Deep mutational scanning of the human insulin receptor ectodomain to inform precision therapy for insulin resistance

The insulin receptor (INSR) entrains tissue growth and metabolism to nutritional conditions. Complete loss of function in humans leads to extreme insulin resistance and infantile mortality, while loss of 80-90% function permits longevity of decades. Even low-level activation of severely compromised receptors, for example by anti-receptor monoclonal antibodies, thus offers the potential for decisive clinical benefit. A barrier to genetic diagnosis and translational research is the increasing identification of INSR variants of uncertain significance. We employed saturation mutagenesis coupled to multidimensional flow-based assays to stratify approximately 14,000 INSR extracellular domain missense variants by cell surface expression, insulin binding, and insulin- or monoclonal antibody-stimulated signaling. The resulting function scores correlate strongly with clinical syndromes, offer insights into dynamics of insulin binding, and reveal novel potential gain-of-function variants. This INSR sequence-function map has high biochemical, diagnostic and translational utility, aiding rapid identification of variants amenable to activation by non-canonical INSR agonists. Overall design: This study aimed to assess the impact of INSR variants on human insulin receptor surface expression, insulin binding, and insulin signalling. Additionally, it explored whether certain damaging variants could be activated by antibodies. To achieve this, saturation mutagenesis was performed on the INSR gene, generating a library of variants, each linked to a unique barcode sequence via PacBio sequencing. This variant library was then transfected into MEF cells and screened using FACS sorting into four bins based on their effects on receptor expression, insulin binding, and signalling. Cells from each bin were collected and subjected to Illumina sequencing to determine the frequency of each barcode in each bin. These frequencies, combined with the PacBio data, were used to calculate a functional score for each variant.