Pediatric Liddle Syndrome Caused by a Novel SCNN1G Variant in a Chinese Family and Characterized by Early-Onset Hypertension

Abstract BACKGROUND Liddle syndrome (LS), an autosomal dominant disorder, is a common monogenic hypertension in pediatrics. In this study, we reported a novel SCNN1G variant in a Chinese family with pediatric LS, and conduct a systematic review of epithelial sodium channel (ENaC)-gene-positive LS cases to conclude the clinical genetic features of LS in childhood. METHODS Next-generation sequencing and in silico analysis were performed in the proband to discover candidate variants. Sanger sequencing was used to identify the predicted likely pathogenic variant. LS patients in this family were treated with amiloride. The Medline database was searched to summarize clinical features of pediatric LS cases whose age at genetic diagnosis was not more than 18 years. RESULTS Genetic analysis identified a novel SCNN1G missense variant (c.1874C>T, p.Pro625Leu) in the proband with LS in childhood. In silico analysis revealed this heterozygous variant was highly conserved and deleterious. A total of 38 publications described pediatric LS associated with 25 pathogenic variants in SCNN1B and SCNN1G in 54 children. Despite the phenotypic heterogeneity, early-onset hypertension is the most common feature. All LS patients in this family or the reviewed cases showed significantly improvements in hypertension and hypokalemia after treatment with ENaC inhibitors. CONCLUSIONS This study identified a novel SCNN1G missense variant in a patient with pediatric LS, expanding the genetic spectrum of SCNN1G and demonstrating the PY motif of γ-ENaC as a potential mutant region. Early identification and specific management of LS in children and adolescents are important to prevent the development of hypertensive end-organ disease.

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In silico analysis of CDC73 gene revealing 11 novel SNPs associated with Jaw Tumor Syndrome

10.1101/729764 ◽

2019 ◽

Author(s):

Abdelrahman H. Abdelmoneim ◽

Alaa I. Mohammed ◽

Esraa O. Gadim ◽

Mayada A.Mohammed ◽

Sara H. Hamza ◽

...

Keyword(s):

In Silico ◽

Autosomal Dominant ◽

In Silico Analysis ◽

Autosomal Dominant Disorder ◽

Variable Expression ◽

Back Ground ◽

And Function ◽

Molecular Aberrations ◽

The Impact ◽

Silico Analysis

AbstractBack groundhyperparathyroidism-jaw tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing CDC73 (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the effect of SNPs on CDC73 structure and function using different bioinformatics tools.MethodComputational analysis using eight different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&GO, PMut and Imutant were used to identify the impact on the structure and/or function of CDC73 gene that might be causing jaw tumour.ResultsFrom (733) SNPs identified in the CDC73 gene we found that only Eleven were deleterious to the function and structure of protein and expected to cause syndrome.ConclusionEleven substantial genetic/molecular aberrations in CDC73 gene were identified that could serve as actionable targets for chemotherapeutic intervention in patients whose disease is no longer surgically curable.

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A Novel Missense Variant in the ESRRB Gene Causing Nonsyndromic Hearing Loss: In Silico Analyses of a Case

10.21203/rs.3.rs-698699/v1 ◽

2021 ◽

Author(s):

Tohid Ghasemnejad ◽

Mahmoud Shekari Khaniani ◽

Jafar Nouri Nojadeh ◽

Sima Mansoori Derakhshan

Keyword(s):

Hearing Loss ◽

In Silico ◽

Standard Procedure ◽

In Silico Analysis ◽

Missense Variant ◽

12S Rrna ◽

Pathogenic Variants ◽

Genetic Hearing Loss ◽

Syndromic Hearing Loss ◽

Silico Analysis

Abstract Background: Genetic hearing loss (GHL) is a common heterogeneous disorder that can affect all ages, ethnicities, and genders. The most common form of hearing loss (HL) is autosomal recessive non-syndromic hearing loss (ARNSHL) and in most cases specific genotype-phenotype correlation is indistinguishable. This study aimed to identify the genetic cause of hearing loss in an Iranian Azeri Turkish ethnicity family with consanguine marriage which is negative for GJB2, GJB6 and mitochondrially encoded 12S RRNA (MT-RNR1) deleterious mutations.Methods: Targeted genome sequencing was applied for the detection of possible genetic causes of HL in this family. Co-segregation and in silico analysis of variant was performed by standard procedure.Results: A missense variant, c.499G>A, was identified in the ESRRB gene. Healthy and affected members of the family confirmed co-segregation of the variant with ARNSHL in the pedigree and then the pathogenicity of the variant was confirmed by in silico analysis and ACMG Guidelines. Conclusion: We report a novel missense variant in the ESRRB gene which seems to be a pathogenic variant. The result of this study suggests that the genetic background of hearing loss patients plays important role in the pathogenicity; moreover, targeted genomic capture is a powerful method that can discover pathogenic variants in heterogeneous disorders.

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A family with Milroy disease caused by the FLT4/VEGFR3 gene variant c.2774 T > A

BMC Medical Genomics ◽

10.1186/s12920-021-00997-w ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Yu Sui ◽

Yongping Lu ◽

Meina Lin ◽

Xiang Ni ◽

Xinren Chen ◽

...

Keyword(s):

Tyrosine Kinase ◽

In Silico ◽

Sanger Sequencing ◽

Autosomal Dominant ◽

Large Family ◽

Missense Variant ◽

Chinese Family ◽

Healthy Controls ◽

Gene Variant ◽

Autosomal Dominant Disorder

Abstract Background Milroy disease (MD) is a rare, autosomal-dominant disorder. Variants in the Fms-related tyrosine kinase 4 (FLT4/VEGFR3) gene cause the symptoms of this disease. In this report, we investigated the variant in a large Chinese family with MD. Methods We conducted Sanger sequencing of exons 17–26 of FLT4/VEGFR3 (NM_182925.4). We assessed its pathogenicity based on the ACMG criteria and predicted it with an in silico program. Results A heterozygous substitution (NM_182925.4 (FLT4/VEGFR3):c.2774 T>A, p. (Val925Glu)) was detected in all patients with MD but not in any healthy controls. The variant was evaluated as pathogenic according to the ACMG criteria and was predicted to be pathogenic using an in silico program. Conclusions In this report, we described a large family with MD caused by a missense variant in FLT4/VEGFR3 (NM_182925.4 (FLT4/VEGFR3_v001):c.2774 T>A, p. (Val925Glu)). There are phenotypic heterogeneities among family members, and further research should be conducted to explore the possible reasons.

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

10.1101/519926 ◽

2019 ◽

Author(s):

Mujahed I. Mustafa ◽

Abdelrahman H. Abdelmoneim ◽

Nafisa M. Elfadol ◽

Soada A. osman ◽

Tebyan A. Abdelhameed ◽

...

Keyword(s):

In Silico ◽

In Silico Analysis ◽

Structure And Function ◽

Incomplete Penetrance ◽

Nucleotide Polymorphisms ◽

Hereditary Pancreatitis ◽

Novel Mutations ◽

Autosomal Dominant Disorder ◽

And Function ◽

Silico Analysis

AbstractBackgroundHereditary 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.Materials 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.ResultTwo novel mutations out of 339 nsSNPs that are found be deleterious effect on the PRSS1 structure and function.ConclusionThis is the first in silico analysis in PRSS1 gene, which will be a valuable resource for future targeted mechanistic and population-based studies.

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Biallelic variants in PCDHGC4 cause a novel neurodevelopmental syndrome with progressive microcephaly, seizures, and joint anomalies

Genetics in Medicine ◽

10.1038/s41436-021-01260-4 ◽

2021 ◽

Author(s):

Maria Iqbal ◽

Reza Maroofian ◽

Büşranur Çavdarlı ◽

Florence Riccardi ◽

Michael Field ◽

...

Keyword(s):

In Silico ◽

Autosomal Recessive ◽

Neurodevelopmental Disorder ◽

Three Dimensional ◽

In Silico Analysis ◽

Missense Variant ◽

The Third ◽

Dimerization Interface ◽

Silico Analysis

Abstract Purpose We aimed to define a novel autosomal recessive neurodevelopmental disorder, characterize its clinical features, and identify the underlying genetic cause for this condition. Methods We performed a detailed clinical characterization of 19 individuals from nine unrelated, consanguineous families with a neurodevelopmental disorder. We used genome/exome sequencing approaches, linkage and cosegregation analyses to identify disease-causing variants, and we performed three-dimensional molecular in silico analysis to predict causality of variants where applicable. Results In all affected individuals who presented with a neurodevelopmental syndrome with progressive microcephaly, seizures, and intellectual disability we identified biallelic disease-causing variants in Protocadherin-gamma-C4 (PCDHGC4). Five variants were predicted to induce premature protein truncation leading to a loss of PCDHGC4 function. The three detected missense variants were located in extracellular cadherin (EC) domains EC5 and EC6 of PCDHGC4, and in silico analysis of the affected residues showed that two of these substitutions were predicted to influence the Ca2+-binding affinity, which is essential for multimerization of the protein, whereas the third missense variant directly influenced the cis-dimerization interface of PCDHGC4. Conclusion We show that biallelic variants in PCDHGC4 are causing a novel autosomal recessive neurodevelopmental disorder and link PCDHGC4 as a member of the clustered PCDH family to a Mendelian disorder in humans.

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In silico analysis of CDC73 gene revealing 11 novel SNPs with possible association to Hyperparathyroidism-Jaw Tumor syndrome

The EuroBiotech Journal ◽

10.2478/ebtj-2020-0008 ◽

2020 ◽

Vol 4 (2) ◽

pp. 67-81

Author(s):

Abdelrahman H. Abdelmoneim ◽

Alaa I. Mohammed ◽

Esraa O. Gadim ◽

Mayada Alhibir Mohammed ◽

Sara H. Hamza ◽

...

Keyword(s):

In Silico ◽

Autosomal Dominant ◽

In Silico Analysis ◽

Autosomal Dominant Disorder ◽

Variable Expression ◽

Project Hope ◽

Molecular Aberrations ◽

Structural Levels ◽

The Impact ◽

Silico Analysis

AbstractHyperparathyroidism-Jaw Tumor (HPT-JT) is an autosomal dominant disorder with variable expression, with an estimated prevalence of 6.7 per 1,000 population. Genetic testing for predisposing CDC73 (HRPT2) mutations has been an important clinical advance, aimed at early detection and/or treatment to prevent advanced disease. The aim of this study is to assess the most deleterious SNPs mutations on CDC73 gene and to predict their influence on the functional and structural levels using different bioinformatics tools. Method: Computational analysis using twelve different in-silico tools including SIFT, PROVEAN, PolyPhen-2, SNAP2, PhD-SNP, SNPs&GO, P-Mut, I-Mutant ,Project Hope, Chimera, COSMIC and dbSNP Short Genetic Variations were used to identify the impact of mutations in CDC73 gene that might be causing jaw tumor. Results: From (733) SNPs identified in the CDC73 gene we found that only Eleven SNPs (G49C, L63P, L64P, D90H, R222G, W231R, P360S, R441C, R441H, R504S and R504H) has deleterious effect on the function and structure of protein and expected to cause the syndrome. Conclusion: Eleven substantial genetic/molecular aberrations in CDC73 gene identified that could serve as diagnostic markers for hyperparathyroidism-jaw tumor (HPT-JT).

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A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

2020 ◽

Vol 47 (6) ◽

pp. 398-408

Author(s):

Sonam Tulsyan ◽

Showket Hussain ◽

Balraj Mittal ◽

Sundeep Singh Saluja ◽

Pranay Tanwar ◽

...

Keyword(s):

Systematic Review ◽

Gallbladder Carcinoma ◽

In Silico ◽

In Silico Analysis ◽

Transcriptomic Profile ◽

Silico Analysis

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

Author(s):

Antreas Afantitis ◽

Andreas Tsoumanis ◽

Georgia Melagraki

Keyword(s):

Data Analysis ◽

Virtual Screening ◽

In Silico ◽

Model Development ◽

In Silico Analysis ◽

Material Design ◽

Efficient Solutions ◽

Biological Data ◽

Cost Efficient ◽

Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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