicular fluid is composed of serum difused by local capillaries and secretions secreted by peripheral GCs, as well as exudates from plasma, primarily containing hormones, interleukins, growth factors,anti-apoptosis factors, proteins, carbohydrates, amino acids, active oxygen, and antioxidant enzymes.It provides special microenvironment for oocytes that has an effect on oocyte maturation, follicular wall rupture, fertilization, and early embryo development [11, 12]. Clear differences in a range of glycerolipid, glycerophospholipids, sphingolipids, and carboxylic acids have been found in PCOS FF [13].Granulosa cells (GCs) are specifically located around oocytes and play a critical role in oocyte maturation and ovulation [14]. GCs serve as a helpful biological model to study these challenging situations because they are also implicated in the aberrant folliculogenesis seen in diseases like PCOS. ApoA-I abundance was observed to be reduced in PCOS-afflicted women's visceral adipose tissue, entire ovarian tissue, and granulosa cells, which may have an impact on the disordered production of steroid hormones in PCOS patients [15, 16]. Since impaired oocyte quality and outcomes of IVF are associated with changes in FF and GCs components among patients with PCOS [17], the identification of the components may help to better understand and reveal potential lipid biomarkers of PCOS.


Clinical data and ART outcomes

As shown in Table 1, we counted the mean age, body mass index (BMI), AMH, bFSH, bLH, bE2, the numbers of oocytes retrieved, MII oocytes, 2PN fertilization,number of frozen embryos,number of blastocysts,frozen embryo rate, blastocyst rate and high-quality embryos of the participants.

Table 1 The demographic and clinical characteristics of patients with PCOS and CON

Age, BMI, bFSH, bE2, No. of retrieved oocytes, MII oocytes, fertilization,number of frozen embryos,number of blastocysts,frozen embryo rate, blastocyst rate and high-quality embryo levels were not significantly different from the CON group (P > 0.05). However, when compared to the CON group, the PCOS group's bLH, LH/FSH, and TT levels were considerably greater (P < 0.05). The pregnancy rate was slightly lower in the PCOS group than in the CON group.

Multivariate analysis of lipid metabolites

Multivariate analysis of metabolites

16 experimental samples and 4 quality control (QC) samples made up the original data. To reduce the effect of detection system error on the results, so that the results can better highlight the biology significance, we carried out a series of preparation and collation of the original data. It mainly includes the following steps: deviation value filtering, missing value filtering, missing value imputation and data normalization. After the data had been preprocessed, SIMCA software (V16.0.2, Sartorius Stedim Data Analytics AB, Umea, Sweden) was used to analyze the data.The FF metabolic profiles were then analyzed by principal component analysis (PCA) and OPLS-DA model analysis to reflect the degree of difference between groups. Figure 1a displays the PCA score scatter plot for all samples. According to Hotelling's T squared ellipse, all samples were distributed within the 95% confidence interval. The OPLS-DA score plot results revealed that the PCOS and CON groups had significantly different lipid metabolites in FF(Fig. 1b). According to the OPLS-DA model's permutation test findings, the R2Y(cum) and Q2(cum) values of the PCOS group vs the Con group were 0.95 and -0.38, respectively (Fig. 1c). The results showed that the OPLS-DA model did not overfit and had a high predictive ability, which was suitable for subsequent optimization analysis.

Fig. 1
figure 1

PCA score plots, OPLS-DA score plots, and corresponding validation plot of OPLS-DA results derived from FF metabolomics profiles comparing PCOS and CON.a:The PCA score scatter plot of all samples;b:The OPLS-DA score plot;c: The permutation test results of OPLS-DA model(red squares represent the control group and blue triangles represent the PCOS group).PCA: Principal component analysis;OPLS-DA:orthogonal projections to latent structures- discriminant analysis;PCOS: Polycystic ovary syndrome

Significant lipid differential metabolite analysis between CON and PCOS group

In this study, 145 FF metabolites in the OPLS-DA analysis (VIP values > 1) and the univariate analysis (P-values < 0.05) were detected between PCOS and CON groups (Table 2). Differential lipid metabolites were represented by volcano plots (Fig. 2a) and the elevated and downregulated metabolites in each group were distinguished by a heatmap (Fig. 2b). Most lipid metabolites were increased in the FF from patients PCOS group compared to the CON group: levels of triacylglycerol(TAG), Phosphatidylethanolamine(PE), Diacylglycerol(DAG) and Hexosylceramide alpha-hydroxy fatty acid-phytospingosine(HexCer-AP) were significantly higher in PCOS FF. Whereas, when comparison to the CON group, PCOS FF had significantly lower levels of Lysophophatidylcholine(LPC), Sphingomyelin(SM), SulfurHexosylceramide hydroxyfatty acid(SHexCer) and Phosphatidylcholine(PC). Changes in other compound classes were also detected, such as Phosphatidylinositol(PI), Diacylglyceryl trimethylhomoserine (DGTS), glucuronosyldiacylglycerol(GlcADG) and etc.

Table 2 List of the 144 differential expressed proteins in PCOS patients’ GCs
Fig. 2
figure 2

ldentification of the differential metabolomics profiles of FF between PCOS and CON patients based on a volcano plot and hierarchical clustering analysis. a. Volcano plot, down-requlated and up-regulatedmetabolites in PCOS compared to CON are marked in blue and red, respectively. The -axis represents the log2 fold change of metabolites, while the Y-axis represents the fold change of the -log10 P value determined by the Student's t-test The variable importance in the projection (VIP) value is represented by the dot size. b. Heatmap of the hierarchical clustering analysis.There are 77 distinct metabolites presented

Correlation analysis between lipid metabolite concentrations and clinical parameters

We performed an association analysis of differential lipid metabolites and clinical indicators between the PCOS and CON groups (Fig. 3). The results were presented by hierarchical clustering heatmap: in the PCOS versus CON group,the concentrations TAG,DAG,PE,PC and HexCer-AP were found to be positively associated with TT,AMH, No. of oocytes retrieved, MII oocytes, and fertilization (P < 0.05). Significant negative correlations were found between LPC,SM, SHexCer,PC and BMI, TT,AMH, No. of oocytes retrieved,MII oocytes and fertilization(P < 0.05). However, TAG and PE were inversely linked with age,frozen embryo rate and high-quality embryo rate.

Fig. 3
figure 3

Spearman correlation analysis. The horizontal and vertical coordinates represent the metabolites and clinical indicators in this group, and the color blocks at different positions represent the correlation coefficients between metabolites and clinical indicators at corresponding positions. Red indicates positive correlation, blue indicates negative correlation, and the darker the color, the stronger the correlation. Significant associations are marked with asterisked (p < 0.05)

Analysis of inflammatory factors and lipid metabolites

The concentrations of TNF-α and IL-6 in follicular fluid of PCOS patients were significantly higher than those of the CON group(P < 0.05).In order to further explore the relationship between inflammatory factors and lipid metabolism, we selected the inflammatory factors TNF-α and IL-6, which are closely related to lipid and PCOS, for comprehensive correlation analysis.The results showed that proinflammatory variables correlated with lipid metabolites(Fig. 4c).IL-6 was positively correlated with SM(d14:0/23:1) and DAG(18:1/18:2),TNF-α was positively correlated with PC(20:3e/26:4),SM(d14:0/23:1) and LPC(18:2)(P < 0.05).

Fig. 4
figure 4

a, b FF TNF-α and IL-6 concentrationsof PCOS and CON groups;c:Correlation analysis between inflammatory factor concentrations and lipid metabolites performed by Mantel test in RStudio

Proteomic analysis

Mass spectrometry quality control detection

As shown in the figure, the length distribution of peptides found by mass spectrometry in this experiment met the quality control requirements, and the molecular weight and coverage of the experimental protein were in line with expectations (Fig. 5a–c). The mass error of most spectra is less than 5 ppm, which accords with the high precision of orbital trap mass spectrometry(Fig. 5d). Most of the proteins correspond to more than two peptides, indicating the accuracy and credibility of the quantitative results(Fig. 5e).

Fig. 5
figure 5

A total of 70,296 peptides and 7423 proteins were identified, of which 7326 proteins could be quantified. ac The length distribution of peptides identified by mass spectrometry in this experiment met the quality control requirements, and the molecular weight and coverage of the experimental protein were in line with expectations. d The mass error of most spectra is less than 5 ppm, which accords with the high precision of orbital trap mass spectrometry. e Most of the proteins correspond to more than two peptides, indicating the accuracy and credibility of the quantitative results

Protein analysis that differs between groups

The differentially expressed proteins were screened and evaluated (Use a change horizon of a substantial increase of > 0.05 and a differential expression ratio > 1.5), and the differential multiple less than -0.67 and p.value < 0.05 as the degree of modification required for meaningful downregulation,the contents of 144 proteins in patients with PCOS were significantly different from those in controls. Details of these proteins are shown in Table 3. Of these proteins, 105 are down-regulated and 39 are up-regulated (Fig. 6a). The heat map (Fig. 6b) also shows the level of these proteins' expression, which is noticeably aggregating in samples from the PCOS and the CON groups.

Table 3 Differentially Expressed Metabolites between PCOS and CON
Fig. 6
figure 6

Analysis of differentially expressed protein data between PCOS and CON. a Protein quantification results differential analysis statistics are presented in the form of volcano plots. Lower panel a volcano plot generated for two group comparison. It is a visualized graph by plotting "" log2 fold change "" on the x-axis versus; b The heat map (P < 0.05)

GO functional enrichment analysis was performed for up-regulated and down-regulated proteins respectively, and the results were shown in Fig. 7a. Terms that up-regulated protein enrichment include blood microparticle, external encapsulating structure, extracellular matrix, collagen − containing extracellular matrix, platelet degranulation, platelet alpha granule, receptor − mediated endocytosis, hemoglobin binding, spherical high-density lipoprotein particle, platelet aggregation, platelet alpha granule lumen and etc. Terms that down-regulated protein enrichment include mitochondrial inner membrane, mitochondrial membrane, organelle inner membrane, mitochondrial envelope, mitochondrial protein-containing complex, respirasome, inner mitochondrial membrane protein complex, electron transfer activity, electron transport chain, respiratory electron transport chain, oxidative phosphorylation, electron transport, mitochondrial ATP synthesis coupled, ATP synthesis coupled electron transport, respiratory chain complex, mitochondrial respirasome, cellular respiration and etc. Statistics on the distribution of differentially expressed proteins were performed in GO secondary annotations, including three categories: Biological Process, Cellular Component and Molecular Function, which explain the biological roles of proteins from different perspectives. GO analysis of up-regulated proteins showed that(Fig. 7b): enriched BP include regulated exocytosis, leukocyte mediated immunity, endocytosis, receptor-mediated endocytosis, platelet degranulation, myeloid leukocyte activation and etc. The down-regulated proteins were enriched in mitochondrion organization, ion transmembrane transport,generation of precursor metabolites and energy, electron transport chain, ATP metabolic process, oxidative phosphorylation, leukocyte activation engaged in immune response, energy derivation by oxidation of organic compounds, cellular respiration,cell activation engaged in immune response.

Fig. 7
figure 7

a GO functional enrichment analysis; b GO functional enrichment analysis. Statistics on the distribution of differentially expressed proteins were performed in GO secondary annotations, including three categories: Biological Process, Cellular Component and Molecular Function, which explain the biological roles of proteins from different perspectives


PCOS,a complex endocrine and metabolic syndrome brought on by the interplay of environmental, genetic, and lifestyle factors,is considered to cause infertility. Clinical features of PCOS include hyperandrogenism, obesity and insulin resistance, accompanied by many long-term complications, such as diabetes, hypertension, hyperlipidemia, cardiovascular diseases and so on. Nowadays, due to technological advances, it is now possible to detect this pathology at an early stage and allow ART treatment to improve the pregnancy rate of PCOS. It has been possible to identify novel proteins and metabolites that are involved in a variety of metabolic and cellular pathways, including the pathogenesis of PCOS, thanks to the advancement of proteomics and metabolomics studies [18]. However, due to the limitation of time and materials, the study of PCOS is still insufficient. In this study,we collected the samples of FF for lipidomic analysis and GCs for proteomic analysis to study the pathogenesis of PCOS.

There were no significant differences in clinical data between the two groups except bLH, LH/FSH, and TT levels.The limited sample size may be the primary cause of these outcomes. There should be a clear clinical difference between the two groups of individuals when the sample size is increased to a specific point.To ensure the freshness of the sample, we selected recent patients from our center, which resulted in a limited sample.Nonetheless, the chosen sample's clinical data showed the fundamental clinical traits of PCOS.The statistical results suggested that there was a typical metabolic syndrome in PCOS patients without statistically significant difference in age and BMI.The results of the expression of proteins and lipids in this investigation are consistent with large sample investigations reported in the literature.

Changes of lipid metabolism in FF of young women with PCOS

About 70% of PCOS patients have abnormal lipid metabolism, which leads to a series of related clinical manifestations and complications[19]. PCOS affects the patient's oocytes and leads to a low fertilization rate during ART. The FF constitutes the microenvironment for follicle development and oocyte maturation, including secretions from GCS, thecal cells, and oocytes in or around the follicle. Several metabolomic studies of FF have been conducted, including those on carbohydrate, amino acid, and lipid metabolism. In studies that have been done, a number of studies have found that abnormal lipid metabolism involves a number of metabolic pathways such as fatty acid metabolism, glyceripid metabolism, glycerophospholipid metabolism and so on [6, 13].

In this study,we collected FFs of patients and performed a UHPLC-MS–MS analysis. The patients were split into two groups(CON and PCOS) to study differences of each group. The identified lipid species belong to diverse lipid classes,including 53 different types such as ceramides, Lysophophatidylcholine, Phosphatidylcholine, Sphingomyelin, triacylglycerol and etc.

Here, the majority of lipid metabolites were greater in the FF of PCOS group than in the CON group,mainly including TAG,PE,DAG,HexCer-AP. TG usually maintains a high concentration of water during oocyte maturation: during oocyte maturation, lipolysis activity is high, leading to intracellular glycerol biosynthesis. Glycerol kinase expression is also increased in oocytes to facilitate the conversion of glycerol to glycerol 3P and synthesis of TAG to provide energy [20]. In vitro maturation(IVM) of follicles from animal experiments suggests that glycerol and glycerol kinase are essential for follicular development, but that high levels of TG reduce follicular maturation rates. Overweight patients with PCOS often have dyslipidemia, including elevated serum TAG levels [7, 21]. In this study, the BMI of PCOS group has no difference in that of CON group(P > 0.05). Through Spearman correlation analysis, TAG was highly correlated with high-quality embryos and showed a negative correlation trend. The up-regulated hemolytic PE may be due to impaired lysophosphatidyltransferase (LPCAT4) and/or LYPLA1 activity. From this, we hypothesized that the level of TG was increased in the FF of PCOS which was independent of BMI. TG plays a role in oocyte maturation and is an essential component. However, excessive TG levels can affect oocyte development.

Meanwhile, the level of LPC, SM, SHexCer and PC were considerably lower in PCOS FF compared to the CON group,indicating disease-related dysfunction of glycerophospholipid metabolism. Similar trends in PC,LPC decline were discovered in PCOS research using serum and urine [6, 13]. Glycerophospholipids [22], as major structural lipids in mammalian cell membranes, they are essential for controlling transport, signaling, and protein activity. Hemolytic PC is highly associated with apoptosis, inflammation, and glucose regulation.LPC can transmit signals across the cell membrane and has a significant role in glucose regulation [23]. This study also showed that LPC, PC was highly correlated with BMI. In particular, hemolytic PE was positively correlated with No. of oocytes retrieved,MII oocytes, 2PN fertilization, high-quality embryos. All these points to the important role of glycerophospholipid metabolism in ART outcome.

The results found lower levels of SM and SHexCer observed in the FF of the PCOS group, indicating that sphingolipid metabolism is down-regulated in PCOS follicles. The majority of sphingolipids can serve as both structural and signaling molecules for membranes. Sphingolipids have been linked to a number of biological processes, including cell death, proliferation, immunological response, tissue invasion, and metastasis, despite the fact that their involvement in these processes are not fully understood [24]. Glycerophospholipid metabolism is interconnected with sphingolipid metabolism through PC and ceramide synthesis of SM [25]. Y. Shi et al. found that the activation of the PCOS gene YAP (Yes-associated protein) may be impacted by the down-regulated sphingolipid metabolism [26].

Proteomics of GCs in women with PCOS suggests molecular defects in follicular development

According to the findings of granulosa cell proteomics, 144 proteins were significantly expressed differently in PCOS patients compared to controls. Go functional enrichment analysis revealed that the main enrichment functions of the differentially expressed proteins could be summarized as platelet degranulation, blood coagulation and inflammatory response. PCOS is characterized by a prothrombotic state, often accompanied by lipid abnormalities and platelet dysfunction that induce inflammation. Coagulation and fibrinolytic parameters typically need to be assessed in PCOS patients[27]. Platelets are involved in the formation of corpus luteum and angiogenesis of ovarian granulosa cells, and dysregulation of ovarian angiogenesis can cause abnormal follicular development in PCOS[28, 29].Aye et al. found that in young women with PCOS, acute hypertriglyceridemia caused IR and enhanced platelet activation[30].In this study, the abundant protein changes in platelet degranulation and blood coagulation in patients with PCOS may be the cause of platelet dysfunction, and are also related to IR and inflammation. And we speculated that these differential proteins may be related to follicular development disorder in PCOS patients, thereby participating in the pathogenesis of PCOS.

In addition, inflammation-rich proteins were identified in this analysis, and their association with PCOS has been reported in the literature,such as A1BG,APOC1,AZGP1,CCN4 and etc. [31,32,33,34], have a tight connection to the metabolism of lipids, glucose, and obesity, inflammatory response, and insulin resistance,indicating their diagnostic value in PCOS.

Adipocyte factor α-2-Glycoprotein 1 (AZGP1) is a 41 kDa protein that controls insulin sensitivity and glucose lipid metabolism.Several reports suggest that ZAG is associated with inflammation. Liu Y et al. proposed that ZAG may inhibit ERK phosphorylation mediated by TGF-β and inhibit neuroinflammation mediated by TNF-α and IL-6 [35].β-α2-Glycoprotein (ZAG) has been shown to be associated with IR and PCOS [32], respectively. ZAG has been demonstrated to facilitate in vitro fat breakdown and weight loss by enhancing fat loss and reducing TG levels as well as other components of metabolic syndrome (MetS). In a different study, it was observed that IR obese people had lower levels of ZAG mRNA and protein in their adipose tissue compared to lean persons. Lai et al. [36] observed that compared to healthy women, PCOS patients had significantly lower levels of circulating ZAG. However, no research has shown a connection between circulating ZAG levels and the many traits or symptoms used to identify PCOS.

CCN4,known as airfoil-free (Wnt) -induced signaling pathway protein-1 (WISP-1), lately been reported as a novel adipokine obese and insulin resistant people have higher quantities of it in their blood.WISP1 is a downstream target protein of Wnt/ β-catenin. Human adipose-derived stem cells' ability to differentiate into adipocytes can be impacted by the control of the Wnt/-catenin signaling pathway, according to research conducted on animals [37]. WISP1 has been reported to avoid neuronal cell damage and apoptotic degeneration by activating the PI3K/Akt pathway during oxidative damage. Ferrand N [38] found that CCN4 inhibits adipocyte differentiation by inhibiting PPARγ activity. It has been verified that the serum WISP1 value of PCOS group is higher than that of control group. In addition, the patients in obese PCOS subgroup had higher serum WISP1 levels than people who had normal weight and those in obese control subgroups[38]. It is anticipated that WISP1 will emerge as a new therapeutic target for obesity. WISP1 may have a function in the interaction between insulin resistance, inflammation, and obesity. It can be speculated that WISP1 can also be a treatment's therapeutic objective of abnormal lipid metabolism in PCOS patients.

Relationship between proteomics and lipid metabolomics

The development of oocytes, ovulation, fertilization, embryonic growth and development, and the success of pregnancy are all intimately linked to lipid metabolism.He et al. [39] found that inflammation and abnormal lipid metabolism in PCOS patients have adverse effects on oocyte quality and IVF reproductive outcome.Proteomics analysis showed that the differentially expressed proteins were enriched in inflammation and lipid metabolism.Furthermore, differential lipids and PCOS-related inflammatory markers are tightly associated.Embryo rate in clinical indicators is also correlated with lipid metabolites.It is concluded that the chronic inflammatory state of PCOS patients causes abnormal lipid metabolism, which affects the changes of related proteins and embryo quality in IVF.


This study showed that significant alterations in glycerolipid, glycerophospholipid, and sphingolipid metabolism have been observed in FF of PCOS patients, and these changes are intimately linked to inflammatory variables.The differential proteins in GCS of PCOS patients are related to lipid metabolism and inflammation.These metabolic dysfunctions are intimately tied to oocyte development in ART. In this experiment, we screened out proteins closely related to lipid metabolism, such as ZAG and WISP1. The limited sample size we examined and the high degree of individual variation in clinical samples may have ramifications for both clinical data and experimental analysis, to sum up. Among the patients we included, there were no statistically significant variations in the results of ART, which is different from the outcomes in a larger trial. This point is the deficiency of the present experiment.

Despite the sample size was limited, these results led us to further investigate the characteristics of the follicular environment related to lipid metabolism and metabolism in PCOS. In clinical practice, it is important to screen lipid metabolic profile and correct chronic inflammatory state before assisted reproductive treatment.Further studies will verify the relationship between differential proteins related to lipid metabolism and follicular development of PCOS, and provide targets and directions for the treatment of PCOS.


Study participants

Patients with PCOS (n = 8) and normal patients with tubal or male factor infertility (n = 8, control group) who underwent IVF were recruited from the Reproductive Medicine Center of Affiliated Hospital of Nanjing University of Chinese Medicine, between May 2022 and January 2023. PCOS was diagnosed using the American Society of Reproductive Medicine's (ASRM) updated criteria. The Nanjing Medical University Ethics Committee authorized our experimental plan(Ethics No. 2022NL-200-03). All the research participants signed the informed consent of the patients. Women with tubal factor infertility met the inclusion criteria for ART. Women who had undergone preimplantation genetic testing (PGT), had a donor oocyte/embryo recipient cycle, diabetes, heart disease, or chronic hypertension were not eligible.

Collection of follicular fluid and Granulosa cells

All patients were treated with a classical mid-luteal gonadotropin-releasing hormone agonist (GnRHa) long-term regimen.Ovulation was induced by injection of 10,000 IU human chorionic gonadotropin (hCG) on the trigger day. After 36 h, the oocytes were collected by transvaginal B-ultrasound guided puncture. The follicular fluid was extracted from the patient's large follicles (> 18 mm), and the first tube of follicular fluid without blood pollution was collected. The samples were centrifuged at 1500 g for 15 min at room temperature, and the supernatant was removed and stored for future use at – 80 °C. For granulosa cell collection,follicular fluid was centrifuged at 2500 g for 15 min.The precipitate was aspirated and resuspended with percoll, and centrifugation was continued at 2000 g for 20 min to separate blood cells. After the granule cells being collected, they were washed twice with phosphate buffer solution (PBS) for later use.

Determination of cytokine concentration

ELISA was used to measure the concentrations of TNF-α and IL-6 in follicular fluid.

Lipid metabolome analysis

Metabolites extraction

In an Eppendorf tube, a 100 L aliquot of the sample was transferred, and 480 L of the extract solution (MTBE: methanol1 = 5: 1) was then added. The materials were sonicated for 10 min after being vortexed for 30 s in a cold water bath. Following this, they were centrifuged at 3000 rpm for 15 min at 4 °C (RCF = 900(g), R = 8.6 cm) and then incubated at − 40 °C for 1 h. 350 L of supernatant were moved to a fresh tube and dried at 37 °C in a vacuum concentrator. In order to rehydrate the dried materials, 100 L of a 50% methanol/dichloromethane solution was added. And then the samples were vortexed for 30 s, sonicated in ice water for ten minutes,centrifuged at 13,000 rpm for fifteen minutes at four degrees Celsius. A fresh glass vial was filled with 70 L of supernatant for LC/MS analysis. To create the quality control (QC) samples, 20 L of the supernatant from each sample was combined.

LC–MS/MS analysis

The LC–MS/MS studies were carried out using a ultra high performance liquid chromatography-Q exactive orbitrap-mass spectrometry (UPLC-QE-MS) system (1290, Agilent Technologies) and a Kinetex C18 column (2.1 × 100 mm, 1.7 m, Phenomen). Mix mobile phases A and B as required.Per 1000 mL of combined solvent, 50 mL of ammonium formate (10 mmol/L) were added. Set elution gradient feed, autosampler temperature, column temperature, injection volume for positive and negative results, and mobile phase flow rate according to instructions.

Using a QE mass spectrometer and acquisition software (Xcalibur 4.0.27, Thermo), mass spectra were collected in data-dependent acquisition (DDA) mode. In this mode, the acquisition program continuously evaluates the entire scan MS spectrum.

Data preprocessing and annotation

Using ProteoWizard's ‘msconvert’ application, the original data files were converted to mzXML files. Peak detection, extraction, alignment, and integration were carried out using the XCMS CentWave algorithm,with minfrac for annotation set to 0.5 and cutoff for annotation set to 0.3. The LipidBlast package, created by R program based on XCMS for spectrum matching, was used to identify lipids.

Proteomic analysis

Protein extraction

GCs samples from 3 normal controls and 3 patients with PCOS were analyzed.Samples of GCs were taken out from − 80 °C, and appropriate 8 M urea cracking solution was added to samples for each group. The samples were placed on ice for 1 h after ice bath ultrasound and vortex fully broken. During this period, vortices were rotated for several seconds every 15 min, and then centrifuged at 12000g at 4 °C for 1 h. The supernatant was transferred to a fresh centrifuge tube for protein concentration determination using the Bradford kit.

Trypsin digest

In a 56 °C water bath for 25 min, an equal volume of the protein solution was added with dithiothreitol at a concentration of 5 mm. Then, 14 mm of iodoacetamide was added, and it was held at ambient temperature and covered from light for 30 min. Using 25 mm Tris HCl, the samples' urea concentration was reduced to below 2 m. Trypsin was added at a mass ratio of 1 to 100 (protein: trypsin), and the mixture was then enzymatically digested for 16 to 18 h at 37  C.

TMT labelling

After being digested, the peptides were desalted using a C18 column and vacuum-freeze dried. According to the TMT kit's operating instructions, peptides were tagged after being dissolved at 0.2 m teab.

HPLC fractionation

The XBridge BEH130 C18 column (300 mm 150 mm, 1.7 mm, Waters) was used to grade the peptides using high pH reverse HPLC. These were the operations: 20 mM ammonium formate (pH = 10) made up mobile phase A, and 100% acetonitrile made up mobile phase B, and the gradient of the peptide segment was 3%-41% acetonitrile. The peptides were separated and cyclically collected into 20 components for 73 min, and each component was concentrated into the peptide powder by vacuum and stored at − 20 °C.

LC–MS analysis

With 0.1% (v/v) formic acid added, the peptide powder was redissolved, and the EASY-nLC 1200 ultra-high performance liquid system was used to elute it.Formic acid aqueous solution containing 0.1% (v/v) is the mobile phase A, and formic acid acetonitrile solution containing 0.1% is the mobile phase B. Elution gradient: 0 to 5 min, 3% to 5%; 5 to 24 min, 5% to 15%; 24 to 45 min, 15% to 28%; 45 to 52 min, 28% to 38%; 52 to 65 min, 100%; flow rate: 300 nL/min.

The peptides were eluted, ionized, and subjected to series mass spectrometric analyses using Thermo Scientific's Orbitrap Fusion™ Lumos™ Tribrid™. High-resolution Orbitrap was used to detect and analyze the parent ions and secondary fragments of the peptide at 2.1 kV source voltage.Two mass spectrometry analyses' scanning range and resolution were established in accordance with the guidelines, and the data-dependent scanning (DDA) data acquisition mode was used. The steps were as follows: after primary scanning, the top 20 parent ions of the peptide segment with the highest signal intensity were selected, sequentially added to the HCD collision pool, fragmented with 32% fragmentation energy, and then secondary mass spectrometry was carried out. Automatic gain control (AGC) was set to 1.25 E5 and serial mass spectrometry was employed for scanning in order to increase the efficiency of mass spectrometry. To prevent parent ion duplication, the dynamic exclusion time is set at 60 s.

Databases search

Secondary mass spectrometry data were extracted with Maxquant (v1.6.14). Retrieval parameters were set: Uniprot-Hom-Sapiens-proteome_up000005640 (2021.06) was used in the database, and Trypsin/P was selected as the technique of enzyme digestion. The number of missing cuts is set to 2; The minimum length of peptide was set to 7 amino acid residues. Methionine oxidation and acetylation of the protein's N-terminal were defined as variable modifications, whereas the alkylation of cysteine was set as a fixed modification. The quantitative technique was set to TPT-6plex, and the FDR for identifying proteins and PSMs was set to 1%.

Analysis of clinical data

Data were analyzed with SPSS 26.0 to highlight the fundamental characteristics of the study population. Means and standard deviations (SDs) were used to express continuous variables with normally or nearly normally distributed distribution.

Data availability

The data underlying this article are available in the article and in its online supplementary material. Portions of the data from this article will be shared upon reasonable request from the corresponding author.


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We would like to thank everyone who took part in the study, including the organizations and individuals.


National Natural Science Foundation of China, Grant/Award Number: 82074479; Special Foundation of Clinical Medicine of Jiangsu Provincial Bureau of Science and Technology, Grant/Award Number: ZT202107.

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Authors and Affiliations



Yu Qian and Yun Tong contributed to study design; Yaqiong Zeng,Huang Jingyu, Kailu Liu and Juan Chen contributed to manuscript editing; Mengya Gao and Li Liu contributed to experimental studies; Yanli Hong contributed to data analysis. All authors read and approved the final manuscript.

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Correspondence to Yanli Hong or Xiaowei Nie.

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The Nanjing Medical University Ethics Committee authorized our experimental plan(Ethics No. 2022NL-200-03). All the research participants signed the informed consent of the patients.

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Qian, Y., Tong, Y., Zeng, Y. et al. Integrated lipid metabolomics and proteomics analysis reveal the pathogenesis of polycystic ovary syndrome. J Transl Med 22, 364 (2024).

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