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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

Component – protein–metabolite network.The active components, target proteins, pathway proteins and metabolites are represented by the red circles, blue hexagons, green round rectangles, and yellow diamonds, respectively. GATM represented by a purple round rectangle is both target protein and pathway protein. The interactions between active components and proteins, between proteins and proteins and between proteins and metabolites are linked by edges, respectively.
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f4: Component – protein–metabolite network.The active components, target proteins, pathway proteins and metabolites are represented by the red circles, blue hexagons, green round rectangles, and yellow diamonds, respectively. GATM represented by a purple round rectangle is both target protein and pathway protein. The interactions between active components and proteins, between proteins and proteins and between proteins and metabolites are linked by edges, respectively.

Mentions: To reduce the number of candidate targets and identify more potential targets based on targets identified from network pharmacology, a component –protein – metabolite interaction network (CPMI) was established through the integration of NATPI and MPPI, as shown in Fig. 4. This network consists of chemical components, target proteins, pathway proteins, and biomarkers, including 130 nodes and 375 edges. The components acting on target proteins would cause the up- or down- regulation of related metabolic enzymes in pathway, thereby resulting in the changes in concentration of biomarkers. The interactions between target proteins and pathway proteins were obtained from STRING with a high confidence (>0.8). We consider the active components as the initial node and the metabolites as the terminal node. As shown in Fig. 4, 48 components interacted with 25 target proteins. These proteins can be related to 13 biomarkers through 44 pathway proteins. In this network, 25 target proteins could be more likely to be the true targets of active components in SND compared to 61 target proteins identified from network pharmacology. This deduction has been proved by references. The detailed evidence is that a total of 61 putative target proteins were identified by network pharmacology, only 13 target proteins were validated by reference. While among 26 target proteins identified by network analysis, nine target proteins are in agreement with existing research results. Target proteins of active components validated by references were summarized in supplementary data (Supplementary Table S3).


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)

Component – protein–metabolite network.The active components, target proteins, pathway proteins and metabolites are represented by the red circles, blue hexagons, green round rectangles, and yellow diamonds, respectively. GATM represented by a purple round rectangle is both target protein and pathway protein. The interactions between active components and proteins, between proteins and proteins and between proteins and metabolites are linked by edges, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Component – protein–metabolite network.The active components, target proteins, pathway proteins and metabolites are represented by the red circles, blue hexagons, green round rectangles, and yellow diamonds, respectively. GATM represented by a purple round rectangle is both target protein and pathway protein. The interactions between active components and proteins, between proteins and proteins and between proteins and metabolites are linked by edges, respectively.
Mentions: To reduce the number of candidate targets and identify more potential targets based on targets identified from network pharmacology, a component –protein – metabolite interaction network (CPMI) was established through the integration of NATPI and MPPI, as shown in Fig. 4. This network consists of chemical components, target proteins, pathway proteins, and biomarkers, including 130 nodes and 375 edges. The components acting on target proteins would cause the up- or down- regulation of related metabolic enzymes in pathway, thereby resulting in the changes in concentration of biomarkers. The interactions between target proteins and pathway proteins were obtained from STRING with a high confidence (>0.8). We consider the active components as the initial node and the metabolites as the terminal node. As shown in Fig. 4, 48 components interacted with 25 target proteins. These proteins can be related to 13 biomarkers through 44 pathway proteins. In this network, 25 target proteins could be more likely to be the true targets of active components in SND compared to 61 target proteins identified from network pharmacology. This deduction has been proved by references. The detailed evidence is that a total of 61 putative target proteins were identified by network pharmacology, only 13 target proteins were validated by reference. While among 26 target proteins identified by network analysis, nine target proteins are in agreement with existing research results. Target proteins of active components validated by references were summarized in supplementary data (Supplementary Table S3).

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