All of the PCOS patients and healthy controls were recruited from the Zhejiang Provincial Hospital of Chinese Medicine (Hangzhou, China). This study was approved by the Ethics Committee of Zhejiang Provincial Hospital of Chinese Medicine. The signed informed consents were obtained from all the participators prior to inclusion in this study.
According to the Rotterdam criteria, 2003, PCOS patients can be diagnosed if two of the three criteria are present after excluding congenial adrenal hyperplasia, Cushing’s syndrome, androgen secreting tumors, or other related disorders. The three criteria are (1) oligo- and/or anovulation; (2) clinical and/or biochemical signs of hyperandrogenism (clinical manifestations of hyperandrogenism include presence of acne, hirsutism, and androgenic alopecia); (3) polycystic ovaries by ultrasound examination: presence of 12 or more follicles in each ovary measuring 2−9 mm in diameter and/or ovarian volume >10 cm3.
The inclusion criteria for PCOS cases in this study were: diagnosed with PCOS according to the Rotterdam criteria, 2003 ; adolescent females (18-40 years old); had at least 2 years of menstrual history. Exclusion criteria: had received any androgenic drug or sex steroid therapy in the past 3 months before the study; current pregnant, delivery or miscarriage within the preceding 3 months; congenital adrenal hyperplasia, androgen-secreting tumors, and other diseases with hyperandrogenism, thyroid dysfunction, hyperprolactinemia, cardiovascular diseases, diabetes or any chronic diseases. Controls group were healthy female volunteers, 18-40 years old, regular menstrual cycles and normal androgen levels, no PCOS and IR, and no evident disease was detected in them during the course of the study. According to the above mentioned inclusion/exclusion criteria, a total of 31 PCOS patients and 31 health participants were included from December of 2018 to April of 2019 in the present study.
The clinical characteristics data of the enrolled participators were recorded at the time of recruitment. After fasting for 8 h, blood sample from each participator was collected. The serum samples were stored at -80 ℃ for subsequent assay.
Clinical laboratory tests
Serum concentrations of Fasting glucose, Fasting insulin, follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), prolactin (PRL), testosterone (T), progesterone (P), TC, TG, HDL-c, LDL-c in all PCOS patients and control participants were detected by Immulite 2000 analyzer (Siemens Healthcare Diagnostics Products Ltd., UK) using two site chemiluminescent immunometric assays.
Sample preparation and metabolite extraction
The polar metabolome extraction: After thawed at 4℃,a 100 μL serum samples were added with 400 μL methanol-acetonitrile (1:1, v:v; including isotope internal standard tryptophan -d5, cetylic acid-[13C]12), centrifugated at 15,000 g for 15 min. Then a 200 μL supernatants were dried under low temperature vacuum (Thermo Scientific, USA) to obtained the sample for UPLC-HRMS analysis. Before analysis, the samples were redissolved with 100 μL 10% methanol (including multiple internal standards).
The lipidomic metabolome extraction: After thawed at 4℃,a 50 μL serum samples were added with 300 μL methanol (including internal standards: Ceramide (d18:1/17:0)、PC(17:0/17:0)、TG(15:0/15:0/15:0)), swirled for 120 s, and added with 900 μL MTBE, 250 μL ultrapure water. After vortex mixed and vibrated at room temperature for 15 min, solution was placed under 4℃, 30 min for stratify. Then a 900 μL supernatants were transferred into EP pipe and dried under low temperature vacuum (Thermo Scientific, USA) to obtained the sample for UPLC-HRMS analysis. Before analysis, the samples were redissolved with 600 μL acetonitrile-isopropanol mixture.
UPLC-HRMS instrumentation and measurement conditions
Untargeted metabolomics analysis was conducted by using three different analytical methods (M1-3) on a Ultimate 3000 ultra-high performance liquid chromatograph coupled with Q ExactiveTM quadrupole-Orbitrap high resolution mass spectrometer (UPLC-HRMS) system (Thermo Scientific,USA).
Untargeted metabolomics analysis was conducted by using three different analytical methods (M1-3). Method 1 and 2 (M1, 2) was used for the polar metabolome extracts analysis on UPLC-HRMS system with positive and negative ionization detection, respectively. Metabolites were separated by an AcquityTM HSS C18 column (Waters Co., USA, 2.1 × 100 mm) for M1, and eluted by 0.1% formate/water (A) and acetonitrile (B) in linear gradient from 2% organic mobile phase to 98% in 10 min. Furthermore, other mobile phases consisting of water and ammonium acetonitrile/methanol both containing ammonium bicarbonate buffer salt were employed to eluted metabolites separated on an AcquityTM BEH C18 column (Waters Co., USA, 1.7 μm, 2.1 × 100 mm), the gradient was used as follow: from 0 ~10 min, 2% organic phase ramped to 100%, and from 10~15 min, column washing and equilibrating. Untargeted lipidomic analysis was operated based on Method 3, the chromatographic separation conditions were maintained under positive and negative ionization detection mode, respectively. The used column was an Accucore C30 core-shell column, the mobile phase were 60% acetonitrile in water (A) and 10% acetonitrile in isopropanol (B) both containing 10 mM ammonium formate and 0.1% formate. The separation gradient was optimized as follow: initial 10% B, ramping to 50% in 5 min, and further increasing to 100% in 23 min, the the rest 7 min for column washing and equilibration. For Method 1~3, the flow rate was 0.4 mL/min, injection volume was 5 μL, and column temperature was 50℃.
Mass spectrometer system
For Method 1~2, the quadrupole-Orbitrap mass spectrometer was all operated under identical ionization parameters with a heated electrospray ionization source except ionization voltage including sheath gas 45 arb, aux gas 10 arb, heater temperature 355℃, capillary temperature 320℃ and S-Lens RF level 55%. The metabolome extracts were profiled with full scan mode under 70,000 FWHM resolution with AGC 1E6 and 200 ms max injection time. The scan range was 70~1000 m/z. QC samples were repeatedly injected to acquired Top 10 data dependent MS2 spectra (full scan-ddMS2) for comprehensive metabolite and lipid structural annotation. 17,500 FWHM resolution setting were used for full MS/MS data acquisition. Apex trigger, dynamic exclusion and isotope exclusion was turned on, precursor isolation window as set at 1.0 Da. Stepped normalized collision energy was employed for collision induced disassociation of metabolite using ultra-pure nitrogen as fragmentation gas. All the data acquired as centroid format. For Method 3, the ionized lipid molecules were detected using the same parameters as previous description 6.3.1. 300-2000 m/z lipid extracts were profiled with the same parameters as the metabolome used. Lipid were structurally identified through acquiring data dependent MS2 spectra, the key settings included 70,000 FWHM full scan resolution, 17,500 FWHM MS/MS resolution, loop count 10, AGC target 3e6, maximum injection time 200 ms and 80 ms for full scan and MS/MS respectively, dynamic exclusion 8 s. Stepped normalized collision energy 25%+40% and 35% were employed for positive and negative mode after optimization.
Metabolomics data analysis
The full scan and data-dependent MS2 metabolic profiles data were further processed with Compound Discoverer software for comprehensive component extraction. The polar metabolites were structural annotated through searching acquired MS2 against a local proprietary iPhenomeTM SMOL high resolution MS/MS spectrum library created using authentic standards, NIST 17 Tandem MS/MS library (National Institute of Standards and Technology), local version MoNA (MassBank of North America), as well as mzCloud library (Thermo Scientific, USA). Besides, exact m/z of MS1 spectra was searched against a local KEGG, HMDB metabolite chemical database. For metabolite identification or structural annotation, mass accuracy of precursor within ± 5ppm was prerequisite, meanwhile, isotopic information including at least 1 isotopes within 10 ppm and fit score of relative isotopic abundance pattern 70% were introduced to confirm the chemical formula in addition to exact mass. Furthermore, retention time information as well as high resolution MS/MS spectra similarity was employed to strictly confirm the structural annotation of metabolites. The area under curve (AUC) values as extracted as quantitative information of metabolites with XCalibur Quan Browser information, all peak areas data for the annotated metabolites were exported into Excel software for trim and organization before statistics (Microsoft, USA). And on the other hand, untargeted lipidomics data was processed with LipidSearch software including peak picking, lipid identification. The acquired MS2 spectra were searching against in silico predicted spectra of diverse phospholipid, neutral glycerolipid, spingolipid, neutral glycosphingolipids, glycosphingolipids, steroids, fatty ester, etc. The mass accuracy for precursor and MS/MS product ions searching were 5 ppm and 5 mDa, respectively. The MS/MS similarity score threshold was set at 5. The potential ionization adduct including hydrogen, sodium, ammonium for positive and hydrogen loss, formate and acetate adduct for negative mode. The lipid identification was strictly manually checked and investigated one-by-one to eliminate false positive chiefly basing on peak shake, adduct ions behavior, fragmentation pattern, and chromatographic behavior.
All the clinical data were computed using SPSS18.0 version software. An unpaired, two-tailed student t test was performed on clinical biochemical data, the chi-square test was used for comparison of categorical variables. p-value < 0.05 was considered to be statistically significant. The metabolome and lipidome data deriving from different measurements were normalized to sample weight used prior to further process, respectively. Then, the resultant quantitative information from foregoing methods were merged together and those detected with multiple methods was excluded to guaranteed uniqueness of metabolite and lipid, and then Log10 tranformed for final statistical analysis. The principal component analysis was conducted with SIMCA-P software (Umetrics, Sweden), and other univariate analysis including independent sample t-test and p value FDR adjust, as well as metabolic pathway analysis was conduct on MetaboAnalyst website.