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Drug target prioritization by perturbed gene expression and network information.

Isik Z, Baldow C, Cannistraci CV, Schroeder M - Sci Rep (2015)

Bottom Line: We performed the first systematic analysis of over 500 drugs from the Connectivity Map.We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information.The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Group, Biotechnology Center (BIOTEC), Technische Universitat Dresden, Tatzberg 47-49, 01307 Dresden, Germany.

ABSTRACT
Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

No MeSH data available.


Related in: MedlinePlus

A sub-network of selected targets and deregulated genes.Four drugs (methylprednisolone, nimesulide, prednicarbate, and simvastatin) and their differentially expressed genes are shown in different colors in the STRING network. A rectangle node shape represents a target protein, and circles indicate interconnecting genes. Differentially expressed genes (including possible targets) are colored in the color of the appropriate drug. Therefore, each colored sub-network might represent affected downstream pathways of the given drug. Thus, the view of target-affected genes community helps experimentalists design new drug experiments.
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f6: A sub-network of selected targets and deregulated genes.Four drugs (methylprednisolone, nimesulide, prednicarbate, and simvastatin) and their differentially expressed genes are shown in different colors in the STRING network. A rectangle node shape represents a target protein, and circles indicate interconnecting genes. Differentially expressed genes (including possible targets) are colored in the color of the appropriate drug. Therefore, each colored sub-network might represent affected downstream pathways of the given drug. Thus, the view of target-affected genes community helps experimentalists design new drug experiments.

Mentions: Although the prediction of a drug target is crucial, the generation of the expected phenotype is also important for drug treatment experiments. The pathway databases can help to formalize the expected phenotype, but incomplete databases limit the investigation of effects on the pathway level. Specifically, the knowledge about molecular pathways might be incomplete and inconsistent between different sources14 because biochemical reactions are not fully understood for all genes and diseases. If such information is not covered for a drug target and the affected genes in public databases, the PPI networks might provide some hints for possible reactions between these genes. Therefore, we used the LR method to obtain more insights into affected downstream pathways (see details in the Methods Section). The extraction of shortest paths between deregulated genes and known targets exposes the topological mapping of perturbation data in a functional interaction network (Fig. 6). Each selected target-deregulated gene sub-network is clearly separated from other nodes in this example. Each colored sub-network might be interpreted as affected downstream pathways of the given drug. Such deregulated paths explain the observed phenotype after a drug treatment. Moreover, the sub-network of selected targets-deregulated genes might point out potential new targets for the given drug. Thus, this network-level visualization helps experimentalists design new drug experiments.


Drug target prioritization by perturbed gene expression and network information.

Isik Z, Baldow C, Cannistraci CV, Schroeder M - Sci Rep (2015)

A sub-network of selected targets and deregulated genes.Four drugs (methylprednisolone, nimesulide, prednicarbate, and simvastatin) and their differentially expressed genes are shown in different colors in the STRING network. A rectangle node shape represents a target protein, and circles indicate interconnecting genes. Differentially expressed genes (including possible targets) are colored in the color of the appropriate drug. Therefore, each colored sub-network might represent affected downstream pathways of the given drug. Thus, the view of target-affected genes community helps experimentalists design new drug experiments.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663505&req=5

f6: A sub-network of selected targets and deregulated genes.Four drugs (methylprednisolone, nimesulide, prednicarbate, and simvastatin) and their differentially expressed genes are shown in different colors in the STRING network. A rectangle node shape represents a target protein, and circles indicate interconnecting genes. Differentially expressed genes (including possible targets) are colored in the color of the appropriate drug. Therefore, each colored sub-network might represent affected downstream pathways of the given drug. Thus, the view of target-affected genes community helps experimentalists design new drug experiments.
Mentions: Although the prediction of a drug target is crucial, the generation of the expected phenotype is also important for drug treatment experiments. The pathway databases can help to formalize the expected phenotype, but incomplete databases limit the investigation of effects on the pathway level. Specifically, the knowledge about molecular pathways might be incomplete and inconsistent between different sources14 because biochemical reactions are not fully understood for all genes and diseases. If such information is not covered for a drug target and the affected genes in public databases, the PPI networks might provide some hints for possible reactions between these genes. Therefore, we used the LR method to obtain more insights into affected downstream pathways (see details in the Methods Section). The extraction of shortest paths between deregulated genes and known targets exposes the topological mapping of perturbation data in a functional interaction network (Fig. 6). Each selected target-deregulated gene sub-network is clearly separated from other nodes in this example. Each colored sub-network might be interpreted as affected downstream pathways of the given drug. Such deregulated paths explain the observed phenotype after a drug treatment. Moreover, the sub-network of selected targets-deregulated genes might point out potential new targets for the given drug. Thus, this network-level visualization helps experimentalists design new drug experiments.

Bottom Line: We performed the first systematic analysis of over 500 drugs from the Connectivity Map.We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information.The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Group, Biotechnology Center (BIOTEC), Technische Universitat Dresden, Tatzberg 47-49, 01307 Dresden, Germany.

ABSTRACT
Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

No MeSH data available.


Related in: MedlinePlus