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Plasma metabolomics in a deep vein thrombosis rat model based on ultra-high performance liquid chromatography-electrostatic field orbitrap high resolution mass spectrometry

Se Pu. 2022 Aug;40(8):736-745. doi: 10.3724/SP.J.1123.2021.12024.


Deep vein thrombosis (DVT) is a venous thromboembolic disease characterized by high incidence, mortality, and sequelae. Therefore, the effective prevention of DVT has become a critical public health concern. However, due to its complexity, the pathophysiological mechanism of DVT remains unclear. Metabolomics can be employed to analyze disease characteristics and provide scientific evidence on the underlying mechanisms. In this study, an established left femoral vein ligation rat model of DVT (n=10) was used and compared with sham surgery controls (n=10). In the DVT group, rats were anesthetized using an intraperitoneal injection of 10% chloral hydrate (300 mg/kg), after which the hair was shaved and the groin disinfected. A 2-cm longitudinal incision was made along the midpoint of the left groin area, and then the left femoral vein was separated. The vein was partially ligated at its proximal end to shrink the blood vessel lumen to approximately half. Then, 0.4 mL of 10% hypertonic saline was slowly injected from the distal end of the left femoral vein. At the same time, the femoral vein turned dark red, which indicated the formation of thrombosis. Finally, the incision was sutured after verifying bleeding in the surrounding tissue. Keeping all other procedures the same as the DVT group, the vein in the control group was not ligated or stimulated using hyper-tonic saline. The abdominal aorta plasma from rats in each group was collected seven days later. Untargeted metabolomics analysis based on ultra-high performance liquid chromatography-electrostatic field orbitrap high resolution mass spectrometry (UHPLC-Orbitrap HRMS) was conducted to investigate the plasma metabolic profiles of the sham surgery control and DVT groups. Principal component analysis (PCA) and orthogonal to partial least squares discrimi-nant analysis (OPLS-DA) on metabolome data for multivariate statistical analysis were employed to assess differences in the metabolic profile between the two groups. The results revealed distinct profiles for the DVT and control groups. The selection criteria for the differential metabolites were the variable importance in the projection (VIP) values of OPLS-DA (VIP>1) and fold changes (FC) in the DVT group (FC≤0.5 or FC≥2, P<0.05). The resulting 27 differential metabolites reflecting a metabolic disorder in the DVT group were selected and analyzed. Of these, the levels of 17 metabolites significantly increased in the DVT group, including trimethylamine N-oxide (TMAO), 4-amino-2-methyl-1-naphthol, chenodeoxycholic acid, and 7-ketocholesterol, whereas the levels of 10 metabolites decreased, including 3-dehydroxycarnitine, phosphatidylcholine 22∶6/20∶2 (PC 22∶6/20∶2), diglyceride 18∶3/20∶4 (DG 18∶3/20∶4) and anserine. To identify the changes in the metabolic pathway reflected by these differential metabolites, a differential abundance (DA) analysis based on the Kyoto Encyclopedia of Genes and Genomes metabolic pathway was conducted. The results showed that the differences in the metabolic pathways between the DVT and control groups were mainly manifested in the primary bile acid biosynthesis, bile secretion, histidine metabolism, linoleic acid metabolism, glycerophospholipid metabolism, and β-alanine metabolism pathways. Among them, the primary bile acid biosynthesis and bile secretion pathways were upregulated in the DVT group, whereas the glycerophospholipid metabolism, linoleic acid metabolism, and β-alanine metabolism pathways were downregulated. The histidine metabolism pathway contained upregulated as well as downregulated metabolites, resulting in a DA score of 0. In conclusion, these results indicate that the plasma metabolic profiling of the DVT group was significantly altered, while the disordered metabolites and metabolic pathways could provide a reference to further understand the pathological mechanism of DVT and identify new drug targets.

PMID:35903841 | DOI:10.3724/SP.J.1123.2021.12024

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