PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy.
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.
Dai
Xudong
X
Informatics, Rosetta Inpharmatics, Seattle, Washington, United States of America. xudong_dai@merck.com
De Souza
Angus T
AT
Dai
Hongyue
H
Lewis
David L
DL
Lee
Chang-kyu
CK
Spencer
Andy G
AG
Herweijer
Hans
H
Hagstrom
Jim E
JE
Linsley
Peter S
PS
Bassett
Douglas E
DE
Ulrich
Roger G
RG
He
Yudong D
YD
D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D020868:Gene Silencing; D006984:Hypertrophy / Q000139:chemically induced / Q000378:metabolism; D008099:Liver / Q000187:drug effects / Q000378:metabolism* / Q000473:pathology*; D051379:Mice; D008954:Models, Biological; D047493:PPAR alpha / Q000235:genetics / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D034741:RNA, Small Interfering / Q000008:administration & dosage / Q000235:genetics*; D015398:Signal Transduction
D047493:PPAR alpha; D020543:Proteome; D034741:RNA, Small Interfering