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Drug target identification using network analysis: Taking active components in Sini decoction as an example.

Chen S, Jiang H, Cao Y, Wang Y, Hu Z, Zhu Z, Chai Y - Sci Rep (2016)

Bottom Line: The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database.Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level.Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.

ABSTRACT
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

No MeSH data available.


Related in: MedlinePlus

(A) The Target-Pathway network by STRING database. (B) The enrichment analysis of diseases of these target proteins by STRING database.
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f3: (A) The Target-Pathway network by STRING database. (B) The enrichment analysis of diseases of these target proteins by STRING database.

Mentions: To find the relations between target proteins and the important pathway further, we constructed the target-pathway network (Fig. 3A) based on the data extracted from STRING. There were several target proteins in one pathway and one target protein always existed in many pathways. Logically, the role of one pathway which contain many target proteins that interacted with drug molecules is more vital than the role of one target protein that interacted with drug molecules in many pathways, which is because that the impact of one target protein on the whole pathway maybe little, and the impact of a pathway which contained many target proteins interacted with the drugs on the body could be huge. Therefore, we tried to find the most important pathways through analyzing the target –pathway network. The pathways related with target proteins can be summarized in Fig. 3A. These results firstly demonstrated that SND exerted its protective effects against heart failure primarily by regulating above 15 pathways. Previous studies have demonstrated that SND exerted its cardiotonic effect by regulation of TNF signaling pathway33, Hypertrophic cardiomyopathy (HCM)22, PI3K-Akt signaling pathway34 and Dilated cardiomyopathy2335. Although large amounts of references showed that heart failure was closely related to HIF-1 signaling pathway36, Calcium signaling pathway37, cGMP-PKG signaling pathway38, mTOR signaling pathway39, Renin-angiotensin system40, ErbB signaling pathway41, AMPK signaling pathway42, VEGF signaling pathway43, Vascular smooth muscle contraction44, Adrenergic signaling in cardiomyocytes45 and Estrogen signaling pathway46, further experiments are still needed to identify the relationship between SND and these 11 pathways. In order to further explore the possible mechanism of active components in SND, we classified the targets protein into three parts according their degree in drug-target network: high degree (20–41), middle degree (10–19) and low degree (1–9). Then the p value of every relevant pathway was calculated in the three parts (Supplementary Table S4). We only considered pathways with a p value < 0.05. Lower p values represent that pathways have higher amounts of proteins involved in, and were meaningful in the global pathways. Comparing the results of three parts we found that high degree and low degree targets are mainly related with HIF-1 signaling pathway and Calcium signaling pathway, whereas middle degree targets are primarily bound up with Dilated cardiomyopathy and TNF signaling pathway.


Drug target identification using network analysis: Taking active components in Sini decoction as an example.

Chen S, Jiang H, Cao Y, Wang Y, Hu Z, Zhu Z, Chai Y - Sci Rep (2016)

(A) The Target-Pathway network by STRING database. (B) The enrichment analysis of diseases of these target proteins by STRING database.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837341&req=5

f3: (A) The Target-Pathway network by STRING database. (B) The enrichment analysis of diseases of these target proteins by STRING database.
Mentions: To find the relations between target proteins and the important pathway further, we constructed the target-pathway network (Fig. 3A) based on the data extracted from STRING. There were several target proteins in one pathway and one target protein always existed in many pathways. Logically, the role of one pathway which contain many target proteins that interacted with drug molecules is more vital than the role of one target protein that interacted with drug molecules in many pathways, which is because that the impact of one target protein on the whole pathway maybe little, and the impact of a pathway which contained many target proteins interacted with the drugs on the body could be huge. Therefore, we tried to find the most important pathways through analyzing the target –pathway network. The pathways related with target proteins can be summarized in Fig. 3A. These results firstly demonstrated that SND exerted its protective effects against heart failure primarily by regulating above 15 pathways. Previous studies have demonstrated that SND exerted its cardiotonic effect by regulation of TNF signaling pathway33, Hypertrophic cardiomyopathy (HCM)22, PI3K-Akt signaling pathway34 and Dilated cardiomyopathy2335. Although large amounts of references showed that heart failure was closely related to HIF-1 signaling pathway36, Calcium signaling pathway37, cGMP-PKG signaling pathway38, mTOR signaling pathway39, Renin-angiotensin system40, ErbB signaling pathway41, AMPK signaling pathway42, VEGF signaling pathway43, Vascular smooth muscle contraction44, Adrenergic signaling in cardiomyocytes45 and Estrogen signaling pathway46, further experiments are still needed to identify the relationship between SND and these 11 pathways. In order to further explore the possible mechanism of active components in SND, we classified the targets protein into three parts according their degree in drug-target network: high degree (20–41), middle degree (10–19) and low degree (1–9). Then the p value of every relevant pathway was calculated in the three parts (Supplementary Table S4). We only considered pathways with a p value < 0.05. Lower p values represent that pathways have higher amounts of proteins involved in, and were meaningful in the global pathways. Comparing the results of three parts we found that high degree and low degree targets are mainly related with HIF-1 signaling pathway and Calcium signaling pathway, whereas middle degree targets are primarily bound up with Dilated cardiomyopathy and TNF signaling pathway.

Bottom Line: The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database.Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level.Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.

ABSTRACT
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

No MeSH data available.


Related in: MedlinePlus