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Recovering drug-induced apoptosis subnetwork from Connectivity Map data.

Yu J, Putcha P, Silva JM - Biomed Res Int (2015)

Bottom Line: We then focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network.In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in the literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway.Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as "gateway" genes in the drug-induced intrinsic and extrinsic apoptosis pathways.

View Article: PubMed Central - PubMed

Affiliation: Department of Precision Medicine, Oncology Research Unit, Pfizer Inc., Pearl River, NY 10965, USA.

ABSTRACT
The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. Since the therapeutic goal of most anticancer drugs is to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using CMAP microarray data from breast cancer cell line MCF7, we applied a Gaussian Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular-pathway. We then focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network. In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in the literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway. Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as "gateway" genes in the drug-induced intrinsic and extrinsic apoptosis pathways.

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(a) Predicted subnetwork of 13 selected drug-responsive apoptotic genes: edges in red are validated interactions in literature and edges in dark red are strong validated direct interactions. (b) A subnetwork from literature showing evidences for validated interactions in predicted network including candidate genes (colored in yellow) with their validated interactants (in brown). Each validated edge in predicted network (red in (a)) can be mapped to one path in evidence network (b) between the two corresponding interacting candidate genes.
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fig6: (a) Predicted subnetwork of 13 selected drug-responsive apoptotic genes: edges in red are validated interactions in literature and edges in dark red are strong validated direct interactions. (b) A subnetwork from literature showing evidences for validated interactions in predicted network including candidate genes (colored in yellow) with their validated interactants (in brown). Each validated edge in predicted network (red in (a)) can be mapped to one path in evidence network (b) between the two corresponding interacting candidate genes.

Mentions: Using the described Gaussian Bayesian network modeling framework, a network model was generated for the 13 identified drug-responsive apoptotic genes as shown in Figure 6(a). The network contains 15 interactions and each edge has a confidence of over 75%. The inferred interactions represent dependence among these 13 genes of interest, which may be due to direct or indirect protein-protein interactions, transcriptional regulation, or signal transduction. To validate the inferred interactions, we searched the interactions component of NCBI Gene database (http://www.ncbi.nlm.nih.gov/gene), which contains data from multiple interaction databases such as BIND, HPRD, and BioGRID. We then generated a validated interaction network of the 13 apoptotic genes using their validated interactions (Figure 7). The validated network contained 216 interacting genes, including our 13 genes of interest. The network also contained 243 interactions after removing duplicate interactions (365 interactions with duplicates, Table S3). When compared with our predicted network, 9 out of 15 predicted interactions were found to be direct or indirect interactions in the validated network (marked in red, Figure 6(a)). An indirect interaction means the network does not contain a direct edge between the two genes, but there exists a path between them via intermediate genes.


Recovering drug-induced apoptosis subnetwork from Connectivity Map data.

Yu J, Putcha P, Silva JM - Biomed Res Int (2015)

(a) Predicted subnetwork of 13 selected drug-responsive apoptotic genes: edges in red are validated interactions in literature and edges in dark red are strong validated direct interactions. (b) A subnetwork from literature showing evidences for validated interactions in predicted network including candidate genes (colored in yellow) with their validated interactants (in brown). Each validated edge in predicted network (red in (a)) can be mapped to one path in evidence network (b) between the two corresponding interacting candidate genes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4389823&req=5

fig6: (a) Predicted subnetwork of 13 selected drug-responsive apoptotic genes: edges in red are validated interactions in literature and edges in dark red are strong validated direct interactions. (b) A subnetwork from literature showing evidences for validated interactions in predicted network including candidate genes (colored in yellow) with their validated interactants (in brown). Each validated edge in predicted network (red in (a)) can be mapped to one path in evidence network (b) between the two corresponding interacting candidate genes.
Mentions: Using the described Gaussian Bayesian network modeling framework, a network model was generated for the 13 identified drug-responsive apoptotic genes as shown in Figure 6(a). The network contains 15 interactions and each edge has a confidence of over 75%. The inferred interactions represent dependence among these 13 genes of interest, which may be due to direct or indirect protein-protein interactions, transcriptional regulation, or signal transduction. To validate the inferred interactions, we searched the interactions component of NCBI Gene database (http://www.ncbi.nlm.nih.gov/gene), which contains data from multiple interaction databases such as BIND, HPRD, and BioGRID. We then generated a validated interaction network of the 13 apoptotic genes using their validated interactions (Figure 7). The validated network contained 216 interacting genes, including our 13 genes of interest. The network also contained 243 interactions after removing duplicate interactions (365 interactions with duplicates, Table S3). When compared with our predicted network, 9 out of 15 predicted interactions were found to be direct or indirect interactions in the validated network (marked in red, Figure 6(a)). An indirect interaction means the network does not contain a direct edge between the two genes, but there exists a path between them via intermediate genes.

Bottom Line: We then focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network.In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in the literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway.Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as "gateway" genes in the drug-induced intrinsic and extrinsic apoptosis pathways.

View Article: PubMed Central - PubMed

Affiliation: Department of Precision Medicine, Oncology Research Unit, Pfizer Inc., Pearl River, NY 10965, USA.

ABSTRACT
The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. Since the therapeutic goal of most anticancer drugs is to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using CMAP microarray data from breast cancer cell line MCF7, we applied a Gaussian Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular-pathway. We then focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network. In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in the literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway. Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as "gateway" genes in the drug-induced intrinsic and extrinsic apoptosis pathways.

Show MeSH
Related in: MedlinePlus