Grant: Foundation Grant

There are few cases where we fully understand how DNA sequence variants impact gene function, and how these changes propagate through networks of molecular and cellular interactions to yield human disease. My team is developing genomic technology to address two distinct goals to improve our understanding of genetic disease: 1) Mapping sequence-function relationships to identify human DNA sequence variants affecting their gene product; and 2) Mapping the downstream network of macromolecular interactions, including their dynamics and context-dependence, to better understand how the connection from genotype to phenotype is mediated. Towards the first goal, we developed a hybrid experimental/computational platform for deep mutational scanning (DMS), in which the functional effects of missense variants are exhaustively mapped in specific target human disease genes. These maps have already been validated for two human disease genes: calmodulin and cystathionine beta-synthase, with the former map already in clinical use. We will continue improving the DMS technology: extending it to human cell models and experimentally detecting the impact of both intronic and exonic variation on splicing and gene function, and applying it to ~100 human disease genes over the next 7 years (approximately 2% of the ~4000 annotated human disease genes). Towards the second goal, we developed a 'barcode-fusion genetics' (BFG) strategy, enabling two reagent libraries to be coupled so that all reagent pairs are tracked via combined ('fused') barcodes. This allows efficient mapping of protein interactions under different environments and genetic backgrounds. We recently developed a prototype method, 'messenger BFG', in which fused barcodes are transcribed, sensing interaction dynamics on the time scale of minutes. We will continue developing this approach, adapting it to human cell-based protein interaction assays, and applying it at proteome scale to both human and yeast proteomes.