Structural and functional investigation of mycobacterial HflX protein and its mutational hotspots annotation by in silico approach

BackgroundAlthough rare missense variants in Mendelian disease genes often cluster in specific regions of proteins, it is unclear how to consider this when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene association and variant interpretation that use this powerful signal.MethodsWe present statistical methods to detect missense variant clustering (BIN-test) combined with burden information (ClusterBurden). We introduce a flexible generalised additive modelling (GAM) framework to identify mutational hotspots using burden and clustering information (hotspot model) and supplemented by in silico predictors (hotspot+ model). The methods were applied to synthetic data and a case–control dataset, comprising 5338 hypertrophic cardiomyopathy patients and 125 748 population reference samples over 34 putative cardiomyopathy genes.ResultsIn simulations, the BIN-test was almost twice as powerful as the Anderson-Darling or Kolmogorov-Smirnov tests; ClusterBurden was computationally faster and more powerful than alternative position-informed methods. For 6/8 sarcomeric genes with strong clustering, Clusterburden showed enhanced power over burden-alone, equivalent to increasing the sample size by 50%. Hotspot+ models that combine burden, clustering and in silico predictors outperform generic pathogenicity predictors and effectively integrate ACMG criteria PM1 and PP3 to yield strong or moderate evidence of pathogenicity for 31.8% of examined variants of uncertain significance.ConclusionGAMs represent a unified statistical modelling framework to combine burden, clustering and functional information. Hotspot models can refine maps of regional burden and hotspot+ models can be powerful predictors of variant pathogenicity. The BIN-test is a fast powerful approach to detect missense variant clustering that when combined with burden information (ClusterBurden) may enhance disease-gene discovery.

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Data-driven modelling of mutational hotspots and in-silico predictors in hypertrophic cardiomyopathy

10.1101/826164 ◽

2019 ◽

Author(s):

A.J. Waring ◽

A.R. Harper ◽

S. Salatino ◽

C.M. Kramer ◽

S Neubauer ◽

...

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Rare Variant ◽

In Silico ◽

Missense Variant ◽

Data Driven ◽

Disease Genes ◽

Mendelian Disease ◽

Gene Association ◽

Missense Variants ◽

Mutational Hotspots

ABSTRACTBackgroundAlthough rare-missense variants in Mendelian disease-genes have been noted to cluster in specific regions of proteins, it is not clear how to consider this information when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene-association and variant-interpretation that utilise this powerful signal.MethodsWe present a case-control rare-variant association test, ClusterBurden, that combines information on both variant-burden and variant-clustering. We then introduce a data-driven modelling framework to estimate mutational hotspots in genes with missense variant-clustering and integrate further in-silico predictors into the models.ResultsWe show that ClusterBurden can increase statistical power to scan for putative disease-genes, driven by missense variants, in simulated data and a 34-gene panel dataset of 5,338 cases of hypertrophic cardiomyopathy. We demonstrate that data-driven models can allow quantitative application of the ACMG criteria PM1 and PP3, to resolve a wide range of pathogenicity potential amongst variants of uncertain significance. A web application (Pathogenicity_by_Position) is accessible for missense variant risk prediction of six sarcomeric genes and an R package is available for association testing using ClusterBurden.ConclusionThe inclusion of missense residue position enhances the power of disease-gene association and improves rare-variant pathogenicity interpretation.

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Mutations in CD14 gene causes mastitis in different breeds of buffalo as confirmed by in silico studies and experimental validation

10.21203/rs.2.10779/v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aruna Pal ◽

Abantika Pal ◽

Arjava Sharma ◽

Tarun Kumar Bhattacharya

Keyword(s):

Somatic Cell ◽

In Silico ◽

Lipoteichoic Acid ◽

Transgenic Animal ◽

Post Translational Modification ◽

California Mastitis Test ◽

Nucleotide Conformation ◽

In Silico Studies ◽

Mutational Hotspots ◽

Cd14 Gene

Abstract Background CD 14 is an important pattern recognition receptor having innate immune function and has antibacterial activity. It binds with LPS of gram-negative bacteria, arachidonic acid, and lipoteichoic acid. Being a receptor, it binds with the pathogen with the help of other cytokines. Mutations in CD14 affect the binding ability which in turn affects the biological potentiality. Method The present study was conducted on 228 nos. of buffaloes pertaining to four different breeds as Murrah, Mehsana, Surti and Bhadawari. CD14 gene was characterized and polymorphism was detected through Single nucleotide conformation polymorphism. Association study was conducted for different variants of CD14 with mastitis in buffalo, detected through somatic cell count, california mastitis test. Result Eight variants of CD14 were detected and mutational hotspots were detected in bubaline CD14 with 58 number of non-synonymous SNP, out of which 18 were observed to be deleterious and 34 as thermodynamically unstable. In the present study, we had detected the mutations in CD14 gene and its association with the somatic cell score and other indicators for mastitis. In-silico studies were conducted to understand the molecular mechanism how the mutations affect the biological potentiality by analyzing different domains and structural analysis along with various post-translational modification sites. Conclusion Deleterious mutations were observed in CD14 gene which have significant effect on mastitis of buffalo. It may be employed for marker assisted selection, therapeutic application of recombinant CD14, gene therapy, transgenic animal production with wild type CD14 resistant to mastitis as future strategy.

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Succinic Semialdehyde Dehydrogenase Deficiency: In Vitro and In Silico Characterization of a Novel Pathogenic Missense Variant and Analysis of the Mutational Spectrum of ALDH5A1

International Journal of Molecular Sciences ◽

10.3390/ijms21228578 ◽

2020 ◽

Vol 21 (22) ◽

pp. 8578

Author(s):

Heiko Brennenstuhl ◽

Miroslava Didiasova ◽

Birgit Assmann ◽

Mariarita Bertoldi ◽

Gianluca Molla ◽

...

Keyword(s):

In Silico ◽

Dehydrogenase Deficiency ◽

Succinic Semialdehyde ◽

Succinic Semialdehyde Dehydrogenase ◽

Mutational Spectrum ◽

Protein Secondary Structures ◽

Semialdehyde Dehydrogenase ◽

Mutational Hotspots ◽

Succinic Semialdehyde Dehydrogenase Deficiency

Succinic semialdehyde dehydrogenase deficiency (SSADHD) is a rare, monogenic disorder affecting the degradation of the main inhibitory neurotransmitter γ-amino butyric acid (GABA). Pathogenic variants in the ALDH5A1 gene that cause an enzymatic dysfunction of succinic semialdehyde dehydrogenase (SSADH) lead to an accumulation of potentially toxic metabolites, including γ-hydroxybutyrate (GHB). Here, we present a patient with a severe phenotype of SSADHD caused by a novel genetic variant c.728T > C that leads to an exchange of leucine to proline at residue 243, located within the highly conserved nicotinamide adenine dinucleotide (NAD)+ binding domain of SSADH. Proline harbors a pyrrolidine within its side chain known for its conformational rigidity and disruption of protein secondary structures. We investigate the effect of this novel variant in vivo, in vitro, and in silico. We furthermore examine the mutational spectrum of all previously described disease-causing variants and computationally assess all biologically possible missense variants of ALDH5A1 to identify mutational hotspots.

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In Silico Toxicology

10.1039/9781849732093 ◽

2010 ◽

Cited By ~ 30

Keyword(s):

In Silico

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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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Common and well-documented Allele List: unverzichtbares Hilfsmittel für die moderne HLA-Typisierung

Transfusionsmedizin - Immunhämatologie Hämotherapie Immungenetik Zelltherapie ◽

10.1055/a-0898-1122 ◽

2019 ◽

Vol 9 (04) ◽

pp. 241-245

Author(s):

Nils Lachmann ◽

Diana Stauch ◽

Axel Pruß

Keyword(s):

In Silico ◽

Regionale Unterschiede

ZusammenfassungDie Typisierung der humanen Leukozytenantigene (HLA) vor Organ- und hämatopoetischer Stammzelltransplantation zur Beurteilung der Kompatibilität von Spender und Empfänger wird heutzutage in der Regel molekulargenetisch mittels Amplifikation, Hybridisierung oder Sequenzierung durchgeführt. Durch die exponentiell steigende Anzahl an neu entdeckten HLA-Allelen treten vermehrt Mehrdeutigkeiten, sogenannte Ambiguitäten, in der HLA-Typisierung auf, die aufgelöst werden müssen, um zu einem eindeutigen Ergebnis zu gelangen. Mithilfe kategorisierter Allelfrequenzen (häufig, gut dokumentiert und selten) in Form von CWD-Allellisten (CWD: common and well-documented) ist die In-silico-Auflösung von Ambiguitäten durch den Ausschluss seltener Allele als mögliches Ergebnis realisierbar. Ausgehend von einer amerikanischen CWD-Liste existieren derzeit auch eine europäische, deutsche und chinesische CWD-Liste, die jeweils regionale Unterschiede in den Allelfrequenzen erkennbar werden lassen. Durch die Anwendung von CWD-Allelfiltern in der klinischen HLA-Typisierung können Zeit, Kosten und Arbeitskraft eingespart werden.

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