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Gene Symbol |
GDI1 |
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Aliases |
1A, GDIL, MRX41, MRX48, OPHN2, RABGD1A, RABGDIA, XAP-4 |
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Entrez Gene ID |
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Gene Name |
GDP dissociation inhibitor 1 |
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Chromosomal Location |
Xq28 |
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HGNC ID |
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Summary |
GDP dissociation inhibitors are proteins that regulate the GDP-GTP exchange reaction of members of the rab family, small GTP-binding proteins of the ras superfamily, that are involved in vesicular trafficking of molecules between cellular organelles. GDIs slow the rate of dissociation of GDP from rab proteins and release GDP from membrane-bound rabs. GDI1 is expressed primarily in neural and sensory tissues. Mutations in GDI1 have been linked to X-linked nonspecific cognitive disability. [provided by RefSeq, Jul 2008]
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RefSeq DNA |
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RefSeq mRNA |
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e!Ensembl
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| Protein Information |
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Protein Name |
Rab GDP dissociation inhibitor alpha, GDI-1, guanosine diphosphate dissociation inhibitor 1, mental retardation, X-linked 48, oligophrenin-2, protein XAP-4, rab GDI alpha, rab GDP-dissociation inhibitor, alpha, testis secretory sperm-binding protein Li 208a |
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Function |
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Regulates the GDP/GTP exchange reaction of most Rab proteins by inhibiting the dissociation of GDP from them, and the subsequent binding of GTP to them. Promotes the dissociation of GDP-bound Rab proteins from the membrane and inhibits their activation. Promotes the dissociation of RAB1A, RAB3A, RAB5A and RAB10 from membranes. |
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UniProt |
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Interactions |
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| STRING |
MINT |
IntAct |
| ENSP00000216341 |
P10144 |
P10144 |
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View interactions
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Associated Diseases
| Disease group | Disease Name | References |
| Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| Mental Retardation |
11027356, 9620768, 20159109, 22002931, 9106537, 10473636, 28057534, 8826463, 22291894, 26971292, 18829665, 12354782, 9668174 |
| Endocrine System Diseases |
| PCOS |
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| Nervous System Diseases |
| Lateral Sclerosis |
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| Psychiatric/Brain disorders |
| Mental Retardation |
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References |
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| Atiomo W, Khalid S, Parameshweran S, Houda M, Layfield R |
| Department of Obstetrics and Gynaecology, School of Human Development, University of Nottingham, and Nottingham University Hospitals, Nottingham, UK. william.atiomo@nottingham.ac.uk |
| BJOG. 2009 Jan;116(2):137-43. doi: 10.1111/j.1471-0528.2008.02041.x. |
Abstract
BACKGROUND: The exact causes of polycystic ovary syndrome (PCOS) are uncertain, and treatment could be improved. Discovery-based approaches like 'proteomics' may result in faster insights into the causes of PCOS and improved treatment. OBJECTIVES: To identify the number and nature of proteomic biomarkers found in PCOS so far and to identify their diagnostic and therapeutic potential. SEARCH STRATEGY: All published studies on proteomic biomarkers in women with PCOS identified through the MEDLINE (1966-2008), EMBASE (1980-2008) and the ISI web of knowledge (v4.2) databases. SELECTION CRITERIA: The terms 'polycystic ovary syndrome' and 'proteomic', 'proteomics', 'proteomic biomarker' or 'proteomics biomarker' without any limits/restrictions were used. DATA COLLECTION AND ANALYSIS: Original data were abstracted where available and summarised on a separate Microsoft Excel (2007) database for analysis. MAIN RESULTS: Seventeen articles were identified, of which 6 original papers and 1 review article contained original data. Tissues investigated included serum, omental biopsies, ovarian biopsies, follicular fluid and T lymphocytes. Sample sizes ranged from 3 to 30 women. One hundred and forty-eight biomarkers were identified. The biomarkers were involved in many pathways, for example the regulation of fibrinolysis and thrombosis, insulin resistance, immunity/inflammation and the antioxidant pathway. Eleven groups of biomarkers appeared to be independently validated. The individual sensitivities for the diagnosis of PCOS were reported for 11 named biomarkers and ranged from 57 to 100%. AUTHOR'S CONCLUSIONS: Proteomic biomarker discovery in PCOS offers great potential. Current challenges include reproducibility and data analysis. The establishment of a PCOS-specific biomarker data bank and international consensus on the framework of systematic reviews in this field are required. |
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| © 2019, Biomedical Informatics Centre, NIRRH |
National Institute for Research in Reproductive Health, Jehangir Merwanji Street, Parel, Mumbai-400 012
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