Detection of compound mode of action by computational integration of whole-genome measurements and genetic perturbations.
BACKGROUND
A key problem of drug development is to decide which compounds to evaluate further in expensive clinical trials (Phase I- III). This decision is primarily based on the primary targets and mechanisms of action of the chemical compounds under consideration. Whole-genome expression measurements have shown to be useful for this process but current approaches suffer from requiring either a large number of mutant experiments or a detailed understanding of the regulatory networks.


RESULTS
We have designed an algorithm, CutTree that when applied to whole-genome expression datasets identifies the primary affected genes (PAGs) of a chemical compound by separating them from downstream, indirectly affected genes. Unlike previous methods requiring whole-genome deletion libraries or a complete map of gene network architecture, CutTree identifies PAGs from a limited set of experimental perturbations without requiring any prior information about the underlying pathways. The principle for CutTree is to iteratively filter out PAGs from other recurrently active genes (RAGs) that are not PAGs. The in silico validation predicted that CutTree should be able to identify 3-4 out of 5 known PAGs (approximately 70%). In accordance, when we applied CutTree to whole-genome expression profiles from 17 genetic perturbations in the presence of galactose in Yeast, CutTree identified four out of five known primary galactose targets (80%). Using an exhaustive search strategy to detect these PAGs would not have been feasible (>1012 combinations).


CONCLUSIONS
In combination with genetic perturbation techniques like short interfering RNA (siRNA) followed by whole-genome expression measurements, CutTree sets the stage for compound target identification in less well-characterized but more disease-relevant mammalian cell systems.
Hallén
Kristofer
K
Division of Computational Biology, The Department of Physics and Measurement Technology, Biology and Chemistry, Institute of Technology, Linköping University, S-581 83 Linköping, Sweden. hallen@ifm.liu.se
Björkegren
Johan
J
Tegnér
Jesper
J
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D016503:Drug Delivery Systems / Q000379:methods*; D015195:Drug Design; D004341:Drug Evaluation / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000187:drug effects* / Q000235:genetics; D014644:Genetic Variation; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D018511:Systems Integration
D020543:Proteome