Biochemical and biophysical research communications · 2021
Reconstruction and analysis of correlation networks based on GC-MS metabolomics data for hypercholesterolemia.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
OuYang Ya-Nan, Zhou Lu-Xin, Jin Yue-Xin, Hou Guo-Feng, Yang Peng-Fei, Chen Meng, Tian Zhongmin
Аннотация
BACKGROUND AND AIMS: Hypercholesterolemia is characterized by the elevation of plasma total cholesterol level, especially low-density lipoprotein (LDL) cholesterol. This disease is usually caused by a mutation in genes such as LDL receptor, apolipoprotein B, or proprotein convertase subtilisin/kexin type 9. However, a considerable number of patients with hypercholesterolemia do not have any mutation in these candidate genes. In this study, we examined the difference in the metabolic level between patients with hypercholesterolemia and healthy subjects, and screened the potential biomarkers for this disease.
METHODS: Analysis of plasma metabolomics in hypercholesterolemia patients and healthy controls was performed by gas chromatography-mass spectrometry and metabolic correlation networks were constructed using Gephi-0.9.2.
RESULTS: First, metabolic profile analysis confirmed the distinct metabolic footprints between the patients and the healthy ones. The potential biomarkers screened by orthogonal partial least-squares discrimination analysis included l-lactic acid, cholesterol, phosphoric acid, d-glucose, urea, and d-allose (Variable importance in the projection > 1). Second, arginine and methionine metabolism were significantly perturbed in hypercholesterolemia patients. Finally, we identified that l-lactic acid, l-lysine, l-glutamine, and l-cysteine had high scores of centrality parameters in the metabolic correlation network.
CONCLUSION: Plasma l-lactic acid could be used as a sensitive biomarker for hypercholesterolemia. In addition, arginine biosynthesis and cysteine and methionine metabolism were profoundly altered in patients with hypercholesterolemia.
METHODS: Analysis of plasma metabolomics in hypercholesterolemia patients and healthy controls was performed by gas chromatography-mass spectrometry and metabolic correlation networks were constructed using Gephi-0.9.2.
RESULTS: First, metabolic profile analysis confirmed the distinct metabolic footprints between the patients and the healthy ones. The potential biomarkers screened by orthogonal partial least-squares discrimination analysis included l-lactic acid, cholesterol, phosphoric acid, d-glucose, urea, and d-allose (Variable importance in the projection > 1). Second, arginine and methionine metabolism were significantly perturbed in hypercholesterolemia patients. Finally, we identified that l-lactic acid, l-lysine, l-glutamine, and l-cysteine had high scores of centrality parameters in the metabolic correlation network.
CONCLUSION: Plasma l-lactic acid could be used as a sensitive biomarker for hypercholesterolemia. In addition, arginine biosynthesis and cysteine and methionine metabolism were profoundly altered in patients with hypercholesterolemia.