Insulin-mediated "pseudoacromegaly"

Sam, Amir H.; Tan, Tricia; Meeran, Karim

doi:10.14310/horm.2002.1306

Cited by 14 publications

(11 citation statements)

References 49 publications

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“…Meanwhile, animal studies have indicated that overexpression of murine Elovl6 gene can cause insulin resistance [25]. Combining all the recent studies, enlargement of finger in macrodactyly can be associated with insulin signaling mechanism considering the role of ELOVL6 gene in de novo lipogenesis and insulin resistance like pseudo-acromegaly [26]. Interestingly, the fat tissue was not distinguishable from normal fat tissue, except for the increased size of fat lobules in our case (Fig.…”

Section: Discussionsupporting

confidence: 49%

Signature Genes in Macrodactyly through Transcriptome Network Analysis Reveal their Association of Lipid Metabolism

Choi

Lee

2017

Korean J Phys Anthropol

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: Macrodactyly is one of the most difficult hand anomalies to treat not only surgically but medically as well. Little is known about the molecular pathways and lipid metabolism of this disease. To elucidate the potential mechanism of macrodactyly progress, we used the bioinformatical analysis including quantile normalization, principal component analysis, heatmap and volcano plot. For the functional bioinformatical study, lipid, lipoprotein and phospholipid metabolism of Kyoto Encyclopedia of Genes and Genomes, Wiki Pathways, and Reactome Pathway were utilized to compare the differentially expressed genes in macrodactyly with control group. We found up-regulation of CDK6 and E2F1, which are associated with the mitotic cell cycle of cancer cells. PIK3CG, associated with cancer and lipid metabolism, was also enriched in macrodactyly. In down-regulated genes, PTEN was highlighted in lipid metabolism, phosphatidylinositol signaling system and insulin signaling. ABCD3, related in peroxisomal import of fatty acids, was also down-regulated. In this study, we predicted the pathogenic candidate genes as well as the potential molecular pathways related to macrodactyly by identifying the signature genes. Signature genes through systems bioinformatical analysis can be utilized to catch the insight of the molecular pathogenesis of macrodactyly.

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Section: Discussionsupporting

confidence: 49%

Signature Genes in Macrodactyly through Transcriptome Network Analysis Reveal their Association of Lipid Metabolism

Choi

Lee

2017

Korean J Phys Anthropol

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“…He also has more severe hyperinsulinaemia and is older than his sisters (Table 1). The low serum IGF‐1 levels in our patients may be due to the negative feedback regulation from very high insulin levels acting on the pituitary and hypothalamic IGF‐1 receptors, which has recently been reported in a case with insulin‐mediated pseudoacromegaly 27 …”

Section: Discussionmentioning

confidence: 55%

“…The low serum IGF-1 levels in our patients may be due to the negative feedback regulation from very high insulin levels acting on the pituitary and hypothalamic IGF-1 receptors, which has recently been reported in a case with insulinmediated pseudoacromegaly. 27 Based on the crystal structure of INSR, the central b-sheet of the leucine-rich repeat 1 (L1) domain is a major contributor to ligand binding. 28 The characteristic feature of this L1 ligand-binding surface is a large hydrophobic patch with a cavity at the centre formed by amino acid residues Q34, L36, L62, F64, F88, F89, V94, F96, R118 and E120.…”

Section: Discussionmentioning

confidence: 99%

Molecular characterization of a novel p.R118C mutation in the insulin receptor gene from patients with severe insulin resistance

Alzahrani

Zou²,

Baitei³

et al. 2012

Clinical Endocrinology

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Summary Context Mutations of the insulin receptor gene (INSR) can cause genetic syndromes associated with severe insulin resistance. Objectives We aimed to analyse INSR mutations in Saudi patients with severe insulin resistance. Design Ten patients with Type A insulin resistance syndrome from five unrelated Saudi families were investigated. The entire coding region of INSR was sequenced. The founder effect was assessed by microsatellite haplotype analysis. The functional effect of the mutation was investigated by in vitro functional assays. Results A novel biallelic c.433 C>T (p.R118C) mutation was detected in all patients. The c.433 C>T (p.R118C) sequence variation was not found in 100 population controls. The arginine residue at position 118 is located in the ligand‐binding domain of INSR and is highly conserved across species. Microsatellite haplotype analysis of these patients indicated that p.R118C was a founder mutation created approximately 2900 years ago. The wild‐type and mutant R118C INSR were cloned and expressed in CHO cells for functional analysis. Specific insulin binding to the mutant receptor was reduced by 83% as compared to wild‐type (WT), although the mutant receptor was processed and expressed on the cell surface. Insulin‐mediated receptor autophosphorylation was also significantly reduced in CHOR118C cells. Conclusions Biallelic c.433 C>T (p.R118C) mutation of INSR causes significant damage to insulin binding and insulin‐mediated signal transduction. p.R118C is a founder mutation frequently present in the Saudi patients with severe insulin resistance.

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“…Patients with acromegaloid have increased growth hormone and insulin resistance, which makes them insulin-dependent. Glibenclamide is used to treat diabetes [ 75 , 76 ].…”

Section: Resultsmentioning

confidence: 99%

Repositioning Drugs for Rare Diseases Based on Biological Features and Computational Approaches

et al. 2022

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Rare diseases are a group of uncommon diseases in the world population. To date, about 7000 rare diseases have been documented. However, most of them do not have a known treatment. As a result of the relatively low demand for their treatments caused by their scarce prevalence, the pharmaceutical industry has not sufficiently encouraged the research to develop drugs to treat them. This work aims to analyse potential drug-repositioning strategies for this kind of disease. Drug repositioning seeks to find new uses for existing drugs. In this context, it seeks to discover if rare diseases could be treated with medicines previously indicated to heal other diseases. Our approaches tackle the problem by employing computational methods that calculate similarities between rare and non-rare diseases, considering biological features such as genes, proteins, and symptoms. Drug candidates for repositioning will be checked against clinical trials found in the scientific literature. In this study, 13 different rare diseases have been selected for which potential drugs could be repositioned. By verifying these drugs in the scientific literature, successful cases were found for 75% of the rare diseases studied. The genetic associations and phenotypical features of the rare diseases were examined. In addition, the verified drugs were classified according to the anatomical therapeutic chemical (ATC) code to highlight the types with a higher predisposition to be repositioned. These promising results open the door for further research in this field of study.

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Insulin-mediated "pseudoacromegaly"

Abstract: AbstrAct

Cited by 14 publications

References 49 publications

Signature Genes in Macrodactyly through Transcriptome Network Analysis Reveal their Association of Lipid Metabolism

Signature Genes in Macrodactyly through Transcriptome Network Analysis Reveal their Association of Lipid Metabolism

Molecular characterization of a novel p.R118C mutation in the insulin receptor gene from patients with severe insulin resistance

Repositioning Drugs for Rare Diseases Based on Biological Features and Computational Approaches

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