AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

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.

Sequence variability of CatSper1 and TNP2 gene in indigenous and crossbred cattle in Bangladesh.

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.

Gut microbiome composition reveals the distinctiveness between the Bengali people and the Indigenous ethnicities in Bangladesh.

Ethnicity has a significant role in shaping the composition of the gut microbiome, which has implications in human physiology. This study intends to investigate the gut microbiome of Bengali people as well as several indigenous ethnicities (Chakma, Marma, Khyang, and Tripura) residing in the Chittagong Hill Tracts areas of Bangladesh. Following fecal sample collection from each population, part of the bacterial 16 s rRNA gene was amplified and sequenced using Illumina NovaSeq platform. Our findings indicated that Bangladeshi gut microbiota have a distinct diversity profile when compared to other countries. We also found out that Bangladeshi indigenous communities had a higher Firmicutes to Bacteroidetes ratio than the Bengali population. The investigation revealed an unclassified bacterium that was differentially abundant in Bengali samples while the genus Alistipes was found to be prevalent in Chakma samples. Further research on these bacteria might help understand diseases associated with these populations. Also, the current small sample-sized pilot study hindered the comprehensive understanding of the gut microbial diversity of the Bangladeshi population and its potential health implications. However, our study will help establish a basic understanding of the gut microbiome of the Bangladeshi population.

An integrated comparative genomics, subtractive proteomics and immunoinformatics framework for the rational design of a Pan-Salmonella multi-epitope vaccine.

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.

High risk genetic variants of human insulin receptor substrate 1(IRS1) infer structural instability and functional interference.

Insulin receptor substrate 1(IRS1) is a signaling adapter protein encoded by the IRS1 gene. This protein delivers signals from insulin and insulin-like growth factor-1(IGF-1) receptors to the phosphatidylinositol 3-kinases (P13K)/protein kinase B (Akt) and Extracellular signal-regulated kinases (Erk) - Mitogen-activated protein (MAP) kinase pathways, which regulate particular cellular processes. Mutations in this gene have been linked to type 2 diabetes mellitus, a heightened risk of insulin resistance, and an increased likelihood of developing a number of different malignancies. The structure and function of IRS1 could be severely compromised as a result of single nucleotide polymorphism (SNP) type genetic variants. In this study, we focused on identification of the most harmful non-synonymous SNPs (nsSNPs) of the IRS1 gene as well as prediction of their structural and functional consequences. Six different algorithms made the initial prediction that 59 of the 1142 IRS1 nsSNPs would have a negative impact on the protein structure. In-depth evaluations detected 26 nsSNPs located inside the functional domains of IRS1. Following that, 16 nsSNPs were identified as more harmful based on conservation profile, hydrophobic interaction, surface accessibility, homology modelling, and inter-atomic interactions. Following an in-depth analysis of protein stability, M249T (rs373826433), I223T (rs1939785175) and V204G (rs1574667052) were identified as three most deleterious SNPs and were subjected to molecular dynamics simulation for further insights. These findings will help us understand the implications for disease susceptibility, cancer progression, and the efficacy of therapeutic development against IRS1 gene mutants.Communicated by Ramaswamy H. Sarma.

Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function.

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.

Identification of repurposable drug targets in Mycoplasma pneumoniae using subtractive genomics, molecular docking and dynamics simulation.

Mycoplasma pneumoniae is a significant causative agent of community-acquired pneumonia, causing acute inflammation in the upper and lower respiratory tract as well as extrapulmonary syndromes. In particular, the elderly and infants are at greater risk of developing severe, life-threatening pneumonia caused by M. pneumoniae. Yet, the global increase in antimicrobial resistance against antibiotics for the treatment of M. pneumoniae infection highlights the urgent need to explore novel drug targets. To this end, bioinformatics approaches, such as subtractive genomics, can be employed to identify specific metabolic pathways and essential proteins unique to the pathogen that could be potential targets for new drugs. In this study, we implemented a subtractive genomics approach to identify 61 metabolic pathways and 42 essential proteins that are unique to M. pneumoniae. A subsequent screening in the DrugBank database revealed three druggable proteins with similarity to FDA-approved small-molecule drugs, and finally, the compound CHEBI:97093 was identified as a promising novel putative drug target. These findings can provide crucial insights for the development of highly effective drugs that selectively inhibit the pathogen-specific metabolic pathways, leading to better management and treatment of M. pneumoniae infections.

Pharmacoinformatics and molecular dynamics simulation approach to identify anti-diarrheal potentials of Centella asiatica (L.) Urb. against Vibrio cholerae.

Vibrio cholerae, the etiological agent of cholera, causes dehydration and severe diarrhea with the production of cholera toxin. Due to the acquired antibiotic resistance, V. cholerae has drawn attention to the establishment of novel medications to counteract the virulence and viability of the pathogen. Centella asiatica is a medicinal herb native to Bangladesh that has a wide range of medicinal and ethnobotanical applications including anti-bacterial properties. In the present investigation, a total of 25 bioactive phytochemicals of C. asiatica have been screened virtually through molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses, and molecular dynamics simulation. Our results revealed four lead compounds as Viridiflorol (-8.7 Kcal/mol), Luteolin (-8.1 Kcal/mol), Quercetin (-8.0 Kcal/mol) and, Geranyl acetate (-7.1 Kcal/mol) against V. cholerae Toxin co-regulated pilus virulence regulatory protein (ToxT). All the lead compounds have been found to possess favorable pharmacokinetic, pharmacodynamics, and molecular dynamics properties. Toxicity analysis revealed satisfactory results with no major side effects. Molecular dynamics simulation was performed for 100 ns that revealed noteworthy conformational stability and structural compactness for all the lead compounds, especially for Quercetin. Target class prediction unveiled enzymes in most of the cases and some experimental and investigational drugs were found as structurally similar analogs of the lead compounds. These findings could aid in the development of novel therapeutics targeting Cholera disease and we strongly recommend in vitro trials of our experimental findings.Communicated by Ramaswamy H. Sarma.

High-Risk Polymorphisms Associated with the Molecular Function of Human HMGCR Gene Infer the Inhibition of Cholesterol Biosynthesis.

HMG-CoA reductase or HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) is a rate-limiting enzyme involved in cholesterol biosynthesis. HMGCR plays an important role in the possible occurrence of hypercholesterolemia leading to atherosclerosis and coronary heart disease. This enzyme is a major target for cholesterol-lowering drugs such as "statin" which blocks the synthesis of mevalonate, a precursor for cholesterol biosynthesis. This study is aimed at characterizing deleterious mutations and classifying functional single nucleotide polymorphisms (SNPs) of the HMGCR gene through analysis of functional and structural evaluation, domain association, solvent accessibility, and energy minimization studies. The functional and characterization tools such as SIFT, PolyPhen, SNPs and GO, Panther, I-Mutant, and Pfam along with programming were employed to explore all the available SNPs in the HMGCR gene in the database. Among 6815 SNP entries from different databases, approximately 388 SNPs were found to be missense. Analysis showed that seven missense SNPs are more likely to have deleterious effects. A tertiary model of the mutant protein was constructed to determine the functional and structural effects of the HMGCR mutation. In addition, the location of the mutations suggests that they may have deleterious effects because most of the mutations are residing in the functional domain of the protein. The findings from the analysis predicted that rs147043821 and rs193026499 missense SNPs could cause significant structural and functional instability in the mutated proteins of the HMGCR gene. The findings of the current study will likely be useful in future efforts to uncover the mechanism and cause of hypercholesterolemia. In addition, the identified SNPs of HMGCR gene could set up a strong foundation for further therapeutic discovery.

Proteome-Based Investigation Identified Potential Drug Repurposable Small Molecules Against Monkeypox Disease.

Monkeypox Virus (MPXV), the causative agent of Monkeypox (MPX) disease, is an emerging zoonotic pathogen spreading in different endemic and non-endemic nations and creating outbreaks. MPX treatment mainly includes Cidofovir and Tecovirimat but they have several side effects and solely depending on these drugs may promote the emergence of drug-resistant variants. Hence, new drugs are required to control the spread of the disease. In this study, we explored the MPXV proteome to suggest repurposable drugs. DrugBank screening revealed drugs such as Brinzolamide, Dorzolamide, Methazolamide, Zidovudine, Gemcitabine, Hydroxyurea, Fludarabine, and Tecovirimat as controls. Structural analogs of these compounds were extracted from ChEMBL Database. After Molecular docking and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET)-based screening, we identified Zidovudine (binding affinity-5.9 kcal/mol) and a Harmala alkaloid (2S,4R)-4-(9H-Pyrido[3,4-b]indol-1-yl)-1,2,4-butanetriol (binding affinity - 6.6 kcal/mol) against L2R receptor (Thymidine Kinase). Moreover, Fludarabine (binding affinity - 6.4 kcal/mol) and 5'-Dehydroadenosine (binding affinity - 6.4 kcal/mol) can strongly interact with the I4L receptor (Ribonucleotide reductase large subunit R1). Molecular Dynamics (MD) simulations suggest all of these compounds can change the C-alpha backbone, residue mobility, compactness, and solvent accessible surface area of L2R and I4L. Our results strongly suggest that these drug repurposing small molecules are worth exploring in vivo and in vitro for clinical applications.