MacMillan: Project 2; Technology and Research Development Project 2: FUSION (Macmillian)

University Of California Santa Cruz, Santa Cruz CA

Investigators

Linked publications & trials

Abstract

Abstract: A pressing challenge in the field of natural products and botanicals is the biological characterization of pure compounds, mixtures and the study of synergistic relationships. Although the rate of discovery of interesting new metabolites is high using phenotypic approaches, the difficulty of target identification and verification is a bottleneck that is difficult to overcome. To fully take advantage of this vast reservoir of biologically active small molecules requires the development of high-throughput methods to mechanistically annotate chemical collections. For botanicals, there is generally anecdotal evidence for their use and in some cases a body of clinical evaluation. However many are not well annotated for their molecular and cellular interactions. We have developed a method for the broad-scale classification of natural products in human cells, by employing an information-rich, endogenous reporter gene expression platform that allows quantitative discrimination of cellular responses to genetic and chemical perturbations. In a proof-of-concept, gene expression-driven functional signatures were employed as cross-platform phenotypic discriminators to link concordant cellular responses to 1124 genetic and 1186 natural product perturbations, providing Functional Signature of Ontology (FUSION) maps, allowing us to predict the mechanism of action of natural products. In this proposal, we will expand both our chemical coverage to include plant, marine invertebrate and microbial extracts, fractions and pure compounds as well as commercial pure compound libraries. We will also expand our biological coverage to include over 20,000 siRNAs in the context of normal human cells, non-small cell lung cancer cell lines and immune cells. Importantly, we will use a combination of experimental and bioinformatic approaches for the selection of an optimized set of high performance reporter genes tuned to the context of the different cell types we will utilize.