Follicular fluid lipid fingerprinting from women with PCOS and hyper response during IVF treatment

Follicular fluid lipidomics analysis reveals altered lipid signatures in patients with polycystic ovary syndrome

BACKGROUND

This research investigates the metabolic profiles of follicular fluid (FF) samples from patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilisation and aims to identify diagnostic and therapeutic biomarkers for PCOS through lipidomic analysis.

METHODS

We performed non-targeted lipid analysis of FF samples from women with PCOS (n = 6) and normal controls (n = 6) using ultra-high-performance liquid chromatography-tandem mass spectrometry. Differential lipids between the two groups were screened using multidimensional statistical analysis, followed by fold change analysis and t-tests to identify potential PCOS biomarkers.

RESULTS

Multivariate statistical analysis revealed significant differences in FF lipid levels between the PCOS and control groups. Five different lipids were selected as standards, with p < .05. Phosphatidylcholine (PC), the main differentially expressed lipid, was significantly increased in the FF of the POCS group and was closely related to other lipids.

CONCLUSIONS

Using ultra-high-performance liquid chromatography-tandem mass spectrometry, we investigated lipid biomarkers based on FF lipidomics to provide useful information for the discovery of diagnostic markers for PCOS. Our study identified five distinct lipids as potential markers of PCOS, with PC being the primary aberrant lipid found in the FF of patients with PCOS.

High coverage of targeted lipidomics revealed lipid changes in the follicular fluid of patients with insulin-resistant polycystic ovary syndrome and a positive correlation between plasmalogens and oocyte quality

Background

Polycystic ovary syndrome with insulin resistance (PCOS-IR) is the most common endocrine and metabolic disease in women of reproductive age, and low fertility in PCOS patients may be associated with oocyte quality; however, the molecular mechanism through which PCOS-IR affects oocyte quality remains unknown.

Methods

A total of 22 women with PCOS-IR and 23 women without polycystic ovary syndrome (control) who underwent in vitro fertilization and embryo transfer were recruited, and clinical information pertaining to oocyte quality was analyzed. Lipid components of follicular fluid (FF) were detected using high-coverage targeted lipidomics, which identified 344 lipid species belonging to 19 lipid classes. The exact lipid species associated with oocyte quality were identified.

Results

The number (rate) of two pronuclear (2PN) zygotes, the number (rate) of 2PN cleaved embryos, and the number of high-quality embryos were significantly lower in the PCOS-IR group. A total of 19 individual lipid classes and 344 lipid species were identified and quantified. The concentrations of the 19 lipid species in the normal follicular fluid (control) ranged between 10-3 mol/L and 10-9 mol/L. In addition, 39 lipid species were significantly reduced in the PCOS-IR group, among which plasmalogens were positively correlated with oocyte quality.

Conclusions

This study measured the levels of various lipids in follicular fluid, identified a significantly altered lipid profile in the FF of PCOS-IR patients, and established a correlation between poor oocyte quality and plasmalogens in PCOS-IR patients. These findings have contributed to the development of plasmalogen replacement therapy to enhance oocyte quality and have improved culture medium formulations for oocyte in vitro maturation (IVM).

Nontargeted metabolomics analysis of follicular fluid in patients with endometriosis provides a new direction for the study of oocyte quality

Endometriosis is a common, estrogen-dependent chronic gynecological disease that endangers the reproductive system and systemic metabolism of patients. We aimed to investigate the differences in metabolic profiles in the follicular fluid between infertile patients with endometriosis and controls. A total of 25 infertile patients with endometriosis and 25 infertile controls who were similar in age, BMI, fertilization method and ovulation induction treatment were recruited in this study. Metabolomics analysis of follicular fluid was performed by two methods of high-performance liquid chromatography tandem mass spectrometry. There were 36 upregulated and 17 downregulated metabolites in the follicular fluid of patients in the endometriosis group. KEGG pathway analysis revealed that these metabolites were enriched in phenylalanine, tyrosine and tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, phenylalanine metabolism and pyrimidine metabolism pathways. A biomarker panel consisting of 20 metabolites was constructed by random forest, with an accuracy of 0.946 and an AUC of 0.988. This study characterizes differences in follicular fluid metabolites and associated pathway profiles in infertile patients with endometriosis. These findings can provide a better comprehensive understanding of the disease and a new direction for the study of oocyte quality, as well as potential metabolic markers for the prognosis of endometriosis.

Upregulated Ribosomal Pathway Impairs Follicle Development in a Polycystic Ovary Syndrome Mouse Model: Differential Gene Expression Analysis of Oocytes

Polycystic ovary syndrome (PCOS), a common endocrine disorder, is associated with impaired oocyte development, leading to infertility. However, the pathogenesis of PCOS has not been completely elucidated. This study aimed to determine the differentially expressed genes (DEGs) and epigenetic changes in the oocytes from a PCOS mouse model to identify the etiological factors. RNA-sequencing analysis revealed that 90 DEGs were upregulated and 27 DEGs were downregulated in mice with PCOS compared with control mice. DNA methylation analysis revealed 30 hypomethylated and 10 hypermethylated regions in the PCOS group. However, the DNA methylation status did not correlate with differential gene expression. The pathway enrichment analysis revealed that five DEGs (Rps21, Rpl36, Rpl36a, Rpl37a, and Rpl22l1) were enriched in ribosome-related pathways in the oocytes of mice with PCOS, and the immunohistochemical analysis revealed significantly upregulated expression levels of Rps21 and Rpl36. These results suggest that differential gene expression in the oocytes of mice in PCOS is related to impaired folliculogenesis. These findings improve our understanding of PCOS pathogenesis.

Serum Metabolomic Signature Predicts Ovarian Response to Controlled Stimulation

In in vitro fertilization (IVF), it is meaningful to find novel biomarkers predicting ovarian response in advance. The aim of the study was to identify serum metabolomics predicting ovarian response after controlled ovarian stimulation (COS). Blood samples collected at the start of pituitary downregulation and on the fifth day after COS using Liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods were analyzed to quantify metabolites. Demographic data were calculated with SPSS version 22.0 software. Multivariate statistics were used to analyze metabolomics dataset. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic model. Analyses revealed 50 different metabolomics between the pre- and post-COS groups. Compared with baseline, amino acids increased significantly following COS. At baseline, acetylglycine was more abundant in FOI<1 group, while glycine and lipids increased in FOI≥1 group. After COS, glycine, N-acetyl-L-alanine, D-alanine, and 2-aminomuconic acid were higher in those with FOI≥1, but L-glutamine was abundant in FOI<1. ROC curves indicated that combination of glycine, acetylglycine, and lipids predicts different responses to COS (AUC=0.866). Serum metabolism might reflect the response to ovarian stimulation. Higher glycine and PC may be a good predictor for response to COS.

Follicular Fluid: A Powerful Tool for the Understanding and Diagnosis of Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) represents one of the leading causes of anovulatory infertility and affects 5% to 20% of women worldwide. Until today, both the subsequent etiology and pathophysiology of PCOS remain unclear, and patients with PCOS that undergo assisted reproductive techniques (ART) might present a poor to exaggerated response, low oocyte quality, ovarian hyperstimulation syndrome, as well as changes in the follicular fluid metabolites pattern. These abnormalities originate a decrease of Metaphase II (MII) oocytes and decreased rates for fertilization, cleavage, implantation, blastocyst conversion, poor egg to follicle ratio, and increased miscarriages. Focus on obtaining high-quality embryos has been taken into more consideration over the years. Nowadays, the use of metabolomic analysis in the quantification of proteins and peptides in biological matrices might predict, with more accuracy, the success in assisted reproductive technology. In this article, we review the use of human follicular fluid as the matrix in metabolomic analysis for diagnostic and ART predictor of success for PCOS patients.

Lipidomics analysis of human follicular fluid form normal-weight patients with polycystic ovary syndrome: a pilot study

Background

The polycystic ovary syndrome (PCOS) is the most common endocrine associated with insulin resistance, even in the absence of overweight. The global lipid profile of the follicular fluid in PCOS with normal weight as yet has not been investigated. The objection of this pilot study was to explore the changes of lipids in the follicular fluid of PCOS with normal weight.

Methods

Follicular fluid samples were collected from patients who underwent IVF, including normal-weight women without PCOS (control group, n = 10) and normal-weight women with PCOS (PCOS group, n = 8). A lipidomic analysis was performed by high performance liquid chromatography/ mass spectrometry (HPLC-MS). Multidimensional statistical analysis was performed to disclose the global differences between the two groups. Further, differential lipid analysis between the two groups was performed by Fold Change Analysis (FC Analysis) and T-test to screen potential markers.

Results

All 812 species of 32 subclasses of lipids were identified by lipidomics analysis. 108 kinds of lipids were considered as the potential candidate differential metabolites with the score of variable importance in the project (VIP) more than 1 by the orthogonal partial least squares discriminant analysis. 32 lipids were significantly different between the PCOS group and the control group simultaneously with FC > 1.5 or FC < 0.67, p-value < 0.05 and VIP value > 1. These differential species of lipid belong to lipid subclasses including triglycerides (TG), phosphatidylethanolamines (PE) and phosphatidylinositols (PI).

Conclusion

The identified differential lipids in the follicular fluid may be considered as candidate biomarkers as well as therapeutic targets of PCOS with normal-weight.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00885-y.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

Ovarian environment aging: follicular fluid lipidomic and related metabolic pathways

PURPOSE

The decline in female fecundity with age may be caused by decreased oocyte quality, a factor that may be associated with the altered composition of follicular fluid (FF).

METHODS

In an effort to better understand follicular aging and the role of lipids in a given biological system, we present a prospective study that compares lipid profiles of FF from women older than 35 years (aging group, n = 12) to women equal or younger than 35 years old (control group, n = 17). FF lipids were extracted, and mass spectra were generated using a Waters Synapt G1 Q-TOF in MS mode. MS data was evaluated for both multi- and univariate statistics. The lipids identified as potential biomarkers of follicle aging were attributed by the online databases Lipid Maps, followed by pathway network analysis using Cytoscape software.

RESULTS

The in vitro fertilization (IVF) parameters showed significant differences in aging, number of follicles, total number of oocytes and oocytes in MII, and number of injected oocytes. Additionally, FF from the aging group revealed 11 lipids with higher abundance, while FF from the control group included 4 lipids with higher abundance.

CONCLUSIONS

We suspect that aging may influence lipid metabolism in a downstream cascade leading, ultimately, to decreased oocyte quality. The discovery of target lipids may assist oocyte selection for IVF in the future. Furthermore, systems biology approach based on post-genomic medicine may help unravel a number of altered mechanisms not previously understood.

Targeted metabolomics reveals reduced levels of polyunsaturated choline plasmalogens and a smaller dimethylarginine/arginine ratio in the follicular fluid of patients with a diminished ovarian reserve

STUDY QUESTION

Does the metabolomic profile of the follicular fluid (FF) of patients with a diminished ovarian reserve (DOR) differ from that of patients with a normal ovarian reserve (NOR)?

SUMMARY ANSWER

The metabolomic signature of the FF reveals a significant decrease in polyunsaturated choline plasmalogens and methyl arginine transferase activity in DOR patients compared to NOR patients.

WHAT IS KNOWN ALREADY

The composition of the FF reflects the exchanges between the oocyte and its microenvironment during its acquisition of gametic competence. Studies of the FF have allowed identification of biomarkers and metabolic pathways involved in various pathologies affecting oocyte quality, but no large metabolomic analysis in the context of ovarian ageing and DOR has been undertaken so far.

STUDY DESIGN, SIZE, DURATION

This was an observational study of the FF retrieved from 57 women undergoing in vitro fertilization at the University Hospital of Angers, France, from November 2015 to September 2016. The women were classified in two groups: one including 28 DOR patients, and the other including 29 NOR patients, serving as controls.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Patients were enrolled in the morning of oocyte retrieval after ovarian stimulation. Once the oocytes were isolated for fertilization and culture, the FF was pooled and centrifuged for analysis. A targeted quantitative metabolomic analysis was performed using high-performance liquid chromatography coupled with tandem mass spectrometry, and the Biocrates Absolute IDQ p180 kit. The FF levels of 188 metabolites and several sums and ratios of metabolic significance were assessed by multivariate and univariate analyses.

MAIN RESULTS AND THE ROLE OF CHANCE

A total of 136 metabolites were accurately quantified and used for calculating 23 sums and ratios. Samples were randomly divided into training and validation sets. The training set, allowed the construction of multivariate statistical models with a projection-supervised method, i.e. orthogonal partial least squares discriminant analysis (OPLS-DA), applied to the full set of metabolites, or the penalized least absolute shrinkage and selection operator with logistic regression (LASSO-LR), applied to the ratios and sums of the metabolites. Both multivariate models showed good predictive performances when applied to the validation set. The final penalized model retained the three most significant variables, i.e. the total dimethylarginine-to-arginine ratio (Total DMA/Arginine), the sum of the polyunsaturated choline plasmalogens (PUFA ae), and the patient's age. The negative coefficients of Total DMA/Arginine and PUFA ae indicated that these FF variables had lower values in DOR patients than in NOR patients.

LARGE SCALE DATA

N/A.

LIMITATIONS REASONS FOR CAUTION

This study presents two limitations. First, with this targeted metabolomics analysis, we have explored only a limited portion of the FF metabolome. Second, although the signature found was highly significant, the mechanism underlying the dysfunction remains undetermined.

WIDER IMPLICATIONS OF THE FINDINGS

The understanding of the mechanisms implied in ovarian ageing is essential for providing an adequate response to affected women desiring pregnancy. Our study proposes an incoming signature that may open new paths towards this goal.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by the University Hospital of Angers, the University of Angers, and the French national research centers, INSERM and the CNRS. There were no competing interests.

Hyper response to ovarian stimulation affects the follicular fluid metabolomic profile of women undergoing IVF similarly to polycystic ovary syndrome

INTRODUCTION

During in vitro fertilization (IVF), the hyper response to controlled ovarian stimulation (COS) is a common characteristic among patients diagnosed with polycystic ovary syndrome (PCOS), although non-diagnosed patients may also demonstrate this response.

OBJECTIVES

In an effort to investigate follicular metabolic characteristics associated with hyper response to COS, the present study analyzed follicular fluid (FF) samples from patients undergoing IVF.

METHODS

FF samples were obtained from patients with PCOS and hyper response during IVF (PCOS group, N = 15), patients without PCOS but with hyper response during IVF (HR group, N = 44), and normo-responder patients receiving IVF (control group, N = 22). FF samples underwent Bligh and Dyer extraction, followed by metabolomic analysis by ultra-performance liquid chromatography mass spectrometry, considering two technical replicates. Clinical data was analyzed by ANOVA and chi-square tests. The metabolomic dataset was analyzed by multivariate statistics, and the significance of biomarkers was confirmed by ANOVA.

RESULTS

Clinical data showed differences regarding follicles production, oocyte and embryo quality. From the 15 proposed biomarkers, 14 were of increased abundance in the control group and attributed as fatty acids, diacylglycerol, triacylglycerol, ceramide, ceramide-phosphate, phosphatidylcholine, and sphingomyelin. The PCOS patients showed increased abundance of a metabolite of m/z 144.0023 that was not attributed to a class.

CONCLUSION

The clinical and metabolic similarities observed in the FF of hyper responders with and without PCOS diagnosis indicate common biomarkers that could assist on the development of accessory tools for assessment of IVF parameters.

Multiple reaction monitoring (MRM)-profiling for biomarker discovery applied to human polycystic ovarian syndrome

RATIONALE

We describe multiple reaction monitoring (MRM)-profiling, which provides accelerated discovery of discriminating molecular features, and its application to human polycystic ovary syndrome (PCOS) diagnosis. The discovery phase of the MRM-profiling seeks molecular features based on some prior knowledge of the chemical functional groups likely to be present in the sample. It does this through use of a limited number of pre-chosen and chemically specific neutral loss and/or precursor ion MS/MS scans. The output of the discovery phase is a set of precursor/product transitions. In the screening phase these MRM transitions are used to interrogate multiple samples (hence the name MRM-profiling).

METHODS

MRM-profiling was applied to follicular fluid samples of 22 controls and 29 clinically diagnosed PCOS patients. Representative samples were delivered by flow injection to a triple quadrupole mass spectrometer set to perform a number of pre-chosen and chemically specific neutral loss and/or precursor ion MS/MS scans. The output of this discovery phase was a set of 1012 precursor/product transitions. In the screening phase each individual sample was interrogated for these MRM transitions. Principal component analysis (PCA) and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

To evaluate the method's performance, half the samples were used to build a classification model (testing set) and half were blinded (validation set). Twenty transitions were used for the classification of the blind samples, most of them (N = 19) showed lower abundances in the PCOS group and corresponded to phosphatidylethanolamine (PE) and phosphatidylserine (PS) lipids. Agreement of 73% with clinical diagnosis was found when classifying the 26 blind samples.

CONCLUSIONS

MRM-profiling is a supervised method characterized by its simplicity, speed and the absence of chromatographic separation. It can be used to rapidly isolate discriminating molecules in healthy/disease conditions by tailored screening of signals associated with hundreds of molecules in complex samples.