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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

Downstream affected pathways for the Pioglitazone treatment.(A) The shortest paths network. The colored nodes represent deregulated genes and bold circled nodes have specific Gene Ontology annotations (e.g., angiogenesis, apoptosis). (B) The core pathway affected by the activation of PPARG. The color indicates the gene expression value of the node. An edge represents an activation or inhibition between two genes.
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f7: Downstream affected pathways for the Pioglitazone treatment.(A) The shortest paths network. The colored nodes represent deregulated genes and bold circled nodes have specific Gene Ontology annotations (e.g., angiogenesis, apoptosis). (B) The core pathway affected by the activation of PPARG. The color indicates the gene expression value of the node. An edge represents an activation or inhibition between two genes.

Mentions: In particular, we investigated Pioglitazone as well as its targets and altered pathways. Pioglitazone was approved for the treatment of type 2 diabetes. It regulates the peroxisome proliferator-activated receptor gamma (PPARG) as an agonist. Connective tissue growth factor (CTGF) is reported as a functional target of Pioglitazone in the STITCH database. CTGF is involved in endothelial cell proliferation, migration, and angiogenesis. Several network measures ranked PPARG and CTGF in the 1st percentile of possible targets on the prostate cancer (PC3) tissue. Thus, Pioglitazone might be a new repositioning candidate for prostate cancer treatment. Although high expression of CTGF was observed in tumor-promoting prostate stromal cell lines15, it is significantly down-regulated by the Pioglitazone treatment; thus it can no longer trigger the angiogenesis path. The affected pathways due to the Pioglitazone treatment were analyzed using the LR measure. There were 70 deregulated genes in this treatment with /FC/ ≥ 2. The shortest paths network (SP-net) is built by compiling the shortest paths passing through PPARG, CTGF (targets) and deregulated genes based on the STRING network. The initial SP-net contains 322 genes and 1125 edges. Figure 7a shows possible affected paths after application of a filtering procedure (see Methods). The most interesting genes are SMAD3, NFKB1, IL8, KLF4, and FABP4. We performed a literature search to find transcriptome-level responses of these genes. PPARG agonists inhibit CTGF expression through SMAD3-(4)16. Similarly, PPARG agonists reduce SMAD3 activity and inhibit metastasis of lung cancer cells in mice17. Due to the down-regulation of CTGF through SMAD3 inhibition, these observations could be accurate for Pioglitazone treatment on PC3 tissue. Activation of PPARG represses the transcriptional activity of NFKB that reduces IL8 production and proliferation of PC3 cells18. A similar mode of action might work in the Pioglitazone treatment because of the down-regulation of IL8. Epidermis-associated FABP is strongly down-regulated in prostate cancer cells1920. Correlated with such an observation, PPARG activation leads to a significant up-regulation of FABP4 in the Pioglitazone treatment. KLF4 regulates cell proliferation, apoptosis, and inflammation. KLF4 works as a tumor suppressor21 and PPARG binds to the promoter region of KLF4 in prostate cancer22. The up-regulation of KLF4 in the Pioglitazone treatment also supports previous findings that it might reduce tumor proliferation. All of these observations, which are obtained by the affected pathway analysis and validated by pathway databases (e.g., KEGG, Reactome, and WikiPathways), uncovered the hypothetical pathway in Fig. 7b. In summary, previous studies highlighting the relationship between PPARG and CTGF, IL8, and KLF4 were also confirmed by the affected pathway analysis, which helps in the discovery of a pathway-level phenotype for drug treatment.


Drug target prioritization by perturbed gene expression and network information.

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

Downstream affected pathways for the Pioglitazone treatment.(A) The shortest paths network. The colored nodes represent deregulated genes and bold circled nodes have specific Gene Ontology annotations (e.g., angiogenesis, apoptosis). (B) The core pathway affected by the activation of PPARG. The color indicates the gene expression value of the node. An edge represents an activation or inhibition between two genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f7: Downstream affected pathways for the Pioglitazone treatment.(A) The shortest paths network. The colored nodes represent deregulated genes and bold circled nodes have specific Gene Ontology annotations (e.g., angiogenesis, apoptosis). (B) The core pathway affected by the activation of PPARG. The color indicates the gene expression value of the node. An edge represents an activation or inhibition between two genes.
Mentions: In particular, we investigated Pioglitazone as well as its targets and altered pathways. Pioglitazone was approved for the treatment of type 2 diabetes. It regulates the peroxisome proliferator-activated receptor gamma (PPARG) as an agonist. Connective tissue growth factor (CTGF) is reported as a functional target of Pioglitazone in the STITCH database. CTGF is involved in endothelial cell proliferation, migration, and angiogenesis. Several network measures ranked PPARG and CTGF in the 1st percentile of possible targets on the prostate cancer (PC3) tissue. Thus, Pioglitazone might be a new repositioning candidate for prostate cancer treatment. Although high expression of CTGF was observed in tumor-promoting prostate stromal cell lines15, it is significantly down-regulated by the Pioglitazone treatment; thus it can no longer trigger the angiogenesis path. The affected pathways due to the Pioglitazone treatment were analyzed using the LR measure. There were 70 deregulated genes in this treatment with /FC/ ≥ 2. The shortest paths network (SP-net) is built by compiling the shortest paths passing through PPARG, CTGF (targets) and deregulated genes based on the STRING network. The initial SP-net contains 322 genes and 1125 edges. Figure 7a shows possible affected paths after application of a filtering procedure (see Methods). The most interesting genes are SMAD3, NFKB1, IL8, KLF4, and FABP4. We performed a literature search to find transcriptome-level responses of these genes. PPARG agonists inhibit CTGF expression through SMAD3-(4)16. Similarly, PPARG agonists reduce SMAD3 activity and inhibit metastasis of lung cancer cells in mice17. Due to the down-regulation of CTGF through SMAD3 inhibition, these observations could be accurate for Pioglitazone treatment on PC3 tissue. Activation of PPARG represses the transcriptional activity of NFKB that reduces IL8 production and proliferation of PC3 cells18. A similar mode of action might work in the Pioglitazone treatment because of the down-regulation of IL8. Epidermis-associated FABP is strongly down-regulated in prostate cancer cells1920. Correlated with such an observation, PPARG activation leads to a significant up-regulation of FABP4 in the Pioglitazone treatment. KLF4 regulates cell proliferation, apoptosis, and inflammation. KLF4 works as a tumor suppressor21 and PPARG binds to the promoter region of KLF4 in prostate cancer22. The up-regulation of KLF4 in the Pioglitazone treatment also supports previous findings that it might reduce tumor proliferation. All of these observations, which are obtained by the affected pathway analysis and validated by pathway databases (e.g., KEGG, Reactome, and WikiPathways), uncovered the hypothetical pathway in Fig. 7b. In summary, previous studies highlighting the relationship between PPARG and CTGF, IL8, and KLF4 were also confirmed by the affected pathway analysis, which helps in the discovery of a pathway-level phenotype for drug treatment.

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