Recessive Mutations in <i>PCBD1</i> Cause a New Type of Early-Onset Diabetes

Šimaitė, Deimantė; Kofent, Julia; Gong, Maolian; Rüschendorf, Franz; Jia, Shiqi; Arn, Pamela; Bentler, Kristi; Ellaway, Carolyn; Kühnen, Peter; Hoffmann, Georg F.; Blau, Nenad; Spagnoli, Francesca M.; Hübner, Norbert; Raile, Klemens

doi:10.2337/db13-1784

Cited by 44 publications

(41 citation statements)

References 24 publications

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“…Re-evaluation of earlier identified patients with this syndrome revealed that hypomagnesemia and MODY5-like diabetes are later manifestations of PCBD1 mutations [53, 54]. This finding also has implications for the importance of looking for alternative genetic diagnoses (including PCBD1 mutations) if screening for mutations of PAH (phenylalanine hydroxylase) is negative after a positive result for the Guthrie test.…”

Section: Hereditary Hypomagnesemiasmentioning

confidence: 99%

Genetic causes of hypomagnesemia, a clinical overview

et al. 2016

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Magnesium is essential to the proper functioning of numerous cellular processes. Magnesium ion (Mg2+) deficits, as reflected in hypomagnesemia, can cause neuromuscular irritability, seizures and cardiac arrhythmias. With normal Mg2+ intake, homeostasis is maintained primarily through the regulated reabsorption of Mg2+ by the thick ascending limb of Henle’s loop and distal convoluted tubule of the kidney. Inadequate reabsorption results in renal Mg2+ wasting, as evidenced by an inappropriately high fractional Mg2+ excretion. Familial renal Mg2+ wasting is suggestive of a genetic cause, and subsequent studies in these hypomagnesemic families have revealed over a dozen genes directly or indirectly involved in Mg2+ transport. Those can be classified into four groups: hypercalciuric hypomagnesemias (encompassing mutations in CLDN16, CLDN19, CASR, CLCNKB), Gitelman-like hypomagnesemias (CLCNKB, SLC12A3, BSND, KCNJ10, FYXD2, HNF1B, PCBD1), mitochondrial hypomagnesemias (SARS2, MT-TI, Kearns–Sayre syndrome) and other hypomagnesemias (TRPM6, CNMM2, EGF, EGFR, KCNA1, FAM111A). Although identification of these genes has not yet changed treatment, which remains Mg2+ supplementation, it has contributed enormously to our understanding of Mg2+ transport and renal function. In this review, we discuss general mechanisms and symptoms of genetic causes of hypomagnesemia as well as the specific molecular mechanisms and clinical phenotypes associated with each syndrome.

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Section: Hereditary Hypomagnesemiasmentioning

confidence: 99%

Genetic causes of hypomagnesemia, a clinical overview

et al. 2016

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“…Simaite et al observe an abundant expression of Pcbd1 in the developing pancreas of both mouse and Xenopus embryos [51]. The genetic evidence obtained by them displays that PCBD1 variations can lead to early-onset nonautoimmune diabetes with characteristics like dominantly inherited HNF1A-diabetes.…”

Section: Resultsmentioning

confidence: 99%

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

Tang

Yang

et al. 2016

BMC Genomics

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BackgroundDiabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.ResultsIn this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.ConclusionsThe results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

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“…In the pancreas, heterozygous mutations in DCoH1 that cause mild neonatal hyperphenylalaninemia also increase the risk of monogenic diabetes (50). Mutations in HNF-1␣ cause maturity-onset diabetes of the young type 3 (MODY3) (51).…”

Section: Discussionmentioning

confidence: 99%

Interactions with the Bifunctional Interface of the Transcriptional Coactivator DCoH1 Are Kinetically Regulated

Wang

Coco

Rose

2015

Journal of Biological Chemistry

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Background:The protein dimerization cofactor of HNF-1 (DCoH1)/pterin-carbinolamine dehydratase (PCD) accomplishes two unrelated activities, forming two complexes with the same interface. Results: The DCoH1 homotetramer is kinetically trapped; a single mutation in the interface increases the unfolding rate 10 9 -fold. Conclusion: Kinetic regulation allows DCoH to assume two unrelated functions. Significance: Mutations excluding water from interfaces represent kinetic "hot spots," dramatically affecting dissociation rates.

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Recessive Mutations in PCBD1 Cause a New Type of Early-Onset Diabetes

Cited by 44 publications

References 24 publications

Genetic causes of hypomagnesemia, a clinical overview

Genetic causes of hypomagnesemia, a clinical overview

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

Interactions with the Bifunctional Interface of the Transcriptional Coactivator DCoH1 Are Kinetically Regulated

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