The over-arching goal of functional genomics is to globally define the function of all genomic loci-to directly connect genotype with phenotype. A successful functional genomics screen requires technology to specifically inhibit or activate each gene in the genome. The discovery of microRNAs and the realization that the miRNA processing machinery could be hijacked for sequence-specific gene silencing (RNAi technologies) created a high-throughput loss-of-function approach for genome annotation. The recent discovery and employment of near-haploid human cells for functional genomic screening further empowers mammalian loss-of-function genome annotation. In contrast, genome-scale gain-of-function technologies have yet to be fully realized. Although progress is being made, the incomplete nature of human open reading frame clone collections and the often prohibitive costs of establishing an arrayed expression-ready library dramatically limit its application for the vast majority of academic research laboratories. The principle goal of this grant is to realize a high throughput, cost-effective and genome-wide gain-of-function screening platform. We propose to use a mass spectrometry-based approach to detect proteins over-expressed in a 'foot-printed'random mutagenesis screen. In addition to revealing the genes that when over-expressed result in a given phenotype, this approach illuminates protein interaction networks and protein post translational modifications. In proof-of-concept experiments, we discovered and validated novel activators of the b-catenin dependent WNT signaling pathway. This work supports the transformative potential of the discovery platform, in part by demonstrating scalability, general applicability and low cost. This proposal has two primary goals. First, although our preliminary screens were successful, additional molecular engineering is needed to improve detection of the over-expressed protein by mass spectrometry. Second, using the screening platform and a new panel of pathway-specific transcriptional reporters, we will identify and validate activators of th following pathways: WNT/ b-catenin, Retinoic Acid, Notch, NFkB, TGFb and NRF2. The resulting data promises to reveal gain-of-function genotype- phenotype relationships at the protein-level. We will integrate our data with cancer-derived genomic copy number alterations to provide a data reduction strategy for future mechanistic studies with translational promise.

Public Health Relevance

If we knew the functions of every human gene, we might be able to predict the effect of a given mutation on cell behavior-to know whether that genetic mutation may give rise to cancer. Whereas recent technological breakthroughs allow cancer researchers to systematically inactivate every gene in the genome, comparable efficient technologies allowing for the activation of all human genes do not exist. Our research will develop an affordable and robust gain-of-function discovery platform to help cancer researchers predict the cellular effects of gene mutation.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZCA1)
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Knowlton, John R
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University of North Carolina Chapel Hill
Schools of Medicine
Chapel Hill
United States
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Madan, Babita; Walker, Matthew P; Young, Robert et al. (2016) USP6 oncogene promotes Wnt signaling by deubiquitylating Frizzleds. Proc Natl Acad Sci U S A 113:E2945-54
Goldfarb, Dennis; Wang, Wei; Major, Michael B (2016) MSAcquisitionSimulator: data-dependent acquisition simulator for LC-MS shotgun proteomics. Bioinformatics 32:1269-71
Walker, Matthew P; Stopford, Charles M; Cederlund, Maria et al. (2015) FOXP1 potentiates Wnt/?-catenin signaling in diffuse large B cell lymphoma. Sci Signal 8:ra12
Goldfarb, Dennis; Hast, Bridgid E; Wang, Wei et al. (2014) Spotlite: web application and augmented algorithms for predicting co-complexed proteins from affinity purification--mass spectrometry data. J Proteome Res 13:5944-55