Screening Biomarkers Of Polycystic Ovary Syndrome By Proteomics And Metabolomics

BackgroundPolycystic ovary syndrome(PCOS)is the most common secretion and metabolism disorder in women of childbearing age and is the main cause of anovulatory infertility and hyperandrogenemia.Without effective intervention and treatment,polycystic ovary syndrome may lead to diabetes,hyperlipidemia,cardiovascular diseases and even endometrial cancer,seriously affecting women's health.At present,the etiology and pathogenesis of androgenemia are still unclear and the diagnostic criteria are constantly updated and controversial,therefore,further investigation of the pathogenesis and search for biomarkers are still of great importance.Proteomics and metabolomics are the popular new disciplines in systems biology in recent years,mainly through highthroughput,high-sensitivity modern analytical techniques to achieve the detection of all proteins and metabolites in biological samples,so as to reflect the complex and dynamic changes in the organism,and reveal the underlying mechanisms of biological phenotypes.These two omics have now become important tools for the discovery of metabolic pathway changes in diseases and diagnosis of biomarkers.ObjectiveIn this study,we conducted metabolomic analysis of serum samples from PCOS patients and healthy control subjects to search for specific biomarkers that can be used in the diagnosis of PCOS based on the latest metabolomics and proteomics research platform of ultra performance liquid chromatography-mass spectrometry(UPLCMS/MS).It also explores the pathogenesis of PCOS and new targets for treatment at the protein and metabolic level.Method1.Application of DIA quantitative proteomics technology for screening biomarkers of polycystic ovary syndrome31 serum samples from patients with polycystic ovary syndrome and healthy controls were collected respectively and pretreated with high peak protein removal,sample enzymatic digestion,and peptide desalting.Based on the liquid chromatography-mass spectrometry platform,the serum samples were subjected to proteomic analysis using data non-dependent acquisition label-free quantitative proteomics(DIA)technology.Then,the DIA data were analyzed in depth,and the protein quantification results were subjected to t-test or ANOVA test,and the proteins with adj p-value<0.01 were screened as differential proteins,and the results were presented as volcano plots,cluster plots,and PCA graphs;at the same time,the differential proteins were functionally annotated,and their biological functions were known by GO and KEGG annotation analysis.The results were presented in the form of volcano maps,cluster maps and PCA patterns.Finally,the potential biomarkers were screened by ROC curve analysis,and the corresponding differential proteins were validated by western blotting.2.Screening of biomarkers for polycystic ovary syndrome using UPLC-HRMS highresolution metabolomics31 serum samples were collected from patients with polycystic ovary syndrome and healthy controls respectively,and the serum was subjected to polar metabolite extraction and lipid extraction pre-treatment,respectively.The samples were qualitatively characterized and quantitatively detected with deep coverage and systematic non-targeted metabolites based on UPLC-HRMS technology.The integrated metabolomic data were then analyzed in depth,and the different metabolites were screened based on the Student's t-test results between the two groups and the FDR multiple test corrected p-values using the Benjamin-Hocheberg method,as well as the fold Change information of metabolite changes between the two groups.Plotted volcanoes and clustered heat maps were then drawn.Pathway analysis was performed on the list of differential metabolites using the Over-representation method,and metabolite set enrichment analysis was performed using the Metabolites Set of the KEGG database using quantitative metabolic data.Finally,potential biomarkers were screened using ROC curve analysis.3.Development and evaluation of a diagnostic model for polycystic ovary syndromeIn addition,30 clinical serum samples from patients with polycystic ovary syndrome and healthy controls were collected respectively.Based on the application of UPLC-HRMS high-resolution metabolomics technology for non-targeted metabolomic screening of potential metabolic markers,metabolites with significant differential expression(p<0.05 and FDP<0.05)with VIP values greater than 1 in the OPLS-DA model were used as candidate markers for targeted metabolomic analysis.Metabolic marker models were developed using binary logistic regression in SPSS software and receiver operating characteristic curves(ROC)were used to evaluate the marker model effects.Result1.DIA quantitative proteomics technology screens 80 differentially expressed biomarkers for polycystic ovary syndrome1)In this study,by quantitative DIA proteomics,we aimed to investigate whether proteomics changes in PCOS women plasma compared to healthy controls.The results provided evidence of plasma proteomic profile alterations in PCOS female.There were 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.2)ROC curves analysis showed that the AUC of HIST1H4A,HIST1H2AB,TREML1 were all over than 0.9,indicated promising diagnosis values of these proteins.2.UPLC-HRMS based untargeted metabolomic approach to study the serum metabolites in women with polycystic ovary syndrome1)In this study,we utilized metabolomics approach by UPLC-HRMS technology to study the metabolic changes of in 31 PCOS patients and 31 healthy controls.The metabolomics analysis showed that in PCOS patients serum,there were 146 significantly varied metabolites,among them 68 were downregulated,78 were upregulated.These metabolites mainly belong to Triacylglycerols,Glycerophosphocholines,Acylcarnitines,Diacylglycerols,Peptides,Amino acids,Glycerophosphoethanolamines and FA.Pathway analysis showed that these metabolites were enriched in pathways including Glycerophospholipid metabolism,Fatty acid degradation,Fatty acid biosynthesis,Ether lipid metabolism etc.2)Diagnosis value assessment by ROC analysis showed that the changed metabolites including Leu-Ala/Ile-Ala,3-(4-Hydroxyphenyl)propionic acid,Ile-Val/Leu-Val,Gly-Val/Val-Gly,aspartic acid,DG(3 4:2)_DG(16:0/18:2),DG(34:1)_DG(16:0/18:1),Phe-Trp,DG(36:1)_DG(18:0/18:1),Leu-Leu/Leu-Ile had higher AUC values,indicated significant roles in PCOS.3.Establishment of a diagnostic model for polycystic ovary syndrome based on serum metabolic markers1)In this study,based on the UPLC-HRMS high-resolution metabolomics technology for non-targeted metabolomic screening of potential metabolic markers,targeted metabolomic testing and validation were performed with newly collected clinical samples to identify and validate novel markers with clinical utility and corresponding predictive models.The results of the study revealed that two metabolite combination markers,including 3-(4-hydroxyphenyl)propionic acid(DAT)and phenylalanyltryptophan(Phe-Trp),were considered to be the optimal combination for discriminating PCOS.The equation of the combined marker model is as follows:Logit[p=PCOS]=1.765 ×[DAT]+1.844 ×[Phe-Trp]-46.914.The AUC of its combined marker model in distinguishing PCOS from healthy controls reached 0.9432)3-(4-Hydroxyphenyl)propionic acid(DAT),a gut microbiota-dependent metabolite,has been reported to be a potent anti-inflammatory modulator with relevance to the development of obesity as well as insulin resistance.Phe-Trp,as a dipeptide,is also closely associated with intestinal flora metabolism.Both metabolic markers in the combination may be associated with intestinal flora disorders in PCOS patients.It predicts that the regulation of intestinal flora is of great value in the treatment of PCOS,and the study provides a new perspective for the prevention and treatment of polycystic ovary syndrome.ConclusionDIA quantitative proteomics analysis showed that 80 proteins were significantly differentially expressed in patients with polycystic ovary syndrome compared with controls(54 down-regulated and 26 up-regulated),among which HIST1H4A,HIST1H2AB and TREML1 had good diagnostic value for polycystic ovary syndrome;UPLC-HRMS high-resolution metabolomics analysis showed that polycystic ovary syndrome patients with a total of 146 significantly altered metabolites in serum,and nine of them were significant in the diagnosis of polycystic ovary syndrome,among which two metabolite combination markers,3-(4-hydroxyphenyl)propionic acid(DAT)and phenylalanyltryptophan(Phe-Trp),were the optimal combination to discriminate polycystic ovary syndrome.In addition,3-(4-hydroxyphenyl)propionic acid(DAT)and phenyl al any ltryptophan(Phe-Trp)seem to be associated with intestinal flora disorders in patients with polycystic ovary syndrome.