From September 24 to 25, Baofeng Biology cooperated with the Occupational Health Research Center of the State Health and Construction Commission, Xuanwu Hospital, Tianjin Eye Hospital, Shengjing Hospital affiliated to China Medical University, Waters (Shanghai) and other cooperative units. carry out internal training on the basis of metabonomics and metabonomics data analysis.
Metabolomics is a new technology developed after genomics and proteomics, and it is an important part of systems biology. according to statistics, the research of metabonomics has become more and more active in recent years. NIH of the National Institutes of Health has incorporated the development plan of metabonomics into the national road map for the development of biotechnology. Scientific research institutions in many countries have also carried out the research work of metabonomics.
Dr. Chen Xianyang of Baofeng Biology explained various problems in metabonomics in detail. Dr. Chen Xianyang pointed out that metabonomics research has produced a large number of data, which have complex characteristics such as high-dimensional, small sample, high noise and so on. How to extract valuable information from complex metabonomic data and screen potential biomarkers has become a hot and difficult point in metabonomics research in recent years.
The first thing is to preprocess the sample data, and the processing methods mainly include normalized (normalizationn) and standardized (scaling). In metabonomics studies, univariate analysis is usually used to quickly investigate the differences of various metabolites among different categories. However, the problem of multiple hypothesis testing should be considered in the processing, and the commonly used methods are Bonferion correction and local FDR. Metabonomics produces high-dimensional data, so multivariate statistical analysis plays an important role in metabonomics data analysis. The main multivariate statistical analysis methods for metabonomics data include partial least squares discriminant analysis (PLS-DA), partial least squares discriminant analysis based on orthogonal signal correction (OPLS-DA), artificial neural network (ANN), support vector machine (SVM) and so on.
The ultimate goal of metabonomics analysis is to screen out potential biomarkers, so as to explore the mechanism of metabolism. A common strategy is to carry out univariate analysis at first, and then combine the importance score of variables in the multivariate model as screening criteria, such as selecting variables with FDR≤ 0.05and VIP > 1.0 as potential biomarkers. The screened potential biomarkers were used to predict the external test data set, and the prediction effect was evaluated. Finally, the interaction and relationship between different biomarkers can be analyzed by studying the biological function and metabolic pathway of biomarkers, so as to provide important clues and information for exploring the mechanism of biological metabolism.
As a new discipline in the post-gene era, the development of metabonomics in recent years has shown its application and development prospects. It is closely related to the efficacy, toxicity screening, evaluation, safety evaluation, mechanism of action and rational use of drugs. From the understanding of the consistency between the overall concept of metabonomics and the overall concept of function, it is suitable for a series of directions such as early diagnosis, prevention and evaluation of therapeutic effects of diseases.
The research and development of nervonic acid in Baofeng Bio also draws lessons from the means of metabonomics. Through the detection of UPLC-TOF-MS, the skeleton structure of nervonic acid suitable for absorption and function has been identified. At the same time, by means of targeted metabonomics, it is proved that nervonic acid is an important molecular marker of many brain diseases and has great medicinal potential. Based on the research of comparative genomics and metabonomics, Baofeng Biological developed a series of compound formula products, which filled the gap of brain health food in the market.