A comprehensive map of human glucokinase variant activity

Abstract

Diabetes is a complex disease spanning from the heterogeneous etiology of type 1 and type 2 diabetes to monogenic diabetes. A common monogenic form of diabetes is glucokinase (GCK) maturity-onset diabetes of the young (GCK-MODY), which is caused by heterozygous inactivating variants in the gene encoding GCK. GCK is known as the pancreatic glucose sensor, as it regulates insulin secretion to maintain appropriate blood glucose levels. Accordingly, variants that alter GCK activity can cause hypo- and hyperglycemia, associated with hyperinsulinemic hypoglycemia (HH) and GCK-MODY, respectively, affecting up to 10 million people worldwide. Patients with GCK-MODY, in contrast to other people with diabetes, often do not require treatment but are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants. To address this, we generated a comprehensive map of GCK variant activity in yeast. The activity map includes 97% of the possible missense and nonsense variants and correlates with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and evolutionary conservation analysis. Activity scores include both hyper- and hypoactive variants. We found that some hyperactive variants shift the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation. As expected, hypoactive variants were concentrated at buried positions, near the active site, and at a partially surface-exposed region involved in GCK conformational dynamics. In conclusion, we provide a comprehensive assessment of GCK variant activity to facilitate variant interpretation and diagnosis, and we expand the mechanistic understanding of hyperactive variants to support development and refinement of drugs targeting GCK.

A power-based sliding window approach to evaluate the clinical impact of rare genetic variants

“…Future studies with even larger sample sizes may reclassify some portions of the genes tested here, identifying some regions we erroneously excluded or identifying new regions to include. Other improvements will involve focusing on certain classes of variants in the gene, such as those computationally predicted to be gain or loss of function, those with functional data from screenings, and those in transcripts expressed in tissues of interest 19,26,27 .…”

“…This finding is also supported by a recent deep scan mutagenesis study which found that 5'-end mutations were more likely to lead to lower glucose levels. 26 .…”

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

“…We compared MisFit_D with published methods [34][35][36][37]51,[59][60][61] on predicting damaging variants in DMS for individual genes. First, we collected functional readout scores from 32 DMS assays in 26 genes [11][12][13][14][15][16][17][19][20][21][22][23][24][25][26][27][28][29][30][31] with 44,100 single amino acid substitutions (Supplementary Table 3). We calculated the Spearman correlation between the functional scores and computational scores (Fig.…”

Patients with Type 2 Diabetes and aGCKvariant are still at risk for T2D-related secondary complications

“…Variants were classified as LoF if the consequence was stop_lost, start_lost, splice_donor_variant, frameshift_variant, splice_acceptor_variant, or stop_gained and MAF<0.1% in all ancestry populations in gnomAD and the analyzed cohorts. For coding variants classified as missense_variant, inframe_deletion, or inframe_insertion, non-benign (by Polyphen or SIFT), and MAF<0.1%, we also used a combination of a functional evidence map (urn:mavedb:00000096-a), with scores for each variant assigned using a yeast complementation assay, and a statistical evidence map created using the Power Window technique to predict variants that likely increased glucose levels 25,26,39,40 . Briefly, Power Window is a sliding window analysis that groups variants located near each other into one unit and analyzes them together to improve power, much like a gene-based collapsing analysis but at a smaller scale, as previously described 26 .…”