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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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Predictability of Liver Hypertrophy Induced by Non-PPARα Compounds from Inferred PPARα–AILH Causal GenesNine selected transcripts for the logistic regression model from the 16 inferred causal genes were coordinately regulated in the training set (A). A total of 211 expression profiles for the nine measured transcripts were aligned with liver hypertrophy, defined by the liver/body weight ratio (B) in corresponding rats (color bar: −0.3:0.3 at log10 scale). The expected normal liver and liver hypertrophy (LH), based on measured liver/body weight ratio in the training set, are indicated by blue dots and diamonds, respectively, while the predicted normal liver and LH derived from the established model are indicated by red dots and diamonds, respectively (B). Based on the logistic regression model built from the training set, the pathological condition of the liver in the independent testing set (normal or hypertrophy) was predicted (D). The expected normal liver and LH based on measured liver/body weight ratio in the testing set are illustrated by blue dots and diamonds, respectively. The predicted normal liver and LH derived from the established model from the training set are indicated by red dots and diamonds, respectively. Distinctive patterns of the selected biomarkers are evident between the normal and hypertrophic livers [(A,C); color bar: −0.3:0.3 at log10 scale]. The probability of obtaining such a set of predictive biomarkers from the genes correlated with the liver/body weight ratio in the PPAR minicompendium was significantly small in both the training dataset [(E), p < 0.001] and the testing dataset [(F), p < 0.005]. The AUCs for the ROC of the built model based on causal transcripts are indicated by the blue and red bars for the training and testing dataset, respectively, among the distribution of AUCs for 10,000 trials of 16 genes randomly selected from 757 genes correlated with the endpoint in the minicompendium (E,F).
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pcbi-0030030-g004: Predictability of Liver Hypertrophy Induced by Non-PPARα Compounds from Inferred PPARα–AILH Causal GenesNine selected transcripts for the logistic regression model from the 16 inferred causal genes were coordinately regulated in the training set (A). A total of 211 expression profiles for the nine measured transcripts were aligned with liver hypertrophy, defined by the liver/body weight ratio (B) in corresponding rats (color bar: −0.3:0.3 at log10 scale). The expected normal liver and liver hypertrophy (LH), based on measured liver/body weight ratio in the training set, are indicated by blue dots and diamonds, respectively, while the predicted normal liver and LH derived from the established model are indicated by red dots and diamonds, respectively (B). Based on the logistic regression model built from the training set, the pathological condition of the liver in the independent testing set (normal or hypertrophy) was predicted (D). The expected normal liver and LH based on measured liver/body weight ratio in the testing set are illustrated by blue dots and diamonds, respectively. The predicted normal liver and LH derived from the established model from the training set are indicated by red dots and diamonds, respectively. Distinctive patterns of the selected biomarkers are evident between the normal and hypertrophic livers [(A,C); color bar: −0.3:0.3 at log10 scale]. The probability of obtaining such a set of predictive biomarkers from the genes correlated with the liver/body weight ratio in the PPAR minicompendium was significantly small in both the training dataset [(E), p < 0.001] and the testing dataset [(F), p < 0.005]. The AUCs for the ROC of the built model based on causal transcripts are indicated by the blue and red bars for the training and testing dataset, respectively, among the distribution of AUCs for 10,000 trials of 16 genes randomly selected from 757 genes correlated with the endpoint in the minicompendium (E,F).

Mentions: We assumed that liver hypertrophy is mediated by a common essential molecular mechanism, even if it can be induced by differing top signals. Therefore, there should be a set of core genes that are essential for liver hypertrophy regardless of whether the hypertrophy results from PPARα activation or other conditions. If some of the 16 identified transcripts are essential for liver hypertrophy, we would expect them to have predictive power for compound-induced liver hypertrophy regardless of the compounds' mode of action; otherwise, the derived causal effect of these 16 transcripts for liver hypertrophy cannot be sustained. To test this hypothesis, we profiled 211 rats that were treated with 30 non-PPAR compounds and determined their liver/body weight ratio, as an indicator of liver hypertrophy (Table S3). A logistic regression-based classifier for liver hypertrophy was built based on nine of the 16 identified causal transcripts from the 211 profiles as the training set. The optimal number of transcripts among the 16 inferred transcripts was determined by their contribution to the fit of the model for predicting liver hypertrophy. The accuracy of the model was measured by the root-mean-squared error estimated by leave-one-out validation. Nine transcripts were selected to build the best-fitted model with the least difference between the measured and predicted ratios of liver to body weight in the training set. The built model predicted liver hypertrophy in the training set with 96% sensitivity and 80% specificity (Figure 4B, Table 2).


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)

Predictability of Liver Hypertrophy Induced by Non-PPARα Compounds from Inferred PPARα–AILH Causal GenesNine selected transcripts for the logistic regression model from the 16 inferred causal genes were coordinately regulated in the training set (A). A total of 211 expression profiles for the nine measured transcripts were aligned with liver hypertrophy, defined by the liver/body weight ratio (B) in corresponding rats (color bar: −0.3:0.3 at log10 scale). The expected normal liver and liver hypertrophy (LH), based on measured liver/body weight ratio in the training set, are indicated by blue dots and diamonds, respectively, while the predicted normal liver and LH derived from the established model are indicated by red dots and diamonds, respectively (B). Based on the logistic regression model built from the training set, the pathological condition of the liver in the independent testing set (normal or hypertrophy) was predicted (D). The expected normal liver and LH based on measured liver/body weight ratio in the testing set are illustrated by blue dots and diamonds, respectively. The predicted normal liver and LH derived from the established model from the training set are indicated by red dots and diamonds, respectively. Distinctive patterns of the selected biomarkers are evident between the normal and hypertrophic livers [(A,C); color bar: −0.3:0.3 at log10 scale]. The probability of obtaining such a set of predictive biomarkers from the genes correlated with the liver/body weight ratio in the PPAR minicompendium was significantly small in both the training dataset [(E), p < 0.001] and the testing dataset [(F), p < 0.005]. The AUCs for the ROC of the built model based on causal transcripts are indicated by the blue and red bars for the training and testing dataset, respectively, among the distribution of AUCs for 10,000 trials of 16 genes randomly selected from 757 genes correlated with the endpoint in the minicompendium (E,F).
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pcbi-0030030-g004: Predictability of Liver Hypertrophy Induced by Non-PPARα Compounds from Inferred PPARα–AILH Causal GenesNine selected transcripts for the logistic regression model from the 16 inferred causal genes were coordinately regulated in the training set (A). A total of 211 expression profiles for the nine measured transcripts were aligned with liver hypertrophy, defined by the liver/body weight ratio (B) in corresponding rats (color bar: −0.3:0.3 at log10 scale). The expected normal liver and liver hypertrophy (LH), based on measured liver/body weight ratio in the training set, are indicated by blue dots and diamonds, respectively, while the predicted normal liver and LH derived from the established model are indicated by red dots and diamonds, respectively (B). Based on the logistic regression model built from the training set, the pathological condition of the liver in the independent testing set (normal or hypertrophy) was predicted (D). The expected normal liver and LH based on measured liver/body weight ratio in the testing set are illustrated by blue dots and diamonds, respectively. The predicted normal liver and LH derived from the established model from the training set are indicated by red dots and diamonds, respectively. Distinctive patterns of the selected biomarkers are evident between the normal and hypertrophic livers [(A,C); color bar: −0.3:0.3 at log10 scale]. The probability of obtaining such a set of predictive biomarkers from the genes correlated with the liver/body weight ratio in the PPAR minicompendium was significantly small in both the training dataset [(E), p < 0.001] and the testing dataset [(F), p < 0.005]. The AUCs for the ROC of the built model based on causal transcripts are indicated by the blue and red bars for the training and testing dataset, respectively, among the distribution of AUCs for 10,000 trials of 16 genes randomly selected from 757 genes correlated with the endpoint in the minicompendium (E,F).
Mentions: We assumed that liver hypertrophy is mediated by a common essential molecular mechanism, even if it can be induced by differing top signals. Therefore, there should be a set of core genes that are essential for liver hypertrophy regardless of whether the hypertrophy results from PPARα activation or other conditions. If some of the 16 identified transcripts are essential for liver hypertrophy, we would expect them to have predictive power for compound-induced liver hypertrophy regardless of the compounds' mode of action; otherwise, the derived causal effect of these 16 transcripts for liver hypertrophy cannot be sustained. To test this hypothesis, we profiled 211 rats that were treated with 30 non-PPAR compounds and determined their liver/body weight ratio, as an indicator of liver hypertrophy (Table S3). A logistic regression-based classifier for liver hypertrophy was built based on nine of the 16 identified causal transcripts from the 211 profiles as the training set. The optimal number of transcripts among the 16 inferred transcripts was determined by their contribution to the fit of the model for predicting liver hypertrophy. The accuracy of the model was measured by the root-mean-squared error estimated by leave-one-out validation. Nine transcripts were selected to build the best-fitted model with the least difference between the measured and predicted ratios of liver to body weight in the training set. The built model predicted liver hypertrophy in the training set with 96% sensitivity and 80% specificity (Figure 4B, Table 2).

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