Abstract
Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, “Clone-seq”, leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.
Highlights
Owing to rapid advances in next-generation sequencing technologies, tens of thousands of disease-associated mutations [1] and millions of single nucleotide polymorphisms (SNPs) [2,3] have been identified in the human population
We focus on coding variants, firstly because traitand disease-associated SNPs are significantly over-represented in nonsynonymous sites [4], and secondly because the vast majority of disease-associated mutations identified to date reside within coding regions [1]
We evaluate the functional impact of coding variants by examining their effects on corresponding proteinprotein interactions, because most proteins carry out their functions by interacting with other proteins [5]
Summary
Owing to rapid advances in next-generation sequencing technologies, tens of thousands of disease-associated mutations [1] and millions of single nucleotide polymorphisms (SNPs) [2,3] have been identified in the human population. By integrating structural details with high-quality protein networks, we created a 3D interactome network where the interface for each interaction has been structurally resolved [7]. Using this 3D network, we demonstrated that in-frame disease mutations (missense mutations and in-frame insertions/deletions) are significantly enriched at the interaction interfaces of the corresponding proteins [7]. Our results indicate that alteration of specific interactions is very important for the pathogenesis of many disease genes, highlighting the importance of 3D structural models
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