1. To investigate Genkwa Flos hepatotoxicity, a cell metabolomics strategy combined with serum pharmacology was performed on human HL-7702 liver cells in this study.
2. Firstly, cell viability and biochemical indicators were determined and the cell morphology was observed to confirm the cell injury and develop a cell hepatotoxicity model. Then, with the help of cell metabolomics based on UPLC-MS, the Genkwa Flos group samples were completely separated from the blank group samples in the score plots and seven upregulated as well as two down-regulated putative biomarkers in the loading plot were identified and confirmed. Besides, two signal molecules and four enzymes involved in biosynthesis pathway of lysophosphatidylcholine and the sphingosine kinase/sphingosine-1-phosphate pathway were determined to investigate the relationship between Genkwa Flos hepatotoxicity and these two classic pathways. Finally, the metabolic pathways related to specific biomarkers and two classic metabolic pathways were analyzed to explain the possible mechanism of Genkwa Flos hepatotoxicity.
3. Based on the results, lipid peroxidation and oxidative stress, phospholipase A2/lysophosphatidylcholine pathway, the disturbance of sphingosine-1-phosphate metabolic profile centered on sphingosine kinase/sphingosine-1-phosphate pathway and fatty acid metabolism might be critical participators in the progression of liver injury induced by Genkwa Flos. 相似文献
Spinal muscular atrophy (SMA), the main genetic cause of infant death, is a neurodegenerative disease characterized by the selective loss of motor neurons in the anterior horn of the spinal cord, accompanied by muscle wasting. Pathomechanically, SMA is caused by low levels of the survival motor neuron protein (SMN) resulting from the loss of the SMN1 gene. However, emerging research extends the pathogenic effect of SMN deficiency beyond motor neurons. A variety of metabolic abnormalities, especially altered fatty acid metabolism and impaired glucose tolerance, has been described in isolated cases of SMA; therefore, the impact of SMN deficiency in metabolic abnormalities has been speculated. Although the life expectancy of these patients has increased due to novel disease-modifying therapies and standardization of care, understanding of the involvement of metabolism and nutrition in SMA is still limited. Optimal nutrition support and metabolic monitoring are essential for patients with SMA, and a comprehensive nutritional assessment can guide personalized nutritional therapy for this vulnerable population. It has recently been suggested that metabolomics studies before and after the onset of SMA in patients can provide valuable information about the direct or indirect effects of SMN deficiency on metabolic abnormalities. Furthermore, identifying and quantifying the specific metabolites in SMA patients may serve as an authentic biomarker or therapeutic target for SMA. Here, we review the main epidemiological and mechanistic findings that link metabolic changes to SMA and further discuss the principles of metabolomics as a novel approach to seek biomarkers and therapeutic insights in SMA. 相似文献
Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case–control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD = 0.77, 95% confidence interval 0.66–0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD = 0.72, 0.57–0.90), or lysophosphatidylcholines (OR1SD = 0.81, 0.69–0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD = 0.77, 0.61–0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer. 相似文献