Search
×

Sign up

Use your Facebook account for quick registration

OR

Create a Shvoong account from scratch

Already a Member? Sign In!
×

Sign In

Sign in using your Facebook account

OR

Not a Member? Sign up!
×

Sign up

Use your Facebook account for quick registration

OR

Sign In

Sign in using your Facebook account

Shvoong Home>Science>Biology>PPAR - siRNA - Treated Expression Profiles Uncover the Causal Sufficiency Network for Compou Summary

PPAR - siRNA - Treated Expression Profiles Uncover the Causal Sufficiency Network for Compou

ª
 
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 (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 PPAR-induced liver hypertrophy is supported by their ability to predict non-PPARinduced 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.
Published: March 02, 2007   
Please Rate this Summary : 1 2 3 4 5
Translate Send Link Print
X

.