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.

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.

Subtractive genomics study for the identification of therapeutic targets against Cronobacter sakazakii: A threat to infants.

Cronobacter sakazakii is an opportunistic pathogen that has been associated with severe infection in neonates such as necrotizing enterocolitis (NEC), neonatal meningitis, and bacteremia. This pathogen can survive in a relatively dry environment, especially in powdered infant formula (PIF). Unfortunately, conventional drugs that were once effective against C. sakazakii are gradually losing their efficacy due to rising antibiotic resistance. In this study, a subtractive genomic approach was followed in order to identify potential therapeutic targets in the pathogen. The whole proteome of the pathogen was filtered through a step-by-step process, which involved removing paralogous proteins, human homologs, sequences that are less essential for survival, proteins with shared metabolic pathways, and proteins that are located in cells other than the cytoplasmic membrane. As a result, nine novel drug targets were identified. Further, the analysis also unveiled that the FDA-approved drug Terbinafine can be repurposed against the Glutathione/l-cysteine transport system ATP-binding/permease protein CydC of C. sakazakii. Moreover, molecular docking and dynamics studies of Terbinafine and CydC suggested that this drug can be used to treat C. sakazakii infection in neonates. However, for clinical purposes further in vitro and in vivo studies are necessary.

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.

Subtractive genomics study of Xanthomonas oryzae pv. Oryzae reveals repurposable drug candidate for the treatment of bacterial leaf blight in rice.

BACKGROUND: Xanthomonas oryzae pv. oryzae is a plant pathogen responsible for causing one of the most severe bacterial diseases in rice, known as bacterial leaf blight that poses a major threat to global rice production. Even though several experimental compounds and chemical agents have been tested against X. oryzae pv. oryzae, still no approved drug is available. In this study, a subtractive genomic approach was used to identify potential therapeutic targets and repurposible drug candidates that could control of bacterial leaf blight in rice plants. RESULTS: The entire proteome of the pathogen underwent an extensive filtering process which involved removal of the paralogous proteins, rice homologs, non-essential proteins. Out of the 4382 proteins present in Xoo proteome, five hub proteins such as dnaA, dnaN, recJ, ruvA, and recR were identified for the druggability analysis. This analysis led to the identification of dnaN-encoded Beta sliding clamp protein as a potential therapeutic target and one experimental drug named [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid that can be repurposed against it. Molecular docking and 100 ns long molecular dynamics simulation suggested that the drug can form stable complexes with the target protein over time. CONCLUSION: Findings from our study indicated that the proposed drug showed potential effectiveness against bacterial leaf blight in rice caused by X. oryzae pv. oryzae. It is essential to keep in consideration that the procedure for developing novel drugs can be challenging and complicated. Even the most promising results from in silico studies should be validated through further in vitro and in vivo investigation before approval.

Type 2 diabetes linked FTO gene variant rs8050136 is significantly associated with gravidity in gestational diabetes in a sample of Bangladeshi women: Meta-analysis and case-control study.

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.

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.

Investigation of pathogenic germline variants in gastric cancer and development of "GasCanBase" database.

BACKGROUND: Gastric cancer, which is also known as stomach cancer, can be influenced by both germline and somatic mutations. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in germline have long been reported to play a pivotal role in cancer progression. AIM: The aim of this study is to examine the nsSNP in GC-associated genes. The study also aims to develop a database with extensive information regarding the nsSNPs in the GC-associated genes and their impacts. METHODS AND RESULTS: A total of 34,588 nsSNPs from 1,493,460 SNPs of the 40 genes were extracted from the available SNP database. Drug binding and energy minimization were examined by molecular docking and YASARA. To validate the existence of the germline CDH1 gene mutation (rs34466743) in the isolated blood DNA of gastric cancer (GC) patients, polymerase chain reaction (PCR) and DNA sequencing were performed. According to the results of the gene network analysis, 17 genes may interact with other types of cancer. A total of 11,363 nsSNPs were detected within the 40 GC genes. Among these, 474 nsSNPs were predicted to be damaging and 40 to be the most damaging. The SNPs in domain regions were thought to be strong candidates that alter protein functions. Our findings proposed that most of the selected nsSNPs were within the domains or motif regions. Free Energy Deviation calculation of protein structure pointed toward noteworthy changes in the structure of each protein that can demolish its natural function. Subsequently, drug binding confirmed the structural variation and the ineffectiveness of the drug against the mutant model in individuals with these germline variants. Furthermore, in vitro analysis of the rs34466743 germline variant from the CDH1 gene confirmed the strength and robustness of the pipeline that could expand the somatic alteration for causing cancer. In addition, a comprehensive gastric cancer polymorphism database named "GasCanBase" was developed to make data available to researchers. CONCLUSION: The findings of this study and the "GasCanBase" database may greatly contribute to our understanding of molecular epidemiology and the development of precise therapeutics for gastric cancer. GasCanBase is available at: https://www.gascanbase.com/.

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.

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.

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.

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.

Virtual screening reveals liquiritigenin as a broad-spectrum inhibitor of SARS-CoV-2 variants of concern: an in silico study.

The SARS-CoV-2 has severely impacted the lives of people worldwide. Global concern is on the rise due to a large number of unexpected mutations in the viral genome, resulting in new variants. Nature-based bioactive phytochemicals hold great promise as inhibitors against pathogenic viruses. The current study was aimed at evaluating some bioactive antiviral phytochemicals against SARS-CoV-2 variants of concern. A total of 46 phytochemicals were screened against the pathogenic spike protein of Alpha, Beta, Delta, Gamma, and Omicron variants. In addition to molecular docking, screening for favorable pharmacokinetic and pharmacodynamic properties such as absorption, distribution, metabolism, excretion, and toxicity was undertaken. For each of the aforementioned five SARS-CoV-2 variants of concern, a 100 ns molecular dynamics simulation was run to assess the stability of the complexes between their respective spike protein receptor-binding domain and the best-selected compound. From our current investigation, the natural compound liquiritigenin turned out to be the most promising potential lead compound against almost all the variants. These findings could pave the way for the development of effective medications against SARS-CoV-2 variants. However, in vivo trials in future studies are necessary for further validation of our results.Communicated by Ramaswamy H. Sarma.

Pathogen-driven gene expression patterns lead to a novel approach to the identification of common therapeutic targets.

Developing a common medication strategy for disease control and management could be greatly beneficial. Investigating the differences between diseased and healthy states using differentially expressed genes aids in understanding disease pathophysiology and enables the exploration of protein-drug interactions. This study aimed to find the most common genes in diarrhea-causing bacteria such as Salmonella enterica serovar Typhimurium, Campylobacter jejuni, Escherichia coli, Shigella dysenteriae (CESS) to find new drugs. Thus, differential gene expression datasets of CESS were screened through computational algorithms and programming. Subsequently, hub and common genes were prioritized from the analysis of extensive protein-protein interactions. Binding predictions were performed to identify the common potential therapeutic targets of CESS. We identified a total of 827 dysregulated genes that are highly linked to CESS. Notably, no common gene interaction was found among all CESS bacteria, but we identified 3 common genes in both Salmonella-Escherichia and Escherichia-Campylobacter infections. Later, out of 73 protein complexes, molecular simulations confirmed 5 therapeutic candidates from the CESS. We have developed a new pipeline for identifying therapeutic targets for a common medication strategy against CESS. However, further wet-lab validation is needed to confirm their effectiveness.

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.

Exploration of Streptococcus core genome to reveal druggable targets and novel therapeutics against S. pneumoniae.

Streptococcus pneumoniae (S. pneumoniae), the major etiological agent of community-acquired pneumonia (CAP) contributes significantly to the global burden of infectious diseases which is getting resistant day by day. Nearly 30% of the S. pneumoniae genomes encode hypothetical proteins (HPs), and better understandings of these HPs in virulence and pathogenicity plausibly decipher new treatments. Some of the HPs are present across many Streptococcus species, systematic assessment of these unexplored HPs will disclose prospective drug targets. In this study, through a stringent bioinformatics analysis of the core genome and proteome of S. pneumoniae PCS8235, we identified and analyzed 28 HPs that are common in many Streptococcus species and might have a potential role in the virulence or pathogenesis of the bacteria. Functional annotations of the proteins were conducted based on the physicochemical properties, subcellular localization, virulence prediction, protein-protein interactions, and identification of essential genes, to find potentially druggable proteins among 28 HPs. The majority of the HPs are involved in bacterial transcription and translation. Besides, some of them were homologs of enzymes, binding proteins, transporters, and regulators. Protein-protein interactions revealed HP PCS8235_RS05845 made the highest interactions with other HPs and also has TRP structural motif along with virulent and pathogenic properties indicating it has critical cellular functions and might go under unconventional protein secretions. The second highest interacting protein HP PCS8235_RS02595 interacts with the Regulator of chromosomal segregation (RocS) which participates in chromosome segregation and nucleoid protection in S. pneumoniae. In this interacting network, 54% of protein members have virulent properties and 40% contain pathogenic properties. Among them, most of these proteins circulate in the cytoplasmic area and have hydrophilic properties. Finally, molecular docking and dynamics simulation demonstrated that the antimalarial drug Artenimol can act as a drug repurposing candidate against HP PCS8235_RS 04650 of S. pneumoniae. Hence, the present study could aid in drugs against S. pneumoniae.

Identification of a novel variant of hepatitis B virus isolated from patient co-infected with hepatitis C virus.

Hepatitis B virus (HBV) is a major public health concern worldwide. Co-infection of hepatitis B patients with other pathogens intensifies the severity of the disease. We report a novel variant of hepatitis B virus (HBV) in Bangladesh isolated from a patient co-infected with hepatitis C virus (HCV) who exhibited liver cirrhosis. From 150 collected plasma samples, we sequenced HBV complete genome from one HBV-HCV co-infected patient. The complete genome was analysed using bioinformatics tools, NCBI BLAST, Geno2Pheno, and SnapGene software. The strain belongs to genotype A and subgenotype A1. Upon analysing the complete genome of this strain, we found a frameshift deletion of 54 nucleotides at the pre-S2 region, a functional regulator of HBV surface protein. Furthermore, we observed a Y126H mutation in the polymerase protein of this strain. This is the first report with such an unusual pre-S deletion event of the HBV genome in an HCV-co-infected patient associated with liver cirrhosis. These findings may inform scientists about genomic modifications in the HBV genome associated with HCV co-infection.

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.

Community Structure and Functional Annotations of the Skin Microbiome in Healthy and Diseased Catfish, Heteropneustes fossilis.

The skin mucosa of fish serves as a primary barrier against pathogens. In lesion sites in diseased fish, the mucosal barrier is expected to be compromised, with a substantial presence of potential pathogens. An understanding of the skin microbiome and its functional repertoire would provide important insights into host-microbe interactions, which has important implications for prophylactic measures in aquaculture. This study revealed the skin microbiomes and their functional annotations from healthy and diseased stinging catfish (Heteropneustes fossilis) based on 16S rRNA metagenomics. The OTUs consisted of four major phyla, Proteobacteria, Bacteroidota, Actinobacteriota and Firmicutes. Among members of the predominant phyla, Proteobacteria were rich in healthy fishes, but Bacteroidota and Firmicutes were significantly differentiated in healthy and diseased fish. The diversified microbiome was high in the skin of healthy fishes and did not significantly differ from that of the diseased groups. At the genus level, Pseudomonas showed the highest abundance in healthy fish but was nearly absent in diseased fish, whereas Flavobacterium showed the highest abundance in diseased fish. Linear discriminant analysis identified two phyla (Bacteroidota, Firmicutes) and two genera (Flavobacterium, Allorhizobium) that were consistently identified in diseased fishes. Functional prediction analysis specified that the genes related to physiological functions such as metabolism, immune and digestive systems and environmental adaptations could be highly expressed in diseased fishes. The present study indicates that the compositions, richness and functions of the bacterial community could influence the health status of cultured stinging catfish. Aquaculture-associated pathogenic bacteria may be identified, and preventive measures can be taken for the surveillance of fish health.

CDKAL1 gene rs7756992 A/G and rs7754840 G/C polymorphisms are associated with gestational diabetes mellitus in a sample of Bangladeshi population: implication for future T2DM prophylaxis.

BACKGROUND: Association of single nucleotide polymorphisms (SNP) rs7756992 A/G and rs7754840 G/C of cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) gene with the susceptibility of gestational diabetes mellitus (GDM) has been studied in a group of Bangladeshi women. METHODS: In this case-control study, 212 GDM patients and 256 control subjects were genotyped for rs7756992 and rs7754840 by PCR-RFLP and TaqMan™ allelic discrimination assay method respectively. Genotyping results were confirmed by DNA sequencing and replicated TaqMan™ assay. The odds ratios and their 95% confidence interval were calculated by logistic regression to determine the associations between genotypes and GDM. RESULTS: The genotype frequencies of rs7756992-AA/AG/GG in the GDM group and the control group were 37%/48%, 53%/45%, 10%/7% and those of rs7754840-CC/CG/GG were 51%/55%, 40.1%/39.8%, 9%/5% respectively. Under dominant and log additive models rs7756992 was revealed significantly associated with GDM after being adjusted for family history of diabetes (FHD) and gravidity. Conversely, rs7754840 was significantly associated (P = 0.047) with GDM only under the recessive model after the same adjustment. The risk allele frequency of both SNPs was higher in the GDM group but significantly (P = 0.029) increased prevalence was observed in the rs7756992 G allele. When positive FHD and risk alleles of these SNPs were synergistically present in any pregnant woman, the chance of developing GDM was augmented by many folds. The codominant model revealed 2.5 and 2.1 folds increase in odds by AG (rs7756992) and GC (rs7754840) genotypes and 3.7 and 4.5 folds by GG (rs7756992) and CC (rs7754840) genotypes respectively. A significant 2.7 folds (P = 0.038) increase in odds of GDM resulted from the interaction of rs7756992 and family history of diabetes under the dominant model. The cumulative effect of multigravidity and risk alleles of these SNPs increased the odds of GDM more than 1.5 folds in different genotypes. CONCLUSION: This study not only revealed a significant association between rs7756992 and rs7754840 with GDM but also provided the possibility as potential markers for foretelling about GDM and type 2 diabetes mellitus in Bangladeshi women.