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The association of FKBP5 polymorphism with asthma susceptibility in asthmatic patients
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Abstract<sec id="j_jbcpp-2020-0450_abs_001"> <title>Objectives

Inhaled corticosteroids are the most effective controllers of asthma, although asthmatics vary in their response. FKBP51 is a major component of the glucocorticoid receptor which regulates its responses to corticosteroids. Therefore, the present study aims to identify the role of FKBP5 gene polymorphism in asthma susceptibility and corticosteroid resistance.

Methods

DNA was extracted from the blood of 68 asthmatic and 40 control subjects. FKBP5 gene fragments were amplified by PCR and sequenced by the Sanger method. The sequencing results were aligned by mapping on the reference sequences of National center of Biotechnology Information (NCBI) and single nucleotide polymorphisms (SNPs) which were checked. Finally, the genotype, allele frequency and odds ratio (OR) were calculated.

Results

The FKBP5 fragment sequencing revealed the presence of rs1360780 and one novel SNP found in 17 samples taken from asthmatic patients as compared to db SNP data in the NCBI database. The FKBP5 variant (rs1360780) indicated that the allele frequency of risk allele T was 41.18% in patients and 20% in control group members p<0.001 and OR=2.8 when compared to a wild C allele frequency of 58.82% in patients and 64% in the control group members. The novel SNP FKBP5 was compared to the SNP database in the NCBI database in which wild T allele was substituted with G. The novel SNP was submitted to the ClinVar Submission Portal at NCBI with accession number: rs1581842283 and confirmed an asthma susceptibility risk factor with allele G frequency of 11.76% in asthmatics and 2.5% in the control group members (OR=5.2, p<0.05), as compared to a wild T allele frequency of 88.24% in asthmatics and 97.5% in the control group members.

Conclusions

The risk allele T of rs1360780 and the novel SNP rs1581842283 risk allele G predict asthma susceptibility but show no association with corticosteroid resistant.

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Publication Date
Sun Jun 11 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Synthesis and Characterization Palladium(II) Complexes of benz-1,3-imidazoline-2-thione, benz-1,3-thiazoline-2thione and Diamine Ligands
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Sun Jan 01 2023
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Materials Today: Proceedings
Preparation, characterization and preliminary cytotoxic evaluation of 6-mercaptopurine-coated biotinylated carbon dots nanoparticles as a drug delivery system
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Synthesis, Characterization and Biological Activates Studies of some New Derivatives From 2-aminoo-5-mercapto-1, 3, 4-thiadiazole
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Synthesis and Antioxidant Ability of Some 4-(((4-(5-(Aryl)-1,3,4-Oxadiazol-2-Yl)Benzyl)Oxy)Methyl)-2,6-Dimethoxyphenol
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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