Functional characterization of MODY2 mutations highlights the importance of the fine-tuning of glucokinase and its role in glucose sensing

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

BACKGROUND

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms underlying variant effects in human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity.

RESULTS

Using a yeast growth-based assay, we score the abundance of 95% of GCK missense and nonsense variants. When combining the abundance scores with our previously determined activity scores, we find that 43% of hypoactive variants also decrease cellular protein abundance. The low-abundance variants are enriched in the large domain, while residues in the small domain are tolerant to mutations with respect to abundance. Instead, many variants in the small domain perturb GCK conformational dynamics which are essential for appropriate activity.

CONCLUSIONS

In this study, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms of human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. We assayed the abundance of 95% of GCK missense and nonsense variants, and found that 43% of hypoactive variants have a decreased cellular abundance. By combining our abundance scores with predictions of protein thermodynamic stability, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.

A comprehensive map of human glucokinase variant activity

BACKGROUND

Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants.

RESULT

Here, we exploit a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlate with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants are concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shift the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation.

CONCLUSION

Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.

Identification of Novel GCK and HNF4α Gene Variants in Japanese Pediatric Patients with Onset of Diabetes before 17 Years of Age

BACKGROUND

Maturity-onset diabetes of the young (MODY) is commonly misdiagnosed as type 1 or type 2 diabetes. Common reasons for misdiagnosis are related to limitations in genetic testing. A precise molecular diagnosis is essential for the optimal treatment of patients and allows for early diagnosis of their asymptomatic family members.

OBJECTIVE

The aim of this study was to identify rare monogenic variants of common MODY genes in Japanese pediatric patients.

METHODS

We investigated 45 Japanese pediatric patients based on the following clinical criteria: development of diabetes before 17 years of age, a family history of diabetes, testing negative for glutamate decarboxylase-65 (GAD 65) antibodies and insulinoma-2-associated autoantibodies (IA-2A), no significant obesity, and evidence of endogenous insulin production. Genetic screening for MODY1 (HNF4α), MODY2 (GCK), MODY3 (HNF1α), and MODY5 (HNF1β) was performed by direct sequencing followed by multiplex ligation amplification assays.

RESULTS

We identified 22 missense variants (3 novel variants) in 27 patients (60.0%) in the GCK, HNF4α, and HNF1α genes. We also detected a whole exon deletion in the HNF1β gene and an exon 5-6 aberration in the GCK gene, each in one proband (4.4%). There were a total of 29 variations (64.4%), giving a relative frequency of 53.3% (24/45) for GCK, 2.2% (1/45) for HNF4α, 6.7% (3/45) for HNF1α, and 2.2% (1/45) for HNF1β genes.

CONCLUSIONS

Clinicians should consider collecting and assessing detailed clinical information, especially regarding GCK gene variants, in young antibody-negative patients with diabetes. Correct molecular diagnosis of MODY better predicts the clinical course of diabetes and facilitates individualized management.

Pathophysiology of diabetes: An overview

Diabetes mellitus is a chronic heterogeneous metabolic disorder with complex pathogenesis. It is characterized by elevated blood glucose levels or hyperglycemia, which results from abnormalities in either insulin secretion or insulin action or both. Hyperglycemia manifests in various forms with a varied presentation and results in carbohydrate, fat, and protein metabolic dysfunctions. Long-term hyperglycemia often leads to various microvascular and macrovascular diabetic complications, which are mainly responsible for diabetes-associated morbidity and mortality. Hyperglycemia serves as the primary biomarker for the diagnosis of diabetes as well. In this review, we would be focusing on the classification of diabetes and its pathophysiology including that of its various types.

L-Arginine prevents cereblon-mediated ubiquitination of glucokinase and stimulates glucose-6-phosphate production in pancreatic β-cells

We sought to determine a mechanism by which L-arginine increases glucose-stimulated insulin secretion (GSIS) in β-cells by finding a protein with affinity to L-arginine using arginine-immobilized magnetic nanobeads technology. Glucokinase (GCK), the key regulator of GSIS and a disease-causing gene of maturity-onset diabetes of the young type 2 (MODY2), was found to bind L-arginine. L-Arginine stimulated production of glucose-6-phosphate (G6P) and induced insulin secretion. We analyzed glucokinase mutants and identified three glutamate residues that mediate binding to L-arginine. One MODY2 patient with GCK^E442* demonstrated lower C-peptide-to-glucose ratio after arginine administration. In β-cell line, GCK^E442* reduced L-arginine-induced insulin secretion compared with GCK^WT. In addition, we elucidated that the binding of arginine protects glucokinase from degradation by E3 ubiquitin ligase cereblon mediated ubiquitination. We conclude that L-arginine induces insulin secretion by increasing G6P production by glucokinase through direct stimulation and by prevention of degradation.

Using arginine-immobilized magnetic nanobeads, Cho et al. show that glucokinase, the key regulator of glucose-stimulated insulin secretion, binds L-arginine, which protects glucokinase from ubiquitination-mediated degradation while inducing insulin secretion. This study provides mechanistic insights into how L-arginine increases insulin production.

How Heterogeneity in Glucokinase and Gap-Junction Coupling Determines the Islet [Ca2+] Response

Understanding how cell subpopulations in a tissue impact overall system function is challenging. There is extensive heterogeneity among insulin-secreting β-cells within islets of Langerhans, including their insulin secretory response and gene expression profile, and this heterogeneity can be altered in diabetes. Several studies have identified variations in nutrient sensing between β-cells, including glucokinase (GK) levels, mitochondrial function, or expression of genes important for glucose metabolism. Subpopulations of β-cells with defined electrical properties can disproportionately influence islet-wide free-calcium activity ([Ca2+]) and insulin secretion via gap-junction electrical coupling. However, it is poorly understood how subpopulations of β-cells with altered glucose metabolism may impact islet function. To address this, we utilized a multicellular computational model of the islet in which a population of cells deficient in GK activity and glucose metabolism was imposed on the islet or in which β-cells were heterogeneous in glucose metabolism and GK kinetics were altered. This included simulating GK gene (GCK) mutations that cause monogenic diabetes. We combined these approaches with experimental models in which gck was genetically deleted in a population of cells or GK was pharmacologically inhibited. In each case, we modulated gap-junction electrical coupling. Both the simulated islet and the experimental system required 30-50% of the cells to have near-normal glucose metabolism, fewer than cells with normal KATP conductance. Below this number, the islet lacked any glucose-stimulated [Ca2+] elevations. In the absence of electrical coupling, the change in [Ca2+] was more gradual. As such, electrical coupling allows a large minority of cells with normal glucose metabolism to promote glucose-stimulated [Ca2+]. If insufficient numbers of cells are present, which we predict can be caused by a subset of GCK mutations that cause monogenic diabetes, electrical coupling exacerbates [Ca2+] suppression. This demonstrates precisely how metabolically heterogeneous β-cell populations interact to impact islet function.

Gluconeogenic Enzymes in β-Cells: Pharmacological Targets for Improving Insulin Secretion

Pancreatic β-cells express the gluconeogenic enzymes glucose 6-phosphatase (G6Pase), fructose 1,6-bisphosphatase (FBP), and phosphoenolpyruvate (PEP) carboxykinase (PCK), which modulate glucose-stimulated insulin secretion (GSIS) through their ability to reverse otherwise irreversible glycolytic steps. Here, we review current knowledge about the expression and regulation of these enzymes in the context of manipulating them to improve insulin secretion in diabetics. Because the regulation of gluconeogenic enzymes in β-cells is so poorly understood, we propose novel research avenues to study these enzymes as modulators of insulin secretion and β-cell dysfunction, with especial attention to FBP, which constitutes an attractive target with an inhibitor under clinical evaluation at present.

Functional characterization of MODY2 mutations in the nuclear export signal of glucokinase

Glucokinase (GCK) plays a key role in glucose homeostasis. Heterozygous inactivating mutations in the GCK gene cause the familial, mild fasting hyperglycaemia named MODY2. Besides its particular kinetic characteristics, glucokinase is regulated by subcellular compartmentation in hepatocytes. Glucokinase regulatory protein (GKRP) binds to GCK, leading to enzyme inhibition and import into the nucleus at fasting. When glucose concentration increases, GCK-GKRP dissociates and GCK is exported to the cytosol due to a nuclear export signal (NES). With the aim to characterize the GCK-NES, we have functionally analysed nine MODY2 mutations located within the NES sequence. Recombinant GCK mutants showed reduced catalytic activity and, in most cases, protein instability. Most of the mutants interact normally with GKRP, although mutations L306R and L309P impair GCK nuclear import in cotransfected cells. We demonstrated that GCK-NES function depends on exportin 1. We further showed that none of the mutations fully inactivate the NES, with the exception of mutation L304P, which likely destabilizes its α-helicoidal structure. Finally, we found that residue Glu300 negatively modulates the NES activity, whereas other residues have the opposite effect, thus suggesting that some of the NES spacer residues contribute to the low affinity of the NES for exportin 1, which is required for its proper functioning. In conclusion, our results have provided functional and structural insights regarding the GCK-NES and contributed to a better knowledge of the molecular mechanisms involved in the nucleo-cytoplasmic shuttling of glucokinase. Impairment of this regulatory mechanism by some MODY2 mutations might contribute to the hyperglycaemia in the patients.

Evidence-based tailoring of bioinformatics approaches to optimize methods that predict the effects of nonsynonymous amino acid substitutions in glucokinase

Computational methods that allow predicting the effects of nonsynonymous substitutions are an integral part of exome studies. Here, we validated and improved their specificity by performing a comprehensive bioinformatics analysis combined with experimental and clinical data on a model of glucokinase (GCK): 8835 putative variations, including 515 disease-associated variations from 1596 families with diagnoses of monogenic diabetes (GCK-MODY) or persistent hyperinsulinemic hypoglycemia of infancy (PHHI), and 126 variations with available or newly reported (19 variations) data on enzyme kinetics. We also proved that high frequency of disease-associated variations found in patients is closely related to their evolutionary conservation. The default set prediction methods predicted correctly the effects of only a part of the GCK-MODY-associated variations and completely failed to predict the normoglycemic or PHHI-associated variations. Therefore, we calculated evidence-based thresholds that improved significantly the specificity of predictions (≤75%). The combined prediction analysis even allowed to distinguish activating from inactivating variations and identified a group of putatively highly pathogenic variations (EVmutation score <−7.5 and SNAP2 score >70), which were surprisingly underrepresented among MODY patients and thus under negative selection during molecular evolution. We suggested and validated the first robust evidence-based thresholds, which allow improved, highly specific predictions of disease-associated GCK variations.

Targeted next-generation sequencing reveals MODY in up to 6.5% of antibody-negative diabetes cases listed in the Norwegian Childhood Diabetes Registry

AIMS/HYPOTHESIS

MODY can be wrongly diagnosed as type 1 diabetes in children. We aimed to find the prevalence of MODY in a nationwide population-based registry of childhood diabetes.

METHODS

Using next-generation sequencing, we screened the HNF1A, HNF4A, HNF1B, GCK and INS genes in all 469 children (12.1%) negative for both GAD and IA-2 autoantibodies and 469 antibody-positive matched controls selected from the Norwegian Childhood Diabetes Registry (3882 children). Variants were classified using clinical diagnostic criteria for pathogenicity ranging from class 1 (neutral) to class 5 (pathogenic).

RESULTS

We identified 58 rare exonic and splice variants in cases and controls. Among antibody-negative patients, 6.5% had genetic variants of classes 3-5 (vs 2.4% in controls; p = 0.002). For the stricter classification (classes 4 and 5), the corresponding number was 4.1% (vs 0.2% in controls; p = 1.6 × 10^-5). HNF1A showed the strongest enrichment of class 3-5 variants, with 3.9% among antibody-negative patients (vs 0.4% in controls; p = 0.0002). Antibody-negative carriers of variants in class 3 had a similar phenotype to those carrying variants in classes 4 and 5.

CONCLUSIONS/INTERPRETATION

This is the first study screening for MODY in all antibody-negative children in a nationwide population-based registry. Our results suggest that the prevalence of MODY in antibody-negative childhood diabetes may reach 6.5%. One-third of these MODY cases had not been recognised by clinicians. Since a precise diagnosis is important for treatment and genetic counselling, molecular screening of all antibody-negative children should be considered in routine diagnostics.

Lack of glibenclamide response in a case of permanent neonatal diabetes caused by incomplete inactivation of glucokinase

BACKGROUND

Hypoglycaemic drugs that close the KATP channel have been tested in patients with permanent neonatal diabetes due to glucokinase mutations (PNDM-GCK). From the results obtained, it has been suggested that this treatment may be beneficial in patients carrying GCK mutations with mild kinetic defects. The aim of this study was to evaluate the kinetic analysis of glucokinase activity as a predictive factor for response to sulphonylureas in PNDM-GCK.

METHODS

The clinical characteristics of two siblings with PNDM born to non-consanguineous parents are described. Mutation analysis of KCNJ11, INS and GCK genes was done by sequencing. A comprehensive functional characterisation of GCK mutation was undertaken. Glibenclamide treatment was assayed for 16 weeks in one child. Response to treatment was evaluated by means of fasting glycaemia, C-peptide and HbA1c levels.

RESULTS

Compound heterozygous GCK mutations (p.Ile19Asn and p.Ser441Trp) were identified. Functional analysis of GCK(p.Ile19Asn) indicated that this mutant retained more than 70% of wild-type catalytic activity in vitro, with a slight increase of thermolability. This mutation did not impair the interaction with the glucokinase regulatory protein, and the enzymatic activity of the GCK(p.Ile19Asn) mutant is restored to wild-type levels in the presence of GCK allosteric activator LY2121260. However, glibenclamide treatment of the patient on a reduced dose of insulin did not reduce HbA1c levels, and C-peptide increased only very slightly.

CONCLUSION

Hypoglycaemic drugs acting on the KATP channel might not be useful in the treatment of PNDM-GCK, even in patients carrying GCK mutations with mild kinetic defects.

Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming

Background

The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites.

Results

The result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs.

Conclusions

QCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF’s algorithm, we leveraged Compute Unified Device Architecture (CUDA).

The QCMF server is freely accessible at http://qcmf.informatik.uni-goettingen.de/.

Evolution- and structure-based computational strategy reveals the impact of deleterious missense mutations on MODY 2 (maturity-onset diabetes of the young, type 2)

Heterozygous mutations in the central glycolytic enzyme glucokinase (GCK) can result in an autosomal dominant inherited disease, namely maturity-onset diabetes of the young, type 2 (MODY 2). MODY 2 is characterised by early onset: it usually appears before 25 years of age and presents as a mild form of hyperglycaemia. In recent years, the number of known GCK mutations has markedly increased. As a result, interpreting which mutations cause a disease or confer susceptibility to a disease and characterising these deleterious mutations can be a difficult task in large-scale analyses and may be impossible when using a structural perspective. The laborious and time-consuming nature of the experimental analysis led us to attempt to develop a cost-effective computational pipeline for diabetic research that is based on the fundamentals of protein biophysics and that facilitates our understanding of the relationship between phenotypic effects and evolutionary processes. In this study, we investigate missense mutations in the GCK gene by using a wide array of evolution- and structure-based computational methods, such as SIFT, PolyPhen2, PhD-SNP, SNAP, SNPs&GO, fathmm, and Align GVGD. Based on the computational prediction scores obtained using these methods, three mutations, namely E70K, A188T, and W257R, were identified as highly deleterious on the basis of their effects on protein structure and function. Using the evolutionary conservation predictors Consurf and Scorecons, we further demonstrated that most of the predicted deleterious mutations, including E70K, A188T, and W257R, occur in highly conserved regions of GCK. The effects of the mutations on protein stability were computed using PoPMusic 2.1, I-mutant 3.0, and Dmutant. We also conducted molecular dynamics (MD) simulation analysis through in silico modelling to investigate the conformational differences between the native and the mutant proteins and found that the identified deleterious mutations alter the stability, flexibility, and solvent-accessible surface area of the protein. Furthermore, the functional role of each SNP in GCK was identified and characterised using SNPeffect 4.0, F-SNP, and FASTSNP. We hope that the observed results aid in the identification of disease-associated mutations that affect protein structure and function. Our in silico findings provide a new perspective on the role of GCK mutations in MODY2 from an evolution-based structure-centric point of view. The computational architecture described in this paper can be used to predict the most appropriate disease phenotypes for large-genome sequencing projects and to provide individualised drug therapy for complex diseases such as diabetes.

Phytochemicals targeting genes relevant for type 2 diabetes

Nutrigenomic approaches based on ethnopharmacology and phytotherapy concepts have revealed that type 2 diabetes mellitus (T2DM) may be susceptible to dietary intervention. Interaction between bioactive food components and the genome may influence cell processes and modulate the onset and progression of the disease. T2DM, characterized by insulin resistance and beta cell dysfunction, is one of the leading causes of death and disability. Despite the great advances that have been made in the understanding and management of this complex, multifactorial disease, T2DM has become a worldwide epidemic in the 21st century. Population and family studies have revealed a strong genetic component of T2DM, and a number of candidate genes have been identified in humans. Variations in the gene sequences such as single nucleotide polymorphisms, explain the individual differences in traits like disease susceptibility and response to treatment. A clear understanding of how nutrients affect the expression of genes should facilitate the development of individualized intervention and, eventually, treatment strategies for T2DM. Review of the literature identified many phytochemicals/extracts from traditional medicinal plants that can target diabetogenic genes. This review focuses on the genetic aspects of T2DM, nutrient modification of genes relevant for diabetes, and future prospects of nutritional therapy of T2DM.

Coupled mutation finder: a new entropy-based method quantifying phylogenetic noise for the detection of compensatory mutations

BACKGROUND

The detection of significant compensatory mutation signals in multiple sequence alignments (MSAs) is often complicated by noise. A challenging problem in bioinformatics is remains the separation of significant signals between two or more non-conserved residue sites from the phylogenetic noise and unrelated pair signals. Determination of these non-conserved residue sites is as important as the recognition of strictly conserved positions for understanding of the structural basis of protein functions and identification of functionally important residue regions. In this study, we developed a new method, the Coupled Mutation Finder (CMF) quantifying the phylogenetic noise for the detection of compensatory mutations.

RESULTS

To demonstrate the effectiveness of this method, we analyzed essential sites of two human proteins: epidermal growth factor receptor (EGFR) and glucokinase (GCK). Our results suggest that the CMF is able to separate significant compensatory mutation signals from the phylogenetic noise and unrelated pair signals. The vast majority of compensatory mutation sites found by the CMF are related to essential sites of both proteins and they are likely to affect protein stability or functionality.

CONCLUSIONS

The CMF is a new method, which includes an MSA-specific statistical model based on multiple testing procedures that quantify the error made in terms of the false discovery rate and a novel entropy-based metric to upscale BLOSUM62 dissimilar compensatory mutations. Therefore, it is a helpful tool to predict and investigate compensatory mutation sites of structural or functional importance in proteins. We suggest that the CMF could be used as a novel automated function prediction tool that is required for a better understanding of the structural basis of proteins. The CMF server is freely accessible at http://cmf.bioinf.med.uni-goettingen.de.