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Functional Characterization of Glucokinase Variants to Aid Clinical Interpretation of Monogenic Diabetes

Abstract:
Precision medicine starts with a precision diagnosis. Yet up to 80% of cases of monogenic diabetes, a form of diabetes characterized by mutations in a single gene, are either overlooked or misdiagnosed. A genetic test for monogenic diabetes does not always lead to a precise diagnosis, as novel variants are often classified as variants of unknown significance. Variant interpretation requires collation of a framework of evidence, including population, computational, and segregation data, and can be assisted by functional analysis. The inclusion of functional data can be challenging, depending on the number of benign and pathogenic variants available for benchmarking assays. Glucokinase is the rate-limiting step for glucose metabolism in the pancreatic beta-cell and governs the threshold for glucose-stimulated insulin release. Loss-of-function alleles in the glucokinase (GCK) gene are a cause of stable fasting hyperglycemia from birth and/or diabetes. In this study, we functionally characterized 25 variants identified during diagnostic testing or in exome sequencing studies. We assessed their kinetic characteristics, stability, and interaction with pharmacological and physiological regulators. We integrated our functional data with existing data from the ClinGen Monogenic Diabetes Variant Curation Expert Review panel using a gene-specific framework to assist variant classification. We show how functional evidence can aid variant classification, thus enabling diagnostic certainty.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3390/ijms27010156

Authors

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Role:
Author
ORCID:
0009-0004-0222-8954
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Role:
Author
ORCID:
0000-0002-0615-2836
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-1727-3633


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Funder identifier:
https://ror.org/029chgv08


Publisher:
MDPI
Journal:
International Journal of Molecular Sciences More from this journal
Volume:
27
Issue:
1
Pages:
156
Article number:
156
Publication date:
2025-12-23
Acceptance date:
2025-12-17
DOI:
EISSN:
1422-0067
ISSN:
1661-6596


Language:
English
Keywords:
Pubs id:
2355377
UUID:
uuid_baf3dac7-bff7-468b-9a30-25924a400a01
Local pid:
pubs:2355377
Source identifiers:
3643361
Deposit date:
2026-01-08
ARK identifier:
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