Identifying novel interactions of the colon-cancer related APC protein with Wnt-pathway nuclear transcription factors.

BACKGROUND: Colon cancer is often driven by mutations of the adenomatous polyposis coli (APC) gene, an essential tumor suppressor gene of the Wnt ß-catenin signaling pathway. APC and its cytoplasmic interactions have been well studied. However, various groups have also observed its presence in the nucleus. Identifying novel interactions of APC in the Wnt pathway will provide an opportunity to understand APC's nuclear role better and ultimately identify potential cancer treatment targets. METHODS: We used the all-vs-all sequencing (AVA-Seq) method to interrogate the interactome of protein fragments spanning most of the 60 Wnt ß-catenin pathway proteins. Using protein fragments identified the interacting regions between the proteins with more resolution than a full-length protein approach. Pull-down assays were used to validate a subset of these interactions. RESULTS: 74 known and 703 novel Wnt ß-catenin pathway protein-protein interactions were recovered in this study. There were 8 known and 31 novel APC protein-protein interactions. Novel interactions of APC and nuclear transcription factors TCF7, JUN, FOSL1, and SOX17 were particularly interesting and confirmed in validation assays. CONCLUSION: Based on our findings of novel interactions between APC and transcription factors and previous evidence of APC localizing to the nucleus, we suggest APC may compete and repress CTNNB1. This would occur through APC binding to the transcription factors (JUN, FOSL1, TCF7) to regulate the Wnt signaling pathway including through enhanced marking of CTNNB1 for degradation in the nucleus by APC binding with SOX17. Additional novel Wnt ß-catenin pathway protein-protein interactions from this study could lead researchers to novel drug designs for cancer.

Novel protein contact points among TP53 and minichromosome maintenance complex proteins 2, 3, and 5.

OBJECTIVE: Identify protein contact points between TP53 and minichromosome maintenance (MCM) complex proteins 2, 3, and 5 with high resolution allowing for potential novel Cancer drug design. METHODS: A next-generation sequencing-based protein-protein interaction method developed in our laboratory called AVA-Seq was applied to a gold-standard human protein interaction set. Proteins including TP53, MCM2, MCM3, MCM5, HSP90AA1, PCNA, NOD1, and others were sheared and ligated into the AVA-Seq system. Protein-protein interactions were then identified in both mild and stringent selective conditions. RESULTS: Known interactions among MCM2, MCM3, and MCM5 were identified with the AVA-Seq system. The interacting regions detected between these three proteins overlap with the structural data of the MCM complex, and novel domains were identified with high resolution determined by multiple overlapping fragments. Fragments of wild type TP53 were shown to interact with MCM2, MCM3, and MCM5, and details on the location of the interactions were provided. Finally, a mini-network of known and novel cancer protein interactions was provided, which could have implications for fundamental changes in multiple cancers. CONCLUSION: We provide a high-resolution mini-interactome that could direct novel drug targets and implicate possible effects of specific cancer mutations.

Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

Protein-protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all-vs-all sequencing (AVA-Seq) approach using a gold-standard human protein interaction set (hsPRS-v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA-Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome-wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto-activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA-Seq recovered >500 known and novel PPIs, including interactions between wild-type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

High-resolution protein-protein interaction mapping using all-versus-all sequencing (AVA-Seq).

Two-hybrid systems can be used for investigating protein-protein interactions and may provide important information about gene products with unknown function. Despite their success in mapping protein interactions, two-hybrid systems have remained mostly untouched by improvements in next-generation DNA sequencing. The two-hybrid systems rely on one-versus-all methods in which each bait is sequentially screened against an entire library. Here, we developed a screening method that joins both bait and prey as a convergent fusion into one bacterial plasmid vector that can then be amplified and paired-end sequencing by next-generation sequencing (NGS). Our method enables all-versus-all sequencing (AVA-Seq) and utilizes NGS to remove multiple bottlenecks of the two-hybrid system. AVA-Seq allows for high-resolution protein-protein interaction mapping of a small set of proteins and has the potential for lower-resolution mapping of entire proteomes. Features of the system include ORF selection to improve efficiency, high bacterial transformation efficiency, a convergent fusion vector to allow paired-end sequencing of interactors, and the use of protein fragments rather than full-length proteins to better resolve specific protein contact points. We demonstrate the system's strengths and limitations on a set of proteins known to interact in humans and provide a framework for future large-scale projects.