Nontargeted lipidomic analysis of sera from sepsis patients based on ultra-high performance liquid chromatography-mass spectrometry/mass spectrometry
10.3760/cma.j.cn121430-20210612-00875
- VernacularTitle:基于UPLC-MS/MS对脓毒症患者进行血清非靶向脂质组学分析
- Author:
Shan WANG
1
;
Jifang LIANG
;
Haipeng SHI
;
Yanmei XIA
;
Jing LI
;
Wenjing WU
;
Hongxiong WANG
;
Weidong WU
Author Information
1. 山西中医药大学，山西晋中 030619
- Keywords:
Sepsis;
Lipidomics;
Biomarker
- From:
Chinese Critical Care Medicine
2022;34(4):346-351
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the changes of serum lipidomics in patients with sepsis and healthy controls, search for the differences of lipid metabolites, and reveal the changes of lipidomics in the process of sepsis.Methods:A prospective observational study was conducted. From September 2019 to April 2020, morning blood samples of upper extremity superficial veins were collected from 30 patients with definite sepsis diagnosed in intensive care unit (ICU) of Shanxi Bethune Hospital and 30 age-matched healthy subjects during the same period. Serum lipid metabolites were analyzed by ultra-high performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS), and the quality control samples were analyzed by base peak spectroscopy (BPC) and verified experimental repetition. Student t-test and fold change (FC) were used for screening significant differences in lipid metabolites and determining their expression changes. Principal component analysis (PCA) and orthogonal projectionto latent structure discriminant analysis (OPLS-DA) were used to determine the entire allocation of experimental groups apiece, access the quality of being near to the true value of model, and screen the differential lipid metabolites with variable importance of projection (VIP). Finally, Metabo Analyst platform database was used to analyze lipid molecular metabolic pathways. Results:BPC results showed that the experimental repeatability was good and the experimental data was reliable. The main parameter model interpretation rate of PCA model R 2X = 0.511, indicating that the model was reliable. The main parameter model interpretation rate of OPLS-DA model R 2Y = 0.954, Q 2 = 0.913, indicating that the model was stable and reliable. With FC > 2.0 or FC < 0.5, P < 0.05, a total of 72 differential lipid metabolites were obtained based on VIP > 1. Based on Metabo Analyst 5.0, 24 distinguishable lipid metabolites were identified including 8 phosphatidylethanolamine (PE), 7 lysophosphatidylcholine (LPC), 6 phosphatidylcholine (PC), 2 lysophosphatidylethanolamine (LPE) and 1 phosphatidylserine (PS). Compared with healthy volunteers, the lipid molecules expression proved down-regulated in most sepsis patients, including PC, LPC, LPE, and some PE, while some PE and PS were up-regulated, which was mainly related to the PE (18∶0p/20∶4), PC (16∶0/16∶0) and LPC (18∶1) metabolic pathways in glycerophospholipids. Conclusions:There are significant differences in lipid metabolites between the sera of sepsis patients and healthy volunteers. PE (18∶0p/20∶4), PC (16∶0/16∶0) and LPC (18∶1) may be new targets for sepsis prediction and intervention.
