With the development of science medical practice is moving forward to precision medicine.Metabolomics methods for large-scale clinical samples,as an indispensable technology in precision medicine researches,are very important to realize the aim of precision medicine.The metabolomics study in relation to diseases often needs to detect hundreds to thousands of metabolites in large-scale cohort clinical biological samples,which poses a great challenge to the existing metabolomics methods.To this end,the present work started with the development of new metabolomics methods,was followed with the investigation on the discovery of disease biomarkers and the analysis of metabolomics features caused by drug intervention.The main results are summarized as follows:1.To improve the coverage of metabolites with the situation of low intensity and co-elution,a novel pseudotargeted metabolomics method based on Sequential Windowed Acquisition of All Theoretical Fragment Ion(SWATH)was established and a related software was also developed for data process.The main processes of method development contained metabolite data acquisition by SWATH mode with variable isolation windows in different collision energy,MRM transition selection in each collision energy,MRM transition selection and validation,integration of final MRM transition for pseudotargeted method.SRM 1950 was taken as an example,a total of 1373 metabolite MRM transitions were acquired.When compared with pseudotargeted method based on DDA mode,this method could detect additional 253 characteristic MRM transitions.The established pseudotargeted method was stable and suitable for high coverage metabolomics study.2.To improve the analytical throughput of samples in clinic,a strategy for metabolite annotation and quantitation based on nESI DI-DIA MS was developed.The annotation process included accurate precursor ion match,isotopic distribution evaluation,MS/MS similarity evaluation,precursor and fragment ion correlation evaluation.According to the results of method evaluation to estimate the strategy for metabolite annotation,the accuracy could reach to more than 94%for the top 3 candidates.Besides,the use of characteristic fragment ions for isomer quantification could be realized.In this experiment,10 μL serum was used and the metabolites were analyzed within 2 min.This metabolomics method has the advantages of high throughput and small sample amount requirement.It is preferable for large-scale cohort study.3.To obtain the stable and reliable biomarkers for diabetes screening in clinic,a knowledge-based targeted metabolomics method was developed.According to 108 metabolites related to diabetes from our previous researches and literatures,hexose,phenylalanine and 2-hydroxybutyric acid/2-hydroxyisobutyric acid were defined as biomarkers from 1859 samples of two centers and could be combined as a serum metabolite biomarker panel for diabetes screening.The panel was well complementary to fasting glucose and it had a higher diagnosis sensitivity than fasting glucose(>79.6%vs<68%).Moreover,the predictive ability of the biomarkers and metabolite panel was also tested by a cohort study,and the results revealed they had good capability to predict further diabetes risk.Collectively,the serum metabolite biomarker panel was promising for clinic application in the future.4.To investigate the alterations in chronic kidney patients caused by drug treatment more deeply,lipidomics and metabolomics platforms were combined to investigate the serum metabolism of patients after Roxadustat treatment.The lipidomics study showed that phospholipids and sphingolipids were significantly reduced after drug therapy.Meanwhile,the metabolomics results also indicated the bile acids were significantly reduced after drug therapy,and amino acids,carnitines were significantly increased in response to medication.The present study demonstrated that,the combination of lipidomics and metabolomics helps to well understand the alterations of metabolism caused by Roxadustat and it was promising for providing technical support for the treatment and prognosis in clinic. |