Heterozygous nonsense mutations near the C‐terminal region of IGF1R in two patients with small‐for‐gestational‐age‐related short stature

Fujimoto, Masanobu; Sonoyama, Yuki Kawashima; Hamajima, Naoki; Hamajima, Takashi; Kumura, Yumiko; Miyahara, Naoki; Nishimura, R.; Adachi, Kaori; Nanba, Eiji; Hanaki, Keiichi; Kanzaki, Sho

doi:10.1111/cen.12791

Cited by 21 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IGF1R mediates the function of the insulin-like growth factor 1 and is crucial for tumor transformation and survival of malignant cell 53 . It can be activated by autophosphorylation at Tyr-1165, Tyr-1161 and Tyr-1166 54 . Thus, based on these results, we hypothesize that SHP-1 might be an inhibitor of IGF1R through dephosphorylation at these three sites.…”

Section: Resultsmentioning

confidence: 99%

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

Wang

Yan

Song

2016

Sci Rep

View full text Add to dashboard Cite

Protein dephosphorylation, which is an inverse process of phosphorylation, plays a crucial role in a myriad of cellular processes, including mitotic cycle, proliferation, differentiation, and cell growth. Compared with tyrosine kinase substrate and phosphorylation site prediction, there is a paucity of studies focusing on computational methods of predicting protein tyrosine phosphatase substrates and dephosphorylation sites. In this work, we developed two elegant models for predicting the substrate dephosphorylation sites of three specific phosphatases, namely, PTP1B, SHP-1, and SHP-2. The first predictor is called MGPS-DEPHOS, which is modified from the GPS (Group-based Prediction System) algorithm with an interpretable capability. The second predictor is called CKSAAP-DEPHOS, which is built through the combination of support vector machine (SVM) and the composition of k-spaced amino acid pairs (CKSAAP) encoding scheme. Benchmarking experiments using jackknife cross validation and 30 repeats of 5-fold cross validation tests show that MGPS-DEPHOS and CKSAAP-DEPHOS achieved AUC values of 0.921, 0.914 and 0.912, for predicting dephosphorylation sites of the three phosphatases PTP1B, SHP-1, and SHP-2, respectively. Both methods outperformed the previously developed kNN-DEPHOS algorithm. In addition, a web server implementing our algorithms is publicly available at http://genomics.fzu.edu.cn/dephossite/ for the research community.

show abstract

Section: Resultsmentioning

confidence: 99%

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

Wang

Yan

Song

2016

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The collected cord blood (from the umbilical vein) was immediately placed into a PAXgene Blood RNA Tube (Becton Dickinson and Company, Franklin Lakes, NJ); total RNA from the umbilical cord blood was purified using the reagents provided in the PAX gene Blood RNA Kit (QIAGEN, Germanton, MD) within 4 days, and reverse transcription PCR was performed. First-strand complementary DNA synthesis was performed as described previously [ 21 ]. Quantitative real-time PCR analysis was applied for evaluating the expression of IGF1R , INSR , IRS1 , IRS2 , SLC2A2 , and SLC2A4 mRNA using the Applied Biosystems 7900HT Fast Real-Time PCR System (Life Technologies Japan Ltd., Tokyo, Japan) with a TaqMan probe and 2× EXPRESS qPCR Supermix with Premixed ROX (Life Technologies, Carlsbad, CA).…”

Section: Methodsmentioning

confidence: 99%

Increased IRS2 mRNA Expression in SGA Neonates: PCR Analysis of Insulin/IGF Signaling in Cord Blood

Fujimoto

Sonoyama

Fukushima

et al. 2017

Journal of the Endocrine Society

Self Cite

View full text Add to dashboard Cite

Context:Hypoglycemia is the most common metabolic problem among small-for-gestational-age (SGA) neonates. However, the pathological mechanism and insulin/ insulin-like growth factor (IGF) signaling axis in neonates remain unknown.Objective:To determine the insulin/IGF axis in neonates, we analyzed the messenger RNA (mRNA) expression of insulin/IGF signaling in fetal umbilical cord blood.Setting:The Perinatal Medical Center of Tottori University Hospital.Participants:Fifty-two [42 appropriate-for-gestational-age (AGA) and 10 SGA] neonates.Interventions:Immediately collected cord blood was placed into a PAXgene Blood RNA Tube. Total RNA from the blood was purified using reagents provided in the PAXgene Blood RNA Kit within 4 days, and reverse transcription polymerase chain reaction (PCR) was performed.Main Outcome Measure:Quantitative real-time PCR analysis was applied to evaluate the mRNA expression of insulin receptor (INSR), IGF-I receptor (IGF1R), insulin receptor substrate 1 (IRS1), IRS2, and glucose transporters (SLC2A2 and SLC2A4). β-Actin was used as a control gene.Results:Serum glucose and IGF-I levels in SGA neonates were significantly lower. The cord serum insulin levels were similar between AGA and SGA neonates. The IRS2 mRNA expression was significantly higher in SGA than in AGA neonates (P < 0.05). The IRS2 mRNA expression was significantly higher in hypoglycemic SGA neonates than in normoglycemic SGA neonates.Conclusions:We determined that intrauterine growth restriction induces increased IRS2 mRNA expression in cord blood, without hyperinsulinemia. The increased expression of IRS2 mRNA might be associated with abnormal glucose metabolism in SGA neonates. Our findings might lead to the elucidation of abnormal glucose metabolism in SGA neonates.

show abstract

“…In 2009, Fang et al demonstrated IGF1R haploinsufficiency due to a mRNA decay phenomenon in a nonsense variant in exon 18 10. Most groups have not observed any effect on the expression of the transmembrane IGF1R in patients with missense variants 6 10 19 20…”

Section: Introductionmentioning

confidence: 99%

Increasing knowledge in IGF1R defects: lessons from 35 new patients

et al. 2019

View full text Add to dashboard Cite

BackgroundThe type 1 insulin-like growth factor receptor (IGF1R) is a keystone of fetal growth regulation by mediating the effects of IGF-I and IGF-II. Recently, a cohort of patients carrying an IGF1R defect was described, from which a clinical score was established for diagnosis. We assessed this score in a large cohort of patients with identified IGF1R defects, as no external validation was available. Furthermore, we aimed to develop a functional test to allow the classification of variants of unknown significance (VUS) in vitro.MethodsDNA was tested for either deletions or single nucleotide variant (SNV) and the phosphorylation of downstream pathways studied after stimulation with IGF-I by western blot analysis of fibroblast of nine patients.ResultsWe detected 21 IGF1R defects in 35 patients, including 8 deletions and 10 heterozygous, 1 homozygous and 1 compound-heterozygous SNVs. The main clinical characteristics of these patients were being born small for gestational age (90.9%), short stature (88.2%) and microcephaly (74.1%). Feeding difficulties and varying degrees of developmental delay were highly prevalent (54.5%). There were no differences in phenotypes between patients with deletions and SNVs of IGF1R. Functional studies showed that the SNVs tested were associated with decreased AKT phosphorylation.ConclusionWe report eight new pathogenic variants of IGF1R and an original case with a homozygous SNV. We found the recently proposed clinical score to be accurate for the diagnosis of IGF1R defects with a sensitivity of 95.2%. We developed an efficient functional test to assess the pathogenicity of SNVs, which is useful, especially for VUS.

show abstract

Heterozygous nonsense mutations near the C‐terminal region of IGF1R in two patients with small‐for‐gestational‐age‐related short stature

Abstract: Heterozygous nonsense mutations affecting the C-terminal region (p.Q1250X, p.W1249X) of IGF1R decreased the expression of IGF1R through the ERAD pathway. Our study revealed the importance of the C-terminal region and the dosage of this receptor for growth.

Cited by 21 publications

References 38 publications

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

Increased IRS2 mRNA Expression in SGA Neonates: PCR Analysis of Insulin/IGF Signaling in Cord Blood

Increasing knowledge in IGF1R defects: lessons from 35 new patients

Contact Info

Product

Resources

About