DIA proteomics analysis through serum profiles reveals the significant proteins as candidate biomarkers in women with PCOS.

The clinical characteristics and biochemical data of the study subjects were collected and analyzed, and the relative results were presented in Table 1 . In this study, the study subjects included 31 healthy women as controls and 31 PCOS women. There are no statistically differences for the age and BMI between the two groups ( p value > 0.05). For PCOS related biochemical data, the levels of fasting glucose, LH, T, TG, LDL-c and LH/FSH ratio were significantly higher in PCOS patients than those in the controls, the levels of PRL, HDL-c were significantly lower in PCOS patients than those in the controls ( p value < 0.05). Table 1 Clinical characteristic of the study subjects Control (n = 31) PCOS (n = 31) p value Age [years] 24.52 ± 2.31 24.20 ± 4.49 0.750 BMI [kg/m 2 ] 20.48 ± 2.67 22.27 ± 3.56 0.081 Fasting glucose [mmol/L] 4.68 ± 0.42 5.25 ± 1.20 0.026 < 0.05 FSH [IU/L] 5.15 ± 1.32 5.59 ± 2.86 0.440 LH [IU/L] 5.85 ± 2.74 9.82 ± 8.57 0.017 < 0.05 LH/FSH 1.14 ± 0.50 1.73 ± 0.97 0.003 < 0.01 PRL [mIU/L] 466.83 ± 231.05 309.64 ± 158.59 0.003 < 0.01 E2 [pmol/L] 209.95 ± 127.20 220.25 ± 246.80 0.837 T [nmol/L] 1.12 ± 0.40 1.65 ± 0.67 < 0.001 P [nmol/L] 0.93 ± 0.36 1.05 ± 0.80 0.451 TC [mmol/L] 4.44 ± 0.63 4.65 ± 0.76 0.306 TG [mmol/L] 0.75 ± 0.29 1.13 ± 0.51 0.002 < 0.05 HDL-c [mmol/L] 2.23 ± 0.52 1.46 ± 0.41 < 0.001 LDL-c [mmol/L] 1.69 ± 0.29 2.54 ± 0.62 < 0.001 Clinical characteristic of the study subjects The intensity of all identified proteins showed that the abundance of these proteins spans largely, covering 5 orders of magnitude, suggesting that the instrument is more sensitive (Fig.  1 a). Coefficient of variation (CV) of the control group and PCOS group samples were 37.7% and 32.9%, respectively. Pearson correlation analysis of the protein intensity showed that the correlation index was among 0.8–1, suggested that the experimental procedures are reproducible (Fig.  1 b, c). Blood coagulation is a interference factor for serum proteomics analysis, the decrease of fibrinogen indicates the coagulation event. We detected the serum levels of fibrinogen alpha chain (FGA), fibrinogen beta chain (FGB), fibrinogen gamma chain (FGG), the results showed that there are no significant decreases of FGA, FGB and FGG in all samples, suggested there is no coagulation event in our tested samples (Fig.  1 d). Fig. 1 Quality control analysis of the proteomics analysis. a Dynamic range of 809 quantified proteins. X-protein numbers, Y-Log10 protein intensities. b Box plot of CV (%) in each sample group, PCOS, Control and QC group. Median CV were also indicated. c Person correlation in 62 samples. d Protein Intensity of FGA, FGB and FGG in 62 serum samples. No of samples much lower abundance of FGA, FGB and FGG indicated coagulation during serum collection. CV: coefficient of variation; QC: quality control; FGA: Fibrinogen alpha chain; FGB: Fibrinogen beta chain; FGG: Fibrinogen gamma chain Quality control analysis of the proteomics analysis. a Dynamic range of 809 quantified proteins. X-protein numbers, Y-Log10 protein intensities. b Box plot of CV (%) in each sample group, PCOS, Control and QC group. Median CV were also indicated. c Person correlation in 62 samples. d Protein Intensity of FGA, FGB and FGG in 62 serum samples. No of samples much lower abundance of FGA, FGB and FGG indicated coagulation during serum collection. CV: coefficient of variation; QC: quality control; FGA: Fibrinogen alpha chain; FGB: Fibrinogen beta chain; FGG: Fibrinogen gamma chain There were 550 proteins were identified and filtered from the dataset. The PCA and OPLS-DA were performed based on the quantitative data of these proteins to determine the principal axes on protein abundance variations in PCOS cases and controls. The PCA (Fig.  2 a) showed moderate separation of the serum proteins between the PCOS and control group at t(2) axis, but with overlaps at t(1) axis. Further OPLS-DA result (Fig.  2 b, c) showed complete separation of the serum proteins between the PCOS and control group. The quality parameters of the model were: R 2 Y = 0.94; Q 2  = 0.71. The OPLS-DA model was also verified by 999 times permutation test (Fig.  2 d). Fig. 2 Principal component analysis of proteome profiles. a Score plot of PCA analysis to overview classification trend of proteome profiles of PCOS and Control groups. Model parameter: R2X = 0.46 (cumulative variance proportion of 8 principal components). b Score plot of OPLS-DA modeling to maximize inter-group differentiation of proteomic data between PCOS and Control groups. Model parameter: 1 predictive component + 2 orthogonal components, R 2 Y = 0.94, Q 2  = 0.71. c S-plot of OPLS-DA modeling using DIA proteomics quantitative data. d 999 times permutation test of OPLS-DA modeling Principal component analysis of proteome profiles. a Score plot of PCA analysis to overview classification trend of proteome profiles of PCOS and Control groups. Model parameter: R2X = 0.46 (cumulative variance proportion of 8 principal components). b Score plot of OPLS-DA modeling to maximize inter-group differentiation of proteomic data between PCOS and Control groups. Model parameter: 1 predictive component + 2 orthogonal components, R 2 Y = 0.94, Q 2  = 0.71. c S-plot of OPLS-DA modeling using DIA proteomics quantitative data. d 999 times permutation test of OPLS-DA modeling A total of 80 proteins were significantly differentially abundant in PCOS patients compared to the healthy controls from the criterion p value after Benjamin-Hocheberg FDR adjustment p value was less than 0.05. The detailed information of these proteins were presented in Table 2 . Among these proteins, 56 were down-regulated, 24 were up-regulated (Fig.  3 a). Heatmap (Fig.  3 b) also showed the expression intensity of these proteins, and these proteins were clustered significantly in the samples between the PCOS and control groups. Table 2 List of the 80 differential expressed proteins in PCOS patients plasma No Protein accessions Proteins Proteins FDR adj p value PCOS_Con PCOS/Con ratio − Log10 (FDR adj p value PCOS/Con) 1 O60814 H2B Histone H2B 1.87E−03 3.852 2.729 2 P04908 H2A Histone H2A 3.29E−07 3.658 6.482 3 P11277 SPTB Spectrin beta chain, erythrocytic 1.13E−03 3.475 2.945 4 P02730 SLC4A1 Band 3 anion transport protein 2.70E−05 3.279 4.569 5 P62805 H4 Histone H4 4.47E−09 2.784 8.350 6 P05388 RPLP0 60S acidic ribosomal protein P0 1.30E−02 2.660 1.886 7 O75460 ERN1 Serine/threonine-protein kinase/endoribonuclease IRE1 1.66E−02 2.201 1.781 8 P30041 PRDX6 Peroxiredoxin-6 6.48E−03 2.160 2.189 9 B9A064 IGLL5 Immunoglobulin lambda-like polypeptide 5 3.17E−02 2.154 1.498 10 P04040 CAT Catalase 2.76E−02 1.934 1.559 11 P00738 HP Haptoglobin 4.10E−02 1.926 1.387 12 P13716 ALAD Delta-aminolevulinic acid dehydratase 2.41E−02 1.903 1.618 13 Q9P2P1 NYNRIN Protein NYNRIN 3.62E−02 1.875 1.441 14 P01857 IGHG1 Ig gamma-1 chain C region 4.16E−02 1.835 1.380 15 Q06830 PRDX1 Peroxiredoxin-1 5.92E−05 1.832 4.228 16 P32119 PRDX2 Peroxiredoxin-2 9.11E−03 1.629 2.040 17 P05062 ALDOB Fructose-bisphosphate aldolase B 3.85E−02 1.614 1.415 18 P30043 BLVRB Flavin reductase (NADPH) 1.48E−02 1.612 1.830 19 P00918 CA2 Carbonic anhydrase 2 1.66E−02 1.563 1.781 20 Q15293 RCN1 Reticulocalbin-1 3.62E−02 1.450 1.441 21 P00558 PGK1 Phosphoglycerate kinase 1 4.75E−02 1.376 1.323 22 P02751 FN1 Fibronectin 1.25E−03 1.298 2.904 23 Q92954 PRG4 Proteoglycan 4 4.37E−02 1.289 1.359 24 P78417 GSTO1 Glutathione S-transferase omega-1 3.04E−02 1.276 1.517 25 P04275 VWF von Willebrand factor 2.76E−02 1.275 1.559 26 Q9H3P2 NELFA Negative elongation factor A 3.45E−02 1.160 1.462 27 Q6UX71 PLXDC2 Plexin domain-containing protein 2 2.94E−02 0.876 1.531 28 P08195 SLC3A2 4F2 cell-surface antigen heavy chain 4.39E−02 0.872 1.357 29 P51884 LUM Lumican 4.10E−02 0.866 1.388 30 O75882 ATRN Attractin 4.08E−02 0.864 1.390 31 P20851 C4BPB C4b-binding protein beta chain 4.66E−02 0.857 1.331 32 P00734 F2 Prothrombin 4.45E−03 0.852 2.351 33 P14151 SELL L-selectin 2.59E−02 0.851 1.586 34 P06396 GSN Gelsolin 4.16E−02 0.844 1.380 35 O00533 CHL1 Neural cell adhesion molecule L1-like protein 1.15E−02 0.838 1.938 36 P55058 PLTP Phospholipid transfer protein 2.88E−02 0.824 1.541 37 P07359 GP1BA Platelet glycoprotein Ib alpha chain 1.89E−02 0.819 1.723 38 P08253 MMP2 72 kDa type IV collagenase 2.59E−02 0.816 1.586 39 Q14126 DSG2 Desmoglein-2 1.87E−02 0.814 1.728 40 Q9NPH3 IL1RAP Interleukin-1 receptor accessory protein 8.88E−03 0.811 2.051 41 P07998 RNASE1 Ribonuclease pancreatic 2.59E−02 0.811 1.586 42 P02776 PF4 Platelet factor 4 2.70E−02 0.806 1.569 43 P07996 THBS1 Thrombospondin-1 2.59E−02 0.797 1.586 44 Q07954 LRP1 Prolow-density lipoprotein receptor-related protein 1 3.81E−02 0.793 1.419 45 Q15166 PON3 Serum paraoxonase/lactonase 3 1.66E−02 0.790 1.781 46 P05452 CLEC3B Tetranectin 9.11E−03 0.789 2.040 47 P10643 C7 Complement component C7 8.88E−03 0.788 2.051 48 P54289 CACNA2D1 Voltage-dependent calcium channel subunit alpha-2/delta-1 3.49E−02 0.780 1.457 49 P05067 APP Amyloid beta A4 protein 2.70E−02 0.764 1.569 50 Q12860 CNTN1 Contactin-1 1.25E−03 0.762 2.904 51 Q99784 OLFM1 Noelin 3.71E−02 0.758 1.431 52 P40189 IL6ST Interleukin-6 receptor subunit beta 3.17E−02 0.756 1.498 53 P02671 FGA Fibrinogen alpha chain 1.25E−03 0.723 2.904 54 P40197 GP5 Platelet glycoprotein V 2.65E−03 0.720 2.576 55 Q9BZE9 ASPSCR1 Tether containing UBX domain for GLUT4 4.13E−02 0.720 1.384 56 Q10588 BST1 ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase 2 3.71E−02 0.688 1.431 57 P13473 LAMP2 Lysosome-associated membrane glycoprotein 2 4.49E−02 0.682 1.348 58 P04066 FUCA1 Tissue alpha-L-fucosidase 4.37E−02 0.680 1.359 59 Q8IVU3 HERC6 Probable E3 ubiquitin-protein ligase HERC6 3.17E−02 0.668 1.498 60 P02786 TFRC Transferrin receptor protein 1 2.66E−03 0.667 2.575 61 P25311 AZGP1 Zinc-alpha-2-glycoprotein 1.66E−02 0.656 1.781 62 Q8WZ42 TTN Titin 4.10E−03 0.653 2.388 63 P01009 SERPINA1 Alpha-1-antitrypsin 2.70E−02 0.645 1.569 64 P05090 APOD Apolipoprotein D 3.17E−02 0.629 1.498 65 P16109 SELP P-selectin 3.88E−02 0.618 1.412 66 Q15848 ADIPOQ Adiponectin 4.16E−02 0.607 1.380 67 P04745 AMY1A Alpha-amylase 1 2.70E−02 0.582 1.569 68 Q16853 AOC3 Membrane primary amine oxidase 2.45E−02 0.575 1.611 69 P54108 CRISP3 Cysteine-rich secretory protein 3 2.11E−02 0.573 1.676 70 P58335 ANTXR2 Anthrax toxin receptor 2 3.17E−02 0.565 1.498 71 Q5VY43 PEAR1 Platelet endothelial aggregation receptor 1 2.13E−02 0.563 1.671 72 P18065 IGFBP2 Insulin-like growth factor-binding protein 2 5.87E−03 0.551 2.231 73 P0DMV8 HSPA1A Heat shock 70 kDa protein 1A 2.70E−02 0.455 1.569 74 Q86YW5 TLT-1 Trem-like transcript 1 protein 3.11E−07 0.452 6.507 75 P21926 CD9 CD9 antigen 3.64E−03 0.411 2.438 76 P08567 PLEK Pleckstrin 4.45E−03 0.392 2.351 77 P37802 TAGLN2 Transgelin-2 2.37E−04 0.352 3.626 78 O15394 NCAM2 Neural cell adhesion molecule 2 1.10E−02 0.350 1.957 79 Q15942 ZYX Zyxin 3.91E−05 0.219 4.408 80 Q8IVT5 KSR1 Kinase suppressor of Ras 1 2.04E−03 0.060 2.691 Fig. 3 Identification of 26 up-regulated and 54 down regulated proteins. a Volcano plot of quantitative DIA proteome data visualizing A/Con. Proteins with Q < 0.05 as significant proteins were red highlight. Number of upregulated and down-regulated proteins were indicated in each volcano plot. b Heatmap and clustering analysis result of differential proteins in p value of significance level p value < 0.05 after FDR adjustment List of the 80 differential expressed proteins in PCOS patients plasma Identification of 26 up-regulated and 54 down regulated proteins. a Volcano plot of quantitative DIA proteome data visualizing A/Con. Proteins with Q < 0.05 as significant proteins were red highlight. Number of upregulated and down-regulated proteins were indicated in each volcano plot. b Heatmap and clustering analysis result of differential proteins in p value of significance level p value < 0.05 after FDR adjustment The GO terms of these 56 down-regulated and 24 up-regulated proteins enriched were shown in Fig.  4 . GO terms were divided into three categories, biological process (BP), cellular component (CC), and molecular function (MF). GO analysis of the downregulated proteins showed that the enriched BP terms include response to stimulus, biological regulation, metabolic process, cell proliferation, and especially reproduction term. The GO terms that upregulated proteins enriched were similar to those downregulated proteins. Volcano plot (Fig.  5 ) of GO and KEGG enrichment results showed that 54 downregulated proteins were enriched in terms including platelet degranulation, cell adhesion, biological adhesion, cell activation, regulated exocytosis, blood coagulation, coagulation, hemostasis, defense response and inflammatory response. 26 upregulated proteins were enriched in terms including viral carcinogenesis, alcoholism, systemic lupus erythematosus, cofactor catabolic process, hydrogen peroxide catabolic process, antioxidant activity, cellular oxidant detoxification, cellular detoxification, antibiotic catabolic process and hydrogen peroxide metabolic process. Fig. 4 Volcano plot of GO (GOBP, GOCC and GOMF) and KEGG enrichment result using over-representation method. Left: 54 DOWN-regulated proteins in PCOS group compared to sample Control group; Right: 26 Up-regulated proteins in PCOS group compared to Control group. Those genesets with FDR adjusted p value < 0.05 were labeled Fig. 5 Gene ontology analysis of the proteins. a GOBP, GOCC and GOMP enrichment results of 54 downregulated proteins. b GOBP, GOCC and GOMP enrichment results of 26 upregulated proteins. Y axis: protein numbers. GOBP: gene ontology biological process; GOCC: gene ontology cellular component; GOMP: gene ontology molecular function Volcano plot of GO (GOBP, GOCC and GOMF) and KEGG enrichment result using over-representation method. Left: 54 DOWN-regulated proteins in PCOS group compared to sample Control group; Right: 26 Up-regulated proteins in PCOS group compared to Control group. Those genesets with FDR adjusted p value < 0.05 were labeled Gene ontology analysis of the proteins. a GOBP, GOCC and GOMP enrichment results of 54 downregulated proteins. b GOBP, GOCC and GOMP enrichment results of 26 upregulated proteins. Y axis: protein numbers. GOBP: gene ontology biological process; GOCC: gene ontology cellular component; GOMP: gene ontology molecular function In order to explore the diagnoses potential of these 80 proteins in PCOS cases, all the 80 proteins were further introduced for ROC curves analysis. And top 10 proteins with the higher area under curve (AUC) were presented in Fig.  6 and Table 3 , including H4, H2A, TLT-1, PRDX1, SLC4A1, Protein NYNRIN (NYNRIN), Zyxin (ZYX), Transgelin-2 (TAGLN2), Contactin-1 (CNTN1), Histone H2B (H2B). It could be observed that the AUC of H4, H2A, TLT-1 were all over than 0.9 (AUC = 0.966, 0.940, 0.912, respectively), and they were all differentially abundant in PCOS and control subjects significantly, indicated promising diagnosis values of these proteins. Fig. 6 ROC-curve analysis of top 10 proteins ranked AUC values Table 3 Top 10 proteins ranked based on area under ROC curve Name Protein AUC FC(PCOS/Con) p value P62805 Histone H4 0.966 2.79 4.47E−09 P04908 Histone H2A 0.940 3.66 3.29E−07 Q86YW5 Trem-like transcript 1 protein 0.912 0.45 3.11E−07 Q06830 Peroxiredoxin-1 0.855 1.83 5.92E−05 P02730 Band 3 anion transport protein 0.854 3.27 2.70E−05 Q9P2P1 Protein NYNRIN 0.852 1.88 3.62E−02 Q15942 Zyxin 0.843 0.22 3.91E−05 P37802 Transgelin-2 0.828 0.35 2.37E−04 Q12860 Contactin-1 0.807 0.76 1.25E−03 O60814 Histone H2B 0.805 3.86 1.87E−03 ROC, receiver operating characteristic; AUC, area under curve; FC, fold change ROC-curve analysis of top 10 proteins ranked AUC values Top 10 proteins ranked based on area under ROC curve ROC, receiver operating characteristic; AUC, area under curve; FC, fold change Based on the former DIA proteomics results and the identified significant biomarkers, we chose the expression of top five significant proteins (H4, H2A, TLT-1, PRDX1, SLC4A1) from the ROC curves results for further validation using Western blot analysis. As shown in Fig.  7 , the expression of H4, H2A, PRDX1, SLC4A1 in PCOS group were significantly increased comparing to the control group, but the expression of TLT-1 was significantly decreased in PCOS group comparing to the control group. More importantly, tendency of these detected proteins between the PCOS and control groups were consisted with the DIA proteomics results. Fig. 7 Western blot analysis of top five significant proteins (H4, H2A, TLT-1, PRDX1, SLC4A1). n = 3, compared to the control group, ▲ p  < 0.05, ▲▲ p  < 0.01 Western blot analysis of top five significant proteins (H4, H2A, TLT-1, PRDX1, SLC4A1). n = 3, compared to the control group, ▲ p  < 0.05, ▲▲ p  < 0.01

UltraPure™ Tris Hydrochloride (Tris–HCl) (Invitrogen, CA, USA), Ammonium biocarbonate (NH 4 HCO 3 ) (Sigma-Aldrich, Shanghai, China), Trifluoroacetic Acid (TFA) (Sigma-Aldrich, Shanghai, China), DT-Dithiothreitol (DTT) (Sigma-Aldrich, Shanghai, China), Iodoacetamide (IAM) (Sigma-Aldrich, Shanghai, China), Lysyl Endopeptidase, MS Grade (LysC) (Wako, Japan), Sequencing Grade Modified Trypsin (Trypsin) (Promega, WI, USA), SOLAμ HRP 2 mg/1 mL 96 well plate (Thermo Scientific, Shanghai, China), Methanol, Optima ™ LC/MS Grade (Fisher Scientific, PA, USA), Acetonitrile, Optima™ LC/MS Grade, (Fisher Scientific, PA, USA), ddH 2 O (water from thermo ScientificTM water Purification system), High Select™ Top14 Abundant Protein Depletion Mini Spin Columns (Fisher Scientific, PA, USA). All the participators including 31 PCOS patients and 31 paired healthy female controls were recruited from the Chinese Medicine Hospital of Zhejiang province (Hangzhou, China). This study received the ethical approval from the Ethics Committee of Zhejiang Provincial Hospital of Chinese Medicine [approve no. 2020-KL-155-02]. All the participators signed informed consent forms before the start of the study. The inclusion criteria for PCOS cases were: adolescent females, diagnosed with PCOS, had at least 2 years of menstrual history. And in order to exclude the luteal phase, progesterone (P) level was less than 3.82. PCOS was diagnosed based on the recent androgen excess and Rotterdam criteria, 2003: clinical or biochemical hyperandrogenism and ovarian dysfunction: oligomenorrhea (menstrual cycle of more than 45 days) and/or polycystic ovaries on ultrasound (ovarian volume > 10 mL in at least one ovary) [ 10 ]. Exclusion criteria: other disorders with similar presentation (hyperprolactinemia, thyroid disorders, late-onset congenital adrenal hyperplasia, androgen-secreting ovarian or adrenal tumors, Cushing syndrome, or other related disorders) were excluded [ 11 ]. Healthy controls were volunteers with age and gender matched with PCOS patients. And no evident disease was detected in them during the course of the study. The clinical characteristics data of the enrolled participators were recorded at the time of recruitment. After fasting for 8 h, a venous blood sample from each participator was collected. The serum samples were stored at -80 ℃ for subsequent assay. Serum concentrations of follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), prolactin (PRL), testosterone (T), P in all PCOS patients and control participators were detected by Immulite 2000 analyzer (Siemens Healthcare Diagnostics Products Ltd., UK) using two site chemiluminescent immunometric assays. 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. Samples were prepared following the manufacturer’s instructions. The details were as following. All 62 samples were depleted with top 14 high-abundant depletion spin columns (Thermo) was placed at room temperature, 10 μl (about 600 μg) of serum sample was added, and incubation was carried out for 30 min at room temperature. The supernatant was collected by centrifugation at 1,000 g for 2 min, and the volume was about 320 μl, the solution system was 10 mM PBS, 0.15 M NaCl, 0.02% azide, pH 7.4. 320 μl of high-abundance protein-depleted sample was added to a 10 kD ultrafiltration tube (Millipore), 12,000 g , centrifuged for 10 min; added 200 μl of 8 M Urea, 12,000 g , centrifuged for 10 min, repeated once, and finally 50 μl of 8 M urea were added. Then the sample was added into a 96-well plate. 10 mM DTT solution was added and reacted at 37 °C for 30 min; IAM solution was added to a final concentration of 20 mM, and reacted at 37 °C for 30 min at room temperature in the dark for 30 min; DTT solution was added to a final concentration of 10 mM to quench the reaction. 1 μg of LysC was added to each sample, incubate at 37 °C for 2 h; 1 μg Trypsin was then added and incubated at 37 °C overnight; finally 10 μl of 10% TFA was added to terminate the reaction. Add 100 μl of methanol to SoLAμ HRP plate, 600 g , centrifuge for 1 min; add 100 μl of 80% ACN 0.1% TFA, 1000 g , centrifuge for 1 min; add 200 μl of 0.1% THA, 1000 g , centrifuge for 1 min; add the sample to SoLAμ In the HRP plate, 1000 g , centrifuge for 2 min; repeat loading the sample once; add 200 μl 0.1% THA, 1000 g , centrifuge for 2 min to wash the column; add 100 μl 80% ACN 0.1% TFA, 1000 g , centrifuge for 3 min, collect the elution; the elution is concentrated in the centrifuge. The peptides were dissolved to 0.5 μg/μl with 0.1% FA, and loaded with 1 μg of mass spectrometry for DIA analysis. DIA proteomic analysis was performed using a Thermo U3000nano RSLC nanoLC (Thermo Fisher Scientific) with Orbitrap Fusion LumosTM quadrupole-linear ion trap-electrostatic field orbitrap high resolution mass spectrometry (Thermo Fisher Scientific, USA), data acquisition software XCalibur 4.3 (Thermo Fisher Scientific). The analysis was performed following the manufacturer’s instructions, and the main parameters are as follows. Nano LC-UltiMateTM 3000 RSLC nano System; two Column Mode: Trap Column (Acclaim PepMap C18, 3 μm, 100 Å, 75 μm × 2 cm), Analytical Column (Acclaim PepMap C18, 2 μm, 100 Å, 75 μm × 25 cm); mobile phase: A: 0.1% formic acid in water; B: 0.1% formic acid in 80% acetonitrile; gradient: 3–8% B in 6 min, 8–30% B in 102 min, 30–100% B in 8 min, 100–100% B in 4 min; flow rate: 300 nL/min. Thermo Scientific Orbitrap Fusion Lumos; spray voltage: 2.1 kV; capillary temperature: 300 °C; S-lens: 50%; collision energy: 32% HCD; resolution setting: first level 60,000 m/z 200, two Level 30,000 m/z 200; Max IT: full MS 20 ms, full MS/MS 54 ms; parent ion scanning range: m/z 350–1200; product ion scanning range: start from m/z 200; number of windows: 70; isolation window: adjust the variable isolation window based on the eluted peptide m/z. Spectronaut X system was used for DIA proteomics data analysis. Briefly, 62 samples and QC data were imported into Spectronaut X, targeted extracted with laboratory built serum proteomics database, and the remaining parameters were controlled by default to control FDR < 1% of peptide and protein levels. Protein intensity was calculated by Spectronaut from the average of the top3 peptide was exported. This protein intensity was imported into Perseus and Metaboanalyst for statistical analysis. Log 2 transformation, feature filtering, missing value filling, and data normalization of raw data were done before statistical analysis. S0 0.1 and FDR p  < 0.05 was used as the threshold value for differential proteins selection. PCA, cluster analysis, and correlation analysis were performed in Metaboanalyst. The up-regulated and down-regulated proteins were imported into the Protein Center (Thermo) for functional enrichment annotation of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) with FDR p  < 0.05. Receiver operating characteristic (ROC) curves analysis was performed using SPSS software. Based on the former DIA proteomics results and the identified significant protein biomarkers, Western blot assay was further performed to validate the significance of biomarkers in PCOS. Serum samples were homogenized in RIPA buffer containing protease inhibitor and the protein concentration was measured using a BCA method. 10% sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) was used to separate the proteins, then the proteins were electrotransferred onto the nitrocellulose (NC) membranes. After blocking with non-fat mike for 1 h and washed with Tris-buffered saline containing Tween 20 (TBST), the membranes were probed with the primary antibodies against Histone H4 (H4, Affinity, dilution 1:2000), Histone H2A (H2A, Affinity, dilution 1:1000), Trem-like transcript 1 protein (TLT-1, R&D systems, 0.1 µg/mL), Peroxiredoxin-1 (PRDX1, Affinity, dilution 1:2000), Band 3 anion transport protein (SLC4A1, Affinity, dilution 1:2000), Transferrin (Affinity, dilution 1:2000) at 4 °C over-night. And subsequently followed by incubated with horseradish peroxidase-conjugated secondary antibodies for 2 h at room temperature. Transferrin was detected as an internal reference protein [ 12 ]. Protein bands were visualized by enhanced chemiluminescence reagents and the protein intensity was quantified using Image-J software. Data was expressed as mean ± standard deviation, group comparisons were processed using the two-tailed Student’s t -test, p  < 0.05 was considered as statistical significance.

Despite the great potential of -omic sciences in elucidating biological processes and monitoring disease progression, comprehensive proteomic analysis on PCOS patients has not been investigate fully to date [ 8 , 13 ]. In this study, by quantitative DIA proteomics, we aimed to investigate whether proteomics change in PCOS women serum samples compared to the healthy controls, and the results evidenced the serum proteomic profile alterations in PCOS female. As a result, there were 80 proteins significantly differentially expressed between PCOS patients and controls, including 54 downregulated and 26 upregulated proteins. GO and KEGG analysis showed that downregulated proteins were enriched in platelet degranulation, cell adhesion, cell activation, blood coagulation, hemostasis, defense response and inflammatory response terms; upregulated proteins were enriched in cofactor catabolic process, hydrogen peroxide catabolic process, antioxidant activity, cellular oxidant detoxification, cellular detoxification, antibiotic catabolic process and hydrogen peroxide metabolic process. ROC curves analysis showed that the AUC of H4, H2A, TLT-1 were all over than 0.9, indicated promising diagnosis values of these proteins. Serum proteomics results showed that there were 80 proteins differentially expressed between PCOS patients and controls significantly. Further GO and KEGG enrichment analysis indicated that the downregulated proteins were enriched in platelet degranulation, blood coagulation and inflammatory response. Reproductive disorders, such as PCOS, are often accompanied by platelet dysfunctions and thereby induced inflammation [ 14 ]. In patients with PCOS, coagulation and fibrinolysis parameters are usually evaluated. Platelet involves in granulosa cell corpus luteum formation and angiogenesis in ovary, and abnormal follicular development in PCOS is partly contributed by ovarian angiogenesis dysregulation. Aye et al. examined the effect of hypertriglyceridemia on IR and platelet function in young women with PCOS, and reported that acute hypertriglyceridemia induced IR, increased platelet activation both in control and PCOS groups [ 15 ]. PCOS patients also have risk to induce prothrombotic state, and platelet dysfunction might responsible for it. This study showed that changed proteins were enriched in platelet degranulation and blood coagulation in PCOS group, which indicated that these protein changes might contributed to the platelet dysfunction, and also associated with IR and inflammation. GO results also showed that these changed proteins were also enriched in reproduction biological process, which indicated that the proteomics changes in PCOS cases were directly associated with the PCOS pathogenesis and induced infertility. In addition, PCOS is also a low-grade chronic inflammation disease, continuous releasing of inflammatory mediators could perpetuate the inflammatory condition in women with PCOS [ 16 , 17 ]. Proteomics changes can also impact on inflammatory response and reveal the presence of inflammation in disease [ 18 ]. For the identified proteins that enriched in inflammatory response in this analysis, some proteins were related to blood coagulation, including thrombin (also called coagulation factor II), platelet factor 4, thrombospondin 1. Some publications also reported the associations of them with inflammation of PCOS [ 19 – 21 ]. Platelets are inflammatory anuclear cells, blood coagulation is an intrinsic pathway for proinflammation, blood coagulation factors also are important inflammatory mediators than just promoting or inhibiting blood coagulation [ 22 – 24 ]. The activation of coagulation factors could cause a proinflammatory response and initiate coagulation and downstream cellular signaling pathways. Our ROC curves results showed that the AUC of H4, H2A, TLT-1 were all over than 0.9, indicated a significant role of these three proteins in differentiating PCOS from controls. Among these three proteins, proteomics analysis showed that H4 and H2A was upregulated in PCOD patients with a PCOS/control ratio of 2.79 and 3.66, respectively. Histone is one of the critical components of chromatin, the amino acid residues at its N-terminus can be covalently modified, thus change the chromatin conformation and induce transcription or gene silencing [ 25 ]. This modification mainly including acetylation/deacetylation, methylation/demethylation, ubiquitination/deubiquitination, phosphorylation, sumo, biotin, etc. Excepting for gene expression controlling, histone modification also participates in cell division, cell apoptosis and memory formation by recruiting protein complex and affecting downstream proteins, and also has the impact on immune system and inflammatory reaction [ 26 ]. Histone subunits include H2A, H2B, H3 and H4, the histone modifications aforementioned could all be occurred and thus exhibit multiple functions. But previous studies about H4 or H2A in PCOS is rare. Monteiro’s study reported that in endometriosis patients, lesions had significantly lower levels of Histone H3K9ac and Histone H4K16 acetylation compared to eutopic endometrium from controls, and comparing to the control endometrium, the hypoacetylation of Histone H3/H4 within promoter regions of candidate genes known to be downregulated in endometriosis lesions, while the stereoidogenic factor 1 promoter region was enriched for acetylated H3 and H4 in lesions versus control tissues, correlating with its reported high expression in lesions [ 27 ]. Neonatal exposure to diethylstilbestrol (DES) can cause permanent alterations in female reproductive tract gene expression, infertility, and uterine cancer in mice, after DES treatment, three histone modifications associated with active transcription, including Histone H4 lysine 5 acetylation (H4K5ac), which were found to enriched at specific lactoferrin (Ltf) promoter regions in uterine [ 28 ]. This suggested that the alteration expression of multiple chromatin-modifying proteins and epigenetic marks might lead to altered reproductive function and increased cancer risk. TLT-1 is another protein with AUC over than 0.9 in ROC analysis. But different from the aforementioned two proteins, our proteomics analysis showed that TLT-1 was downregulated in PCOD patients with a PCOS/control ratio of 0.452. TLT-1 belongs to a kind of triggering receptors expressed on myeloid cells that play important roles in innate and adaptive immune responses, platelet aggregation, inflammation and insulted bleeding [ 29 ]. It mediates blood coagulation via binding fibrinogen. Based on the critical role in immune, inflammation and platelets, previous studies of TLT-1 are mainly focused on these associated diseases. In patients with systemic lupus erythematosus, the soluble TLT-1 levels were significantly lower than healthy individuals [ 30 ]. In a model of acute lung injury, results showed that infusion of sTLT-1 restored normal fibrinogen deposition and alleviated pulmonary hemorrhage by 40% and tissue damage by 25% [ 31 ]. In thrombocytopenia and platelet function defect, the content of TLT-1 was reduced, recombinant soluble TLT-1 could potentiate fibrinogen binding to patient platelets, and TLT-1 was found to be positively regulated by RUNX1 [ 32 ]. Derive et al. reported that TLT-1 is a potent endogenous regulator of sepsis-associated inflammation via suppressing leukocyte activation and modulating platelet-neutrophil crosstalk [ 33 ]. In this study, the expression of TLT-1 was also downregulated, and AUC of ROC curves analysis was 0.912. In this cases, we suspected that the changed expression of TLT-1 in PCOS might involved in the mediation of chronic inflammation and blood coagulation, thus impacting on the pathogenesis of PCOS. In conclusion, this study enrolled 31 PCOS patients and 31 matched healthy control participators, by quantitative DIA proteomics analysis, we provided evidence of serum proteomic profile alterations in female with PCOS. 80 proteins were significantly differentially expressed between PCOS patients and controls, including 54 downregulated and 26 upregulated proteins. GO and KEGG analysis showed that downregulated proteins were enriched in platelet degranulation, cell adhesion, cell activation, blood coagulation, hemostasis, defense response and inflammatory response terms; upregulated proteins were enriched in cofactor catabolic process, hydrogen peroxide catabolic process, antioxidant activity, cellular oxidant detoxification, cellular detoxification, antibiotic catabolic process and hydrogen peroxide metabolic process. ROC curves analysis showed that the AUC of H4, H2A, TLT-1 were all over than 0.9, indicated promising diagnosis values of these proteins in PCOS. Western blotting also validated that H4, H2A, TLT-1, PRDX1, SLC4A1 were differently expressed in PCOS and control groups significantly. Future studies comparing systemic expression and exact role of these candidate biomarkers in PCOS are essential for confirmation of this hypothesis.

Polycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders, 5–15% female in reproductive age live with a PCOS diagnosis worldwide. The etiopathogenesis of PCOS is complex, genetic, environmental and lifestyle interaction contribute to the etiology of PCOS. Hyperandrogenism and insulin resistance (IR) are the major characteristics of PCOS [ 1 ]. And normally, except for the above-mentioned characteristics, the clinical features of PCOS usually including menstrual cycle irregular, ovarian abnormalities, follicular dysplasia (with multiple cystic ovarian follicles), etc. [ 2 ]. But the diagnosis of PCOS is quite difficult since the symptoms of PCOS patients are not unified. Except for the PCOS caused physical hazards in reproductive age women, PCOS also has negative effects on life quality and mental health. In the female with PCOS, the long-term risks of endometrial carcinoma, diabetes mellitus, and cardiovascular diseases also seem to be higher than the normal populations [ 3 , 4 ]. Population based studies show that PCOS and thereby caused infertility are associated with lower life satisfaction, poor health status and increased psychological distress, including depression, anxiety and perceived stress [ 5 , 6 ]. Proteomics is an emerging tool involves comprehensive study of qualitative and quantitative profiling of proteins present in tested samples [ 7 ]. It can systematically characterize a large-scale of dynamic changes in protein expression, which can provide basic information for the study of complex diseases. Thus allowing the capacity to unravel new mechanistic explanations and offering richer source of potential diagnostic biomarkers associated with complex metabolic disorders. In the past few years, substantial efforts have been used to study the pathogenesis of PCOS via proteomic approaches, but challenges still exist [ 8 ]. The identification of novel proteins in PCOS is of great interest for developing more precise diagnostic strategy and new therapeutic targets. Data independent acquisition (DIA) recommended with high reproducibility and high-throughput is a powerful technique in proteomics studies. The mass spectrometer in DIA systematically acquires MS/MS spectra without regard to whether a precursor signal is detected, which is in comparison to datadependent acquisition (DDA) for proteomics research [ 9 ]. In this experiment, DIA proteomic techniques was employed to explore a broad spectrum of functional proteins in PCOS patients and controls. The aim of this study was to use proteomic methodologies for the identification of biomarkers over- or under expressed in women with PCOS compared with the controls, and provide the potentiality to help us understand PCOS better.