Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC

Comparative evaluation of different cultivars of Flos Chrysanthemi by an anti-inflammatory-based NF-κB reporter gene assay coupled to UPLC-Q/TOF MS with PCA and ANN

ETHNOPHARMACOLOGICAL RELEVANCE

Flos Chrysanthemi (FC), a commonly used traditional Chinese medicine, has five major cultivars ("Boju", "Chuju", "Gongju", "Hangbaiju" and "Huaiju") from different sources. However, the active constituents of these cultivars have not been studied or characterized with respect to their bioactivity, which is a serious problem when considering quality and safety.

AIM OF THE STUDY

To evaluate the differences among the five cultivars of FC, and to establish a method for the standardization and quality control of FC related to its bioactivity.

MATERIALS AND METHODS

In this study, the different ingredients in five cultivars of FC were identified by UPLC-Q/TOF and PCA, and the anti-inflammatory ingredients of FC were predicted and screened by artificial neural network (ANN) and an NF-κB luciferase reporter gene assay system. Using this comprehensive method, we successfully screened the anti-inflammatory markers of different cultivars of FC.

RESULTS

Nineteen marker ingredients were confirmed to contribute strongly to the cluster, and eleven compounds in the five cultivars of FC were found to exert potential anti-inflammatory effects. Among these compounds, the NF-κB inhibitor activity of apigenin-7-O-6″-malonyl-glucoside, luteolin-7-O-rutinoside, quercetin-7-O-galactoside, quercetin-3-O-glucoside, apigenin-7-O-rutinoside and apigenin-7-O-glucoside were first reported here. Chlorogenic acid, luteolin-7-O-glucoside, 3,5-dicaffeoylquinic acid and luteolin were confirmed to be the most important anti-inflammatory marker ingredients useful for the quality control of FC.

CONCLUSIONS

The proposed efficient and systematic method is helpful for the standardization and quality control of FC. Moreover, this comprehensive strategy may prove to be a powerful technique for the rapid establishment of quality control procedures related to bioactivity for other herbal samples and foods.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.