A cell based screening approach for identifying protein degradation regulators

<p>Cellular transitions are achieved by the concerted actions of regulated degradation pathways. In the case of the cell cycle, ubiquitin mediated degradation ensures unidirectional transition from one phase to another. For instance, turnover of the cell cycle regulator cyclin B1 occurs after metaphase to induce mitotic exit. To better understand pathways controlling cyclin B1 turnover, the N-terminal domain of cyclin B1 was fused to luciferase to generate an N-cyclin B1-luciferase protein that can be used as a reporter for protein turnover. Prior studies demonstrated that cell-based screens using this reporter identified small molecules inhibiting the ubiquitin ligase controlling cyclin B1-turnover. Our group adapted this approach for the G2-M regulator Wee1 where a Wee1-luciferase construct was used to identify selective small molecules inhibiting an upstream kinase that controls Wee1 turnover. In the present study we present a screening approach where cell cycle regulators are fused to luciferase and overexpressed with cDNAs to identify specific regulators of protein turnover. We overexpressed approximately 14,000 cDNAs with the N-cyclin B1-luciferase fusion protein and determined its steady-state level relative to other luciferase fusion proteins. We identified the known APC/C regulator Cdh1 and the F-box protein Fbxl15 as specific modulators of N-cyclin B1-luciferase steady-state levels and turnover. Collectively, our studies suggest that analyzing the steady-state levels of luciferase fusion proteins in parallel facilitates identification of specific regulators of protein turnover.</p

Correlation networks identified that intersect epigenetic pathways/signaling pathways with patient specific DE genes.

<p>Connections were calculated for gene-gene pairs emanating from epigenetic pathways or genes in the Notch, SHH, or WNT pathways and genes that were Differentially Expressed in each patient.</p

Gene Networks created by Pairs with high PCC (greater than 0.7) and high hypergeometric p-value yield less experimentally verified interactions.

<p>The number of connections identified was calculated for gene pairs with high PCC and high hypergeometric p-values. These connections were then compared to those identified in the literature. Note that few connections were found to be experimentally validated.</p

Correlation networks created by using the top gene pairs for each patient.

<p>The number of connections we identified were compared to those previously described in the literature (red). Yellow indicates connections, which were identified in protein-protein interaction databases.</p

Pipeline for identifying patient-specific gene association in GBM.

<p>Our first step in our pipeline is to identify Differentially Expressed (DE) genes that are represented in 3 out of 4 algorithms. Next, we filter this DE gene list for those genes that overlapped with DE genes in the TCGA GBM Database. We then calculate the Correlation Coefficient and a hypergeometric p-value for every gene pair. Finally, by selecting the gene pairs with the highest correlation values we create a patient specific gene correlation network, which can be experimentally verified. As a starting point for our experiments, we can use the sub-networks in which, already verified connections exist in the literature.</p