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Screening the active compounds of Phellodendri Amurensis cortex for treating prostate cancer by high-throughput chinmedomics

View Article: PubMed Central - PubMed

ABSTRACT

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 disturbedmetabolic 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.

No MeSH data available.


Metabolic profile characterisation and multivariate data analysis.(A) PCA score plots for control, model, and treatment groups in positive mode; (B) PCA score plots for control, model, and treatment groups in negative mode; (C) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in positive mode; (D) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in negative mode; (E) Dendrogram visualisation for serum samples from the control, model, and treatment groups; (F) Heatmap visualisation for serum samples from the control, model, and treatment groups.
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f6: Metabolic profile characterisation and multivariate data analysis.(A) PCA score plots for control, model, and treatment groups in positive mode; (B) PCA score plots for control, model, and treatment groups in negative mode; (C) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in positive mode; (D) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in negative mode; (E) Dendrogram visualisation for serum samples from the control, model, and treatment groups; (F) Heatmap visualisation for serum samples from the control, model, and treatment groups.

Mentions: Principal component analysis (PCA) revealed the metabolic profile of the control group, model group, and treatment group, and provided a score plot diagram which can reflect the degree of separation among the different groups. Results showed that there was a clear separation between the control group, model group and treatment group and the metabolic profile of the treatment group was closer to the control group than the model group (Fig. 6A–D). The MetaboAnalyst system revealed the effects of PAC against prostate cancer. Hierarchical cluster analysis showed that the metabolic profile in the treatment group was close to that of the control group, it was indicated that PAC had therapeutic efficacy, the dendrogram and heatmap are shown in Fig. 6E and F. Through analysis of the VIP scores of biomarkers in different groups, it was shown that the relative concentration of these metabolites could be reversed aftertaking PAC, the VIP scores are shown in Fig. 7A. The result indicated that PAC could return the metabolic profile to its normal state (Fig. 7B). PAC could completely reverse 24 biomarkers to abnormal levels, including 16(R)-HETE, LysoPC, SM, Eicosapentaenoic acid, Ceramide (d18:1/12:0), Sphingosine 1-phosphate, LysoPC(P-18:0), Arachidonic acid, 8-Isoprostane, Linoleic acid, Prostaglandin A1, Neoxanthin, Ceramide (d18:1/18:0), Uric acid, Isocitric acid, 5′-Deoxyadenosine, PGF2a ethanolamide, Beta-Tyrosine, 2-Furoic acid, Thromboxane, 2-Hydroxycinnamic acid, All-trans-retinoic acid, Prostaglandin A2, and PC, these metabolite biomarkers were involved in six metabolic pathways. Results indicated that PAC displayed an obvious effect in the treatment of prostate cancer through adjusting the disturbed metabolic pathways such as purine metabolism, citrate cycle (TCA cycle), arachidonic acid metabolism, retinolmetabolism, sphingolipid metabolism, glycerophospholipid metabolism, and many other metabolic pathways.


Screening the active compounds of Phellodendri Amurensis cortex for treating prostate cancer by high-throughput chinmedomics
Metabolic profile characterisation and multivariate data analysis.(A) PCA score plots for control, model, and treatment groups in positive mode; (B) PCA score plots for control, model, and treatment groups in negative mode; (C) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in positive mode; (D) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in negative mode; (E) Dendrogram visualisation for serum samples from the control, model, and treatment groups; (F) Heatmap visualisation for serum samples from the control, model, and treatment groups.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382783&req=5

f6: Metabolic profile characterisation and multivariate data analysis.(A) PCA score plots for control, model, and treatment groups in positive mode; (B) PCA score plots for control, model, and treatment groups in negative mode; (C) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in positive mode; (D) 3-d score plots of OPLS-DA based on serum metabolites discriminating between control, model, and treatment groups in negative mode; (E) Dendrogram visualisation for serum samples from the control, model, and treatment groups; (F) Heatmap visualisation for serum samples from the control, model, and treatment groups.
Mentions: Principal component analysis (PCA) revealed the metabolic profile of the control group, model group, and treatment group, and provided a score plot diagram which can reflect the degree of separation among the different groups. Results showed that there was a clear separation between the control group, model group and treatment group and the metabolic profile of the treatment group was closer to the control group than the model group (Fig. 6A–D). The MetaboAnalyst system revealed the effects of PAC against prostate cancer. Hierarchical cluster analysis showed that the metabolic profile in the treatment group was close to that of the control group, it was indicated that PAC had therapeutic efficacy, the dendrogram and heatmap are shown in Fig. 6E and F. Through analysis of the VIP scores of biomarkers in different groups, it was shown that the relative concentration of these metabolites could be reversed aftertaking PAC, the VIP scores are shown in Fig. 7A. The result indicated that PAC could return the metabolic profile to its normal state (Fig. 7B). PAC could completely reverse 24 biomarkers to abnormal levels, including 16(R)-HETE, LysoPC, SM, Eicosapentaenoic acid, Ceramide (d18:1/12:0), Sphingosine 1-phosphate, LysoPC(P-18:0), Arachidonic acid, 8-Isoprostane, Linoleic acid, Prostaglandin A1, Neoxanthin, Ceramide (d18:1/18:0), Uric acid, Isocitric acid, 5′-Deoxyadenosine, PGF2a ethanolamide, Beta-Tyrosine, 2-Furoic acid, Thromboxane, 2-Hydroxycinnamic acid, All-trans-retinoic acid, Prostaglandin A2, and PC, these metabolite biomarkers were involved in six metabolic pathways. Results indicated that PAC displayed an obvious effect in the treatment of prostate cancer through adjusting the disturbed metabolic pathways such as purine metabolism, citrate cycle (TCA cycle), arachidonic acid metabolism, retinolmetabolism, sphingolipid metabolism, glycerophospholipid metabolism, and many other metabolic pathways.

View Article: PubMed Central - PubMed

ABSTRACT

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 disturbedmetabolic 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.

No MeSH data available.