Recent explosion of biological data brings a great challenge for the traditional methods. With increasing scale of large data sets, much advanced tools are required for the depth interpretation problems. As a rapid-developing technology, metabolomics can provide a useful method to discover the pathogenesis of diseases. This study was explored the dynamic changes of metabolic profiling in cells model and Balb/C nude-mouse model of prostate cancer, to clarify the therapeutic mechanism of berberine, as a case study. Here, we report the findings of comprehensive metabolomic investigation of berberine on prostate cancer by high-throughput ultra performance liquid chromatography-mass spectrometry coupled with pattern recognition methods and network pathway analysis. A total of 30 metabolite biomarkers in blood and 14 metabolites in prostate cancer cell were found from large-scale biological data sets (serum and cell metabolome), respectively. We have constructed a comprehensive metabolic characterization network of berberine to protect against prostate cancer. Furthermore, the results showed that berberine could provide satisfactory effects on prostate cancer via regulating the perturbed pathway. Overall, these findings illustrated the power of the ultra performance liquid chromatography-mass spectrometry with the pattern recognition analysis for large-scale biological data sets may be promising to yield a valuable tool that insight into the drug action mechanisms and drug discovery as well as help guide testable predictions.
Screening the active compounds of herbal medicines is of importance to modern drug discovery. In this work, an integrative strategy was established to discover the effective compounds and their therapeutic targets using Phellodendri Amurensis cortex (PAC) aimed at inhibiting prostate cancer as a case study. We found that PAC could be inhibited the growth of xenograft tumours of prostate cancer. Global constituents and serum metabolites were analysed by UPLC-MS based on the established chinmedomics analysis method, a total of 54 peaks in the spectrum of PAC were characterised in vitro and 38 peaks were characterised in vivo. Among the 38 compounds characterised in vivo, 29 prototype components were absorbed in serum and nine metabolites were identified in vivo. Thirty-four metabolic biomarkers were related to prostate cancer, and PAC could observably reverse these metabolic biomarkers to their normal level and regulate the disturbed
metabolic profile to a healthy state. A chinmedomics approach showed that ten absorbed constituents, as effective compounds, were associated with the therapeutic effect of PAC. In combination with bioactivity assays, the action targets were also predicted and discovered. As an illustrative case study, the strategy was successfully applied to high-throughput screening of active compounds from herbal medicine.
Traditional Chinese medicine is the clinical experience accumulated by Chinese people against diseases. Da-Bu-Yin-Wan is a famous traditional Chinese medicine formula consisting of Phellodendri amurensis Rupr., Anemarrhenae asphodeloides Bge., Radix Rehmanniae Preparata and Chinemys reevesii. In this study, ultra high performance liquid chromatography with electrospray ionization quadrupole time-of-flight high-definition mass spectrometry with the control software of Masslynx (V4.1) was established for comprehensive screening and identification of the chemical constituents and serum metabolites of Da-Bu-Yin-Wan in vivo and in vitro. Consequently, 70 peaks in the methanol extract from Da-Bu-Yin-Wan and 38 peaks absorbed into rat blood were characterized. The 70 constituents in vitro included alkaloids, flavonoids, polysaccharide, limonoids, flavonoid, etc. And the 38 constituents consist of 22 absorbed prototypes and 16 metabolites of Da-Bu-Yin-Wan absorbed in vivo. We fully clarified the chemical constituents of Da-Bu-Yin-Wan and provided a scientific strategy for the screening and characterization of the chemical constituents and metabolites of traditional Chinese medicine in vitro and in vivo.
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