Non-targeted metabolomics is not based on limited relevant research and background knowledge, systematic and comprehensive analysis of the entire metabolome, acquisition of data on a large number of metabolites, and processing them to identify differential metabolites a research method. Currently, non-targeted metabolomic analysis is widely used in biomarker discovery, disease diagnosis, and mechanism research, and provides new ideas and directions for solving some bottlenecks in disease mechanism research.
Basic process
1.1 Sample collection, preprocessing and analysis
Sample collection is the initial step and one of the most important steps in metabolomics research. Samples commonly used for metabolomics analysis include plasma, urine, various tissues, cells, and organelles. Metabolomics research requires strict experimental design, and a sufficiently large sample size can effectively reduce the impact of individual differences in biological samples on the analysis results, so as to obtain statistically significant data.
The purpose of sample pretreatment is to extract analytes from biological samples with complex components and reduce the interference of other components on the determination results, which is also a key factor affecting the results of metabolomics research. Commonly used sample pretreatment methods include solid-phase extraction, liquid-liquid extraction, supercritical fluid extraction, accelerated solvent extraction, protein precipitation, differential centrifugation, etc.
Using a single analytical technique often cannot meet the analytical requirements of metabolomics. Therefore, metabolomics often requires a combination of multiple analytical methods. In recent years, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS) have been widely used in in metabolomics research. The advancement of analytical methods is inseparable from the development of metabolomics, and is one of the main driving forces for the development of metabolomics.
1.2 Data processing
Metabolomics data requires analysis and comparison to identify differential metabolites. Commonly used data analysis methods include principal component analysis (PCA), partial least squares projection to latent structures discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (orthogonal projections to latent) structuresdiscriminantanalysis, OPLS-DA) et al.
Appropriate analysis methods should be selected according to the actual situation to obtain credible analysis results. At the same time, the differential metabolites should be screened by combining t-test and variable importance in projection (VIP) values. It is generally considered that P is less than Variables with a VIP value of 0.05 and greater than 1.0 were differential metabolites.
With the maturity of metabolomics analysis technology, various metabolomics databases and processing software are also increasing. According to different analysis methods, compound spectra can be assigned in different databases, so as to obtain differential metabolites. Chemical structure and name. In the process of drug target discovery, further mechanism research is often required. At this time, metabolic pathway analysis can be performed using databases such as KEGG and Metacyc, so as to master the relevant metabolic pathways and carry out follow-up research.
application
1. Discovery of potential targets for the treatment of cardiovascular disease
Elevated levels of trimethylamine N-oxide (TMAO) can cause abnormal accumulation of lipids, thereby increasing the risk of cardiovascular disease. In the study of cardiovascular disease, non-targeted metabolomic analysis showed that the content of TMAO in the plasma of patients with cardiovascular disease was significantly increased. Choline ingested from food can generate TMA under the action of choline-trimethylamine (TMA) lyase in intestinal flora, which is further metabolized by flavinmonooxygenase 3 (FMO3) in the liver Generate TMAO.
Further studies showed that the plasma TMAO content of FMO3-overexpressing transgenic mice was significantly higher than that of wild-type mice under the same high choline diet. The content of TMAO in the plasma of mice silenced by antisense oligonucleotide technology was significantly lower than that of the control group, indicating that FMO3 can further affect the pathogenesis of atherosclerosis and other cardiovascular diseases by regulating the content of TMAO in plasma. . The above metabolomics-derived studies suggest that FMO3 may be a potential target for the treatment of cardiovascular disease.
2. Discover potential targets for tumor therapy
Using capillary electrophoresis-mass spectrometry, non-targeted metabolomic analysis of glioma samples found that the hypotaurine content in tumor tissues was significantly higher than that in paracancerous control tissues, and it was closely related to glioma. Tumor grade (degree of malignancy) was positively correlated. After molecular docking computer simulation, it was found that hypotaurine can competitively inhibit the catalytic activity of prolyl hydroxylase domain-2 (PHD2) and affect the hydroxylation of hypoxia-inducible factor 1α (HIF-1α). 1α) and promote its entry into the nucleus, thereby initiating the expression of many tumor-related genes.
Therefore, increased intracellular hypotaurine content can promote the occurrence and development of tumors. Studies have found that intracellular hypotaurine is synthesized from cystine as a precursor, and cystine needs to be transported by the cystine/glutamate antiporter (XC-glutamate-cystineantiporter) to enter the cell, inhibiting the This transporter can block hypotaurine biosynthesis, thereby inhibiting tumor cell proliferation and invasion. Therefore, the cystine/glutamate antiporter may serve as a potential target for the treatment of glioma.
In a study of acute myeloid leukemia, Bhanot et al. treated leukemia cancer cell lines with a variety of tyrosine kinase inhibitors, and performed non-targeted metabolomic analysis of different treated cell lines. The endogenous metabolome was significantly changed, and a series of endogenous metabolites, such as those related to energy metabolism such as glycolysis, tricarboxylic acid cycle, and glycogen synthesis, were significantly altered, among which the precursor of glycogen synthesis, UDP-D-glucose Levels are abnormally elevated in cancerous cell lines.
Further mechanism studies have shown that the metabolic reprogramming of tumor cells relies on the glycogen synthesis pathway for energy storage. Patients with abnormally elevated glycogensynthase 1 (GYS1) activity tend to have poorer prognosis. Inhibition of GYS1 not only reduces glycolytic flux, but also results in inhibition of tumor cell growth. New targeted therapy strategies can target and inhibit GYS1, thereby affecting the energy metabolism and growth of tumor cells, so GYS1 is expected to become a new therapeutic target.
