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Salmonella infections pose a significant global public health concern due to the substantial expenses associated with monitoring, preventing, and treating the infection. In this study, we explored the core proteome of Salmonella to design a multi-epitope vaccine through Subtractive Proteomics and immunoinformatics approaches. A total of 2395 core proteins were curated from 30 different isolates of Salmonella (strain NZ CP014051 was taken as reference). Utilizing the subtractive proteomics approach on the Salmonella core proteome, Curlin major subunit A (CsgA) was selected as the vaccine candidate. csgA is a conserved gene that is related to biofilm formation. Immunodominant B and T cell epitopes from CsgA were predicted using numerous immunoinformatics tools. T lymphocyte epitopes had adequate population coverage and their corresponding MHC alleles showed significant binding scores after peptide-protein based molecular docking. Afterward, a multi-epitope vaccine was constructed with peptide linkers and Human Beta Defensin-2 (as an adjuvant). The vaccine could be highly antigenic, non-toxic, non-allergic, and have suitable physicochemical properties. Additionally, Molecular Dynamics Simulation and Immune Simulation demonstrated that the vaccine can bind with Toll Like Receptor 4 and elicit a robust immune response. Using in vitro, in vivo, and clinical trials, our findings could yield a Pan-Salmonella vaccine that might provide protection against various Salmonella species. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1 , EPHA2 , CLSPN , OLFML3 , and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ. [ABSTRACT FROM AUTHOR]

CatSper1 and TNP2 genes are known to affect semen quality and fertility parameters, including sperm motility and maturation. However, studies are yet to examine the genes in indigenous and crossbred cattle in Bangladesh. Therefore, this study was conducted to determine the genetic variants of CatSper1 and TNP2 in indigenous and crossbred cattle in Bangladesh. Blood samples were collected from 130 indigenous and 70 crossbred (Holstein Friesian × indigenous) cattle. Nucleotide variation was evaluated by PCR-RFLP and sequencing. The results of the study showed that the indigenous cattle possessed only TT genotype (1.0), whereas the crossbreds possessed both TT (0.91) and CT (0.09) genotypes, which was validated by gene sequencing. Additionally, the CatSper1 was conserved in both the indigenous and crossbred cattle, suggesting good semen quality and fertility. However, the TNP2 was conserved in the indigenous breeds and mostly conserved in the crossbreds. The findings of this study reveal the diversity of CatSper1 and TNP2 genes in indigenous and crossbred cattle. [ABSTRACT FROM AUTHOR]

Objective: Gestational diabetes mellitus (GDM) is a growing public health concern that has not been extensively studied. Numerous studies have indicated that a variant (rs8050136) of the fat mass-associated gene, FTO, is associated with both GDM and Type 2 diabetes mellitus(T2DM). We conducted a meta-analysis on the association between the FTO single nucleotide polymorphism (SNP) rs8050136 and T2DM, followed by a case-control study on the association of the said SNP and GDM in a sample of Bangladeshi women. Method: A total of 25 studies were selected after exploring various databases and search engines, which were assessed using the Newcastle-Ottawa Scale (NOS). The MetaGenyo web tool was used to conduct this meta-analysis. A case-control study was performed on 218 GDM patients and 284 controls to observe any association between FTO rs8050136 and GDM. Genotyping was performed using the tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS) method, and statistical analyses were performed using various statistical softwares. Results: In the meta-analysis 26231 cases and 43839 controls were examined. Pooled association analyses revealed a statistically significant relationship between the FTO rs8050136 polymorphism and an elevated risk of T2DM under all genetic models (P<0.05). In the case-control study, synergistic analyses of the SNP and gravida with GDM revealed a significant (P<0.01) association with an increase in odds by 1.6 to 2.4 folds in multigravida and decrease in odds by 2 folds in primigravida. A positive family history of diabetes and the minor allele of this SNP collectively increased the risk of developing GDM by many-fold (1.8 to 2.7 folds). However, after accounting for family history of diabetes and gravidity, analyses showed no significant association with GDM. Conclusion: Our meta-analysis revealed a significant association between SNP rs8050136 of FTO with T2DM, and this variant was substantially associated with an increased risk of GDM in a sample of Bangladeshi multigravida women. [ABSTRACT FROM AUTHOR]

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases. [ABSTRACT FROM AUTHOR]