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Research On Diagnosis Markers Of Systemic Lupus Erythematosus Based On Multi-omics Technology

Posted on:2023-10-08Degree:MasterType:Thesis
Country:ChinaCandidate:Y Z WangFull Text:PDF
GTID:2544306938482174Subject:Pharmacology
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Background and ObjectiveSystemic lupus erythematosus(SLE)is an autoimmune disease associated with various factors such as sex hormones,heredity and infection.It is mainly characterized by the production of a large number of pathogenic autoantibodies in the body,systemic multi-organ and multisystem involvement,repeated remission and aggravation.Patients can show butterfly erythema,mental state changes,anemia and other clinical symptoms.At present,antinuclear antibodies,anti-double-stranded DNA antibodies and complement activation are commonly used in the diagnosis of SLE or the evaluation of disease activity.However,considering the complexity of the disease,it is quite difficult to evaluate SLE with a single biomarker.Therefore,more in-depth researches on diagnostic markers of SLE are urgently needed.The purpose of this study is to analyze metabolic disorders in patients with SLE by using multi-omics technology,to find novel biomarkers that can be used for clinical diagnosis of SLE,and to conduct a preliminary investigation of the pathogenesis of SLE.MethodsIn this study,a total of 534 subjects were enrolled from Jiangsu Province Hospital of Chinese Medicine,including 267 healthy controls(HCs)and 267 patients with SLE.The serum of the subjects was collected for lipidomics,metabolomic,pilot proteomics and multi-omics integrated analysis by using ultrahigh-performance liquid chromatography coupled with Q Exactive spectrometry(UPLC-QE),gas chromatography-mass spectrometry(GC-MS),tandem mass tag(TMT)and machine learning method.Results1.The results of lipidomics showed that lysophosphatidylethanolamine(LPE)(18:0),phosphatidylethanolamine(PE)(16:0/18:2)and acylcarnine(ACar)(11:0)were closely related to the progression of SLE.The levels of LPE and PE were significantly increased,while the level of ACar was decreased in patients with SLE.Therefore,the combination of PE(16:0/18:2),LPE(18:0)and ACar(11:0)is ideal biomarker to differentiate between patients with SLE and HCs.2.The results of metabolomic showed that the level of arabitol was decreased significantly,while the levels of asparagine and stearic acid were increased in patients with SLE.These three metabolites can be used as potential biomarkers for the diagnosis of SLE and have high diagnostic efficacy.“Aspartate metabolism”was considered the most significantly changed pathways.3.TMT-based quantitative proteomics showed that the differential proteins were mainly involved in many biological processes,including immune response,signal transduction,inflammatory response,proteolysis,innate immune response and apoptosis.ELISA confirmed that there were significant differences in the expression levels of endosialin(CD248)and serum amyloid A(SAA1),and the two proteins and their combinations could differentiate well between patients with SLE and HCs.4.The integrated analysis of metabolomics and proteomics showed that the differential metabolites and proteins in the serum of patients with SLE were mainly involved in the "alanine,aspartate and glutamate metabolism" pathway.The combination of metabolomics and proteomics improves the accuracy of diagnosis and prediction of SLE status.ConclusionsIn the thesis,metabolic alterations in patients with SLE were investigated from the perspectives of lipidomics,metabolomics and proteomics.It was found that serum lipids such as PE(16:0/18:2),LPE(18:0)and ACar(11:0),small molecular metabolites such as arabitol,asparagine and stearic acid,and proteins such as CD248 and SAA1 have changed significantly in patients with SLE.Based on the above differential metabolites,the pathogenesis of SLE was preliminarily explored,which provides a novel potential biomarker for clinical diagnosis and disease activity evaluation of SLE.
Keywords/Search Tags:Systemic lupus erythematosus, Biomarkers, Lipidomics, Metabolomics, Proteomics, Multi-omics integrated analysis
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