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.
Zi Shen Wan is a typical formula consisting of three herbs, Phellodendri Amurensis Cortex, Rhizoma Anemarrhenae, and Cortex Cinnamomi, and has been widely used for treating prostatitis and infection diseases. However, it lacks in-depth research of the constituents of Zi Shen Wan in vivo and in vitro. In this work, ultra high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry and MassLynx software was established to characterize the chemical compositions of Zi Shen Wan in vivo and in vitro. In total, 92 peaks were characterized in vitro and 33 peaks were characterized in vivo based on mass spectrometry and tandem mass spectrometry data. Among the 33 compounds characterized in rat plasma, 22 prototype components absorbed in rat serum and 11 metabolites were identified in vivo. This work was fully reports the chemical constituents of traditional Chinese formula of Zi Shen Wan, it demonstrated that ultra high performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry coupled to MassLynx software and multivariate data processing approach could be successfully applied for rapid screening and comprehensive analysis of chemical constituents in vitro and prototype components or metabolites in vivo of traditional Chinese medicine.
BackgroundPolycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder. And metabolic syndrome (MS) is an important bridge for PCOS patients to develop other diseases, such as diabetes and coronary heart disease. Our aim was to study the potential metabolic characteristics of PCOS-MS and identify sensitive biomarkers so as to provide targets for clinical screening, diagnosis, and treatment.MethodsIn this study, 44 PCOS patients with MS, 34 PCOS patients without MS, and 32 healthy controls were studied. Plasma samples of subjects were tested by ultraperformance liquid chromatography (UPLC) system combined with LTQ-orbi-trap mass spectrometry. The changes of metabolic characteristics from PCOS to PCOS-MS were systematically analyzed. Correlations between differential metabolites and clinical characteristics of PCOS-MS were assessed. Differential metabolites with high correlation were further evaluated by the receiver operating characteristic (ROC) curve to identify their sensitivity as screening indicators.ResultsThere were significant differences in general characteristics, reproductive hormone, and metabolic parameters in the PCOS-MS group when compared with the PCOS group and healthy controls. We found 40 differential metabolites which were involved in 23 pathways when compared with the PCOS group. The metabolic network further reflected the metabolic environment, including the interaction between metabolic pathways, modules, enzymes, reactions, and metabolites. In the correlation analysis, there were 11 differential metabolites whose correlation coefficient with clinical parameters was greater than 0.4, which were expected to be taken as biomarkers for clinical diagnosis. Besides, these 11 differential metabolites were assessed by ROC, and the areas under curve (AUCs) were all greater than 0.7, with a good sensitivity. Furthermore, combinational metabolic biomarkers, such as glutamic acid + leucine + phenylalanine and carnitine C 4: 0 + carnitine C18:1 + carnitine C5:0 were expected to be sensitive combinational biomarkers in clinical practice.ConclusionOur study provides a new insight to understand the pathogenesis mechanism, and the discriminating metabolites may help screen high-risk of MS in patients with PCOS and provide sensitive biomarkers for clinical diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.