Abstract: A critical step in drug development is the optimization of the efficacy and specificity of candidate therapeutic compounds. Ideally, optimization is carried out using knowledge of the drug’s mode of action, i.e., the gene products with which a drug functionally interacts (drug targets). These drug targets may include genes that mediate the therapeutic effects of the drug, as well as genes that mediate undesirable side-effects. However, for many drug candidates the targets are unknown and difficult to identify among the thousands of genes in a typical genome. Previously, we developed an algorithm to identify drug targets in yeast using multiple perturbations to a cell and by measuring the response at steady-state (di Bernardo et al, Nature Biotechnology, in press). Here, we report a novel computational approach for rapidly identifying drug targets using time-course gene expression profiles. The approach filters expression profiles using a reverse-engineered gene-network model to distinguish the targets of compounds from the genes that exhibit only secondary responses. We tested this approach experimentally in E coli and show that it can overcome some of the experimental and computational limitations of existing chemogenetic approach for identifying a drug’s mode of action.

Abstract: Genome-wide gene expression profiles provide a means to discover the direct mediators of biologically active compounds. We have already shown that it is possible to infer a predictive model of a genetic network by overexpressing each gene of the network and measuring the resulting expression at steady state of all the genes in the network. This approach however requires the perturbation of each gene and the measurement of the perturbation magnitude. In this work we explored the possibility of inferring predictive models of large genetic networks without requiring the knowledge of which genes have been perturbed and by what amount. The network identification algorithm here described allows to infer a model of a genetic network from perturbation experiments for which the perturbed genes are not known. This model can be used to identify the target gene, or genes, of a given drug.

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