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PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy.

Dai X, De Souza AT, Dai H, Lewis DL, Lee CK, Spencer AG, Herweijer H, Hagstrom JE, Linsley PS, Bassett DE, Ulrich RG, He YD - PLoS Comput. Biol. (2007)

Bottom Line: Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005).Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy.When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.

View Article: PubMed Central - PubMed

Affiliation: Informatics, Rosetta Inpharmatics, Seattle, Washington, United States of America. xudong_dai@merck.com

ABSTRACT
Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor alpha (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARalpha-induced liver hypertrophy is supported by their ability to predict non-PPARalpha-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.

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The Sufficiency Order Network of the 15 Causal Genes Mediating PPARα–AILH, as Determined by CI Tests against Liver Hypertrophy
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pcbi-0030030-g005: The Sufficiency Order Network of the 15 Causal Genes Mediating PPARα–AILH, as Determined by CI Tests against Liver Hypertrophy

Mentions: Finally, we determined the sufficiency order for mediating PPARα–AILH among the 16 transcripts by exhaustive pairwise conditional independent (CI) tests (Figure 5 and Figure S1). There were 15 genes with known or predicted proteins among the 16 derived causal transcripts. CI tests identified nine of these genes as the most sufficient ones in inducing liver hypertrophy. For instance, as revealed by conditional dependency between Pck1 and PPARα–AILH given the constraint of the inferred causal relationship between Acadm and PPARα–AILH, Pck1 is more sufficient than Acadm in mediating PPARα–AILH (Figure S1A).


PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy.

Dai X, De Souza AT, Dai H, Lewis DL, Lee CK, Spencer AG, Herweijer H, Hagstrom JE, Linsley PS, Bassett DE, Ulrich RG, He YD - PLoS Comput. Biol. (2007)

The Sufficiency Order Network of the 15 Causal Genes Mediating PPARα–AILH, as Determined by CI Tests against Liver Hypertrophy
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1808491&req=5

pcbi-0030030-g005: The Sufficiency Order Network of the 15 Causal Genes Mediating PPARα–AILH, as Determined by CI Tests against Liver Hypertrophy
Mentions: Finally, we determined the sufficiency order for mediating PPARα–AILH among the 16 transcripts by exhaustive pairwise conditional independent (CI) tests (Figure 5 and Figure S1). There were 15 genes with known or predicted proteins among the 16 derived causal transcripts. CI tests identified nine of these genes as the most sufficient ones in inducing liver hypertrophy. For instance, as revealed by conditional dependency between Pck1 and PPARα–AILH given the constraint of the inferred causal relationship between Acadm and PPARα–AILH, Pck1 is more sufficient than Acadm in mediating PPARα–AILH (Figure S1A).

Bottom Line: Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005).Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy.When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.

View Article: PubMed Central - PubMed

Affiliation: Informatics, Rosetta Inpharmatics, Seattle, Washington, United States of America. xudong_dai@merck.com

ABSTRACT
Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor alpha (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARalpha-induced liver hypertrophy is supported by their ability to predict non-PPARalpha-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events.

Show MeSH
Related in: MedlinePlus