Functional Stratification of Metabolic Disease Gene Variants to Accelerate Diagnosis and Guide Treatment

The increasing use of next generation sequencing in diagnostic laboratories means that rare variants in disease genes are being discovered at an exponential level. However, the ability to class all these variants into pathogenic or benign at the same pace hasn't been possible, which has resulted in many them being classed as "Variants of Unknown Significance" (VUS).

A transformational approach known as multiplexed assays of variant effects (MAVE) has allowed for the assessment of up to tens of thousands of variants in a single experiment.

For the purpose of this project MAVEs will be applied to multiple genes involved in metabolic disease. The current candidate genes are LMNA, POLD1, MFN2 and PIK3R1. These genes have been selected due to all of them having a substantial number of VUS, have been shown to have a complex genotype-phenotype correlation, are implicated in the pathogenicity of more than one disease, and have unanswered structure-function questions.

Additionally, MAVEs are yet to be applied to high content cellular imaging, and it is therefore one of the aims of this project to develop assays that can achieve this. This will be achieved by adopting an established workflow using a large pool of primers including "NNS" codons for all wild type codons. Primers will be used in an established nicking mutagenesis strategy to construct a barcoded variant library in plasmids.

Long read sequencing will phase barcodes and mutations. In parallel with library generation, high content cellular imaging will be optimized for known disease-causing mutations in the gene of interest, both tailored to the cellular phenotype caused by known disease mutations and cellular process agnostic. Genotype-phenotype relationships of all known mutations in these genes will be investigated using "cell painting" (using multiple dyes to mark different cell organelles) and observing morphological changes within the cells.

Finally, barcode sequencing will be used to count every occurrence of every mutation in different phenotypic groups, allowing stratification of the whole mutational library by functional effect.

The aims of this project are to classify all VUS in the target genes as benign or pathogenic, to associate them with specific disease phenotypes, to make data available freely accessible to accelerate genetic diagnosis, to facilitate development of targeted therapies, and to test candidate treatments on the variant library.