Biology subjects

Abdelmoneim, A. H.

Publications and source records attributed to Abdelmoneim, A. H..

7 recordsLinked to original sources

bioRxiv2019

Identification of novel key biomarkers in Simpson-Golabi-Behmel Syndrome: Evidence from bioinformatics analysis

BackgroundThe Simpson-Golabi-Behmel Syndrome (SGBS) or overgrowth Syndrome is a rare inherited X-linked condition characterized by pre- and postnatal overgrowth. The aim of the present study is to identify functional non-synonymous SNPs of GPC3 gene using various in silico approaches. These SNPs are supposed to have a direct effect on protein stability through conformation changes.\n\nMaterial and methodsThe SNPs were retrieved from the Single Nucleotide Polymorphism database (dbSNP) and further used to investigate a damaging effect using SIFT, PolyPhen, PROVEAN, SNAP2, SNPs&GO, PHD-SNP and P-mut, While we used I-mutant and MUPro to study the effect of SNPs on GPC3 protein structure. The 3D structure of human GPC3 protein is not available in the Protein Data Bank, so we used RaptorX to generate a 3D structural model for wild-type GPC3 to visualize the amino acids changes by UCSF Chimera. For biophysical validation we used project HOPE. Lastly we run conservational analysis by BioEdit and Consurf web server respectively.\n\nResultsour results revealed three novel missense mutations (rs1460413167, rs1295603457 and rs757475450) that are found to be the most deleterious which effect on the GPC3 structure and function.\n\nConclusionThis present study could provide a novel insight into the molecular basis of overgrowth Syndrome.

bioinformatics↗

bioRxiv2019

In Silico Genetics Revealing Novel Mutations in CEBPA Gene Associated with Acute Myeloid Leukemia

BackgroundMyelodysplastic syndrome/Acute myeloid leukemia (MDS/AML) is a highly heterogeneous malignant disease; affects children and adults of all ages. AML is one of the main causes of death in children with cancer. However, It is the most common acute leukemia in adults, with a frequency of over 20 000 cases per year in the United States of America alone.\n\nMethodsThe SNPs were retrieved from the dbSNP database. this SNPs were submitted into various functional analysis tools that done by SIFT, PolyPhen-2, PROVEAN, SNAP2, SNPs&GO, PhD-SNP and PANTHER, while structural analysis were done by I-mutant3 and MUPro. The most damaging SNPs were selected for further analysis by Mutation3D, Project hope, ConSurf and BioEdit softwares.\n\nResultsA total of five novel nsSNPs out of 248 missense mutations were predicted to be responsible for the structural and functional variations of CEBPA protein.\n\nConclusionIn this study the impact of functional SNPs in the CEBPA gene was investigated through different computational methods, which determined that (R339W, R288P, N292S N292T and D63N) are novel SNPs have a potential functional effect and can thus be used as diagnostic markers and may facilitate in genetic studies with a special consideration of the large heterogeneity of AML among the different populations.

bioinformatics↗

bioRxiv2019

Comprehensive bioinformatics analysis of L1CAM gene revealed Novel Pathological mutations associated with L1 syndrome

BackgroundMutations in the human L1CAM gene cause a group of neurodevelopmental disorders known as L1 syndrome (CRASH syndrome). The L1CAM gene provides instructions for producing the L1 protein, which is found all over the nervous system on the surface of neurons. L1 syndrome involves a variety of characteristics but the most common characteristic is muscle stiffness. Patients with L1 syndrome can also suffer from difficulty speaking, seizures, and underdeveloped or absent tissue connecting the left and right halves of the brain. MethodThe human L1CAM gene was studied from dbSNP/NCBI, 1499 SNPs were Homo sapiens; of which 450 were missense mutations. This selected for Comprehensive bioinformatics analysis by several in silico tools to investigate the effect of SNPs on L1CAM proteins structure and function. Results34 missense mutations (26 novel mutations) out of 450 nsSNPs that are found to be the most deleterious that effect on the L1CAM structural and functional level. ConclusionBetter understanding of L1 syndrome caused by mutations in L1CAM gene was achieved using Comprehensive bioinformatics analysis. These findings describe 35 novel L1 mutations which improve our understanding on genotype-phenotype correlation. And can be used as diagnostic markers for L1 syndrome and besides in cancer diagnosis specifically in breast cancer.

bioinformatics↗

bioRxiv2019

In silico analysis of IDH3A gene revealed Novel mutations associated with Retinitis Pigmentosa

BackgroundRetinitis Pigmentosa (RP) refers to a group of inherited disorders characterized by the death of photoreceptor cells leading to blindness. The aim of this study is to identify the pathogenic SNPs in the IDH3A gene and their effect on the structure and function of the protein. Methodwe used different bioinformatics tools to predict the effect of each SNP on the structure and function of the protein. Result20 deleterious SNPs out of 178 were found to have a damaging effect on the protein structure and function. Conclusionthis is the first in silico analysis of IDH3A gene and 20 novel mutations were found using different bioinformatics tools, and they could be used as diagnostic markers for Retinitis Pigmentosa.

bioinformatics↗

bioRxiv2019

In Silico Analysis and Modeling of Novel Pathogenic Single Nucleotide Polymorphisms (SNPs) in Human CD40LG Gene

AbstractO_ST_ABSBackgroundC_ST_ABSThe X-linked hyper-immunoglobulin M syndrome (XHIGM) is a rare, inherited immune deficiency disorder. It is more common in males. Characterized by elevated serum IgM levels and low to undetectable levels of serum IgG, IgA and IgE. Hyper-IgM syndrome is caused by mutations in the CD40LG gene. Located in human Xq26. CD40LG acts as an immune modulator in activated T cells. MethodWe used different bioinformatics tools to predict the effect of each SNP on the structure and function of the protein. Result8 novel SNPs out of 233 were found to have most deleterious effect on the protein structure and function. While modeling of nsSNPs was studied by Project HOPE software. ConclusionBetter understanding of Hyper-IgM syndrome caused by mutations in CD40LG gene was achieved using in silico analysis. This is the first in silico functional analysis of CD40LG gene and 8 novel mutations were found using different bioinformatics tools, and they could be used as diagnostic markers for hyper-IgM syndrome. These 8 novel SNPs may be important candidates for the cause of different types of human diseases by CD40LG gene.

bioinformatics↗

bioRxiv2019

In Silico Genetics: Identification of pathogenic nsSNPs in human STAT3 gene associated with Job's syndrome

BackgroundAutosomal dominant hyper-IgE syndrome (AD-HIES) or Jobs syndrome is a rare immunodeficiesncy disease that classically presents in early childhood, characterized by eczematoid dermatitis, characteristic facies, pneumatoceles, hyperextensibility of joints, multiple bone fractures, scoliosis, atopic dermatitis and elevated levels of serum IgE (>2000 IU/ml). The term Autosomal dominant hyper-IgE syndrome has primarily been associated with mutations in STAT3 gene, Located in human chromosome 17q21. MethodsThe human STAT3 gene was investigated in dbSNP/NCBI, 962 SNPs were Homo sapiens; of which 255 were missense SNPs. This selected for in silico analysis by multiple in silico tools to investigate the effect of SNPs on STAT3 proteins structure and function. ResultEleven novel mutations out of 255 nsSNPs that are found to be deleterious effect on the STAT3 structure and function. ConclusionA total of eleven novel nsSNPs were predicted to be responsible for the structural and functional modifications of STAT3 protein. The newly recognized genetic cause of the hyper-IgE syndrome affects complex, compartmentalized somatic and immune regulation. This study will opens new doors to facilitate the development of novel diagnostic markers for associated diseases.

bioinformatics↗

bioRxiv2019

Novel mutations within PRSS1 Gene that could potentially cause hereditary pancreatitis: Using Comprehensive in silico Approach

BackgroundHereditary pancreatitis (HP) is an autosomal dominant disorder with incomplete penetrance characterized by recurring episodes of severe abdominal pain often presenting in childhood. The comprehensive in silico analysis of coding SNPs, and their functional impacts on protein level, still remains unknown. In this study, we aimed to identify the pathogenic SNPs in PRSS1 gene by computational analysis approach.\n\nMaterials and MethodsWe carried out in silico analysis of structural effect of each SNP using different bioinformatics tools to predict Single-nucleotide polymorphisms influence on protein structure and function.\n\nResultTwo novel mutations out of 339 nsSNPs that are found be deleterious effect on the PRSS1 structure and function.\n\nConclusionThis is the first in silico analysis in PRSS1 gene, which will be a valuable resource for future targeted mechanistic and population-based studies.

bioinformatics↗