Thousands of human genomes are being sequenced at an unprecedented rate, ushering in an era of extraordinary data generation that is revealing a multitude of genetic variants within individuals. However, a large proportion of these variants have unknown functional importance, which presents the formidable challenge of discerning any potential health implications of each sequence alteration. To address this challenge, researchers have turned to synthetic biology to functionally replace human biological processes in a simplified cellular setup, such as Saccharomyces cerevisiae (baker’s yeast), generating ‘humanized yeast’. The underlying principle of humanized yeast involves substituting yeast genes with their disease-associated human counterparts. This substitution enables the fitness of a variant human protein to be linked to the overall fitness of the yeast cell, especially when the gene is essential for optimal growth and survival. This cost-effective approach (which is also faster than traditional methods such as animal studies and clinical trials) provides a surrogate system for the mechanistic exploration of human gene function, the effect of genetic variation on function and drug discovery at scale.
In a recent paper in the American Journal of Human Genetics, van Loggerenberg et al. used a synthetic yeast system in combination with innovative techniques to unravel the effect of missense variants in the human gene HMBS (which encodes hydroxymethylbilane synthase, HMBS; also known as porphobilinogen deaminase, PBG-D) on acute intermittent porphyria (AIP). AIP is a rare autosomal dominant disease resulting from a deficiency of HMBS. Approximately one-third of clinically reported HMBS variants are missense variants, with the vast majority classified as variants of unknown significance. The approach by van Loggerenberg et al. integrated humanized yeast, deep mutational scanning mutagenesis (DMS), pooled selection and next-generation sequencing. More specifically, the authors used multiplexed assays of variant effect (known as MAVEs) to comprehensively assess all single variants within a target protein. The variant effect maps comprehensively report protein function at single-amino-acid resolution, illustrating the influence of mutations on protein structure and function, which can lead to diverse phenotypes. Developing such maps holds the potential to aid in the diagnosis, prognosis and treatment of diseases, especially for clinically relevant mutations and undiscovered variants in patients. These maps offer clinicians a precise reference to identify dysfunctional alleles, substantially advancing disease understanding and expediting treatment development.
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