Clinical features and mutational analysis of X-linked agammaglobulinemia patients in Malaysia

BackgroundBruton’s tyrosine kinase (BTK) is a cytoplasmic protein involved in the B cell development. X-linked agammaglobulinemia (XLA) is caused by mutation in the BTK gene, which results in very low or absent B cells. Affected males have markedly reduced immunoglobulin levels, which render them susceptible to recurrent and severe bacterial infections. Methods: Patients suspected with X-linked agammaglobulinemia were enrolled during the period of 2010-2018. Clinical summary, and immunological profiles of these patients were recorded. Peripheral blood samples were collected for monocyte BTK protein expression detection and BTK genetic analysis. The medical records between January 2020 and June 2023 were reviewed to investigate COVID-19 in XLA.ResultsTwenty-two patients (from 16 unrelated families) were molecularly diagnosed as XLA. Genetic testing revealed fifteen distinct mutations, including four splicing mutations, four missense mutations, three nonsense mutations, three short deletions, and one large indel mutation. These mutations scattered throughout the BTK gene and mostly affected the kinase domain. All mutations including five novel mutations were predicted to be pathogenic or deleterious by in silico prediction tools. Genetic testing confirmed that eleven mothers and seven sisters were carriers for the disease, while three mutations were de novo. Flow cytometric analysis showed that thirteen patients had minimal BTK expression (0-15%) while eight patients had reduced BTK expression (16-64%). One patient was not tested for monocyte BTK expression due to insufficient sample. Pneumonia (n=13) was the most common manifestation, while Pseudomonas aeruginosa was the most frequently isolated pathogen from the patients (n=4). Mild or asymptomatic COVID-19 was reported in four patients.ConclusionThis report provides the first overview of demographic, clinical, immunological and genetic data of XLA in Malaysia. The combination of flow cytometric assessment and BTK genetic analysis provides a definitive diagnosis for XLA patients, especially with atypical clinical presentation. In addition, it may also allow carrier detection and assist in genetic counselling and prenatal diagnosis

Amino Acid Sequence and Structural Comparison of BACE1 and BACE2 Using Evolutionary Trace Method

Beta-amyloid precursor protein cleavage enzyme 1 (BACE1) and beta-amyloid precursor protein cleavage enzyme 2 (BACE2), members of aspartyl protease family, are close homologues and have high similarity in their protein crystal structures. However, their enzymatic properties differ leading to disparate clinical consequences. In order to identify the residues that are responsible for such differences, we used evolutionary trace (ET) method to compare the amino acid conservation patterns of BACE1 and BACE2 in several mammalian species. We found that, in BACE1 and BACE2 structures, most of the ligand binding sites are conserved which indicate their enzymatic property of aspartyl protease family members. The other conserved residues are more or less randomly localized in other parts of the structures. Four group-specific residues were identified at the ligand binding site of BACE1 and BACE2. We postulated that these residues would be essential for selectivity of BACE1 and BACE2 biological functions and could be sites of interest for the design of selective inhibitors targeting either BACE1 or BACE2

Marginal expansion planning of infrastructure at a container terminal

Globalization push container terminal grown rapidly in decade. To fulfill the future traffic requirement, this study highlights marginal expansion planning of infrastructure in a container terminal. By using marginal approach, the expansion plan can be determine correctly and economically stage by stage. A mathematical model has generated to calculate the expansion size, expansion time, interval of expansion, expansion cost, and significant of expansion for each infrastructure respectively. It recommended determining the expansion plan for each infrastructure respectively. This is because one of the infrastructures needs to be expanding but the other may not. The generated model was verified with others model and validated with case study to investigate the practicability of the model. The model serves as expansion decision making tools to assist port expansion planners

A Comparative Analysis of Synonymous Codon Usage Bias Pattern in Human Albumin Superfamily

Synonymous codon usage bias is an inevitable phenomenon in organismic taxa across the three domains of life. Though the frequency of codon usage is not equal across species and within genome in the same species, the phenomenon is non random and is tissue-specific. Several factors such as GC content, nucleotide distribution, protein hydropathy, protein secondary structure, and translational selection are reported to contribute to codon usage preference. The synonymous codon usage patterns can be helpful in revealing the expression pattern of genes as well as the evolutionary relationship between the sequences. In this study, synonymous codon usage bias patterns were determined for the evolutionarily close proteins of albumin superfamily, namely, albumin, α-fetoprotein, afamin, and vitamin D-binding protein. Our study demonstrated that the genes of the four albumin superfamily members have low GC content and high values of effective number of codons (ENC) suggesting high expressivity of these genes and less bias in codon usage preferences. This study also provided evidence that the albumin superfamily members are not subjected to mutational selection pressure

Experimental and computational approaches to reveal the potential of <i>Ficus deltoidea</i> leaves extract as α-amylase inhibitor

<p><i>Ficus deltoidea</i> leaves extract are known to have good therapeutic properties such as antioxidant, anti-inflammatory and anti-diabetic. We showed that 50% ethanol-water extract of <i>F. deltoidea</i> leaves and its pungent compounds vitexin and isovitexin exhibited significant (<i>p</i> < 0.05) α-amylase inhibition with IC₅₀ (vitexin: 4.6 μM [0.02 μg/mL]; isovitexin: 0.06 μg/mL [13.8 μM] and DPPH scavenging with IC₅₀ (vitexin: 92.5 μM [0.4 μg/mL]; isovitexin: 0.5 μg/mL [115.4 μM]). Additionally, molecular docking analysis confirmed that vitexin has a higher binding affinity (-7.54 kcal/mol) towards α-amylase compared to isovitexin (−5.61 kcal/mol). On the other hand, the molecular dynamics findings showed that vitexin-α-amylase complex is more stable during the simulation of 20 ns when compared to the isovitexin-α-amylase complex. Our results suggest that vitexin is more potent and stable against α-amylase enzyme, thus it could develop as a therapeutic drug for the treatment of diabetes.</p

Feature extension of gut microbiome data for deep neural network-based colorectal cancer classification

Colorectal cancer (CRC) is the third most deadly cancer worldwide. The use of gut microbiome in early detection of the disease has attracted much attention from the research community, mainly because of its noninvasive nature. Recent achievements in next generation sequencing technology have led to increased availability of sequence data and enabled an environment for the growth of gut microbiome research. The use of conventional machine learning algorithms for automatic detection of CRC based on the microbiome is limited by factors such as low accuracy and the need for manual selection of features. Despite their success in other fields, Deep Neural Network (DNN) algorithms have limitations in microbiome-based CRC classification. These limitations include high dimensionality of microbiome data and other characteristics associated with sequence data such as feature dominance. In this paper, we propose a feature augmentation approach that aggregates data normalization methods to extend existing features of a dataset. The proposed method combines feature extension with data augmentation to improve CRC classification performance of a DNN model. The proposed model obtained area under the curve (AUC) scores of 0.96 and 0.89 on two publicly available microbiome datasets

Correction to &#x201C;Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification&#x201D;

In the above article [1], the acknowledgment for the research grant was referencing an incorrect grant reference number