Metabonomics measures the overall and the dynamic metabolic responses and is consistent with determining the overall efficacy of traditional Chinese medicine. Changes in the drug components due to the body's metabolic response can be determined with the metabonomics. The scope of screening the active ingredients of TCM can be narrowed by utilizing this technique.
One-sidedness can be avoided by studying the individual constituents. This method can be simultaneous, determine the endogenous metabolites and exogenous constituents absorbed into the blood. Metabonomics has been widely used in studies on TCM, drug toxicology, health management, sports, food, and other fields.
To begin, determine the required extract of Cyperi rhizoma, or CR, or Cyperi rhizoma processed with vinegar, or CRV, to treat a group of six Sprague Dawley rats for three days. Calculate the volume of CR or CRV to be applied per rat using this equation. To process the CRV, thoroughly mix 100 grams of CR and 20 grams of vinegar, which has more than 5.5 grams of acetic acid per 100 milliliters, and incubate for 12 hours.
After the incubation, stir fry the mixture in an iron pan for 10 minutes at 110 to 120 degrees Celsius. Then, remove the mixture, and allow it to cool at room temperature. To prepare the CR extract, soak the CR for two hours with pure water 10 times the CR amount taken, making sure that the medicinal materials are below the liquid level.
Next, bring the mixture of water and medicine to boil over high heat, and keep it boiling on low heat for 20 minutes. Then, filter the contents through a 100 mesh filter cloth, and collect the filtrate. Then, concentrate the collected filtrate into an extract with a rotary evaporator to one gram per milliliter.
To prepare the CRV extract, perform the steps of soaking, boiling, and concentrating as demonstrated for the CR extraction. For testing the CR and CRV extracts, pipette 500 microliters of the extract to 500 microliters of methanol in 1.5-milliliter microcentrifuge tubes, and vortex for 30 seconds to mix. Centrifuge the samples for 15 minutes at 16, 500 g at four degrees Celsius.
Then, remove the supernatant, and transfer it to the sample vial for testing. After testing the samples, perform the principal component analysis, or PCA, and the modeling using the analysis software. Import the standardized data of the metabolites to the software, and then use Autofit to build the analysis model.
Finally, use the scores to obtain the score scatter plot of the PCA. To perform the orthogonal partial least square discriminant analysis, or OPLS-DA, import the standardized data on the metabolites and the CR and CRV groups into the software. Then, import the CR and CRV data into their respective groups created.
Then, use Autofit to build the analysis model, and use the score to obtain the score scatter plot of the OPLS-DA. Finally, use VIP to obtain the variable significance in the projection or VIP value in the OPLS-DA. To identify the potentially differential metabolites, screen out the metabolites with VIP values greater than one.
Then, use the statistical software to calculate the P-value of the screened metabolites by the student's T-test. Next, to identify the differential metabolites, use the annotated metabolites, and screen out the differential metabolites to be matched in the KEGG database. Show the changes in the differential metabolites in the CR and CRV groups by drawing a heat map.
To examine the potential metabolic pathways, go to the MetaboAnalyst database. Use Pathways Analysis to upload the different metabolites to obtain the potential metabolic pathways. Upload the different metabolites to the KEGG database to analyze the potential metabolic pathways.
The analyzed dysmenorrhea model experiment showed significant differences in the prostaglandin levels. The rats in the model CR and CRV groups displayed substantial writhing reactions following the oxytocin injection. The PCA analysis results demonstrated that the clusters of the CR and CRV compared to the model groups were significantly separated in both the positive and negative ion modes.
The OPLS-DA was used to screen for metabolic differences, and the scatter plot results demonstrated that the CR and CRV groups were separated. Univariate statistical analyses were performed to identify metabolic variations. A volcano plot is shown, wherein each point corresponds to a different metabolite.
Significant changes were observed in 63 metabolites in the positive mode and 30 in the negative mode. The differential metabolites were determined using the KEGG and HDMB databases, and the accurately matched compounds were listed. The quantitative values of the differential metabolites between the CR and CRV groups were calculated and clustered.
The color patches indicate how each metabolite is expressed relative to the others. Compared with the CR group, the levels of four differential metabolites in the CRV group increased while 11 metabolites decreased in the positive ion mode. In the case of negative ion mode, four differential metabolites increased, and seven metabolites decreased.
KEGG pathway analysis results showed that the differential metabolites were associated with nine pathways in the positive and negative modes. The more differential metabolites, the better the results, so enough metabolic pathways can be linked and the key metabolic pathways screened will be more accurate.
