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

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Biomarker; Follicular fluid; Granulosa cells; Lipid metabolomics; Polycystic ovary syndrome; Proteomics.

Fig. 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

Fig. 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

Fig. 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)

Fig. 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

Fig. 5

A total of 70,296 peptides and 7423 proteins were identified, of which 7326 proteins could be quantified. a–c 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

Fig. 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)

Fig. 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